rocksdb/tools/db_bench_tool.cc

8725 lines
323 KiB
C++
Raw Normal View History

// Copyright (c) 2011-present, Facebook, Inc. All rights reserved.
// This source code is licensed under both the GPLv2 (found in the
// COPYING file in the root directory) and Apache 2.0 License
// (found in the LICENSE.Apache file in the root directory).
//
// Copyright (c) 2011 The LevelDB Authors. All rights reserved.
// Use of this source code is governed by a BSD-style license that can be
// found in the LICENSE file. See the AUTHORS file for names of contributors.
#ifdef GFLAGS
Adding NUMA support to db_bench tests Summary: Changes: - Adding numa_aware flag to db_bench.cc - Using numa.h library to bind memory and cpu of threads to a fixed NUMA node Result: There seems to be no significant change in the micros/op time with numa_aware enabled. I also tried this with other implementations, including a combination of pthread_setaffinity_np, sched_setaffinity and set_mempolicy methods. It'd be great if someone could point out where I'm going wrong and if we can achieve a better micors/op. Test Plan: Ran db_bench tests using following command: ./db_bench --db=/mnt/tmp --num_levels=6 --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --block_size=4096 --cache_size=17179869184 --cache_numshardbits=6 --compression_type=none --compression_ratio=1 --min_level_to_compress=-1 --disable_seek_compaction=1 --hard_rate_limit=2 --write_buffer_size=134217728 --max_write_buffer_number=2 --level0_file_num_compaction_trigger=8 --target_file_size_base=134217728 --max_bytes_for_level_base=1073741824 --disable_wal=0 --wal_dir=/mnt/tmp --sync=0 --disable_data_sync=1 --verify_checksum=1 --delete_obsolete_files_period_micros=314572800 --max_grandparent_overlap_factor=10 --max_background_compactions=4 --max_background_flushes=0 --level0_slowdown_writes_trigger=16 --level0_stop_writes_trigger=24 --statistics=0 --stats_per_interval=0 --stats_interval=1048576 --histogram=0 --use_plain_table=1 --open_files=-1 --mmap_read=1 --mmap_write=0 --memtablerep=prefix_hash --bloom_bits=10 --bloom_locality=1 --perf_level=0 --duration=300 --benchmarks=readwhilewriting --use_existing_db=1 --num=157286400 --threads=24 --writes_per_second=10240 --numa_aware=[False/True] The tests were run in private devserver with 24 cores and the db was prepopulated using filluniquerandom test. The tests resulted in 0.145 us/op with numa_aware=False and 0.161 us/op with numa_aware=True. Reviewers: sdong, yhchiang, ljin, igor Reviewed By: ljin, igor Subscribers: igor, leveldb Differential Revision: https://reviews.facebook.net/D19353
2014-07-07 17:53:31 +00:00
#ifdef NUMA
#include <numa.h>
#endif
#ifndef OS_WIN
#include <unistd.h>
#endif
#include <fcntl.h>
#include <sys/types.h>
#include <cstdio>
#include <cstdlib>
#ifdef __APPLE__
#include <mach/host_info.h>
#include <mach/mach_host.h>
#include <sys/sysctl.h>
#endif
#ifdef __FreeBSD__
#include <sys/sysctl.h>
#endif
#include <atomic>
#include <cinttypes>
#include <condition_variable>
#include <cstddef>
#include <iostream>
#include <memory>
#include <mutex>
#include <optional>
Create fillanddeleteuniquerandom benchmark (db_bench), with new option flags. (#8593) Summary: Introduction of a new `fillanddeleteuniquerandom` benchmark (`db_bench`) with 5 new option flags to simulate a benchmark where the following sequence is repeated multiple times: "A set of keys S1 is inserted ('`disposable entries`'), then after some delay another set of keys S2 is inserted ('`persistent entries`') and the first set of keys S1 is deleted. S2 artificially represents the insertion of hypothetical results from some undefined computation done on the first set of keys S1. The next sequence can start as soon as the last disposable entry in the set S1 of this sequence is inserted, if the `delay` is non negligible." New flags: - `disposable_entries_delete_delay`: minimum delay in microseconds between insertion of the last `disposable` entry, and the start of the insertion of the first `persistent` entry. - `disposable_entries_batch_size`: number of `disposable` entries inserted at the beginning of each sequence. - `disposable_entries_value_size`: size of the random `value` string for the `disposable` entries. - `persistent_entries_batch_size`: number of `persistent` entries inserted at the end of each sequence, right before the deletion of the `disposable` entries starts. - `persistent_entries_value_size`: size of the random value string for the `persistent` entries. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8593 Reviewed By: pdillinger Differential Revision: D29974436 Pulled By: bjlemaire fbshipit-source-id: f578033e5b45e8268ba6fa6f38f4770c2e6e801d
2021-07-29 21:57:03 +00:00
#include <queue>
#include <thread>
#include <unordered_map>
#include "db/db_impl/db_impl.h"
#include "db/malloc_stats.h"
#include "db/version_set.h"
#include "monitoring/histogram.h"
#include "monitoring/statistics_impl.h"
#include "options/cf_options.h"
#include "port/port.h"
#include "port/stack_trace.h"
#include "rocksdb/cache.h"
#include "rocksdb/convenience.h"
#include "rocksdb/db.h"
#include "rocksdb/env.h"
Implement full filter for block based table. Summary: 1. Make filter_block.h a base class. Derive block_based_filter_block and full_filter_block. The previous one is the traditional filter block. The full_filter_block is newly added. It would generate a filter block that contain all the keys in SST file. 2. When querying a key, table would first check if full_filter is available. If not, it would go to the exact data block and check using block_based filter. 3. User could choose to use full_filter or tradional(block_based_filter). They would be stored in SST file with different meta index name. "filter.filter_policy" or "full_filter.filter_policy". Then, Table reader is able to know the fllter block type. 4. Some optimizations have been done for full_filter_block, thus it requires a different interface compared to the original one in filter_policy.h. 5. Actual implementation of filter bits coding/decoding is placed in util/bloom_impl.cc Benchmark: base commit 1d23b5c470844c1208301311f0889eca750431c0 Command: db_bench --db=/dev/shm/rocksdb --num_levels=6 --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --write_buffer_size=134217728 --max_write_buffer_number=2 --target_file_size_base=33554432 --max_bytes_for_level_base=1073741824 --verify_checksum=false --max_background_compactions=4 --use_plain_table=0 --memtablerep=prefix_hash --open_files=-1 --mmap_read=1 --mmap_write=0 --bloom_bits=10 --bloom_locality=1 --memtable_bloom_bits=500000 --compression_type=lz4 --num=393216000 --use_hash_search=1 --block_size=1024 --block_restart_interval=16 --use_existing_db=1 --threads=1 --benchmarks=readrandom —disable_auto_compactions=1 Read QPS increase for about 30% from 2230002 to 2991411. Test Plan: make all check valgrind db_test db_stress --use_block_based_filter = 0 ./auto_sanity_test.sh Reviewers: igor, yhchiang, ljin, sdong Reviewed By: sdong Subscribers: dhruba, leveldb Differential Revision: https://reviews.facebook.net/D20979
2014-09-08 17:37:05 +00:00
#include "rocksdb/filter_policy.h"
#include "rocksdb/memtablerep.h"
#include "rocksdb/options.h"
#include "rocksdb/perf_context.h"
#include "rocksdb/persistent_cache.h"
#include "rocksdb/rate_limiter.h"
#include "rocksdb/secondary_cache.h"
#include "rocksdb/slice.h"
#include "rocksdb/slice_transform.h"
#include "rocksdb/stats_history.h"
Implement XXH3 block checksum type (#9069) Summary: XXH3 - latest hash function that is extremely fast on large data, easily faster than crc32c on most any x86_64 hardware. In integrating this hash function, I have handled the compression type byte in a non-standard way to avoid using the streaming API (extra data movement and active code size because of hash function complexity). This approach got a thumbs-up from Yann Collet. Existing functionality change: * reject bad ChecksumType in options with InvalidArgument This change split off from https://github.com/facebook/rocksdb/issues/9058 because context-aware checksum is likely to be handled through different configuration than ChecksumType. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9069 Test Plan: tests updated, and substantially expanded. Unit tests now check that we don't accidentally change the values generated by the checksum algorithms ("schema test") and that we properly handle invalid/unrecognized checksum types in options or in file footer. DBTestBase::ChangeOptions (etc.) updated from two to one configuration changing from default CRC32c ChecksumType. The point of this test code is to detect possible interactions among features, and the likelihood of some bad interaction being detected by including configurations other than XXH3 and CRC32c--and then not detected by stress/crash test--is extremely low. Stress/crash test also updated (manual run long enough to see it accepts new checksum type). db_bench also updated for microbenchmarking checksums. ### Performance microbenchmark (PORTABLE=0 DEBUG_LEVEL=0, Broadwell processor) ./db_bench -benchmarks=crc32c,xxhash,xxhash64,xxh3,crc32c,xxhash,xxhash64,xxh3,crc32c,xxhash,xxhash64,xxh3 crc32c : 0.200 micros/op 5005220 ops/sec; 19551.6 MB/s (4096 per op) xxhash : 0.807 micros/op 1238408 ops/sec; 4837.5 MB/s (4096 per op) xxhash64 : 0.421 micros/op 2376514 ops/sec; 9283.3 MB/s (4096 per op) xxh3 : 0.171 micros/op 5858391 ops/sec; 22884.3 MB/s (4096 per op) crc32c : 0.206 micros/op 4859566 ops/sec; 18982.7 MB/s (4096 per op) xxhash : 0.793 micros/op 1260850 ops/sec; 4925.2 MB/s (4096 per op) xxhash64 : 0.410 micros/op 2439182 ops/sec; 9528.1 MB/s (4096 per op) xxh3 : 0.161 micros/op 6202872 ops/sec; 24230.0 MB/s (4096 per op) crc32c : 0.203 micros/op 4924686 ops/sec; 19237.1 MB/s (4096 per op) xxhash : 0.839 micros/op 1192388 ops/sec; 4657.8 MB/s (4096 per op) xxhash64 : 0.424 micros/op 2357391 ops/sec; 9208.6 MB/s (4096 per op) xxh3 : 0.162 micros/op 6182678 ops/sec; 24151.1 MB/s (4096 per op) As you can see, especially once warmed up, xxh3 is fastest. ### Performance macrobenchmark (PORTABLE=0 DEBUG_LEVEL=0, Broadwell processor) Test for I in `seq 1 50`; do for CHK in 0 1 2 3 4; do TEST_TMPDIR=/dev/shm/rocksdb$CHK ./db_bench -benchmarks=fillseq -memtablerep=vector -allow_concurrent_memtable_write=false -num=30000000 -checksum_type=$CHK 2>&1 | grep 'micros/op' | tee -a results-$CHK & done; wait; done Results (ops/sec) for FILE in results*; do echo -n "$FILE "; awk '{ s += $5; c++; } END { print 1.0 * s / c; }' < $FILE; done results-0 252118 # kNoChecksum results-1 251588 # kCRC32c results-2 251863 # kxxHash results-3 252016 # kxxHash64 results-4 252038 # kXXH3 Reviewed By: mrambacher Differential Revision: D31905249 Pulled By: pdillinger fbshipit-source-id: cb9b998ebe2523fc7c400eedf62124a78bf4b4d1
2021-10-29 05:13:47 +00:00
#include "rocksdb/table.h"
Support read rate-limiting in SequentialFileReader (#9973) Summary: Added rate limiter and read rate-limiting support to SequentialFileReader. I've updated call sites to SequentialFileReader::Read with appropriate IO priority (or left a TODO and specified IO_TOTAL for now). The PR is separated into four commits: the first one added the rate-limiting support, but with some fixes in the unit test since the number of request bytes from rate limiter in SequentialFileReader are not accurate (there is overcharge at EOF). The second commit fixed this by allowing SequentialFileReader to check file size and determine how many bytes are left in the file to read. The third commit added benchmark related code. The fourth commit moved the logic of using file size to avoid overcharging the rate limiter into backup engine (the main user of SequentialFileReader). Pull Request resolved: https://github.com/facebook/rocksdb/pull/9973 Test Plan: - `make check`, backup_engine_test covers usage of SequentialFileReader with rate limiter. - Run db_bench to check if rate limiting is throttling as expected: Verified that reads and writes are together throttled at 2MB/s, and at 0.2MB chunks that are 100ms apart. - Set up: `./db_bench --benchmarks=fillrandom -db=/dev/shm/test_rocksdb` - Benchmark: ``` strace -ttfe read,write ./db_bench --benchmarks=backup -db=/dev/shm/test_rocksdb --backup_rate_limit=2097152 --use_existing_db strace -ttfe read,write ./db_bench --benchmarks=restore -db=/dev/shm/test_rocksdb --restore_rate_limit=2097152 --use_existing_db ``` - db bench on backup and restore to ensure no performance regression. - backup (avg over 50 runs): pre-change: 1.90443e+06 micros/op; post-change: 1.8993e+06 micros/op (improve by 0.2%) - restore (avg over 50 runs): pre-change: 1.79105e+06 micros/op; post-change: 1.78192e+06 micros/op (improve by 0.5%) ``` # Set up ./db_bench --benchmarks=fillrandom -db=/tmp/test_rocksdb -num=10000000 # benchmark TEST_TMPDIR=/tmp/test_rocksdb NUM_RUN=50 for ((j=0;j<$NUM_RUN;j++)) do ./db_bench -db=$TEST_TMPDIR -num=10000000 -benchmarks=backup -use_existing_db | egrep 'backup' # Restore #./db_bench -db=$TEST_TMPDIR -num=10000000 -benchmarks=restore -use_existing_db done > rate_limit.txt && awk -v NUM_RUN=$NUM_RUN '{sum+=$3;sum_sqrt+=$3^2}END{print sum/NUM_RUN, sqrt(sum_sqrt/NUM_RUN-(sum/NUM_RUN)^2)}' rate_limit.txt >> rate_limit_2.txt ``` Reviewed By: hx235 Differential Revision: D36327418 Pulled By: cbi42 fbshipit-source-id: e75d4307cff815945482df5ba630c1e88d064691
2022-05-24 17:28:57 +00:00
#include "rocksdb/utilities/backup_engine.h"
#include "rocksdb/utilities/object_registry.h"
#include "rocksdb/utilities/optimistic_transaction_db.h"
#include "rocksdb/utilities/options_type.h"
#include "rocksdb/utilities/options_util.h"
#include "rocksdb/utilities/replayer.h"
add simulator Cache as class SimCache/SimLRUCache(with test) Summary: add class SimCache(base class with instrumentation api) and SimLRUCache(derived class with detailed implementation) which is used as an instrumented block cache that can predict hit rate for different cache size Test Plan: Add a test case in `db_block_cache_test.cc` called `SimCacheTest` to test basic logic of SimCache. Also add option `-simcache_size` in db_bench. if set with a value other than -1, then the benchmark will use this value as the size of the simulator cache and finally output the simulation result. ``` [gzh@dev9927.prn1 ~/local/rocksdb] ./db_bench -benchmarks "fillseq,readrandom" -cache_size 1000000 -simcache_size 1000000 RocksDB: version 4.8 Date: Tue May 17 16:56:16 2016 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Compression: Snappy Memtablerep: skip_list Perf Level: 0 WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ DB path: [/tmp/rocksdbtest-112628/dbbench] fillseq : 6.809 micros/op 146874 ops/sec; 16.2 MB/s DB path: [/tmp/rocksdbtest-112628/dbbench] readrandom : 6.343 micros/op 157665 ops/sec; 17.4 MB/s (1000000 of 1000000 found) SIMULATOR CACHE STATISTICS: SimCache LOOKUPs: 986559 SimCache HITs: 264760 SimCache HITRATE: 26.84% [gzh@dev9927.prn1 ~/local/rocksdb] ./db_bench -benchmarks "fillseq,readrandom" -cache_size 1000000 -simcache_size 10000000 RocksDB: version 4.8 Date: Tue May 17 16:57:10 2016 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Compression: Snappy Memtablerep: skip_list Perf Level: 0 WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ DB path: [/tmp/rocksdbtest-112628/dbbench] fillseq : 5.066 micros/op 197394 ops/sec; 21.8 MB/s DB path: [/tmp/rocksdbtest-112628/dbbench] readrandom : 6.457 micros/op 154870 ops/sec; 17.1 MB/s (1000000 of 1000000 found) SIMULATOR CACHE STATISTICS: SimCache LOOKUPs: 1059764 SimCache HITs: 374501 SimCache HITRATE: 35.34% [gzh@dev9927.prn1 ~/local/rocksdb] ./db_bench -benchmarks "fillseq,readrandom" -cache_size 1000000 -simcache_size 100000000 RocksDB: version 4.8 Date: Tue May 17 16:57:32 2016 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Compression: Snappy Memtablerep: skip_list Perf Level: 0 WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ DB path: [/tmp/rocksdbtest-112628/dbbench] fillseq : 5.632 micros/op 177572 ops/sec; 19.6 MB/s DB path: [/tmp/rocksdbtest-112628/dbbench] readrandom : 6.892 micros/op 145094 ops/sec; 16.1 MB/s (1000000 of 1000000 found) SIMULATOR CACHE STATISTICS: SimCache LOOKUPs: 1150767 SimCache HITs: 1034535 SimCache HITRATE: 89.90% ``` Reviewers: IslamAbdelRahman, andrewkr, sdong Reviewed By: sdong Subscribers: MarkCallaghan, andrewkr, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D57999
2016-05-24 06:35:23 +00:00
#include "rocksdb/utilities/sim_cache.h"
#include "rocksdb/utilities/transaction.h"
#include "rocksdb/utilities/transaction_db.h"
#include "rocksdb/write_batch.h"
#include "test_util/testutil.h"
#include "test_util/transaction_test_util.h"
#include "tools/simulated_hybrid_file_system.h"
#include "util/cast_util.h"
2015-01-09 21:04:06 +00:00
#include "util/compression.h"
#include "util/crc32c.h"
Add rate limiter priority to ReadOptions (#9424) Summary: Users can set the priority for file reads associated with their operation by setting `ReadOptions::rate_limiter_priority` to something other than `Env::IO_TOTAL`. Rate limiting `VerifyChecksum()` and `VerifyFileChecksums()` is the motivation for this PR, so it also includes benchmarks and minor bug fixes to get that working. `RandomAccessFileReader::Read()` already had support for rate limiting compaction reads. I changed that rate limiting to be non-specific to compaction, but rather performed according to the passed in `Env::IOPriority`. Now the compaction read rate limiting is supported by setting `rate_limiter_priority = Env::IO_LOW` on its `ReadOptions`. There is no default value for the new `Env::IOPriority` parameter to `RandomAccessFileReader::Read()`. That means this PR goes through all callers (in some cases multiple layers up the call stack) to find a `ReadOptions` to provide the priority. There are TODOs for cases I believe it would be good to let user control the priority some day (e.g., file footer reads), and no TODO in cases I believe it doesn't matter (e.g., trace file reads). The API doc only lists the missing cases where a file read associated with a provided `ReadOptions` cannot be rate limited. For cases like file ingestion checksum calculation, there is no API to provide `ReadOptions` or `Env::IOPriority`, so I didn't count that as missing. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9424 Test Plan: - new unit tests - new benchmarks on ~50MB database with 1MB/s read rate limit and 100ms refill interval; verified with strace reads are chunked (at 0.1MB per chunk) and spaced roughly 100ms apart. - setup command: `./db_bench -benchmarks=fillrandom,compact -db=/tmp/testdb -target_file_size_base=1048576 -disable_auto_compactions=true -file_checksum=true` - benchmarks command: `strace -ttfe pread64 ./db_bench -benchmarks=verifychecksum,verifyfilechecksums -use_existing_db=true -db=/tmp/testdb -rate_limiter_bytes_per_sec=1048576 -rate_limit_bg_reads=1 -rate_limit_user_ops=true -file_checksum=true` - crash test using IO_USER priority on non-validation reads with https://github.com/facebook/rocksdb/issues/9567 reverted: `python3 tools/db_crashtest.py blackbox --max_key=1000000 --write_buffer_size=524288 --target_file_size_base=524288 --level_compaction_dynamic_level_bytes=true --duration=3600 --rate_limit_bg_reads=true --rate_limit_user_ops=true --rate_limiter_bytes_per_sec=10485760 --interval=10` Reviewed By: hx235 Differential Revision: D33747386 Pulled By: ajkr fbshipit-source-id: a2d985e97912fba8c54763798e04f006ccc56e0c
2022-02-17 07:17:03 +00:00
#include "util/file_checksum_helper.h"
#include "util/gflags_compat.h"
#include "util/mutexlock.h"
#include "util/random.h"
#include "util/stderr_logger.h"
#include "util/string_util.h"
#include "util/xxhash.h"
#include "utilities/blob_db/blob_db.h"
#include "utilities/counted_fs.h"
#include "utilities/merge_operators.h"
#include "utilities/merge_operators/bytesxor.h"
New API to get all merge operands for a Key (#5604) Summary: This is a new API added to db.h to allow for fetching all merge operands associated with a Key. The main motivation for this API is to support use cases where doing a full online merge is not necessary as it is performance sensitive. Example use-cases: 1. Update subset of columns and read subset of columns - Imagine a SQL Table, a row is encoded as a K/V pair (as it is done in MyRocks). If there are many columns and users only updated one of them, we can use merge operator to reduce write amplification. While users only read one or two columns in the read query, this feature can avoid a full merging of the whole row, and save some CPU. 2. Updating very few attributes in a value which is a JSON-like document - Updating one attribute can be done efficiently using merge operator, while reading back one attribute can be done more efficiently if we don't need to do a full merge. ---------------------------------------------------------------------------------------------------- API : Status GetMergeOperands( const ReadOptions& options, ColumnFamilyHandle* column_family, const Slice& key, PinnableSlice* merge_operands, GetMergeOperandsOptions* get_merge_operands_options, int* number_of_operands) Example usage : int size = 100; int number_of_operands = 0; std::vector<PinnableSlice> values(size); GetMergeOperandsOptions merge_operands_info; db_->GetMergeOperands(ReadOptions(), db_->DefaultColumnFamily(), "k1", values.data(), merge_operands_info, &number_of_operands); Description : Returns all the merge operands corresponding to the key. If the number of merge operands in DB is greater than merge_operands_options.expected_max_number_of_operands no merge operands are returned and status is Incomplete. Merge operands returned are in the order of insertion. merge_operands-> Points to an array of at-least merge_operands_options.expected_max_number_of_operands and the caller is responsible for allocating it. If the status returned is Incomplete then number_of_operands will contain the total number of merge operands found in DB for key. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5604 Test Plan: Added unit test and perf test in db_bench that can be run using the command: ./db_bench -benchmarks=getmergeoperands --merge_operator=sortlist Differential Revision: D16657366 Pulled By: vjnadimpalli fbshipit-source-id: 0faadd752351745224ee12d4ae9ef3cb529951bf
2019-08-06 21:22:34 +00:00
#include "utilities/merge_operators/sortlist.h"
#include "utilities/persistent_cache/block_cache_tier.h"
Provide an allocator for new memory type to be used with RocksDB block cache (#6214) Summary: New memory technologies are being developed by various hardware vendors (Intel DCPMM is one such technology currently available). These new memory types require different libraries for allocation and management (such as PMDK and memkind). The high capacities available make it possible to provision large caches (up to several TBs in size), beyond what is achievable with DRAM. The new allocator provided in this PR uses the memkind library to allocate memory on different media. **Performance** We tested the new allocator using db_bench. - For each test, we vary the size of the block cache (relative to the size of the uncompressed data in the database). - The database is filled sequentially. Throughput is then measured with a readrandom benchmark. - We use a uniform distribution as a worst-case scenario. The plot shows throughput (ops/s) relative to a configuration with no block cache and default allocator. For all tests, p99 latency is below 500 us. ![image](https://user-images.githubusercontent.com/26400080/71108594-42479100-2178-11ea-8231-8a775bbc92db.png) **Changes** - Add MemkindKmemAllocator - Add --use_cache_memkind_kmem_allocator db_bench option (to create an LRU block cache with the new allocator) - Add detection of memkind library with KMEM DAX support - Add test for MemkindKmemAllocator **Minimum Requirements** - kernel 5.3.12 - ndctl v67 - https://github.com/pmem/ndctl - memkind v1.10.0 - https://github.com/memkind/memkind **Memory Configuration** The allocator uses the MEMKIND_DAX_KMEM memory kind. Follow the instructions on[ memkind’s GitHub page](https://github.com/memkind/memkind) to set up NVDIMM memory accordingly. Note on memory allocation with NVDIMM memory exposed as system memory. - The MemkindKmemAllocator will only allocate from NVDIMM memory (using memkind_malloc with MEMKIND_DAX_KMEM kind). - The default allocator is not restricted to RAM by default. Based on NUMA node latency, the kernel should allocate from local RAM preferentially, but it’s a kernel decision. numactl --preferred/--membind can be used to allocate preferentially/exclusively from the local RAM node. **Usage** When creating an LRU cache, pass a MemkindKmemAllocator object as argument. For example (replace capacity with the desired value in bytes): ``` #include "rocksdb/cache.h" #include "memory/memkind_kmem_allocator.h" NewLRUCache( capacity /*size_t*/, 6 /*cache_numshardbits*/, false /*strict_capacity_limit*/, false /*cache_high_pri_pool_ratio*/, std::make_shared<MemkindKmemAllocator>()); ``` Refer to [RocksDB’s block cache documentation](https://github.com/facebook/rocksdb/wiki/Block-Cache) to assign the LRU cache as block cache for a database. Pull Request resolved: https://github.com/facebook/rocksdb/pull/6214 Reviewed By: cheng-chang Differential Revision: D19292435 fbshipit-source-id: 7202f47b769e7722b539c86c2ffd669f64d7b4e1
2020-04-10 03:45:17 +00:00
#ifdef MEMKIND
#include "memory/memkind_kmem_allocator.h"
#endif
#ifdef OS_WIN
#include <io.h> // open/close
#endif
using GFLAGS_NAMESPACE::ParseCommandLineFlags;
using GFLAGS_NAMESPACE::RegisterFlagValidator;
using GFLAGS_NAMESPACE::SetUsageMessage;
using GFLAGS_NAMESPACE::SetVersionString;
DEFINE_string(
benchmarks,
"fillseq,"
"fillseqdeterministic,"
"fillsync,"
"fillrandom,"
"filluniquerandomdeterministic,"
"overwrite,"
"readrandom,"
"newiterator,"
"newiteratorwhilewriting,"
"seekrandom,"
"seekrandomwhilewriting,"
"seekrandomwhilemerging,"
"readseq,"
"readreverse,"
"compact,"
"compactall,"
"flush,"
"compact0,"
"compact1,"
"waitforcompaction,"
"multireadrandom,"
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 18:40:44 +00:00
"mixgraph,"
"readseq,"
"readtorowcache,"
"readtocache,"
"readreverse,"
"readwhilewriting,"
"readwhilemerging,"
"readwhilescanning,"
"readrandomwriterandom,"
"updaterandom,"
"xorupdaterandom,"
For ApproximateSizes, pro-rate table metadata size over data blocks (#6784) Summary: The implementation of GetApproximateSizes was inconsistent in its treatment of the size of non-data blocks of SST files, sometimes including and sometimes now. This was at its worst with large portion of table file used by filters and querying a small range that crossed a table boundary: the size estimate would include large filter size. It's conceivable that someone might want only to know the size in terms of data blocks, but I believe that's unlikely enough to ignore for now. Similarly, there's no evidence the internal function AppoximateOffsetOf is used for anything other than a one-sided ApproximateSize, so I intend to refactor to remove redundancy in a follow-up commit. So to fix this, GetApproximateSizes (and implementation details ApproximateSize and ApproximateOffsetOf) now consistently include in their returned sizes a portion of table file metadata (incl filters and indexes) based on the size portion of the data blocks in range. In other words, if a key range covers data blocks that are X% by size of all the table's data blocks, returned approximate size is X% of the total file size. It would technically be more accurate to attribute metadata based on number of keys, but that's not computationally efficient with data available and rarely a meaningful difference. Also includes miscellaneous comment improvements / clarifications. Also included is a new approximatesizerandom benchmark for db_bench. No significant performance difference seen with this change, whether ~700 ops/sec with cache_index_and_filter_blocks and small cache or ~150k ops/sec without cache_index_and_filter_blocks. Pull Request resolved: https://github.com/facebook/rocksdb/pull/6784 Test Plan: Test added to DBTest.ApproximateSizesFilesWithErrorMargin. Old code running new test... [ RUN ] DBTest.ApproximateSizesFilesWithErrorMargin db/db_test.cc:1562: Failure Expected: (size) <= (11 * 100), actual: 9478 vs 1100 Other tests updated to reflect consistent accounting of metadata. Reviewed By: siying Differential Revision: D21334706 Pulled By: pdillinger fbshipit-source-id: 6f86870e45213334fedbe9c73b4ebb1d8d611185
2020-06-02 19:27:59 +00:00
"approximatesizerandom,"
"randomwithverify,"
"fill100K,"
"crc32c,"
"xxhash,"
Implement XXH3 block checksum type (#9069) Summary: XXH3 - latest hash function that is extremely fast on large data, easily faster than crc32c on most any x86_64 hardware. In integrating this hash function, I have handled the compression type byte in a non-standard way to avoid using the streaming API (extra data movement and active code size because of hash function complexity). This approach got a thumbs-up from Yann Collet. Existing functionality change: * reject bad ChecksumType in options with InvalidArgument This change split off from https://github.com/facebook/rocksdb/issues/9058 because context-aware checksum is likely to be handled through different configuration than ChecksumType. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9069 Test Plan: tests updated, and substantially expanded. Unit tests now check that we don't accidentally change the values generated by the checksum algorithms ("schema test") and that we properly handle invalid/unrecognized checksum types in options or in file footer. DBTestBase::ChangeOptions (etc.) updated from two to one configuration changing from default CRC32c ChecksumType. The point of this test code is to detect possible interactions among features, and the likelihood of some bad interaction being detected by including configurations other than XXH3 and CRC32c--and then not detected by stress/crash test--is extremely low. Stress/crash test also updated (manual run long enough to see it accepts new checksum type). db_bench also updated for microbenchmarking checksums. ### Performance microbenchmark (PORTABLE=0 DEBUG_LEVEL=0, Broadwell processor) ./db_bench -benchmarks=crc32c,xxhash,xxhash64,xxh3,crc32c,xxhash,xxhash64,xxh3,crc32c,xxhash,xxhash64,xxh3 crc32c : 0.200 micros/op 5005220 ops/sec; 19551.6 MB/s (4096 per op) xxhash : 0.807 micros/op 1238408 ops/sec; 4837.5 MB/s (4096 per op) xxhash64 : 0.421 micros/op 2376514 ops/sec; 9283.3 MB/s (4096 per op) xxh3 : 0.171 micros/op 5858391 ops/sec; 22884.3 MB/s (4096 per op) crc32c : 0.206 micros/op 4859566 ops/sec; 18982.7 MB/s (4096 per op) xxhash : 0.793 micros/op 1260850 ops/sec; 4925.2 MB/s (4096 per op) xxhash64 : 0.410 micros/op 2439182 ops/sec; 9528.1 MB/s (4096 per op) xxh3 : 0.161 micros/op 6202872 ops/sec; 24230.0 MB/s (4096 per op) crc32c : 0.203 micros/op 4924686 ops/sec; 19237.1 MB/s (4096 per op) xxhash : 0.839 micros/op 1192388 ops/sec; 4657.8 MB/s (4096 per op) xxhash64 : 0.424 micros/op 2357391 ops/sec; 9208.6 MB/s (4096 per op) xxh3 : 0.162 micros/op 6182678 ops/sec; 24151.1 MB/s (4096 per op) As you can see, especially once warmed up, xxh3 is fastest. ### Performance macrobenchmark (PORTABLE=0 DEBUG_LEVEL=0, Broadwell processor) Test for I in `seq 1 50`; do for CHK in 0 1 2 3 4; do TEST_TMPDIR=/dev/shm/rocksdb$CHK ./db_bench -benchmarks=fillseq -memtablerep=vector -allow_concurrent_memtable_write=false -num=30000000 -checksum_type=$CHK 2>&1 | grep 'micros/op' | tee -a results-$CHK & done; wait; done Results (ops/sec) for FILE in results*; do echo -n "$FILE "; awk '{ s += $5; c++; } END { print 1.0 * s / c; }' < $FILE; done results-0 252118 # kNoChecksum results-1 251588 # kCRC32c results-2 251863 # kxxHash results-3 252016 # kxxHash64 results-4 252038 # kXXH3 Reviewed By: mrambacher Differential Revision: D31905249 Pulled By: pdillinger fbshipit-source-id: cb9b998ebe2523fc7c400eedf62124a78bf4b4d1
2021-10-29 05:13:47 +00:00
"xxhash64,"
"xxh3,"
"compress,"
"uncompress,"
"acquireload,"
"fillseekseq,"
"randomtransaction,"
"randomreplacekeys,"
New API to get all merge operands for a Key (#5604) Summary: This is a new API added to db.h to allow for fetching all merge operands associated with a Key. The main motivation for this API is to support use cases where doing a full online merge is not necessary as it is performance sensitive. Example use-cases: 1. Update subset of columns and read subset of columns - Imagine a SQL Table, a row is encoded as a K/V pair (as it is done in MyRocks). If there are many columns and users only updated one of them, we can use merge operator to reduce write amplification. While users only read one or two columns in the read query, this feature can avoid a full merging of the whole row, and save some CPU. 2. Updating very few attributes in a value which is a JSON-like document - Updating one attribute can be done efficiently using merge operator, while reading back one attribute can be done more efficiently if we don't need to do a full merge. ---------------------------------------------------------------------------------------------------- API : Status GetMergeOperands( const ReadOptions& options, ColumnFamilyHandle* column_family, const Slice& key, PinnableSlice* merge_operands, GetMergeOperandsOptions* get_merge_operands_options, int* number_of_operands) Example usage : int size = 100; int number_of_operands = 0; std::vector<PinnableSlice> values(size); GetMergeOperandsOptions merge_operands_info; db_->GetMergeOperands(ReadOptions(), db_->DefaultColumnFamily(), "k1", values.data(), merge_operands_info, &number_of_operands); Description : Returns all the merge operands corresponding to the key. If the number of merge operands in DB is greater than merge_operands_options.expected_max_number_of_operands no merge operands are returned and status is Incomplete. Merge operands returned are in the order of insertion. merge_operands-> Points to an array of at-least merge_operands_options.expected_max_number_of_operands and the caller is responsible for allocating it. If the status returned is Incomplete then number_of_operands will contain the total number of merge operands found in DB for key. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5604 Test Plan: Added unit test and perf test in db_bench that can be run using the command: ./db_bench -benchmarks=getmergeoperands --merge_operator=sortlist Differential Revision: D16657366 Pulled By: vjnadimpalli fbshipit-source-id: 0faadd752351745224ee12d4ae9ef3cb529951bf
2019-08-06 21:22:34 +00:00
"timeseries,"
Support read rate-limiting in SequentialFileReader (#9973) Summary: Added rate limiter and read rate-limiting support to SequentialFileReader. I've updated call sites to SequentialFileReader::Read with appropriate IO priority (or left a TODO and specified IO_TOTAL for now). The PR is separated into four commits: the first one added the rate-limiting support, but with some fixes in the unit test since the number of request bytes from rate limiter in SequentialFileReader are not accurate (there is overcharge at EOF). The second commit fixed this by allowing SequentialFileReader to check file size and determine how many bytes are left in the file to read. The third commit added benchmark related code. The fourth commit moved the logic of using file size to avoid overcharging the rate limiter into backup engine (the main user of SequentialFileReader). Pull Request resolved: https://github.com/facebook/rocksdb/pull/9973 Test Plan: - `make check`, backup_engine_test covers usage of SequentialFileReader with rate limiter. - Run db_bench to check if rate limiting is throttling as expected: Verified that reads and writes are together throttled at 2MB/s, and at 0.2MB chunks that are 100ms apart. - Set up: `./db_bench --benchmarks=fillrandom -db=/dev/shm/test_rocksdb` - Benchmark: ``` strace -ttfe read,write ./db_bench --benchmarks=backup -db=/dev/shm/test_rocksdb --backup_rate_limit=2097152 --use_existing_db strace -ttfe read,write ./db_bench --benchmarks=restore -db=/dev/shm/test_rocksdb --restore_rate_limit=2097152 --use_existing_db ``` - db bench on backup and restore to ensure no performance regression. - backup (avg over 50 runs): pre-change: 1.90443e+06 micros/op; post-change: 1.8993e+06 micros/op (improve by 0.2%) - restore (avg over 50 runs): pre-change: 1.79105e+06 micros/op; post-change: 1.78192e+06 micros/op (improve by 0.5%) ``` # Set up ./db_bench --benchmarks=fillrandom -db=/tmp/test_rocksdb -num=10000000 # benchmark TEST_TMPDIR=/tmp/test_rocksdb NUM_RUN=50 for ((j=0;j<$NUM_RUN;j++)) do ./db_bench -db=$TEST_TMPDIR -num=10000000 -benchmarks=backup -use_existing_db | egrep 'backup' # Restore #./db_bench -db=$TEST_TMPDIR -num=10000000 -benchmarks=restore -use_existing_db done > rate_limit.txt && awk -v NUM_RUN=$NUM_RUN '{sum+=$3;sum_sqrt+=$3^2}END{print sum/NUM_RUN, sqrt(sum_sqrt/NUM_RUN-(sum/NUM_RUN)^2)}' rate_limit.txt >> rate_limit_2.txt ``` Reviewed By: hx235 Differential Revision: D36327418 Pulled By: cbi42 fbshipit-source-id: e75d4307cff815945482df5ba630c1e88d064691
2022-05-24 17:28:57 +00:00
"getmergeoperands,",
"readrandomoperands,"
Support read rate-limiting in SequentialFileReader (#9973) Summary: Added rate limiter and read rate-limiting support to SequentialFileReader. I've updated call sites to SequentialFileReader::Read with appropriate IO priority (or left a TODO and specified IO_TOTAL for now). The PR is separated into four commits: the first one added the rate-limiting support, but with some fixes in the unit test since the number of request bytes from rate limiter in SequentialFileReader are not accurate (there is overcharge at EOF). The second commit fixed this by allowing SequentialFileReader to check file size and determine how many bytes are left in the file to read. The third commit added benchmark related code. The fourth commit moved the logic of using file size to avoid overcharging the rate limiter into backup engine (the main user of SequentialFileReader). Pull Request resolved: https://github.com/facebook/rocksdb/pull/9973 Test Plan: - `make check`, backup_engine_test covers usage of SequentialFileReader with rate limiter. - Run db_bench to check if rate limiting is throttling as expected: Verified that reads and writes are together throttled at 2MB/s, and at 0.2MB chunks that are 100ms apart. - Set up: `./db_bench --benchmarks=fillrandom -db=/dev/shm/test_rocksdb` - Benchmark: ``` strace -ttfe read,write ./db_bench --benchmarks=backup -db=/dev/shm/test_rocksdb --backup_rate_limit=2097152 --use_existing_db strace -ttfe read,write ./db_bench --benchmarks=restore -db=/dev/shm/test_rocksdb --restore_rate_limit=2097152 --use_existing_db ``` - db bench on backup and restore to ensure no performance regression. - backup (avg over 50 runs): pre-change: 1.90443e+06 micros/op; post-change: 1.8993e+06 micros/op (improve by 0.2%) - restore (avg over 50 runs): pre-change: 1.79105e+06 micros/op; post-change: 1.78192e+06 micros/op (improve by 0.5%) ``` # Set up ./db_bench --benchmarks=fillrandom -db=/tmp/test_rocksdb -num=10000000 # benchmark TEST_TMPDIR=/tmp/test_rocksdb NUM_RUN=50 for ((j=0;j<$NUM_RUN;j++)) do ./db_bench -db=$TEST_TMPDIR -num=10000000 -benchmarks=backup -use_existing_db | egrep 'backup' # Restore #./db_bench -db=$TEST_TMPDIR -num=10000000 -benchmarks=restore -use_existing_db done > rate_limit.txt && awk -v NUM_RUN=$NUM_RUN '{sum+=$3;sum_sqrt+=$3^2}END{print sum/NUM_RUN, sqrt(sum_sqrt/NUM_RUN-(sum/NUM_RUN)^2)}' rate_limit.txt >> rate_limit_2.txt ``` Reviewed By: hx235 Differential Revision: D36327418 Pulled By: cbi42 fbshipit-source-id: e75d4307cff815945482df5ba630c1e88d064691
2022-05-24 17:28:57 +00:00
"backup,"
"restore"
"Comma-separated list of operations to run in the specified"
" order. Available benchmarks:\n"
"\tfillseq -- write N values in sequential key"
" order in async mode\n"
"\tfillseqdeterministic -- write N values in the specified"
" key order and keep the shape of the LSM tree\n"
"\tfillrandom -- write N values in random key order in async"
" mode\n"
"\tfilluniquerandomdeterministic -- write N values in a random"
" key order and keep the shape of the LSM tree\n"
"\toverwrite -- overwrite N values in random key order in "
"async mode\n"
"\tfillsync -- write N/1000 values in random key order in "
"sync mode\n"
"\tfill100K -- write N/1000 100K values in random order in"
" async mode\n"
"\tdeleteseq -- delete N keys in sequential order\n"
"\tdeleterandom -- delete N keys in random order\n"
"\treadseq -- read N times sequentially\n"
"\treadtocache -- 1 thread reading database sequentially\n"
"\treadreverse -- read N times in reverse order\n"
"\treadrandom -- read N times in random order\n"
"\treadmissing -- read N missing keys in random order\n"
"\treadwhilewriting -- 1 writer, N threads doing random "
"reads\n"
"\treadwhilemerging -- 1 merger, N threads doing random "
"reads\n"
"\treadwhilescanning -- 1 thread doing full table scan, "
"N threads doing random reads\n"
"\treadrandomwriterandom -- N threads doing random-read, "
"random-write\n"
"\tupdaterandom -- N threads doing read-modify-write for random "
"keys\n"
"\txorupdaterandom -- N threads doing read-XOR-write for "
"random keys\n"
"\tappendrandom -- N threads doing read-modify-write with "
"growing values\n"
"\tmergerandom -- same as updaterandom/appendrandom using merge"
" operator. "
"Must be used with merge_operator\n"
"\treadrandommergerandom -- perform N random read-or-merge "
"operations. Must be used with merge_operator\n"
"\tnewiterator -- repeated iterator creation\n"
"\tseekrandom -- N random seeks, call Next seek_nexts times "
"per seek\n"
"\tseekrandomwhilewriting -- seekrandom and 1 thread doing "
"overwrite\n"
"\tseekrandomwhilemerging -- seekrandom and 1 thread doing "
"merge\n"
Implement XXH3 block checksum type (#9069) Summary: XXH3 - latest hash function that is extremely fast on large data, easily faster than crc32c on most any x86_64 hardware. In integrating this hash function, I have handled the compression type byte in a non-standard way to avoid using the streaming API (extra data movement and active code size because of hash function complexity). This approach got a thumbs-up from Yann Collet. Existing functionality change: * reject bad ChecksumType in options with InvalidArgument This change split off from https://github.com/facebook/rocksdb/issues/9058 because context-aware checksum is likely to be handled through different configuration than ChecksumType. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9069 Test Plan: tests updated, and substantially expanded. Unit tests now check that we don't accidentally change the values generated by the checksum algorithms ("schema test") and that we properly handle invalid/unrecognized checksum types in options or in file footer. DBTestBase::ChangeOptions (etc.) updated from two to one configuration changing from default CRC32c ChecksumType. The point of this test code is to detect possible interactions among features, and the likelihood of some bad interaction being detected by including configurations other than XXH3 and CRC32c--and then not detected by stress/crash test--is extremely low. Stress/crash test also updated (manual run long enough to see it accepts new checksum type). db_bench also updated for microbenchmarking checksums. ### Performance microbenchmark (PORTABLE=0 DEBUG_LEVEL=0, Broadwell processor) ./db_bench -benchmarks=crc32c,xxhash,xxhash64,xxh3,crc32c,xxhash,xxhash64,xxh3,crc32c,xxhash,xxhash64,xxh3 crc32c : 0.200 micros/op 5005220 ops/sec; 19551.6 MB/s (4096 per op) xxhash : 0.807 micros/op 1238408 ops/sec; 4837.5 MB/s (4096 per op) xxhash64 : 0.421 micros/op 2376514 ops/sec; 9283.3 MB/s (4096 per op) xxh3 : 0.171 micros/op 5858391 ops/sec; 22884.3 MB/s (4096 per op) crc32c : 0.206 micros/op 4859566 ops/sec; 18982.7 MB/s (4096 per op) xxhash : 0.793 micros/op 1260850 ops/sec; 4925.2 MB/s (4096 per op) xxhash64 : 0.410 micros/op 2439182 ops/sec; 9528.1 MB/s (4096 per op) xxh3 : 0.161 micros/op 6202872 ops/sec; 24230.0 MB/s (4096 per op) crc32c : 0.203 micros/op 4924686 ops/sec; 19237.1 MB/s (4096 per op) xxhash : 0.839 micros/op 1192388 ops/sec; 4657.8 MB/s (4096 per op) xxhash64 : 0.424 micros/op 2357391 ops/sec; 9208.6 MB/s (4096 per op) xxh3 : 0.162 micros/op 6182678 ops/sec; 24151.1 MB/s (4096 per op) As you can see, especially once warmed up, xxh3 is fastest. ### Performance macrobenchmark (PORTABLE=0 DEBUG_LEVEL=0, Broadwell processor) Test for I in `seq 1 50`; do for CHK in 0 1 2 3 4; do TEST_TMPDIR=/dev/shm/rocksdb$CHK ./db_bench -benchmarks=fillseq -memtablerep=vector -allow_concurrent_memtable_write=false -num=30000000 -checksum_type=$CHK 2>&1 | grep 'micros/op' | tee -a results-$CHK & done; wait; done Results (ops/sec) for FILE in results*; do echo -n "$FILE "; awk '{ s += $5; c++; } END { print 1.0 * s / c; }' < $FILE; done results-0 252118 # kNoChecksum results-1 251588 # kCRC32c results-2 251863 # kxxHash results-3 252016 # kxxHash64 results-4 252038 # kXXH3 Reviewed By: mrambacher Differential Revision: D31905249 Pulled By: pdillinger fbshipit-source-id: cb9b998ebe2523fc7c400eedf62124a78bf4b4d1
2021-10-29 05:13:47 +00:00
"\tcrc32c -- repeated crc32c of <block size> data\n"
"\txxhash -- repeated xxHash of <block size> data\n"
"\txxhash64 -- repeated xxHash64 of <block size> data\n"
"\txxh3 -- repeated XXH3 of <block size> data\n"
"\tacquireload -- load N*1000 times\n"
"\tfillseekseq -- write N values in sequential key, then read "
"them by seeking to each key\n"
"\trandomtransaction -- execute N random transactions and "
"verify correctness\n"
"\trandomreplacekeys -- randomly replaces N keys by deleting "
"the old version and putting the new version\n\n"
"\ttimeseries -- 1 writer generates time series data "
"and multiple readers doing random reads on id\n\n"
"Meta operations:\n"
"\tcompact -- Compact the entire DB; If multiple, randomly choose one\n"
"\tcompactall -- Compact the entire DB\n"
"\tcompact0 -- compact L0 into L1\n"
"\tcompact1 -- compact L1 into L2\n"
"\twaitforcompaction - pause until compaction is (probably) done\n"
"\tflush - flush the memtable\n"
"\tstats -- Print DB stats\n"
"\tresetstats -- Reset DB stats\n"
"\tlevelstats -- Print the number of files and bytes per level\n"
"\tmemstats -- Print memtable stats\n"
"\tsstables -- Print sstable info\n"
"\theapprofile -- Dump a heap profile (if supported by this port)\n"
New API to get all merge operands for a Key (#5604) Summary: This is a new API added to db.h to allow for fetching all merge operands associated with a Key. The main motivation for this API is to support use cases where doing a full online merge is not necessary as it is performance sensitive. Example use-cases: 1. Update subset of columns and read subset of columns - Imagine a SQL Table, a row is encoded as a K/V pair (as it is done in MyRocks). If there are many columns and users only updated one of them, we can use merge operator to reduce write amplification. While users only read one or two columns in the read query, this feature can avoid a full merging of the whole row, and save some CPU. 2. Updating very few attributes in a value which is a JSON-like document - Updating one attribute can be done efficiently using merge operator, while reading back one attribute can be done more efficiently if we don't need to do a full merge. ---------------------------------------------------------------------------------------------------- API : Status GetMergeOperands( const ReadOptions& options, ColumnFamilyHandle* column_family, const Slice& key, PinnableSlice* merge_operands, GetMergeOperandsOptions* get_merge_operands_options, int* number_of_operands) Example usage : int size = 100; int number_of_operands = 0; std::vector<PinnableSlice> values(size); GetMergeOperandsOptions merge_operands_info; db_->GetMergeOperands(ReadOptions(), db_->DefaultColumnFamily(), "k1", values.data(), merge_operands_info, &number_of_operands); Description : Returns all the merge operands corresponding to the key. If the number of merge operands in DB is greater than merge_operands_options.expected_max_number_of_operands no merge operands are returned and status is Incomplete. Merge operands returned are in the order of insertion. merge_operands-> Points to an array of at-least merge_operands_options.expected_max_number_of_operands and the caller is responsible for allocating it. If the status returned is Incomplete then number_of_operands will contain the total number of merge operands found in DB for key. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5604 Test Plan: Added unit test and perf test in db_bench that can be run using the command: ./db_bench -benchmarks=getmergeoperands --merge_operator=sortlist Differential Revision: D16657366 Pulled By: vjnadimpalli fbshipit-source-id: 0faadd752351745224ee12d4ae9ef3cb529951bf
2019-08-06 21:22:34 +00:00
"\treplay -- replay the trace file specified with trace_file\n"
"\tgetmergeoperands -- Insert lots of merge records which are a list of "
"sorted ints for a key and then compare performance of lookup for another "
"key by doing a Get followed by binary searching in the large sorted list "
"vs doing a GetMergeOperands and binary searching in the operands which "
"are sorted sub-lists. The MergeOperator used is sortlist.h\n"
"\treadrandomoperands -- read random keys using `GetMergeOperands()`. An "
"operation includes a rare but possible retry in case it got "
"`Status::Incomplete()`. This happens upon encountering more keys than "
Support read rate-limiting in SequentialFileReader (#9973) Summary: Added rate limiter and read rate-limiting support to SequentialFileReader. I've updated call sites to SequentialFileReader::Read with appropriate IO priority (or left a TODO and specified IO_TOTAL for now). The PR is separated into four commits: the first one added the rate-limiting support, but with some fixes in the unit test since the number of request bytes from rate limiter in SequentialFileReader are not accurate (there is overcharge at EOF). The second commit fixed this by allowing SequentialFileReader to check file size and determine how many bytes are left in the file to read. The third commit added benchmark related code. The fourth commit moved the logic of using file size to avoid overcharging the rate limiter into backup engine (the main user of SequentialFileReader). Pull Request resolved: https://github.com/facebook/rocksdb/pull/9973 Test Plan: - `make check`, backup_engine_test covers usage of SequentialFileReader with rate limiter. - Run db_bench to check if rate limiting is throttling as expected: Verified that reads and writes are together throttled at 2MB/s, and at 0.2MB chunks that are 100ms apart. - Set up: `./db_bench --benchmarks=fillrandom -db=/dev/shm/test_rocksdb` - Benchmark: ``` strace -ttfe read,write ./db_bench --benchmarks=backup -db=/dev/shm/test_rocksdb --backup_rate_limit=2097152 --use_existing_db strace -ttfe read,write ./db_bench --benchmarks=restore -db=/dev/shm/test_rocksdb --restore_rate_limit=2097152 --use_existing_db ``` - db bench on backup and restore to ensure no performance regression. - backup (avg over 50 runs): pre-change: 1.90443e+06 micros/op; post-change: 1.8993e+06 micros/op (improve by 0.2%) - restore (avg over 50 runs): pre-change: 1.79105e+06 micros/op; post-change: 1.78192e+06 micros/op (improve by 0.5%) ``` # Set up ./db_bench --benchmarks=fillrandom -db=/tmp/test_rocksdb -num=10000000 # benchmark TEST_TMPDIR=/tmp/test_rocksdb NUM_RUN=50 for ((j=0;j<$NUM_RUN;j++)) do ./db_bench -db=$TEST_TMPDIR -num=10000000 -benchmarks=backup -use_existing_db | egrep 'backup' # Restore #./db_bench -db=$TEST_TMPDIR -num=10000000 -benchmarks=restore -use_existing_db done > rate_limit.txt && awk -v NUM_RUN=$NUM_RUN '{sum+=$3;sum_sqrt+=$3^2}END{print sum/NUM_RUN, sqrt(sum_sqrt/NUM_RUN-(sum/NUM_RUN)^2)}' rate_limit.txt >> rate_limit_2.txt ``` Reviewed By: hx235 Differential Revision: D36327418 Pulled By: cbi42 fbshipit-source-id: e75d4307cff815945482df5ba630c1e88d064691
2022-05-24 17:28:57 +00:00
"have ever been seen by the thread (or eight initially)\n"
"\tbackup -- Create a backup of the current DB and verify that a new backup is corrected. "
"Rate limit can be specified through --backup_rate_limit\n"
"\trestore -- Restore the DB from the latest backup available, rate limit can be specified through --restore_rate_limit\n");
DEFINE_int64(num, 1000000, "Number of key/values to place in database");
DEFINE_int64(numdistinct, 1000,
"Number of distinct keys to use. Used in RandomWithVerify to "
"read/write on fewer keys so that gets are more likely to find the"
" key and puts are more likely to update the same key");
DEFINE_int64(merge_keys, -1,
"Number of distinct keys to use for MergeRandom and "
"ReadRandomMergeRandom. "
"If negative, there will be FLAGS_num keys.");
DEFINE_int32(num_column_families, 1, "Number of Column Families to use.");
DEFINE_int32(
num_hot_column_families, 0,
"Number of Hot Column Families. If more than 0, only write to this "
"number of column families. After finishing all the writes to them, "
"create new set of column families and insert to them. Only used "
"when num_column_families > 1.");
DEFINE_string(column_family_distribution, "",
"Comma-separated list of percentages, where the ith element "
"indicates the probability of an op using the ith column family. "
"The number of elements must be `num_hot_column_families` if "
"specified; otherwise, it must be `num_column_families`. The "
"sum of elements must be 100. E.g., if `num_column_families=4`, "
"and `num_hot_column_families=0`, a valid list could be "
"\"10,20,30,40\".");
DEFINE_int64(reads, -1,
"Number of read operations to do. "
"If negative, do FLAGS_num reads.");
DEFINE_int64(deletes, -1,
"Number of delete operations to do. "
"If negative, do FLAGS_num deletions.");
DEFINE_int32(bloom_locality, 0, "Control bloom filter probes locality");
db_bench should use a good seed when --seed is not set or set to 0 (#9740) Summary: This is for https://github.com/facebook/rocksdb/issues/9737 I have wasted more than a few hours running db_bench benchmarks where --seed was not set and getting better than expected results because cache hit rates are great because multiple invocations of db_bench used the same value for --seed or did not set it, and then all used 0. The result is that all see the same sequence of keys. Others have done the same. The problem is worse in that it is easy to miss and the result is a benchmark with results that are misleading. A good way to avoid this is to set it to the equivalent of gettimeofday() when either --seed is not set or it is set to 0 (the default). With this change the actual seed is printed when it was 0 at process start: Set seed to 1647992570365606 because --seed was 0 Pull Request resolved: https://github.com/facebook/rocksdb/pull/9740 Test Plan: Perf results: ./db_bench --benchmarks=fillseq,readrandom --num=1000000 --reads=4000000 readrandom : 6.469 micros/op 154583 ops/sec; 17.1 MB/s (4000000 of 4000000 found) ./db_bench --benchmarks=fillseq,readrandom --num=1000000 --reads=4000000 --seed=0 readrandom : 6.565 micros/op 152321 ops/sec; 16.9 MB/s (4000000 of 4000000 found) ./db_bench --benchmarks=fillseq,readrandom --num=1000000 --reads=4000000 --seed=1 readrandom : 6.461 micros/op 154777 ops/sec; 17.1 MB/s (4000000 of 4000000 found) ./db_bench --benchmarks=fillseq,readrandom --num=1000000 --reads=4000000 --seed=2 readrandom : 6.525 micros/op 153244 ops/sec; 17.0 MB/s (4000000 of 4000000 found) Reviewed By: jay-zhuang Differential Revision: D35145361 Pulled By: mdcallag fbshipit-source-id: 2b35b153ccec46b27d7c9405997523555fc51267
2022-03-25 17:12:27 +00:00
DEFINE_int64(seed, 0,
"Seed base for random number generators. "
"When 0 it is derived from the current time.");
static std::optional<int64_t> seed_base;
DEFINE_int32(threads, 1, "Number of concurrent threads to run.");
DEFINE_int32(duration, 0,
"Time in seconds for the random-ops tests to run."
" When 0 then num & reads determine the test duration");
DEFINE_string(value_size_distribution_type, "fixed",
"Value size distribution type: fixed, uniform, normal");
DEFINE_int32(value_size, 100, "Size of each value in fixed distribution");
static unsigned int value_size = 100;
DEFINE_int32(value_size_min, 100, "Min size of random value");
DEFINE_int32(value_size_max, 102400, "Max size of random value");
DEFINE_int32(seek_nexts, 0,
"How many times to call Next() after Seek() in "
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
2015-03-30 18:28:25 +00:00
"fillseekseq, seekrandom, seekrandomwhilewriting and "
"seekrandomwhilemerging");
SkipListRep::LookaheadIterator Summary: This diff introduces the `lookahead` argument to `SkipListFactory()`. This is an optimization for the tailing use case which includes many seeks. E.g. consider the following operations on a skip list iterator: Seek(x), Next(), Next(), Seek(x+2), Next(), Seek(x+3), Next(), Next(), ... If `lookahead` is positive, `SkipListRep` will return an iterator which also keeps track of the previously visited node. Seek() then first does a linear search starting from that node (up to `lookahead` steps). As in the tailing example above, this may require fewer than ~log(n) comparisons as with regular skip list search. Test Plan: Added a new benchmark (`fillseekseq`) which simulates the usage pattern. It first writes N records (with consecutive keys), then measures how much time it takes to read them by calling `Seek()` and `Next()`. $ time ./db_bench -num 10000000 -benchmarks fillseekseq -prefix_size 1 \ -key_size 8 -write_buffer_size $[1024*1024*1024] -value_size 50 \ -seekseq_next 2 -skip_list_lookahead=0 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.389 micros/op 2569047 ops/sec; real 0m21.806s user 0m12.106s sys 0m9.672s $ time ./db_bench [...] -skip_list_lookahead=2 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.153 micros/op 6540684 ops/sec; real 0m19.469s user 0m10.192s sys 0m9.252s Reviewers: ljin, sdong, igor Reviewed By: igor Subscribers: dhruba, leveldb, march, lovro Differential Revision: https://reviews.facebook.net/D23997
2014-09-23 22:52:28 +00:00
DEFINE_bool(reverse_iterator, false,
"When true use Prev rather than Next for iterators that do "
"Seek and then Next");
Fix auto_prefix_mode performance with partitioned filters (#10012) Summary: Essentially refactored the RangeMayExist implementation in FullFilterBlockReader to FilterBlockReaderCommon so that it applies to partitioned filters as well. (The function is not called for the block-based filter case.) RangeMayExist is essentially a series of checks around a possible PrefixMayExist, and I'm confident those checks should be the same for partitioned as for full filters. (I think it's likely that bugs remain in those checks, but this change is overall a simplifying one.) Added auto_prefix_mode support to db_bench Other small fixes as well Fixes https://github.com/facebook/rocksdb/issues/10003 Pull Request resolved: https://github.com/facebook/rocksdb/pull/10012 Test Plan: Expanded unit test that uses statistics to check for filter optimization, fails without the production code changes here Performance: populate two DBs with ``` TEST_TMPDIR=/dev/shm/rocksdb_nonpartitioned ./db_bench -benchmarks=fillrandom -num=10000000 -disable_wal=1 -write_buffer_size=30000000 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=8 TEST_TMPDIR=/dev/shm/rocksdb_partitioned ./db_bench -benchmarks=fillrandom -num=10000000 -disable_wal=1 -write_buffer_size=30000000 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=8 -partition_index_and_filters ``` Observe no measurable change in non-partitioned performance ``` TEST_TMPDIR=/dev/shm/rocksdb_nonpartitioned ./db_bench -benchmarks=seekrandom[-X1000] -num=10000000 -readonly -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=8 -auto_prefix_mode -cache_index_and_filter_blocks=1 -cache_size=1000000000 -duration 20 ``` Before: seekrandom [AVG 15 runs] : 11798 (± 331) ops/sec After: seekrandom [AVG 15 runs] : 11724 (± 315) ops/sec Observe big improvement with partitioned (also supported by bloom use statistics) ``` TEST_TMPDIR=/dev/shm/rocksdb_partitioned ./db_bench -benchmarks=seekrandom[-X1000] -num=10000000 -readonly -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=8 -partition_index_and_filters -auto_prefix_mode -cache_index_and_filter_blocks=1 -cache_size=1000000000 -duration 20 ``` Before: seekrandom [AVG 12 runs] : 2942 (± 57) ops/sec After: seekrandom [AVG 12 runs] : 7489 (± 184) ops/sec Reviewed By: siying Differential Revision: D36469796 Pulled By: pdillinger fbshipit-source-id: bcf1e2a68d347b32adb2b27384f945434e7a266d
2022-05-19 20:09:03 +00:00
DEFINE_bool(auto_prefix_mode, false, "Set auto_prefix_mode for seek benchmark");
DEFINE_int64(max_scan_distance, 0,
"Used to define iterate_upper_bound (or iterate_lower_bound "
"if FLAGS_reverse_iterator is set to true) when value is nonzero");
DEFINE_bool(use_uint64_comparator, false, "use Uint64 user comparator");
DEFINE_int64(batch_size, 1, "Batch size");
static bool ValidateKeySize(const char* /*flagname*/, int32_t /*value*/) {
return true;
}
static bool ValidateUint32Range(const char* flagname, uint64_t value) {
if (value > std::numeric_limits<uint32_t>::max()) {
fprintf(stderr, "Invalid value for --%s: %lu, overflow\n", flagname,
(unsigned long)value);
return false;
}
return true;
}
DEFINE_int32(key_size, 16, "size of each key");
DEFINE_int32(user_timestamp_size, 0,
"number of bytes in a user-defined timestamp");
DEFINE_int32(num_multi_db, 0,
"Number of DBs used in the benchmark. 0 means single DB.");
DEFINE_double(compression_ratio, 0.5,
"Arrange to generate values that shrink to this fraction of "
"their original size after compression");
Add overwrite_probability for filluniquerandom benchmark in db_bench (#8569) Summary: Add flags `overwrite_probability` and `overwrite_window_size` flag to `db_bench`. Add the possibility of performing a `filluniquerandom` benchmark with an overwrite probability. For each write operation, there is a probability _p_ that the write is an overwrite (_p_=`overwrite_probability`). When an overwrite is decided, the key is randomly chosen from the last _N_ keys previously inserted into the DB (with _N_=`overwrite_window_size`). When a pure write is decided, the key inserted into the DB is unique and therefore will not be an overwrite. The `overwrite_window_size` is used so that the user can decide if the overwrite are mostly targeting recently inserted keys (when `overwrite_window_size` is small compared to the total number of writes), or can also target keys inserted "a long time ago" (when `overwrite_window_size` is comparable to total number of writes). Note that total number of writes = # of unique insertions + # of overwrites. No unit test specifically added. Local testing show the following **throughputs** for `filluniquerandom` with 1M total writes: - bypass the code inserts (no `overwrite_probability` flag specified): ~14.0MB/s - `overwrite_probability=0.99`, `overwrite_window_size=10`: ~17.0MB/s - `overwrite_probability=0.10`, `overwrite_window_size=10`: ~14.0MB/s - `overwrite_probability=0.99`, `overwrite_window_size=1M`: ~14.5MB/s - `overwrite_probability=0.10`, `overwrite_window_size=1M`: ~14.0MB/s Pull Request resolved: https://github.com/facebook/rocksdb/pull/8569 Reviewed By: pdillinger Differential Revision: D29818631 Pulled By: bjlemaire fbshipit-source-id: d472b4ea4e457a4da7c4ee4f14b40cccd6a4587a
2021-07-21 18:32:36 +00:00
DEFINE_double(
overwrite_probability, 0.0,
"Used in 'filluniquerandom' benchmark: for each write operation, "
"we give a probability to perform an overwrite instead. The key used for "
"the overwrite is randomly chosen from the last 'overwrite_window_size' "
"keys previously inserted into the DB. "
Add overwrite_probability for filluniquerandom benchmark in db_bench (#8569) Summary: Add flags `overwrite_probability` and `overwrite_window_size` flag to `db_bench`. Add the possibility of performing a `filluniquerandom` benchmark with an overwrite probability. For each write operation, there is a probability _p_ that the write is an overwrite (_p_=`overwrite_probability`). When an overwrite is decided, the key is randomly chosen from the last _N_ keys previously inserted into the DB (with _N_=`overwrite_window_size`). When a pure write is decided, the key inserted into the DB is unique and therefore will not be an overwrite. The `overwrite_window_size` is used so that the user can decide if the overwrite are mostly targeting recently inserted keys (when `overwrite_window_size` is small compared to the total number of writes), or can also target keys inserted "a long time ago" (when `overwrite_window_size` is comparable to total number of writes). Note that total number of writes = # of unique insertions + # of overwrites. No unit test specifically added. Local testing show the following **throughputs** for `filluniquerandom` with 1M total writes: - bypass the code inserts (no `overwrite_probability` flag specified): ~14.0MB/s - `overwrite_probability=0.99`, `overwrite_window_size=10`: ~17.0MB/s - `overwrite_probability=0.10`, `overwrite_window_size=10`: ~14.0MB/s - `overwrite_probability=0.99`, `overwrite_window_size=1M`: ~14.5MB/s - `overwrite_probability=0.10`, `overwrite_window_size=1M`: ~14.0MB/s Pull Request resolved: https://github.com/facebook/rocksdb/pull/8569 Reviewed By: pdillinger Differential Revision: D29818631 Pulled By: bjlemaire fbshipit-source-id: d472b4ea4e457a4da7c4ee4f14b40cccd6a4587a
2021-07-21 18:32:36 +00:00
"Valid overwrite_probability values: [0.0, 1.0].");
DEFINE_uint32(overwrite_window_size, 1,
"Used in 'filluniquerandom' benchmark. For each write operation,"
" when the overwrite_probability flag is set by the user, the "
"key used to perform an overwrite is randomly chosen from the "
"last 'overwrite_window_size' keys previously inserted into DB. "
Add overwrite_probability for filluniquerandom benchmark in db_bench (#8569) Summary: Add flags `overwrite_probability` and `overwrite_window_size` flag to `db_bench`. Add the possibility of performing a `filluniquerandom` benchmark with an overwrite probability. For each write operation, there is a probability _p_ that the write is an overwrite (_p_=`overwrite_probability`). When an overwrite is decided, the key is randomly chosen from the last _N_ keys previously inserted into the DB (with _N_=`overwrite_window_size`). When a pure write is decided, the key inserted into the DB is unique and therefore will not be an overwrite. The `overwrite_window_size` is used so that the user can decide if the overwrite are mostly targeting recently inserted keys (when `overwrite_window_size` is small compared to the total number of writes), or can also target keys inserted "a long time ago" (when `overwrite_window_size` is comparable to total number of writes). Note that total number of writes = # of unique insertions + # of overwrites. No unit test specifically added. Local testing show the following **throughputs** for `filluniquerandom` with 1M total writes: - bypass the code inserts (no `overwrite_probability` flag specified): ~14.0MB/s - `overwrite_probability=0.99`, `overwrite_window_size=10`: ~17.0MB/s - `overwrite_probability=0.10`, `overwrite_window_size=10`: ~14.0MB/s - `overwrite_probability=0.99`, `overwrite_window_size=1M`: ~14.5MB/s - `overwrite_probability=0.10`, `overwrite_window_size=1M`: ~14.0MB/s Pull Request resolved: https://github.com/facebook/rocksdb/pull/8569 Reviewed By: pdillinger Differential Revision: D29818631 Pulled By: bjlemaire fbshipit-source-id: d472b4ea4e457a4da7c4ee4f14b40cccd6a4587a
2021-07-21 18:32:36 +00:00
"Warning: large values can affect throughput. "
"Valid overwrite_window_size values: [1, kMaxUint32].");
Create fillanddeleteuniquerandom benchmark (db_bench), with new option flags. (#8593) Summary: Introduction of a new `fillanddeleteuniquerandom` benchmark (`db_bench`) with 5 new option flags to simulate a benchmark where the following sequence is repeated multiple times: "A set of keys S1 is inserted ('`disposable entries`'), then after some delay another set of keys S2 is inserted ('`persistent entries`') and the first set of keys S1 is deleted. S2 artificially represents the insertion of hypothetical results from some undefined computation done on the first set of keys S1. The next sequence can start as soon as the last disposable entry in the set S1 of this sequence is inserted, if the `delay` is non negligible." New flags: - `disposable_entries_delete_delay`: minimum delay in microseconds between insertion of the last `disposable` entry, and the start of the insertion of the first `persistent` entry. - `disposable_entries_batch_size`: number of `disposable` entries inserted at the beginning of each sequence. - `disposable_entries_value_size`: size of the random `value` string for the `disposable` entries. - `persistent_entries_batch_size`: number of `persistent` entries inserted at the end of each sequence, right before the deletion of the `disposable` entries starts. - `persistent_entries_value_size`: size of the random value string for the `persistent` entries. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8593 Reviewed By: pdillinger Differential Revision: D29974436 Pulled By: bjlemaire fbshipit-source-id: f578033e5b45e8268ba6fa6f38f4770c2e6e801d
2021-07-29 21:57:03 +00:00
DEFINE_uint64(
disposable_entries_delete_delay, 0,
"Minimum delay in microseconds for the series of Deletes "
"to be issued. When 0 the insertion of the last disposable entry is "
"immediately followed by the issuance of the Deletes. "
"(only compatible with fillanddeleteuniquerandom benchmark).");
DEFINE_uint64(disposable_entries_batch_size, 0,
"Number of consecutively inserted disposable KV entries "
"that will be deleted after 'delete_delay' microseconds. "
"A series of Deletes is always issued once all the "
"disposable KV entries it targets have been inserted "
"into the DB. When 0 no deletes are issued and a "
"regular 'filluniquerandom' benchmark occurs. "
"(only compatible with fillanddeleteuniquerandom benchmark)");
DEFINE_int32(disposable_entries_value_size, 64,
"Size of the values (in bytes) of the entries targeted by "
"selective deletes. "
"(only compatible with fillanddeleteuniquerandom benchmark)");
DEFINE_uint64(
persistent_entries_batch_size, 0,
"Number of KV entries being inserted right before the deletes "
"targeting the disposable KV entries are issued. These "
"persistent keys are not targeted by the deletes, and will always "
"remain valid in the DB. (only compatible with "
"--benchmarks='fillanddeleteuniquerandom' "
"and used when--disposable_entries_batch_size is > 0).");
DEFINE_int32(persistent_entries_value_size, 64,
"Size of the values (in bytes) of the entries not targeted by "
"deletes. (only compatible with "
"--benchmarks='fillanddeleteuniquerandom' "
"and used when--disposable_entries_batch_size is > 0).");
DEFINE_double(read_random_exp_range, 0.0,
"Read random's key will be generated using distribution of "
"num * exp(-r) where r is uniform number from 0 to this value. "
"The larger the number is, the more skewed the reads are. "
"Only used in readrandom and multireadrandom benchmarks.");
DEFINE_bool(histogram, false, "Print histogram of operation timings");
Add 95% confidence intervals to db_bench output (#9882) Summary: Enhancing `db_bench` output with 95% statistical confidence intervals for better performance evaluation. The goal is to unambiguously separate random variance when running benchmark over multiple iterations. Output enhanced with confidence intervals exposed in brackets: ``` $ ./db_bench --benchmarks=fillseq[-X10] Running benchmark for 10 times fillseq : 4.961 micros/op 201578 ops/sec; 22.3 MB/s fillseq : 5.030 micros/op 198824 ops/sec; 22.0 MB/s fillseq [AVG 2 runs] : 200201 (± 2698) ops/sec; 22.1 (± 0.3) MB/sec fillseq : 4.963 micros/op 201471 ops/sec; 22.3 MB/s fillseq [AVG 3 runs] : 200624 (± 1765) ops/sec; 22.2 (± 0.2) MB/sec fillseq : 5.035 micros/op 198625 ops/sec; 22.0 MB/s fillseq [AVG 4 runs] : 200124 (± 1586) ops/sec; 22.1 (± 0.2) MB/sec fillseq : 4.979 micros/op 200861 ops/sec; 22.2 MB/s fillseq [AVG 5 runs] : 200272 (± 1262) ops/sec; 22.2 (± 0.1) MB/sec fillseq : 4.893 micros/op 204367 ops/sec; 22.6 MB/s fillseq [AVG 6 runs] : 200954 (± 1688) ops/sec; 22.2 (± 0.2) MB/sec fillseq : 4.914 micros/op 203502 ops/sec; 22.5 MB/s fillseq [AVG 7 runs] : 201318 (± 1595) ops/sec; 22.3 (± 0.2) MB/sec fillseq : 4.998 micros/op 200074 ops/sec; 22.1 MB/s fillseq [AVG 8 runs] : 201163 (± 1415) ops/sec; 22.3 (± 0.2) MB/sec fillseq : 4.946 micros/op 202188 ops/sec; 22.4 MB/s fillseq [AVG 9 runs] : 201277 (± 1267) ops/sec; 22.3 (± 0.1) MB/sec fillseq : 5.093 micros/op 196331 ops/sec; 21.7 MB/s fillseq [AVG 10 runs] : 200782 (± 1491) ops/sec; 22.2 (± 0.2) MB/sec fillseq [AVG 10 runs] : 200782 (± 1491) ops/sec; 22.2 (± 0.2) MB/sec fillseq [MEDIAN 10 runs] : 201166 ops/sec; 22.3 MB/s ``` For more explicit interval representation, use `--confidence_interval_only` flag: ``` $ ./db_bench --benchmarks=fillseq[-X10] --confidence_interval_only Running benchmark for 10 times fillseq : 4.935 micros/op 202648 ops/sec; 22.4 MB/s fillseq : 5.078 micros/op 196943 ops/sec; 21.8 MB/s fillseq [CI95 2 runs] : (194205, 205385) ops/sec; (21.5, 22.7) MB/sec fillseq : 5.159 micros/op 193816 ops/sec; 21.4 MB/s fillseq [CI95 3 runs] : (192735, 202869) ops/sec; (21.3, 22.4) MB/sec fillseq : 4.947 micros/op 202158 ops/sec; 22.4 MB/s fillseq [CI95 4 runs] : (194721, 203061) ops/sec; (21.5, 22.5) MB/sec fillseq : 4.908 micros/op 203756 ops/sec; 22.5 MB/s fillseq [CI95 5 runs] : (196113, 203615) ops/sec; (21.7, 22.5) MB/sec fillseq : 5.063 micros/op 197528 ops/sec; 21.9 MB/s fillseq [CI95 6 runs] : (196319, 202631) ops/sec; (21.7, 22.4) MB/sec fillseq : 5.214 micros/op 191799 ops/sec; 21.2 MB/s fillseq [CI95 7 runs] : (194953, 201803) ops/sec; (21.6, 22.3) MB/sec fillseq : 5.260 micros/op 190095 ops/sec; 21.0 MB/s fillseq [CI95 8 runs] : (193749, 200937) ops/sec; (21.4, 22.2) MB/sec fillseq : 5.076 micros/op 196992 ops/sec; 21.8 MB/s fillseq [CI95 9 runs] : (194134, 200474) ops/sec; (21.5, 22.2) MB/sec fillseq : 5.388 micros/op 185603 ops/sec; 20.5 MB/s fillseq [CI95 10 runs] : (192487, 199781) ops/sec; (21.3, 22.1) MB/sec fillseq [AVG 10 runs] : 196134 (± 3647) ops/sec; 21.7 (± 0.4) MB/sec fillseq [MEDIAN 10 runs] : 196968 ops/sec; 21.8 MB/sec ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/9882 Reviewed By: pdillinger Differential Revision: D35796148 Pulled By: vanekjar fbshipit-source-id: 8313712d16728ff982b8aff28195ee56622385b8
2022-04-25 21:49:54 +00:00
DEFINE_bool(confidence_interval_only, false,
"Print 95% confidence interval upper and lower bounds only for "
"aggregate stats.");
Adding NUMA support to db_bench tests Summary: Changes: - Adding numa_aware flag to db_bench.cc - Using numa.h library to bind memory and cpu of threads to a fixed NUMA node Result: There seems to be no significant change in the micros/op time with numa_aware enabled. I also tried this with other implementations, including a combination of pthread_setaffinity_np, sched_setaffinity and set_mempolicy methods. It'd be great if someone could point out where I'm going wrong and if we can achieve a better micors/op. Test Plan: Ran db_bench tests using following command: ./db_bench --db=/mnt/tmp --num_levels=6 --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --block_size=4096 --cache_size=17179869184 --cache_numshardbits=6 --compression_type=none --compression_ratio=1 --min_level_to_compress=-1 --disable_seek_compaction=1 --hard_rate_limit=2 --write_buffer_size=134217728 --max_write_buffer_number=2 --level0_file_num_compaction_trigger=8 --target_file_size_base=134217728 --max_bytes_for_level_base=1073741824 --disable_wal=0 --wal_dir=/mnt/tmp --sync=0 --disable_data_sync=1 --verify_checksum=1 --delete_obsolete_files_period_micros=314572800 --max_grandparent_overlap_factor=10 --max_background_compactions=4 --max_background_flushes=0 --level0_slowdown_writes_trigger=16 --level0_stop_writes_trigger=24 --statistics=0 --stats_per_interval=0 --stats_interval=1048576 --histogram=0 --use_plain_table=1 --open_files=-1 --mmap_read=1 --mmap_write=0 --memtablerep=prefix_hash --bloom_bits=10 --bloom_locality=1 --perf_level=0 --duration=300 --benchmarks=readwhilewriting --use_existing_db=1 --num=157286400 --threads=24 --writes_per_second=10240 --numa_aware=[False/True] The tests were run in private devserver with 24 cores and the db was prepopulated using filluniquerandom test. The tests resulted in 0.145 us/op with numa_aware=False and 0.161 us/op with numa_aware=True. Reviewers: sdong, yhchiang, ljin, igor Reviewed By: ljin, igor Subscribers: igor, leveldb Differential Revision: https://reviews.facebook.net/D19353
2014-07-07 17:53:31 +00:00
DEFINE_bool(enable_numa, false,
"Make operations aware of NUMA architecture and bind memory "
"and cpus corresponding to nodes together. In NUMA, memory "
"in same node as CPUs are closer when compared to memory in "
"other nodes. Reads can be faster when the process is bound to "
"CPU and memory of same node. Use \"$numactl --hardware\" command "
"to see NUMA memory architecture.");
DEFINE_int64(db_write_buffer_size,
ROCKSDB_NAMESPACE::Options().db_write_buffer_size,
"Number of bytes to buffer in all memtables before compacting");
DEFINE_bool(cost_write_buffer_to_cache, false,
"The usage of memtable is costed to the block cache");
DEFINE_int64(arena_block_size, ROCKSDB_NAMESPACE::Options().arena_block_size,
"The size, in bytes, of one block in arena memory allocation.");
DEFINE_int64(write_buffer_size, ROCKSDB_NAMESPACE::Options().write_buffer_size,
"Number of bytes to buffer in memtable before compacting");
DEFINE_int32(max_write_buffer_number,
ROCKSDB_NAMESPACE::Options().max_write_buffer_number,
"The number of in-memory memtables. Each memtable is of size"
" write_buffer_size bytes.");
DEFINE_int32(min_write_buffer_number_to_merge,
ROCKSDB_NAMESPACE::Options().min_write_buffer_number_to_merge,
"The minimum number of write buffers that will be merged together"
"before writing to storage. This is cheap because it is an"
"in-memory merge. If this feature is not enabled, then all these"
"write buffers are flushed to L0 as separate files and this "
"increases read amplification because a get request has to check"
" in all of these files. Also, an in-memory merge may result in"
" writing less data to storage if there are duplicate records "
" in each of these individual write buffers.");
Support saving history in memtable_list Summary: For transactions, we are using the memtables to validate that there are no write conflicts. But after flushing, we don't have any memtables, and transactions could fail to commit. So we want to someone keep around some extra history to use for conflict checking. In addition, we want to provide a way to increase the size of this history if too many transactions fail to commit. After chatting with people, it seems like everyone prefers just using Memtables to store this history (instead of a separate history structure). It seems like the best place for this is abstracted inside the memtable_list. I decide to create a separate list in MemtableListVersion as using the same list complicated the flush/installalflushresults logic too much. This diff adds a new parameter to control how much memtable history to keep around after flushing. However, it sounds like people aren't too fond of adding new parameters. So I am making the default size of flushed+not-flushed memtables be set to max_write_buffers. This should not change the maximum amount of memory used, but make it more likely we're using closer the the limit. (We are now postponing deleting flushed memtables until the max_write_buffer limit is reached). So while we might use more memory on average, we are still obeying the limit set (and you could argue it's better to go ahead and use up memory now instead of waiting for a write stall to happen to test this limit). However, if people are opposed to this default behavior, we can easily set it to 0 and require this parameter be set in order to use transactions. Test Plan: Added a xfunc test to play around with setting different values of this parameter in all tests. Added testing in memtablelist_test and planning on adding more testing here. Reviewers: sdong, rven, igor Reviewed By: igor Subscribers: dhruba, leveldb Differential Revision: https://reviews.facebook.net/D37443
2015-05-28 23:34:24 +00:00
DEFINE_int32(max_write_buffer_number_to_maintain,
ROCKSDB_NAMESPACE::Options().max_write_buffer_number_to_maintain,
Support saving history in memtable_list Summary: For transactions, we are using the memtables to validate that there are no write conflicts. But after flushing, we don't have any memtables, and transactions could fail to commit. So we want to someone keep around some extra history to use for conflict checking. In addition, we want to provide a way to increase the size of this history if too many transactions fail to commit. After chatting with people, it seems like everyone prefers just using Memtables to store this history (instead of a separate history structure). It seems like the best place for this is abstracted inside the memtable_list. I decide to create a separate list in MemtableListVersion as using the same list complicated the flush/installalflushresults logic too much. This diff adds a new parameter to control how much memtable history to keep around after flushing. However, it sounds like people aren't too fond of adding new parameters. So I am making the default size of flushed+not-flushed memtables be set to max_write_buffers. This should not change the maximum amount of memory used, but make it more likely we're using closer the the limit. (We are now postponing deleting flushed memtables until the max_write_buffer limit is reached). So while we might use more memory on average, we are still obeying the limit set (and you could argue it's better to go ahead and use up memory now instead of waiting for a write stall to happen to test this limit). However, if people are opposed to this default behavior, we can easily set it to 0 and require this parameter be set in order to use transactions. Test Plan: Added a xfunc test to play around with setting different values of this parameter in all tests. Added testing in memtablelist_test and planning on adding more testing here. Reviewers: sdong, rven, igor Reviewed By: igor Subscribers: dhruba, leveldb Differential Revision: https://reviews.facebook.net/D37443
2015-05-28 23:34:24 +00:00
"The total maximum number of write buffers to maintain in memory "
"including copies of buffers that have already been flushed. "
"Unlike max_write_buffer_number, this parameter does not affect "
"flushing. This controls the minimum amount of write history "
"that will be available in memory for conflict checking when "
"Transactions are used. If this value is too low, some "
"transactions may fail at commit time due to not being able to "
"determine whether there were any write conflicts. Setting this "
"value to 0 will cause write buffers to be freed immediately "
"after they are flushed. If this value is set to -1, "
"'max_write_buffer_number' will be used.");
Refactor trimming logic for immutable memtables (#5022) Summary: MyRocks currently sets `max_write_buffer_number_to_maintain` in order to maintain enough history for transaction conflict checking. The effectiveness of this approach depends on the size of memtables. When memtables are small, it may not keep enough history; when memtables are large, this may consume too much memory. We are proposing a new way to configure memtable list history: by limiting the memory usage of immutable memtables. The new option is `max_write_buffer_size_to_maintain` and it will take precedence over the old `max_write_buffer_number_to_maintain` if they are both set to non-zero values. The new option accounts for the total memory usage of flushed immutable memtables and mutable memtable. When the total usage exceeds the limit, RocksDB may start dropping immutable memtables (which is also called trimming history), starting from the oldest one. The semantics of the old option actually works both as an upper bound and lower bound. History trimming will start if number of immutable memtables exceeds the limit, but it will never go below (limit-1) due to history trimming. In order the mimic the behavior with the new option, history trimming will stop if dropping the next immutable memtable causes the total memory usage go below the size limit. For example, assuming the size limit is set to 64MB, and there are 3 immutable memtables with sizes of 20, 30, 30. Although the total memory usage is 80MB > 64MB, dropping the oldest memtable will reduce the memory usage to 60MB < 64MB, so in this case no memtable will be dropped. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5022 Differential Revision: D14394062 Pulled By: miasantreble fbshipit-source-id: 60457a509c6af89d0993f988c9b5c2aa9e45f5c5
2019-08-23 20:54:09 +00:00
DEFINE_int64(max_write_buffer_size_to_maintain,
ROCKSDB_NAMESPACE::Options().max_write_buffer_size_to_maintain,
Refactor trimming logic for immutable memtables (#5022) Summary: MyRocks currently sets `max_write_buffer_number_to_maintain` in order to maintain enough history for transaction conflict checking. The effectiveness of this approach depends on the size of memtables. When memtables are small, it may not keep enough history; when memtables are large, this may consume too much memory. We are proposing a new way to configure memtable list history: by limiting the memory usage of immutable memtables. The new option is `max_write_buffer_size_to_maintain` and it will take precedence over the old `max_write_buffer_number_to_maintain` if they are both set to non-zero values. The new option accounts for the total memory usage of flushed immutable memtables and mutable memtable. When the total usage exceeds the limit, RocksDB may start dropping immutable memtables (which is also called trimming history), starting from the oldest one. The semantics of the old option actually works both as an upper bound and lower bound. History trimming will start if number of immutable memtables exceeds the limit, but it will never go below (limit-1) due to history trimming. In order the mimic the behavior with the new option, history trimming will stop if dropping the next immutable memtable causes the total memory usage go below the size limit. For example, assuming the size limit is set to 64MB, and there are 3 immutable memtables with sizes of 20, 30, 30. Although the total memory usage is 80MB > 64MB, dropping the oldest memtable will reduce the memory usage to 60MB < 64MB, so in this case no memtable will be dropped. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5022 Differential Revision: D14394062 Pulled By: miasantreble fbshipit-source-id: 60457a509c6af89d0993f988c9b5c2aa9e45f5c5
2019-08-23 20:54:09 +00:00
"The total maximum size of write buffers to maintain in memory "
"including copies of buffers that have already been flushed. "
"Unlike max_write_buffer_number, this parameter does not affect "
"flushing. This controls the minimum amount of write history "
"that will be available in memory for conflict checking when "
"Transactions are used. If this value is too low, some "
"transactions may fail at commit time due to not being able to "
"determine whether there were any write conflicts. Setting this "
"value to 0 will cause write buffers to be freed immediately "
"after they are flushed. If this value is set to -1, "
"'max_write_buffer_number' will be used.");
DEFINE_int32(max_background_jobs,
ROCKSDB_NAMESPACE::Options().max_background_jobs,
"The maximum number of concurrent background jobs that can occur "
"in parallel.");
Introduce bottom-pri thread pool for large universal compactions Summary: When we had a single thread pool for compactions, a thread could be busy for a long time (minutes) executing a compaction involving the bottom level. In multi-instance setups, the entire thread pool could be consumed by such bottom-level compactions. Then, top-level compactions (e.g., a few L0 files) would be blocked for a long time ("head-of-line blocking"). Such top-level compactions are critical to prevent compaction stalls as they can quickly reduce number of L0 files / sorted runs. This diff introduces a bottom-priority queue for universal compactions including the bottom level. This alleviates the head-of-line blocking situation for fast, top-level compactions. - Added `Env::Priority::BOTTOM` thread pool. This feature is only enabled if user explicitly configures it to have a positive number of threads. - Changed `ThreadPoolImpl`'s default thread limit from one to zero. This change is invisible to users as we call `IncBackgroundThreadsIfNeeded` on the low-pri/high-pri pools during `DB::Open` with values of at least one. It is necessary, though, for bottom-pri to start with zero threads so the feature is disabled by default. - Separated `ManualCompaction` into two parts in `PrepickedCompaction`. `PrepickedCompaction` is used for any compaction that's picked outside of its execution thread, either manual or automatic. - Forward universal compactions involving last level to the bottom pool (worker thread's entry point is `BGWorkBottomCompaction`). - Track `bg_bottom_compaction_scheduled_` so we can wait for bottom-level compactions to finish. We don't count them against the background jobs limits. So users of this feature will get an extra compaction for free. Closes https://github.com/facebook/rocksdb/pull/2580 Differential Revision: D5422916 Pulled By: ajkr fbshipit-source-id: a74bd11f1ea4933df3739b16808bb21fcd512333
2017-08-03 22:36:28 +00:00
DEFINE_int32(num_bottom_pri_threads, 0,
"The number of threads in the bottom-priority thread pool (used "
"by universal compaction only).");
DEFINE_int32(num_high_pri_threads, 0,
"The maximum number of concurrent background compactions"
" that can occur in parallel.");
DEFINE_int32(num_low_pri_threads, 0,
"The maximum number of concurrent background compactions"
" that can occur in parallel.");
DEFINE_int32(max_background_compactions,
ROCKSDB_NAMESPACE::Options().max_background_compactions,
"The maximum number of concurrent background compactions"
" that can occur in parallel.");
DEFINE_uint64(subcompactions, 1,
"For CompactRange, set max_subcompactions for each compaction "
"job in this CompactRange, for auto compactions, this is "
"Maximum number of subcompactions to divide L0-L1 compactions "
"into.");
static const bool FLAGS_subcompactions_dummy __attribute__((__unused__)) =
RegisterFlagValidator(&FLAGS_subcompactions, &ValidateUint32Range);
DEFINE_int32(max_background_flushes,
ROCKSDB_NAMESPACE::Options().max_background_flushes,
"The maximum number of concurrent background flushes"
" that can occur in parallel.");
static ROCKSDB_NAMESPACE::CompactionStyle FLAGS_compaction_style_e;
DEFINE_int32(compaction_style,
(int32_t)ROCKSDB_NAMESPACE::Options().compaction_style,
"style of compaction: level-based, universal and fifo");
static ROCKSDB_NAMESPACE::CompactionPri FLAGS_compaction_pri_e;
DEFINE_int32(compaction_pri,
(int32_t)ROCKSDB_NAMESPACE::Options().compaction_pri,
"priority of files to compaction: by size or by data age");
DEFINE_int32(universal_size_ratio, 0,
"Percentage flexibility while comparing file size "
"(for universal compaction only).");
DEFINE_int32(universal_min_merge_width, 0,
"The minimum number of files in a single compaction run "
"(for universal compaction only).");
DEFINE_int32(universal_max_merge_width, 0,
"The max number of files to compact in universal style "
"compaction");
DEFINE_int32(universal_max_size_amplification_percent, 0,
"The max size amplification for universal style compaction");
DEFINE_int32(universal_compression_size_percent, -1,
"The percentage of the database to compress for universal "
"compaction. -1 means compress everything.");
DEFINE_bool(universal_allow_trivial_move, false,
"Allow trivial move in universal compaction.");
Incremental Space Amp Compactions in Universal Style (#8655) Summary: This commit introduces incremental compaction in univeral style for space amplification. This follows the first improvement mentioned in https://rocksdb.org/blog/2021/04/12/universal-improvements.html . The implemention simply picks up files about size of max_compaction_bytes to compact and execute if the penalty is not too big. More optimizations can be done in the future, e.g. prioritizing between this compaction and other types. But for now, the feature is supposed to be functional and can often reduce frequency of full compactions, although it can introduce penalty. In order to add cut files more efficiently so that more files from upper levels can be included, SST file cutting threshold (for current file + overlapping parent level files) is set to 1.5X of target file size. A 2MB target file size will generate files like this: https://gist.github.com/siying/29d2676fba417404f3c95e6c013c7de8 Number of files indeed increases but it is not out of control. Two set of write benchmarks are run: 1. For ingestion rate limited scenario, we can see full compaction is mostly eliminated: https://gist.github.com/siying/959bc1186066906831cf4c808d6e0a19 . The write amp increased from 7.7 to 9.4, as expected. After applying file cutting, the number is improved to 8.9. In another benchmark, the write amp is even better with the incremental approach: https://gist.github.com/siying/d1c16c286d7c59c4d7bba718ca198163 2. For ingestion rate unlimited scenario, incremental compaction turns out to be too expensive most of the time and is not executed, as expected. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8655 Test Plan: Add unit tests to the functionality. Reviewed By: ajkr Differential Revision: D31787034 fbshipit-source-id: ce813e63b15a61d5a56e97bf8902a1b28e011beb
2021-10-20 17:03:03 +00:00
DEFINE_bool(universal_incremental, false,
"Enable incremental compactions in universal compaction.");
DEFINE_int64(cache_size, 32 << 20, // 32MB
"Number of bytes to use as a cache of uncompressed data");
Revamp, optimize new experimental clock cache (#10626) Summary: * Consolidates most metadata into a single word per slot so that more can be accomplished with a single atomic update. In the common case, Lookup was previously about 4 atomic updates, now just 1 atomic update. Common case Release was previously 1 atomic read + 1 atomic update, now just 1 atomic update. * Eliminate spins / waits / yields, which likely threaten some "lock free" benefits. Compare-exchange loops are only used in explicit Erase, and strict_capacity_limit=true Insert. Eviction uses opportunistic compare- exchange. * Relaxes some aggressiveness and guarantees. For example, * Duplicate Inserts will sometimes go undetected and the shadow duplicate will age out with eviction. * In many cases, the older Inserted value for a given cache key will be kept (i.e. Insert does not support overwrite). * Entries explicitly erased (rather than evicted) might not be freed immediately in some rare cases. * With strict_capacity_limit=false, capacity limit is not tracked/enforced as precisely as LRUCache, but is self-correcting and should only deviate by a very small number of extra or fewer entries. * Use smaller "computed default" number of cache shards in many cases, because benefits to larger usage tracking / eviction pools outweigh the small cost of more lock-free atomic contention. The improvement in CPU and I/O is dramatic in some limit-memory cases. * Even without the sharding change, the eviction algorithm is likely more effective than LRU overall because it's more stateful, even though the "hot path" state tracking for it is essentially free with ref counting. It is like a generalized CLOCK with aging (see code comments). I don't have performance numbers showing a specific improvement, but in theory, for a Poisson access pattern to each block, keeping some state allows better estimation of time to next access (Poisson interval) than strict LRU. The bounded randomness in CLOCK can also reduce "cliff" effect for repeated range scans approaching and exceeding cache size. ## Hot path algorithm comparison Rough descriptions, focusing on number and kind of atomic operations: * Old `Lookup()` (2-5 atomic updates per probe): ``` Loop: Increment internal ref count at slot If possible hit: Check flags atomic (and non-atomic fields) If cache hit: Three distinct updates to 'flags' atomic Increment refs for internal-to-external Return Decrement internal ref count while atomic read 'displacements' > 0 ``` * New `Lookup()` (1-2 atomic updates per probe): ``` Loop: Increment acquire counter in meta word (optimistic) If visible entry (already read meta word): If match (read non-atomic fields): Return Else: Decrement acquire counter in meta word Else if invisible entry (rare, already read meta word): Decrement acquire counter in meta word while atomic read 'displacements' > 0 ``` * Old `Release()` (1 atomic update, conditional on atomic read, rarely more): ``` Read atomic ref count If last reference and invisible (rare): Use CAS etc. to remove Return Else: Decrement ref count ``` * New `Release()` (1 unconditional atomic update, rarely more): ``` Increment release counter in meta word If last reference and invisible (rare): Use CAS etc. to remove Return ``` ## Performance test setup Build DB with ``` TEST_TMPDIR=/dev/shm ./db_bench -benchmarks=fillrandom -num=30000000 -disable_wal=1 -bloom_bits=16 ``` Test with ``` TEST_TMPDIR=/dev/shm ./db_bench -benchmarks=readrandom -readonly -num=30000000 -bloom_bits=16 -cache_index_and_filter_blocks=1 -cache_size=${CACHE_MB}000000 -duration 60 -threads=$THREADS -statistics ``` Numbers on a single socket Skylake Xeon system with 48 hardware threads, DEBUG_LEVEL=0 PORTABLE=0. Very similar story on a dual socket system with 80 hardware threads. Using (every 2nd) Fibonacci MB cache sizes to sample the territory between powers of two. Configurations: base: LRUCache before this change, but with db_bench change to default cache_numshardbits=-1 (instead of fixed at 6) folly: LRUCache before this change, with folly enabled (distributed mutex) but on an old compiler (sorry) gt_clock: experimental ClockCache before this change new_clock: experimental ClockCache with this change ## Performance test results First test "hot path" read performance, with block cache large enough for whole DB: 4181MB 1thread base -> kops/s: 47.761 4181MB 1thread folly -> kops/s: 45.877 4181MB 1thread gt_clock -> kops/s: 51.092 4181MB 1thread new_clock -> kops/s: 53.944 4181MB 16thread base -> kops/s: 284.567 4181MB 16thread folly -> kops/s: 249.015 4181MB 16thread gt_clock -> kops/s: 743.762 4181MB 16thread new_clock -> kops/s: 861.821 4181MB 24thread base -> kops/s: 303.415 4181MB 24thread folly -> kops/s: 266.548 4181MB 24thread gt_clock -> kops/s: 975.706 4181MB 24thread new_clock -> kops/s: 1205.64 (~= 24 * 53.944) 4181MB 32thread base -> kops/s: 311.251 4181MB 32thread folly -> kops/s: 274.952 4181MB 32thread gt_clock -> kops/s: 1045.98 4181MB 32thread new_clock -> kops/s: 1370.38 4181MB 48thread base -> kops/s: 310.504 4181MB 48thread folly -> kops/s: 268.322 4181MB 48thread gt_clock -> kops/s: 1195.65 4181MB 48thread new_clock -> kops/s: 1604.85 (~= 24 * 1.25 * 53.944) 4181MB 64thread base -> kops/s: 307.839 4181MB 64thread folly -> kops/s: 272.172 4181MB 64thread gt_clock -> kops/s: 1204.47 4181MB 64thread new_clock -> kops/s: 1615.37 4181MB 128thread base -> kops/s: 310.934 4181MB 128thread folly -> kops/s: 267.468 4181MB 128thread gt_clock -> kops/s: 1188.75 4181MB 128thread new_clock -> kops/s: 1595.46 Whether we have just one thread on a quiet system or an overload of threads, the new version wins every time in thousand-ops per second, sometimes dramatically so. Mutex-based implementation quickly becomes contention-limited. New clock cache shows essentially perfect scaling up to number of physical cores (24), and then each hyperthreaded core adding about 1/4 the throughput of an additional physical core (see 48 thread case). Block cache miss rates (omitted above) are negligible across the board. With partitioned instead of full filters, the maximum speed-up vs. base is more like 2.5x rather than 5x. Now test a large block cache with low miss ratio, but some eviction is required: 1597MB 1thread base -> kops/s: 46.603 io_bytes/op: 1584.63 miss_ratio: 0.0201066 max_rss_mb: 1589.23 1597MB 1thread folly -> kops/s: 45.079 io_bytes/op: 1530.03 miss_ratio: 0.019872 max_rss_mb: 1550.43 1597MB 1thread gt_clock -> kops/s: 48.711 io_bytes/op: 1566.63 miss_ratio: 0.0198923 max_rss_mb: 1691.4 1597MB 1thread new_clock -> kops/s: 51.531 io_bytes/op: 1589.07 miss_ratio: 0.0201969 max_rss_mb: 1583.56 1597MB 32thread base -> kops/s: 301.174 io_bytes/op: 1439.52 miss_ratio: 0.0184218 max_rss_mb: 1656.59 1597MB 32thread folly -> kops/s: 273.09 io_bytes/op: 1375.12 miss_ratio: 0.0180002 max_rss_mb: 1586.8 1597MB 32thread gt_clock -> kops/s: 904.497 io_bytes/op: 1411.29 miss_ratio: 0.0179934 max_rss_mb: 1775.89 1597MB 32thread new_clock -> kops/s: 1182.59 io_bytes/op: 1440.77 miss_ratio: 0.0185449 max_rss_mb: 1636.45 1597MB 128thread base -> kops/s: 309.91 io_bytes/op: 1438.25 miss_ratio: 0.018399 max_rss_mb: 1689.98 1597MB 128thread folly -> kops/s: 267.605 io_bytes/op: 1394.16 miss_ratio: 0.0180286 max_rss_mb: 1631.91 1597MB 128thread gt_clock -> kops/s: 691.518 io_bytes/op: 9056.73 miss_ratio: 0.0186572 max_rss_mb: 1982.26 1597MB 128thread new_clock -> kops/s: 1406.12 io_bytes/op: 1440.82 miss_ratio: 0.0185463 max_rss_mb: 1685.63 610MB 1thread base -> kops/s: 45.511 io_bytes/op: 2279.61 miss_ratio: 0.0290528 max_rss_mb: 615.137 610MB 1thread folly -> kops/s: 43.386 io_bytes/op: 2217.29 miss_ratio: 0.0289282 max_rss_mb: 600.996 610MB 1thread gt_clock -> kops/s: 46.207 io_bytes/op: 2275.51 miss_ratio: 0.0290057 max_rss_mb: 637.934 610MB 1thread new_clock -> kops/s: 48.879 io_bytes/op: 2283.1 miss_ratio: 0.0291253 max_rss_mb: 613.5 610MB 32thread base -> kops/s: 306.59 io_bytes/op: 2250 miss_ratio: 0.0288721 max_rss_mb: 683.402 610MB 32thread folly -> kops/s: 269.176 io_bytes/op: 2187.86 miss_ratio: 0.0286938 max_rss_mb: 628.742 610MB 32thread gt_clock -> kops/s: 855.097 io_bytes/op: 2279.26 miss_ratio: 0.0288009 max_rss_mb: 733.062 610MB 32thread new_clock -> kops/s: 1121.47 io_bytes/op: 2244.29 miss_ratio: 0.0289046 max_rss_mb: 666.453 610MB 128thread base -> kops/s: 305.079 io_bytes/op: 2252.43 miss_ratio: 0.0288884 max_rss_mb: 723.457 610MB 128thread folly -> kops/s: 269.583 io_bytes/op: 2204.58 miss_ratio: 0.0287001 max_rss_mb: 676.426 610MB 128thread gt_clock -> kops/s: 53.298 io_bytes/op: 8128.98 miss_ratio: 0.0292452 max_rss_mb: 956.273 610MB 128thread new_clock -> kops/s: 1301.09 io_bytes/op: 2246.04 miss_ratio: 0.0289171 max_rss_mb: 788.812 The new version is still winning every time, sometimes dramatically so, and we can tell from the maximum resident memory numbers (which contain some noise, by the way) that the new cache is not cheating on memory usage. IMPORTANT: The previous generation experimental clock cache appears to hit a serious bottleneck in the higher thread count configurations, presumably due to some of its waiting functionality. (The same bottleneck is not seen with partitioned index+filters.) Now we consider even smaller cache sizes, with higher miss ratios, eviction work, etc. 233MB 1thread base -> kops/s: 10.557 io_bytes/op: 227040 miss_ratio: 0.0403105 max_rss_mb: 247.371 233MB 1thread folly -> kops/s: 15.348 io_bytes/op: 112007 miss_ratio: 0.0372238 max_rss_mb: 245.293 233MB 1thread gt_clock -> kops/s: 6.365 io_bytes/op: 244854 miss_ratio: 0.0413873 max_rss_mb: 259.844 233MB 1thread new_clock -> kops/s: 47.501 io_bytes/op: 2591.93 miss_ratio: 0.0330989 max_rss_mb: 242.461 233MB 32thread base -> kops/s: 96.498 io_bytes/op: 363379 miss_ratio: 0.0459966 max_rss_mb: 479.227 233MB 32thread folly -> kops/s: 109.95 io_bytes/op: 314799 miss_ratio: 0.0450032 max_rss_mb: 400.738 233MB 32thread gt_clock -> kops/s: 2.353 io_bytes/op: 385397 miss_ratio: 0.048445 max_rss_mb: 500.688 233MB 32thread new_clock -> kops/s: 1088.95 io_bytes/op: 2567.02 miss_ratio: 0.0330593 max_rss_mb: 303.402 233MB 128thread base -> kops/s: 84.302 io_bytes/op: 378020 miss_ratio: 0.0466558 max_rss_mb: 1051.84 233MB 128thread folly -> kops/s: 89.921 io_bytes/op: 338242 miss_ratio: 0.0460309 max_rss_mb: 812.785 233MB 128thread gt_clock -> kops/s: 2.588 io_bytes/op: 462833 miss_ratio: 0.0509158 max_rss_mb: 1109.94 233MB 128thread new_clock -> kops/s: 1299.26 io_bytes/op: 2565.94 miss_ratio: 0.0330531 max_rss_mb: 361.016 89MB 1thread base -> kops/s: 0.574 io_bytes/op: 5.35977e+06 miss_ratio: 0.274427 max_rss_mb: 91.3086 89MB 1thread folly -> kops/s: 0.578 io_bytes/op: 5.16549e+06 miss_ratio: 0.27276 max_rss_mb: 96.8984 89MB 1thread gt_clock -> kops/s: 0.512 io_bytes/op: 4.13111e+06 miss_ratio: 0.242817 max_rss_mb: 119.441 89MB 1thread new_clock -> kops/s: 48.172 io_bytes/op: 2709.76 miss_ratio: 0.0346162 max_rss_mb: 100.754 89MB 32thread base -> kops/s: 5.779 io_bytes/op: 6.14192e+06 miss_ratio: 0.320399 max_rss_mb: 311.812 89MB 32thread folly -> kops/s: 5.601 io_bytes/op: 5.83838e+06 miss_ratio: 0.313123 max_rss_mb: 252.418 89MB 32thread gt_clock -> kops/s: 0.77 io_bytes/op: 3.99236e+06 miss_ratio: 0.236296 max_rss_mb: 396.422 89MB 32thread new_clock -> kops/s: 1064.97 io_bytes/op: 2687.23 miss_ratio: 0.0346134 max_rss_mb: 155.293 89MB 128thread base -> kops/s: 4.959 io_bytes/op: 6.20297e+06 miss_ratio: 0.323945 max_rss_mb: 823.43 89MB 128thread folly -> kops/s: 4.962 io_bytes/op: 5.9601e+06 miss_ratio: 0.319857 max_rss_mb: 626.824 89MB 128thread gt_clock -> kops/s: 1.009 io_bytes/op: 4.1083e+06 miss_ratio: 0.242512 max_rss_mb: 1095.32 89MB 128thread new_clock -> kops/s: 1224.39 io_bytes/op: 2688.2 miss_ratio: 0.0346207 max_rss_mb: 218.223 ^ Now something interesting has happened: the new clock cache has gained a dramatic lead in the single-threaded case, and this is because the cache is so small, and full filters are so big, that dividing the cache into 64 shards leads to significant (random) imbalances in cache shards and excessive churn in imbalanced shards. This new clock cache only uses two shards for this configuration, and that helps to ensure that entries are part of a sufficiently big pool that their eviction order resembles the single-shard order. (This effect is not seen with partitioned index+filters.) Even smaller cache size: 34MB 1thread base -> kops/s: 0.198 io_bytes/op: 1.65342e+07 miss_ratio: 0.939466 max_rss_mb: 48.6914 34MB 1thread folly -> kops/s: 0.201 io_bytes/op: 1.63416e+07 miss_ratio: 0.939081 max_rss_mb: 45.3281 34MB 1thread gt_clock -> kops/s: 0.448 io_bytes/op: 4.43957e+06 miss_ratio: 0.266749 max_rss_mb: 100.523 34MB 1thread new_clock -> kops/s: 1.055 io_bytes/op: 1.85439e+06 miss_ratio: 0.107512 max_rss_mb: 75.3125 34MB 32thread base -> kops/s: 3.346 io_bytes/op: 1.64852e+07 miss_ratio: 0.93596 max_rss_mb: 180.48 34MB 32thread folly -> kops/s: 3.431 io_bytes/op: 1.62857e+07 miss_ratio: 0.935693 max_rss_mb: 137.531 34MB 32thread gt_clock -> kops/s: 1.47 io_bytes/op: 4.89704e+06 miss_ratio: 0.295081 max_rss_mb: 392.465 34MB 32thread new_clock -> kops/s: 8.19 io_bytes/op: 3.70456e+06 miss_ratio: 0.20826 max_rss_mb: 519.793 34MB 128thread base -> kops/s: 2.293 io_bytes/op: 1.64351e+07 miss_ratio: 0.931866 max_rss_mb: 449.484 34MB 128thread folly -> kops/s: 2.34 io_bytes/op: 1.6219e+07 miss_ratio: 0.932023 max_rss_mb: 396.457 34MB 128thread gt_clock -> kops/s: 1.798 io_bytes/op: 5.4241e+06 miss_ratio: 0.324881 max_rss_mb: 1104.41 34MB 128thread new_clock -> kops/s: 10.519 io_bytes/op: 2.39354e+06 miss_ratio: 0.136147 max_rss_mb: 1050.52 As the miss ratio gets higher (say, above 10%), the CPU time spent in eviction starts to erode the advantage of using fewer shards (13% miss rate much lower than 94%). LRU's O(1) eviction time can eventually pay off when there's enough block cache churn: 13MB 1thread base -> kops/s: 0.195 io_bytes/op: 1.65732e+07 miss_ratio: 0.946604 max_rss_mb: 45.6328 13MB 1thread folly -> kops/s: 0.197 io_bytes/op: 1.63793e+07 miss_ratio: 0.94661 max_rss_mb: 33.8633 13MB 1thread gt_clock -> kops/s: 0.519 io_bytes/op: 4.43316e+06 miss_ratio: 0.269379 max_rss_mb: 100.684 13MB 1thread new_clock -> kops/s: 0.176 io_bytes/op: 1.54148e+07 miss_ratio: 0.91545 max_rss_mb: 66.2383 13MB 32thread base -> kops/s: 3.266 io_bytes/op: 1.65544e+07 miss_ratio: 0.943386 max_rss_mb: 132.492 13MB 32thread folly -> kops/s: 3.396 io_bytes/op: 1.63142e+07 miss_ratio: 0.943243 max_rss_mb: 101.863 13MB 32thread gt_clock -> kops/s: 2.758 io_bytes/op: 5.13714e+06 miss_ratio: 0.310652 max_rss_mb: 396.121 13MB 32thread new_clock -> kops/s: 3.11 io_bytes/op: 1.23419e+07 miss_ratio: 0.708425 max_rss_mb: 321.758 13MB 128thread base -> kops/s: 2.31 io_bytes/op: 1.64823e+07 miss_ratio: 0.939543 max_rss_mb: 425.539 13MB 128thread folly -> kops/s: 2.339 io_bytes/op: 1.6242e+07 miss_ratio: 0.939966 max_rss_mb: 346.098 13MB 128thread gt_clock -> kops/s: 3.223 io_bytes/op: 5.76928e+06 miss_ratio: 0.345899 max_rss_mb: 1087.77 13MB 128thread new_clock -> kops/s: 2.984 io_bytes/op: 1.05341e+07 miss_ratio: 0.606198 max_rss_mb: 898.27 gt_clock is clearly blowing way past its memory budget for lower miss rates and best throughput. new_clock also seems to be exceeding budgets, and this warrants more investigation but is not the use case we are targeting with the new cache. With partitioned index+filter, the miss ratio is much better, and although still high enough that the eviction CPU time is definitely offsetting mutex contention: 13MB 1thread base -> kops/s: 16.326 io_bytes/op: 23743.9 miss_ratio: 0.205362 max_rss_mb: 65.2852 13MB 1thread folly -> kops/s: 15.574 io_bytes/op: 19415 miss_ratio: 0.184157 max_rss_mb: 56.3516 13MB 1thread gt_clock -> kops/s: 14.459 io_bytes/op: 22873 miss_ratio: 0.198355 max_rss_mb: 63.9688 13MB 1thread new_clock -> kops/s: 16.34 io_bytes/op: 24386.5 miss_ratio: 0.210512 max_rss_mb: 61.707 13MB 128thread base -> kops/s: 289.786 io_bytes/op: 23710.9 miss_ratio: 0.205056 max_rss_mb: 103.57 13MB 128thread folly -> kops/s: 185.282 io_bytes/op: 19433.1 miss_ratio: 0.184275 max_rss_mb: 116.219 13MB 128thread gt_clock -> kops/s: 354.451 io_bytes/op: 23150.6 miss_ratio: 0.200495 max_rss_mb: 102.871 13MB 128thread new_clock -> kops/s: 295.359 io_bytes/op: 24626.4 miss_ratio: 0.212452 max_rss_mb: 121.109 Pull Request resolved: https://github.com/facebook/rocksdb/pull/10626 Test Plan: updated unit tests, stress/crash test runs including with TSAN, ASAN, UBSAN Reviewed By: anand1976 Differential Revision: D39368406 Pulled By: pdillinger fbshipit-source-id: 5afc44da4c656f8f751b44552bbf27bd3ca6fef9
2022-09-16 07:24:11 +00:00
DEFINE_int32(cache_numshardbits, -1,
"Number of shards for the block cache"
" is 2 ** cache_numshardbits. Negative means use default settings."
" This is applied only if FLAGS_cache_size is non-negative.");
DEFINE_double(cache_high_pri_pool_ratio, 0.0,
"Ratio of block cache reserve for high pri blocks. "
"If > 0.0, we also enable "
"cache_index_and_filter_blocks_with_high_priority.");
DEFINE_double(cache_low_pri_pool_ratio, 0.0,
"Ratio of block cache reserve for low pri blocks.");
DEFINE_string(cache_type, "lru_cache", "Type of block cache.");
add simulator Cache as class SimCache/SimLRUCache(with test) Summary: add class SimCache(base class with instrumentation api) and SimLRUCache(derived class with detailed implementation) which is used as an instrumented block cache that can predict hit rate for different cache size Test Plan: Add a test case in `db_block_cache_test.cc` called `SimCacheTest` to test basic logic of SimCache. Also add option `-simcache_size` in db_bench. if set with a value other than -1, then the benchmark will use this value as the size of the simulator cache and finally output the simulation result. ``` [gzh@dev9927.prn1 ~/local/rocksdb] ./db_bench -benchmarks "fillseq,readrandom" -cache_size 1000000 -simcache_size 1000000 RocksDB: version 4.8 Date: Tue May 17 16:56:16 2016 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Compression: Snappy Memtablerep: skip_list Perf Level: 0 WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ DB path: [/tmp/rocksdbtest-112628/dbbench] fillseq : 6.809 micros/op 146874 ops/sec; 16.2 MB/s DB path: [/tmp/rocksdbtest-112628/dbbench] readrandom : 6.343 micros/op 157665 ops/sec; 17.4 MB/s (1000000 of 1000000 found) SIMULATOR CACHE STATISTICS: SimCache LOOKUPs: 986559 SimCache HITs: 264760 SimCache HITRATE: 26.84% [gzh@dev9927.prn1 ~/local/rocksdb] ./db_bench -benchmarks "fillseq,readrandom" -cache_size 1000000 -simcache_size 10000000 RocksDB: version 4.8 Date: Tue May 17 16:57:10 2016 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Compression: Snappy Memtablerep: skip_list Perf Level: 0 WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ DB path: [/tmp/rocksdbtest-112628/dbbench] fillseq : 5.066 micros/op 197394 ops/sec; 21.8 MB/s DB path: [/tmp/rocksdbtest-112628/dbbench] readrandom : 6.457 micros/op 154870 ops/sec; 17.1 MB/s (1000000 of 1000000 found) SIMULATOR CACHE STATISTICS: SimCache LOOKUPs: 1059764 SimCache HITs: 374501 SimCache HITRATE: 35.34% [gzh@dev9927.prn1 ~/local/rocksdb] ./db_bench -benchmarks "fillseq,readrandom" -cache_size 1000000 -simcache_size 100000000 RocksDB: version 4.8 Date: Tue May 17 16:57:32 2016 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Compression: Snappy Memtablerep: skip_list Perf Level: 0 WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ DB path: [/tmp/rocksdbtest-112628/dbbench] fillseq : 5.632 micros/op 177572 ops/sec; 19.6 MB/s DB path: [/tmp/rocksdbtest-112628/dbbench] readrandom : 6.892 micros/op 145094 ops/sec; 16.1 MB/s (1000000 of 1000000 found) SIMULATOR CACHE STATISTICS: SimCache LOOKUPs: 1150767 SimCache HITs: 1034535 SimCache HITRATE: 89.90% ``` Reviewers: IslamAbdelRahman, andrewkr, sdong Reviewed By: sdong Subscribers: MarkCallaghan, andrewkr, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D57999
2016-05-24 06:35:23 +00:00
Prevent double caching in the compressed secondary cache (#9747) Summary: ### **Summary:** When both LRU Cache and CompressedSecondaryCache are configured together, there possibly are some data blocks double cached. **Changes include:** 1. Update IS_PROMOTED to IS_IN_SECONDARY_CACHE to prevent confusions. 2. This PR updates SecondaryCacheResultHandle and use IsErasedFromSecondaryCache to determine whether the handle is erased in the secondary cache. Then, the caller can determine whether to SetIsInSecondaryCache(). 3. Rename LRUSecondaryCache to CompressedSecondaryCache. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9747 Test Plan: **Test Scripts:** 1. Populate a DB. The on disk footprint is 482 MB. The data is set to be 50% compressible, so the total decompressed size is expected to be 964 MB. ./db_bench --benchmarks=fillrandom --num=10000000 -db=/db_bench_1 2. overwrite it to a stable state: ./db_bench --benchmarks=overwrite,stats --num=10000000 -use_existing_db -duration=10 --benchmark_write_rate_limit=2000000 -db=/db_bench_1 4. Run read tests with diffeernt cache setting: T1: ./db_bench --benchmarks=seekrandom,stats --threads=16 --num=10000000 -use_existing_db -duration=120 --benchmark_write_rate_limit=52000000 -use_direct_reads --cache_size=520000000 --statistics -db=/db_bench_1 T2: ./db_bench --benchmarks=seekrandom,stats --threads=16 --num=10000000 -use_existing_db -duration=120 --benchmark_write_rate_limit=52000000 -use_direct_reads --cache_size=320000000 -compressed_secondary_cache_size=400000000 --statistics -use_compressed_secondary_cache -db=/db_bench_1 T3: ./db_bench --benchmarks=seekrandom,stats --threads=16 --num=10000000 -use_existing_db -duration=120 --benchmark_write_rate_limit=52000000 -use_direct_reads --cache_size=520000000 -compressed_secondary_cache_size=400000000 --statistics -use_compressed_secondary_cache -db=/db_bench_1 T4: ./db_bench --benchmarks=seekrandom,stats --threads=16 --num=10000000 -use_existing_db -duration=120 --benchmark_write_rate_limit=52000000 -use_direct_reads --cache_size=20000000 -compressed_secondary_cache_size=500000000 --statistics -use_compressed_secondary_cache -db=/db_bench_1 **Before this PR** | Cache Size | Compressed Secondary Cache Size | Cache Hit Rate | |------------|-------------------------------------|----------------| |520 MB | 0 MB | 85.5% | |320 MB | 400 MB | 96.2% | |520 MB | 400 MB | 98.3% | |20 MB | 500 MB | 98.8% | **Before this PR** | Cache Size | Compressed Secondary Cache Size | Cache Hit Rate | |------------|-------------------------------------|----------------| |520 MB | 0 MB | 85.5% | |320 MB | 400 MB | 99.9% | |520 MB | 400 MB | 99.9% | |20 MB | 500 MB | 99.2% | Reviewed By: anand1976 Differential Revision: D35117499 Pulled By: gitbw95 fbshipit-source-id: ea2657749fc13efebe91a8a1b56bc61d6a224a12
2022-04-11 20:28:33 +00:00
DEFINE_bool(use_compressed_secondary_cache, false,
"Use the CompressedSecondaryCache as the secondary cache.");
Add a secondary cache implementation based on LRUCache 1 (#9518) Summary: **Summary:** RocksDB uses a block cache to reduce IO and make queries more efficient. The block cache is based on the LRU algorithm (LRUCache) and keeps objects containing uncompressed data, such as Block, ParsedFullFilterBlock etc. It allows the user to configure a second level cache (rocksdb::SecondaryCache) to extend the primary block cache by holding items evicted from it. Some of the major RocksDB users, like MyRocks, use direct IO and would like to use a primary block cache for uncompressed data and a secondary cache for compressed data. The latter allows us to mitigate the loss of the Linux page cache due to direct IO. This PR includes a concrete implementation of rocksdb::SecondaryCache that integrates with compression libraries such as LZ4 and implements an LRU cache to hold compressed blocks. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9518 Test Plan: In this PR, the lru_secondary_cache_test.cc includes the following tests: 1. The unit tests for the secondary cache with either compression or no compression, such as basic tests, fails tests. 2. The integration tests with both primary cache and this secondary cache . **Follow Up:** 1. Statistics (e.g. compression ratio) will be added in another PR. 2. Once this implementation is ready, I will do some shadow testing and benchmarking with UDB to measure the impact. Reviewed By: anand1976 Differential Revision: D34430930 Pulled By: gitbw95 fbshipit-source-id: 218d78b672a2f914856d8a90ff32f2f5b5043ded
2022-02-24 00:06:27 +00:00
DEFINE_int64(compressed_secondary_cache_size, 32 << 20, // 32MB
Prevent double caching in the compressed secondary cache (#9747) Summary: ### **Summary:** When both LRU Cache and CompressedSecondaryCache are configured together, there possibly are some data blocks double cached. **Changes include:** 1. Update IS_PROMOTED to IS_IN_SECONDARY_CACHE to prevent confusions. 2. This PR updates SecondaryCacheResultHandle and use IsErasedFromSecondaryCache to determine whether the handle is erased in the secondary cache. Then, the caller can determine whether to SetIsInSecondaryCache(). 3. Rename LRUSecondaryCache to CompressedSecondaryCache. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9747 Test Plan: **Test Scripts:** 1. Populate a DB. The on disk footprint is 482 MB. The data is set to be 50% compressible, so the total decompressed size is expected to be 964 MB. ./db_bench --benchmarks=fillrandom --num=10000000 -db=/db_bench_1 2. overwrite it to a stable state: ./db_bench --benchmarks=overwrite,stats --num=10000000 -use_existing_db -duration=10 --benchmark_write_rate_limit=2000000 -db=/db_bench_1 4. Run read tests with diffeernt cache setting: T1: ./db_bench --benchmarks=seekrandom,stats --threads=16 --num=10000000 -use_existing_db -duration=120 --benchmark_write_rate_limit=52000000 -use_direct_reads --cache_size=520000000 --statistics -db=/db_bench_1 T2: ./db_bench --benchmarks=seekrandom,stats --threads=16 --num=10000000 -use_existing_db -duration=120 --benchmark_write_rate_limit=52000000 -use_direct_reads --cache_size=320000000 -compressed_secondary_cache_size=400000000 --statistics -use_compressed_secondary_cache -db=/db_bench_1 T3: ./db_bench --benchmarks=seekrandom,stats --threads=16 --num=10000000 -use_existing_db -duration=120 --benchmark_write_rate_limit=52000000 -use_direct_reads --cache_size=520000000 -compressed_secondary_cache_size=400000000 --statistics -use_compressed_secondary_cache -db=/db_bench_1 T4: ./db_bench --benchmarks=seekrandom,stats --threads=16 --num=10000000 -use_existing_db -duration=120 --benchmark_write_rate_limit=52000000 -use_direct_reads --cache_size=20000000 -compressed_secondary_cache_size=500000000 --statistics -use_compressed_secondary_cache -db=/db_bench_1 **Before this PR** | Cache Size | Compressed Secondary Cache Size | Cache Hit Rate | |------------|-------------------------------------|----------------| |520 MB | 0 MB | 85.5% | |320 MB | 400 MB | 96.2% | |520 MB | 400 MB | 98.3% | |20 MB | 500 MB | 98.8% | **Before this PR** | Cache Size | Compressed Secondary Cache Size | Cache Hit Rate | |------------|-------------------------------------|----------------| |520 MB | 0 MB | 85.5% | |320 MB | 400 MB | 99.9% | |520 MB | 400 MB | 99.9% | |20 MB | 500 MB | 99.2% | Reviewed By: anand1976 Differential Revision: D35117499 Pulled By: gitbw95 fbshipit-source-id: ea2657749fc13efebe91a8a1b56bc61d6a224a12
2022-04-11 20:28:33 +00:00
"Number of bytes to use as a cache of data");
Add a secondary cache implementation based on LRUCache 1 (#9518) Summary: **Summary:** RocksDB uses a block cache to reduce IO and make queries more efficient. The block cache is based on the LRU algorithm (LRUCache) and keeps objects containing uncompressed data, such as Block, ParsedFullFilterBlock etc. It allows the user to configure a second level cache (rocksdb::SecondaryCache) to extend the primary block cache by holding items evicted from it. Some of the major RocksDB users, like MyRocks, use direct IO and would like to use a primary block cache for uncompressed data and a secondary cache for compressed data. The latter allows us to mitigate the loss of the Linux page cache due to direct IO. This PR includes a concrete implementation of rocksdb::SecondaryCache that integrates with compression libraries such as LZ4 and implements an LRU cache to hold compressed blocks. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9518 Test Plan: In this PR, the lru_secondary_cache_test.cc includes the following tests: 1. The unit tests for the secondary cache with either compression or no compression, such as basic tests, fails tests. 2. The integration tests with both primary cache and this secondary cache . **Follow Up:** 1. Statistics (e.g. compression ratio) will be added in another PR. 2. Once this implementation is ready, I will do some shadow testing and benchmarking with UDB to measure the impact. Reviewed By: anand1976 Differential Revision: D34430930 Pulled By: gitbw95 fbshipit-source-id: 218d78b672a2f914856d8a90ff32f2f5b5043ded
2022-02-24 00:06:27 +00:00
Prevent double caching in the compressed secondary cache (#9747) Summary: ### **Summary:** When both LRU Cache and CompressedSecondaryCache are configured together, there possibly are some data blocks double cached. **Changes include:** 1. Update IS_PROMOTED to IS_IN_SECONDARY_CACHE to prevent confusions. 2. This PR updates SecondaryCacheResultHandle and use IsErasedFromSecondaryCache to determine whether the handle is erased in the secondary cache. Then, the caller can determine whether to SetIsInSecondaryCache(). 3. Rename LRUSecondaryCache to CompressedSecondaryCache. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9747 Test Plan: **Test Scripts:** 1. Populate a DB. The on disk footprint is 482 MB. The data is set to be 50% compressible, so the total decompressed size is expected to be 964 MB. ./db_bench --benchmarks=fillrandom --num=10000000 -db=/db_bench_1 2. overwrite it to a stable state: ./db_bench --benchmarks=overwrite,stats --num=10000000 -use_existing_db -duration=10 --benchmark_write_rate_limit=2000000 -db=/db_bench_1 4. Run read tests with diffeernt cache setting: T1: ./db_bench --benchmarks=seekrandom,stats --threads=16 --num=10000000 -use_existing_db -duration=120 --benchmark_write_rate_limit=52000000 -use_direct_reads --cache_size=520000000 --statistics -db=/db_bench_1 T2: ./db_bench --benchmarks=seekrandom,stats --threads=16 --num=10000000 -use_existing_db -duration=120 --benchmark_write_rate_limit=52000000 -use_direct_reads --cache_size=320000000 -compressed_secondary_cache_size=400000000 --statistics -use_compressed_secondary_cache -db=/db_bench_1 T3: ./db_bench --benchmarks=seekrandom,stats --threads=16 --num=10000000 -use_existing_db -duration=120 --benchmark_write_rate_limit=52000000 -use_direct_reads --cache_size=520000000 -compressed_secondary_cache_size=400000000 --statistics -use_compressed_secondary_cache -db=/db_bench_1 T4: ./db_bench --benchmarks=seekrandom,stats --threads=16 --num=10000000 -use_existing_db -duration=120 --benchmark_write_rate_limit=52000000 -use_direct_reads --cache_size=20000000 -compressed_secondary_cache_size=500000000 --statistics -use_compressed_secondary_cache -db=/db_bench_1 **Before this PR** | Cache Size | Compressed Secondary Cache Size | Cache Hit Rate | |------------|-------------------------------------|----------------| |520 MB | 0 MB | 85.5% | |320 MB | 400 MB | 96.2% | |520 MB | 400 MB | 98.3% | |20 MB | 500 MB | 98.8% | **Before this PR** | Cache Size | Compressed Secondary Cache Size | Cache Hit Rate | |------------|-------------------------------------|----------------| |520 MB | 0 MB | 85.5% | |320 MB | 400 MB | 99.9% | |520 MB | 400 MB | 99.9% | |20 MB | 500 MB | 99.2% | Reviewed By: anand1976 Differential Revision: D35117499 Pulled By: gitbw95 fbshipit-source-id: ea2657749fc13efebe91a8a1b56bc61d6a224a12
2022-04-11 20:28:33 +00:00
DEFINE_int32(compressed_secondary_cache_numshardbits, 6,
Add a secondary cache implementation based on LRUCache 1 (#9518) Summary: **Summary:** RocksDB uses a block cache to reduce IO and make queries more efficient. The block cache is based on the LRU algorithm (LRUCache) and keeps objects containing uncompressed data, such as Block, ParsedFullFilterBlock etc. It allows the user to configure a second level cache (rocksdb::SecondaryCache) to extend the primary block cache by holding items evicted from it. Some of the major RocksDB users, like MyRocks, use direct IO and would like to use a primary block cache for uncompressed data and a secondary cache for compressed data. The latter allows us to mitigate the loss of the Linux page cache due to direct IO. This PR includes a concrete implementation of rocksdb::SecondaryCache that integrates with compression libraries such as LZ4 and implements an LRU cache to hold compressed blocks. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9518 Test Plan: In this PR, the lru_secondary_cache_test.cc includes the following tests: 1. The unit tests for the secondary cache with either compression or no compression, such as basic tests, fails tests. 2. The integration tests with both primary cache and this secondary cache . **Follow Up:** 1. Statistics (e.g. compression ratio) will be added in another PR. 2. Once this implementation is ready, I will do some shadow testing and benchmarking with UDB to measure the impact. Reviewed By: anand1976 Differential Revision: D34430930 Pulled By: gitbw95 fbshipit-source-id: 218d78b672a2f914856d8a90ff32f2f5b5043ded
2022-02-24 00:06:27 +00:00
"Number of shards for the block cache"
Prevent double caching in the compressed secondary cache (#9747) Summary: ### **Summary:** When both LRU Cache and CompressedSecondaryCache are configured together, there possibly are some data blocks double cached. **Changes include:** 1. Update IS_PROMOTED to IS_IN_SECONDARY_CACHE to prevent confusions. 2. This PR updates SecondaryCacheResultHandle and use IsErasedFromSecondaryCache to determine whether the handle is erased in the secondary cache. Then, the caller can determine whether to SetIsInSecondaryCache(). 3. Rename LRUSecondaryCache to CompressedSecondaryCache. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9747 Test Plan: **Test Scripts:** 1. Populate a DB. The on disk footprint is 482 MB. The data is set to be 50% compressible, so the total decompressed size is expected to be 964 MB. ./db_bench --benchmarks=fillrandom --num=10000000 -db=/db_bench_1 2. overwrite it to a stable state: ./db_bench --benchmarks=overwrite,stats --num=10000000 -use_existing_db -duration=10 --benchmark_write_rate_limit=2000000 -db=/db_bench_1 4. Run read tests with diffeernt cache setting: T1: ./db_bench --benchmarks=seekrandom,stats --threads=16 --num=10000000 -use_existing_db -duration=120 --benchmark_write_rate_limit=52000000 -use_direct_reads --cache_size=520000000 --statistics -db=/db_bench_1 T2: ./db_bench --benchmarks=seekrandom,stats --threads=16 --num=10000000 -use_existing_db -duration=120 --benchmark_write_rate_limit=52000000 -use_direct_reads --cache_size=320000000 -compressed_secondary_cache_size=400000000 --statistics -use_compressed_secondary_cache -db=/db_bench_1 T3: ./db_bench --benchmarks=seekrandom,stats --threads=16 --num=10000000 -use_existing_db -duration=120 --benchmark_write_rate_limit=52000000 -use_direct_reads --cache_size=520000000 -compressed_secondary_cache_size=400000000 --statistics -use_compressed_secondary_cache -db=/db_bench_1 T4: ./db_bench --benchmarks=seekrandom,stats --threads=16 --num=10000000 -use_existing_db -duration=120 --benchmark_write_rate_limit=52000000 -use_direct_reads --cache_size=20000000 -compressed_secondary_cache_size=500000000 --statistics -use_compressed_secondary_cache -db=/db_bench_1 **Before this PR** | Cache Size | Compressed Secondary Cache Size | Cache Hit Rate | |------------|-------------------------------------|----------------| |520 MB | 0 MB | 85.5% | |320 MB | 400 MB | 96.2% | |520 MB | 400 MB | 98.3% | |20 MB | 500 MB | 98.8% | **Before this PR** | Cache Size | Compressed Secondary Cache Size | Cache Hit Rate | |------------|-------------------------------------|----------------| |520 MB | 0 MB | 85.5% | |320 MB | 400 MB | 99.9% | |520 MB | 400 MB | 99.9% | |20 MB | 500 MB | 99.2% | Reviewed By: anand1976 Differential Revision: D35117499 Pulled By: gitbw95 fbshipit-source-id: ea2657749fc13efebe91a8a1b56bc61d6a224a12
2022-04-11 20:28:33 +00:00
" is 2 ** compressed_secondary_cache_numshardbits."
Add a secondary cache implementation based on LRUCache 1 (#9518) Summary: **Summary:** RocksDB uses a block cache to reduce IO and make queries more efficient. The block cache is based on the LRU algorithm (LRUCache) and keeps objects containing uncompressed data, such as Block, ParsedFullFilterBlock etc. It allows the user to configure a second level cache (rocksdb::SecondaryCache) to extend the primary block cache by holding items evicted from it. Some of the major RocksDB users, like MyRocks, use direct IO and would like to use a primary block cache for uncompressed data and a secondary cache for compressed data. The latter allows us to mitigate the loss of the Linux page cache due to direct IO. This PR includes a concrete implementation of rocksdb::SecondaryCache that integrates with compression libraries such as LZ4 and implements an LRU cache to hold compressed blocks. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9518 Test Plan: In this PR, the lru_secondary_cache_test.cc includes the following tests: 1. The unit tests for the secondary cache with either compression or no compression, such as basic tests, fails tests. 2. The integration tests with both primary cache and this secondary cache . **Follow Up:** 1. Statistics (e.g. compression ratio) will be added in another PR. 2. Once this implementation is ready, I will do some shadow testing and benchmarking with UDB to measure the impact. Reviewed By: anand1976 Differential Revision: D34430930 Pulled By: gitbw95 fbshipit-source-id: 218d78b672a2f914856d8a90ff32f2f5b5043ded
2022-02-24 00:06:27 +00:00
" Negative means use default settings."
" This is applied only if FLAGS_cache_size is non-negative.");
Prevent double caching in the compressed secondary cache (#9747) Summary: ### **Summary:** When both LRU Cache and CompressedSecondaryCache are configured together, there possibly are some data blocks double cached. **Changes include:** 1. Update IS_PROMOTED to IS_IN_SECONDARY_CACHE to prevent confusions. 2. This PR updates SecondaryCacheResultHandle and use IsErasedFromSecondaryCache to determine whether the handle is erased in the secondary cache. Then, the caller can determine whether to SetIsInSecondaryCache(). 3. Rename LRUSecondaryCache to CompressedSecondaryCache. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9747 Test Plan: **Test Scripts:** 1. Populate a DB. The on disk footprint is 482 MB. The data is set to be 50% compressible, so the total decompressed size is expected to be 964 MB. ./db_bench --benchmarks=fillrandom --num=10000000 -db=/db_bench_1 2. overwrite it to a stable state: ./db_bench --benchmarks=overwrite,stats --num=10000000 -use_existing_db -duration=10 --benchmark_write_rate_limit=2000000 -db=/db_bench_1 4. Run read tests with diffeernt cache setting: T1: ./db_bench --benchmarks=seekrandom,stats --threads=16 --num=10000000 -use_existing_db -duration=120 --benchmark_write_rate_limit=52000000 -use_direct_reads --cache_size=520000000 --statistics -db=/db_bench_1 T2: ./db_bench --benchmarks=seekrandom,stats --threads=16 --num=10000000 -use_existing_db -duration=120 --benchmark_write_rate_limit=52000000 -use_direct_reads --cache_size=320000000 -compressed_secondary_cache_size=400000000 --statistics -use_compressed_secondary_cache -db=/db_bench_1 T3: ./db_bench --benchmarks=seekrandom,stats --threads=16 --num=10000000 -use_existing_db -duration=120 --benchmark_write_rate_limit=52000000 -use_direct_reads --cache_size=520000000 -compressed_secondary_cache_size=400000000 --statistics -use_compressed_secondary_cache -db=/db_bench_1 T4: ./db_bench --benchmarks=seekrandom,stats --threads=16 --num=10000000 -use_existing_db -duration=120 --benchmark_write_rate_limit=52000000 -use_direct_reads --cache_size=20000000 -compressed_secondary_cache_size=500000000 --statistics -use_compressed_secondary_cache -db=/db_bench_1 **Before this PR** | Cache Size | Compressed Secondary Cache Size | Cache Hit Rate | |------------|-------------------------------------|----------------| |520 MB | 0 MB | 85.5% | |320 MB | 400 MB | 96.2% | |520 MB | 400 MB | 98.3% | |20 MB | 500 MB | 98.8% | **Before this PR** | Cache Size | Compressed Secondary Cache Size | Cache Hit Rate | |------------|-------------------------------------|----------------| |520 MB | 0 MB | 85.5% | |320 MB | 400 MB | 99.9% | |520 MB | 400 MB | 99.9% | |20 MB | 500 MB | 99.2% | Reviewed By: anand1976 Differential Revision: D35117499 Pulled By: gitbw95 fbshipit-source-id: ea2657749fc13efebe91a8a1b56bc61d6a224a12
2022-04-11 20:28:33 +00:00
DEFINE_double(compressed_secondary_cache_high_pri_pool_ratio, 0.0,
Add a secondary cache implementation based on LRUCache 1 (#9518) Summary: **Summary:** RocksDB uses a block cache to reduce IO and make queries more efficient. The block cache is based on the LRU algorithm (LRUCache) and keeps objects containing uncompressed data, such as Block, ParsedFullFilterBlock etc. It allows the user to configure a second level cache (rocksdb::SecondaryCache) to extend the primary block cache by holding items evicted from it. Some of the major RocksDB users, like MyRocks, use direct IO and would like to use a primary block cache for uncompressed data and a secondary cache for compressed data. The latter allows us to mitigate the loss of the Linux page cache due to direct IO. This PR includes a concrete implementation of rocksdb::SecondaryCache that integrates with compression libraries such as LZ4 and implements an LRU cache to hold compressed blocks. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9518 Test Plan: In this PR, the lru_secondary_cache_test.cc includes the following tests: 1. The unit tests for the secondary cache with either compression or no compression, such as basic tests, fails tests. 2. The integration tests with both primary cache and this secondary cache . **Follow Up:** 1. Statistics (e.g. compression ratio) will be added in another PR. 2. Once this implementation is ready, I will do some shadow testing and benchmarking with UDB to measure the impact. Reviewed By: anand1976 Differential Revision: D34430930 Pulled By: gitbw95 fbshipit-source-id: 218d78b672a2f914856d8a90ff32f2f5b5043ded
2022-02-24 00:06:27 +00:00
"Ratio of block cache reserve for high pri blocks. "
"If > 0.0, we also enable "
"cache_index_and_filter_blocks_with_high_priority.");
DEFINE_double(compressed_secondary_cache_low_pri_pool_ratio, 0.0,
"Ratio of block cache reserve for low pri blocks.");
Prevent double caching in the compressed secondary cache (#9747) Summary: ### **Summary:** When both LRU Cache and CompressedSecondaryCache are configured together, there possibly are some data blocks double cached. **Changes include:** 1. Update IS_PROMOTED to IS_IN_SECONDARY_CACHE to prevent confusions. 2. This PR updates SecondaryCacheResultHandle and use IsErasedFromSecondaryCache to determine whether the handle is erased in the secondary cache. Then, the caller can determine whether to SetIsInSecondaryCache(). 3. Rename LRUSecondaryCache to CompressedSecondaryCache. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9747 Test Plan: **Test Scripts:** 1. Populate a DB. The on disk footprint is 482 MB. The data is set to be 50% compressible, so the total decompressed size is expected to be 964 MB. ./db_bench --benchmarks=fillrandom --num=10000000 -db=/db_bench_1 2. overwrite it to a stable state: ./db_bench --benchmarks=overwrite,stats --num=10000000 -use_existing_db -duration=10 --benchmark_write_rate_limit=2000000 -db=/db_bench_1 4. Run read tests with diffeernt cache setting: T1: ./db_bench --benchmarks=seekrandom,stats --threads=16 --num=10000000 -use_existing_db -duration=120 --benchmark_write_rate_limit=52000000 -use_direct_reads --cache_size=520000000 --statistics -db=/db_bench_1 T2: ./db_bench --benchmarks=seekrandom,stats --threads=16 --num=10000000 -use_existing_db -duration=120 --benchmark_write_rate_limit=52000000 -use_direct_reads --cache_size=320000000 -compressed_secondary_cache_size=400000000 --statistics -use_compressed_secondary_cache -db=/db_bench_1 T3: ./db_bench --benchmarks=seekrandom,stats --threads=16 --num=10000000 -use_existing_db -duration=120 --benchmark_write_rate_limit=52000000 -use_direct_reads --cache_size=520000000 -compressed_secondary_cache_size=400000000 --statistics -use_compressed_secondary_cache -db=/db_bench_1 T4: ./db_bench --benchmarks=seekrandom,stats --threads=16 --num=10000000 -use_existing_db -duration=120 --benchmark_write_rate_limit=52000000 -use_direct_reads --cache_size=20000000 -compressed_secondary_cache_size=500000000 --statistics -use_compressed_secondary_cache -db=/db_bench_1 **Before this PR** | Cache Size | Compressed Secondary Cache Size | Cache Hit Rate | |------------|-------------------------------------|----------------| |520 MB | 0 MB | 85.5% | |320 MB | 400 MB | 96.2% | |520 MB | 400 MB | 98.3% | |20 MB | 500 MB | 98.8% | **Before this PR** | Cache Size | Compressed Secondary Cache Size | Cache Hit Rate | |------------|-------------------------------------|----------------| |520 MB | 0 MB | 85.5% | |320 MB | 400 MB | 99.9% | |520 MB | 400 MB | 99.9% | |20 MB | 500 MB | 99.2% | Reviewed By: anand1976 Differential Revision: D35117499 Pulled By: gitbw95 fbshipit-source-id: ea2657749fc13efebe91a8a1b56bc61d6a224a12
2022-04-11 20:28:33 +00:00
DEFINE_string(compressed_secondary_cache_compression_type, "lz4",
Add a secondary cache implementation based on LRUCache 1 (#9518) Summary: **Summary:** RocksDB uses a block cache to reduce IO and make queries more efficient. The block cache is based on the LRU algorithm (LRUCache) and keeps objects containing uncompressed data, such as Block, ParsedFullFilterBlock etc. It allows the user to configure a second level cache (rocksdb::SecondaryCache) to extend the primary block cache by holding items evicted from it. Some of the major RocksDB users, like MyRocks, use direct IO and would like to use a primary block cache for uncompressed data and a secondary cache for compressed data. The latter allows us to mitigate the loss of the Linux page cache due to direct IO. This PR includes a concrete implementation of rocksdb::SecondaryCache that integrates with compression libraries such as LZ4 and implements an LRU cache to hold compressed blocks. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9518 Test Plan: In this PR, the lru_secondary_cache_test.cc includes the following tests: 1. The unit tests for the secondary cache with either compression or no compression, such as basic tests, fails tests. 2. The integration tests with both primary cache and this secondary cache . **Follow Up:** 1. Statistics (e.g. compression ratio) will be added in another PR. 2. Once this implementation is ready, I will do some shadow testing and benchmarking with UDB to measure the impact. Reviewed By: anand1976 Differential Revision: D34430930 Pulled By: gitbw95 fbshipit-source-id: 218d78b672a2f914856d8a90ff32f2f5b5043ded
2022-02-24 00:06:27 +00:00
"The compression algorithm to use for large "
Prevent double caching in the compressed secondary cache (#9747) Summary: ### **Summary:** When both LRU Cache and CompressedSecondaryCache are configured together, there possibly are some data blocks double cached. **Changes include:** 1. Update IS_PROMOTED to IS_IN_SECONDARY_CACHE to prevent confusions. 2. This PR updates SecondaryCacheResultHandle and use IsErasedFromSecondaryCache to determine whether the handle is erased in the secondary cache. Then, the caller can determine whether to SetIsInSecondaryCache(). 3. Rename LRUSecondaryCache to CompressedSecondaryCache. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9747 Test Plan: **Test Scripts:** 1. Populate a DB. The on disk footprint is 482 MB. The data is set to be 50% compressible, so the total decompressed size is expected to be 964 MB. ./db_bench --benchmarks=fillrandom --num=10000000 -db=/db_bench_1 2. overwrite it to a stable state: ./db_bench --benchmarks=overwrite,stats --num=10000000 -use_existing_db -duration=10 --benchmark_write_rate_limit=2000000 -db=/db_bench_1 4. Run read tests with diffeernt cache setting: T1: ./db_bench --benchmarks=seekrandom,stats --threads=16 --num=10000000 -use_existing_db -duration=120 --benchmark_write_rate_limit=52000000 -use_direct_reads --cache_size=520000000 --statistics -db=/db_bench_1 T2: ./db_bench --benchmarks=seekrandom,stats --threads=16 --num=10000000 -use_existing_db -duration=120 --benchmark_write_rate_limit=52000000 -use_direct_reads --cache_size=320000000 -compressed_secondary_cache_size=400000000 --statistics -use_compressed_secondary_cache -db=/db_bench_1 T3: ./db_bench --benchmarks=seekrandom,stats --threads=16 --num=10000000 -use_existing_db -duration=120 --benchmark_write_rate_limit=52000000 -use_direct_reads --cache_size=520000000 -compressed_secondary_cache_size=400000000 --statistics -use_compressed_secondary_cache -db=/db_bench_1 T4: ./db_bench --benchmarks=seekrandom,stats --threads=16 --num=10000000 -use_existing_db -duration=120 --benchmark_write_rate_limit=52000000 -use_direct_reads --cache_size=20000000 -compressed_secondary_cache_size=500000000 --statistics -use_compressed_secondary_cache -db=/db_bench_1 **Before this PR** | Cache Size | Compressed Secondary Cache Size | Cache Hit Rate | |------------|-------------------------------------|----------------| |520 MB | 0 MB | 85.5% | |320 MB | 400 MB | 96.2% | |520 MB | 400 MB | 98.3% | |20 MB | 500 MB | 98.8% | **Before this PR** | Cache Size | Compressed Secondary Cache Size | Cache Hit Rate | |------------|-------------------------------------|----------------| |520 MB | 0 MB | 85.5% | |320 MB | 400 MB | 99.9% | |520 MB | 400 MB | 99.9% | |20 MB | 500 MB | 99.2% | Reviewed By: anand1976 Differential Revision: D35117499 Pulled By: gitbw95 fbshipit-source-id: ea2657749fc13efebe91a8a1b56bc61d6a224a12
2022-04-11 20:28:33 +00:00
"values stored in CompressedSecondaryCache.");
Add a secondary cache implementation based on LRUCache 1 (#9518) Summary: **Summary:** RocksDB uses a block cache to reduce IO and make queries more efficient. The block cache is based on the LRU algorithm (LRUCache) and keeps objects containing uncompressed data, such as Block, ParsedFullFilterBlock etc. It allows the user to configure a second level cache (rocksdb::SecondaryCache) to extend the primary block cache by holding items evicted from it. Some of the major RocksDB users, like MyRocks, use direct IO and would like to use a primary block cache for uncompressed data and a secondary cache for compressed data. The latter allows us to mitigate the loss of the Linux page cache due to direct IO. This PR includes a concrete implementation of rocksdb::SecondaryCache that integrates with compression libraries such as LZ4 and implements an LRU cache to hold compressed blocks. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9518 Test Plan: In this PR, the lru_secondary_cache_test.cc includes the following tests: 1. The unit tests for the secondary cache with either compression or no compression, such as basic tests, fails tests. 2. The integration tests with both primary cache and this secondary cache . **Follow Up:** 1. Statistics (e.g. compression ratio) will be added in another PR. 2. Once this implementation is ready, I will do some shadow testing and benchmarking with UDB to measure the impact. Reviewed By: anand1976 Differential Revision: D34430930 Pulled By: gitbw95 fbshipit-source-id: 218d78b672a2f914856d8a90ff32f2f5b5043ded
2022-02-24 00:06:27 +00:00
static enum ROCKSDB_NAMESPACE::CompressionType
Prevent double caching in the compressed secondary cache (#9747) Summary: ### **Summary:** When both LRU Cache and CompressedSecondaryCache are configured together, there possibly are some data blocks double cached. **Changes include:** 1. Update IS_PROMOTED to IS_IN_SECONDARY_CACHE to prevent confusions. 2. This PR updates SecondaryCacheResultHandle and use IsErasedFromSecondaryCache to determine whether the handle is erased in the secondary cache. Then, the caller can determine whether to SetIsInSecondaryCache(). 3. Rename LRUSecondaryCache to CompressedSecondaryCache. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9747 Test Plan: **Test Scripts:** 1. Populate a DB. The on disk footprint is 482 MB. The data is set to be 50% compressible, so the total decompressed size is expected to be 964 MB. ./db_bench --benchmarks=fillrandom --num=10000000 -db=/db_bench_1 2. overwrite it to a stable state: ./db_bench --benchmarks=overwrite,stats --num=10000000 -use_existing_db -duration=10 --benchmark_write_rate_limit=2000000 -db=/db_bench_1 4. Run read tests with diffeernt cache setting: T1: ./db_bench --benchmarks=seekrandom,stats --threads=16 --num=10000000 -use_existing_db -duration=120 --benchmark_write_rate_limit=52000000 -use_direct_reads --cache_size=520000000 --statistics -db=/db_bench_1 T2: ./db_bench --benchmarks=seekrandom,stats --threads=16 --num=10000000 -use_existing_db -duration=120 --benchmark_write_rate_limit=52000000 -use_direct_reads --cache_size=320000000 -compressed_secondary_cache_size=400000000 --statistics -use_compressed_secondary_cache -db=/db_bench_1 T3: ./db_bench --benchmarks=seekrandom,stats --threads=16 --num=10000000 -use_existing_db -duration=120 --benchmark_write_rate_limit=52000000 -use_direct_reads --cache_size=520000000 -compressed_secondary_cache_size=400000000 --statistics -use_compressed_secondary_cache -db=/db_bench_1 T4: ./db_bench --benchmarks=seekrandom,stats --threads=16 --num=10000000 -use_existing_db -duration=120 --benchmark_write_rate_limit=52000000 -use_direct_reads --cache_size=20000000 -compressed_secondary_cache_size=500000000 --statistics -use_compressed_secondary_cache -db=/db_bench_1 **Before this PR** | Cache Size | Compressed Secondary Cache Size | Cache Hit Rate | |------------|-------------------------------------|----------------| |520 MB | 0 MB | 85.5% | |320 MB | 400 MB | 96.2% | |520 MB | 400 MB | 98.3% | |20 MB | 500 MB | 98.8% | **Before this PR** | Cache Size | Compressed Secondary Cache Size | Cache Hit Rate | |------------|-------------------------------------|----------------| |520 MB | 0 MB | 85.5% | |320 MB | 400 MB | 99.9% | |520 MB | 400 MB | 99.9% | |20 MB | 500 MB | 99.2% | Reviewed By: anand1976 Differential Revision: D35117499 Pulled By: gitbw95 fbshipit-source-id: ea2657749fc13efebe91a8a1b56bc61d6a224a12
2022-04-11 20:28:33 +00:00
FLAGS_compressed_secondary_cache_compression_type_e =
Add a secondary cache implementation based on LRUCache 1 (#9518) Summary: **Summary:** RocksDB uses a block cache to reduce IO and make queries more efficient. The block cache is based on the LRU algorithm (LRUCache) and keeps objects containing uncompressed data, such as Block, ParsedFullFilterBlock etc. It allows the user to configure a second level cache (rocksdb::SecondaryCache) to extend the primary block cache by holding items evicted from it. Some of the major RocksDB users, like MyRocks, use direct IO and would like to use a primary block cache for uncompressed data and a secondary cache for compressed data. The latter allows us to mitigate the loss of the Linux page cache due to direct IO. This PR includes a concrete implementation of rocksdb::SecondaryCache that integrates with compression libraries such as LZ4 and implements an LRU cache to hold compressed blocks. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9518 Test Plan: In this PR, the lru_secondary_cache_test.cc includes the following tests: 1. The unit tests for the secondary cache with either compression or no compression, such as basic tests, fails tests. 2. The integration tests with both primary cache and this secondary cache . **Follow Up:** 1. Statistics (e.g. compression ratio) will be added in another PR. 2. Once this implementation is ready, I will do some shadow testing and benchmarking with UDB to measure the impact. Reviewed By: anand1976 Differential Revision: D34430930 Pulled By: gitbw95 fbshipit-source-id: 218d78b672a2f914856d8a90ff32f2f5b5043ded
2022-02-24 00:06:27 +00:00
ROCKSDB_NAMESPACE::kLZ4Compression;
DEFINE_int32(compressed_secondary_cache_compression_level,
ROCKSDB_NAMESPACE::CompressionOptions().level,
"Compression level. The meaning of this value is library-"
"dependent. If unset, we try to use the default for the library "
"specified in `--compressed_secondary_cache_compression_type`");
Add a secondary cache implementation based on LRUCache 1 (#9518) Summary: **Summary:** RocksDB uses a block cache to reduce IO and make queries more efficient. The block cache is based on the LRU algorithm (LRUCache) and keeps objects containing uncompressed data, such as Block, ParsedFullFilterBlock etc. It allows the user to configure a second level cache (rocksdb::SecondaryCache) to extend the primary block cache by holding items evicted from it. Some of the major RocksDB users, like MyRocks, use direct IO and would like to use a primary block cache for uncompressed data and a secondary cache for compressed data. The latter allows us to mitigate the loss of the Linux page cache due to direct IO. This PR includes a concrete implementation of rocksdb::SecondaryCache that integrates with compression libraries such as LZ4 and implements an LRU cache to hold compressed blocks. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9518 Test Plan: In this PR, the lru_secondary_cache_test.cc includes the following tests: 1. The unit tests for the secondary cache with either compression or no compression, such as basic tests, fails tests. 2. The integration tests with both primary cache and this secondary cache . **Follow Up:** 1. Statistics (e.g. compression ratio) will be added in another PR. 2. Once this implementation is ready, I will do some shadow testing and benchmarking with UDB to measure the impact. Reviewed By: anand1976 Differential Revision: D34430930 Pulled By: gitbw95 fbshipit-source-id: 218d78b672a2f914856d8a90ff32f2f5b5043ded
2022-02-24 00:06:27 +00:00
DEFINE_uint32(
Prevent double caching in the compressed secondary cache (#9747) Summary: ### **Summary:** When both LRU Cache and CompressedSecondaryCache are configured together, there possibly are some data blocks double cached. **Changes include:** 1. Update IS_PROMOTED to IS_IN_SECONDARY_CACHE to prevent confusions. 2. This PR updates SecondaryCacheResultHandle and use IsErasedFromSecondaryCache to determine whether the handle is erased in the secondary cache. Then, the caller can determine whether to SetIsInSecondaryCache(). 3. Rename LRUSecondaryCache to CompressedSecondaryCache. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9747 Test Plan: **Test Scripts:** 1. Populate a DB. The on disk footprint is 482 MB. The data is set to be 50% compressible, so the total decompressed size is expected to be 964 MB. ./db_bench --benchmarks=fillrandom --num=10000000 -db=/db_bench_1 2. overwrite it to a stable state: ./db_bench --benchmarks=overwrite,stats --num=10000000 -use_existing_db -duration=10 --benchmark_write_rate_limit=2000000 -db=/db_bench_1 4. Run read tests with diffeernt cache setting: T1: ./db_bench --benchmarks=seekrandom,stats --threads=16 --num=10000000 -use_existing_db -duration=120 --benchmark_write_rate_limit=52000000 -use_direct_reads --cache_size=520000000 --statistics -db=/db_bench_1 T2: ./db_bench --benchmarks=seekrandom,stats --threads=16 --num=10000000 -use_existing_db -duration=120 --benchmark_write_rate_limit=52000000 -use_direct_reads --cache_size=320000000 -compressed_secondary_cache_size=400000000 --statistics -use_compressed_secondary_cache -db=/db_bench_1 T3: ./db_bench --benchmarks=seekrandom,stats --threads=16 --num=10000000 -use_existing_db -duration=120 --benchmark_write_rate_limit=52000000 -use_direct_reads --cache_size=520000000 -compressed_secondary_cache_size=400000000 --statistics -use_compressed_secondary_cache -db=/db_bench_1 T4: ./db_bench --benchmarks=seekrandom,stats --threads=16 --num=10000000 -use_existing_db -duration=120 --benchmark_write_rate_limit=52000000 -use_direct_reads --cache_size=20000000 -compressed_secondary_cache_size=500000000 --statistics -use_compressed_secondary_cache -db=/db_bench_1 **Before this PR** | Cache Size | Compressed Secondary Cache Size | Cache Hit Rate | |------------|-------------------------------------|----------------| |520 MB | 0 MB | 85.5% | |320 MB | 400 MB | 96.2% | |520 MB | 400 MB | 98.3% | |20 MB | 500 MB | 98.8% | **Before this PR** | Cache Size | Compressed Secondary Cache Size | Cache Hit Rate | |------------|-------------------------------------|----------------| |520 MB | 0 MB | 85.5% | |320 MB | 400 MB | 99.9% | |520 MB | 400 MB | 99.9% | |20 MB | 500 MB | 99.2% | Reviewed By: anand1976 Differential Revision: D35117499 Pulled By: gitbw95 fbshipit-source-id: ea2657749fc13efebe91a8a1b56bc61d6a224a12
2022-04-11 20:28:33 +00:00
compressed_secondary_cache_compress_format_version, 2,
Add a secondary cache implementation based on LRUCache 1 (#9518) Summary: **Summary:** RocksDB uses a block cache to reduce IO and make queries more efficient. The block cache is based on the LRU algorithm (LRUCache) and keeps objects containing uncompressed data, such as Block, ParsedFullFilterBlock etc. It allows the user to configure a second level cache (rocksdb::SecondaryCache) to extend the primary block cache by holding items evicted from it. Some of the major RocksDB users, like MyRocks, use direct IO and would like to use a primary block cache for uncompressed data and a secondary cache for compressed data. The latter allows us to mitigate the loss of the Linux page cache due to direct IO. This PR includes a concrete implementation of rocksdb::SecondaryCache that integrates with compression libraries such as LZ4 and implements an LRU cache to hold compressed blocks. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9518 Test Plan: In this PR, the lru_secondary_cache_test.cc includes the following tests: 1. The unit tests for the secondary cache with either compression or no compression, such as basic tests, fails tests. 2. The integration tests with both primary cache and this secondary cache . **Follow Up:** 1. Statistics (e.g. compression ratio) will be added in another PR. 2. Once this implementation is ready, I will do some shadow testing and benchmarking with UDB to measure the impact. Reviewed By: anand1976 Differential Revision: D34430930 Pulled By: gitbw95 fbshipit-source-id: 218d78b672a2f914856d8a90ff32f2f5b5043ded
2022-02-24 00:06:27 +00:00
"compress_format_version can have two values: "
"compress_format_version == 1 -- decompressed size is not included"
" in the block header."
"compress_format_version == 2 -- decompressed size is included"
" in the block header in varint32 format.");
DEFINE_bool(use_tiered_cache, false,
Integrate CacheReservationManager with compressed secondary cache (#11449) Summary: This draft PR implements charging of reserved memory, for write buffers, table readers, and other purposes, proportionally to the block cache and the compressed secondary cache. The basic flow of memory reservation is maintained - clients use ```CacheReservationManager``` to request reservations, and ```CacheReservationManager``` inserts placeholder entries, i.e null value and non-zero charge, into the block cache. The ```CacheWithSecondaryAdapter``` wrapper uses its own instance of ```CacheReservationManager``` to keep track of reservations charged to the secondary cache, while the placeholder entries are inserted into the primary block cache. The design is as follows. When ```CacheWithSecondaryAdapter``` is constructed with the ```distribute_cache_res``` parameter set to true, it manages the entire memory budget across the primary and secondary cache. The secondary cache is assumed to be in memory, such as the ```CompressedSecondaryCache```. When a placeholder entry is inserted by a CacheReservationManager instance to reserve memory, the ```CacheWithSecondaryAdapter```ensures that the reservation is distributed proportionally across the primary/secondary caches. The primary block cache is initially sized to the sum of the primary cache budget + the secondary cache budget, as follows - |--------- Primary Cache Configured Capacity -----------| |---Secondary Cache Budget----|----Primary Cache Budget-----| A ```ConcurrentCacheReservationManager``` member in the ```CacheWithSecondaryAdapter```, ```pri_cache_res_```, is used to help with tracking the distribution of memory reservations. Initially, it accounts for the entire secondary cache budget as a reservation against the primary cache. This shrinks the usable capacity of the primary cache to the budget that the user originally desired. |--Reservation for Sec Cache--|-Pri Cache Usable Capacity---| When a reservation placeholder is inserted into the adapter, it is inserted directly into the primary cache. This means the entire charge of the placeholder is counted against the primary cache. To compensate and count a portion of it against the secondary cache, the secondary cache ```Deflate()``` method is called to shrink it. Since the ```Deflate()``` causes the secondary actual usage to shrink, it is reflected here by releasing an equal amount from the ```pri_cache_res_``` reservation. For example, if the pri/sec ratio is 50/50, this would be the state after placeholder insertion - |-Reservation for Sec Cache-|-Pri Cache Usable Capacity-|-R-| Likewise, when the user inserted placeholder is released, the secondary cache ```Inflate()``` method is called to grow it, and the ```pri_cache_res_``` reservation is increased by an equal amount. Other alternatives - 1. Another way of implementing this would have been to simply split the user reservation in ```CacheWithSecondaryAdapter``` into primary and secondary components. However, this would require allocating a structure to track the associated secondary cache reservation, which adds some complexity and overhead. 2. Yet another option is to implement the splitting directly in ```CacheReservationManager```. However, there are multiple instances of ```CacheReservationManager``` in a DB instance, making it complicated to keep track of them. The PR contains the following changes - 1. A new cache allocator, ```NewTieredVolatileCache()```, is defined for allocating a tiered primary block cache and compressed secondary cache. This internally allocates an instance of ```CacheWithSecondaryAdapter```. 3. New interfaces, ```Deflate()``` and ```Inflate()```, are added to the ```SecondaryCache``` interface. The default implementaion returns ```NotSupported``` with overrides in ```CompressedSecondaryCache```. 4. The ```CompressedSecondaryCache``` uses a ```ConcurrentCacheReservationManager``` instance to manage reservations done using ```Inflate()/Deflate()```. 5. The ```CacheWithSecondaryAdapter``` optionally distributes memory reservations across the primary and secondary caches. The primary cache is sized to the total memory budget (primary + secondary), and the capacity allocated to secondary cache is "reserved" against the primary cache. For any subsequent reservations, the primary cache pre-reserved capacity is adjusted. Benchmarks - Baseline ``` time ~/rocksdb_anand76/db_bench --db=/dev/shm/comp_cache_res/base --use_existing_db=true --benchmarks="readseq,readwhilewriting" --key_size=32 --value_size=1024 --num=20000000 --threads=32 --bloom_bits=10 --cache_size=30000000000 --use_compressed_secondary_cache=true --compressed_secondary_cache_size=5000000000 --duration=300 --cost_write_buffer_to_cache=true ``` ``` readseq : 3.301 micros/op 9694317 ops/sec 66.018 seconds 640000000 operations; 9763.0 MB/s readwhilewriting : 22.921 micros/op 1396058 ops/sec 300.021 seconds 418846968 operations; 1405.9 MB/s (13068999 of 13068999 found) real 6m31.052s user 152m5.660s sys 26m18.738s ``` With TieredVolatileCache ``` time ~/rocksdb_anand76/db_bench --db=/dev/shm/comp_cache_res/base --use_existing_db=true --benchmarks="readseq,readwhilewriting" --key_size=32 --value_size=1024 --num=20000000 --threads=32 --bloom_bits=10 --cache_size=30000000000 --use_compressed_secondary_cache=true --compressed_secondary_cache_size=5000000000 --duration=300 --cost_write_buffer_to_cache=true --use_tiered_volatile_cache=true ``` ``` readseq : 4.064 micros/op 7873915 ops/sec 81.281 seconds 640000000 operations; 7929.7 MB/s readwhilewriting : 20.944 micros/op 1527827 ops/sec 300.020 seconds 458378968 operations; 1538.6 MB/s (14296999 of 14296999 found) real 6m42.743s user 157m58.972s sys 33m16.671 ``` ``` readseq : 3.484 micros/op 9184967 ops/sec 69.679 seconds 640000000 operations; 9250.0 MB/s readwhilewriting : 21.261 micros/op 1505035 ops/sec 300.024 seconds 451545968 operations; 1515.7 MB/s (14101999 of 14101999 found) real 6m31.469s user 155m16.570s sys 27m47.834s ``` ToDo - 1. Add to db_stress Pull Request resolved: https://github.com/facebook/rocksdb/pull/11449 Reviewed By: pdillinger Differential Revision: D46197388 Pulled By: anand1976 fbshipit-source-id: 42d16f0254df683db4929db20d06ff26030e90df
2023-05-30 21:05:48 +00:00
"If use_compressed_secondary_cache is true and "
"use_tiered_volatile_cache is true, then allocate a tiered cache "
"that distributes cache reservations proportionally over both "
"the caches.");
DEFINE_string(
tiered_adm_policy, "auto",
"Admission policy to use for the secondary cache(s) in the tiered cache. "
"Allowed values are auto, placeholder, allow_cache_hits, and three_queue.");
add simulator Cache as class SimCache/SimLRUCache(with test) Summary: add class SimCache(base class with instrumentation api) and SimLRUCache(derived class with detailed implementation) which is used as an instrumented block cache that can predict hit rate for different cache size Test Plan: Add a test case in `db_block_cache_test.cc` called `SimCacheTest` to test basic logic of SimCache. Also add option `-simcache_size` in db_bench. if set with a value other than -1, then the benchmark will use this value as the size of the simulator cache and finally output the simulation result. ``` [gzh@dev9927.prn1 ~/local/rocksdb] ./db_bench -benchmarks "fillseq,readrandom" -cache_size 1000000 -simcache_size 1000000 RocksDB: version 4.8 Date: Tue May 17 16:56:16 2016 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Compression: Snappy Memtablerep: skip_list Perf Level: 0 WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ DB path: [/tmp/rocksdbtest-112628/dbbench] fillseq : 6.809 micros/op 146874 ops/sec; 16.2 MB/s DB path: [/tmp/rocksdbtest-112628/dbbench] readrandom : 6.343 micros/op 157665 ops/sec; 17.4 MB/s (1000000 of 1000000 found) SIMULATOR CACHE STATISTICS: SimCache LOOKUPs: 986559 SimCache HITs: 264760 SimCache HITRATE: 26.84% [gzh@dev9927.prn1 ~/local/rocksdb] ./db_bench -benchmarks "fillseq,readrandom" -cache_size 1000000 -simcache_size 10000000 RocksDB: version 4.8 Date: Tue May 17 16:57:10 2016 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Compression: Snappy Memtablerep: skip_list Perf Level: 0 WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ DB path: [/tmp/rocksdbtest-112628/dbbench] fillseq : 5.066 micros/op 197394 ops/sec; 21.8 MB/s DB path: [/tmp/rocksdbtest-112628/dbbench] readrandom : 6.457 micros/op 154870 ops/sec; 17.1 MB/s (1000000 of 1000000 found) SIMULATOR CACHE STATISTICS: SimCache LOOKUPs: 1059764 SimCache HITs: 374501 SimCache HITRATE: 35.34% [gzh@dev9927.prn1 ~/local/rocksdb] ./db_bench -benchmarks "fillseq,readrandom" -cache_size 1000000 -simcache_size 100000000 RocksDB: version 4.8 Date: Tue May 17 16:57:32 2016 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Compression: Snappy Memtablerep: skip_list Perf Level: 0 WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ DB path: [/tmp/rocksdbtest-112628/dbbench] fillseq : 5.632 micros/op 177572 ops/sec; 19.6 MB/s DB path: [/tmp/rocksdbtest-112628/dbbench] readrandom : 6.892 micros/op 145094 ops/sec; 16.1 MB/s (1000000 of 1000000 found) SIMULATOR CACHE STATISTICS: SimCache LOOKUPs: 1150767 SimCache HITs: 1034535 SimCache HITRATE: 89.90% ``` Reviewers: IslamAbdelRahman, andrewkr, sdong Reviewed By: sdong Subscribers: MarkCallaghan, andrewkr, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D57999
2016-05-24 06:35:23 +00:00
DEFINE_int64(simcache_size, -1,
"Number of bytes to use as a simcache of "
"uncompressed data. Nagative value disables simcache.");
DEFINE_bool(cache_index_and_filter_blocks, false,
"Cache index/filter blocks in block cache.");
DEFINE_bool(use_cache_jemalloc_no_dump_allocator, false,
"Use JemallocNodumpAllocator for block/blob cache.");
Provide an allocator for new memory type to be used with RocksDB block cache (#6214) Summary: New memory technologies are being developed by various hardware vendors (Intel DCPMM is one such technology currently available). These new memory types require different libraries for allocation and management (such as PMDK and memkind). The high capacities available make it possible to provision large caches (up to several TBs in size), beyond what is achievable with DRAM. The new allocator provided in this PR uses the memkind library to allocate memory on different media. **Performance** We tested the new allocator using db_bench. - For each test, we vary the size of the block cache (relative to the size of the uncompressed data in the database). - The database is filled sequentially. Throughput is then measured with a readrandom benchmark. - We use a uniform distribution as a worst-case scenario. The plot shows throughput (ops/s) relative to a configuration with no block cache and default allocator. For all tests, p99 latency is below 500 us. ![image](https://user-images.githubusercontent.com/26400080/71108594-42479100-2178-11ea-8231-8a775bbc92db.png) **Changes** - Add MemkindKmemAllocator - Add --use_cache_memkind_kmem_allocator db_bench option (to create an LRU block cache with the new allocator) - Add detection of memkind library with KMEM DAX support - Add test for MemkindKmemAllocator **Minimum Requirements** - kernel 5.3.12 - ndctl v67 - https://github.com/pmem/ndctl - memkind v1.10.0 - https://github.com/memkind/memkind **Memory Configuration** The allocator uses the MEMKIND_DAX_KMEM memory kind. Follow the instructions on[ memkind’s GitHub page](https://github.com/memkind/memkind) to set up NVDIMM memory accordingly. Note on memory allocation with NVDIMM memory exposed as system memory. - The MemkindKmemAllocator will only allocate from NVDIMM memory (using memkind_malloc with MEMKIND_DAX_KMEM kind). - The default allocator is not restricted to RAM by default. Based on NUMA node latency, the kernel should allocate from local RAM preferentially, but it’s a kernel decision. numactl --preferred/--membind can be used to allocate preferentially/exclusively from the local RAM node. **Usage** When creating an LRU cache, pass a MemkindKmemAllocator object as argument. For example (replace capacity with the desired value in bytes): ``` #include "rocksdb/cache.h" #include "memory/memkind_kmem_allocator.h" NewLRUCache( capacity /*size_t*/, 6 /*cache_numshardbits*/, false /*strict_capacity_limit*/, false /*cache_high_pri_pool_ratio*/, std::make_shared<MemkindKmemAllocator>()); ``` Refer to [RocksDB’s block cache documentation](https://github.com/facebook/rocksdb/wiki/Block-Cache) to assign the LRU cache as block cache for a database. Pull Request resolved: https://github.com/facebook/rocksdb/pull/6214 Reviewed By: cheng-chang Differential Revision: D19292435 fbshipit-source-id: 7202f47b769e7722b539c86c2ffd669f64d7b4e1
2020-04-10 03:45:17 +00:00
DEFINE_bool(use_cache_memkind_kmem_allocator, false,
"Use memkind kmem allocator for block/blob cache.");
Provide an allocator for new memory type to be used with RocksDB block cache (#6214) Summary: New memory technologies are being developed by various hardware vendors (Intel DCPMM is one such technology currently available). These new memory types require different libraries for allocation and management (such as PMDK and memkind). The high capacities available make it possible to provision large caches (up to several TBs in size), beyond what is achievable with DRAM. The new allocator provided in this PR uses the memkind library to allocate memory on different media. **Performance** We tested the new allocator using db_bench. - For each test, we vary the size of the block cache (relative to the size of the uncompressed data in the database). - The database is filled sequentially. Throughput is then measured with a readrandom benchmark. - We use a uniform distribution as a worst-case scenario. The plot shows throughput (ops/s) relative to a configuration with no block cache and default allocator. For all tests, p99 latency is below 500 us. ![image](https://user-images.githubusercontent.com/26400080/71108594-42479100-2178-11ea-8231-8a775bbc92db.png) **Changes** - Add MemkindKmemAllocator - Add --use_cache_memkind_kmem_allocator db_bench option (to create an LRU block cache with the new allocator) - Add detection of memkind library with KMEM DAX support - Add test for MemkindKmemAllocator **Minimum Requirements** - kernel 5.3.12 - ndctl v67 - https://github.com/pmem/ndctl - memkind v1.10.0 - https://github.com/memkind/memkind **Memory Configuration** The allocator uses the MEMKIND_DAX_KMEM memory kind. Follow the instructions on[ memkind’s GitHub page](https://github.com/memkind/memkind) to set up NVDIMM memory accordingly. Note on memory allocation with NVDIMM memory exposed as system memory. - The MemkindKmemAllocator will only allocate from NVDIMM memory (using memkind_malloc with MEMKIND_DAX_KMEM kind). - The default allocator is not restricted to RAM by default. Based on NUMA node latency, the kernel should allocate from local RAM preferentially, but it’s a kernel decision. numactl --preferred/--membind can be used to allocate preferentially/exclusively from the local RAM node. **Usage** When creating an LRU cache, pass a MemkindKmemAllocator object as argument. For example (replace capacity with the desired value in bytes): ``` #include "rocksdb/cache.h" #include "memory/memkind_kmem_allocator.h" NewLRUCache( capacity /*size_t*/, 6 /*cache_numshardbits*/, false /*strict_capacity_limit*/, false /*cache_high_pri_pool_ratio*/, std::make_shared<MemkindKmemAllocator>()); ``` Refer to [RocksDB’s block cache documentation](https://github.com/facebook/rocksdb/wiki/Block-Cache) to assign the LRU cache as block cache for a database. Pull Request resolved: https://github.com/facebook/rocksdb/pull/6214 Reviewed By: cheng-chang Differential Revision: D19292435 fbshipit-source-id: 7202f47b769e7722b539c86c2ffd669f64d7b4e1
2020-04-10 03:45:17 +00:00
DEFINE_bool(partition_index_and_filters, false,
"Partition index and filter blocks.");
DEFINE_bool(partition_index, false, "Partition index blocks");
DEFINE_bool(index_with_first_key, false, "Include first key in the index");
Minimize memory internal fragmentation for Bloom filters (#6427) Summary: New experimental option BBTO::optimize_filters_for_memory builds filters that maximize their use of "usable size" from malloc_usable_size, which is also used to compute block cache charges. Rather than always "rounding up," we track state in the BloomFilterPolicy object to mix essentially "rounding down" and "rounding up" so that the average FP rate of all generated filters is the same as without the option. (YMMV as heavily accessed filters might be unluckily lower accuracy.) Thus, the option near-minimizes what the block cache considers as "memory used" for a given target Bloom filter false positive rate and Bloom filter implementation. There are no forward or backward compatibility issues with this change, though it only works on the format_version=5 Bloom filter. With Jemalloc, we see about 10% reduction in memory footprint (and block cache charge) for Bloom filters, but 1-2% increase in storage footprint, due to encoding efficiency losses (FP rate is non-linear with bits/key). Why not weighted random round up/down rather than state tracking? By only requiring malloc_usable_size, we don't actually know what the next larger and next smaller usable sizes for the allocator are. We pick a requested size, accept and use whatever usable size it has, and use the difference to inform our next choice. This allows us to narrow in on the right balance without tracking/predicting usable sizes. Why not weight history of generated filter false positive rates by number of keys? This could lead to excess skew in small filters after generating a large filter. Results from filter_bench with jemalloc (irrelevant details omitted): (normal keys/filter, but high variance) $ ./filter_bench -quick -impl=2 -average_keys_per_filter=30000 -vary_key_count_ratio=0.9 Build avg ns/key: 29.6278 Number of filters: 5516 Total size (MB): 200.046 Reported total allocated memory (MB): 220.597 Reported internal fragmentation: 10.2732% Bits/key stored: 10.0097 Average FP rate %: 0.965228 $ ./filter_bench -quick -impl=2 -average_keys_per_filter=30000 -vary_key_count_ratio=0.9 -optimize_filters_for_memory Build avg ns/key: 30.5104 Number of filters: 5464 Total size (MB): 200.015 Reported total allocated memory (MB): 200.322 Reported internal fragmentation: 0.153709% Bits/key stored: 10.1011 Average FP rate %: 0.966313 (very few keys / filter, optimization not as effective due to ~59 byte internal fragmentation in blocked Bloom filter representation) $ ./filter_bench -quick -impl=2 -average_keys_per_filter=1000 -vary_key_count_ratio=0.9 Build avg ns/key: 29.5649 Number of filters: 162950 Total size (MB): 200.001 Reported total allocated memory (MB): 224.624 Reported internal fragmentation: 12.3117% Bits/key stored: 10.2951 Average FP rate %: 0.821534 $ ./filter_bench -quick -impl=2 -average_keys_per_filter=1000 -vary_key_count_ratio=0.9 -optimize_filters_for_memory Build avg ns/key: 31.8057 Number of filters: 159849 Total size (MB): 200 Reported total allocated memory (MB): 208.846 Reported internal fragmentation: 4.42297% Bits/key stored: 10.4948 Average FP rate %: 0.811006 (high keys/filter) $ ./filter_bench -quick -impl=2 -average_keys_per_filter=1000000 -vary_key_count_ratio=0.9 Build avg ns/key: 29.7017 Number of filters: 164 Total size (MB): 200.352 Reported total allocated memory (MB): 221.5 Reported internal fragmentation: 10.5552% Bits/key stored: 10.0003 Average FP rate %: 0.969358 $ ./filter_bench -quick -impl=2 -average_keys_per_filter=1000000 -vary_key_count_ratio=0.9 -optimize_filters_for_memory Build avg ns/key: 30.7131 Number of filters: 160 Total size (MB): 200.928 Reported total allocated memory (MB): 200.938 Reported internal fragmentation: 0.00448054% Bits/key stored: 10.1852 Average FP rate %: 0.963387 And from db_bench (block cache) with jemalloc: $ ./db_bench -db=/dev/shm/dbbench.no_optimize -benchmarks=fillrandom -format_version=5 -value_size=90 -bloom_bits=10 -num=2000000 -threads=8 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=false $ ./db_bench -db=/dev/shm/dbbench -benchmarks=fillrandom -format_version=5 -value_size=90 -bloom_bits=10 -num=2000000 -threads=8 -optimize_filters_for_memory -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=false $ (for FILE in /dev/shm/dbbench.no_optimize/*.sst; do ./sst_dump --file=$FILE --show_properties | grep 'filter block' ; done) | awk '{ t += $4; } END { print t; }' 17063835 $ (for FILE in /dev/shm/dbbench/*.sst; do ./sst_dump --file=$FILE --show_properties | grep 'filter block' ; done) | awk '{ t += $4; } END { print t; }' 17430747 $ #^ 2.1% additional filter storage $ ./db_bench -db=/dev/shm/dbbench.no_optimize -use_existing_db -benchmarks=readrandom,stats -statistics -bloom_bits=10 -num=2000000 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=false -duration=10 -cache_index_and_filter_blocks -cache_size=1000000000 rocksdb.block.cache.index.add COUNT : 33 rocksdb.block.cache.index.bytes.insert COUNT : 8440400 rocksdb.block.cache.filter.add COUNT : 33 rocksdb.block.cache.filter.bytes.insert COUNT : 21087528 rocksdb.bloom.filter.useful COUNT : 4963889 rocksdb.bloom.filter.full.positive COUNT : 1214081 rocksdb.bloom.filter.full.true.positive COUNT : 1161999 $ #^ 1.04 % observed FP rate $ ./db_bench -db=/dev/shm/dbbench -use_existing_db -benchmarks=readrandom,stats -statistics -bloom_bits=10 -num=2000000 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=false -optimize_filters_for_memory -duration=10 -cache_index_and_filter_blocks -cache_size=1000000000 rocksdb.block.cache.index.add COUNT : 33 rocksdb.block.cache.index.bytes.insert COUNT : 8448592 rocksdb.block.cache.filter.add COUNT : 33 rocksdb.block.cache.filter.bytes.insert COUNT : 18220328 rocksdb.bloom.filter.useful COUNT : 5360933 rocksdb.bloom.filter.full.positive COUNT : 1321315 rocksdb.bloom.filter.full.true.positive COUNT : 1262999 $ #^ 1.08 % observed FP rate, 13.6% less memory usage for filters (Due to specific key density, this example tends to generate filters that are "worse than average" for internal fragmentation. "Better than average" cases can show little or no improvement.) Pull Request resolved: https://github.com/facebook/rocksdb/pull/6427 Test Plan: unit test added, 'make check' with gcc, clang and valgrind Reviewed By: siying Differential Revision: D22124374 Pulled By: pdillinger fbshipit-source-id: f3e3aa152f9043ddf4fae25799e76341d0d8714e
2020-06-22 20:30:57 +00:00
DEFINE_bool(
optimize_filters_for_memory,
ROCKSDB_NAMESPACE::BlockBasedTableOptions().optimize_filters_for_memory,
"Minimize memory footprint of filters");
DEFINE_int64(
index_shortening_mode, 2,
"mode to shorten index: 0 for no shortening; 1 for only shortening "
"separaters; 2 for shortening shortening and successor");
DEFINE_int64(metadata_block_size,
ROCKSDB_NAMESPACE::BlockBasedTableOptions().metadata_block_size,
"Max partition size when partitioning index/filters");
// The default reduces the overhead of reading time with flash. With HDD, which
// offers much less throughput, however, this number better to be set to 1.
DEFINE_int32(ops_between_duration_checks, 1000,
"Check duration limit every x ops");
DEFINE_bool(pin_l0_filter_and_index_blocks_in_cache, false,
"Pin index/filter blocks of L0 files in block cache.");
DEFINE_bool(
pin_top_level_index_and_filter, false,
"Pin top-level index of partitioned index/filter blocks in block cache.");
DEFINE_int32(block_size,
static_cast<int32_t>(
ROCKSDB_NAMESPACE::BlockBasedTableOptions().block_size),
"Number of bytes in a block.");
DEFINE_int32(format_version,
static_cast<int32_t>(
ROCKSDB_NAMESPACE::BlockBasedTableOptions().format_version),
"Format version of SST files.");
DEFINE_int32(block_restart_interval,
ROCKSDB_NAMESPACE::BlockBasedTableOptions().block_restart_interval,
"Number of keys between restart points "
"for delta encoding of keys in data block.");
DEFINE_int32(
index_block_restart_interval,
ROCKSDB_NAMESPACE::BlockBasedTableOptions().index_block_restart_interval,
"Number of keys between restart points "
"for delta encoding of keys in index block.");
DEFINE_int32(read_amp_bytes_per_bit,
ROCKSDB_NAMESPACE::BlockBasedTableOptions().read_amp_bytes_per_bit,
"Number of bytes per bit to be used in block read-amp bitmap");
DEFINE_bool(
enable_index_compression,
ROCKSDB_NAMESPACE::BlockBasedTableOptions().enable_index_compression,
"Compress the index block");
DEFINE_bool(block_align,
ROCKSDB_NAMESPACE::BlockBasedTableOptions().block_align,
"Align data blocks on page size");
DEFINE_int64(prepopulate_block_cache, 0,
"Pre-populate hot/warm blocks in block cache. 0 to disable and 1 "
"to insert during flush");
DEFINE_bool(use_data_block_hash_index, false,
"if use kDataBlockBinaryAndHash "
"instead of kDataBlockBinarySearch. "
"This is valid if only we use BlockTable");
DEFINE_double(data_block_hash_table_util_ratio, 0.75,
"util ratio for data block hash index table. "
"This is only valid if use_data_block_hash_index is "
"set to true");
DEFINE_int64(compressed_cache_size, -1,
"Number of bytes to use as a cache of compressed data.");
DEFINE_int64(row_cache_size, 0,
"Number of bytes to use as a cache of individual rows"
" (0 = disabled).");
DEFINE_int32(open_files, ROCKSDB_NAMESPACE::Options().max_open_files,
"Maximum number of files to keep open at the same time"
" (use default if == 0)");
DEFINE_int32(file_opening_threads,
ROCKSDB_NAMESPACE::Options().max_file_opening_threads,
"If open_files is set to -1, this option set the number of "
"threads that will be used to open files during DB::Open()");
DEFINE_uint64(compaction_readahead_size,
ROCKSDB_NAMESPACE::Options().compaction_readahead_size,
"Compaction readahead size");
DEFINE_int32(log_readahead_size, 0, "WAL and manifest readahead size");
2015-10-29 18:34:34 +00:00
DEFINE_int32(random_access_max_buffer_size, 1024 * 1024,
"Maximum windows randomaccess buffer size");
DEFINE_int32(writable_file_max_buffer_size, 1024 * 1024,
"Maximum write buffer for Writable File");
DEFINE_int32(bloom_bits, -1,
"Bloom filter bits per key. Negative means use default."
"Zero disables.");
Support optimize_filters_for_memory for Ribbon filter (#7774) Summary: Primarily this change refactors the optimize_filters_for_memory code for Bloom filters, based on malloc_usable_size, to also work for Ribbon filters. This change also replaces the somewhat slow but general BuiltinFilterBitsBuilder::ApproximateNumEntries with implementation-specific versions for Ribbon (new) and Legacy Bloom (based on a recently deleted version). The reason is to emphasize speed in ApproximateNumEntries rather than 100% accuracy. Justification: ApproximateNumEntries (formerly CalculateNumEntry) is only used by RocksDB for range-partitioned filters, called each time we start to construct one. (In theory, it should be possible to reuse the estimate, but the abstractions provided by FilterPolicy don't really make that workable.) But this is only used as a heuristic estimate for hitting a desired partitioned filter size because of alignment to data blocks, which have various numbers of unique keys or prefixes. The two factors lead us to prioritize reasonable speed over 100% accuracy. optimize_filters_for_memory adds extra complication, because precisely calculating num_entries for some allowed number of bytes depends on state with optimize_filters_for_memory enabled. And the allocator-agnostic implementation of optimize_filters_for_memory, using malloc_usable_size, means we would have to actually allocate memory, many times, just to precisely determine how many entries (keys) could be added and stay below some size budget, for the current state. (In a draft, I got this working, and then realized the balance of speed vs. accuracy was all wrong.) So related to that, I have made CalculateSpace, an internal-only API only used for testing, non-authoritative also if optimize_filters_for_memory is enabled. This simplifies some code. Pull Request resolved: https://github.com/facebook/rocksdb/pull/7774 Test Plan: unit test updated, and for FilterSize test, range of tested values is greatly expanded (still super fast) Also tested `db_bench -benchmarks=fillrandom,stats -bloom_bits=10 -num=1000000 -partition_index_and_filters -format_version=5 [-optimize_filters_for_memory] [-use_ribbon_filter]` with temporary debug output of generated filter sizes. Bloom+optimize_filters_for_memory: 1 Filter size: 197 (224 in memory) 134 Filter size: 3525 (3584 in memory) 107 Filter size: 4037 (4096 in memory) Total on disk: 904,506 Total in memory: 918,752 Ribbon+optimize_filters_for_memory: 1 Filter size: 3061 (3072 in memory) 110 Filter size: 3573 (3584 in memory) 58 Filter size: 4085 (4096 in memory) Total on disk: 633,021 (-30.0%) Total in memory: 634,880 (-30.9%) Bloom (no offm): 1 Filter size: 261 (320 in memory) 1 Filter size: 3333 (3584 in memory) 240 Filter size: 3717 (4096 in memory) Total on disk: 895,674 (-1% on disk vs. +offm; known tolerable overhead of offm) Total in memory: 986,944 (+7.4% vs. +offm) Ribbon (no offm): 1 Filter size: 2949 (3072 in memory) 1 Filter size: 3381 (3584 in memory) 167 Filter size: 3701 (4096 in memory) Total on disk: 624,397 (-30.3% vs. Bloom) Total in memory: 690,688 (-30.0% vs. Bloom) Note that optimize_filters_for_memory is even more effective for Ribbon filter than for cache-local Bloom, because it can close the unused memory gap even tighter than Bloom filter, because of 16 byte increments for Ribbon vs. 64 byte increments for Bloom. Reviewed By: jay-zhuang Differential Revision: D25592970 Pulled By: pdillinger fbshipit-source-id: 606fdaa025bb790d7e9c21601e8ea86e10541912
2020-12-18 22:29:48 +00:00
DEFINE_bool(use_ribbon_filter, false, "Use Ribbon instead of Bloom filter");
DEFINE_double(memtable_bloom_size_ratio, 0,
"Ratio of memtable size used for bloom filter. 0 means no bloom "
"filter.");
DEFINE_bool(memtable_whole_key_filtering, false,
"Try to use whole key bloom filter in memtables.");
DEFINE_bool(memtable_use_huge_page, false,
"Try to use huge page in memtables.");
Ignore `total_order_seek` in DB::Get (#9427) Summary: Apparently setting total_order_seek=true for DB::Get was intended to allow accurate read semantics if the current prefix extractor doesn't match what was used to generate SST files on disk. But since prefix_extractor was made a mutable option in 5.14.0, we have been able to detect this case and provide the correct semantics regardless of the total_order_seek option. Since that time, the option has only made Get() slower in a reasonably common case: prefix_extractor unchanged and whole_key_filtering=false. So this change primarily removes unnecessary effect of total_order_seek on Get. Also cleans up some related comments. Also adds a -total_order_seek option to db_bench and canonicalizes handling of ReadOptions in db_bench so that command line options have the expected association with library features. (There is potential for change in regression test behavior, but the old behavior is likely indefensible, or some other inconsistency would need to be fixed.) TODO in follow-up work: there should be no reason for Get() to depend on current prefix extractor at all. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9427 Test Plan: Unit tests updated. Performance (using db_bench update) Create DB with `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -benchmarks=fillrandom -num=10000000 -disable_wal=1 -write_buffer_size=10000000 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12 -whole_key_filtering=0` Test with and without `-total_order_seek` on `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -use_existing_db -readonly -benchmarks=readrandom -num=10000000 -duration=40 -disable_wal=1 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12` Before this change, total_order_seek=false: 25188 ops/sec Before this change, total_order_seek=true: 1222 ops/sec (~20x slower) After this change, total_order_seek=false: 24570 ops/sec After this change, total_order_seek=true: 25012 ops/sec (indistinguishable) Reviewed By: siying Differential Revision: D33753458 Pulled By: pdillinger fbshipit-source-id: bf892f34907a5e407d9c40bd4d42f0adbcbe0014
2022-02-01 03:45:17 +00:00
DEFINE_bool(whole_key_filtering,
ROCKSDB_NAMESPACE::BlockBasedTableOptions().whole_key_filtering,
"Use whole keys (in addition to prefixes) in SST bloom filter.");
DEFINE_bool(use_existing_db, false,
"If true, do not destroy the existing database. If you set this "
"flag and also specify a benchmark that wants a fresh database, "
"that benchmark will fail.");
DEFINE_bool(use_existing_keys, false,
"If true, uses existing keys in the DB, "
"rather than generating new ones. This involves some startup "
"latency to load all keys into memory. It is supported for the "
"same read/overwrite benchmarks as `-use_existing_db=true`, which "
"must also be set for this flag to be enabled. When this flag is "
"set, the value for `-num` will be ignored.");
Add argument --show_table_properties to db_bench Summary: Add argument --show_table_properties to db_bench -show_table_properties (If true, then per-level table properties will be printed on every stats-interval when stats_interval is set and stats_per_interval is on.) type: bool default: false Test Plan: ./db_bench --show_table_properties=1 --stats_interval=100000 --stats_per_interval=1 ./db_bench --show_table_properties=1 --stats_interval=100000 --stats_per_interval=1 --num_column_families=2 Sample Output: Compaction Stats [column_family_name_000001] Level Files Size(MB) Score Read(GB) Rn(GB) Rnp1(GB) Write(GB) Wnew(GB) Moved(GB) W-Amp Rd(MB/s) Wr(MB/s) Comp(sec) Comp(cnt) Avg(sec) Stall(cnt) KeyIn KeyDrop --------------------------------------------------------------------------------------------------------------------------------------------------------------------- L0 3/0 5 0.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 86.3 0 17 0.021 0 0 0 L1 5/0 9 0.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 0 0.000 0 0 0 L2 9/0 16 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 0 0.000 0 0 0 Sum 17/0 31 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 86.3 0 17 0.021 0 0 0 Int 0/0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 83.9 0 2 0.022 0 0 0 Flush(GB): cumulative 0.030, interval 0.004 Stalls(count): 0 level0_slowdown, 0 level0_numfiles, 0 memtable_compaction, 0 leveln_slowdown_soft, 0 leveln_slowdown_hard Level[0]: # data blocks=2571; # entries=84813; raw key size=2035512; raw average key size=24.000000; raw value size=8481300; raw average value size=100.000000; data block size=5690119; index block size=82415; filter block size=0; (estimated) table size=5772534; filter policy name=N/A; Level[1]: # data blocks=4285; # entries=141355; raw key size=3392520; raw average key size=24.000000; raw value size=14135500; raw average value size=100.000000; data block size=9487353; index block size=137377; filter block size=0; (estimated) table size=9624730; filter policy name=N/A; Level[2]: # data blocks=7713; # entries=254439; raw key size=6106536; raw average key size=24.000000; raw value size=25443900; raw average value size=100.000000; data block size=17077893; index block size=247269; filter block size=0; (estimated) table size=17325162; filter policy name=N/A; Level[3]: # data blocks=0; # entries=0; raw key size=0; raw average key size=0.000000; raw value size=0; raw average value size=0.000000; data block size=0; index block size=0; filter block size=0; (estimated) table size=0; filter policy name=N/A; Level[4]: # data blocks=0; # entries=0; raw key size=0; raw average key size=0.000000; raw value size=0; raw average value size=0.000000; data block size=0; index block size=0; filter block size=0; (estimated) table size=0; filter policy name=N/A; Level[5]: # data blocks=0; # entries=0; raw key size=0; raw average key size=0.000000; raw value size=0; raw average value size=0.000000; data block size=0; index block size=0; filter block size=0; (estimated) table size=0; filter policy name=N/A; Level[6]: # data blocks=0; # entries=0; raw key size=0; raw average key size=0.000000; raw value size=0; raw average value size=0.000000; data block size=0; index block size=0; filter block size=0; (estimated) table size=0; filter policy name=N/A; Reviewers: anthony, IslamAbdelRahman, MarkCallaghan, sdong, igor Reviewed By: igor Subscribers: dhruba, leveldb Differential Revision: https://reviews.facebook.net/D45651
2015-08-27 01:27:23 +00:00
DEFINE_bool(show_table_properties, false,
"If true, then per-level table"
" properties will be printed on every stats-interval when"
" stats_interval is set and stats_per_interval is on.");
DEFINE_string(db, "", "Use the db with the following name.");
DEFINE_bool(progress_reports, true,
"If true, db_bench will report number of finished operations.");
// Read cache flags
DEFINE_string(read_cache_path, "",
"If not empty string, a read cache will be used in this path");
DEFINE_int64(read_cache_size, 4LL * 1024 * 1024 * 1024,
"Maximum size of the read cache");
DEFINE_bool(read_cache_direct_write, true,
"Whether to use Direct IO for writing to the read cache");
DEFINE_bool(read_cache_direct_read, true,
"Whether to use Direct IO for reading from read cache");
DEFINE_bool(use_keep_filter, false, "Whether to use a noop compaction filter");
static bool ValidateCacheNumshardbits(const char* flagname, int32_t value) {
if (value >= 20) {
fprintf(stderr, "Invalid value for --%s: %d, must be < 20\n", flagname,
value);
return false;
}
return true;
}
DEFINE_bool(verify_checksum, true,
"Verify checksum for every block read from storage");
Implement XXH3 block checksum type (#9069) Summary: XXH3 - latest hash function that is extremely fast on large data, easily faster than crc32c on most any x86_64 hardware. In integrating this hash function, I have handled the compression type byte in a non-standard way to avoid using the streaming API (extra data movement and active code size because of hash function complexity). This approach got a thumbs-up from Yann Collet. Existing functionality change: * reject bad ChecksumType in options with InvalidArgument This change split off from https://github.com/facebook/rocksdb/issues/9058 because context-aware checksum is likely to be handled through different configuration than ChecksumType. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9069 Test Plan: tests updated, and substantially expanded. Unit tests now check that we don't accidentally change the values generated by the checksum algorithms ("schema test") and that we properly handle invalid/unrecognized checksum types in options or in file footer. DBTestBase::ChangeOptions (etc.) updated from two to one configuration changing from default CRC32c ChecksumType. The point of this test code is to detect possible interactions among features, and the likelihood of some bad interaction being detected by including configurations other than XXH3 and CRC32c--and then not detected by stress/crash test--is extremely low. Stress/crash test also updated (manual run long enough to see it accepts new checksum type). db_bench also updated for microbenchmarking checksums. ### Performance microbenchmark (PORTABLE=0 DEBUG_LEVEL=0, Broadwell processor) ./db_bench -benchmarks=crc32c,xxhash,xxhash64,xxh3,crc32c,xxhash,xxhash64,xxh3,crc32c,xxhash,xxhash64,xxh3 crc32c : 0.200 micros/op 5005220 ops/sec; 19551.6 MB/s (4096 per op) xxhash : 0.807 micros/op 1238408 ops/sec; 4837.5 MB/s (4096 per op) xxhash64 : 0.421 micros/op 2376514 ops/sec; 9283.3 MB/s (4096 per op) xxh3 : 0.171 micros/op 5858391 ops/sec; 22884.3 MB/s (4096 per op) crc32c : 0.206 micros/op 4859566 ops/sec; 18982.7 MB/s (4096 per op) xxhash : 0.793 micros/op 1260850 ops/sec; 4925.2 MB/s (4096 per op) xxhash64 : 0.410 micros/op 2439182 ops/sec; 9528.1 MB/s (4096 per op) xxh3 : 0.161 micros/op 6202872 ops/sec; 24230.0 MB/s (4096 per op) crc32c : 0.203 micros/op 4924686 ops/sec; 19237.1 MB/s (4096 per op) xxhash : 0.839 micros/op 1192388 ops/sec; 4657.8 MB/s (4096 per op) xxhash64 : 0.424 micros/op 2357391 ops/sec; 9208.6 MB/s (4096 per op) xxh3 : 0.162 micros/op 6182678 ops/sec; 24151.1 MB/s (4096 per op) As you can see, especially once warmed up, xxh3 is fastest. ### Performance macrobenchmark (PORTABLE=0 DEBUG_LEVEL=0, Broadwell processor) Test for I in `seq 1 50`; do for CHK in 0 1 2 3 4; do TEST_TMPDIR=/dev/shm/rocksdb$CHK ./db_bench -benchmarks=fillseq -memtablerep=vector -allow_concurrent_memtable_write=false -num=30000000 -checksum_type=$CHK 2>&1 | grep 'micros/op' | tee -a results-$CHK & done; wait; done Results (ops/sec) for FILE in results*; do echo -n "$FILE "; awk '{ s += $5; c++; } END { print 1.0 * s / c; }' < $FILE; done results-0 252118 # kNoChecksum results-1 251588 # kCRC32c results-2 251863 # kxxHash results-3 252016 # kxxHash64 results-4 252038 # kXXH3 Reviewed By: mrambacher Differential Revision: D31905249 Pulled By: pdillinger fbshipit-source-id: cb9b998ebe2523fc7c400eedf62124a78bf4b4d1
2021-10-29 05:13:47 +00:00
DEFINE_int32(checksum_type,
ROCKSDB_NAMESPACE::BlockBasedTableOptions().checksum,
"ChecksumType as an int");
DEFINE_bool(statistics, false, "Database statistics");
DEFINE_int32(stats_level, ROCKSDB_NAMESPACE::StatsLevel::kExceptDetailedTimers,
"stats level for statistics");
DEFINE_string(statistics_string, "", "Serialized statistics string");
static class std::shared_ptr<ROCKSDB_NAMESPACE::Statistics> dbstats;
DEFINE_int64(writes, -1,
"Number of write operations to do. If negative, do --num reads.");
DEFINE_bool(finish_after_writes, false,
"Write thread terminates after all writes are finished");
DEFINE_bool(sync, false, "Sync all writes to disk");
DEFINE_bool(use_fsync, false, "If true, issue fsync instead of fdatasync");
DEFINE_bool(disable_wal, false, "If true, do not write WAL for write.");
DEFINE_bool(manual_wal_flush, false,
"If true, buffer WAL until buffer is full or a manual FlushWAL().");
DEFINE_string(wal_compression, "none",
"Algorithm to use for WAL compression. none to disable.");
static enum ROCKSDB_NAMESPACE::CompressionType FLAGS_wal_compression_e =
ROCKSDB_NAMESPACE::kNoCompression;
DEFINE_string(wal_dir, "", "If not empty, use the given dir for WAL");
DEFINE_string(truth_db, "/dev/shm/truth_db/dbbench",
"Truth key/values used when using verify");
DEFINE_int32(num_levels, 7, "The total number of levels");
DEFINE_int64(target_file_size_base,
ROCKSDB_NAMESPACE::Options().target_file_size_base,
"Target file size at level-1");
DEFINE_int32(target_file_size_multiplier,
ROCKSDB_NAMESPACE::Options().target_file_size_multiplier,
"A multiplier to compute target level-N file size (N >= 2)");
DEFINE_uint64(max_bytes_for_level_base,
ROCKSDB_NAMESPACE::Options().max_bytes_for_level_base,
"Max bytes for level-1");
DEFINE_bool(level_compaction_dynamic_level_bytes, false,
"Whether level size base is dynamic");
DEFINE_double(max_bytes_for_level_multiplier, 10,
"A multiplier to compute max bytes for level-N (N >= 2)");
static std::vector<int> FLAGS_max_bytes_for_level_multiplier_additional_v;
DEFINE_string(max_bytes_for_level_multiplier_additional, "",
"A vector that specifies additional fanout per level");
DEFINE_int32(level0_stop_writes_trigger,
ROCKSDB_NAMESPACE::Options().level0_stop_writes_trigger,
"Number of files in level-0 that will trigger put stop.");
DEFINE_int32(level0_slowdown_writes_trigger,
ROCKSDB_NAMESPACE::Options().level0_slowdown_writes_trigger,
"Number of files in level-0 that will slow down writes.");
DEFINE_int32(level0_file_num_compaction_trigger,
ROCKSDB_NAMESPACE::Options().level0_file_num_compaction_trigger,
"Number of files in level-0 when compactions start.");
DEFINE_uint64(periodic_compaction_seconds,
ROCKSDB_NAMESPACE::Options().periodic_compaction_seconds,
"Files older than this will be picked up for compaction and"
" rewritten to the same level");
Try to start TTL earlier with kMinOverlappingRatio is used (#8749) Summary: Right now, when options.ttl is set, compactions are triggered around the time when TTL is reached. This might cause extra compactions which are often bursty. This commit tries to mitigate it by picking those files earlier in normal compaction picking process. This is only implemented using kMinOverlappingRatio with Leveled compaction as it is the default value and it is more complicated to change other styles. When a file is aged more than ttl/2, RocksDB starts to boost the compaction priority of files in normal compaction picking process, and hope by the time TTL is reached, very few extra compaction is needed. In order for this to work, another change is made: during a compaction, if an output level file is older than ttl/2, cut output files based on original boundary (if it is not in the last level). This is to make sure that after an old file is moved to the next level, and new data is merged from the upper level, the new data falling into this range isn't reset with old timestamp. Without this change, in many cases, most files from one level will keep having old timestamp, even if they have newer data and we stuck in it. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8749 Test Plan: Add a unit test to test the boosting logic. Will add a unit test to test it end-to-end. Reviewed By: jay-zhuang Differential Revision: D30735261 fbshipit-source-id: 503c2d89250b22911eb99e72b379be154de3428e
2021-11-01 21:32:12 +00:00
DEFINE_uint64(ttl_seconds, ROCKSDB_NAMESPACE::Options().ttl, "Set options.ttl");
static bool ValidateInt32Percent(const char* flagname, int32_t value) {
if (value <= 0 || value >= 100) {
fprintf(stderr, "Invalid value for --%s: %d, 0< pct <100 \n", flagname,
value);
return false;
}
return true;
}
DEFINE_int32(readwritepercent, 90,
"Ratio of reads to reads/writes (expressed as percentage) for "
"the ReadRandomWriteRandom workload. The default value 90 means "
"90% operations out of all reads and writes operations are "
"reads. In other words, 9 gets for every 1 put.");
DEFINE_int32(mergereadpercent, 70,
"Ratio of merges to merges&reads (expressed as percentage) for "
"the ReadRandomMergeRandom workload. The default value 70 means "
"70% out of all read and merge operations are merges. In other "
"words, 7 merges for every 3 gets.");
DEFINE_int32(deletepercent, 2,
"Percentage of deletes out of reads/writes/deletes (used in "
"RandomWithVerify only). RandomWithVerify "
"calculates writepercent as (100 - FLAGS_readwritepercent - "
"deletepercent), so deletepercent must be smaller than (100 - "
"FLAGS_readwritepercent)");
DEFINE_bool(optimize_filters_for_hits,
ROCKSDB_NAMESPACE::Options().optimize_filters_for_hits,
"Optimizes bloom filters for workloads for most lookups return "
"a value. For now this doesn't create bloom filters for the max "
"level of the LSM to reduce metadata that should fit in RAM. ");
DEFINE_bool(paranoid_checks, ROCKSDB_NAMESPACE::Options().paranoid_checks,
"RocksDB will aggressively check consistency of the data.");
DEFINE_bool(force_consistency_checks,
ROCKSDB_NAMESPACE::Options().force_consistency_checks,
"Runs consistency checks on the LSM every time a change is "
"applied.");
Speed up FindObsoleteFiles() Summary: There are two versions of FindObsoleteFiles(): * full scan, which is executed every 6 hours (and it's terribly slow) * no full scan, which is executed every time a background process finishes and iterator is deleted This diff is optimizing the second case (no full scan). Here's what we do before the diff: * Get the list of obsolete files (files with ref==0). Some files in obsolete_files set might actually be live. * Get the list of live files to avoid deleting files that are live. * Delete files that are in obsolete_files and not in live_files. After this diff: * The only files with ref==0 that are still live are files that have been part of move compaction. Don't include moved files in obsolete_files. * Get the list of obsolete files (which exclude moved files). * No need to get the list of live files, since all files in obsolete_files need to be deleted. I'll post the benchmark results, but you can get the feel of it here: https://reviews.facebook.net/D30123 This depends on D30123. P.S. We should do full scan only in failure scenarios, not every 6 hours. I'll do this in a follow-up diff. Test Plan: One new unit test. Made sure that unit test fails if we don't have a `if (!f->moved)` safeguard in ~Version. make check Big number of compactions and flushes: ./db_stress --threads=30 --ops_per_thread=20000000 --max_key=10000 --column_families=20 --clear_column_family_one_in=10000000 --verify_before_write=0 --reopen=15 --max_background_compactions=10 --max_background_flushes=10 --db=/fast-rocksdb-tmp/db_stress --prefixpercent=0 --iterpercent=0 --writepercent=75 --db_write_buffer_size=2000000 Reviewers: yhchiang, rven, sdong Reviewed By: sdong Subscribers: dhruba, leveldb Differential Revision: https://reviews.facebook.net/D30249
2014-12-22 11:04:45 +00:00
DEFINE_uint64(delete_obsolete_files_period_micros, 0,
"Ignored. Left here for backward compatibility");
DEFINE_int64(writes_before_delete_range, 0,
"Number of writes before DeleteRange is called regularly.");
DEFINE_int64(writes_per_range_tombstone, 0,
"Number of writes between range tombstones");
DEFINE_int64(range_tombstone_width, 100, "Number of keys in tombstone's range");
DEFINE_int64(max_num_range_tombstones, 0,
"Maximum number of range tombstones to insert.");
DEFINE_bool(expand_range_tombstones, false,
"Expand range tombstone into sequential regular tombstones.");
// Transactions Options
DEFINE_bool(optimistic_transaction_db, false,
"Open a OptimisticTransactionDB instance. "
"Required for randomtransaction benchmark.");
DEFINE_bool(transaction_db, false,
"Open a TransactionDB instance. "
"Required for randomtransaction benchmark.");
DEFINE_uint64(transaction_sets, 2,
"Number of keys each transaction will "
"modify (use in RandomTransaction only). Max: 9999");
DEFINE_bool(transaction_set_snapshot, false,
"Setting to true will have each transaction call SetSnapshot()"
" upon creation.");
DEFINE_int32(transaction_sleep, 0,
"Max microseconds to sleep in between "
"reading and writing a value (used in RandomTransaction only). ");
DEFINE_uint64(transaction_lock_timeout, 100,
"If using a transaction_db, specifies the lock wait timeout in"
" milliseconds before failing a transaction waiting on a lock");
DEFINE_string(
options_file, "",
"The path to a RocksDB options file. If specified, then db_bench will "
"run with the RocksDB options in the default column family of the "
"specified options file. "
"Note that with this setting, db_bench will ONLY accept the following "
"RocksDB options related command-line arguments, all other arguments "
"that are related to RocksDB options will be ignored:\n"
"\t--use_existing_db\n"
"\t--use_existing_keys\n"
"\t--statistics\n"
"\t--row_cache_size\n"
"\t--row_cache_numshardbits\n"
"\t--enable_io_prio\n"
"\t--dump_malloc_stats\n"
"\t--num_multi_db\n");
// FIFO Compaction Options
DEFINE_uint64(fifo_compaction_max_table_files_size_mb, 0,
"The limit of total table file sizes to trigger FIFO compaction");
DEFINE_bool(fifo_compaction_allow_compaction, true,
"Allow compaction in FIFO compaction.");
FIFO Compaction with TTL Summary: Introducing FIFO compactions with TTL. FIFO compaction is based on size only which makes it tricky to enable in production as use cases can have organic growth. A user requested an option to drop files based on the time of their creation instead of the total size. To address that request: - Added a new TTL option to FIFO compaction options. - Updated FIFO compaction score to take TTL into consideration. - Added a new table property, creation_time, to keep track of when the SST file is created. - Creation_time is set as below: - On Flush: Set to the time of flush. - On Compaction: Set to the max creation_time of all the files involved in the compaction. - On Repair and Recovery: Set to the time of repair/recovery. - Old files created prior to this code change will have a creation_time of 0. - FIFO compaction with TTL is enabled when ttl > 0. All files older than ttl will be deleted during compaction. i.e. `if (file.creation_time < (current_time - ttl)) then delete(file)`. This will enable cases where you might want to delete all files older than, say, 1 day. - FIFO compaction will fall back to the prior way of deleting files based on size if: - the creation_time of all files involved in compaction is 0. - the total size (of all SST files combined) does not drop below `compaction_options_fifo.max_table_files_size` even if the files older than ttl are deleted. This feature is not supported if max_open_files != -1 or with table formats other than Block-based. **Test Plan:** Added tests. **Benchmark results:** Base: FIFO with max size: 100MB :: ``` svemuri@dev15905 ~/rocksdb (fifo-compaction) $ TEST_TMPDIR=/dev/shm ./db_bench --benchmarks=readwhilewriting --num=5000000 --threads=16 --compaction_style=2 --fifo_compaction_max_table_files_size_mb=100 readwhilewriting : 1.924 micros/op 519858 ops/sec; 13.6 MB/s (1176277 of 5000000 found) ``` With TTL (a low one for testing) :: ``` svemuri@dev15905 ~/rocksdb (fifo-compaction) $ TEST_TMPDIR=/dev/shm ./db_bench --benchmarks=readwhilewriting --num=5000000 --threads=16 --compaction_style=2 --fifo_compaction_max_table_files_size_mb=100 --fifo_compaction_ttl=20 readwhilewriting : 1.902 micros/op 525817 ops/sec; 13.7 MB/s (1185057 of 5000000 found) ``` Example Log lines: ``` 2017/06/26-15:17:24.609249 7fd5a45ff700 (Original Log Time 2017/06/26-15:17:24.609177) [db/compaction_picker.cc:1471] [default] FIFO compaction: picking file 40 with creation time 1498515423 for deletion 2017/06/26-15:17:24.609255 7fd5a45ff700 (Original Log Time 2017/06/26-15:17:24.609234) [db/db_impl_compaction_flush.cc:1541] [default] Deleted 1 files ... 2017/06/26-15:17:25.553185 7fd5a61a5800 [DEBUG] [db/db_impl_files.cc:309] [JOB 0] Delete /dev/shm/dbbench/000040.sst type=2 #40 -- OK 2017/06/26-15:17:25.553205 7fd5a61a5800 EVENT_LOG_v1 {"time_micros": 1498515445553199, "job": 0, "event": "table_file_deletion", "file_number": 40} ``` SST Files remaining in the dbbench dir, after db_bench execution completed: ``` svemuri@dev15905 ~/rocksdb (fifo-compaction) $ ls -l /dev/shm//dbbench/*.sst -rw-r--r--. 1 svemuri users 30749887 Jun 26 15:17 /dev/shm//dbbench/000042.sst -rw-r--r--. 1 svemuri users 30768779 Jun 26 15:17 /dev/shm//dbbench/000044.sst -rw-r--r--. 1 svemuri users 30757481 Jun 26 15:17 /dev/shm//dbbench/000046.sst ``` Closes https://github.com/facebook/rocksdb/pull/2480 Differential Revision: D5305116 Pulled By: sagar0 fbshipit-source-id: 3e5cfcf5dd07ed2211b5b37492eb235b45139174
2017-06-28 00:02:20 +00:00
DEFINE_uint64(fifo_compaction_ttl, 0, "TTL for the SST Files in seconds.");
DEFINE_uint64(fifo_age_for_warm, 0, "age_for_warm for FIFO compaction.");
// Stacked BlobDB Options
DEFINE_bool(use_blob_db, false, "[Stacked BlobDB] Open a BlobDB instance.");
DEFINE_bool(
blob_db_enable_gc,
ROCKSDB_NAMESPACE::blob_db::BlobDBOptions().enable_garbage_collection,
"[Stacked BlobDB] Enable BlobDB garbage collection.");
DEFINE_double(
blob_db_gc_cutoff,
ROCKSDB_NAMESPACE::blob_db::BlobDBOptions().garbage_collection_cutoff,
"[Stacked BlobDB] Cutoff ratio for BlobDB garbage collection.");
DEFINE_bool(blob_db_is_fifo,
ROCKSDB_NAMESPACE::blob_db::BlobDBOptions().is_fifo,
"[Stacked BlobDB] Enable FIFO eviction strategy in BlobDB.");
DEFINE_uint64(blob_db_max_db_size,
ROCKSDB_NAMESPACE::blob_db::BlobDBOptions().max_db_size,
"[Stacked BlobDB] Max size limit of the directory where blob "
"files are stored.");
DEFINE_uint64(blob_db_max_ttl_range, 0,
"[Stacked BlobDB] TTL range to generate BlobDB data (in "
"seconds). 0 means no TTL.");
DEFINE_uint64(
blob_db_ttl_range_secs,
ROCKSDB_NAMESPACE::blob_db::BlobDBOptions().ttl_range_secs,
"[Stacked BlobDB] TTL bucket size to use when creating blob files.");
DEFINE_uint64(
blob_db_min_blob_size,
ROCKSDB_NAMESPACE::blob_db::BlobDBOptions().min_blob_size,
"[Stacked BlobDB] Smallest blob to store in a file. Blobs "
"smaller than this will be inlined with the key in the LSM tree.");
DEFINE_uint64(blob_db_bytes_per_sync,
ROCKSDB_NAMESPACE::blob_db::BlobDBOptions().bytes_per_sync,
"[Stacked BlobDB] Bytes to sync blob file at.");
DEFINE_uint64(blob_db_file_size,
ROCKSDB_NAMESPACE::blob_db::BlobDBOptions().blob_file_size,
"[Stacked BlobDB] Target size of each blob file.");
DEFINE_string(
blob_db_compression_type, "snappy",
"[Stacked BlobDB] Algorithm to use to compress blobs in blob files.");
static enum ROCKSDB_NAMESPACE::CompressionType
FLAGS_blob_db_compression_type_e = ROCKSDB_NAMESPACE::kSnappyCompression;
// Integrated BlobDB options
DEFINE_bool(
enable_blob_files,
ROCKSDB_NAMESPACE::AdvancedColumnFamilyOptions().enable_blob_files,
"[Integrated BlobDB] Enable writing large values to separate blob files.");
DEFINE_uint64(min_blob_size,
ROCKSDB_NAMESPACE::AdvancedColumnFamilyOptions().min_blob_size,
"[Integrated BlobDB] The size of the smallest value to be stored "
"separately in a blob file.");
DEFINE_uint64(blob_file_size,
ROCKSDB_NAMESPACE::AdvancedColumnFamilyOptions().blob_file_size,
"[Integrated BlobDB] The size limit for blob files.");
DEFINE_string(blob_compression_type, "none",
"[Integrated BlobDB] The compression algorithm to use for large "
"values stored in blob files.");
DEFINE_bool(enable_blob_garbage_collection,
ROCKSDB_NAMESPACE::AdvancedColumnFamilyOptions()
.enable_blob_garbage_collection,
"[Integrated BlobDB] Enable blob garbage collection.");
DEFINE_double(blob_garbage_collection_age_cutoff,
ROCKSDB_NAMESPACE::AdvancedColumnFamilyOptions()
.blob_garbage_collection_age_cutoff,
"[Integrated BlobDB] The cutoff in terms of blob file age for "
"garbage collection.");
Make it possible to force the garbage collection of the oldest blob files (#8994) Summary: The current BlobDB garbage collection logic works by relocating the valid blobs from the oldest blob files as they are encountered during compaction, and cleaning up blob files once they contain nothing but garbage. However, with sufficiently skewed workloads, it is theoretically possible to end up in a situation when few or no compactions get scheduled for the SST files that contain references to the oldest blob files, which can lead to increased space amp due to the lack of GC. In order to efficiently handle such workloads, the patch adds a new BlobDB configuration option called `blob_garbage_collection_force_threshold`, which signals to BlobDB to schedule targeted compactions for the SST files that keep alive the oldest batch of blob files if the overall ratio of garbage in the given blob files meets the threshold *and* all the given blob files are eligible for GC based on `blob_garbage_collection_age_cutoff`. (For example, if the new option is set to 0.9, targeted compactions will get scheduled if the sum of garbage bytes meets or exceeds 90% of the sum of total bytes in the oldest blob files, assuming all affected blob files are below the age-based cutoff.) The net result of these targeted compactions is that the valid blobs in the oldest blob files are relocated and the oldest blob files themselves cleaned up (since *all* SST files that rely on them get compacted away). These targeted compactions are similar to periodic compactions in the sense that they force certain SST files that otherwise would not get picked up to undergo compaction and also in the sense that instead of merging files from multiple levels, they target a single file. (Note: such compactions might still include neighboring files from the same level due to the need of having a "clean cut" boundary but they never include any files from any other level.) This functionality is currently only supported with the leveled compaction style and is inactive by default (since the default value is set to 1.0, i.e. 100%). Pull Request resolved: https://github.com/facebook/rocksdb/pull/8994 Test Plan: Ran `make check` and tested using `db_bench` and the stress/crash tests. Reviewed By: riversand963 Differential Revision: D31489850 Pulled By: ltamasi fbshipit-source-id: 44057d511726a0e2a03c5d9313d7511b3f0c4eab
2021-10-12 01:00:44 +00:00
DEFINE_double(blob_garbage_collection_force_threshold,
ROCKSDB_NAMESPACE::AdvancedColumnFamilyOptions()
.blob_garbage_collection_force_threshold,
"[Integrated BlobDB] The threshold for the ratio of garbage in "
"the oldest blob files for forcing garbage collection.");
DEFINE_uint64(blob_compaction_readahead_size,
ROCKSDB_NAMESPACE::AdvancedColumnFamilyOptions()
.blob_compaction_readahead_size,
"[Integrated BlobDB] Compaction readahead for blob files.");
Make it possible to enable blob files starting from a certain LSM tree level (#10077) Summary: Currently, if blob files are enabled (i.e. `enable_blob_files` is true), large values are extracted both during flush/recovery (when SST files are written into level 0 of the LSM tree) and during compaction into any LSM tree level. For certain use cases that have a mix of short-lived and long-lived values, it might make sense to support extracting large values only during compactions whose output level is greater than or equal to a specified LSM tree level (e.g. compactions into L1/L2/... or above). This could reduce the space amplification caused by large values that are turned into garbage shortly after being written at the price of some write amplification incurred by long-lived values whose extraction to blob files is delayed. In order to achieve this, we would like to do the following: - Add a new configuration option `blob_file_starting_level` (default: 0) to `AdvancedColumnFamilyOptions` (and `MutableCFOptions` and extend the related logic) - Instantiate `BlobFileBuilder` in `BuildTable` (used during flush and recovery, where the LSM tree level is L0) and `CompactionJob` iff `enable_blob_files` is set and the LSM tree level is `>= blob_file_starting_level` - Add unit tests for the new functionality, and add the new option to our stress tests (`db_stress` and `db_crashtest.py` ) - Add the new option to our benchmarking tool `db_bench` and the BlobDB benchmark script `run_blob_bench.sh` - Add the new option to the `ldb` tool (see https://github.com/facebook/rocksdb/wiki/Administration-and-Data-Access-Tool) - Ideally extend the C and Java bindings with the new option - Update the BlobDB wiki to document the new option. Pull Request resolved: https://github.com/facebook/rocksdb/pull/10077 Reviewed By: ltamasi Differential Revision: D36884156 Pulled By: gangliao fbshipit-source-id: 942bab025f04633edca8564ed64791cb5e31627d
2022-06-03 03:04:33 +00:00
DEFINE_int32(
blob_file_starting_level,
ROCKSDB_NAMESPACE::AdvancedColumnFamilyOptions().blob_file_starting_level,
"[Integrated BlobDB] The starting level for blob files.");
DEFINE_bool(use_blob_cache, false, "[Integrated BlobDB] Enable blob cache.");
DEFINE_bool(
use_shared_block_and_blob_cache, true,
"[Integrated BlobDB] Use a shared backing cache for both block "
"cache and blob cache. It only takes effect if use_blob_cache is enabled.");
DEFINE_uint64(
blob_cache_size, 8 << 20,
"[Integrated BlobDB] Number of bytes to use as a cache of blobs. It only "
"takes effect if the block and blob caches are different "
"(use_shared_block_and_blob_cache = false).");
DEFINE_int32(blob_cache_numshardbits, 6,
"[Integrated BlobDB] Number of shards for the blob cache is 2 ** "
"blob_cache_numshardbits. Negative means use default settings. "
"It only takes effect if blob_cache_size is greater than 0, and "
"the block and blob caches are different "
"(use_shared_block_and_blob_cache = false).");
DEFINE_int32(prepopulate_blob_cache, 0,
"[Integrated BlobDB] Pre-populate hot/warm blobs in blob cache. 0 "
"to disable and 1 to insert during flush.");
// Secondary DB instance Options
DEFINE_bool(use_secondary_db, false,
"Open a RocksDB secondary instance. A primary instance can be "
"running in another db_bench process.");
DEFINE_string(secondary_path, "",
"Path to a directory used by the secondary instance to store "
"private files, e.g. info log.");
DEFINE_int32(secondary_update_interval, 5,
"Secondary instance attempts to catch up with the primary every "
"secondary_update_interval seconds.");
Basic RocksDB follower implementation (#12540) Summary: A basic implementation of RocksDB follower mode, which opens a remote database (referred to as leader) on a distributed file system by tailing its MANIFEST. It leverages the secondary instance mode, but is different in some key ways - 1. It has its own directory with links to the leader's database 2. Periodically refreshes itself 3. (Future) Snapshot support 4. (Future) Garbage collection of obsolete links 5. (Long term) Memtable replication There are two main classes implementing this functionality - `DBImplFollower` and `OnDemandFileSystem`. The former is derived from `DBImplSecondary`. Similar to `DBImplSecondary`, it implements recovery and catch up through MANIFEST tailing using the `ReactiveVersionSet`, but does not consider logs. In a future PR, we will implement memtable replication, which will eliminate the need to catch up using logs. In addition, the recovery and catch-up tries to avoid directory listing as repeated metadata operations are expensive. The second main piece is the `OnDemandFileSystem`, which plugs in as an `Env` for the follower instance and creates the illusion of the follower directory as a clone of the leader directory. It creates links to SSTs on first reference. When the follower tails the MANIFEST and attempts to create a new `Version`, it calls `VerifyFileMetadata` to verify the size of the file, and optionally the unique ID of the file. During this process, links are created which prevent the underlying files from getting deallocated even if the leader deletes the files. TODOs: Deletion of obsolete links, snapshots, robust checking against misconfigurations, better observability etc. Pull Request resolved: https://github.com/facebook/rocksdb/pull/12540 Reviewed By: jowlyzhang Differential Revision: D56315718 Pulled By: anand1976 fbshipit-source-id: d19e1aca43a6af4000cb8622a718031b69ebd97b
2024-04-20 02:13:31 +00:00
DEFINE_bool(open_as_follower, false,
"Open a RocksDB DB as a follower. The leader instance can be "
"running in another db_bench process.");
DEFINE_string(leader_path, "", "Path to the directory of the leader DB");
DEFINE_bool(report_bg_io_stats, false,
"Measure times spents on I/Os while in compactions. ");
DEFINE_bool(use_stderr_info_logger, false,
"Write info logs to stderr instead of to LOG file. ");
DEFINE_string(trace_file, "", "Trace workload to a file. ");
DEFINE_double(trace_replay_fast_forward, 1.0,
"Fast forward trace replay, must > 0.0.");
DEFINE_int32(block_cache_trace_sampling_frequency, 1,
"Block cache trace sampling frequency, termed s. It uses spatial "
"downsampling and samples accesses to one out of s blocks.");
DEFINE_int64(
block_cache_trace_max_trace_file_size_in_bytes,
uint64_t{64} * 1024 * 1024 * 1024,
"The maximum block cache trace file size in bytes. Block cache accesses "
"will not be logged if the trace file size exceeds this threshold. Default "
"is 64 GB.");
DEFINE_string(block_cache_trace_file, "", "Block cache trace file path.");
DEFINE_int32(trace_replay_threads, 1,
"The number of threads to replay, must >=1.");
DEFINE_bool(io_uring_enabled, true,
"If true, enable the use of IO uring if the platform supports it");
extern "C" bool RocksDbIOUringEnable() { return FLAGS_io_uring_enabled; }
DEFINE_bool(adaptive_readahead, false,
"carry forward internal auto readahead size from one file to next "
"file at each level during iteration");
Add rate limiter priority to ReadOptions (#9424) Summary: Users can set the priority for file reads associated with their operation by setting `ReadOptions::rate_limiter_priority` to something other than `Env::IO_TOTAL`. Rate limiting `VerifyChecksum()` and `VerifyFileChecksums()` is the motivation for this PR, so it also includes benchmarks and minor bug fixes to get that working. `RandomAccessFileReader::Read()` already had support for rate limiting compaction reads. I changed that rate limiting to be non-specific to compaction, but rather performed according to the passed in `Env::IOPriority`. Now the compaction read rate limiting is supported by setting `rate_limiter_priority = Env::IO_LOW` on its `ReadOptions`. There is no default value for the new `Env::IOPriority` parameter to `RandomAccessFileReader::Read()`. That means this PR goes through all callers (in some cases multiple layers up the call stack) to find a `ReadOptions` to provide the priority. There are TODOs for cases I believe it would be good to let user control the priority some day (e.g., file footer reads), and no TODO in cases I believe it doesn't matter (e.g., trace file reads). The API doc only lists the missing cases where a file read associated with a provided `ReadOptions` cannot be rate limited. For cases like file ingestion checksum calculation, there is no API to provide `ReadOptions` or `Env::IOPriority`, so I didn't count that as missing. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9424 Test Plan: - new unit tests - new benchmarks on ~50MB database with 1MB/s read rate limit and 100ms refill interval; verified with strace reads are chunked (at 0.1MB per chunk) and spaced roughly 100ms apart. - setup command: `./db_bench -benchmarks=fillrandom,compact -db=/tmp/testdb -target_file_size_base=1048576 -disable_auto_compactions=true -file_checksum=true` - benchmarks command: `strace -ttfe pread64 ./db_bench -benchmarks=verifychecksum,verifyfilechecksums -use_existing_db=true -db=/tmp/testdb -rate_limiter_bytes_per_sec=1048576 -rate_limit_bg_reads=1 -rate_limit_user_ops=true -file_checksum=true` - crash test using IO_USER priority on non-validation reads with https://github.com/facebook/rocksdb/issues/9567 reverted: `python3 tools/db_crashtest.py blackbox --max_key=1000000 --write_buffer_size=524288 --target_file_size_base=524288 --level_compaction_dynamic_level_bytes=true --duration=3600 --rate_limit_bg_reads=true --rate_limit_user_ops=true --rate_limiter_bytes_per_sec=10485760 --interval=10` Reviewed By: hx235 Differential Revision: D33747386 Pulled By: ajkr fbshipit-source-id: a2d985e97912fba8c54763798e04f006ccc56e0c
2022-02-17 07:17:03 +00:00
DEFINE_bool(rate_limit_user_ops, false,
"When true use Env::IO_USER priority level to charge internal rate "
"limiter for reads associated with user operations.");
DEFINE_bool(file_checksum, false,
"When true use FileChecksumGenCrc32cFactory for "
"file_checksum_gen_factory.");
Rate-limit automatic WAL flush after each user write (#9607) Summary: **Context:** WAL flush is currently not rate-limited by `Options::rate_limiter`. This PR is to provide rate-limiting to auto WAL flush, the one that automatically happen after each user write operation (i.e, `Options::manual_wal_flush == false`), by adding `WriteOptions::rate_limiter_options`. Note that we are NOT rate-limiting WAL flush that do NOT automatically happen after each user write, such as `Options::manual_wal_flush == true + manual FlushWAL()` (rate-limiting multiple WAL flushes), for the benefits of: - being consistent with [ReadOptions::rate_limiter_priority](https://github.com/facebook/rocksdb/blob/7.0.fb/include/rocksdb/options.h#L515) - being able to turn off some WAL flush's rate-limiting but not all (e.g, turn off specific the WAL flush of a critical user write like a service's heartbeat) `WriteOptions::rate_limiter_options` only accept `Env::IO_USER` and `Env::IO_TOTAL` currently due to an implementation constraint. - The constraint is that we currently queue parallel writes (including WAL writes) based on FIFO policy which does not factor rate limiter priority into this layer's scheduling. If we allow lower priorities such as `Env::IO_HIGH/MID/LOW` and such writes specified with lower priorities occurs before ones specified with higher priorities (even just by a tiny bit in arrival time), the former would have blocked the latter, leading to a "priority inversion" issue and contradictory to what we promise for rate-limiting priority. Therefore we only allow `Env::IO_USER` and `Env::IO_TOTAL` right now before improving that scheduling. A pre-requisite to this feature is to support operation-level rate limiting in `WritableFileWriter`, which is also included in this PR. **Summary:** - Renamed test suite `DBRateLimiterTest to DBRateLimiterOnReadTest` for adding a new test suite - Accept `rate_limiter_priority` in `WritableFileWriter`'s private and public write functions - Passed `WriteOptions::rate_limiter_options` to `WritableFileWriter` in the path of automatic WAL flush. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9607 Test Plan: - Added new unit test to verify existing flush/compaction rate-limiting does not break, since `DBTest, RateLimitingTest` is disabled and current db-level rate-limiting tests focus on read only (e.g, `db_rate_limiter_test`, `DBTest2, RateLimitedCompactionReads`). - Added new unit test `DBRateLimiterOnWriteWALTest, AutoWalFlush` - `strace -ftt -e trace=write ./db_bench -benchmarks=fillseq -db=/dev/shm/testdb -rate_limit_auto_wal_flush=1 -rate_limiter_bytes_per_sec=15 -rate_limiter_refill_period_us=1000000 -write_buffer_size=100000000 -disable_auto_compactions=1 -num=100` - verified that WAL flush(i.e, system-call _write_) were chunked into 15 bytes and each _write_ was roughly 1 second apart - verified the chunking disappeared when `-rate_limit_auto_wal_flush=0` - crash test: `python3 tools/db_crashtest.py blackbox --disable_wal=0 --rate_limit_auto_wal_flush=1 --rate_limiter_bytes_per_sec=10485760 --interval=10` killed as normal **Benchmarked on flush/compaction to ensure no performance regression:** - compaction with rate-limiting (see table 1, avg over 1280-run): pre-change: **915635 micros/op**; post-change: **907350 micros/op (improved by 0.106%)** ``` #!/bin/bash TEST_TMPDIR=/dev/shm/testdb START=1 NUM_DATA_ENTRY=8 N=10 rm -f compact_bmk_output.txt compact_bmk_output_2.txt dont_care_output.txt for i in $(eval echo "{$START..$NUM_DATA_ENTRY}") do NUM_RUN=$(($N*(2**($i-1)))) for j in $(eval echo "{$START..$NUM_RUN}") do ./db_bench --benchmarks=fillrandom -db=$TEST_TMPDIR -disable_auto_compactions=1 -write_buffer_size=6710886 > dont_care_output.txt && ./db_bench --benchmarks=compact -use_existing_db=1 -db=$TEST_TMPDIR -level0_file_num_compaction_trigger=1 -rate_limiter_bytes_per_sec=100000000 | egrep 'compact' done > compact_bmk_output.txt && awk -v NUM_RUN=$NUM_RUN '{sum+=$3;sum_sqrt+=$3^2}END{print sum/NUM_RUN, sqrt(sum_sqrt/NUM_RUN-(sum/NUM_RUN)^2)}' compact_bmk_output.txt >> compact_bmk_output_2.txt done ``` - compaction w/o rate-limiting (see table 2, avg over 640-run): pre-change: **822197 micros/op**; post-change: **823148 micros/op (regressed by 0.12%)** ``` Same as above script, except that -rate_limiter_bytes_per_sec=0 ``` - flush with rate-limiting (see table 3, avg over 320-run, run on the [patch](https://github.com/hx235/rocksdb/commit/ee5c6023a9f6533fab9afdc681568daa21da4953) to augment current db_bench ): pre-change: **745752 micros/op**; post-change: **745331 micros/op (regressed by 0.06 %)** ``` #!/bin/bash TEST_TMPDIR=/dev/shm/testdb START=1 NUM_DATA_ENTRY=8 N=10 rm -f flush_bmk_output.txt flush_bmk_output_2.txt for i in $(eval echo "{$START..$NUM_DATA_ENTRY}") do NUM_RUN=$(($N*(2**($i-1)))) for j in $(eval echo "{$START..$NUM_RUN}") do ./db_bench -db=$TEST_TMPDIR -write_buffer_size=1048576000 -num=1000000 -rate_limiter_bytes_per_sec=100000000 -benchmarks=fillseq,flush | egrep 'flush' done > flush_bmk_output.txt && awk -v NUM_RUN=$NUM_RUN '{sum+=$3;sum_sqrt+=$3^2}END{print sum/NUM_RUN, sqrt(sum_sqrt/NUM_RUN-(sum/NUM_RUN)^2)}' flush_bmk_output.txt >> flush_bmk_output_2.txt done ``` - flush w/o rate-limiting (see table 4, avg over 320-run, run on the [patch](https://github.com/hx235/rocksdb/commit/ee5c6023a9f6533fab9afdc681568daa21da4953) to augment current db_bench): pre-change: **487512 micros/op**, post-change: **485856 micors/ops (improved by 0.34%)** ``` Same as above script, except that -rate_limiter_bytes_per_sec=0 ``` | table 1 - compact with rate-limiting| #-run | (pre-change) avg micros/op | std micros/op | (post-change) avg micros/op | std micros/op | change in avg micros/op (%) -- | -- | -- | -- | -- | -- 10 | 896978 | 16046.9 | 901242 | 15670.9 | 0.475373978 20 | 893718 | 15813 | 886505 | 17544.7 | -0.8070778478 40 | 900426 | 23882.2 | 894958 | 15104.5 | -0.6072681153 80 | 906635 | 21761.5 | 903332 | 23948.3 | -0.3643141948 160 | 898632 | 21098.9 | 907583 | 21145 | 0.9960695813 3.20E+02 | 905252 | 22785.5 | 908106 | 25325.5 | 0.3152713278 6.40E+02 | 905213 | 23598.6 | 906741 | 21370.5 | 0.1688000504 **1.28E+03** | **908316** | **23533.1** | **907350** | **24626.8** | **-0.1063506533** average over #-run | 901896.25 | 21064.9625 | 901977.125 | 20592.025 | 0.008967217682 | table 2 - compact w/o rate-limiting| #-run | (pre-change) avg micros/op | std micros/op | (post-change) avg micros/op | std micros/op | change in avg micros/op (%) -- | -- | -- | -- | -- | -- 10 | 811211 | 26996.7 | 807586 | 28456.4 | -0.4468627768 20 | 815465 | 14803.7 | 814608 | 28719.7 | -0.105093413 40 | 809203 | 26187.1 | 797835 | 25492.1 | -1.404839082 80 | 822088 | 28765.3 | 822192 | 32840.4 | 0.01265071379 160 | 821719 | 36344.7 | 821664 | 29544.9 | -0.006693285661 3.20E+02 | 820921 | 27756.4 | 821403 | 28347.7 | 0.05871454135 **6.40E+02** | **822197** | **28960.6** | **823148** | **30055.1** | **0.1156657103** average over #-run | 8.18E+05 | 2.71E+04 | 8.15E+05 | 2.91E+04 | -0.25 | table 3 - flush with rate-limiting| #-run | (pre-change) avg micros/op | std micros/op | (post-change) avg micros/op | std micros/op | change in avg micros/op (%) -- | -- | -- | -- | -- | -- 10 | 741721 | 11770.8 | 740345 | 5949.76 | -0.1855144994 20 | 735169 | 3561.83 | 743199 | 9755.77 | 1.09226586 40 | 743368 | 8891.03 | 742102 | 8683.22 | -0.1703059588 80 | 742129 | 8148.51 | 743417 | 9631.58| 0.1735547324 160 | 749045 | 9757.21 | 746256 | 9191.86 | -0.3723407806 **3.20E+02** | **745752** | **9819.65** | **745331** | **9840.62** | **-0.0564530836** 6.40E+02 | 749006 | 11080.5 | 748173 | 10578.7 | -0.1112140624 average over #-run | 743741.4286 | 9004.218571 | 744117.5714 | 9090.215714 | 0.05057441238 | table 4 - flush w/o rate-limiting| #-run | (pre-change) avg micros/op | std micros/op | (post-change) avg micros/op | std micros/op | change in avg micros/op (%) -- | -- | -- | -- | -- | -- 10 | 477283 | 24719.6 | 473864 | 12379 | -0.7163464863 20 | 486743 | 20175.2 | 502296 | 23931.3 | 3.195320734 40 | 482846 | 15309.2 | 489820 | 22259.5 | 1.444352858 80 | 491490 | 21883.1 | 490071 | 23085.7 | -0.2887139108 160 | 493347 | 28074.3 | 483609 | 21211.7 | -1.973864238 **3.20E+02** | **487512** | **21401.5** | **485856** | **22195.2** | **-0.3396839462** 6.40E+02 | 490307 | 25418.6 | 485435 | 22405.2 | -0.9936631539 average over #-run | 4.87E+05 | 2.24E+04 | 4.87E+05 | 2.11E+04 | 0.00E+00 Reviewed By: ajkr Differential Revision: D34442441 Pulled By: hx235 fbshipit-source-id: 4790f13e1e5c0a95ae1d1cc93ffcf69dc6e78bdd
2022-03-08 21:19:39 +00:00
DEFINE_bool(rate_limit_auto_wal_flush, false,
"When true use Env::IO_USER priority level to charge internal rate "
"limiter for automatic WAL flush (`Options::manual_wal_flush` == "
"false) after the user write operation.");
Rate-limit automatic WAL flush after each user write (#9607) Summary: **Context:** WAL flush is currently not rate-limited by `Options::rate_limiter`. This PR is to provide rate-limiting to auto WAL flush, the one that automatically happen after each user write operation (i.e, `Options::manual_wal_flush == false`), by adding `WriteOptions::rate_limiter_options`. Note that we are NOT rate-limiting WAL flush that do NOT automatically happen after each user write, such as `Options::manual_wal_flush == true + manual FlushWAL()` (rate-limiting multiple WAL flushes), for the benefits of: - being consistent with [ReadOptions::rate_limiter_priority](https://github.com/facebook/rocksdb/blob/7.0.fb/include/rocksdb/options.h#L515) - being able to turn off some WAL flush's rate-limiting but not all (e.g, turn off specific the WAL flush of a critical user write like a service's heartbeat) `WriteOptions::rate_limiter_options` only accept `Env::IO_USER` and `Env::IO_TOTAL` currently due to an implementation constraint. - The constraint is that we currently queue parallel writes (including WAL writes) based on FIFO policy which does not factor rate limiter priority into this layer's scheduling. If we allow lower priorities such as `Env::IO_HIGH/MID/LOW` and such writes specified with lower priorities occurs before ones specified with higher priorities (even just by a tiny bit in arrival time), the former would have blocked the latter, leading to a "priority inversion" issue and contradictory to what we promise for rate-limiting priority. Therefore we only allow `Env::IO_USER` and `Env::IO_TOTAL` right now before improving that scheduling. A pre-requisite to this feature is to support operation-level rate limiting in `WritableFileWriter`, which is also included in this PR. **Summary:** - Renamed test suite `DBRateLimiterTest to DBRateLimiterOnReadTest` for adding a new test suite - Accept `rate_limiter_priority` in `WritableFileWriter`'s private and public write functions - Passed `WriteOptions::rate_limiter_options` to `WritableFileWriter` in the path of automatic WAL flush. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9607 Test Plan: - Added new unit test to verify existing flush/compaction rate-limiting does not break, since `DBTest, RateLimitingTest` is disabled and current db-level rate-limiting tests focus on read only (e.g, `db_rate_limiter_test`, `DBTest2, RateLimitedCompactionReads`). - Added new unit test `DBRateLimiterOnWriteWALTest, AutoWalFlush` - `strace -ftt -e trace=write ./db_bench -benchmarks=fillseq -db=/dev/shm/testdb -rate_limit_auto_wal_flush=1 -rate_limiter_bytes_per_sec=15 -rate_limiter_refill_period_us=1000000 -write_buffer_size=100000000 -disable_auto_compactions=1 -num=100` - verified that WAL flush(i.e, system-call _write_) were chunked into 15 bytes and each _write_ was roughly 1 second apart - verified the chunking disappeared when `-rate_limit_auto_wal_flush=0` - crash test: `python3 tools/db_crashtest.py blackbox --disable_wal=0 --rate_limit_auto_wal_flush=1 --rate_limiter_bytes_per_sec=10485760 --interval=10` killed as normal **Benchmarked on flush/compaction to ensure no performance regression:** - compaction with rate-limiting (see table 1, avg over 1280-run): pre-change: **915635 micros/op**; post-change: **907350 micros/op (improved by 0.106%)** ``` #!/bin/bash TEST_TMPDIR=/dev/shm/testdb START=1 NUM_DATA_ENTRY=8 N=10 rm -f compact_bmk_output.txt compact_bmk_output_2.txt dont_care_output.txt for i in $(eval echo "{$START..$NUM_DATA_ENTRY}") do NUM_RUN=$(($N*(2**($i-1)))) for j in $(eval echo "{$START..$NUM_RUN}") do ./db_bench --benchmarks=fillrandom -db=$TEST_TMPDIR -disable_auto_compactions=1 -write_buffer_size=6710886 > dont_care_output.txt && ./db_bench --benchmarks=compact -use_existing_db=1 -db=$TEST_TMPDIR -level0_file_num_compaction_trigger=1 -rate_limiter_bytes_per_sec=100000000 | egrep 'compact' done > compact_bmk_output.txt && awk -v NUM_RUN=$NUM_RUN '{sum+=$3;sum_sqrt+=$3^2}END{print sum/NUM_RUN, sqrt(sum_sqrt/NUM_RUN-(sum/NUM_RUN)^2)}' compact_bmk_output.txt >> compact_bmk_output_2.txt done ``` - compaction w/o rate-limiting (see table 2, avg over 640-run): pre-change: **822197 micros/op**; post-change: **823148 micros/op (regressed by 0.12%)** ``` Same as above script, except that -rate_limiter_bytes_per_sec=0 ``` - flush with rate-limiting (see table 3, avg over 320-run, run on the [patch](https://github.com/hx235/rocksdb/commit/ee5c6023a9f6533fab9afdc681568daa21da4953) to augment current db_bench ): pre-change: **745752 micros/op**; post-change: **745331 micros/op (regressed by 0.06 %)** ``` #!/bin/bash TEST_TMPDIR=/dev/shm/testdb START=1 NUM_DATA_ENTRY=8 N=10 rm -f flush_bmk_output.txt flush_bmk_output_2.txt for i in $(eval echo "{$START..$NUM_DATA_ENTRY}") do NUM_RUN=$(($N*(2**($i-1)))) for j in $(eval echo "{$START..$NUM_RUN}") do ./db_bench -db=$TEST_TMPDIR -write_buffer_size=1048576000 -num=1000000 -rate_limiter_bytes_per_sec=100000000 -benchmarks=fillseq,flush | egrep 'flush' done > flush_bmk_output.txt && awk -v NUM_RUN=$NUM_RUN '{sum+=$3;sum_sqrt+=$3^2}END{print sum/NUM_RUN, sqrt(sum_sqrt/NUM_RUN-(sum/NUM_RUN)^2)}' flush_bmk_output.txt >> flush_bmk_output_2.txt done ``` - flush w/o rate-limiting (see table 4, avg over 320-run, run on the [patch](https://github.com/hx235/rocksdb/commit/ee5c6023a9f6533fab9afdc681568daa21da4953) to augment current db_bench): pre-change: **487512 micros/op**, post-change: **485856 micors/ops (improved by 0.34%)** ``` Same as above script, except that -rate_limiter_bytes_per_sec=0 ``` | table 1 - compact with rate-limiting| #-run | (pre-change) avg micros/op | std micros/op | (post-change) avg micros/op | std micros/op | change in avg micros/op (%) -- | -- | -- | -- | -- | -- 10 | 896978 | 16046.9 | 901242 | 15670.9 | 0.475373978 20 | 893718 | 15813 | 886505 | 17544.7 | -0.8070778478 40 | 900426 | 23882.2 | 894958 | 15104.5 | -0.6072681153 80 | 906635 | 21761.5 | 903332 | 23948.3 | -0.3643141948 160 | 898632 | 21098.9 | 907583 | 21145 | 0.9960695813 3.20E+02 | 905252 | 22785.5 | 908106 | 25325.5 | 0.3152713278 6.40E+02 | 905213 | 23598.6 | 906741 | 21370.5 | 0.1688000504 **1.28E+03** | **908316** | **23533.1** | **907350** | **24626.8** | **-0.1063506533** average over #-run | 901896.25 | 21064.9625 | 901977.125 | 20592.025 | 0.008967217682 | table 2 - compact w/o rate-limiting| #-run | (pre-change) avg micros/op | std micros/op | (post-change) avg micros/op | std micros/op | change in avg micros/op (%) -- | -- | -- | -- | -- | -- 10 | 811211 | 26996.7 | 807586 | 28456.4 | -0.4468627768 20 | 815465 | 14803.7 | 814608 | 28719.7 | -0.105093413 40 | 809203 | 26187.1 | 797835 | 25492.1 | -1.404839082 80 | 822088 | 28765.3 | 822192 | 32840.4 | 0.01265071379 160 | 821719 | 36344.7 | 821664 | 29544.9 | -0.006693285661 3.20E+02 | 820921 | 27756.4 | 821403 | 28347.7 | 0.05871454135 **6.40E+02** | **822197** | **28960.6** | **823148** | **30055.1** | **0.1156657103** average over #-run | 8.18E+05 | 2.71E+04 | 8.15E+05 | 2.91E+04 | -0.25 | table 3 - flush with rate-limiting| #-run | (pre-change) avg micros/op | std micros/op | (post-change) avg micros/op | std micros/op | change in avg micros/op (%) -- | -- | -- | -- | -- | -- 10 | 741721 | 11770.8 | 740345 | 5949.76 | -0.1855144994 20 | 735169 | 3561.83 | 743199 | 9755.77 | 1.09226586 40 | 743368 | 8891.03 | 742102 | 8683.22 | -0.1703059588 80 | 742129 | 8148.51 | 743417 | 9631.58| 0.1735547324 160 | 749045 | 9757.21 | 746256 | 9191.86 | -0.3723407806 **3.20E+02** | **745752** | **9819.65** | **745331** | **9840.62** | **-0.0564530836** 6.40E+02 | 749006 | 11080.5 | 748173 | 10578.7 | -0.1112140624 average over #-run | 743741.4286 | 9004.218571 | 744117.5714 | 9090.215714 | 0.05057441238 | table 4 - flush w/o rate-limiting| #-run | (pre-change) avg micros/op | std micros/op | (post-change) avg micros/op | std micros/op | change in avg micros/op (%) -- | -- | -- | -- | -- | -- 10 | 477283 | 24719.6 | 473864 | 12379 | -0.7163464863 20 | 486743 | 20175.2 | 502296 | 23931.3 | 3.195320734 40 | 482846 | 15309.2 | 489820 | 22259.5 | 1.444352858 80 | 491490 | 21883.1 | 490071 | 23085.7 | -0.2887139108 160 | 493347 | 28074.3 | 483609 | 21211.7 | -1.973864238 **3.20E+02** | **487512** | **21401.5** | **485856** | **22195.2** | **-0.3396839462** 6.40E+02 | 490307 | 25418.6 | 485435 | 22405.2 | -0.9936631539 average over #-run | 4.87E+05 | 2.24E+04 | 4.87E+05 | 2.11E+04 | 0.00E+00 Reviewed By: ajkr Differential Revision: D34442441 Pulled By: hx235 fbshipit-source-id: 4790f13e1e5c0a95ae1d1cc93ffcf69dc6e78bdd
2022-03-08 21:19:39 +00:00
DEFINE_bool(async_io, false,
"When set true, RocksDB does asynchronous reads for internal auto "
"readahead prefetching.");
MultiGet async IO across multiple levels (#10535) Summary: This PR exploits parallelism in MultiGet across levels. It applies only to the coroutine version of MultiGet. Previously, MultiGet file reads from SST files in the same level were parallelized. With this PR, MultiGet batches with keys distributed across multiple levels are read in parallel. This is accomplished by splitting the keys not present in a level (determined by bloom filtering) into a separate batch, and processing the new batch in parallel with the original batch. Pull Request resolved: https://github.com/facebook/rocksdb/pull/10535 Test Plan: 1. Ensure existing MultiGet unit tests pass, updating them as necessary 2. New unit tests - TODO 3. Run stress test - TODO No noticeable regression (<1%) without async IO - Without PR: `multireadrandom : 7.261 micros/op 1101724 ops/sec 60.007 seconds 66110936 operations; 571.6 MB/s (8168992 of 8168992 found)` With PR: `multireadrandom : 7.305 micros/op 1095167 ops/sec 60.007 seconds 65717936 operations; 568.2 MB/s (8271992 of 8271992 found)` For a fully cached DB, but with async IO option on, no regression observed (<1%) - Without PR: `multireadrandom : 5.201 micros/op 1538027 ops/sec 60.005 seconds 92288936 operations; 797.9 MB/s (11540992 of 11540992 found) ` With PR: `multireadrandom : 5.249 micros/op 1524097 ops/sec 60.005 seconds 91452936 operations; 790.7 MB/s (11649992 of 11649992 found) ` Reviewed By: akankshamahajan15 Differential Revision: D38774009 Pulled By: anand1976 fbshipit-source-id: c955e259749f1c091590ade73105b3ee46cd0007
2022-08-19 23:52:52 +00:00
DEFINE_bool(optimize_multiget_for_io, true,
"When set true, RocksDB does asynchronous reads for SST files in "
"multiple levels for MultiGet.");
Rewrite memory-charging feature's option API (#9926) Summary: **Context:** Previous PR https://github.com/facebook/rocksdb/pull/9748, https://github.com/facebook/rocksdb/pull/9073, https://github.com/facebook/rocksdb/pull/8428 added separate flag for each charged memory area. Such API design is not scalable as we charge more and more memory areas. Also, we foresee an opportunity to consolidate this feature with other cache usage related features such as `cache_index_and_filter_blocks` using `CacheEntryRole`. Therefore we decided to consolidate all these flags with `CacheUsageOptions cache_usage_options` and this PR serves as the first step by consolidating memory-charging related flags. **Summary:** - Replaced old API reference with new ones, including making `kCompressionDictionaryBuildingBuffer` opt-out and added a unit test for that - Added missing db bench/stress test for some memory charging features - Renamed related test suite to indicate they are under the same theme of memory charging - Refactored a commonly used mocked cache component in memory charging related tests to reduce code duplication - Replaced the phrases "memory tracking" / "cache reservation" (other than CacheReservationManager-related ones) with "memory charging" for standard description of this feature. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9926 Test Plan: - New unit test for opt-out `kCompressionDictionaryBuildingBuffer` `TEST_F(ChargeCompressionDictionaryBuildingBufferTest, Basic)` - New unit test for option validation/sanitization `TEST_F(CacheUsageOptionsOverridesTest, SanitizeAndValidateOptions)` - CI - db bench (in case querying new options introduces regression) **+0.5% micros/op**: `TEST_TMPDIR=/dev/shm/testdb ./db_bench -benchmarks=fillseq -db=$TEST_TMPDIR -charge_compression_dictionary_building_buffer=1(remove this for comparison) -compression_max_dict_bytes=10000 -disable_auto_compactions=1 -write_buffer_size=100000 -num=4000000 | egrep 'fillseq'` #-run | (pre-PR) avg micros/op | std micros/op | (post-PR) micros/op | std micros/op | change (%) -- | -- | -- | -- | -- | -- 10 | 3.9711 | 0.264408 | 3.9914 | 0.254563 | 0.5111933721 20 | 3.83905 | 0.0664488 | 3.8251 | 0.0695456 | **-0.3633711465** 40 | 3.86625 | 0.136669 | 3.8867 | 0.143765 | **0.5289363078** - db_stress: `python3 tools/db_crashtest.py blackbox -charge_compression_dictionary_building_buffer=1 -charge_filter_construction=1 -charge_table_reader=1 -cache_size=1` killed as normal Reviewed By: ajkr Differential Revision: D36054712 Pulled By: hx235 fbshipit-source-id: d406e90f5e0c5ea4dbcb585a484ad9302d4302af
2022-05-17 22:01:51 +00:00
DEFINE_bool(charge_compression_dictionary_building_buffer, false,
"Setting for "
"CacheEntryRoleOptions::charged of "
Rewrite memory-charging feature's option API (#9926) Summary: **Context:** Previous PR https://github.com/facebook/rocksdb/pull/9748, https://github.com/facebook/rocksdb/pull/9073, https://github.com/facebook/rocksdb/pull/8428 added separate flag for each charged memory area. Such API design is not scalable as we charge more and more memory areas. Also, we foresee an opportunity to consolidate this feature with other cache usage related features such as `cache_index_and_filter_blocks` using `CacheEntryRole`. Therefore we decided to consolidate all these flags with `CacheUsageOptions cache_usage_options` and this PR serves as the first step by consolidating memory-charging related flags. **Summary:** - Replaced old API reference with new ones, including making `kCompressionDictionaryBuildingBuffer` opt-out and added a unit test for that - Added missing db bench/stress test for some memory charging features - Renamed related test suite to indicate they are under the same theme of memory charging - Refactored a commonly used mocked cache component in memory charging related tests to reduce code duplication - Replaced the phrases "memory tracking" / "cache reservation" (other than CacheReservationManager-related ones) with "memory charging" for standard description of this feature. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9926 Test Plan: - New unit test for opt-out `kCompressionDictionaryBuildingBuffer` `TEST_F(ChargeCompressionDictionaryBuildingBufferTest, Basic)` - New unit test for option validation/sanitization `TEST_F(CacheUsageOptionsOverridesTest, SanitizeAndValidateOptions)` - CI - db bench (in case querying new options introduces regression) **+0.5% micros/op**: `TEST_TMPDIR=/dev/shm/testdb ./db_bench -benchmarks=fillseq -db=$TEST_TMPDIR -charge_compression_dictionary_building_buffer=1(remove this for comparison) -compression_max_dict_bytes=10000 -disable_auto_compactions=1 -write_buffer_size=100000 -num=4000000 | egrep 'fillseq'` #-run | (pre-PR) avg micros/op | std micros/op | (post-PR) micros/op | std micros/op | change (%) -- | -- | -- | -- | -- | -- 10 | 3.9711 | 0.264408 | 3.9914 | 0.254563 | 0.5111933721 20 | 3.83905 | 0.0664488 | 3.8251 | 0.0695456 | **-0.3633711465** 40 | 3.86625 | 0.136669 | 3.8867 | 0.143765 | **0.5289363078** - db_stress: `python3 tools/db_crashtest.py blackbox -charge_compression_dictionary_building_buffer=1 -charge_filter_construction=1 -charge_table_reader=1 -cache_size=1` killed as normal Reviewed By: ajkr Differential Revision: D36054712 Pulled By: hx235 fbshipit-source-id: d406e90f5e0c5ea4dbcb585a484ad9302d4302af
2022-05-17 22:01:51 +00:00
"CacheEntryRole::kCompressionDictionaryBuildingBuffer");
DEFINE_bool(charge_filter_construction, false,
"Setting for "
"CacheEntryRoleOptions::charged of "
Rewrite memory-charging feature's option API (#9926) Summary: **Context:** Previous PR https://github.com/facebook/rocksdb/pull/9748, https://github.com/facebook/rocksdb/pull/9073, https://github.com/facebook/rocksdb/pull/8428 added separate flag for each charged memory area. Such API design is not scalable as we charge more and more memory areas. Also, we foresee an opportunity to consolidate this feature with other cache usage related features such as `cache_index_and_filter_blocks` using `CacheEntryRole`. Therefore we decided to consolidate all these flags with `CacheUsageOptions cache_usage_options` and this PR serves as the first step by consolidating memory-charging related flags. **Summary:** - Replaced old API reference with new ones, including making `kCompressionDictionaryBuildingBuffer` opt-out and added a unit test for that - Added missing db bench/stress test for some memory charging features - Renamed related test suite to indicate they are under the same theme of memory charging - Refactored a commonly used mocked cache component in memory charging related tests to reduce code duplication - Replaced the phrases "memory tracking" / "cache reservation" (other than CacheReservationManager-related ones) with "memory charging" for standard description of this feature. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9926 Test Plan: - New unit test for opt-out `kCompressionDictionaryBuildingBuffer` `TEST_F(ChargeCompressionDictionaryBuildingBufferTest, Basic)` - New unit test for option validation/sanitization `TEST_F(CacheUsageOptionsOverridesTest, SanitizeAndValidateOptions)` - CI - db bench (in case querying new options introduces regression) **+0.5% micros/op**: `TEST_TMPDIR=/dev/shm/testdb ./db_bench -benchmarks=fillseq -db=$TEST_TMPDIR -charge_compression_dictionary_building_buffer=1(remove this for comparison) -compression_max_dict_bytes=10000 -disable_auto_compactions=1 -write_buffer_size=100000 -num=4000000 | egrep 'fillseq'` #-run | (pre-PR) avg micros/op | std micros/op | (post-PR) micros/op | std micros/op | change (%) -- | -- | -- | -- | -- | -- 10 | 3.9711 | 0.264408 | 3.9914 | 0.254563 | 0.5111933721 20 | 3.83905 | 0.0664488 | 3.8251 | 0.0695456 | **-0.3633711465** 40 | 3.86625 | 0.136669 | 3.8867 | 0.143765 | **0.5289363078** - db_stress: `python3 tools/db_crashtest.py blackbox -charge_compression_dictionary_building_buffer=1 -charge_filter_construction=1 -charge_table_reader=1 -cache_size=1` killed as normal Reviewed By: ajkr Differential Revision: D36054712 Pulled By: hx235 fbshipit-source-id: d406e90f5e0c5ea4dbcb585a484ad9302d4302af
2022-05-17 22:01:51 +00:00
"CacheEntryRole::kFilterConstruction");
DEFINE_bool(charge_table_reader, false,
"Setting for "
"CacheEntryRoleOptions::charged of "
Rewrite memory-charging feature's option API (#9926) Summary: **Context:** Previous PR https://github.com/facebook/rocksdb/pull/9748, https://github.com/facebook/rocksdb/pull/9073, https://github.com/facebook/rocksdb/pull/8428 added separate flag for each charged memory area. Such API design is not scalable as we charge more and more memory areas. Also, we foresee an opportunity to consolidate this feature with other cache usage related features such as `cache_index_and_filter_blocks` using `CacheEntryRole`. Therefore we decided to consolidate all these flags with `CacheUsageOptions cache_usage_options` and this PR serves as the first step by consolidating memory-charging related flags. **Summary:** - Replaced old API reference with new ones, including making `kCompressionDictionaryBuildingBuffer` opt-out and added a unit test for that - Added missing db bench/stress test for some memory charging features - Renamed related test suite to indicate they are under the same theme of memory charging - Refactored a commonly used mocked cache component in memory charging related tests to reduce code duplication - Replaced the phrases "memory tracking" / "cache reservation" (other than CacheReservationManager-related ones) with "memory charging" for standard description of this feature. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9926 Test Plan: - New unit test for opt-out `kCompressionDictionaryBuildingBuffer` `TEST_F(ChargeCompressionDictionaryBuildingBufferTest, Basic)` - New unit test for option validation/sanitization `TEST_F(CacheUsageOptionsOverridesTest, SanitizeAndValidateOptions)` - CI - db bench (in case querying new options introduces regression) **+0.5% micros/op**: `TEST_TMPDIR=/dev/shm/testdb ./db_bench -benchmarks=fillseq -db=$TEST_TMPDIR -charge_compression_dictionary_building_buffer=1(remove this for comparison) -compression_max_dict_bytes=10000 -disable_auto_compactions=1 -write_buffer_size=100000 -num=4000000 | egrep 'fillseq'` #-run | (pre-PR) avg micros/op | std micros/op | (post-PR) micros/op | std micros/op | change (%) -- | -- | -- | -- | -- | -- 10 | 3.9711 | 0.264408 | 3.9914 | 0.254563 | 0.5111933721 20 | 3.83905 | 0.0664488 | 3.8251 | 0.0695456 | **-0.3633711465** 40 | 3.86625 | 0.136669 | 3.8867 | 0.143765 | **0.5289363078** - db_stress: `python3 tools/db_crashtest.py blackbox -charge_compression_dictionary_building_buffer=1 -charge_filter_construction=1 -charge_table_reader=1 -cache_size=1` killed as normal Reviewed By: ajkr Differential Revision: D36054712 Pulled By: hx235 fbshipit-source-id: d406e90f5e0c5ea4dbcb585a484ad9302d4302af
2022-05-17 22:01:51 +00:00
"CacheEntryRole::kBlockBasedTableReader");
Account memory of big memory users in BlockBasedTable in global memory limit (#9748) Summary: **Context:** Through heap profiling, we discovered that `BlockBasedTableReader` objects can accumulate and lead to high memory usage (e.g, `max_open_file = -1`). These memories are currently not saved, not tracked, not constrained and not cache evict-able. As a first step to improve this, similar to https://github.com/facebook/rocksdb/pull/8428, this PR is to track an estimate of `BlockBasedTableReader` object's memory in block cache and fail future creation if the memory usage exceeds the available space of cache at the time of creation. **Summary:** - Approximate big memory users (`BlockBasedTable::Rep` and `TableProperties` )' memory usage in addition to the existing estimated ones (filter block/index block/un-compression dictionary) - Charge all of these memory usages to block cache on `BlockBasedTable::Open()` and release them on `~BlockBasedTable()` as there is no memory usage fluctuation of concern in between - Refactor on CacheReservationManager (and its call-sites) to add concurrent support for BlockBasedTable used in this PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9748 Test Plan: - New unit tests - db bench: `OpenDb` : **-0.52% in ms** - Setup `./db_bench -benchmarks=fillseq -db=/dev/shm/testdb -disable_auto_compactions=1 -write_buffer_size=1048576` - Repeated run with pre-change w/o feature and post-change with feature, benchmark `OpenDb`: `./db_bench -benchmarks=readrandom -use_existing_db=1 -db=/dev/shm/testdb -reserve_table_reader_memory=true (remove this when running w/o feature) -file_opening_threads=3 -open_files=-1 -report_open_timing=true| egrep 'OpenDb:'` #-run | (feature-off) avg milliseconds | std milliseconds | (feature-on) avg milliseconds | std milliseconds | change (%) -- | -- | -- | -- | -- | -- 10 | 11.4018 | 5.95173 | 9.47788 | 1.57538 | -16.87382694 20 | 9.23746 | 0.841053 | 9.32377 | 1.14074 | 0.9343477536 40 | 9.0876 | 0.671129 | 9.35053 | 1.11713 | 2.893283155 80 | 9.72514 | 2.28459 | 9.52013 | 1.0894 | -2.108041632 160 | 9.74677 | 0.991234 | 9.84743 | 1.73396 | 1.032752389 320 | 10.7297 | 5.11555 | 10.547 | 1.97692 | **-1.70275031** 640 | 11.7092 | 2.36565 | 11.7869 | 2.69377 | **0.6635807741** - db bench on write with cost to cache in WriteBufferManager (just in case this PR's CRM refactoring accidentally slows down anything in WBM) : `fillseq` : **+0.54% in micros/op** `./db_bench -benchmarks=fillseq -db=/dev/shm/testdb -disable_auto_compactions=1 -cost_write_buffer_to_cache=true -write_buffer_size=10000000000 | egrep 'fillseq'` #-run | (pre-PR) avg micros/op | std micros/op | (post-PR) avg micros/op | std micros/op | change (%) -- | -- | -- | -- | -- | -- 10 | 6.15 | 0.260187 | 6.289 | 0.371192 | 2.260162602 20 | 7.28025 | 0.465402 | 7.37255 | 0.451256 | 1.267813605 40 | 7.06312 | 0.490654 | 7.13803 | 0.478676 | **1.060579461** 80 | 7.14035 | 0.972831 | 7.14196 | 0.92971 | **0.02254791432** - filter bench: `bloom filter`: **-0.78% in ms/key** - ` ./filter_bench -impl=2 -quick -reserve_table_builder_memory=true | grep 'Build avg'` #-run | (pre-PR) avg ns/key | std ns/key | (post-PR) ns/key | std ns/key | change (%) -- | -- | -- | -- | -- | -- 10 | 26.4369 | 0.442182 | 26.3273 | 0.422919 | **-0.4145720565** 20 | 26.4451 | 0.592787 | 26.1419 | 0.62451 | **-1.1465262** - Crash test `python3 tools/db_crashtest.py blackbox --reserve_table_reader_memory=1 --cache_size=1` killed as normal Reviewed By: ajkr Differential Revision: D35136549 Pulled By: hx235 fbshipit-source-id: 146978858d0f900f43f4eb09bfd3e83195e3be28
2022-04-06 17:33:00 +00:00
Account memory of FileMetaData in global memory limit (#9924) Summary: **Context/Summary:** As revealed by heap profiling, allocation of `FileMetaData` for [newly created file added to a Version](https://github.com/facebook/rocksdb/pull/9924/files#diff-a6aa385940793f95a2c5b39cc670bd440c4547fa54fd44622f756382d5e47e43R774) can consume significant heap memory. This PR is to account that toward our global memory limit based on block cache capacity. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9924 Test Plan: - Previous `make check` verified there are only 2 places where the memory of the allocated `FileMetaData` can be released - New unit test `TEST_P(ChargeFileMetadataTestWithParam, Basic)` - db bench (CPU cost of `charge_file_metadata` in write and compact) - **write micros/op: -0.24%** : `TEST_TMPDIR=/dev/shm/testdb ./db_bench -benchmarks=fillseq -db=$TEST_TMPDIR -charge_file_metadata=1 (remove this option for pre-PR) -disable_auto_compactions=1 -write_buffer_size=100000 -num=4000000 | egrep 'fillseq'` - **compact micros/op -0.87%** : `TEST_TMPDIR=/dev/shm/testdb ./db_bench -benchmarks=fillseq -db=$TEST_TMPDIR -charge_file_metadata=1 -disable_auto_compactions=1 -write_buffer_size=100000 -num=4000000 -numdistinct=1000 && ./db_bench -benchmarks=compact -db=$TEST_TMPDIR -use_existing_db=1 -charge_file_metadata=1 -disable_auto_compactions=1 | egrep 'compact'` table 1 - write #-run | (pre-PR) avg micros/op | std micros/op | (post-PR) micros/op | std micros/op | change (%) -- | -- | -- | -- | -- | -- 10 | 3.9711 | 0.264408 | 3.9914 | 0.254563 | 0.5111933721 20 | 3.83905 | 0.0664488 | 3.8251 | 0.0695456 | -0.3633711465 40 | 3.86625 | 0.136669 | 3.8867 | 0.143765 | 0.5289363078 80 | 3.87828 | 0.119007 | 3.86791 | 0.115674 | **-0.2673865734** 160 | 3.87677 | 0.162231 | 3.86739 | 0.16663 | **-0.2419539978** table 2 - compact #-run | (pre-PR) avg micros/op | std micros/op | (post-PR) micros/op | std micros/op | change (%) -- | -- | -- | -- | -- | -- 10 | 2,399,650.00 | 96,375.80 | 2,359,537.00 | 53,243.60 | -1.67 20 | 2,410,480.00 | 89,988.00 | 2,433,580.00 | 91,121.20 | 0.96 40 | 2.41E+06 | 121811 | 2.39E+06 | 131525 | **-0.96** 80 | 2.40E+06 | 134503 | 2.39E+06 | 108799 | **-0.78** - stress test: `python3 tools/db_crashtest.py blackbox --charge_file_metadata=1 --cache_size=1` killed as normal Reviewed By: ajkr Differential Revision: D36055583 Pulled By: hx235 fbshipit-source-id: b60eab94707103cb1322cf815f05810ef0232625
2022-06-14 20:06:40 +00:00
DEFINE_bool(charge_file_metadata, false,
"Setting for "
"CacheEntryRoleOptions::charged of "
Account memory of FileMetaData in global memory limit (#9924) Summary: **Context/Summary:** As revealed by heap profiling, allocation of `FileMetaData` for [newly created file added to a Version](https://github.com/facebook/rocksdb/pull/9924/files#diff-a6aa385940793f95a2c5b39cc670bd440c4547fa54fd44622f756382d5e47e43R774) can consume significant heap memory. This PR is to account that toward our global memory limit based on block cache capacity. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9924 Test Plan: - Previous `make check` verified there are only 2 places where the memory of the allocated `FileMetaData` can be released - New unit test `TEST_P(ChargeFileMetadataTestWithParam, Basic)` - db bench (CPU cost of `charge_file_metadata` in write and compact) - **write micros/op: -0.24%** : `TEST_TMPDIR=/dev/shm/testdb ./db_bench -benchmarks=fillseq -db=$TEST_TMPDIR -charge_file_metadata=1 (remove this option for pre-PR) -disable_auto_compactions=1 -write_buffer_size=100000 -num=4000000 | egrep 'fillseq'` - **compact micros/op -0.87%** : `TEST_TMPDIR=/dev/shm/testdb ./db_bench -benchmarks=fillseq -db=$TEST_TMPDIR -charge_file_metadata=1 -disable_auto_compactions=1 -write_buffer_size=100000 -num=4000000 -numdistinct=1000 && ./db_bench -benchmarks=compact -db=$TEST_TMPDIR -use_existing_db=1 -charge_file_metadata=1 -disable_auto_compactions=1 | egrep 'compact'` table 1 - write #-run | (pre-PR) avg micros/op | std micros/op | (post-PR) micros/op | std micros/op | change (%) -- | -- | -- | -- | -- | -- 10 | 3.9711 | 0.264408 | 3.9914 | 0.254563 | 0.5111933721 20 | 3.83905 | 0.0664488 | 3.8251 | 0.0695456 | -0.3633711465 40 | 3.86625 | 0.136669 | 3.8867 | 0.143765 | 0.5289363078 80 | 3.87828 | 0.119007 | 3.86791 | 0.115674 | **-0.2673865734** 160 | 3.87677 | 0.162231 | 3.86739 | 0.16663 | **-0.2419539978** table 2 - compact #-run | (pre-PR) avg micros/op | std micros/op | (post-PR) micros/op | std micros/op | change (%) -- | -- | -- | -- | -- | -- 10 | 2,399,650.00 | 96,375.80 | 2,359,537.00 | 53,243.60 | -1.67 20 | 2,410,480.00 | 89,988.00 | 2,433,580.00 | 91,121.20 | 0.96 40 | 2.41E+06 | 121811 | 2.39E+06 | 131525 | **-0.96** 80 | 2.40E+06 | 134503 | 2.39E+06 | 108799 | **-0.78** - stress test: `python3 tools/db_crashtest.py blackbox --charge_file_metadata=1 --cache_size=1` killed as normal Reviewed By: ajkr Differential Revision: D36055583 Pulled By: hx235 fbshipit-source-id: b60eab94707103cb1322cf815f05810ef0232625
2022-06-14 20:06:40 +00:00
"CacheEntryRole::kFileMetadata");
DEFINE_bool(charge_blob_cache, false,
"Setting for "
"CacheEntryRoleOptions::charged of "
"CacheEntryRole::kBlobCache");
Support read rate-limiting in SequentialFileReader (#9973) Summary: Added rate limiter and read rate-limiting support to SequentialFileReader. I've updated call sites to SequentialFileReader::Read with appropriate IO priority (or left a TODO and specified IO_TOTAL for now). The PR is separated into four commits: the first one added the rate-limiting support, but with some fixes in the unit test since the number of request bytes from rate limiter in SequentialFileReader are not accurate (there is overcharge at EOF). The second commit fixed this by allowing SequentialFileReader to check file size and determine how many bytes are left in the file to read. The third commit added benchmark related code. The fourth commit moved the logic of using file size to avoid overcharging the rate limiter into backup engine (the main user of SequentialFileReader). Pull Request resolved: https://github.com/facebook/rocksdb/pull/9973 Test Plan: - `make check`, backup_engine_test covers usage of SequentialFileReader with rate limiter. - Run db_bench to check if rate limiting is throttling as expected: Verified that reads and writes are together throttled at 2MB/s, and at 0.2MB chunks that are 100ms apart. - Set up: `./db_bench --benchmarks=fillrandom -db=/dev/shm/test_rocksdb` - Benchmark: ``` strace -ttfe read,write ./db_bench --benchmarks=backup -db=/dev/shm/test_rocksdb --backup_rate_limit=2097152 --use_existing_db strace -ttfe read,write ./db_bench --benchmarks=restore -db=/dev/shm/test_rocksdb --restore_rate_limit=2097152 --use_existing_db ``` - db bench on backup and restore to ensure no performance regression. - backup (avg over 50 runs): pre-change: 1.90443e+06 micros/op; post-change: 1.8993e+06 micros/op (improve by 0.2%) - restore (avg over 50 runs): pre-change: 1.79105e+06 micros/op; post-change: 1.78192e+06 micros/op (improve by 0.5%) ``` # Set up ./db_bench --benchmarks=fillrandom -db=/tmp/test_rocksdb -num=10000000 # benchmark TEST_TMPDIR=/tmp/test_rocksdb NUM_RUN=50 for ((j=0;j<$NUM_RUN;j++)) do ./db_bench -db=$TEST_TMPDIR -num=10000000 -benchmarks=backup -use_existing_db | egrep 'backup' # Restore #./db_bench -db=$TEST_TMPDIR -num=10000000 -benchmarks=restore -use_existing_db done > rate_limit.txt && awk -v NUM_RUN=$NUM_RUN '{sum+=$3;sum_sqrt+=$3^2}END{print sum/NUM_RUN, sqrt(sum_sqrt/NUM_RUN-(sum/NUM_RUN)^2)}' rate_limit.txt >> rate_limit_2.txt ``` Reviewed By: hx235 Differential Revision: D36327418 Pulled By: cbi42 fbshipit-source-id: e75d4307cff815945482df5ba630c1e88d064691
2022-05-24 17:28:57 +00:00
DEFINE_uint64(backup_rate_limit, 0ull,
"If non-zero, db_bench will rate limit reads and writes for DB "
"backup. This "
"is the global rate in ops/second.");
DEFINE_uint64(restore_rate_limit, 0ull,
"If non-zero, db_bench will rate limit reads and writes for DB "
"restore. This "
"is the global rate in ops/second.");
DEFINE_string(backup_dir, "",
"If not empty string, use the given dir for backup.");
DEFINE_string(restore_dir, "",
"If not empty string, use the given dir for restore.");
DEFINE_uint64(
initial_auto_readahead_size,
ROCKSDB_NAMESPACE::BlockBasedTableOptions().initial_auto_readahead_size,
"RocksDB does auto-readahead for iterators on noticing more than two reads "
"for a table file if user doesn't provide readahead_size. The readahead "
"size starts at initial_auto_readahead_size");
DEFINE_uint64(
max_auto_readahead_size,
ROCKSDB_NAMESPACE::BlockBasedTableOptions().max_auto_readahead_size,
"Rocksdb implicit readahead starts at "
"BlockBasedTableOptions.initial_auto_readahead_size and doubles on every "
"additional read upto max_auto_readahead_size");
DEFINE_uint64(
num_file_reads_for_auto_readahead,
ROCKSDB_NAMESPACE::BlockBasedTableOptions()
.num_file_reads_for_auto_readahead,
"Rocksdb implicit readahead is enabled if reads are sequential and "
"num_file_reads_for_auto_readahead indicates after how many sequential "
"reads into that file internal auto prefetching should be start.");
DEFINE_bool(
auto_readahead_size, false,
"When set true, RocksDB does auto tuning of readahead size during Scans");
static enum ROCKSDB_NAMESPACE::CompressionType StringToCompressionType(
const char* ctype) {
assert(ctype);
if (!strcasecmp(ctype, "none")) {
return ROCKSDB_NAMESPACE::kNoCompression;
} else if (!strcasecmp(ctype, "snappy")) {
return ROCKSDB_NAMESPACE::kSnappyCompression;
} else if (!strcasecmp(ctype, "zlib")) {
return ROCKSDB_NAMESPACE::kZlibCompression;
} else if (!strcasecmp(ctype, "bzip2")) {
return ROCKSDB_NAMESPACE::kBZip2Compression;
} else if (!strcasecmp(ctype, "lz4")) {
return ROCKSDB_NAMESPACE::kLZ4Compression;
} else if (!strcasecmp(ctype, "lz4hc")) {
return ROCKSDB_NAMESPACE::kLZ4HCCompression;
} else if (!strcasecmp(ctype, "xpress")) {
return ROCKSDB_NAMESPACE::kXpressCompression;
} else if (!strcasecmp(ctype, "zstd")) {
return ROCKSDB_NAMESPACE::kZSTD;
} else {
fprintf(stderr, "Cannot parse compression type '%s'\n", ctype);
exit(1);
}
}
static enum ROCKSDB_NAMESPACE::TieredAdmissionPolicy StringToAdmissionPolicy(
const char* policy) {
assert(policy);
if (!strcasecmp(policy, "auto")) {
return ROCKSDB_NAMESPACE::kAdmPolicyAuto;
} else if (!strcasecmp(policy, "placeholder")) {
return ROCKSDB_NAMESPACE::kAdmPolicyPlaceholder;
} else if (!strcasecmp(policy, "allow_cache_hits")) {
return ROCKSDB_NAMESPACE::kAdmPolicyAllowCacheHits;
} else if (!strcasecmp(policy, "three_queue")) {
return ROCKSDB_NAMESPACE::kAdmPolicyThreeQueue;
} else if (!strcasecmp(policy, "allow_all")) {
return ROCKSDB_NAMESPACE::kAdmPolicyAllowAll;
} else {
fprintf(stderr, "Cannot parse admission policy %s\n", policy);
exit(1);
}
}
static std::string ColumnFamilyName(size_t i) {
if (i == 0) {
return ROCKSDB_NAMESPACE::kDefaultColumnFamilyName;
} else {
char name[100];
snprintf(name, sizeof(name), "column_family_name_%06zu", i);
return std::string(name);
}
}
DEFINE_string(compression_type, "snappy",
"Algorithm to use to compress the database");
static enum ROCKSDB_NAMESPACE::CompressionType FLAGS_compression_type_e =
ROCKSDB_NAMESPACE::kSnappyCompression;
DEFINE_int64(sample_for_compression, 0, "Sample every N block for compression");
DEFINE_int32(compression_level, ROCKSDB_NAMESPACE::CompressionOptions().level,
"Compression level. The meaning of this value is library-"
"dependent. If unset, we try to use the default for the library "
"specified in `--compression_type`");
DEFINE_int32(compression_max_dict_bytes,
ROCKSDB_NAMESPACE::CompressionOptions().max_dict_bytes,
"Maximum size of dictionary used to prime the compression "
"library.");
DEFINE_int32(compression_zstd_max_train_bytes,
ROCKSDB_NAMESPACE::CompressionOptions().zstd_max_train_bytes,
"Maximum size of training data passed to zstd's dictionary "
"trainer.");
DEFINE_int32(min_level_to_compress, -1,
"If non-negative, compression starts"
" from this level. Levels with number < min_level_to_compress are"
" not compressed. Otherwise, apply compression_type to "
"all levels.");
DEFINE_int32(compression_parallel_threads, 1,
"Number of threads for parallel compression.");
Limit buffering for collecting samples for compression dictionary (#7970) Summary: For dictionary compression, we need to collect some representative samples of the data to be compressed, which we use to either generate or train (when `CompressionOptions::zstd_max_train_bytes > 0`) a dictionary. Previously, the strategy was to buffer all the data blocks during flush, and up to the target file size during compaction. That strategy allowed us to randomly pick samples from as wide a range as possible that'd be guaranteed to land in a single output file. However, some users try to make huge files in memory-constrained environments, where this strategy can cause OOM. This PR introduces an option, `CompressionOptions::max_dict_buffer_bytes`, that limits how much data blocks are buffered before we switch to unbuffered mode (which means creating the per-SST dictionary, writing out the buffered data, and compressing/writing new blocks as soon as they are built). It is not strict as we currently buffer more than just data blocks -- also keys are buffered. But it does make a step towards giving users predictable memory usage. Related changes include: - Changed sampling for dictionary compression to select unique data blocks when there is limited availability of data blocks - Made use of `BlockBuilder::SwapAndReset()` to save an allocation+memcpy when buffering data blocks for building a dictionary - Changed `ParseBoolean()` to accept an input containing characters after the boolean. This is necessary since, with this PR, a value for `CompressionOptions::enabled` is no longer necessarily the final component in the `CompressionOptions` string. Pull Request resolved: https://github.com/facebook/rocksdb/pull/7970 Test Plan: - updated `CompressionOptions` unit tests to verify limit is respected (to the extent expected in the current implementation) in various scenarios of flush/compaction to bottommost/non-bottommost level - looked at jemalloc heap profiles right before and after switching to unbuffered mode during flush/compaction. Verified memory usage in buffering is proportional to the limit set. Reviewed By: pdillinger Differential Revision: D26467994 Pulled By: ajkr fbshipit-source-id: 3da4ef9fba59974e4ef40e40c01611002c861465
2021-02-19 22:06:59 +00:00
DEFINE_uint64(compression_max_dict_buffer_bytes,
ROCKSDB_NAMESPACE::CompressionOptions().max_dict_buffer_bytes,
"Maximum bytes to buffer to collect samples for dictionary.");
Support using ZDICT_finalizeDictionary to generate zstd dictionary (#9857) Summary: An untrained dictionary is currently simply the concatenation of several samples. The ZSTD API, ZDICT_finalizeDictionary(), can improve such a dictionary's effectiveness at low cost. This PR changes how dictionary is created by calling the ZSTD ZDICT_finalizeDictionary() API instead of creating raw content dictionary (when max_dict_buffer_bytes > 0), and pass in all buffered uncompressed data blocks as samples. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9857 Test Plan: #### db_bench test for cpu/memory of compression+decompression and space saving on synthetic data: Set up: change the parameter [here](https://github.com/facebook/rocksdb/blob/fb9a167a55e0970b1ef6f67c1600c8d9c4c6114f/tools/db_bench_tool.cc#L1766) to 16384 to make synthetic data more compressible. ``` # linked local ZSTD with version 1.5.2 # DEBUG_LEVEL=0 ROCKSDB_NO_FBCODE=1 ROCKSDB_DISABLE_ZSTD=1 EXTRA_CXXFLAGS="-DZSTD_STATIC_LINKING_ONLY -DZSTD -I/data/users/changyubi/install/include/" EXTRA_LDFLAGS="-L/data/users/changyubi/install/lib/ -l:libzstd.a" make -j32 db_bench dict_bytes=16384 train_bytes=1048576 echo "========== No Dictionary ==========" TEST_TMPDIR=/dev/shm ./db_bench -benchmarks=filluniquerandom,compact -num=10000000 -compression_type=zstd -compression_max_dict_bytes=0 -block_size=4096 -max_background_jobs=24 -memtablerep=vector -allow_concurrent_memtable_write=false -disable_wal=true -max_write_buffer_number=8 >/dev/null 2>&1 TEST_TMPDIR=/dev/shm /usr/bin/time ./db_bench -use_existing_db=true -benchmarks=compact -compression_type=zstd -compression_max_dict_bytes=0 -block_size=4096 2>&1 | grep elapsed du -hc /dev/shm/dbbench/*sst | grep total echo "========== Raw Content Dictionary ==========" TEST_TMPDIR=/dev/shm ./db_bench_main -benchmarks=filluniquerandom,compact -num=10000000 -compression_type=zstd -compression_max_dict_bytes=$dict_bytes -block_size=4096 -max_background_jobs=24 -memtablerep=vector -allow_concurrent_memtable_write=false -disable_wal=true -max_write_buffer_number=8 >/dev/null 2>&1 TEST_TMPDIR=/dev/shm /usr/bin/time ./db_bench_main -use_existing_db=true -benchmarks=compact -compression_type=zstd -compression_max_dict_bytes=$dict_bytes -block_size=4096 2>&1 | grep elapsed du -hc /dev/shm/dbbench/*sst | grep total echo "========== FinalizeDictionary ==========" TEST_TMPDIR=/dev/shm ./db_bench -benchmarks=filluniquerandom,compact -num=10000000 -compression_type=zstd -compression_max_dict_bytes=$dict_bytes -compression_zstd_max_train_bytes=$train_bytes -compression_use_zstd_dict_trainer=false -block_size=4096 -max_background_jobs=24 -memtablerep=vector -allow_concurrent_memtable_write=false -disable_wal=true -max_write_buffer_number=8 >/dev/null 2>&1 TEST_TMPDIR=/dev/shm /usr/bin/time ./db_bench -use_existing_db=true -benchmarks=compact -compression_type=zstd -compression_max_dict_bytes=$dict_bytes -compression_zstd_max_train_bytes=$train_bytes -compression_use_zstd_dict_trainer=false -block_size=4096 2>&1 | grep elapsed du -hc /dev/shm/dbbench/*sst | grep total echo "========== TrainDictionary ==========" TEST_TMPDIR=/dev/shm ./db_bench -benchmarks=filluniquerandom,compact -num=10000000 -compression_type=zstd -compression_max_dict_bytes=$dict_bytes -compression_zstd_max_train_bytes=$train_bytes -block_size=4096 -max_background_jobs=24 -memtablerep=vector -allow_concurrent_memtable_write=false -disable_wal=true -max_write_buffer_number=8 >/dev/null 2>&1 TEST_TMPDIR=/dev/shm /usr/bin/time ./db_bench -use_existing_db=true -benchmarks=compact -compression_type=zstd -compression_max_dict_bytes=$dict_bytes -compression_zstd_max_train_bytes=$train_bytes -block_size=4096 2>&1 | grep elapsed du -hc /dev/shm/dbbench/*sst | grep total # Result: TrainDictionary is much better on space saving, but FinalizeDictionary seems to use less memory. # before compression data size: 1.2GB dict_bytes=16384 max_dict_buffer_bytes = 1048576 space cpu/memory No Dictionary 468M 14.93user 1.00system 0:15.92elapsed 100%CPU (0avgtext+0avgdata 23904maxresident)k Raw Dictionary 251M 15.81user 0.80system 0:16.56elapsed 100%CPU (0avgtext+0avgdata 156808maxresident)k FinalizeDictionary 236M 11.93user 0.64system 0:12.56elapsed 100%CPU (0avgtext+0avgdata 89548maxresident)k TrainDictionary 84M 7.29user 0.45system 0:07.75elapsed 100%CPU (0avgtext+0avgdata 97288maxresident)k ``` #### Benchmark on 10 sample SST files for spacing saving and CPU time on compression: FinalizeDictionary is comparable to TrainDictionary in terms of space saving, and takes less time in compression. ``` dict_bytes=16384 train_bytes=1048576 for sst_file in `ls ../temp/myrock-sst/` do echo "********** $sst_file **********" echo "========== No Dictionary ==========" ./sst_dump --file="../temp/myrock-sst/$sst_file" --command=recompress --compression_level_from=6 --compression_level_to=6 --compression_types=kZSTD echo "========== Raw Content Dictionary ==========" ./sst_dump --file="../temp/myrock-sst/$sst_file" --command=recompress --compression_level_from=6 --compression_level_to=6 --compression_types=kZSTD --compression_max_dict_bytes=$dict_bytes echo "========== FinalizeDictionary ==========" ./sst_dump --file="../temp/myrock-sst/$sst_file" --command=recompress --compression_level_from=6 --compression_level_to=6 --compression_types=kZSTD --compression_max_dict_bytes=$dict_bytes --compression_zstd_max_train_bytes=$train_bytes --compression_use_zstd_finalize_dict echo "========== TrainDictionary ==========" ./sst_dump --file="../temp/myrock-sst/$sst_file" --command=recompress --compression_level_from=6 --compression_level_to=6 --compression_types=kZSTD --compression_max_dict_bytes=$dict_bytes --compression_zstd_max_train_bytes=$train_bytes done 010240.sst (Size/Time) 011029.sst 013184.sst 021552.sst 185054.sst 185137.sst 191666.sst 7560381.sst 7604174.sst 7635312.sst No Dictionary 28165569 / 2614419 32899411 / 2976832 32977848 / 3055542 31966329 / 2004590 33614351 / 1755877 33429029 / 1717042 33611933 / 1776936 33634045 / 2771417 33789721 / 2205414 33592194 / 388254 Raw Content Dictionary 28019950 / 2697961 33748665 / 3572422 33896373 / 3534701 26418431 / 2259658 28560825 / 1839168 28455030 / 1846039 28494319 / 1861349 32391599 / 3095649 33772142 / 2407843 33592230 / 474523 FinalizeDictionary 27896012 / 2650029 33763886 / 3719427 33904283 / 3552793 26008225 / 2198033 28111872 / 1869530 28014374 / 1789771 28047706 / 1848300 32296254 / 3204027 33698698 / 2381468 33592344 / 517433 TrainDictionary 28046089 / 2740037 33706480 / 3679019 33885741 / 3629351 25087123 / 2204558 27194353 / 1970207 27234229 / 1896811 27166710 / 1903119 32011041 / 3322315 32730692 / 2406146 33608631 / 570593 ``` #### Decompression/Read test: With FinalizeDictionary/TrainDictionary, some data structure used for decompression are in stored in dictionary, so they are expected to be faster in terms of decompression/reads. ``` dict_bytes=16384 train_bytes=1048576 echo "No Dictionary" TEST_TMPDIR=/dev/shm/ ./db_bench -benchmarks=filluniquerandom,compact -compression_type=zstd -compression_max_dict_bytes=0 > /dev/null 2>&1 TEST_TMPDIR=/dev/shm/ ./db_bench -use_existing_db=true -benchmarks=readrandom -cache_size=0 -compression_type=zstd -compression_max_dict_bytes=0 2>&1 | grep MB/s echo "Raw Dictionary" TEST_TMPDIR=/dev/shm/ ./db_bench -benchmarks=filluniquerandom,compact -compression_type=zstd -compression_max_dict_bytes=$dict_bytes > /dev/null 2>&1 TEST_TMPDIR=/dev/shm/ ./db_bench -use_existing_db=true -benchmarks=readrandom -cache_size=0 -compression_type=zstd -compression_max_dict_bytes=$dict_bytes 2>&1 | grep MB/s echo "FinalizeDict" TEST_TMPDIR=/dev/shm/ ./db_bench -benchmarks=filluniquerandom,compact -compression_type=zstd -compression_max_dict_bytes=$dict_bytes -compression_zstd_max_train_bytes=$train_bytes -compression_use_zstd_dict_trainer=false > /dev/null 2>&1 TEST_TMPDIR=/dev/shm/ ./db_bench -use_existing_db=true -benchmarks=readrandom -cache_size=0 -compression_type=zstd -compression_max_dict_bytes=$dict_bytes -compression_zstd_max_train_bytes=$train_bytes -compression_use_zstd_dict_trainer=false 2>&1 | grep MB/s echo "Train Dictionary" TEST_TMPDIR=/dev/shm/ ./db_bench -benchmarks=filluniquerandom,compact -compression_type=zstd -compression_max_dict_bytes=$dict_bytes -compression_zstd_max_train_bytes=$train_bytes > /dev/null 2>&1 TEST_TMPDIR=/dev/shm/ ./db_bench -use_existing_db=true -benchmarks=readrandom -cache_size=0 -compression_type=zstd -compression_max_dict_bytes=$dict_bytes -compression_zstd_max_train_bytes=$train_bytes 2>&1 | grep MB/s No Dictionary readrandom : 12.183 micros/op 82082 ops/sec 12.183 seconds 1000000 operations; 9.1 MB/s (1000000 of 1000000 found) Raw Dictionary readrandom : 12.314 micros/op 81205 ops/sec 12.314 seconds 1000000 operations; 9.0 MB/s (1000000 of 1000000 found) FinalizeDict readrandom : 9.787 micros/op 102180 ops/sec 9.787 seconds 1000000 operations; 11.3 MB/s (1000000 of 1000000 found) Train Dictionary readrandom : 9.698 micros/op 103108 ops/sec 9.699 seconds 1000000 operations; 11.4 MB/s (1000000 of 1000000 found) ``` Reviewed By: ajkr Differential Revision: D35720026 Pulled By: cbi42 fbshipit-source-id: 24d230fdff0fd28a1bb650658798f00dfcfb2a1f
2022-05-20 19:09:09 +00:00
DEFINE_bool(compression_use_zstd_dict_trainer,
ROCKSDB_NAMESPACE::CompressionOptions().use_zstd_dict_trainer,
"If true, use ZSTD_TrainDictionary() to create dictionary, else"
"use ZSTD_FinalizeDictionary() to create dictionary");
static bool ValidateTableCacheNumshardbits(const char* flagname,
int32_t value) {
if (0 >= value || value >= 20) {
fprintf(stderr, "Invalid value for --%s: %d, must be 0 < val < 20\n",
flagname, value);
return false;
}
return true;
}
DEFINE_int32(table_cache_numshardbits, 4, "");
DEFINE_string(env_uri, "",
"URI for registry Env lookup. Mutually exclusive with --fs_uri");
DEFINE_string(fs_uri, "",
"URI for registry Filesystem lookup. Mutually exclusive"
" with --env_uri."
" Creates a default environment with the specified filesystem.");
DEFINE_string(simulate_hybrid_fs_file, "",
"File for Store Metadata for Simulate hybrid FS. Empty means "
"disable the feature. Now, if it is set, last_level_temperature "
"is set to kWarm.");
DEFINE_int32(simulate_hybrid_hdd_multipliers, 1,
"In simulate_hybrid_fs_file or simulate_hdd mode, how many HDDs "
"are simulated.");
DEFINE_bool(simulate_hdd, false, "Simulate read/write latency on HDD.");
DEFINE_int64(
preclude_last_level_data_seconds, 0,
"Preclude the latest data from the last level. (Used for tiered storage)");
DEFINE_int64(preserve_internal_time_seconds, 0,
"Preserve the internal time information which stores with SST.");
static std::shared_ptr<ROCKSDB_NAMESPACE::Env> env_guard;
static ROCKSDB_NAMESPACE::Env* FLAGS_env = ROCKSDB_NAMESPACE::Env::Default();
DEFINE_int64(stats_interval, 0,
"Stats are reported every N operations when this is greater than "
"zero. When 0 the interval grows over time.");
DEFINE_int64(stats_interval_seconds, 0,
"Report stats every N seconds. This overrides stats_interval when"
" both are > 0.");
DEFINE_int32(stats_per_interval, 0,
"Reports additional stats per interval when this is greater than "
"0.");
DEFINE_uint64(slow_usecs, 1000000,
"A message is printed for operations that take at least this "
"many microseconds.");
DEFINE_int64(report_interval_seconds, 0,
"If greater than zero, it will write simple stats in CSV format "
"to --report_file every N seconds");
DEFINE_string(report_file, "report.csv",
"Filename where some simple stats are reported to (if "
"--report_interval_seconds is bigger than 0)");
DEFINE_int32(thread_status_per_interval, 0,
"Takes and report a snapshot of the current status of each thread"
" when this is greater than 0.");
DEFINE_int32(perf_level, ROCKSDB_NAMESPACE::PerfLevel::kDisable,
"Level of perf collection");
DEFINE_uint64(soft_pending_compaction_bytes_limit, 64ull * 1024 * 1024 * 1024,
"Slowdown writes if pending compaction bytes exceed this number");
DEFINE_uint64(hard_pending_compaction_bytes_limit, 128ull * 1024 * 1024 * 1024,
"Stop writes if pending compaction bytes exceed this number");
DEFINE_uint64(delayed_write_rate, 8388608u,
"Limited bytes allowed to DB when soft_rate_limit or "
"level0_slowdown_writes_trigger triggers");
DEFINE_bool(enable_pipelined_write, true,
"Allow WAL and memtable writes to be pipelined");
DEFINE_bool(
unordered_write, false,
"Enable the unordered write feature, which provides higher throughput but "
"relaxes the guarantees around atomic reads and immutable snapshots");
Unordered Writes (#5218) Summary: Performing unordered writes in rocksdb when unordered_write option is set to true. When enabled the writes to memtable are done without joining any write thread. This offers much higher write throughput since the upcoming writes would not have to wait for the slowest memtable write to finish. The tradeoff is that the writes visible to a snapshot might change over time. If the application cannot tolerate that, it should implement its own mechanisms to work around that. Using TransactionDB with WRITE_PREPARED write policy is one way to achieve that. Doing so increases the max throughput by 2.2x without however compromising the snapshot guarantees. The patch is prepared based on an original by siying Existing unit tests are extended to include unordered_write option. Benchmark Results: ``` TEST_TMPDIR=/dev/shm/ ./db_bench_unordered --benchmarks=fillrandom --threads=32 --num=10000000 -max_write_buffer_number=16 --max_background_jobs=64 --batch_size=8 --writes=3000000 -level0_file_num_compaction_trigger=99999 --level0_slowdown_writes_trigger=99999 --level0_stop_writes_trigger=99999 -enable_pipelined_write=false -disable_auto_compactions --unordered_write=1 ``` With WAL - Vanilla RocksDB: 78.6 MB/s - WRITER_PREPARED with unordered_write: 177.8 MB/s (2.2x) - unordered_write: 368.9 MB/s (4.7x with relaxed snapshot guarantees) Without WAL - Vanilla RocksDB: 111.3 MB/s - WRITER_PREPARED with unordered_write: 259.3 MB/s MB/s (2.3x) - unordered_write: 645.6 MB/s (5.8x with relaxed snapshot guarantees) - WRITER_PREPARED with unordered_write disable concurrency control: 185.3 MB/s MB/s (2.35x) Limitations: - The feature is not yet extended to `max_successive_merges` > 0. The feature is also incompatible with `enable_pipelined_write` = true as well as with `allow_concurrent_memtable_write` = false. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5218 Differential Revision: D15219029 Pulled By: maysamyabandeh fbshipit-source-id: 38f2abc4af8780148c6128acdba2b3227bc81759
2019-05-14 00:43:47 +00:00
DEFINE_bool(allow_concurrent_memtable_write, true,
support for concurrent adds to memtable Summary: This diff adds support for concurrent adds to the skiplist memtable implementations. Memory allocation is made thread-safe by the addition of a spinlock, with small per-core buffers to avoid contention. Concurrent memtable writes are made via an additional method and don't impose a performance overhead on the non-concurrent case, so parallelism can be selected on a per-batch basis. Write thread synchronization is an increasing bottleneck for higher levels of concurrency, so this diff adds --enable_write_thread_adaptive_yield (default off). This feature causes threads joining a write batch group to spin for a short time (default 100 usec) using sched_yield, rather than going to sleep on a mutex. If the timing of the yield calls indicates that another thread has actually run during the yield then spinning is avoided. This option improves performance for concurrent situations even without parallel adds, although it has the potential to increase CPU usage (and the heuristic adaptation is not yet mature). Parallel writes are not currently compatible with inplace updates, update callbacks, or delete filtering. Enable it with --allow_concurrent_memtable_write (and --enable_write_thread_adaptive_yield). Parallel memtable writes are performance neutral when there is no actual parallelism, and in my experiments (SSD server-class Linux and varying contention and key sizes for fillrandom) they are always a performance win when there is more than one thread. Statistics are updated earlier in the write path, dropping the number of DB mutex acquisitions from 2 to 1 for almost all cases. This diff was motivated and inspired by Yahoo's cLSM work. It is more conservative than cLSM: RocksDB's write batch group leader role is preserved (along with all of the existing flush and write throttling logic) and concurrent writers are blocked until all memtable insertions have completed and the sequence number has been advanced, to preserve linearizability. My test config is "db_bench -benchmarks=fillrandom -threads=$T -batch_size=1 -memtablerep=skip_list -value_size=100 --num=1000000/$T -level0_slowdown_writes_trigger=9999 -level0_stop_writes_trigger=9999 -disable_auto_compactions --max_write_buffer_number=8 -max_background_flushes=8 --disable_wal --write_buffer_size=160000000 --block_size=16384 --allow_concurrent_memtable_write" on a two-socket Xeon E5-2660 @ 2.2Ghz with lots of memory and an SSD hard drive. With 1 thread I get ~440Kops/sec. Peak performance for 1 socket (numactl -N1) is slightly more than 1Mops/sec, at 16 threads. Peak performance across both sockets happens at 30 threads, and is ~900Kops/sec, although with fewer threads there is less performance loss when the system has background work. Test Plan: 1. concurrent stress tests for InlineSkipList and DynamicBloom 2. make clean; make check 3. make clean; DISABLE_JEMALLOC=1 make valgrind_check; valgrind db_bench 4. make clean; COMPILE_WITH_TSAN=1 make all check; db_bench 5. make clean; COMPILE_WITH_ASAN=1 make all check; db_bench 6. make clean; OPT=-DROCKSDB_LITE make check 7. verify no perf regressions when disabled Reviewers: igor, sdong Reviewed By: sdong Subscribers: MarkCallaghan, IslamAbdelRahman, anthony, yhchiang, rven, sdong, guyg8, kradhakrishnan, dhruba Differential Revision: https://reviews.facebook.net/D50589
2015-08-14 23:59:07 +00:00
"Allow multi-writers to update mem tables in parallel.");
Memtable sampling for mempurge heuristic. (#8628) Summary: Changes the API of the MemPurge process: the `bool experimental_allow_mempurge` and `experimental_mempurge_policy` flags have been replaced by a `double experimental_mempurge_threshold` option. This change of API reflects another major change introduced in this PR: the MemPurgeDecider() function now works by sampling the memtables being flushed to estimate the overall amount of useful payload (payload minus the garbage), and then compare this useful payload estimate with the `double experimental_mempurge_threshold` value. Therefore, when the value of this flag is `0.0` (default value), mempurge is simply deactivated. On the other hand, a value of `DBL_MAX` would be equivalent to always going through a mempurge regardless of the garbage ratio estimate. At the moment, a `double experimental_mempurge_threshold` value else than 0.0 or `DBL_MAX` is opnly supported`with the `SkipList` memtable representation. Regarding the sampling, this PR includes the introduction of a `MemTable::UniqueRandomSample` function that collects (approximately) random entries from the memtable by using the new `SkipList::Iterator::RandomSeek()` under the hood, or by iterating through each memtable entry, depending on the target sample size and the total number of entries. The unit tests have been readapted to support this new API. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8628 Reviewed By: pdillinger Differential Revision: D30149315 Pulled By: bjlemaire fbshipit-source-id: 1feef5390c95db6f4480ab4434716533d3947f27
2021-08-11 01:07:48 +00:00
DEFINE_double(experimental_mempurge_threshold, 0.0,
"Maximum useful payload ratio estimate that triggers a mempurge "
"(memtable garbage collection).");
Add simple heuristics for experimental mempurge. (#8583) Summary: Add `experimental_mempurge_policy` option flag and introduce two new `MemPurge` (Memtable Garbage Collection) policies: 'ALWAYS' and 'ALTERNATE'. Default value: ALTERNATE. `ALWAYS`: every flush will first go through a `MemPurge` process. If the output is too big to fit into a single memtable, then the mempurge is aborted and a regular flush process carries on. `ALWAYS` is designed for user that need to reduce the number of L0 SST file created to a strict minimum, and can afford a small dent in performance (possibly hits to CPU usage, read efficiency, and maximum burst write throughput). `ALTERNATE`: a flush is transformed into a `MemPurge` except if one of the memtables being flushed is the product of a previous `MemPurge`. `ALTERNATE` is a good tradeoff between reduction in number of L0 SST files created and performance. `ALTERNATE` perform particularly well for completely random garbage ratios, or garbage ratios anywhere in (0%,50%], and even higher when there is a wild variability in garbage ratios. This PR also includes support for `experimental_mempurge_policy` in `db_bench`. Testing was done locally by replacing all the `MemPurge` policies of the unit tests with `ALTERNATE`, as well as local testing with `db_crashtest.py` `whitebox` and `blackbox`. Overall, if an `ALWAYS` mempurge policy passes the tests, there is no reasons why an `ALTERNATE` policy would fail, and therefore the mempurge policy was set to `ALWAYS` for all mempurge unit tests. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8583 Reviewed By: pdillinger Differential Revision: D29888050 Pulled By: bjlemaire fbshipit-source-id: e2cf26646d66679f6f5fb29842624615610759c1
2021-07-26 18:55:27 +00:00
DEFINE_bool(inplace_update_support,
ROCKSDB_NAMESPACE::Options().inplace_update_support,
"Support in-place memtable update for smaller or same-size values");
DEFINE_uint64(inplace_update_num_locks,
ROCKSDB_NAMESPACE::Options().inplace_update_num_locks,
"Number of RW locks to protect in-place memtable updates");
DEFINE_bool(enable_write_thread_adaptive_yield, true,
support for concurrent adds to memtable Summary: This diff adds support for concurrent adds to the skiplist memtable implementations. Memory allocation is made thread-safe by the addition of a spinlock, with small per-core buffers to avoid contention. Concurrent memtable writes are made via an additional method and don't impose a performance overhead on the non-concurrent case, so parallelism can be selected on a per-batch basis. Write thread synchronization is an increasing bottleneck for higher levels of concurrency, so this diff adds --enable_write_thread_adaptive_yield (default off). This feature causes threads joining a write batch group to spin for a short time (default 100 usec) using sched_yield, rather than going to sleep on a mutex. If the timing of the yield calls indicates that another thread has actually run during the yield then spinning is avoided. This option improves performance for concurrent situations even without parallel adds, although it has the potential to increase CPU usage (and the heuristic adaptation is not yet mature). Parallel writes are not currently compatible with inplace updates, update callbacks, or delete filtering. Enable it with --allow_concurrent_memtable_write (and --enable_write_thread_adaptive_yield). Parallel memtable writes are performance neutral when there is no actual parallelism, and in my experiments (SSD server-class Linux and varying contention and key sizes for fillrandom) they are always a performance win when there is more than one thread. Statistics are updated earlier in the write path, dropping the number of DB mutex acquisitions from 2 to 1 for almost all cases. This diff was motivated and inspired by Yahoo's cLSM work. It is more conservative than cLSM: RocksDB's write batch group leader role is preserved (along with all of the existing flush and write throttling logic) and concurrent writers are blocked until all memtable insertions have completed and the sequence number has been advanced, to preserve linearizability. My test config is "db_bench -benchmarks=fillrandom -threads=$T -batch_size=1 -memtablerep=skip_list -value_size=100 --num=1000000/$T -level0_slowdown_writes_trigger=9999 -level0_stop_writes_trigger=9999 -disable_auto_compactions --max_write_buffer_number=8 -max_background_flushes=8 --disable_wal --write_buffer_size=160000000 --block_size=16384 --allow_concurrent_memtable_write" on a two-socket Xeon E5-2660 @ 2.2Ghz with lots of memory and an SSD hard drive. With 1 thread I get ~440Kops/sec. Peak performance for 1 socket (numactl -N1) is slightly more than 1Mops/sec, at 16 threads. Peak performance across both sockets happens at 30 threads, and is ~900Kops/sec, although with fewer threads there is less performance loss when the system has background work. Test Plan: 1. concurrent stress tests for InlineSkipList and DynamicBloom 2. make clean; make check 3. make clean; DISABLE_JEMALLOC=1 make valgrind_check; valgrind db_bench 4. make clean; COMPILE_WITH_TSAN=1 make all check; db_bench 5. make clean; COMPILE_WITH_ASAN=1 make all check; db_bench 6. make clean; OPT=-DROCKSDB_LITE make check 7. verify no perf regressions when disabled Reviewers: igor, sdong Reviewed By: sdong Subscribers: MarkCallaghan, IslamAbdelRahman, anthony, yhchiang, rven, sdong, guyg8, kradhakrishnan, dhruba Differential Revision: https://reviews.facebook.net/D50589
2015-08-14 23:59:07 +00:00
"Use a yielding spin loop for brief writer thread waits.");
DEFINE_uint64(
write_thread_max_yield_usec, 100,
"Maximum microseconds for enable_write_thread_adaptive_yield operation.");
DEFINE_uint64(write_thread_slow_yield_usec, 3,
"The threshold at which a slow yield is considered a signal that "
"other processes or threads want the core.");
DEFINE_uint64(rate_limiter_bytes_per_sec, 0, "Set options.rate_limiter value.");
Simplify GenericRateLimiter algorithm (#8602) Summary: `GenericRateLimiter` slow path handles requests that cannot be satisfied immediately. Such requests enter a queue, and their thread stays in `Request()` until they are granted or the rate limiter is stopped. These threads are responsible for unblocking themselves. The work to do so is split into two main duties. (1) Waiting for the next refill time. (2) Refilling the bytes and granting requests. Prior to this PR, the slow path logic involved a leader election algorithm to pick one thread to perform (1) followed by (2). It elected the thread whose request was at the front of the highest priority non-empty queue since that request was most likely to be granted. This algorithm was efficient in terms of reducing intermediate wakeups, which is a thread waking up only to resume waiting after finding its request is not granted. However, the conceptual complexity of this algorithm was too high. It took me a long time to draw a timeline to understand how it works for just one edge case yet there were so many. This PR drops the leader election to reduce conceptual complexity. Now, the two duties can be performed by whichever thread acquires the lock first. The risk of this change is increasing the number of intermediate wakeups, however, we took steps to mitigate that. - `wait_until_refill_pending_` flag ensures only one thread performs (1). This\ prevents the thundering herd problem at the next refill time. The remaining\ threads wait on their condition variable with an unbounded duration -- thus we\ must remember to notify them to ensure forward progress. - (1) is typically done by a thread at the front of a queue. This is trivial\ when the queues are initially empty as the first choice that arrives must be\ the only entry in its queue. When queues are initially non-empty, we achieve\ this by having (2) notify a thread at the front of a queue (preferring higher\ priority) to perform the next duty. - We do not require any additional wakeup for (2). Typically it will just be\ done by the thread that finished (1). Combined, the second and third bullet points above suggest the refill/granting will typically be done by a request at the front of its queue. This is important because one wakeup is saved when a granted request happens to be in an already running thread. Note there are a few cases that still lead to intermediate wakeup, however. The first two are existing issues that also apply to the old algorithm, however, the third (including both subpoints) is new. - No request may be granted (only possible when rate limit dynamically\ decreases). - Requests from a different queue may be granted. - (2) may be run by a non-front request thread causing it to not be granted even\ if some requests in that same queue are granted. It can happen for a couple\ (unlikely) reasons. - A new request may sneak in and grab the lock at the refill time, before the\ thread finishing (1) can wake up and grab it. - A new request may sneak in and grab the lock and execute (1) before (2)'s\ chosen candidate can wake up and grab the lock. Then that non-front request\ thread performing (1) can carry over to perform (2). Pull Request resolved: https://github.com/facebook/rocksdb/pull/8602 Test Plan: - Use existing tests. The edge cases listed in the comment are all performance\ related; I could not really think of any related to correctness. The logic\ looks the same whether a thread wakes up/finishes its work early/on-time/late,\ or whether the thread is chosen vs. "steals" the work. - Verified write throughput and CPU overhead are basically the same with and\ without this change, even in a rate limiter heavy workload: Test command: ``` $ rm -rf /dev/shm/dbbench/ && TEST_TMPDIR=/dev/shm /usr/bin/time ./db_bench -benchmarks=fillrandom -num_multi_db=64 -num_low_pri_threads=64 -num_high_pri_threads=64 -write_buffer_size=262144 -target_file_size_base=262144 -max_bytes_for_level_base=1048576 -rate_limiter_bytes_per_sec=16777216 -key_size=24 -value_size=1000 -num=10000 -compression_type=none -rate_limiter_refill_period_us=1000 ``` Results before this PR: ``` fillrandom : 108.463 micros/op 9219 ops/sec; 9.0 MB/s 7.40user 8.84system 1:26.20elapsed 18%CPU (0avgtext+0avgdata 256140maxresident)k ``` Results after this PR: ``` fillrandom : 108.108 micros/op 9250 ops/sec; 9.0 MB/s 7.45user 8.23system 1:26.68elapsed 18%CPU (0avgtext+0avgdata 255688maxresident)k ``` Reviewed By: hx235 Differential Revision: D30048013 Pulled By: ajkr fbshipit-source-id: 6741bba9d9dfbccab359806d725105817fef818b
2021-08-09 23:46:14 +00:00
DEFINE_int64(rate_limiter_refill_period_us, 100 * 1000,
"Set refill period on rate limiter.");
Simplify GenericRateLimiter algorithm (#8602) Summary: `GenericRateLimiter` slow path handles requests that cannot be satisfied immediately. Such requests enter a queue, and their thread stays in `Request()` until they are granted or the rate limiter is stopped. These threads are responsible for unblocking themselves. The work to do so is split into two main duties. (1) Waiting for the next refill time. (2) Refilling the bytes and granting requests. Prior to this PR, the slow path logic involved a leader election algorithm to pick one thread to perform (1) followed by (2). It elected the thread whose request was at the front of the highest priority non-empty queue since that request was most likely to be granted. This algorithm was efficient in terms of reducing intermediate wakeups, which is a thread waking up only to resume waiting after finding its request is not granted. However, the conceptual complexity of this algorithm was too high. It took me a long time to draw a timeline to understand how it works for just one edge case yet there were so many. This PR drops the leader election to reduce conceptual complexity. Now, the two duties can be performed by whichever thread acquires the lock first. The risk of this change is increasing the number of intermediate wakeups, however, we took steps to mitigate that. - `wait_until_refill_pending_` flag ensures only one thread performs (1). This\ prevents the thundering herd problem at the next refill time. The remaining\ threads wait on their condition variable with an unbounded duration -- thus we\ must remember to notify them to ensure forward progress. - (1) is typically done by a thread at the front of a queue. This is trivial\ when the queues are initially empty as the first choice that arrives must be\ the only entry in its queue. When queues are initially non-empty, we achieve\ this by having (2) notify a thread at the front of a queue (preferring higher\ priority) to perform the next duty. - We do not require any additional wakeup for (2). Typically it will just be\ done by the thread that finished (1). Combined, the second and third bullet points above suggest the refill/granting will typically be done by a request at the front of its queue. This is important because one wakeup is saved when a granted request happens to be in an already running thread. Note there are a few cases that still lead to intermediate wakeup, however. The first two are existing issues that also apply to the old algorithm, however, the third (including both subpoints) is new. - No request may be granted (only possible when rate limit dynamically\ decreases). - Requests from a different queue may be granted. - (2) may be run by a non-front request thread causing it to not be granted even\ if some requests in that same queue are granted. It can happen for a couple\ (unlikely) reasons. - A new request may sneak in and grab the lock at the refill time, before the\ thread finishing (1) can wake up and grab it. - A new request may sneak in and grab the lock and execute (1) before (2)'s\ chosen candidate can wake up and grab the lock. Then that non-front request\ thread performing (1) can carry over to perform (2). Pull Request resolved: https://github.com/facebook/rocksdb/pull/8602 Test Plan: - Use existing tests. The edge cases listed in the comment are all performance\ related; I could not really think of any related to correctness. The logic\ looks the same whether a thread wakes up/finishes its work early/on-time/late,\ or whether the thread is chosen vs. "steals" the work. - Verified write throughput and CPU overhead are basically the same with and\ without this change, even in a rate limiter heavy workload: Test command: ``` $ rm -rf /dev/shm/dbbench/ && TEST_TMPDIR=/dev/shm /usr/bin/time ./db_bench -benchmarks=fillrandom -num_multi_db=64 -num_low_pri_threads=64 -num_high_pri_threads=64 -write_buffer_size=262144 -target_file_size_base=262144 -max_bytes_for_level_base=1048576 -rate_limiter_bytes_per_sec=16777216 -key_size=24 -value_size=1000 -num=10000 -compression_type=none -rate_limiter_refill_period_us=1000 ``` Results before this PR: ``` fillrandom : 108.463 micros/op 9219 ops/sec; 9.0 MB/s 7.40user 8.84system 1:26.20elapsed 18%CPU (0avgtext+0avgdata 256140maxresident)k ``` Results after this PR: ``` fillrandom : 108.108 micros/op 9250 ops/sec; 9.0 MB/s 7.45user 8.23system 1:26.68elapsed 18%CPU (0avgtext+0avgdata 255688maxresident)k ``` Reviewed By: hx235 Differential Revision: D30048013 Pulled By: ajkr fbshipit-source-id: 6741bba9d9dfbccab359806d725105817fef818b
2021-08-09 23:46:14 +00:00
DEFINE_bool(rate_limiter_auto_tuned, false,
"Enable dynamic adjustment of rate limit according to demand for "
"background I/O");
Decouple `RateLimiter` burst size and refill period (#12379) Summary: When the rate limiter does not have any waiting requests, the first request to arrive may consume all of the available bandwidth, despite potentially having lower priority than requests that arrive later in the same refill interval. Then, those higher priority requests must wait for a refill. So even in scenarios in which we have an overall bandwidth surplus, the highest priority requests can be sporadically delayed up to a whole refill period. Alone, this isn't necessarily problematic as the refill period is configurable via `refill_period_us` and can be tuned down as needed until the max sporadic delay is tolerable. However, tuning down `refill_period_us` had a side effect of reducing burst size. Some users require a certain burst size to issue optimal I/O sizes to the underlying storage system. To satisfy those users, this PR decouples the refill period from the burst size. That way, the max sporadic delay can be limited without impacting I/O sizes issued to the underlying storage system. Pull Request resolved: https://github.com/facebook/rocksdb/pull/12379 Test Plan: The goal is to show we can now limit the max sporadic delay without impacting compaction's I/O size. The benchmark runs compaction with a large I/O size, while user reads simultaneously run at a low rate that does not consume all of the available bandwidth. The max sporadic delay is measured using the P100 of rocksdb.file.read.get.micros. I just used strace to verify the compaction reads follow `rate_limiter_single_burst_bytes` Setup: `./db_bench -benchmarks=fillrandom,flush -write_buffer_size=67108864 -disable_auto_compactions=true -value_size=256 -num=1048576` Benchmark: `./db_bench -benchmarks=readrandom -use_existing_db=true -num=1048576 -duration=10 -benchmark_read_rate_limit=4096 -rate_limiter_bytes_per_sec=67108864 -rate_limiter_refill_period_us=$refill_micros -rate_limiter_single_burst_bytes=16777216 -rate_limit_bg_reads=true -rate_limit_user_ops=true -statistics=true -cache_size=0 -stats_level=5 -compaction_readahead_size=16777216 -use_direct_reads=true` Results: refill_micros | rocksdb.file.read.get.micros (P100) -- | -- 10000 | 10802 100000 | 100240 1000000 | 922061 For verifying compaction read sizes: `strace -fye pread64 ./db_bench -benchmarks=compact -use_existing_db=true -rate_limiter_bytes_per_sec=67108864 -rate_limiter_refill_period_us=$refill_micros -rate_limiter_single_burst_bytes=16777216 -rate_limit_bg_reads=true -compaction_readahead_size=16777216 -use_direct_reads=true` Reviewed By: hx235 Differential Revision: D54165675 Pulled By: ajkr fbshipit-source-id: c5968486316cbfb7ff8e5b7d75d3589883dd1105
2024-02-27 00:55:13 +00:00
DEFINE_int64(rate_limiter_single_burst_bytes, 0,
"Set single burst bytes on background I/O rate limiter.");
DEFINE_bool(sine_write_rate, false, "Use a sine wave write_rate_limit");
DEFINE_uint64(
sine_write_rate_interval_milliseconds, 10000,
"Interval of which the sine wave write_rate_limit is recalculated");
DEFINE_double(sine_a, 1, "A in f(x) = A sin(bx + c) + d");
DEFINE_double(sine_b, 1, "B in f(x) = A sin(bx + c) + d");
DEFINE_double(sine_c, 0, "C in f(x) = A sin(bx + c) + d");
DEFINE_double(sine_d, 1, "D in f(x) = A sin(bx + c) + d");
DEFINE_bool(rate_limit_bg_reads, false,
"Use options.rate_limiter on compaction reads");
DEFINE_uint64(
benchmark_write_rate_limit, 0,
"If non-zero, db_bench will rate-limit the writes going into RocksDB. This "
"is the global rate in bytes/second.");
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 18:40:44 +00:00
// the parameters of mix_graph
Workload generator (Mixgraph) based on prefix hotness (#5953) Summary: In the previous PR https://github.com/facebook/rocksdb/issues/4788, user can use db_bench mix_graph option to generate the workload that is from the social graph. The key is generated based on the key access hotness. In this PR, user can further model the key-range hotness and fit those to two-term-exponential distribution. First, user cuts the whole key space into small key ranges (e.g., key-ranges are the same size and the key-range number is the number of SST files). Then, user calculates the average access count per key of each key-range as the key-range hotness. Next, user fits the key-range hotness to two-term-exponential distribution (f(x) = f(x) = a*exp(b*x) + c*exp(d*x)) and generate the value of a, b, c, and d. They are the parameters in db_bench: prefix_dist_a, prefix_dist_b, prefix_dist_c, and prefix_dist_d. Finally, user can run db_bench by specify the parameters. For example: `./db_bench --benchmarks="mixgraph" -use_direct_io_for_flush_and_compaction=true -use_direct_reads=true -cache_size=268435456 -key_dist_a=0.002312 -key_dist_b=0.3467 -keyrange_dist_a=14.18 -keyrange_dist_b=-2.917 -keyrange_dist_c=0.0164 -keyrange_dist_d=-0.08082 -keyrange_num=30 -value_k=0.2615 -value_sigma=25.45 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.85 -mix_put_ratio=0.14 -mix_seek_ratio=0.01 -sine_mix_rate_interval_milliseconds=5000 -sine_a=350 -sine_b=0.0105 -sine_d=50000 --perf_level=2 -reads=1000000 -num=5000000 -key_size=48` Pull Request resolved: https://github.com/facebook/rocksdb/pull/5953 Test Plan: run db_bench with different parameters and checked the results. Differential Revision: D18053527 Pulled By: zhichao-cao fbshipit-source-id: 171f8b3142bd76462f1967c58345ad7e4f84bab7
2019-11-06 20:50:33 +00:00
DEFINE_double(keyrange_dist_a, 0.0,
"The parameter 'a' of prefix average access distribution "
"f(x)=a*exp(b*x)+c*exp(d*x)");
DEFINE_double(keyrange_dist_b, 0.0,
"The parameter 'b' of prefix average access distribution "
"f(x)=a*exp(b*x)+c*exp(d*x)");
DEFINE_double(keyrange_dist_c, 0.0,
"The parameter 'c' of prefix average access distribution"
"f(x)=a*exp(b*x)+c*exp(d*x)");
DEFINE_double(keyrange_dist_d, 0.0,
"The parameter 'd' of prefix average access distribution"
"f(x)=a*exp(b*x)+c*exp(d*x)");
DEFINE_int64(keyrange_num, 1,
"The number of key ranges that are in the same prefix "
"group, each prefix range will have its key access distribution");
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 18:40:44 +00:00
DEFINE_double(key_dist_a, 0.0,
"The parameter 'a' of key access distribution model f(x)=a*x^b");
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 18:40:44 +00:00
DEFINE_double(key_dist_b, 0.0,
"The parameter 'b' of key access distribution model f(x)=a*x^b");
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 18:40:44 +00:00
DEFINE_double(value_theta, 0.0,
"The parameter 'theta' of Generized Pareto Distribution "
"f(x)=(1/sigma)*(1+k*(x-theta)/sigma)^-(1/k+1)");
2022-03-22 00:30:51 +00:00
// Use reasonable defaults based on the mixgraph paper
DEFINE_double(value_k, 0.2615,
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 18:40:44 +00:00
"The parameter 'k' of Generized Pareto Distribution "
"f(x)=(1/sigma)*(1+k*(x-theta)/sigma)^-(1/k+1)");
2022-03-22 00:30:51 +00:00
// Use reasonable defaults based on the mixgraph paper
DEFINE_double(value_sigma, 25.45,
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 18:40:44 +00:00
"The parameter 'theta' of Generized Pareto Distribution "
"f(x)=(1/sigma)*(1+k*(x-theta)/sigma)^-(1/k+1)");
DEFINE_double(iter_theta, 0.0,
"The parameter 'theta' of Generized Pareto Distribution "
"f(x)=(1/sigma)*(1+k*(x-theta)/sigma)^-(1/k+1)");
2022-03-22 00:30:51 +00:00
// Use reasonable defaults based on the mixgraph paper
DEFINE_double(iter_k, 2.517,
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 18:40:44 +00:00
"The parameter 'k' of Generized Pareto Distribution "
"f(x)=(1/sigma)*(1+k*(x-theta)/sigma)^-(1/k+1)");
2022-03-22 00:30:51 +00:00
// Use reasonable defaults based on the mixgraph paper
DEFINE_double(iter_sigma, 14.236,
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 18:40:44 +00:00
"The parameter 'sigma' of Generized Pareto Distribution "
"f(x)=(1/sigma)*(1+k*(x-theta)/sigma)^-(1/k+1)");
DEFINE_double(mix_get_ratio, 1.0,
"The ratio of Get queries of mix_graph workload");
DEFINE_double(mix_put_ratio, 0.0,
"The ratio of Put queries of mix_graph workload");
DEFINE_double(mix_seek_ratio, 0.0,
"The ratio of Seek queries of mix_graph workload");
DEFINE_int64(mix_max_scan_len, 10000, "The max scan length of Iterator");
DEFINE_int64(mix_max_value_size, 1024, "The max value size of this workload");
DEFINE_double(
sine_mix_rate_noise, 0.0,
"Add the noise ratio to the sine rate, it is between 0.0 and 1.0");
DEFINE_bool(sine_mix_rate, false,
"Enable the sine QPS control on the mix workload");
DEFINE_uint64(
sine_mix_rate_interval_milliseconds, 10000,
"Interval of which the sine wave read_rate_limit is recalculated");
DEFINE_int64(mix_accesses, -1,
"The total query accesses of mix_graph workload");
DEFINE_uint64(
benchmark_read_rate_limit, 0,
"If non-zero, db_bench will rate-limit the reads from RocksDB. This "
"is the global rate in ops/second.");
DEFINE_uint64(max_compaction_bytes,
ROCKSDB_NAMESPACE::Options().max_compaction_bytes,
"Max bytes allowed in one compaction");
DEFINE_bool(readonly, false, "Run read only benchmarks.");
DEFINE_bool(print_malloc_stats, false,
"Print malloc stats to stdout after benchmarks finish.");
DEFINE_bool(disable_auto_compactions, false, "Do not auto trigger compactions");
DEFINE_uint64(wal_ttl_seconds, 0, "Set the TTL for the WAL Files in seconds.");
DEFINE_uint64(wal_size_limit_MB, 0,
"Set the size limit for the WAL Files in MB.");
DEFINE_uint64(max_total_wal_size, 0, "Set total max WAL size");
DEFINE_bool(mmap_read, ROCKSDB_NAMESPACE::Options().allow_mmap_reads,
"Allow reads to occur via mmap-ing files");
DEFINE_bool(mmap_write, ROCKSDB_NAMESPACE::Options().allow_mmap_writes,
"Allow writes to occur via mmap-ing files");
DEFINE_bool(use_direct_reads, ROCKSDB_NAMESPACE::Options().use_direct_reads,
"Use O_DIRECT for reading data");
DEFINE_bool(use_direct_io_for_flush_and_compaction,
ROCKSDB_NAMESPACE::Options().use_direct_io_for_flush_and_compaction,
"Use O_DIRECT for background flush and compaction writes");
DEFINE_bool(advise_random_on_open,
ROCKSDB_NAMESPACE::Options().advise_random_on_open,
"Advise random access on table file open");
DEFINE_bool(use_tailing_iterator, false,
"Use tailing iterator to access a series of keys instead of get");
DEFINE_bool(use_adaptive_mutex, ROCKSDB_NAMESPACE::Options().use_adaptive_mutex,
"Use adaptive mutex");
DEFINE_uint64(bytes_per_sync, ROCKSDB_NAMESPACE::Options().bytes_per_sync,
"Allows OS to incrementally sync SST files to disk while they are"
" being written, in the background. Issue one request for every"
" bytes_per_sync written. 0 turns it off.");
DEFINE_uint64(wal_bytes_per_sync,
ROCKSDB_NAMESPACE::Options().wal_bytes_per_sync,
"Allows OS to incrementally sync WAL files to disk while they are"
" being written, in the background. Issue one request for every"
" wal_bytes_per_sync written. 0 turns it off.");
Support for SingleDelete() Summary: This patch fixes #7460559. It introduces SingleDelete as a new database operation. This operation can be used to delete keys that were never overwritten (no put following another put of the same key). If an overwritten key is single deleted the behavior is undefined. Single deletion of a non-existent key has no effect but multiple consecutive single deletions are not allowed (see limitations). In contrast to the conventional Delete() operation, the deletion entry is removed along with the value when the two are lined up in a compaction. Note: The semantics are similar to @igor's prototype that allowed to have this behavior on the granularity of a column family ( https://reviews.facebook.net/D42093 ). This new patch, however, is more aggressive when it comes to removing tombstones: It removes the SingleDelete together with the value whenever there is no snapshot between them while the older patch only did this when the sequence number of the deletion was older than the earliest snapshot. Most of the complex additions are in the Compaction Iterator, all other changes should be relatively straightforward. The patch also includes basic support for single deletions in db_stress and db_bench. Limitations: - Not compatible with cuckoo hash tables - Single deletions cannot be used in combination with merges and normal deletions on the same key (other keys are not affected by this) - Consecutive single deletions are currently not allowed (and older version of this patch supported this so it could be resurrected if needed) Test Plan: make all check Reviewers: yhchiang, sdong, rven, anthony, yoshinorim, igor Reviewed By: igor Subscribers: maykov, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D43179
2015-09-17 18:42:56 +00:00
DEFINE_bool(use_single_deletes, true,
"Use single deletes (used in RandomReplaceKeys only).");
DEFINE_double(stddev, 2000.0,
"Standard deviation of normal distribution used for picking keys"
" (used in RandomReplaceKeys only).");
Write a benchmark to emulate time series data Summary: Add a benchmark to `db_bench`. In this benchmark, a write thread will populate time series data in the format of 'id | timestamp', and multiple read threads will randomly retrieve all data from one id at a time. Test Plan: Run the benchmark: `num=134217728;bpl=536870912;mb=67108864;overlap=10;mcz=2;del=300000000;levels=6;ctrig=4;delay=8;stop=12;wbn=3;mbc=20;wbs=134217728;dds=0;sync=0;t=32;vs=800;bs=4096;cs=17179869184;of=500000;wps=0;si=10000000; kir=100000; dir=/data/users/jhli/test/; ./db_bench --benchmarks=timeseries --disable_seek_compaction=1 --mmap_read=0 --statistics=1 --histogram=1 --num=$num --threads=$t --value_size=$vs --block_size=$bs --cache_size=$cs --bloom_bits=10 --cache_numshardbits=6 --open_files=$of --verify_checksum=1 --db=$dir --sync=$sync --disable_wal=0 --compression_type=none --stats_interval=$si --compression_ratio=1 --disable_data_sync=$dds --write_buffer_size=$wbs --target_file_size_base=$mb --max_write_buffer_number=$wbn --max_background_compactions=$mbc --level0_file_num_compaction_trigger=$ctrig --level0_slowdown_writes_trigger=$delay --level0_stop_writes_trigger=$stop --num_levels=$levels --delete_obsolete_files_period_micros=$del --min_level_to_compress=$mcz --max_grandparent_overlap_factor=$overlap --stats_per_interval=1 --max_bytes_for_level_base=$bpl --use_existing_db=0 --key_id_range=$kir` Reviewers: andrewkr, sdong Reviewed By: sdong Subscribers: lgalanis, andrewkr, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D60651
2016-08-01 19:28:51 +00:00
DEFINE_int32(key_id_range, 100000,
"Range of possible value of key id (used in TimeSeries only).");
DEFINE_string(expire_style, "none",
"Style to remove expired time entries. Can be one of the options "
"below: none (do not expired data), compaction_filter (use a "
"compaction filter to remove expired data), delete (seek IDs and "
"remove expired data) (used in TimeSeries only).");
DEFINE_uint64(
time_range, 100000,
"Range of timestamp that store in the database (used in TimeSeries"
" only).");
DEFINE_int32(num_deletion_threads, 1,
"Number of threads to do deletion (used in TimeSeries and delete "
"expire_style only).");
DEFINE_int32(max_successive_merges, 0,
"Maximum number of successive merge operations on a key in the "
"memtable");
DEFINE_bool(strict_max_successive_merges, false,
"Whether to issue filesystem reads to keep within "
"`max_successive_merges` limit");
static bool ValidatePrefixSize(const char* flagname, int32_t value) {
if (value < 0 || value >= 2000000000) {
fprintf(stderr, "Invalid value for --%s: %d. 0<= PrefixSize <=2000000000\n",
flagname, value);
return false;
}
return true;
}
DEFINE_int32(prefix_size, 0,
"control the prefix size for HashSkipList and plain table");
DEFINE_int64(keys_per_prefix, 0,
"control average number of keys generated per prefix, 0 means no "
"special handling of the prefix, i.e. use the prefix comes with "
"the generated random number.");
DEFINE_bool(total_order_seek, false,
"Enable total order seek regardless of index format.");
DEFINE_bool(prefix_same_as_start, false,
"Enforce iterator to return keys with prefix same as seek key.");
DEFINE_bool(
seek_missing_prefix, false,
"Iterator seek to keys with non-exist prefixes. Require prefix_size > 8");
DEFINE_int32(memtable_insert_with_hint_prefix_size, 0,
"If non-zero, enable "
"memtable insert with hint with the given prefix size.");
DEFINE_bool(enable_io_prio, false,
"Lower the background flush/compaction threads' IO priority");
DEFINE_bool(enable_cpu_prio, false,
"Lower the background flush/compaction threads' CPU priority");
DEFINE_bool(identity_as_first_hash, false,
"the first hash function of cuckoo table becomes an identity "
"function. This is only valid when key is 8 bytes");
DEFINE_bool(dump_malloc_stats, true, "Dump malloc stats in LOG ");
DEFINE_uint64(stats_dump_period_sec,
ROCKSDB_NAMESPACE::Options().stats_dump_period_sec,
"Gap between printing stats to log in seconds");
DEFINE_uint64(stats_persist_period_sec,
ROCKSDB_NAMESPACE::Options().stats_persist_period_sec,
"Gap between persisting stats in seconds");
DEFINE_bool(persist_stats_to_disk,
ROCKSDB_NAMESPACE::Options().persist_stats_to_disk,
"whether to persist stats to disk");
DEFINE_uint64(stats_history_buffer_size,
ROCKSDB_NAMESPACE::Options().stats_history_buffer_size,
"Max number of stats snapshots to keep in memory");
DEFINE_bool(avoid_flush_during_recovery,
ROCKSDB_NAMESPACE::Options().avoid_flush_during_recovery,
"If true, avoids flushing the recovered WAL data where possible.");
Introduce a new MultiGet batching implementation (#5011) Summary: This PR introduces a new MultiGet() API, with the underlying implementation grouping keys based on SST file and batching lookups in a file. The reason for the new API is twofold - the definition allows callers to allocate storage for status and values on stack instead of std::vector, as well as return values as PinnableSlices in order to avoid copying, and it keeps the original MultiGet() implementation intact while we experiment with batching. Batching is useful when there is some spatial locality to the keys being queries, as well as larger batch sizes. The main benefits are due to - 1. Fewer function calls, especially to BlockBasedTableReader::MultiGet() and FullFilterBlockReader::KeysMayMatch() 2. Bloom filter cachelines can be prefetched, hiding the cache miss latency The next step is to optimize the binary searches in the level_storage_info, index blocks and data blocks, since we could reduce the number of key comparisons if the keys are relatively close to each other. The batching optimizations also need to be extended to other formats, such as PlainTable and filter formats. This also needs to be added to db_stress. Benchmark results from db_bench for various batch size/locality of reference combinations are given below. Locality was simulated by offsetting the keys in a batch by a stride length. Each SST file is about 8.6MB uncompressed and key/value size is 16/100 uncompressed. To focus on the cpu benefit of batching, the runs were single threaded and bound to the same cpu to eliminate interference from other system events. The results show a 10-25% improvement in micros/op from smaller to larger batch sizes (4 - 32). Batch Sizes 1 | 2 | 4 | 8 | 16 | 32 Random pattern (Stride length 0) 4.158 | 4.109 | 4.026 | 4.05 | 4.1 | 4.074 - Get 4.438 | 4.302 | 4.165 | 4.122 | 4.096 | 4.075 - MultiGet (no batching) 4.461 | 4.256 | 4.277 | 4.11 | 4.182 | 4.14 - MultiGet (w/ batching) Good locality (Stride length 16) 4.048 | 3.659 | 3.248 | 2.99 | 2.84 | 2.753 4.429 | 3.728 | 3.406 | 3.053 | 2.911 | 2.781 4.452 | 3.45 | 2.833 | 2.451 | 2.233 | 2.135 Good locality (Stride length 256) 4.066 | 3.786 | 3.581 | 3.447 | 3.415 | 3.232 4.406 | 4.005 | 3.644 | 3.49 | 3.381 | 3.268 4.393 | 3.649 | 3.186 | 2.882 | 2.676 | 2.62 Medium locality (Stride length 4096) 4.012 | 3.922 | 3.768 | 3.61 | 3.582 | 3.555 4.364 | 4.057 | 3.791 | 3.65 | 3.57 | 3.465 4.479 | 3.758 | 3.316 | 3.077 | 2.959 | 2.891 dbbench command used (on a DB with 4 levels, 12 million keys)- TEST_TMPDIR=/dev/shm numactl -C 10 ./db_bench.tmp -use_existing_db=true -benchmarks="readseq,multireadrandom" -write_buffer_size=4194304 -target_file_size_base=4194304 -max_bytes_for_level_base=16777216 -num=12000000 -reads=12000000 -duration=90 -threads=1 -compression_type=none -cache_size=4194304000 -batch_size=32 -disable_auto_compactions=true -bloom_bits=10 -cache_index_and_filter_blocks=true -pin_l0_filter_and_index_blocks_in_cache=true -multiread_batched=true -multiread_stride=4 Pull Request resolved: https://github.com/facebook/rocksdb/pull/5011 Differential Revision: D14348703 Pulled By: anand1976 fbshipit-source-id: 774406dab3776d979c809522a67bedac6c17f84b
2019-04-11 21:24:09 +00:00
DEFINE_int64(multiread_stride, 0,
"Stride length for the keys in a MultiGet batch");
DEFINE_bool(multiread_batched, false, "Use the new MultiGet API");
DEFINE_string(memtablerep, "skip_list", "");
DEFINE_int64(hash_bucket_count, 1024 * 1024, "hash bucket count");
DEFINE_bool(use_plain_table, false,
"if use plain table instead of block-based table format");
DEFINE_bool(use_cuckoo_table, false, "if use cuckoo table format");
DEFINE_double(cuckoo_hash_ratio, 0.9, "Hash ratio for Cuckoo SST table.");
DEFINE_bool(use_hash_search, false,
"if use kHashSearch instead of kBinarySearch. "
"This is valid if only we use BlockTable");
DEFINE_string(merge_operator, "",
"The merge operator to use with the database."
"If a new merge operator is specified, be sure to use fresh"
" database The possible merge operators are defined in"
" utilities/merge_operators.h");
DEFINE_int32(skip_list_lookahead, 0,
"Used with skip_list memtablerep; try linear search first for "
"this many steps from the previous position");
DEFINE_bool(report_file_operations, false,
"if report number of file operations");
Add -report_open_timing to db_bench (#8464) Summary: Hello and thanks for RocksDB, This PR adds support for ```-report_open_timing true``` to ```db_bench```. It can be useful when tuning RocksDB on filesystem/env with high latencies for file level operations (create/delete/rename...) seen during ```((Optimistic)Transaction)DB::Open```. Some examples: ``` > db_bench -benchmarks updaterandom -num 1 -db /dev/shm/db_bench > db_bench -benchmarks updaterandom -num 0 -db /dev/shm/db_bench -use_existing_db true -report_open_timing true -readonly true 2>&1 | grep OpenDb OpenDb: 3.90133 milliseconds > db_bench -benchmarks updaterandom -num 0 -db /dev/shm/db_bench -use_existing_db true -report_open_timing true -use_secondary_db true 2>&1 | grep OpenDb OpenDb: 3.33414 milliseconds > db_bench -benchmarks updaterandom -num 0 -db /dev/shm/db_bench -use_existing_db true -report_open_timing true 2>&1 | grep -A1 OpenDb OpenDb: 6.05423 milliseconds > db_bench -benchmarks updaterandom -num 1 > db_bench -benchmarks updaterandom -num 0 -use_existing_db true -report_open_timing true -readonly true 2>&1 | grep OpenDb OpenDb: 4.06859 milliseconds > db_bench -benchmarks updaterandom -num 0 -use_existing_db true -report_open_timing true -use_secondary_db true 2>&1 | grep OpenDb OpenDb: 2.85794 milliseconds > db_bench -benchmarks updaterandom -num 0 -use_existing_db true -report_open_timing true 2>&1 | grep OpenDb OpenDb: 6.46376 milliseconds > db_bench -benchmarks updaterandom -num 1 -db /clustered_fs/db_bench > db_bench -benchmarks updaterandom -num 0 -db /clustered_fs/db_bench -use_existing_db true -report_open_timing true -readonly true 2>&1 | grep OpenDb OpenDb: 3.79805 milliseconds > db_bench -benchmarks updaterandom -num 0 -db /clustered_fs/db_bench -use_existing_db true -report_open_timing true -use_secondary_db true 2>&1 | grep OpenDb OpenDb: 3.00174 milliseconds > db_bench -benchmarks updaterandom -num 0 -db /clustered_fs/db_bench -use_existing_db true -report_open_timing true 2>&1 | grep OpenDb OpenDb: 24.8732 milliseconds ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/8464 Reviewed By: hx235 Differential Revision: D29398096 Pulled By: zhichao-cao fbshipit-source-id: 8f05dc3284f084612a3f30234e39e1c37548f50c
2021-07-02 01:41:20 +00:00
DEFINE_bool(report_open_timing, false, "if report open timing");
DEFINE_int32(readahead_size, 0, "Iterator readahead size");
DEFINE_bool(read_with_latest_user_timestamp, true,
"If true, always use the current latest timestamp for read. If "
"false, choose a random timestamp from the past.");
DEFINE_string(secondary_cache_uri, "",
"Full URI for creating a custom secondary cache object");
static class std::shared_ptr<ROCKSDB_NAMESPACE::SecondaryCache> secondary_cache;
static const bool FLAGS_prefix_size_dummy __attribute__((__unused__)) =
RegisterFlagValidator(&FLAGS_prefix_size, &ValidatePrefixSize);
static const bool FLAGS_key_size_dummy __attribute__((__unused__)) =
RegisterFlagValidator(&FLAGS_key_size, &ValidateKeySize);
static const bool FLAGS_cache_numshardbits_dummy __attribute__((__unused__)) =
RegisterFlagValidator(&FLAGS_cache_numshardbits,
&ValidateCacheNumshardbits);
static const bool FLAGS_readwritepercent_dummy __attribute__((__unused__)) =
RegisterFlagValidator(&FLAGS_readwritepercent, &ValidateInt32Percent);
DEFINE_int32(disable_seek_compaction, false,
"Not used, left here for backwards compatibility");
DEFINE_bool(allow_data_in_errors,
ROCKSDB_NAMESPACE::Options().allow_data_in_errors,
"If true, allow logging data, e.g. key, value in LOG files.");
static const bool FLAGS_deletepercent_dummy __attribute__((__unused__)) =
RegisterFlagValidator(&FLAGS_deletepercent, &ValidateInt32Percent);
static const bool FLAGS_table_cache_numshardbits_dummy
__attribute__((__unused__)) = RegisterFlagValidator(
&FLAGS_table_cache_numshardbits, &ValidateTableCacheNumshardbits);
DEFINE_uint32(write_batch_protection_bytes_per_key, 0,
"Size of per-key-value checksum in each write batch. Currently "
"only value 0 and 8 are supported.");
Add memtable per key-value checksum (#10281) Summary: Append per key-value checksum to internal key. These checksums are verified on read paths including Get, Iterator and during Flush. Get and Iterator will return `Corruption` status if there is a checksum verification failure. Flush will make DB become read-only upon memtable entry checksum verification failure. Pull Request resolved: https://github.com/facebook/rocksdb/pull/10281 Test Plan: - Added new unit test cases: `make check` - Benchmark on memtable insert ``` TEST_TMPDIR=/dev/shm/memtable_write ./db_bench -benchmarks=fillseq -disable_wal=true -max_write_buffer_number=100 -num=10000000 -min_write_buffer_number_to_merge=100 # avg over 10 runs Baseline: 1166936 ops/sec memtable 2 bytes kv checksum : 1.11674e+06 ops/sec (-4%) memtable 2 bytes kv checksum + write batch 8 bytes kv checksum: 1.08579e+06 ops/sec (-6.95%) write batch 8 bytes kv checksum: 1.17979e+06 ops/sec (+1.1%) ``` - Benchmark on only memtable read: ops/sec dropped 31% for `readseq` due to time spend on verifying checksum. ops/sec for `readrandom` dropped ~6.8%. ``` # Readseq sudo TEST_TMPDIR=/dev/shm/memtable_read ./db_bench -benchmarks=fillseq,readseq"[-X20]" -disable_wal=true -max_write_buffer_number=100 -num=10000000 -min_write_buffer_number_to_merge=100 readseq [AVG 20 runs] : 7432840 (± 212005) ops/sec; 822.3 (± 23.5) MB/sec readseq [MEDIAN 20 runs] : 7573878 ops/sec; 837.9 MB/sec With -memtable_protection_bytes_per_key=2: readseq [AVG 20 runs] : 5134607 (± 119596) ops/sec; 568.0 (± 13.2) MB/sec readseq [MEDIAN 20 runs] : 5232946 ops/sec; 578.9 MB/sec # Readrandom sudo TEST_TMPDIR=/dev/shm/memtable_read ./db_bench -benchmarks=fillrandom,readrandom"[-X10]" -disable_wal=true -max_write_buffer_number=100 -num=1000000 -min_write_buffer_number_to_merge=100 readrandom [AVG 10 runs] : 140236 (± 3938) ops/sec; 9.8 (± 0.3) MB/sec readrandom [MEDIAN 10 runs] : 140545 ops/sec; 9.8 MB/sec With -memtable_protection_bytes_per_key=2: readrandom [AVG 10 runs] : 130632 (± 2738) ops/sec; 9.1 (± 0.2) MB/sec readrandom [MEDIAN 10 runs] : 130341 ops/sec; 9.1 MB/sec ``` - Stress test: `python3 -u tools/db_crashtest.py whitebox --duration=1800` Reviewed By: ajkr Differential Revision: D37607896 Pulled By: cbi42 fbshipit-source-id: fdaefb475629d2471780d4a5f5bf81b44ee56113
2022-08-12 20:51:32 +00:00
DEFINE_uint32(
memtable_protection_bytes_per_key, 0,
"Enable memtable per key-value checksum protection. "
"Each entry in memtable will be suffixed by a per key-value checksum. "
"This options determines the size of such checksums. "
"Supported values: 0, 1, 2, 4, 8.");
Block per key-value checksum (#11287) Summary: add option `block_protection_bytes_per_key` and implementation for block per key-value checksum. The main changes are 1. checksum construction and verification in block.cc/h 2. pass the option `block_protection_bytes_per_key` around (mainly for methods defined in table_cache.h) 3. unit tests/crash test updates Tests: * Added unit tests * Crash test: `python3 tools/db_crashtest.py blackbox --simple --block_protection_bytes_per_key=1 --write_buffer_size=1048576` Follow up (maybe as a separate PR): make sure corruption status returned from BlockIters are correctly handled. Performance: Turning on block per KV protection has a non-trivial negative impact on read performance and costs additional memory. For memory, each block includes additional 24 bytes for checksum-related states beside checksum itself. For CPU, I set up a DB of size ~1.2GB with 5M keys (32 bytes key and 200 bytes value) which compacts to ~5 SST files (target file size 256 MB) in L6 without compression. I tested readrandom performance with various block cache size (to mimic various cache hit rates): ``` SETUP make OPTIMIZE_LEVEL="-O3" USE_LTO=1 DEBUG_LEVEL=0 -j32 db_bench ./db_bench -benchmarks=fillseq,compact0,waitforcompaction,compact,waitforcompaction -write_buffer_size=33554432 -level_compaction_dynamic_level_bytes=true -max_background_jobs=8 -target_file_size_base=268435456 --num=5000000 --key_size=32 --value_size=200 --compression_type=none BENCHMARK ./db_bench --use_existing_db -benchmarks=readtocache,readrandom[-X10] --num=5000000 --key_size=32 --disable_auto_compactions --reads=1000000 --block_protection_bytes_per_key=[0|1] --cache_size=$CACHESIZE The readrandom ops/sec looks like the following: Block cache size: 2GB 1.2GB * 0.9 1.2GB * 0.8 1.2GB * 0.5 8MB Main 240805 223604 198176 161653 139040 PR prot_bytes=0 238691 226693 200127 161082 141153 PR prot_bytes=1 214983 193199 178532 137013 108211 prot_bytes=1 vs -10% -15% -10.8% -15% -23% prot_bytes=0 ``` The benchmark has a lot of variance, but there was a 5% to 25% regression in this benchmark with different cache hit rates. Pull Request resolved: https://github.com/facebook/rocksdb/pull/11287 Reviewed By: ajkr Differential Revision: D43970708 Pulled By: cbi42 fbshipit-source-id: ef98d898b71779846fa74212b9ec9e08b7183940
2023-04-25 19:08:23 +00:00
DEFINE_uint32(block_protection_bytes_per_key, 0,
"Enable block per key-value checksum protection. "
"Supported values: 0, 1, 2, 4, 8.");
DEFINE_bool(build_info, false,
"Print the build info via GetRocksBuildInfoAsString");
Stop tracking syncing live WAL for performance (#10330) Summary: With https://github.com/facebook/rocksdb/issues/10087, applications calling `SyncWAL()` or writing with `WriteOptions::sync=true` can suffer from performance regression. This PR reverts to original behavior of tracking the syncing of closed WALs. After we revert back to old behavior, recovery, whether kPointInTime or kAbsoluteConsistency, may fail to detect corruption in synced WALs if the corruption is in the live WAL. Pull Request resolved: https://github.com/facebook/rocksdb/pull/10330 Test Plan: make check Before https://github.com/facebook/rocksdb/issues/10087 ```bash fillsync : 750.269 micros/op 1332 ops/sec 75.027 seconds 100000 operations; 0.1 MB/s (100 ops) fillsync : 776.492 micros/op 1287 ops/sec 77.649 seconds 100000 operations; 0.1 MB/s (100 ops) fillsync [AVG 2 runs] : 1310 (± 44) ops/sec; 0.1 (± 0.0) MB/sec fillsync : 805.625 micros/op 1241 ops/sec 80.563 seconds 100000 operations; 0.1 MB/s (100 ops) fillsync [AVG 3 runs] : 1287 (± 51) ops/sec; 0.1 (± 0.0) MB/sec fillsync [AVG 3 runs] : 1287 (± 51) ops/sec; 0.1 (± 0.0) MB/sec fillsync [MEDIAN 3 runs] : 1287 ops/sec; 0.1 MB/sec ``` Before this PR and after https://github.com/facebook/rocksdb/issues/10087 ```bash fillsync : 1479.601 micros/op 675 ops/sec 147.960 seconds 100000 operations; 0.1 MB/s (100 ops) fillsync : 1626.080 micros/op 614 ops/sec 162.608 seconds 100000 operations; 0.1 MB/s (100 ops) fillsync [AVG 2 runs] : 645 (± 59) ops/sec; 0.1 (± 0.0) MB/sec fillsync : 1588.402 micros/op 629 ops/sec 158.840 seconds 100000 operations; 0.1 MB/s (100 ops) fillsync [AVG 3 runs] : 640 (± 35) ops/sec; 0.1 (± 0.0) MB/sec fillsync [AVG 3 runs] : 640 (± 35) ops/sec; 0.1 (± 0.0) MB/sec fillsync [MEDIAN 3 runs] : 629 ops/sec; 0.1 MB/sec ``` After this PR ```bash fillsync : 749.621 micros/op 1334 ops/sec 74.962 seconds 100000 operations; 0.1 MB/s (100 ops) fillsync : 865.577 micros/op 1155 ops/sec 86.558 seconds 100000 operations; 0.1 MB/s (100 ops) fillsync [AVG 2 runs] : 1244 (± 175) ops/sec; 0.1 (± 0.0) MB/sec fillsync : 845.837 micros/op 1182 ops/sec 84.584 seconds 100000 operations; 0.1 MB/s (100 ops) fillsync [AVG 3 runs] : 1223 (± 109) ops/sec; 0.1 (± 0.0) MB/sec fillsync [AVG 3 runs] : 1223 (± 109) ops/sec; 0.1 (± 0.0) MB/sec fillsync [MEDIAN 3 runs] : 1182 ops/sec; 0.1 MB/sec ``` Reviewed By: ajkr Differential Revision: D37725212 Pulled By: riversand963 fbshipit-source-id: 8fa7d13b3c7662be5d56351c42caf3266af937ae
2022-07-13 00:16:57 +00:00
DEFINE_bool(track_and_verify_wals_in_manifest, false,
"If true, enable WAL tracking in the MANIFEST");
namespace ROCKSDB_NAMESPACE {
namespace {
static Status CreateMemTableRepFactory(
const ConfigOptions& config_options,
std::shared_ptr<MemTableRepFactory>* factory) {
Status s;
if (!strcasecmp(FLAGS_memtablerep.c_str(), SkipListFactory::kNickName())) {
factory->reset(new SkipListFactory(FLAGS_skip_list_lookahead));
} else if (!strcasecmp(FLAGS_memtablerep.c_str(), "prefix_hash")) {
factory->reset(NewHashSkipListRepFactory(FLAGS_hash_bucket_count));
} else if (!strcasecmp(FLAGS_memtablerep.c_str(),
VectorRepFactory::kNickName())) {
factory->reset(new VectorRepFactory());
} else if (!strcasecmp(FLAGS_memtablerep.c_str(), "hash_linkedlist")) {
factory->reset(NewHashLinkListRepFactory(FLAGS_hash_bucket_count));
} else {
std::unique_ptr<MemTableRepFactory> unique;
s = MemTableRepFactory::CreateFromString(config_options, FLAGS_memtablerep,
&unique);
if (s.ok()) {
factory->reset(unique.release());
}
}
return s;
}
} // namespace
enum DistributionType : unsigned char { kFixed = 0, kUniform, kNormal };
static enum DistributionType FLAGS_value_size_distribution_type_e = kFixed;
static enum DistributionType StringToDistributionType(const char* ctype) {
assert(ctype);
if (!strcasecmp(ctype, "fixed")) {
return kFixed;
} else if (!strcasecmp(ctype, "uniform")) {
return kUniform;
} else if (!strcasecmp(ctype, "normal")) {
return kNormal;
}
fprintf(stdout, "Cannot parse distribution type '%s'\n", ctype);
exit(1);
}
class BaseDistribution {
public:
BaseDistribution(unsigned int _min, unsigned int _max)
: min_value_size_(_min), max_value_size_(_max) {}
virtual ~BaseDistribution() = default;
unsigned int Generate() {
auto val = Get();
if (NeedTruncate()) {
val = std::max(min_value_size_, val);
val = std::min(max_value_size_, val);
}
return val;
}
private:
virtual unsigned int Get() = 0;
virtual bool NeedTruncate() { return true; }
unsigned int min_value_size_;
unsigned int max_value_size_;
};
class FixedDistribution : public BaseDistribution {
public:
FixedDistribution(unsigned int size)
: BaseDistribution(size, size), size_(size) {}
private:
unsigned int Get() override { return size_; }
bool NeedTruncate() override { return false; }
unsigned int size_;
};
class NormalDistribution : public BaseDistribution,
public std::normal_distribution<double> {
public:
NormalDistribution(unsigned int _min, unsigned int _max)
: BaseDistribution(_min, _max),
// 99.7% values within the range [min, max].
std::normal_distribution<double>(
(double)(_min + _max) / 2.0 /*mean*/,
(double)(_max - _min) / 6.0 /*stddev*/),
gen_(rd_()) {}
private:
unsigned int Get() override {
return static_cast<unsigned int>((*this)(gen_));
}
std::random_device rd_;
std::mt19937 gen_;
};
class UniformDistribution : public BaseDistribution,
public std::uniform_int_distribution<unsigned int> {
public:
UniformDistribution(unsigned int _min, unsigned int _max)
: BaseDistribution(_min, _max),
std::uniform_int_distribution<unsigned int>(_min, _max),
gen_(rd_()) {}
private:
unsigned int Get() override { return (*this)(gen_); }
bool NeedTruncate() override { return false; }
std::random_device rd_;
std::mt19937 gen_;
};
// Helper for quickly generating random data.
class RandomGenerator {
private:
std::string data_;
unsigned int pos_;
std::unique_ptr<BaseDistribution> dist_;
public:
RandomGenerator() {
auto max_value_size = FLAGS_value_size_max;
switch (FLAGS_value_size_distribution_type_e) {
case kUniform:
dist_.reset(new UniformDistribution(FLAGS_value_size_min,
FLAGS_value_size_max));
break;
case kNormal:
dist_.reset(
new NormalDistribution(FLAGS_value_size_min, FLAGS_value_size_max));
break;
case kFixed:
default:
dist_.reset(new FixedDistribution(value_size));
max_value_size = value_size;
}
// We use a limited amount of data over and over again and ensure
// that it is larger than the compression window (32KB), and also
// large enough to serve all typical value sizes we want to write.
Random rnd(301);
std::string piece;
while (data_.size() < (unsigned)std::max(1048576, max_value_size)) {
// Add a short fragment that is as compressible as specified
// by FLAGS_compression_ratio.
test::CompressibleString(&rnd, FLAGS_compression_ratio, 100, &piece);
data_.append(piece);
}
pos_ = 0;
}
Slice Generate(unsigned int len) {
assert(len <= data_.size());
if (pos_ + len > data_.size()) {
pos_ = 0;
}
pos_ += len;
return Slice(data_.data() + pos_ - len, len);
}
Slice Generate() {
auto len = dist_->Generate();
return Generate(len);
}
};
static void AppendWithSpace(std::string* str, Slice msg) {
if (msg.empty()) {
return;
}
if (!str->empty()) {
str->push_back(' ');
}
str->append(msg.data(), msg.size());
}
struct DBWithColumnFamilies {
std::vector<ColumnFamilyHandle*> cfh;
DB* db;
OptimisticTransactionDB* opt_txn_db;
std::atomic<size_t> num_created; // Need to be updated after all the
// new entries in cfh are set.
size_t num_hot; // Number of column families to be queried at each moment.
// After each CreateNewCf(), another num_hot number of new
// Column families will be created and used to be queried.
port::Mutex create_cf_mutex; // Only one thread can execute CreateNewCf()
std::vector<int> cfh_idx_to_prob; // ith index holds probability of operating
// on cfh[i].
DBWithColumnFamilies()
: db(nullptr)
,
opt_txn_db(nullptr)
{
cfh.clear();
num_created = 0;
num_hot = 0;
}
DBWithColumnFamilies(const DBWithColumnFamilies& other)
: cfh(other.cfh),
db(other.db),
opt_txn_db(other.opt_txn_db),
num_created(other.num_created.load()),
num_hot(other.num_hot),
cfh_idx_to_prob(other.cfh_idx_to_prob) {
}
void DeleteDBs() {
std::for_each(cfh.begin(), cfh.end(),
[](ColumnFamilyHandle* cfhi) { delete cfhi; });
cfh.clear();
if (opt_txn_db) {
delete opt_txn_db;
opt_txn_db = nullptr;
} else {
delete db;
db = nullptr;
}
}
ColumnFamilyHandle* GetCfh(int64_t rand_num) {
assert(num_hot > 0);
size_t rand_offset = 0;
if (!cfh_idx_to_prob.empty()) {
assert(cfh_idx_to_prob.size() == num_hot);
int sum = 0;
while (sum + cfh_idx_to_prob[rand_offset] < rand_num % 100) {
sum += cfh_idx_to_prob[rand_offset];
++rand_offset;
}
assert(rand_offset < cfh_idx_to_prob.size());
} else {
rand_offset = rand_num % num_hot;
}
return cfh[num_created.load(std::memory_order_acquire) - num_hot +
rand_offset];
}
// stage: assume CF from 0 to stage * num_hot has be created. Need to create
// stage * num_hot + 1 to stage * (num_hot + 1).
void CreateNewCf(ColumnFamilyOptions options, int64_t stage) {
MutexLock l(&create_cf_mutex);
if ((stage + 1) * num_hot <= num_created) {
// Already created.
return;
}
auto new_num_created = num_created + num_hot;
assert(new_num_created <= cfh.size());
for (size_t i = num_created; i < new_num_created; i++) {
Status s =
db->CreateColumnFamily(options, ColumnFamilyName(i), &(cfh[i]));
if (!s.ok()) {
fprintf(stderr, "create column family error: %s\n",
s.ToString().c_str());
abort();
}
}
num_created.store(new_num_created, std::memory_order_release);
}
};
// A class that reports stats to CSV file.
class ReporterAgent {
public:
ReporterAgent(Env* env, const std::string& fname,
uint64_t report_interval_secs)
: env_(env),
total_ops_done_(0),
last_report_(0),
report_interval_secs_(report_interval_secs),
stop_(false) {
auto s = env_->NewWritableFile(fname, &report_file_, EnvOptions());
if (s.ok()) {
s = report_file_->Append(Header() + "\n");
}
if (s.ok()) {
s = report_file_->Flush();
}
if (!s.ok()) {
fprintf(stderr, "Can't open %s: %s\n", fname.c_str(),
s.ToString().c_str());
abort();
}
reporting_thread_ = port::Thread([&]() { SleepAndReport(); });
}
~ReporterAgent() {
{
std::unique_lock<std::mutex> lk(mutex_);
stop_ = true;
stop_cv_.notify_all();
}
reporting_thread_.join();
}
// thread safe
void ReportFinishedOps(int64_t num_ops) {
total_ops_done_.fetch_add(num_ops);
}
private:
std::string Header() const { return "secs_elapsed,interval_qps"; }
void SleepAndReport() {
auto* clock = env_->GetSystemClock().get();
auto time_started = clock->NowMicros();
while (true) {
{
std::unique_lock<std::mutex> lk(mutex_);
if (stop_ ||
stop_cv_.wait_for(lk, std::chrono::seconds(report_interval_secs_),
[&]() { return stop_; })) {
// stopping
break;
}
// else -> timeout, which means time for a report!
}
auto total_ops_done_snapshot = total_ops_done_.load();
// round the seconds elapsed
auto secs_elapsed =
(clock->NowMicros() - time_started + kMicrosInSecond / 2) /
kMicrosInSecond;
std::string report =
std::to_string(secs_elapsed) + "," +
std::to_string(total_ops_done_snapshot - last_report_) + "\n";
auto s = report_file_->Append(report);
if (s.ok()) {
s = report_file_->Flush();
}
if (!s.ok()) {
fprintf(stderr,
"Can't write to report file (%s), stopping the reporting\n",
s.ToString().c_str());
break;
}
last_report_ = total_ops_done_snapshot;
}
}
Env* env_;
std::unique_ptr<WritableFile> report_file_;
std::atomic<int64_t> total_ops_done_;
int64_t last_report_;
const uint64_t report_interval_secs_;
ROCKSDB_NAMESPACE::port::Thread reporting_thread_;
std::mutex mutex_;
// will notify on stop
std::condition_variable stop_cv_;
bool stop_;
};
enum OperationType : unsigned char {
kRead = 0,
kWrite,
kDelete,
kSeek,
kMerge,
kUpdate,
kCompress,
kUncompress,
kCrc,
kHash,
kOthers
};
static std::unordered_map<OperationType, std::string, std::hash<unsigned char>>
OperationTypeString = {{kRead, "read"}, {kWrite, "write"},
{kDelete, "delete"}, {kSeek, "seek"},
{kMerge, "merge"}, {kUpdate, "update"},
{kCompress, "compress"}, {kCompress, "uncompress"},
{kCrc, "crc"}, {kHash, "hash"},
{kOthers, "op"}};
[db_bench] Support single benchmark arguments (Repeat for X times, Warm up for X times), Support CombinedStats (AVG / MEDIAN) Summary: This diff allow us to run a single benchmark X times and warm it up for Y times. and see the AVG & MEDIAN throughput of these X runs for example ``` $ ./db_bench --benchmarks="fillseq,readseq[X5-W2]" Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags RocksDB: version 4.12 Date: Wed Aug 24 10:45:26 2016 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Compression: Snappy Memtablerep: skip_list Perf Level: 1 WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags DB path: [/tmp/rocksdbtest-8616/dbbench] fillseq : 4.695 micros/op 212971 ops/sec; 23.6 MB/s DB path: [/tmp/rocksdbtest-8616/dbbench] Warming up benchmark by running 2 times readseq : 0.214 micros/op 4677005 ops/sec; 517.4 MB/s readseq : 0.212 micros/op 4706834 ops/sec; 520.7 MB/s Running benchmark for 5 times readseq : 0.218 micros/op 4588187 ops/sec; 507.6 MB/s readseq : 0.208 micros/op 4816538 ops/sec; 532.8 MB/s readseq : 0.213 micros/op 4685376 ops/sec; 518.3 MB/s readseq : 0.214 micros/op 4676787 ops/sec; 517.4 MB/s readseq : 0.217 micros/op 4618532 ops/sec; 510.9 MB/s readseq [AVG 5 runs] : 4677084 ops/sec; 517.4 MB/sec readseq [MEDIAN 5 runs] : 4676787 ops/sec; 517.4 MB/sec ``` Test Plan: run db_bench Reviewers: sdong, andrewkr, yhchiang Reviewed By: yhchiang Subscribers: andrewkr, dhruba Differential Revision: https://reviews.facebook.net/D62235
2016-08-25 19:57:35 +00:00
class CombinedStats;
class Stats {
private:
SystemClock* clock_;
int id_;
uint64_t start_ = 0;
uint64_t sine_interval_;
uint64_t finish_;
double seconds_;
uint64_t done_;
uint64_t last_report_done_;
uint64_t next_report_;
uint64_t bytes_;
uint64_t last_op_finish_;
uint64_t last_report_finish_;
std::unordered_map<OperationType, std::shared_ptr<HistogramImpl>,
std::hash<unsigned char>>
hist_;
std::string message_;
bool exclude_from_merge_;
ReporterAgent* reporter_agent_; // does not own
[db_bench] Support single benchmark arguments (Repeat for X times, Warm up for X times), Support CombinedStats (AVG / MEDIAN) Summary: This diff allow us to run a single benchmark X times and warm it up for Y times. and see the AVG & MEDIAN throughput of these X runs for example ``` $ ./db_bench --benchmarks="fillseq,readseq[X5-W2]" Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags RocksDB: version 4.12 Date: Wed Aug 24 10:45:26 2016 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Compression: Snappy Memtablerep: skip_list Perf Level: 1 WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags DB path: [/tmp/rocksdbtest-8616/dbbench] fillseq : 4.695 micros/op 212971 ops/sec; 23.6 MB/s DB path: [/tmp/rocksdbtest-8616/dbbench] Warming up benchmark by running 2 times readseq : 0.214 micros/op 4677005 ops/sec; 517.4 MB/s readseq : 0.212 micros/op 4706834 ops/sec; 520.7 MB/s Running benchmark for 5 times readseq : 0.218 micros/op 4588187 ops/sec; 507.6 MB/s readseq : 0.208 micros/op 4816538 ops/sec; 532.8 MB/s readseq : 0.213 micros/op 4685376 ops/sec; 518.3 MB/s readseq : 0.214 micros/op 4676787 ops/sec; 517.4 MB/s readseq : 0.217 micros/op 4618532 ops/sec; 510.9 MB/s readseq [AVG 5 runs] : 4677084 ops/sec; 517.4 MB/sec readseq [MEDIAN 5 runs] : 4676787 ops/sec; 517.4 MB/sec ``` Test Plan: run db_bench Reviewers: sdong, andrewkr, yhchiang Reviewed By: yhchiang Subscribers: andrewkr, dhruba Differential Revision: https://reviews.facebook.net/D62235
2016-08-25 19:57:35 +00:00
friend class CombinedStats;
public:
Stats() : clock_(FLAGS_env->GetSystemClock().get()) { Start(-1); }
void SetReporterAgent(ReporterAgent* reporter_agent) {
reporter_agent_ = reporter_agent;
}
void Start(int id) {
id_ = id;
next_report_ = FLAGS_stats_interval ? FLAGS_stats_interval : 100;
last_op_finish_ = start_;
hist_.clear();
done_ = 0;
last_report_done_ = 0;
bytes_ = 0;
seconds_ = 0;
start_ = clock_->NowMicros();
sine_interval_ = clock_->NowMicros();
finish_ = start_;
last_report_finish_ = start_;
message_.clear();
// When set, stats from this thread won't be merged with others.
exclude_from_merge_ = false;
}
void Merge(const Stats& other) {
if (other.exclude_from_merge_) {
return;
}
for (auto it = other.hist_.begin(); it != other.hist_.end(); ++it) {
auto this_it = hist_.find(it->first);
if (this_it != hist_.end()) {
this_it->second->Merge(*(other.hist_.at(it->first)));
} else {
hist_.insert({it->first, it->second});
}
}
done_ += other.done_;
bytes_ += other.bytes_;
seconds_ += other.seconds_;
if (other.start_ < start_) {
start_ = other.start_;
}
if (other.finish_ > finish_) {
finish_ = other.finish_;
}
// Just keep the messages from one thread.
if (message_.empty()) {
message_ = other.message_;
}
}
void Stop() {
finish_ = clock_->NowMicros();
seconds_ = (finish_ - start_) * 1e-6;
}
void AddMessage(Slice msg) { AppendWithSpace(&message_, msg); }
void SetId(int id) { id_ = id; }
void SetExcludeFromMerge() { exclude_from_merge_ = true; }
void PrintThreadStatus() {
std::vector<ThreadStatus> thread_list;
FLAGS_env->GetThreadList(&thread_list);
fprintf(stderr, "\n%18s %10s %12s %20s %13s %45s %12s %s\n", "ThreadID",
"ThreadType", "cfName", "Operation", "ElapsedTime", "Stage",
"State", "OperationProperties");
int64_t current_time = 0;
clock_->GetCurrentTime(&current_time).PermitUncheckedError();
for (auto ts : thread_list) {
Allow GetThreadList() to report basic compaction operation properties. Summary: Now we're able to show more details about a compaction in GetThreadList() :) This patch allows GetThreadList() to report basic compaction operation properties. Basic compaction properties include: 1. job id 2. compaction input / output level 3. compaction property flags (is_manual, is_deletion, .. etc) 4. total input bytes 5. the number of bytes has been read currently. 6. the number of bytes has been written currently. Flush operation properties will be done in a seperate diff. Test Plan: /db_bench --threads=30 --num=1000000 --benchmarks=fillrandom --thread_status_per_interval=1 Sample output of tracking same job: ThreadID ThreadType cfName Operation ElapsedTime Stage State OperationProperties 140664171987072 Low Pri default Compaction 31.357 ms CompactionJob::FinishCompactionOutputFile BaseInputLevel 1 | BytesRead 2264663 | BytesWritten 1934241 | IsDeletion 0 | IsManual 0 | IsTrivialMove 0 | JobID 277 | OutputLevel 2 | TotalInputBytes 3964158 | ThreadID ThreadType cfName Operation ElapsedTime Stage State OperationProperties 140664171987072 Low Pri default Compaction 59.440 ms CompactionJob::FinishCompactionOutputFile BaseInputLevel 1 | BytesRead 2264663 | BytesWritten 1934241 | IsDeletion 0 | IsManual 0 | IsTrivialMove 0 | JobID 277 | OutputLevel 2 | TotalInputBytes 3964158 | ThreadID ThreadType cfName Operation ElapsedTime Stage State OperationProperties 140664171987072 Low Pri default Compaction 226.375 ms CompactionJob::Install BaseInputLevel 1 | BytesRead 3958013 | BytesWritten 3621940 | IsDeletion 0 | IsManual 0 | IsTrivialMove 0 | JobID 277 | OutputLevel 2 | TotalInputBytes 3964158 | Reviewers: sdong, rven, igor Reviewed By: igor Subscribers: dhruba, leveldb Differential Revision: https://reviews.facebook.net/D37653
2015-05-07 05:50:35 +00:00
fprintf(stderr, "%18" PRIu64 " %10s %12s %20s %13s %45s %12s",
ts.thread_id,
ThreadStatus::GetThreadTypeName(ts.thread_type).c_str(),
ts.cf_name.c_str(),
ThreadStatus::GetOperationName(ts.operation_type).c_str(),
ThreadStatus::MicrosToString(ts.op_elapsed_micros).c_str(),
ThreadStatus::GetOperationStageName(ts.operation_stage).c_str(),
ThreadStatus::GetStateName(ts.state_type).c_str());
Allow GetThreadList() to report basic compaction operation properties. Summary: Now we're able to show more details about a compaction in GetThreadList() :) This patch allows GetThreadList() to report basic compaction operation properties. Basic compaction properties include: 1. job id 2. compaction input / output level 3. compaction property flags (is_manual, is_deletion, .. etc) 4. total input bytes 5. the number of bytes has been read currently. 6. the number of bytes has been written currently. Flush operation properties will be done in a seperate diff. Test Plan: /db_bench --threads=30 --num=1000000 --benchmarks=fillrandom --thread_status_per_interval=1 Sample output of tracking same job: ThreadID ThreadType cfName Operation ElapsedTime Stage State OperationProperties 140664171987072 Low Pri default Compaction 31.357 ms CompactionJob::FinishCompactionOutputFile BaseInputLevel 1 | BytesRead 2264663 | BytesWritten 1934241 | IsDeletion 0 | IsManual 0 | IsTrivialMove 0 | JobID 277 | OutputLevel 2 | TotalInputBytes 3964158 | ThreadID ThreadType cfName Operation ElapsedTime Stage State OperationProperties 140664171987072 Low Pri default Compaction 59.440 ms CompactionJob::FinishCompactionOutputFile BaseInputLevel 1 | BytesRead 2264663 | BytesWritten 1934241 | IsDeletion 0 | IsManual 0 | IsTrivialMove 0 | JobID 277 | OutputLevel 2 | TotalInputBytes 3964158 | ThreadID ThreadType cfName Operation ElapsedTime Stage State OperationProperties 140664171987072 Low Pri default Compaction 226.375 ms CompactionJob::Install BaseInputLevel 1 | BytesRead 3958013 | BytesWritten 3621940 | IsDeletion 0 | IsManual 0 | IsTrivialMove 0 | JobID 277 | OutputLevel 2 | TotalInputBytes 3964158 | Reviewers: sdong, rven, igor Reviewed By: igor Subscribers: dhruba, leveldb Differential Revision: https://reviews.facebook.net/D37653
2015-05-07 05:50:35 +00:00
auto op_properties = ThreadStatus::InterpretOperationProperties(
ts.operation_type, ts.op_properties);
for (const auto& op_prop : op_properties) {
fprintf(stderr, " %s %" PRIu64 " |", op_prop.first.c_str(),
op_prop.second);
Allow GetThreadList() to report basic compaction operation properties. Summary: Now we're able to show more details about a compaction in GetThreadList() :) This patch allows GetThreadList() to report basic compaction operation properties. Basic compaction properties include: 1. job id 2. compaction input / output level 3. compaction property flags (is_manual, is_deletion, .. etc) 4. total input bytes 5. the number of bytes has been read currently. 6. the number of bytes has been written currently. Flush operation properties will be done in a seperate diff. Test Plan: /db_bench --threads=30 --num=1000000 --benchmarks=fillrandom --thread_status_per_interval=1 Sample output of tracking same job: ThreadID ThreadType cfName Operation ElapsedTime Stage State OperationProperties 140664171987072 Low Pri default Compaction 31.357 ms CompactionJob::FinishCompactionOutputFile BaseInputLevel 1 | BytesRead 2264663 | BytesWritten 1934241 | IsDeletion 0 | IsManual 0 | IsTrivialMove 0 | JobID 277 | OutputLevel 2 | TotalInputBytes 3964158 | ThreadID ThreadType cfName Operation ElapsedTime Stage State OperationProperties 140664171987072 Low Pri default Compaction 59.440 ms CompactionJob::FinishCompactionOutputFile BaseInputLevel 1 | BytesRead 2264663 | BytesWritten 1934241 | IsDeletion 0 | IsManual 0 | IsTrivialMove 0 | JobID 277 | OutputLevel 2 | TotalInputBytes 3964158 | ThreadID ThreadType cfName Operation ElapsedTime Stage State OperationProperties 140664171987072 Low Pri default Compaction 226.375 ms CompactionJob::Install BaseInputLevel 1 | BytesRead 3958013 | BytesWritten 3621940 | IsDeletion 0 | IsManual 0 | IsTrivialMove 0 | JobID 277 | OutputLevel 2 | TotalInputBytes 3964158 | Reviewers: sdong, rven, igor Reviewed By: igor Subscribers: dhruba, leveldb Differential Revision: https://reviews.facebook.net/D37653
2015-05-07 05:50:35 +00:00
}
fprintf(stderr, "\n");
}
}
void ResetSineInterval() { sine_interval_ = clock_->NowMicros(); }
uint64_t GetSineInterval() { return sine_interval_; }
uint64_t GetStart() { return start_; }
void ResetLastOpTime() {
// Set to now to avoid latency from calls to SleepForMicroseconds.
last_op_finish_ = clock_->NowMicros();
}
void FinishedOps(DBWithColumnFamilies* db_with_cfh, DB* db, int64_t num_ops,
enum OperationType op_type = kOthers) {
if (reporter_agent_) {
reporter_agent_->ReportFinishedOps(num_ops);
}
if (FLAGS_histogram) {
uint64_t now = clock_->NowMicros();
uint64_t micros = now - last_op_finish_;
if (hist_.find(op_type) == hist_.end()) {
auto hist_temp = std::make_shared<HistogramImpl>();
hist_.insert({op_type, std::move(hist_temp)});
}
hist_[op_type]->Add(micros);
if (micros >= FLAGS_slow_usecs && !FLAGS_stats_interval) {
fprintf(stderr, "long op: %" PRIu64 " micros%30s\r", micros, "");
fflush(stderr);
}
last_op_finish_ = now;
}
done_ += num_ops;
if (done_ >= next_report_ && FLAGS_progress_reports) {
if (!FLAGS_stats_interval) {
if (next_report_ < 1000) {
next_report_ += 100;
} else if (next_report_ < 5000) {
next_report_ += 500;
} else if (next_report_ < 10000) {
next_report_ += 1000;
} else if (next_report_ < 50000) {
next_report_ += 5000;
} else if (next_report_ < 100000) {
next_report_ += 10000;
} else if (next_report_ < 500000) {
next_report_ += 50000;
} else {
next_report_ += 100000;
}
fprintf(stderr, "... finished %" PRIu64 " ops%30s\r", done_, "");
} else {
uint64_t now = clock_->NowMicros();
int64_t usecs_since_last = now - last_report_finish_;
// Determine whether to print status where interval is either
// each N operations or each N seconds.
if (FLAGS_stats_interval_seconds &&
usecs_since_last < (FLAGS_stats_interval_seconds * 1000000)) {
// Don't check again for this many operations.
next_report_ += FLAGS_stats_interval;
} else {
fprintf(stderr,
"%s ... thread %d: (%" PRIu64 ",%" PRIu64
") ops and "
"(%.1f,%.1f) ops/second in (%.6f,%.6f) seconds\n",
clock_->TimeToString(now / 1000000).c_str(), id_,
done_ - last_report_done_, done_,
(done_ - last_report_done_) / (usecs_since_last / 1000000.0),
done_ / ((now - start_) / 1000000.0),
(now - last_report_finish_) / 1000000.0,
(now - start_) / 1000000.0);
if (id_ == 0 && FLAGS_stats_per_interval) {
std::string stats;
if (db_with_cfh && db_with_cfh->num_created.load()) {
for (size_t i = 0; i < db_with_cfh->num_created.load(); ++i) {
if (db->GetProperty(db_with_cfh->cfh[i], "rocksdb.cfstats",
&stats)) {
fprintf(stderr, "%s\n", stats.c_str());
}
Add argument --show_table_properties to db_bench Summary: Add argument --show_table_properties to db_bench -show_table_properties (If true, then per-level table properties will be printed on every stats-interval when stats_interval is set and stats_per_interval is on.) type: bool default: false Test Plan: ./db_bench --show_table_properties=1 --stats_interval=100000 --stats_per_interval=1 ./db_bench --show_table_properties=1 --stats_interval=100000 --stats_per_interval=1 --num_column_families=2 Sample Output: Compaction Stats [column_family_name_000001] Level Files Size(MB) Score Read(GB) Rn(GB) Rnp1(GB) Write(GB) Wnew(GB) Moved(GB) W-Amp Rd(MB/s) Wr(MB/s) Comp(sec) Comp(cnt) Avg(sec) Stall(cnt) KeyIn KeyDrop --------------------------------------------------------------------------------------------------------------------------------------------------------------------- L0 3/0 5 0.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 86.3 0 17 0.021 0 0 0 L1 5/0 9 0.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 0 0.000 0 0 0 L2 9/0 16 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 0 0.000 0 0 0 Sum 17/0 31 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 86.3 0 17 0.021 0 0 0 Int 0/0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 83.9 0 2 0.022 0 0 0 Flush(GB): cumulative 0.030, interval 0.004 Stalls(count): 0 level0_slowdown, 0 level0_numfiles, 0 memtable_compaction, 0 leveln_slowdown_soft, 0 leveln_slowdown_hard Level[0]: # data blocks=2571; # entries=84813; raw key size=2035512; raw average key size=24.000000; raw value size=8481300; raw average value size=100.000000; data block size=5690119; index block size=82415; filter block size=0; (estimated) table size=5772534; filter policy name=N/A; Level[1]: # data blocks=4285; # entries=141355; raw key size=3392520; raw average key size=24.000000; raw value size=14135500; raw average value size=100.000000; data block size=9487353; index block size=137377; filter block size=0; (estimated) table size=9624730; filter policy name=N/A; Level[2]: # data blocks=7713; # entries=254439; raw key size=6106536; raw average key size=24.000000; raw value size=25443900; raw average value size=100.000000; data block size=17077893; index block size=247269; filter block size=0; (estimated) table size=17325162; filter policy name=N/A; Level[3]: # data blocks=0; # entries=0; raw key size=0; raw average key size=0.000000; raw value size=0; raw average value size=0.000000; data block size=0; index block size=0; filter block size=0; (estimated) table size=0; filter policy name=N/A; Level[4]: # data blocks=0; # entries=0; raw key size=0; raw average key size=0.000000; raw value size=0; raw average value size=0.000000; data block size=0; index block size=0; filter block size=0; (estimated) table size=0; filter policy name=N/A; Level[5]: # data blocks=0; # entries=0; raw key size=0; raw average key size=0.000000; raw value size=0; raw average value size=0.000000; data block size=0; index block size=0; filter block size=0; (estimated) table size=0; filter policy name=N/A; Level[6]: # data blocks=0; # entries=0; raw key size=0; raw average key size=0.000000; raw value size=0; raw average value size=0.000000; data block size=0; index block size=0; filter block size=0; (estimated) table size=0; filter policy name=N/A; Reviewers: anthony, IslamAbdelRahman, MarkCallaghan, sdong, igor Reviewed By: igor Subscribers: dhruba, leveldb Differential Revision: https://reviews.facebook.net/D45651
2015-08-27 01:27:23 +00:00
if (FLAGS_show_table_properties) {
for (int level = 0; level < FLAGS_num_levels; ++level) {
if (db->GetProperty(
db_with_cfh->cfh[i],
"rocksdb.aggregated-table-properties-at-level" +
std::to_string(level),
Add argument --show_table_properties to db_bench Summary: Add argument --show_table_properties to db_bench -show_table_properties (If true, then per-level table properties will be printed on every stats-interval when stats_interval is set and stats_per_interval is on.) type: bool default: false Test Plan: ./db_bench --show_table_properties=1 --stats_interval=100000 --stats_per_interval=1 ./db_bench --show_table_properties=1 --stats_interval=100000 --stats_per_interval=1 --num_column_families=2 Sample Output: Compaction Stats [column_family_name_000001] Level Files Size(MB) Score Read(GB) Rn(GB) Rnp1(GB) Write(GB) Wnew(GB) Moved(GB) W-Amp Rd(MB/s) Wr(MB/s) Comp(sec) Comp(cnt) Avg(sec) Stall(cnt) KeyIn KeyDrop --------------------------------------------------------------------------------------------------------------------------------------------------------------------- L0 3/0 5 0.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 86.3 0 17 0.021 0 0 0 L1 5/0 9 0.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 0 0.000 0 0 0 L2 9/0 16 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 0 0.000 0 0 0 Sum 17/0 31 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 86.3 0 17 0.021 0 0 0 Int 0/0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 83.9 0 2 0.022 0 0 0 Flush(GB): cumulative 0.030, interval 0.004 Stalls(count): 0 level0_slowdown, 0 level0_numfiles, 0 memtable_compaction, 0 leveln_slowdown_soft, 0 leveln_slowdown_hard Level[0]: # data blocks=2571; # entries=84813; raw key size=2035512; raw average key size=24.000000; raw value size=8481300; raw average value size=100.000000; data block size=5690119; index block size=82415; filter block size=0; (estimated) table size=5772534; filter policy name=N/A; Level[1]: # data blocks=4285; # entries=141355; raw key size=3392520; raw average key size=24.000000; raw value size=14135500; raw average value size=100.000000; data block size=9487353; index block size=137377; filter block size=0; (estimated) table size=9624730; filter policy name=N/A; Level[2]: # data blocks=7713; # entries=254439; raw key size=6106536; raw average key size=24.000000; raw value size=25443900; raw average value size=100.000000; data block size=17077893; index block size=247269; filter block size=0; (estimated) table size=17325162; filter policy name=N/A; Level[3]: # data blocks=0; # entries=0; raw key size=0; raw average key size=0.000000; raw value size=0; raw average value size=0.000000; data block size=0; index block size=0; filter block size=0; (estimated) table size=0; filter policy name=N/A; Level[4]: # data blocks=0; # entries=0; raw key size=0; raw average key size=0.000000; raw value size=0; raw average value size=0.000000; data block size=0; index block size=0; filter block size=0; (estimated) table size=0; filter policy name=N/A; Level[5]: # data blocks=0; # entries=0; raw key size=0; raw average key size=0.000000; raw value size=0; raw average value size=0.000000; data block size=0; index block size=0; filter block size=0; (estimated) table size=0; filter policy name=N/A; Level[6]: # data blocks=0; # entries=0; raw key size=0; raw average key size=0.000000; raw value size=0; raw average value size=0.000000; data block size=0; index block size=0; filter block size=0; (estimated) table size=0; filter policy name=N/A; Reviewers: anthony, IslamAbdelRahman, MarkCallaghan, sdong, igor Reviewed By: igor Subscribers: dhruba, leveldb Differential Revision: https://reviews.facebook.net/D45651
2015-08-27 01:27:23 +00:00
&stats)) {
if (stats.find("# entries=0") == std::string::npos) {
fprintf(stderr, "Level[%d]: %s\n", level,
stats.c_str());
}
}
}
}
}
} else if (db) {
if (db->GetProperty("rocksdb.stats", &stats)) {
fprintf(stderr, "%s", stats.c_str());
}
if (db->GetProperty("rocksdb.num-running-compactions", &stats)) {
fprintf(stderr, "num-running-compactions: %s\n", stats.c_str());
}
if (db->GetProperty("rocksdb.num-running-flushes", &stats)) {
fprintf(stderr, "num-running-flushes: %s\n\n", stats.c_str());
Add argument --show_table_properties to db_bench Summary: Add argument --show_table_properties to db_bench -show_table_properties (If true, then per-level table properties will be printed on every stats-interval when stats_interval is set and stats_per_interval is on.) type: bool default: false Test Plan: ./db_bench --show_table_properties=1 --stats_interval=100000 --stats_per_interval=1 ./db_bench --show_table_properties=1 --stats_interval=100000 --stats_per_interval=1 --num_column_families=2 Sample Output: Compaction Stats [column_family_name_000001] Level Files Size(MB) Score Read(GB) Rn(GB) Rnp1(GB) Write(GB) Wnew(GB) Moved(GB) W-Amp Rd(MB/s) Wr(MB/s) Comp(sec) Comp(cnt) Avg(sec) Stall(cnt) KeyIn KeyDrop --------------------------------------------------------------------------------------------------------------------------------------------------------------------- L0 3/0 5 0.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 86.3 0 17 0.021 0 0 0 L1 5/0 9 0.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 0 0.000 0 0 0 L2 9/0 16 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 0 0.000 0 0 0 Sum 17/0 31 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 86.3 0 17 0.021 0 0 0 Int 0/0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 83.9 0 2 0.022 0 0 0 Flush(GB): cumulative 0.030, interval 0.004 Stalls(count): 0 level0_slowdown, 0 level0_numfiles, 0 memtable_compaction, 0 leveln_slowdown_soft, 0 leveln_slowdown_hard Level[0]: # data blocks=2571; # entries=84813; raw key size=2035512; raw average key size=24.000000; raw value size=8481300; raw average value size=100.000000; data block size=5690119; index block size=82415; filter block size=0; (estimated) table size=5772534; filter policy name=N/A; Level[1]: # data blocks=4285; # entries=141355; raw key size=3392520; raw average key size=24.000000; raw value size=14135500; raw average value size=100.000000; data block size=9487353; index block size=137377; filter block size=0; (estimated) table size=9624730; filter policy name=N/A; Level[2]: # data blocks=7713; # entries=254439; raw key size=6106536; raw average key size=24.000000; raw value size=25443900; raw average value size=100.000000; data block size=17077893; index block size=247269; filter block size=0; (estimated) table size=17325162; filter policy name=N/A; Level[3]: # data blocks=0; # entries=0; raw key size=0; raw average key size=0.000000; raw value size=0; raw average value size=0.000000; data block size=0; index block size=0; filter block size=0; (estimated) table size=0; filter policy name=N/A; Level[4]: # data blocks=0; # entries=0; raw key size=0; raw average key size=0.000000; raw value size=0; raw average value size=0.000000; data block size=0; index block size=0; filter block size=0; (estimated) table size=0; filter policy name=N/A; Level[5]: # data blocks=0; # entries=0; raw key size=0; raw average key size=0.000000; raw value size=0; raw average value size=0.000000; data block size=0; index block size=0; filter block size=0; (estimated) table size=0; filter policy name=N/A; Level[6]: # data blocks=0; # entries=0; raw key size=0; raw average key size=0.000000; raw value size=0; raw average value size=0.000000; data block size=0; index block size=0; filter block size=0; (estimated) table size=0; filter policy name=N/A; Reviewers: anthony, IslamAbdelRahman, MarkCallaghan, sdong, igor Reviewed By: igor Subscribers: dhruba, leveldb Differential Revision: https://reviews.facebook.net/D45651
2015-08-27 01:27:23 +00:00
}
if (FLAGS_show_table_properties) {
for (int level = 0; level < FLAGS_num_levels; ++level) {
if (db->GetProperty(
"rocksdb.aggregated-table-properties-at-level" +
std::to_string(level),
Add argument --show_table_properties to db_bench Summary: Add argument --show_table_properties to db_bench -show_table_properties (If true, then per-level table properties will be printed on every stats-interval when stats_interval is set and stats_per_interval is on.) type: bool default: false Test Plan: ./db_bench --show_table_properties=1 --stats_interval=100000 --stats_per_interval=1 ./db_bench --show_table_properties=1 --stats_interval=100000 --stats_per_interval=1 --num_column_families=2 Sample Output: Compaction Stats [column_family_name_000001] Level Files Size(MB) Score Read(GB) Rn(GB) Rnp1(GB) Write(GB) Wnew(GB) Moved(GB) W-Amp Rd(MB/s) Wr(MB/s) Comp(sec) Comp(cnt) Avg(sec) Stall(cnt) KeyIn KeyDrop --------------------------------------------------------------------------------------------------------------------------------------------------------------------- L0 3/0 5 0.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 86.3 0 17 0.021 0 0 0 L1 5/0 9 0.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 0 0.000 0 0 0 L2 9/0 16 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 0 0.000 0 0 0 Sum 17/0 31 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 86.3 0 17 0.021 0 0 0 Int 0/0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 83.9 0 2 0.022 0 0 0 Flush(GB): cumulative 0.030, interval 0.004 Stalls(count): 0 level0_slowdown, 0 level0_numfiles, 0 memtable_compaction, 0 leveln_slowdown_soft, 0 leveln_slowdown_hard Level[0]: # data blocks=2571; # entries=84813; raw key size=2035512; raw average key size=24.000000; raw value size=8481300; raw average value size=100.000000; data block size=5690119; index block size=82415; filter block size=0; (estimated) table size=5772534; filter policy name=N/A; Level[1]: # data blocks=4285; # entries=141355; raw key size=3392520; raw average key size=24.000000; raw value size=14135500; raw average value size=100.000000; data block size=9487353; index block size=137377; filter block size=0; (estimated) table size=9624730; filter policy name=N/A; Level[2]: # data blocks=7713; # entries=254439; raw key size=6106536; raw average key size=24.000000; raw value size=25443900; raw average value size=100.000000; data block size=17077893; index block size=247269; filter block size=0; (estimated) table size=17325162; filter policy name=N/A; Level[3]: # data blocks=0; # entries=0; raw key size=0; raw average key size=0.000000; raw value size=0; raw average value size=0.000000; data block size=0; index block size=0; filter block size=0; (estimated) table size=0; filter policy name=N/A; Level[4]: # data blocks=0; # entries=0; raw key size=0; raw average key size=0.000000; raw value size=0; raw average value size=0.000000; data block size=0; index block size=0; filter block size=0; (estimated) table size=0; filter policy name=N/A; Level[5]: # data blocks=0; # entries=0; raw key size=0; raw average key size=0.000000; raw value size=0; raw average value size=0.000000; data block size=0; index block size=0; filter block size=0; (estimated) table size=0; filter policy name=N/A; Level[6]: # data blocks=0; # entries=0; raw key size=0; raw average key size=0.000000; raw value size=0; raw average value size=0.000000; data block size=0; index block size=0; filter block size=0; (estimated) table size=0; filter policy name=N/A; Reviewers: anthony, IslamAbdelRahman, MarkCallaghan, sdong, igor Reviewed By: igor Subscribers: dhruba, leveldb Differential Revision: https://reviews.facebook.net/D45651
2015-08-27 01:27:23 +00:00
&stats)) {
if (stats.find("# entries=0") == std::string::npos) {
fprintf(stderr, "Level[%d]: %s\n", level, stats.c_str());
}
}
}
}
}
}
Improve statistics Summary: This adds more statistics to be reported by GetProperty("leveldb.stats"). The new stats include time spent waiting on stalls in MakeRoomForWrite. This also includes the total amplification rate where that is: (#bytes of sequential IO during compaction) / (#bytes from Put) This also includes a lot more data for the per-level compaction report. * Rn(MB) - MB read from level N during compaction between levels N and N+1 * Rnp1(MB) - MB read from level N+1 during compaction between levels N and N+1 * Wnew(MB) - new data written to the level during compaction * Amplify - ( Write(MB) + Rnp1(MB) ) / Rn(MB) * Rn - files read from level N during compaction between levels N and N+1 * Rnp1 - files read from level N+1 during compaction between levels N and N+1 * Wnp1 - files written to level N+1 during compaction between levels N and N+1 * NewW - new files written to level N+1 during compaction * Count - number of compactions done for this level This is the new output from DB::GetProperty("leveldb.stats"). The old output stopped at Write(MB) Compactions Level Files Size(MB) Time(sec) Read(MB) Write(MB) Rn(MB) Rnp1(MB) Wnew(MB) Amplify Read(MB/s) Write(MB/s) Rn Rnp1 Wnp1 NewW Count ------------------------------------------------------------------------------------------------------------------------------------- 0 3 6 33 0 576 0 0 576 -1.0 0.0 1.3 0 0 0 0 290 1 127 242 351 5316 5314 570 4747 567 17.0 12.1 12.1 287 2399 2685 286 32 2 161 328 54 822 824 326 496 328 4.0 1.9 1.9 160 251 411 160 161 Amplification: 22.3 rate, 0.56 GB in, 12.55 GB out Uptime(secs): 439.8 Stalls(secs): 206.938 level0_slowdown, 0.000 level0_numfiles, 24.129 memtable_compaction Task ID: # Blame Rev: Test Plan: run db_bench Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - (cherry picked from commit ecdeead38f86cc02e754d0032600742c4f02fec8) Reviewers: dhruba Differential Revision: https://reviews.facebook.net/D6153
2012-10-23 17:34:09 +00:00
next_report_ += FLAGS_stats_interval;
last_report_finish_ = now;
last_report_done_ = done_;
}
}
if (id_ == 0 && FLAGS_thread_status_per_interval) {
PrintThreadStatus();
}
fflush(stderr);
}
}
void AddBytes(int64_t n) { bytes_ += n; }
void Report(const Slice& name) {
// Pretend at least one op was done in case we are running a benchmark
// that does not call FinishedOps().
if (done_ < 1) {
done_ = 1;
}
std::string extra;
2022-05-04 17:15:49 +00:00
double elapsed = (finish_ - start_) * 1e-6;
if (bytes_ > 0) {
// Rate is computed on actual elapsed time, not the sum of per-thread
// elapsed times.
char rate[100];
snprintf(rate, sizeof(rate), "%6.1f MB/s",
(bytes_ / 1048576.0) / elapsed);
extra = rate;
}
AppendWithSpace(&extra, message_);
double throughput = (double)done_ / elapsed;
2022-05-04 17:15:49 +00:00
fprintf(stdout,
"%-12s : %11.3f micros/op %ld ops/sec %.3f seconds %" PRIu64
" operations;%s%s\n",
name.ToString().c_str(), seconds_ * 1e6 / done_, (long)throughput,
elapsed, done_, (extra.empty() ? "" : " "), extra.c_str());
if (FLAGS_histogram) {
for (auto it = hist_.begin(); it != hist_.end(); ++it) {
fprintf(stdout, "Microseconds per %s:\n%s\n",
OperationTypeString[it->first].c_str(),
it->second->ToString().c_str());
}
}
if (FLAGS_report_file_operations) {
auto* counted_fs =
FLAGS_env->GetFileSystem()->CheckedCast<CountedFileSystem>();
assert(counted_fs);
fprintf(stdout, "%s", counted_fs->PrintCounters().c_str());
counted_fs->ResetCounters();
}
fflush(stdout);
}
};
[db_bench] Support single benchmark arguments (Repeat for X times, Warm up for X times), Support CombinedStats (AVG / MEDIAN) Summary: This diff allow us to run a single benchmark X times and warm it up for Y times. and see the AVG & MEDIAN throughput of these X runs for example ``` $ ./db_bench --benchmarks="fillseq,readseq[X5-W2]" Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags RocksDB: version 4.12 Date: Wed Aug 24 10:45:26 2016 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Compression: Snappy Memtablerep: skip_list Perf Level: 1 WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags DB path: [/tmp/rocksdbtest-8616/dbbench] fillseq : 4.695 micros/op 212971 ops/sec; 23.6 MB/s DB path: [/tmp/rocksdbtest-8616/dbbench] Warming up benchmark by running 2 times readseq : 0.214 micros/op 4677005 ops/sec; 517.4 MB/s readseq : 0.212 micros/op 4706834 ops/sec; 520.7 MB/s Running benchmark for 5 times readseq : 0.218 micros/op 4588187 ops/sec; 507.6 MB/s readseq : 0.208 micros/op 4816538 ops/sec; 532.8 MB/s readseq : 0.213 micros/op 4685376 ops/sec; 518.3 MB/s readseq : 0.214 micros/op 4676787 ops/sec; 517.4 MB/s readseq : 0.217 micros/op 4618532 ops/sec; 510.9 MB/s readseq [AVG 5 runs] : 4677084 ops/sec; 517.4 MB/sec readseq [MEDIAN 5 runs] : 4676787 ops/sec; 517.4 MB/sec ``` Test Plan: run db_bench Reviewers: sdong, andrewkr, yhchiang Reviewed By: yhchiang Subscribers: andrewkr, dhruba Differential Revision: https://reviews.facebook.net/D62235
2016-08-25 19:57:35 +00:00
class CombinedStats {
public:
void AddStats(const Stats& stat) {
uint64_t total_ops = stat.done_;
uint64_t total_bytes_ = stat.bytes_;
double elapsed;
if (total_ops < 1) {
total_ops = 1;
}
elapsed = (stat.finish_ - stat.start_) * 1e-6;
throughput_ops_.emplace_back(total_ops / elapsed);
if (total_bytes_ > 0) {
double mbs = (total_bytes_ / 1048576.0);
throughput_mbs_.emplace_back(mbs / elapsed);
}
}
void Report(const std::string& bench_name) {
Add 95% confidence intervals to db_bench output (#9882) Summary: Enhancing `db_bench` output with 95% statistical confidence intervals for better performance evaluation. The goal is to unambiguously separate random variance when running benchmark over multiple iterations. Output enhanced with confidence intervals exposed in brackets: ``` $ ./db_bench --benchmarks=fillseq[-X10] Running benchmark for 10 times fillseq : 4.961 micros/op 201578 ops/sec; 22.3 MB/s fillseq : 5.030 micros/op 198824 ops/sec; 22.0 MB/s fillseq [AVG 2 runs] : 200201 (± 2698) ops/sec; 22.1 (± 0.3) MB/sec fillseq : 4.963 micros/op 201471 ops/sec; 22.3 MB/s fillseq [AVG 3 runs] : 200624 (± 1765) ops/sec; 22.2 (± 0.2) MB/sec fillseq : 5.035 micros/op 198625 ops/sec; 22.0 MB/s fillseq [AVG 4 runs] : 200124 (± 1586) ops/sec; 22.1 (± 0.2) MB/sec fillseq : 4.979 micros/op 200861 ops/sec; 22.2 MB/s fillseq [AVG 5 runs] : 200272 (± 1262) ops/sec; 22.2 (± 0.1) MB/sec fillseq : 4.893 micros/op 204367 ops/sec; 22.6 MB/s fillseq [AVG 6 runs] : 200954 (± 1688) ops/sec; 22.2 (± 0.2) MB/sec fillseq : 4.914 micros/op 203502 ops/sec; 22.5 MB/s fillseq [AVG 7 runs] : 201318 (± 1595) ops/sec; 22.3 (± 0.2) MB/sec fillseq : 4.998 micros/op 200074 ops/sec; 22.1 MB/s fillseq [AVG 8 runs] : 201163 (± 1415) ops/sec; 22.3 (± 0.2) MB/sec fillseq : 4.946 micros/op 202188 ops/sec; 22.4 MB/s fillseq [AVG 9 runs] : 201277 (± 1267) ops/sec; 22.3 (± 0.1) MB/sec fillseq : 5.093 micros/op 196331 ops/sec; 21.7 MB/s fillseq [AVG 10 runs] : 200782 (± 1491) ops/sec; 22.2 (± 0.2) MB/sec fillseq [AVG 10 runs] : 200782 (± 1491) ops/sec; 22.2 (± 0.2) MB/sec fillseq [MEDIAN 10 runs] : 201166 ops/sec; 22.3 MB/s ``` For more explicit interval representation, use `--confidence_interval_only` flag: ``` $ ./db_bench --benchmarks=fillseq[-X10] --confidence_interval_only Running benchmark for 10 times fillseq : 4.935 micros/op 202648 ops/sec; 22.4 MB/s fillseq : 5.078 micros/op 196943 ops/sec; 21.8 MB/s fillseq [CI95 2 runs] : (194205, 205385) ops/sec; (21.5, 22.7) MB/sec fillseq : 5.159 micros/op 193816 ops/sec; 21.4 MB/s fillseq [CI95 3 runs] : (192735, 202869) ops/sec; (21.3, 22.4) MB/sec fillseq : 4.947 micros/op 202158 ops/sec; 22.4 MB/s fillseq [CI95 4 runs] : (194721, 203061) ops/sec; (21.5, 22.5) MB/sec fillseq : 4.908 micros/op 203756 ops/sec; 22.5 MB/s fillseq [CI95 5 runs] : (196113, 203615) ops/sec; (21.7, 22.5) MB/sec fillseq : 5.063 micros/op 197528 ops/sec; 21.9 MB/s fillseq [CI95 6 runs] : (196319, 202631) ops/sec; (21.7, 22.4) MB/sec fillseq : 5.214 micros/op 191799 ops/sec; 21.2 MB/s fillseq [CI95 7 runs] : (194953, 201803) ops/sec; (21.6, 22.3) MB/sec fillseq : 5.260 micros/op 190095 ops/sec; 21.0 MB/s fillseq [CI95 8 runs] : (193749, 200937) ops/sec; (21.4, 22.2) MB/sec fillseq : 5.076 micros/op 196992 ops/sec; 21.8 MB/s fillseq [CI95 9 runs] : (194134, 200474) ops/sec; (21.5, 22.2) MB/sec fillseq : 5.388 micros/op 185603 ops/sec; 20.5 MB/s fillseq [CI95 10 runs] : (192487, 199781) ops/sec; (21.3, 22.1) MB/sec fillseq [AVG 10 runs] : 196134 (± 3647) ops/sec; 21.7 (± 0.4) MB/sec fillseq [MEDIAN 10 runs] : 196968 ops/sec; 21.8 MB/sec ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/9882 Reviewed By: pdillinger Differential Revision: D35796148 Pulled By: vanekjar fbshipit-source-id: 8313712d16728ff982b8aff28195ee56622385b8
2022-04-25 21:49:54 +00:00
if (throughput_ops_.size() < 2) {
// skip if there are not enough samples
return;
}
const char* name = bench_name.c_str();
int num_runs = static_cast<int>(throughput_ops_.size());
if (throughput_mbs_.size() == throughput_ops_.size()) {
fprintf(stdout,
"%s [AVG %d runs] : %d (\xC2\xB1 %d) ops/sec; %6.1f (\xC2\xB1 "
"%.1f) MB/sec\n",
Add 95% confidence intervals to db_bench output (#9882) Summary: Enhancing `db_bench` output with 95% statistical confidence intervals for better performance evaluation. The goal is to unambiguously separate random variance when running benchmark over multiple iterations. Output enhanced with confidence intervals exposed in brackets: ``` $ ./db_bench --benchmarks=fillseq[-X10] Running benchmark for 10 times fillseq : 4.961 micros/op 201578 ops/sec; 22.3 MB/s fillseq : 5.030 micros/op 198824 ops/sec; 22.0 MB/s fillseq [AVG 2 runs] : 200201 (± 2698) ops/sec; 22.1 (± 0.3) MB/sec fillseq : 4.963 micros/op 201471 ops/sec; 22.3 MB/s fillseq [AVG 3 runs] : 200624 (± 1765) ops/sec; 22.2 (± 0.2) MB/sec fillseq : 5.035 micros/op 198625 ops/sec; 22.0 MB/s fillseq [AVG 4 runs] : 200124 (± 1586) ops/sec; 22.1 (± 0.2) MB/sec fillseq : 4.979 micros/op 200861 ops/sec; 22.2 MB/s fillseq [AVG 5 runs] : 200272 (± 1262) ops/sec; 22.2 (± 0.1) MB/sec fillseq : 4.893 micros/op 204367 ops/sec; 22.6 MB/s fillseq [AVG 6 runs] : 200954 (± 1688) ops/sec; 22.2 (± 0.2) MB/sec fillseq : 4.914 micros/op 203502 ops/sec; 22.5 MB/s fillseq [AVG 7 runs] : 201318 (± 1595) ops/sec; 22.3 (± 0.2) MB/sec fillseq : 4.998 micros/op 200074 ops/sec; 22.1 MB/s fillseq [AVG 8 runs] : 201163 (± 1415) ops/sec; 22.3 (± 0.2) MB/sec fillseq : 4.946 micros/op 202188 ops/sec; 22.4 MB/s fillseq [AVG 9 runs] : 201277 (± 1267) ops/sec; 22.3 (± 0.1) MB/sec fillseq : 5.093 micros/op 196331 ops/sec; 21.7 MB/s fillseq [AVG 10 runs] : 200782 (± 1491) ops/sec; 22.2 (± 0.2) MB/sec fillseq [AVG 10 runs] : 200782 (± 1491) ops/sec; 22.2 (± 0.2) MB/sec fillseq [MEDIAN 10 runs] : 201166 ops/sec; 22.3 MB/s ``` For more explicit interval representation, use `--confidence_interval_only` flag: ``` $ ./db_bench --benchmarks=fillseq[-X10] --confidence_interval_only Running benchmark for 10 times fillseq : 4.935 micros/op 202648 ops/sec; 22.4 MB/s fillseq : 5.078 micros/op 196943 ops/sec; 21.8 MB/s fillseq [CI95 2 runs] : (194205, 205385) ops/sec; (21.5, 22.7) MB/sec fillseq : 5.159 micros/op 193816 ops/sec; 21.4 MB/s fillseq [CI95 3 runs] : (192735, 202869) ops/sec; (21.3, 22.4) MB/sec fillseq : 4.947 micros/op 202158 ops/sec; 22.4 MB/s fillseq [CI95 4 runs] : (194721, 203061) ops/sec; (21.5, 22.5) MB/sec fillseq : 4.908 micros/op 203756 ops/sec; 22.5 MB/s fillseq [CI95 5 runs] : (196113, 203615) ops/sec; (21.7, 22.5) MB/sec fillseq : 5.063 micros/op 197528 ops/sec; 21.9 MB/s fillseq [CI95 6 runs] : (196319, 202631) ops/sec; (21.7, 22.4) MB/sec fillseq : 5.214 micros/op 191799 ops/sec; 21.2 MB/s fillseq [CI95 7 runs] : (194953, 201803) ops/sec; (21.6, 22.3) MB/sec fillseq : 5.260 micros/op 190095 ops/sec; 21.0 MB/s fillseq [CI95 8 runs] : (193749, 200937) ops/sec; (21.4, 22.2) MB/sec fillseq : 5.076 micros/op 196992 ops/sec; 21.8 MB/s fillseq [CI95 9 runs] : (194134, 200474) ops/sec; (21.5, 22.2) MB/sec fillseq : 5.388 micros/op 185603 ops/sec; 20.5 MB/s fillseq [CI95 10 runs] : (192487, 199781) ops/sec; (21.3, 22.1) MB/sec fillseq [AVG 10 runs] : 196134 (± 3647) ops/sec; 21.7 (± 0.4) MB/sec fillseq [MEDIAN 10 runs] : 196968 ops/sec; 21.8 MB/sec ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/9882 Reviewed By: pdillinger Differential Revision: D35796148 Pulled By: vanekjar fbshipit-source-id: 8313712d16728ff982b8aff28195ee56622385b8
2022-04-25 21:49:54 +00:00
name, num_runs, static_cast<int>(CalcAvg(throughput_ops_)),
static_cast<int>(CalcConfidence95(throughput_ops_)),
CalcAvg(throughput_mbs_), CalcConfidence95(throughput_mbs_));
} else {
fprintf(stdout, "%s [AVG %d runs] : %d (\xC2\xB1 %d) ops/sec\n", name,
num_runs, static_cast<int>(CalcAvg(throughput_ops_)),
Add 95% confidence intervals to db_bench output (#9882) Summary: Enhancing `db_bench` output with 95% statistical confidence intervals for better performance evaluation. The goal is to unambiguously separate random variance when running benchmark over multiple iterations. Output enhanced with confidence intervals exposed in brackets: ``` $ ./db_bench --benchmarks=fillseq[-X10] Running benchmark for 10 times fillseq : 4.961 micros/op 201578 ops/sec; 22.3 MB/s fillseq : 5.030 micros/op 198824 ops/sec; 22.0 MB/s fillseq [AVG 2 runs] : 200201 (± 2698) ops/sec; 22.1 (± 0.3) MB/sec fillseq : 4.963 micros/op 201471 ops/sec; 22.3 MB/s fillseq [AVG 3 runs] : 200624 (± 1765) ops/sec; 22.2 (± 0.2) MB/sec fillseq : 5.035 micros/op 198625 ops/sec; 22.0 MB/s fillseq [AVG 4 runs] : 200124 (± 1586) ops/sec; 22.1 (± 0.2) MB/sec fillseq : 4.979 micros/op 200861 ops/sec; 22.2 MB/s fillseq [AVG 5 runs] : 200272 (± 1262) ops/sec; 22.2 (± 0.1) MB/sec fillseq : 4.893 micros/op 204367 ops/sec; 22.6 MB/s fillseq [AVG 6 runs] : 200954 (± 1688) ops/sec; 22.2 (± 0.2) MB/sec fillseq : 4.914 micros/op 203502 ops/sec; 22.5 MB/s fillseq [AVG 7 runs] : 201318 (± 1595) ops/sec; 22.3 (± 0.2) MB/sec fillseq : 4.998 micros/op 200074 ops/sec; 22.1 MB/s fillseq [AVG 8 runs] : 201163 (± 1415) ops/sec; 22.3 (± 0.2) MB/sec fillseq : 4.946 micros/op 202188 ops/sec; 22.4 MB/s fillseq [AVG 9 runs] : 201277 (± 1267) ops/sec; 22.3 (± 0.1) MB/sec fillseq : 5.093 micros/op 196331 ops/sec; 21.7 MB/s fillseq [AVG 10 runs] : 200782 (± 1491) ops/sec; 22.2 (± 0.2) MB/sec fillseq [AVG 10 runs] : 200782 (± 1491) ops/sec; 22.2 (± 0.2) MB/sec fillseq [MEDIAN 10 runs] : 201166 ops/sec; 22.3 MB/s ``` For more explicit interval representation, use `--confidence_interval_only` flag: ``` $ ./db_bench --benchmarks=fillseq[-X10] --confidence_interval_only Running benchmark for 10 times fillseq : 4.935 micros/op 202648 ops/sec; 22.4 MB/s fillseq : 5.078 micros/op 196943 ops/sec; 21.8 MB/s fillseq [CI95 2 runs] : (194205, 205385) ops/sec; (21.5, 22.7) MB/sec fillseq : 5.159 micros/op 193816 ops/sec; 21.4 MB/s fillseq [CI95 3 runs] : (192735, 202869) ops/sec; (21.3, 22.4) MB/sec fillseq : 4.947 micros/op 202158 ops/sec; 22.4 MB/s fillseq [CI95 4 runs] : (194721, 203061) ops/sec; (21.5, 22.5) MB/sec fillseq : 4.908 micros/op 203756 ops/sec; 22.5 MB/s fillseq [CI95 5 runs] : (196113, 203615) ops/sec; (21.7, 22.5) MB/sec fillseq : 5.063 micros/op 197528 ops/sec; 21.9 MB/s fillseq [CI95 6 runs] : (196319, 202631) ops/sec; (21.7, 22.4) MB/sec fillseq : 5.214 micros/op 191799 ops/sec; 21.2 MB/s fillseq [CI95 7 runs] : (194953, 201803) ops/sec; (21.6, 22.3) MB/sec fillseq : 5.260 micros/op 190095 ops/sec; 21.0 MB/s fillseq [CI95 8 runs] : (193749, 200937) ops/sec; (21.4, 22.2) MB/sec fillseq : 5.076 micros/op 196992 ops/sec; 21.8 MB/s fillseq [CI95 9 runs] : (194134, 200474) ops/sec; (21.5, 22.2) MB/sec fillseq : 5.388 micros/op 185603 ops/sec; 20.5 MB/s fillseq [CI95 10 runs] : (192487, 199781) ops/sec; (21.3, 22.1) MB/sec fillseq [AVG 10 runs] : 196134 (± 3647) ops/sec; 21.7 (± 0.4) MB/sec fillseq [MEDIAN 10 runs] : 196968 ops/sec; 21.8 MB/sec ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/9882 Reviewed By: pdillinger Differential Revision: D35796148 Pulled By: vanekjar fbshipit-source-id: 8313712d16728ff982b8aff28195ee56622385b8
2022-04-25 21:49:54 +00:00
static_cast<int>(CalcConfidence95(throughput_ops_)));
}
}
void ReportWithConfidenceIntervals(const std::string& bench_name) {
if (throughput_ops_.size() < 2) {
// skip if there are not enough samples
return;
}
const char* name = bench_name.c_str();
int num_runs = static_cast<int>(throughput_ops_.size());
int ops_avg = static_cast<int>(CalcAvg(throughput_ops_));
int ops_confidence_95 = static_cast<int>(CalcConfidence95(throughput_ops_));
if (throughput_mbs_.size() == throughput_ops_.size()) {
double mbs_avg = CalcAvg(throughput_mbs_);
double mbs_confidence_95 = CalcConfidence95(throughput_mbs_);
fprintf(stdout,
"%s [CI95 %d runs] : (%d, %d) ops/sec; (%.1f, %.1f) MB/sec\n",
name, num_runs, ops_avg - ops_confidence_95,
ops_avg + ops_confidence_95, mbs_avg - mbs_confidence_95,
mbs_avg + mbs_confidence_95);
} else {
fprintf(stdout, "%s [CI95 %d runs] : (%d, %d) ops/sec\n", name, num_runs,
ops_avg - ops_confidence_95, ops_avg + ops_confidence_95);
}
}
void ReportFinal(const std::string& bench_name) {
if (throughput_ops_.size() < 2) {
// skip if there are not enough samples
return;
}
[db_bench] Support single benchmark arguments (Repeat for X times, Warm up for X times), Support CombinedStats (AVG / MEDIAN) Summary: This diff allow us to run a single benchmark X times and warm it up for Y times. and see the AVG & MEDIAN throughput of these X runs for example ``` $ ./db_bench --benchmarks="fillseq,readseq[X5-W2]" Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags RocksDB: version 4.12 Date: Wed Aug 24 10:45:26 2016 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Compression: Snappy Memtablerep: skip_list Perf Level: 1 WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags DB path: [/tmp/rocksdbtest-8616/dbbench] fillseq : 4.695 micros/op 212971 ops/sec; 23.6 MB/s DB path: [/tmp/rocksdbtest-8616/dbbench] Warming up benchmark by running 2 times readseq : 0.214 micros/op 4677005 ops/sec; 517.4 MB/s readseq : 0.212 micros/op 4706834 ops/sec; 520.7 MB/s Running benchmark for 5 times readseq : 0.218 micros/op 4588187 ops/sec; 507.6 MB/s readseq : 0.208 micros/op 4816538 ops/sec; 532.8 MB/s readseq : 0.213 micros/op 4685376 ops/sec; 518.3 MB/s readseq : 0.214 micros/op 4676787 ops/sec; 517.4 MB/s readseq : 0.217 micros/op 4618532 ops/sec; 510.9 MB/s readseq [AVG 5 runs] : 4677084 ops/sec; 517.4 MB/sec readseq [MEDIAN 5 runs] : 4676787 ops/sec; 517.4 MB/sec ``` Test Plan: run db_bench Reviewers: sdong, andrewkr, yhchiang Reviewed By: yhchiang Subscribers: andrewkr, dhruba Differential Revision: https://reviews.facebook.net/D62235
2016-08-25 19:57:35 +00:00
const char* name = bench_name.c_str();
int num_runs = static_cast<int>(throughput_ops_.size());
if (throughput_mbs_.size() == throughput_ops_.size()) {
// \xC2\xB1 is +/- character in UTF-8
[db_bench] Support single benchmark arguments (Repeat for X times, Warm up for X times), Support CombinedStats (AVG / MEDIAN) Summary: This diff allow us to run a single benchmark X times and warm it up for Y times. and see the AVG & MEDIAN throughput of these X runs for example ``` $ ./db_bench --benchmarks="fillseq,readseq[X5-W2]" Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags RocksDB: version 4.12 Date: Wed Aug 24 10:45:26 2016 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Compression: Snappy Memtablerep: skip_list Perf Level: 1 WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags DB path: [/tmp/rocksdbtest-8616/dbbench] fillseq : 4.695 micros/op 212971 ops/sec; 23.6 MB/s DB path: [/tmp/rocksdbtest-8616/dbbench] Warming up benchmark by running 2 times readseq : 0.214 micros/op 4677005 ops/sec; 517.4 MB/s readseq : 0.212 micros/op 4706834 ops/sec; 520.7 MB/s Running benchmark for 5 times readseq : 0.218 micros/op 4588187 ops/sec; 507.6 MB/s readseq : 0.208 micros/op 4816538 ops/sec; 532.8 MB/s readseq : 0.213 micros/op 4685376 ops/sec; 518.3 MB/s readseq : 0.214 micros/op 4676787 ops/sec; 517.4 MB/s readseq : 0.217 micros/op 4618532 ops/sec; 510.9 MB/s readseq [AVG 5 runs] : 4677084 ops/sec; 517.4 MB/sec readseq [MEDIAN 5 runs] : 4676787 ops/sec; 517.4 MB/sec ``` Test Plan: run db_bench Reviewers: sdong, andrewkr, yhchiang Reviewed By: yhchiang Subscribers: andrewkr, dhruba Differential Revision: https://reviews.facebook.net/D62235
2016-08-25 19:57:35 +00:00
fprintf(stdout,
"%s [AVG %d runs] : %d (\xC2\xB1 %d) ops/sec; %6.1f (\xC2\xB1 "
"%.1f) MB/sec\n"
[db_bench] Support single benchmark arguments (Repeat for X times, Warm up for X times), Support CombinedStats (AVG / MEDIAN) Summary: This diff allow us to run a single benchmark X times and warm it up for Y times. and see the AVG & MEDIAN throughput of these X runs for example ``` $ ./db_bench --benchmarks="fillseq,readseq[X5-W2]" Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags RocksDB: version 4.12 Date: Wed Aug 24 10:45:26 2016 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Compression: Snappy Memtablerep: skip_list Perf Level: 1 WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags DB path: [/tmp/rocksdbtest-8616/dbbench] fillseq : 4.695 micros/op 212971 ops/sec; 23.6 MB/s DB path: [/tmp/rocksdbtest-8616/dbbench] Warming up benchmark by running 2 times readseq : 0.214 micros/op 4677005 ops/sec; 517.4 MB/s readseq : 0.212 micros/op 4706834 ops/sec; 520.7 MB/s Running benchmark for 5 times readseq : 0.218 micros/op 4588187 ops/sec; 507.6 MB/s readseq : 0.208 micros/op 4816538 ops/sec; 532.8 MB/s readseq : 0.213 micros/op 4685376 ops/sec; 518.3 MB/s readseq : 0.214 micros/op 4676787 ops/sec; 517.4 MB/s readseq : 0.217 micros/op 4618532 ops/sec; 510.9 MB/s readseq [AVG 5 runs] : 4677084 ops/sec; 517.4 MB/sec readseq [MEDIAN 5 runs] : 4676787 ops/sec; 517.4 MB/sec ``` Test Plan: run db_bench Reviewers: sdong, andrewkr, yhchiang Reviewed By: yhchiang Subscribers: andrewkr, dhruba Differential Revision: https://reviews.facebook.net/D62235
2016-08-25 19:57:35 +00:00
"%s [MEDIAN %d runs] : %d ops/sec; %6.1f MB/sec\n",
name, num_runs, static_cast<int>(CalcAvg(throughput_ops_)),
Add 95% confidence intervals to db_bench output (#9882) Summary: Enhancing `db_bench` output with 95% statistical confidence intervals for better performance evaluation. The goal is to unambiguously separate random variance when running benchmark over multiple iterations. Output enhanced with confidence intervals exposed in brackets: ``` $ ./db_bench --benchmarks=fillseq[-X10] Running benchmark for 10 times fillseq : 4.961 micros/op 201578 ops/sec; 22.3 MB/s fillseq : 5.030 micros/op 198824 ops/sec; 22.0 MB/s fillseq [AVG 2 runs] : 200201 (± 2698) ops/sec; 22.1 (± 0.3) MB/sec fillseq : 4.963 micros/op 201471 ops/sec; 22.3 MB/s fillseq [AVG 3 runs] : 200624 (± 1765) ops/sec; 22.2 (± 0.2) MB/sec fillseq : 5.035 micros/op 198625 ops/sec; 22.0 MB/s fillseq [AVG 4 runs] : 200124 (± 1586) ops/sec; 22.1 (± 0.2) MB/sec fillseq : 4.979 micros/op 200861 ops/sec; 22.2 MB/s fillseq [AVG 5 runs] : 200272 (± 1262) ops/sec; 22.2 (± 0.1) MB/sec fillseq : 4.893 micros/op 204367 ops/sec; 22.6 MB/s fillseq [AVG 6 runs] : 200954 (± 1688) ops/sec; 22.2 (± 0.2) MB/sec fillseq : 4.914 micros/op 203502 ops/sec; 22.5 MB/s fillseq [AVG 7 runs] : 201318 (± 1595) ops/sec; 22.3 (± 0.2) MB/sec fillseq : 4.998 micros/op 200074 ops/sec; 22.1 MB/s fillseq [AVG 8 runs] : 201163 (± 1415) ops/sec; 22.3 (± 0.2) MB/sec fillseq : 4.946 micros/op 202188 ops/sec; 22.4 MB/s fillseq [AVG 9 runs] : 201277 (± 1267) ops/sec; 22.3 (± 0.1) MB/sec fillseq : 5.093 micros/op 196331 ops/sec; 21.7 MB/s fillseq [AVG 10 runs] : 200782 (± 1491) ops/sec; 22.2 (± 0.2) MB/sec fillseq [AVG 10 runs] : 200782 (± 1491) ops/sec; 22.2 (± 0.2) MB/sec fillseq [MEDIAN 10 runs] : 201166 ops/sec; 22.3 MB/s ``` For more explicit interval representation, use `--confidence_interval_only` flag: ``` $ ./db_bench --benchmarks=fillseq[-X10] --confidence_interval_only Running benchmark for 10 times fillseq : 4.935 micros/op 202648 ops/sec; 22.4 MB/s fillseq : 5.078 micros/op 196943 ops/sec; 21.8 MB/s fillseq [CI95 2 runs] : (194205, 205385) ops/sec; (21.5, 22.7) MB/sec fillseq : 5.159 micros/op 193816 ops/sec; 21.4 MB/s fillseq [CI95 3 runs] : (192735, 202869) ops/sec; (21.3, 22.4) MB/sec fillseq : 4.947 micros/op 202158 ops/sec; 22.4 MB/s fillseq [CI95 4 runs] : (194721, 203061) ops/sec; (21.5, 22.5) MB/sec fillseq : 4.908 micros/op 203756 ops/sec; 22.5 MB/s fillseq [CI95 5 runs] : (196113, 203615) ops/sec; (21.7, 22.5) MB/sec fillseq : 5.063 micros/op 197528 ops/sec; 21.9 MB/s fillseq [CI95 6 runs] : (196319, 202631) ops/sec; (21.7, 22.4) MB/sec fillseq : 5.214 micros/op 191799 ops/sec; 21.2 MB/s fillseq [CI95 7 runs] : (194953, 201803) ops/sec; (21.6, 22.3) MB/sec fillseq : 5.260 micros/op 190095 ops/sec; 21.0 MB/s fillseq [CI95 8 runs] : (193749, 200937) ops/sec; (21.4, 22.2) MB/sec fillseq : 5.076 micros/op 196992 ops/sec; 21.8 MB/s fillseq [CI95 9 runs] : (194134, 200474) ops/sec; (21.5, 22.2) MB/sec fillseq : 5.388 micros/op 185603 ops/sec; 20.5 MB/s fillseq [CI95 10 runs] : (192487, 199781) ops/sec; (21.3, 22.1) MB/sec fillseq [AVG 10 runs] : 196134 (± 3647) ops/sec; 21.7 (± 0.4) MB/sec fillseq [MEDIAN 10 runs] : 196968 ops/sec; 21.8 MB/sec ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/9882 Reviewed By: pdillinger Differential Revision: D35796148 Pulled By: vanekjar fbshipit-source-id: 8313712d16728ff982b8aff28195ee56622385b8
2022-04-25 21:49:54 +00:00
static_cast<int>(CalcConfidence95(throughput_ops_)),
CalcAvg(throughput_mbs_), CalcConfidence95(throughput_mbs_), name,
num_runs, static_cast<int>(CalcMedian(throughput_ops_)),
[db_bench] Support single benchmark arguments (Repeat for X times, Warm up for X times), Support CombinedStats (AVG / MEDIAN) Summary: This diff allow us to run a single benchmark X times and warm it up for Y times. and see the AVG & MEDIAN throughput of these X runs for example ``` $ ./db_bench --benchmarks="fillseq,readseq[X5-W2]" Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags RocksDB: version 4.12 Date: Wed Aug 24 10:45:26 2016 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Compression: Snappy Memtablerep: skip_list Perf Level: 1 WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags DB path: [/tmp/rocksdbtest-8616/dbbench] fillseq : 4.695 micros/op 212971 ops/sec; 23.6 MB/s DB path: [/tmp/rocksdbtest-8616/dbbench] Warming up benchmark by running 2 times readseq : 0.214 micros/op 4677005 ops/sec; 517.4 MB/s readseq : 0.212 micros/op 4706834 ops/sec; 520.7 MB/s Running benchmark for 5 times readseq : 0.218 micros/op 4588187 ops/sec; 507.6 MB/s readseq : 0.208 micros/op 4816538 ops/sec; 532.8 MB/s readseq : 0.213 micros/op 4685376 ops/sec; 518.3 MB/s readseq : 0.214 micros/op 4676787 ops/sec; 517.4 MB/s readseq : 0.217 micros/op 4618532 ops/sec; 510.9 MB/s readseq [AVG 5 runs] : 4677084 ops/sec; 517.4 MB/sec readseq [MEDIAN 5 runs] : 4676787 ops/sec; 517.4 MB/sec ``` Test Plan: run db_bench Reviewers: sdong, andrewkr, yhchiang Reviewed By: yhchiang Subscribers: andrewkr, dhruba Differential Revision: https://reviews.facebook.net/D62235
2016-08-25 19:57:35 +00:00
CalcMedian(throughput_mbs_));
} else {
fprintf(stdout,
"%s [AVG %d runs] : %d (\xC2\xB1 %d) ops/sec\n"
[db_bench] Support single benchmark arguments (Repeat for X times, Warm up for X times), Support CombinedStats (AVG / MEDIAN) Summary: This diff allow us to run a single benchmark X times and warm it up for Y times. and see the AVG & MEDIAN throughput of these X runs for example ``` $ ./db_bench --benchmarks="fillseq,readseq[X5-W2]" Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags RocksDB: version 4.12 Date: Wed Aug 24 10:45:26 2016 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Compression: Snappy Memtablerep: skip_list Perf Level: 1 WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags DB path: [/tmp/rocksdbtest-8616/dbbench] fillseq : 4.695 micros/op 212971 ops/sec; 23.6 MB/s DB path: [/tmp/rocksdbtest-8616/dbbench] Warming up benchmark by running 2 times readseq : 0.214 micros/op 4677005 ops/sec; 517.4 MB/s readseq : 0.212 micros/op 4706834 ops/sec; 520.7 MB/s Running benchmark for 5 times readseq : 0.218 micros/op 4588187 ops/sec; 507.6 MB/s readseq : 0.208 micros/op 4816538 ops/sec; 532.8 MB/s readseq : 0.213 micros/op 4685376 ops/sec; 518.3 MB/s readseq : 0.214 micros/op 4676787 ops/sec; 517.4 MB/s readseq : 0.217 micros/op 4618532 ops/sec; 510.9 MB/s readseq [AVG 5 runs] : 4677084 ops/sec; 517.4 MB/sec readseq [MEDIAN 5 runs] : 4676787 ops/sec; 517.4 MB/sec ``` Test Plan: run db_bench Reviewers: sdong, andrewkr, yhchiang Reviewed By: yhchiang Subscribers: andrewkr, dhruba Differential Revision: https://reviews.facebook.net/D62235
2016-08-25 19:57:35 +00:00
"%s [MEDIAN %d runs] : %d ops/sec\n",
Add 95% confidence intervals to db_bench output (#9882) Summary: Enhancing `db_bench` output with 95% statistical confidence intervals for better performance evaluation. The goal is to unambiguously separate random variance when running benchmark over multiple iterations. Output enhanced with confidence intervals exposed in brackets: ``` $ ./db_bench --benchmarks=fillseq[-X10] Running benchmark for 10 times fillseq : 4.961 micros/op 201578 ops/sec; 22.3 MB/s fillseq : 5.030 micros/op 198824 ops/sec; 22.0 MB/s fillseq [AVG 2 runs] : 200201 (± 2698) ops/sec; 22.1 (± 0.3) MB/sec fillseq : 4.963 micros/op 201471 ops/sec; 22.3 MB/s fillseq [AVG 3 runs] : 200624 (± 1765) ops/sec; 22.2 (± 0.2) MB/sec fillseq : 5.035 micros/op 198625 ops/sec; 22.0 MB/s fillseq [AVG 4 runs] : 200124 (± 1586) ops/sec; 22.1 (± 0.2) MB/sec fillseq : 4.979 micros/op 200861 ops/sec; 22.2 MB/s fillseq [AVG 5 runs] : 200272 (± 1262) ops/sec; 22.2 (± 0.1) MB/sec fillseq : 4.893 micros/op 204367 ops/sec; 22.6 MB/s fillseq [AVG 6 runs] : 200954 (± 1688) ops/sec; 22.2 (± 0.2) MB/sec fillseq : 4.914 micros/op 203502 ops/sec; 22.5 MB/s fillseq [AVG 7 runs] : 201318 (± 1595) ops/sec; 22.3 (± 0.2) MB/sec fillseq : 4.998 micros/op 200074 ops/sec; 22.1 MB/s fillseq [AVG 8 runs] : 201163 (± 1415) ops/sec; 22.3 (± 0.2) MB/sec fillseq : 4.946 micros/op 202188 ops/sec; 22.4 MB/s fillseq [AVG 9 runs] : 201277 (± 1267) ops/sec; 22.3 (± 0.1) MB/sec fillseq : 5.093 micros/op 196331 ops/sec; 21.7 MB/s fillseq [AVG 10 runs] : 200782 (± 1491) ops/sec; 22.2 (± 0.2) MB/sec fillseq [AVG 10 runs] : 200782 (± 1491) ops/sec; 22.2 (± 0.2) MB/sec fillseq [MEDIAN 10 runs] : 201166 ops/sec; 22.3 MB/s ``` For more explicit interval representation, use `--confidence_interval_only` flag: ``` $ ./db_bench --benchmarks=fillseq[-X10] --confidence_interval_only Running benchmark for 10 times fillseq : 4.935 micros/op 202648 ops/sec; 22.4 MB/s fillseq : 5.078 micros/op 196943 ops/sec; 21.8 MB/s fillseq [CI95 2 runs] : (194205, 205385) ops/sec; (21.5, 22.7) MB/sec fillseq : 5.159 micros/op 193816 ops/sec; 21.4 MB/s fillseq [CI95 3 runs] : (192735, 202869) ops/sec; (21.3, 22.4) MB/sec fillseq : 4.947 micros/op 202158 ops/sec; 22.4 MB/s fillseq [CI95 4 runs] : (194721, 203061) ops/sec; (21.5, 22.5) MB/sec fillseq : 4.908 micros/op 203756 ops/sec; 22.5 MB/s fillseq [CI95 5 runs] : (196113, 203615) ops/sec; (21.7, 22.5) MB/sec fillseq : 5.063 micros/op 197528 ops/sec; 21.9 MB/s fillseq [CI95 6 runs] : (196319, 202631) ops/sec; (21.7, 22.4) MB/sec fillseq : 5.214 micros/op 191799 ops/sec; 21.2 MB/s fillseq [CI95 7 runs] : (194953, 201803) ops/sec; (21.6, 22.3) MB/sec fillseq : 5.260 micros/op 190095 ops/sec; 21.0 MB/s fillseq [CI95 8 runs] : (193749, 200937) ops/sec; (21.4, 22.2) MB/sec fillseq : 5.076 micros/op 196992 ops/sec; 21.8 MB/s fillseq [CI95 9 runs] : (194134, 200474) ops/sec; (21.5, 22.2) MB/sec fillseq : 5.388 micros/op 185603 ops/sec; 20.5 MB/s fillseq [CI95 10 runs] : (192487, 199781) ops/sec; (21.3, 22.1) MB/sec fillseq [AVG 10 runs] : 196134 (± 3647) ops/sec; 21.7 (± 0.4) MB/sec fillseq [MEDIAN 10 runs] : 196968 ops/sec; 21.8 MB/sec ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/9882 Reviewed By: pdillinger Differential Revision: D35796148 Pulled By: vanekjar fbshipit-source-id: 8313712d16728ff982b8aff28195ee56622385b8
2022-04-25 21:49:54 +00:00
name, num_runs, static_cast<int>(CalcAvg(throughput_ops_)),
static_cast<int>(CalcConfidence95(throughput_ops_)), name,
[db_bench] Support single benchmark arguments (Repeat for X times, Warm up for X times), Support CombinedStats (AVG / MEDIAN) Summary: This diff allow us to run a single benchmark X times and warm it up for Y times. and see the AVG & MEDIAN throughput of these X runs for example ``` $ ./db_bench --benchmarks="fillseq,readseq[X5-W2]" Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags RocksDB: version 4.12 Date: Wed Aug 24 10:45:26 2016 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Compression: Snappy Memtablerep: skip_list Perf Level: 1 WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags DB path: [/tmp/rocksdbtest-8616/dbbench] fillseq : 4.695 micros/op 212971 ops/sec; 23.6 MB/s DB path: [/tmp/rocksdbtest-8616/dbbench] Warming up benchmark by running 2 times readseq : 0.214 micros/op 4677005 ops/sec; 517.4 MB/s readseq : 0.212 micros/op 4706834 ops/sec; 520.7 MB/s Running benchmark for 5 times readseq : 0.218 micros/op 4588187 ops/sec; 507.6 MB/s readseq : 0.208 micros/op 4816538 ops/sec; 532.8 MB/s readseq : 0.213 micros/op 4685376 ops/sec; 518.3 MB/s readseq : 0.214 micros/op 4676787 ops/sec; 517.4 MB/s readseq : 0.217 micros/op 4618532 ops/sec; 510.9 MB/s readseq [AVG 5 runs] : 4677084 ops/sec; 517.4 MB/sec readseq [MEDIAN 5 runs] : 4676787 ops/sec; 517.4 MB/sec ``` Test Plan: run db_bench Reviewers: sdong, andrewkr, yhchiang Reviewed By: yhchiang Subscribers: andrewkr, dhruba Differential Revision: https://reviews.facebook.net/D62235
2016-08-25 19:57:35 +00:00
num_runs, static_cast<int>(CalcMedian(throughput_ops_)));
}
}
private:
Add 95% confidence intervals to db_bench output (#9882) Summary: Enhancing `db_bench` output with 95% statistical confidence intervals for better performance evaluation. The goal is to unambiguously separate random variance when running benchmark over multiple iterations. Output enhanced with confidence intervals exposed in brackets: ``` $ ./db_bench --benchmarks=fillseq[-X10] Running benchmark for 10 times fillseq : 4.961 micros/op 201578 ops/sec; 22.3 MB/s fillseq : 5.030 micros/op 198824 ops/sec; 22.0 MB/s fillseq [AVG 2 runs] : 200201 (± 2698) ops/sec; 22.1 (± 0.3) MB/sec fillseq : 4.963 micros/op 201471 ops/sec; 22.3 MB/s fillseq [AVG 3 runs] : 200624 (± 1765) ops/sec; 22.2 (± 0.2) MB/sec fillseq : 5.035 micros/op 198625 ops/sec; 22.0 MB/s fillseq [AVG 4 runs] : 200124 (± 1586) ops/sec; 22.1 (± 0.2) MB/sec fillseq : 4.979 micros/op 200861 ops/sec; 22.2 MB/s fillseq [AVG 5 runs] : 200272 (± 1262) ops/sec; 22.2 (± 0.1) MB/sec fillseq : 4.893 micros/op 204367 ops/sec; 22.6 MB/s fillseq [AVG 6 runs] : 200954 (± 1688) ops/sec; 22.2 (± 0.2) MB/sec fillseq : 4.914 micros/op 203502 ops/sec; 22.5 MB/s fillseq [AVG 7 runs] : 201318 (± 1595) ops/sec; 22.3 (± 0.2) MB/sec fillseq : 4.998 micros/op 200074 ops/sec; 22.1 MB/s fillseq [AVG 8 runs] : 201163 (± 1415) ops/sec; 22.3 (± 0.2) MB/sec fillseq : 4.946 micros/op 202188 ops/sec; 22.4 MB/s fillseq [AVG 9 runs] : 201277 (± 1267) ops/sec; 22.3 (± 0.1) MB/sec fillseq : 5.093 micros/op 196331 ops/sec; 21.7 MB/s fillseq [AVG 10 runs] : 200782 (± 1491) ops/sec; 22.2 (± 0.2) MB/sec fillseq [AVG 10 runs] : 200782 (± 1491) ops/sec; 22.2 (± 0.2) MB/sec fillseq [MEDIAN 10 runs] : 201166 ops/sec; 22.3 MB/s ``` For more explicit interval representation, use `--confidence_interval_only` flag: ``` $ ./db_bench --benchmarks=fillseq[-X10] --confidence_interval_only Running benchmark for 10 times fillseq : 4.935 micros/op 202648 ops/sec; 22.4 MB/s fillseq : 5.078 micros/op 196943 ops/sec; 21.8 MB/s fillseq [CI95 2 runs] : (194205, 205385) ops/sec; (21.5, 22.7) MB/sec fillseq : 5.159 micros/op 193816 ops/sec; 21.4 MB/s fillseq [CI95 3 runs] : (192735, 202869) ops/sec; (21.3, 22.4) MB/sec fillseq : 4.947 micros/op 202158 ops/sec; 22.4 MB/s fillseq [CI95 4 runs] : (194721, 203061) ops/sec; (21.5, 22.5) MB/sec fillseq : 4.908 micros/op 203756 ops/sec; 22.5 MB/s fillseq [CI95 5 runs] : (196113, 203615) ops/sec; (21.7, 22.5) MB/sec fillseq : 5.063 micros/op 197528 ops/sec; 21.9 MB/s fillseq [CI95 6 runs] : (196319, 202631) ops/sec; (21.7, 22.4) MB/sec fillseq : 5.214 micros/op 191799 ops/sec; 21.2 MB/s fillseq [CI95 7 runs] : (194953, 201803) ops/sec; (21.6, 22.3) MB/sec fillseq : 5.260 micros/op 190095 ops/sec; 21.0 MB/s fillseq [CI95 8 runs] : (193749, 200937) ops/sec; (21.4, 22.2) MB/sec fillseq : 5.076 micros/op 196992 ops/sec; 21.8 MB/s fillseq [CI95 9 runs] : (194134, 200474) ops/sec; (21.5, 22.2) MB/sec fillseq : 5.388 micros/op 185603 ops/sec; 20.5 MB/s fillseq [CI95 10 runs] : (192487, 199781) ops/sec; (21.3, 22.1) MB/sec fillseq [AVG 10 runs] : 196134 (± 3647) ops/sec; 21.7 (± 0.4) MB/sec fillseq [MEDIAN 10 runs] : 196968 ops/sec; 21.8 MB/sec ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/9882 Reviewed By: pdillinger Differential Revision: D35796148 Pulled By: vanekjar fbshipit-source-id: 8313712d16728ff982b8aff28195ee56622385b8
2022-04-25 21:49:54 +00:00
double CalcAvg(std::vector<double>& data) {
[db_bench] Support single benchmark arguments (Repeat for X times, Warm up for X times), Support CombinedStats (AVG / MEDIAN) Summary: This diff allow us to run a single benchmark X times and warm it up for Y times. and see the AVG & MEDIAN throughput of these X runs for example ``` $ ./db_bench --benchmarks="fillseq,readseq[X5-W2]" Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags RocksDB: version 4.12 Date: Wed Aug 24 10:45:26 2016 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Compression: Snappy Memtablerep: skip_list Perf Level: 1 WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags DB path: [/tmp/rocksdbtest-8616/dbbench] fillseq : 4.695 micros/op 212971 ops/sec; 23.6 MB/s DB path: [/tmp/rocksdbtest-8616/dbbench] Warming up benchmark by running 2 times readseq : 0.214 micros/op 4677005 ops/sec; 517.4 MB/s readseq : 0.212 micros/op 4706834 ops/sec; 520.7 MB/s Running benchmark for 5 times readseq : 0.218 micros/op 4588187 ops/sec; 507.6 MB/s readseq : 0.208 micros/op 4816538 ops/sec; 532.8 MB/s readseq : 0.213 micros/op 4685376 ops/sec; 518.3 MB/s readseq : 0.214 micros/op 4676787 ops/sec; 517.4 MB/s readseq : 0.217 micros/op 4618532 ops/sec; 510.9 MB/s readseq [AVG 5 runs] : 4677084 ops/sec; 517.4 MB/sec readseq [MEDIAN 5 runs] : 4676787 ops/sec; 517.4 MB/sec ``` Test Plan: run db_bench Reviewers: sdong, andrewkr, yhchiang Reviewed By: yhchiang Subscribers: andrewkr, dhruba Differential Revision: https://reviews.facebook.net/D62235
2016-08-25 19:57:35 +00:00
double avg = 0;
for (double x : data) {
avg += x;
}
avg = avg / data.size();
return avg;
}
Add 95% confidence intervals to db_bench output (#9882) Summary: Enhancing `db_bench` output with 95% statistical confidence intervals for better performance evaluation. The goal is to unambiguously separate random variance when running benchmark over multiple iterations. Output enhanced with confidence intervals exposed in brackets: ``` $ ./db_bench --benchmarks=fillseq[-X10] Running benchmark for 10 times fillseq : 4.961 micros/op 201578 ops/sec; 22.3 MB/s fillseq : 5.030 micros/op 198824 ops/sec; 22.0 MB/s fillseq [AVG 2 runs] : 200201 (± 2698) ops/sec; 22.1 (± 0.3) MB/sec fillseq : 4.963 micros/op 201471 ops/sec; 22.3 MB/s fillseq [AVG 3 runs] : 200624 (± 1765) ops/sec; 22.2 (± 0.2) MB/sec fillseq : 5.035 micros/op 198625 ops/sec; 22.0 MB/s fillseq [AVG 4 runs] : 200124 (± 1586) ops/sec; 22.1 (± 0.2) MB/sec fillseq : 4.979 micros/op 200861 ops/sec; 22.2 MB/s fillseq [AVG 5 runs] : 200272 (± 1262) ops/sec; 22.2 (± 0.1) MB/sec fillseq : 4.893 micros/op 204367 ops/sec; 22.6 MB/s fillseq [AVG 6 runs] : 200954 (± 1688) ops/sec; 22.2 (± 0.2) MB/sec fillseq : 4.914 micros/op 203502 ops/sec; 22.5 MB/s fillseq [AVG 7 runs] : 201318 (± 1595) ops/sec; 22.3 (± 0.2) MB/sec fillseq : 4.998 micros/op 200074 ops/sec; 22.1 MB/s fillseq [AVG 8 runs] : 201163 (± 1415) ops/sec; 22.3 (± 0.2) MB/sec fillseq : 4.946 micros/op 202188 ops/sec; 22.4 MB/s fillseq [AVG 9 runs] : 201277 (± 1267) ops/sec; 22.3 (± 0.1) MB/sec fillseq : 5.093 micros/op 196331 ops/sec; 21.7 MB/s fillseq [AVG 10 runs] : 200782 (± 1491) ops/sec; 22.2 (± 0.2) MB/sec fillseq [AVG 10 runs] : 200782 (± 1491) ops/sec; 22.2 (± 0.2) MB/sec fillseq [MEDIAN 10 runs] : 201166 ops/sec; 22.3 MB/s ``` For more explicit interval representation, use `--confidence_interval_only` flag: ``` $ ./db_bench --benchmarks=fillseq[-X10] --confidence_interval_only Running benchmark for 10 times fillseq : 4.935 micros/op 202648 ops/sec; 22.4 MB/s fillseq : 5.078 micros/op 196943 ops/sec; 21.8 MB/s fillseq [CI95 2 runs] : (194205, 205385) ops/sec; (21.5, 22.7) MB/sec fillseq : 5.159 micros/op 193816 ops/sec; 21.4 MB/s fillseq [CI95 3 runs] : (192735, 202869) ops/sec; (21.3, 22.4) MB/sec fillseq : 4.947 micros/op 202158 ops/sec; 22.4 MB/s fillseq [CI95 4 runs] : (194721, 203061) ops/sec; (21.5, 22.5) MB/sec fillseq : 4.908 micros/op 203756 ops/sec; 22.5 MB/s fillseq [CI95 5 runs] : (196113, 203615) ops/sec; (21.7, 22.5) MB/sec fillseq : 5.063 micros/op 197528 ops/sec; 21.9 MB/s fillseq [CI95 6 runs] : (196319, 202631) ops/sec; (21.7, 22.4) MB/sec fillseq : 5.214 micros/op 191799 ops/sec; 21.2 MB/s fillseq [CI95 7 runs] : (194953, 201803) ops/sec; (21.6, 22.3) MB/sec fillseq : 5.260 micros/op 190095 ops/sec; 21.0 MB/s fillseq [CI95 8 runs] : (193749, 200937) ops/sec; (21.4, 22.2) MB/sec fillseq : 5.076 micros/op 196992 ops/sec; 21.8 MB/s fillseq [CI95 9 runs] : (194134, 200474) ops/sec; (21.5, 22.2) MB/sec fillseq : 5.388 micros/op 185603 ops/sec; 20.5 MB/s fillseq [CI95 10 runs] : (192487, 199781) ops/sec; (21.3, 22.1) MB/sec fillseq [AVG 10 runs] : 196134 (± 3647) ops/sec; 21.7 (± 0.4) MB/sec fillseq [MEDIAN 10 runs] : 196968 ops/sec; 21.8 MB/sec ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/9882 Reviewed By: pdillinger Differential Revision: D35796148 Pulled By: vanekjar fbshipit-source-id: 8313712d16728ff982b8aff28195ee56622385b8
2022-04-25 21:49:54 +00:00
// Calculates 95% CI assuming a normal distribution of samples.
// Samples are not from a normal distribution, but it still
// provides useful approximation.
double CalcConfidence95(std::vector<double>& data) {
assert(data.size() > 1);
double avg = CalcAvg(data);
double std_error = CalcStdDev(data, avg) / std::sqrt(data.size());
// Z score for the 97.5 percentile
// see https://en.wikipedia.org/wiki/1.96
return 1.959964 * std_error;
}
double CalcMedian(std::vector<double>& data) {
[db_bench] Support single benchmark arguments (Repeat for X times, Warm up for X times), Support CombinedStats (AVG / MEDIAN) Summary: This diff allow us to run a single benchmark X times and warm it up for Y times. and see the AVG & MEDIAN throughput of these X runs for example ``` $ ./db_bench --benchmarks="fillseq,readseq[X5-W2]" Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags RocksDB: version 4.12 Date: Wed Aug 24 10:45:26 2016 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Compression: Snappy Memtablerep: skip_list Perf Level: 1 WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags DB path: [/tmp/rocksdbtest-8616/dbbench] fillseq : 4.695 micros/op 212971 ops/sec; 23.6 MB/s DB path: [/tmp/rocksdbtest-8616/dbbench] Warming up benchmark by running 2 times readseq : 0.214 micros/op 4677005 ops/sec; 517.4 MB/s readseq : 0.212 micros/op 4706834 ops/sec; 520.7 MB/s Running benchmark for 5 times readseq : 0.218 micros/op 4588187 ops/sec; 507.6 MB/s readseq : 0.208 micros/op 4816538 ops/sec; 532.8 MB/s readseq : 0.213 micros/op 4685376 ops/sec; 518.3 MB/s readseq : 0.214 micros/op 4676787 ops/sec; 517.4 MB/s readseq : 0.217 micros/op 4618532 ops/sec; 510.9 MB/s readseq [AVG 5 runs] : 4677084 ops/sec; 517.4 MB/sec readseq [MEDIAN 5 runs] : 4676787 ops/sec; 517.4 MB/sec ``` Test Plan: run db_bench Reviewers: sdong, andrewkr, yhchiang Reviewed By: yhchiang Subscribers: andrewkr, dhruba Differential Revision: https://reviews.facebook.net/D62235
2016-08-25 19:57:35 +00:00
assert(data.size() > 0);
std::sort(data.begin(), data.end());
size_t mid = data.size() / 2;
if (data.size() % 2 == 1) {
// Odd number of entries
return data[mid];
} else {
// Even number of entries
return (data[mid] + data[mid - 1]) / 2;
}
}
Add 95% confidence intervals to db_bench output (#9882) Summary: Enhancing `db_bench` output with 95% statistical confidence intervals for better performance evaluation. The goal is to unambiguously separate random variance when running benchmark over multiple iterations. Output enhanced with confidence intervals exposed in brackets: ``` $ ./db_bench --benchmarks=fillseq[-X10] Running benchmark for 10 times fillseq : 4.961 micros/op 201578 ops/sec; 22.3 MB/s fillseq : 5.030 micros/op 198824 ops/sec; 22.0 MB/s fillseq [AVG 2 runs] : 200201 (± 2698) ops/sec; 22.1 (± 0.3) MB/sec fillseq : 4.963 micros/op 201471 ops/sec; 22.3 MB/s fillseq [AVG 3 runs] : 200624 (± 1765) ops/sec; 22.2 (± 0.2) MB/sec fillseq : 5.035 micros/op 198625 ops/sec; 22.0 MB/s fillseq [AVG 4 runs] : 200124 (± 1586) ops/sec; 22.1 (± 0.2) MB/sec fillseq : 4.979 micros/op 200861 ops/sec; 22.2 MB/s fillseq [AVG 5 runs] : 200272 (± 1262) ops/sec; 22.2 (± 0.1) MB/sec fillseq : 4.893 micros/op 204367 ops/sec; 22.6 MB/s fillseq [AVG 6 runs] : 200954 (± 1688) ops/sec; 22.2 (± 0.2) MB/sec fillseq : 4.914 micros/op 203502 ops/sec; 22.5 MB/s fillseq [AVG 7 runs] : 201318 (± 1595) ops/sec; 22.3 (± 0.2) MB/sec fillseq : 4.998 micros/op 200074 ops/sec; 22.1 MB/s fillseq [AVG 8 runs] : 201163 (± 1415) ops/sec; 22.3 (± 0.2) MB/sec fillseq : 4.946 micros/op 202188 ops/sec; 22.4 MB/s fillseq [AVG 9 runs] : 201277 (± 1267) ops/sec; 22.3 (± 0.1) MB/sec fillseq : 5.093 micros/op 196331 ops/sec; 21.7 MB/s fillseq [AVG 10 runs] : 200782 (± 1491) ops/sec; 22.2 (± 0.2) MB/sec fillseq [AVG 10 runs] : 200782 (± 1491) ops/sec; 22.2 (± 0.2) MB/sec fillseq [MEDIAN 10 runs] : 201166 ops/sec; 22.3 MB/s ``` For more explicit interval representation, use `--confidence_interval_only` flag: ``` $ ./db_bench --benchmarks=fillseq[-X10] --confidence_interval_only Running benchmark for 10 times fillseq : 4.935 micros/op 202648 ops/sec; 22.4 MB/s fillseq : 5.078 micros/op 196943 ops/sec; 21.8 MB/s fillseq [CI95 2 runs] : (194205, 205385) ops/sec; (21.5, 22.7) MB/sec fillseq : 5.159 micros/op 193816 ops/sec; 21.4 MB/s fillseq [CI95 3 runs] : (192735, 202869) ops/sec; (21.3, 22.4) MB/sec fillseq : 4.947 micros/op 202158 ops/sec; 22.4 MB/s fillseq [CI95 4 runs] : (194721, 203061) ops/sec; (21.5, 22.5) MB/sec fillseq : 4.908 micros/op 203756 ops/sec; 22.5 MB/s fillseq [CI95 5 runs] : (196113, 203615) ops/sec; (21.7, 22.5) MB/sec fillseq : 5.063 micros/op 197528 ops/sec; 21.9 MB/s fillseq [CI95 6 runs] : (196319, 202631) ops/sec; (21.7, 22.4) MB/sec fillseq : 5.214 micros/op 191799 ops/sec; 21.2 MB/s fillseq [CI95 7 runs] : (194953, 201803) ops/sec; (21.6, 22.3) MB/sec fillseq : 5.260 micros/op 190095 ops/sec; 21.0 MB/s fillseq [CI95 8 runs] : (193749, 200937) ops/sec; (21.4, 22.2) MB/sec fillseq : 5.076 micros/op 196992 ops/sec; 21.8 MB/s fillseq [CI95 9 runs] : (194134, 200474) ops/sec; (21.5, 22.2) MB/sec fillseq : 5.388 micros/op 185603 ops/sec; 20.5 MB/s fillseq [CI95 10 runs] : (192487, 199781) ops/sec; (21.3, 22.1) MB/sec fillseq [AVG 10 runs] : 196134 (± 3647) ops/sec; 21.7 (± 0.4) MB/sec fillseq [MEDIAN 10 runs] : 196968 ops/sec; 21.8 MB/sec ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/9882 Reviewed By: pdillinger Differential Revision: D35796148 Pulled By: vanekjar fbshipit-source-id: 8313712d16728ff982b8aff28195ee56622385b8
2022-04-25 21:49:54 +00:00
double CalcStdDev(std::vector<double>& data, double average) {
assert(data.size() > 1);
double squared_sum = 0.0;
for (double x : data) {
squared_sum += std::pow(x - average, 2);
}
// using samples count - 1 following Bessel's correction
// see https://en.wikipedia.org/wiki/Bessel%27s_correction
return std::sqrt(squared_sum / (data.size() - 1));
}
[db_bench] Support single benchmark arguments (Repeat for X times, Warm up for X times), Support CombinedStats (AVG / MEDIAN) Summary: This diff allow us to run a single benchmark X times and warm it up for Y times. and see the AVG & MEDIAN throughput of these X runs for example ``` $ ./db_bench --benchmarks="fillseq,readseq[X5-W2]" Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags RocksDB: version 4.12 Date: Wed Aug 24 10:45:26 2016 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Compression: Snappy Memtablerep: skip_list Perf Level: 1 WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags DB path: [/tmp/rocksdbtest-8616/dbbench] fillseq : 4.695 micros/op 212971 ops/sec; 23.6 MB/s DB path: [/tmp/rocksdbtest-8616/dbbench] Warming up benchmark by running 2 times readseq : 0.214 micros/op 4677005 ops/sec; 517.4 MB/s readseq : 0.212 micros/op 4706834 ops/sec; 520.7 MB/s Running benchmark for 5 times readseq : 0.218 micros/op 4588187 ops/sec; 507.6 MB/s readseq : 0.208 micros/op 4816538 ops/sec; 532.8 MB/s readseq : 0.213 micros/op 4685376 ops/sec; 518.3 MB/s readseq : 0.214 micros/op 4676787 ops/sec; 517.4 MB/s readseq : 0.217 micros/op 4618532 ops/sec; 510.9 MB/s readseq [AVG 5 runs] : 4677084 ops/sec; 517.4 MB/sec readseq [MEDIAN 5 runs] : 4676787 ops/sec; 517.4 MB/sec ``` Test Plan: run db_bench Reviewers: sdong, andrewkr, yhchiang Reviewed By: yhchiang Subscribers: andrewkr, dhruba Differential Revision: https://reviews.facebook.net/D62235
2016-08-25 19:57:35 +00:00
std::vector<double> throughput_ops_;
std::vector<double> throughput_mbs_;
};
Write a benchmark to emulate time series data Summary: Add a benchmark to `db_bench`. In this benchmark, a write thread will populate time series data in the format of 'id | timestamp', and multiple read threads will randomly retrieve all data from one id at a time. Test Plan: Run the benchmark: `num=134217728;bpl=536870912;mb=67108864;overlap=10;mcz=2;del=300000000;levels=6;ctrig=4;delay=8;stop=12;wbn=3;mbc=20;wbs=134217728;dds=0;sync=0;t=32;vs=800;bs=4096;cs=17179869184;of=500000;wps=0;si=10000000; kir=100000; dir=/data/users/jhli/test/; ./db_bench --benchmarks=timeseries --disable_seek_compaction=1 --mmap_read=0 --statistics=1 --histogram=1 --num=$num --threads=$t --value_size=$vs --block_size=$bs --cache_size=$cs --bloom_bits=10 --cache_numshardbits=6 --open_files=$of --verify_checksum=1 --db=$dir --sync=$sync --disable_wal=0 --compression_type=none --stats_interval=$si --compression_ratio=1 --disable_data_sync=$dds --write_buffer_size=$wbs --target_file_size_base=$mb --max_write_buffer_number=$wbn --max_background_compactions=$mbc --level0_file_num_compaction_trigger=$ctrig --level0_slowdown_writes_trigger=$delay --level0_stop_writes_trigger=$stop --num_levels=$levels --delete_obsolete_files_period_micros=$del --min_level_to_compress=$mcz --max_grandparent_overlap_factor=$overlap --stats_per_interval=1 --max_bytes_for_level_base=$bpl --use_existing_db=0 --key_id_range=$kir` Reviewers: andrewkr, sdong Reviewed By: sdong Subscribers: lgalanis, andrewkr, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D60651
2016-08-01 19:28:51 +00:00
class TimestampEmulator {
private:
std::atomic<uint64_t> timestamp_;
public:
TimestampEmulator() : timestamp_(0) {}
uint64_t Get() const { return timestamp_.load(); }
void Inc() { timestamp_++; }
Slice Allocate(char* scratch) {
// TODO: support larger timestamp sizes
assert(FLAGS_user_timestamp_size == 8);
assert(scratch);
uint64_t ts = timestamp_.fetch_add(1);
EncodeFixed64(scratch, ts);
return Slice(scratch, FLAGS_user_timestamp_size);
}
Slice GetTimestampForRead(Random64& rand, char* scratch) {
assert(FLAGS_user_timestamp_size == 8);
assert(scratch);
if (FLAGS_read_with_latest_user_timestamp) {
return Allocate(scratch);
}
// Choose a random timestamp from the past.
uint64_t ts = rand.Next() % Get();
EncodeFixed64(scratch, ts);
return Slice(scratch, FLAGS_user_timestamp_size);
}
Write a benchmark to emulate time series data Summary: Add a benchmark to `db_bench`. In this benchmark, a write thread will populate time series data in the format of 'id | timestamp', and multiple read threads will randomly retrieve all data from one id at a time. Test Plan: Run the benchmark: `num=134217728;bpl=536870912;mb=67108864;overlap=10;mcz=2;del=300000000;levels=6;ctrig=4;delay=8;stop=12;wbn=3;mbc=20;wbs=134217728;dds=0;sync=0;t=32;vs=800;bs=4096;cs=17179869184;of=500000;wps=0;si=10000000; kir=100000; dir=/data/users/jhli/test/; ./db_bench --benchmarks=timeseries --disable_seek_compaction=1 --mmap_read=0 --statistics=1 --histogram=1 --num=$num --threads=$t --value_size=$vs --block_size=$bs --cache_size=$cs --bloom_bits=10 --cache_numshardbits=6 --open_files=$of --verify_checksum=1 --db=$dir --sync=$sync --disable_wal=0 --compression_type=none --stats_interval=$si --compression_ratio=1 --disable_data_sync=$dds --write_buffer_size=$wbs --target_file_size_base=$mb --max_write_buffer_number=$wbn --max_background_compactions=$mbc --level0_file_num_compaction_trigger=$ctrig --level0_slowdown_writes_trigger=$delay --level0_stop_writes_trigger=$stop --num_levels=$levels --delete_obsolete_files_period_micros=$del --min_level_to_compress=$mcz --max_grandparent_overlap_factor=$overlap --stats_per_interval=1 --max_bytes_for_level_base=$bpl --use_existing_db=0 --key_id_range=$kir` Reviewers: andrewkr, sdong Reviewed By: sdong Subscribers: lgalanis, andrewkr, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D60651
2016-08-01 19:28:51 +00:00
};
// State shared by all concurrent executions of the same benchmark.
struct SharedState {
port::Mutex mu;
port::CondVar cv;
int total;
int perf_level;
std::shared_ptr<RateLimiter> write_rate_limiter;
std::shared_ptr<RateLimiter> read_rate_limiter;
// Each thread goes through the following states:
// (1) initializing
// (2) waiting for others to be initialized
// (3) running
// (4) done
long num_initialized;
long num_done;
bool start;
SharedState() : cv(&mu), perf_level(FLAGS_perf_level) {}
};
// Per-thread state for concurrent executions of the same benchmark.
struct ThreadState {
int tid; // 0..n-1 when running in n threads
Random64 rand; // Has different seeds for different threads
Stats stats;
SharedState* shared;
explicit ThreadState(int index, int my_seed)
: tid(index), rand(*seed_base + my_seed) {}
};
class Duration {
public:
Duration(uint64_t max_seconds, int64_t max_ops, int64_t ops_per_stage = 0) {
max_seconds_ = max_seconds;
max_ops_ = max_ops;
ops_per_stage_ = (ops_per_stage > 0) ? ops_per_stage : max_ops;
ops_ = 0;
start_at_ = FLAGS_env->NowMicros();
}
int64_t GetStage() { return std::min(ops_, max_ops_ - 1) / ops_per_stage_; }
bool Done(int64_t increment) {
if (increment <= 0) {
increment = 1; // avoid Done(0) and infinite loops
}
ops_ += increment;
if (max_seconds_) {
// Recheck every appx 1000 ops (exact iff increment is factor of 1000)
auto granularity = FLAGS_ops_between_duration_checks;
if ((ops_ / granularity) != ((ops_ - increment) / granularity)) {
uint64_t now = FLAGS_env->NowMicros();
return ((now - start_at_) / 1000000) >= max_seconds_;
} else {
return false;
}
} else {
return ops_ > max_ops_;
}
}
private:
uint64_t max_seconds_;
int64_t max_ops_;
int64_t ops_per_stage_;
int64_t ops_;
uint64_t start_at_;
};
class Benchmark {
private:
std::shared_ptr<Cache> cache_;
std::shared_ptr<Cache> compressed_cache_;
Fix auto_prefix_mode performance with partitioned filters (#10012) Summary: Essentially refactored the RangeMayExist implementation in FullFilterBlockReader to FilterBlockReaderCommon so that it applies to partitioned filters as well. (The function is not called for the block-based filter case.) RangeMayExist is essentially a series of checks around a possible PrefixMayExist, and I'm confident those checks should be the same for partitioned as for full filters. (I think it's likely that bugs remain in those checks, but this change is overall a simplifying one.) Added auto_prefix_mode support to db_bench Other small fixes as well Fixes https://github.com/facebook/rocksdb/issues/10003 Pull Request resolved: https://github.com/facebook/rocksdb/pull/10012 Test Plan: Expanded unit test that uses statistics to check for filter optimization, fails without the production code changes here Performance: populate two DBs with ``` TEST_TMPDIR=/dev/shm/rocksdb_nonpartitioned ./db_bench -benchmarks=fillrandom -num=10000000 -disable_wal=1 -write_buffer_size=30000000 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=8 TEST_TMPDIR=/dev/shm/rocksdb_partitioned ./db_bench -benchmarks=fillrandom -num=10000000 -disable_wal=1 -write_buffer_size=30000000 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=8 -partition_index_and_filters ``` Observe no measurable change in non-partitioned performance ``` TEST_TMPDIR=/dev/shm/rocksdb_nonpartitioned ./db_bench -benchmarks=seekrandom[-X1000] -num=10000000 -readonly -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=8 -auto_prefix_mode -cache_index_and_filter_blocks=1 -cache_size=1000000000 -duration 20 ``` Before: seekrandom [AVG 15 runs] : 11798 (± 331) ops/sec After: seekrandom [AVG 15 runs] : 11724 (± 315) ops/sec Observe big improvement with partitioned (also supported by bloom use statistics) ``` TEST_TMPDIR=/dev/shm/rocksdb_partitioned ./db_bench -benchmarks=seekrandom[-X1000] -num=10000000 -readonly -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=8 -partition_index_and_filters -auto_prefix_mode -cache_index_and_filter_blocks=1 -cache_size=1000000000 -duration 20 ``` Before: seekrandom [AVG 12 runs] : 2942 (± 57) ops/sec After: seekrandom [AVG 12 runs] : 7489 (± 184) ops/sec Reviewed By: siying Differential Revision: D36469796 Pulled By: pdillinger fbshipit-source-id: bcf1e2a68d347b32adb2b27384f945434e7a266d
2022-05-19 20:09:03 +00:00
std::shared_ptr<const SliceTransform> prefix_extractor_;
DBWithColumnFamilies db_;
std::vector<DBWithColumnFamilies> multi_dbs_;
int64_t num_;
int key_size_;
int user_timestamp_size_;
int prefix_size_;
Avoid seed reuse when --benchmarks has more than one test (#9733) Summary: When --benchmarks has more than one test then the threads in one benchmark will use the same set of seeds as the threads in the previous benchmark. This diff fixe that. This fixes https://github.com/facebook/rocksdb/issues/9632 Pull Request resolved: https://github.com/facebook/rocksdb/pull/9733 Test Plan: For this command line the block cache is 8GB, so it caches at most 1024 8KB blocks. Note that without this diff the second run of readrandom has a much better response time because seed reuse means the second run reads the same 1000 blocks as the first run and they are cached at that point. But with this diff that does not happen. ./db_bench --benchmarks=fillseq,flush,compact0,waitforcompaction,levelstats,readrandom,readrandom --compression_type=zlib --num=10000000 --reads=1000 --block_size=8192 ... ``` Level Files Size(MB) -------------------- 0 0 0 1 11 238 2 9 253 3 0 0 4 0 0 5 0 0 6 0 0 ``` --- perf results without this diff DB path: [/tmp/rocksdbtest-2260/dbbench] readrandom : 46.212 micros/op 21618 ops/sec; 2.4 MB/s (1000 of 1000 found) DB path: [/tmp/rocksdbtest-2260/dbbench] readrandom : 21.963 micros/op 45450 ops/sec; 5.0 MB/s (1000 of 1000 found) --- perf results with this diff DB path: [/tmp/rocksdbtest-2260/dbbench] readrandom : 47.213 micros/op 21126 ops/sec; 2.3 MB/s (1000 of 1000 found) DB path: [/tmp/rocksdbtest-2260/dbbench] readrandom : 42.880 micros/op 23299 ops/sec; 2.6 MB/s (1000 of 1000 found) Reviewed By: jay-zhuang Differential Revision: D35089763 Pulled By: mdcallag fbshipit-source-id: 1b50143a07afe876b8c8e5fa50dd94a8ce57fc6b
2022-03-24 15:57:48 +00:00
int total_thread_count_;
int64_t keys_per_prefix_;
int64_t entries_per_batch_;
int64_t writes_before_delete_range_;
int64_t writes_per_range_tombstone_;
int64_t range_tombstone_width_;
int64_t max_num_range_tombstones_;
Ignore `total_order_seek` in DB::Get (#9427) Summary: Apparently setting total_order_seek=true for DB::Get was intended to allow accurate read semantics if the current prefix extractor doesn't match what was used to generate SST files on disk. But since prefix_extractor was made a mutable option in 5.14.0, we have been able to detect this case and provide the correct semantics regardless of the total_order_seek option. Since that time, the option has only made Get() slower in a reasonably common case: prefix_extractor unchanged and whole_key_filtering=false. So this change primarily removes unnecessary effect of total_order_seek on Get. Also cleans up some related comments. Also adds a -total_order_seek option to db_bench and canonicalizes handling of ReadOptions in db_bench so that command line options have the expected association with library features. (There is potential for change in regression test behavior, but the old behavior is likely indefensible, or some other inconsistency would need to be fixed.) TODO in follow-up work: there should be no reason for Get() to depend on current prefix extractor at all. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9427 Test Plan: Unit tests updated. Performance (using db_bench update) Create DB with `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -benchmarks=fillrandom -num=10000000 -disable_wal=1 -write_buffer_size=10000000 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12 -whole_key_filtering=0` Test with and without `-total_order_seek` on `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -use_existing_db -readonly -benchmarks=readrandom -num=10000000 -duration=40 -disable_wal=1 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12` Before this change, total_order_seek=false: 25188 ops/sec Before this change, total_order_seek=true: 1222 ops/sec (~20x slower) After this change, total_order_seek=false: 24570 ops/sec After this change, total_order_seek=true: 25012 ops/sec (indistinguishable) Reviewed By: siying Differential Revision: D33753458 Pulled By: pdillinger fbshipit-source-id: bf892f34907a5e407d9c40bd4d42f0adbcbe0014
2022-02-01 03:45:17 +00:00
ReadOptions read_options_;
WriteOptions write_options_;
Options open_options_; // keep options around to properly destroy db later
TraceOptions trace_options_;
TraceOptions block_cache_trace_options_;
int64_t reads_;
int64_t deletes_;
double read_random_exp_range_;
int64_t writes_;
int64_t readwrites_;
int64_t merge_keys_;
bool report_file_operations_;
bool use_blob_db_; // Stacked BlobDB
bool read_operands_; // read via GetMergeOperands()
std::vector<std::string> keys_;
Auto recovery from out of space errors (#4164) Summary: This commit implements automatic recovery from a Status::NoSpace() error during background operations such as write callback, flush and compaction. The broad design is as follows - 1. Compaction errors are treated as soft errors and don't put the database in read-only mode. A compaction is delayed until enough free disk space is available to accomodate the compaction outputs, which is estimated based on the input size. This means that users can continue to write, and we rely on the WriteController to delay or stop writes if the compaction debt becomes too high due to persistent low disk space condition 2. Errors during write callback and flush are treated as hard errors, i.e the database is put in read-only mode and goes back to read-write only fater certain recovery actions are taken. 3. Both types of recovery rely on the SstFileManagerImpl to poll for sufficient disk space. We assume that there is a 1-1 mapping between an SFM and the underlying OS storage container. For cases where multiple DBs are hosted on a single storage container, the user is expected to allocate a single SFM instance and use the same one for all the DBs. If no SFM is specified by the user, DBImpl::Open() will allocate one, but this will be one per DB and each DB will recover independently. The recovery implemented by SFM is as follows - a) On the first occurance of an out of space error during compaction, subsequent compactions will be delayed until the disk free space check indicates enough available space. The required space is computed as the sum of input sizes. b) The free space check requirement will be removed once the amount of free space is greater than the size reserved by in progress compactions when the first error occured c) If the out of space error is a hard error, a background thread in SFM will poll for sufficient headroom before triggering the recovery of the database and putting it in write-only mode. The headroom is calculated as the sum of the write_buffer_size of all the DB instances associated with the SFM 4. EventListener callbacks will be called at the start and completion of automatic recovery. Users can disable the auto recov ery in the start callback, and later initiate it manually by calling DB::Resume() Todo: 1. More extensive testing 2. Add disk full condition to db_stress (follow-on PR) Pull Request resolved: https://github.com/facebook/rocksdb/pull/4164 Differential Revision: D9846378 Pulled By: anand1976 fbshipit-source-id: 80ea875dbd7f00205e19c82215ff6e37da10da4a
2018-09-15 20:36:19 +00:00
class ErrorHandlerListener : public EventListener {
public:
ErrorHandlerListener()
: mutex_(),
cv_(&mutex_),
no_auto_recovery_(false),
recovery_complete_(false) {}
~ErrorHandlerListener() override = default;
Auto recovery from out of space errors (#4164) Summary: This commit implements automatic recovery from a Status::NoSpace() error during background operations such as write callback, flush and compaction. The broad design is as follows - 1. Compaction errors are treated as soft errors and don't put the database in read-only mode. A compaction is delayed until enough free disk space is available to accomodate the compaction outputs, which is estimated based on the input size. This means that users can continue to write, and we rely on the WriteController to delay or stop writes if the compaction debt becomes too high due to persistent low disk space condition 2. Errors during write callback and flush are treated as hard errors, i.e the database is put in read-only mode and goes back to read-write only fater certain recovery actions are taken. 3. Both types of recovery rely on the SstFileManagerImpl to poll for sufficient disk space. We assume that there is a 1-1 mapping between an SFM and the underlying OS storage container. For cases where multiple DBs are hosted on a single storage container, the user is expected to allocate a single SFM instance and use the same one for all the DBs. If no SFM is specified by the user, DBImpl::Open() will allocate one, but this will be one per DB and each DB will recover independently. The recovery implemented by SFM is as follows - a) On the first occurance of an out of space error during compaction, subsequent compactions will be delayed until the disk free space check indicates enough available space. The required space is computed as the sum of input sizes. b) The free space check requirement will be removed once the amount of free space is greater than the size reserved by in progress compactions when the first error occured c) If the out of space error is a hard error, a background thread in SFM will poll for sufficient headroom before triggering the recovery of the database and putting it in write-only mode. The headroom is calculated as the sum of the write_buffer_size of all the DB instances associated with the SFM 4. EventListener callbacks will be called at the start and completion of automatic recovery. Users can disable the auto recov ery in the start callback, and later initiate it manually by calling DB::Resume() Todo: 1. More extensive testing 2. Add disk full condition to db_stress (follow-on PR) Pull Request resolved: https://github.com/facebook/rocksdb/pull/4164 Differential Revision: D9846378 Pulled By: anand1976 fbshipit-source-id: 80ea875dbd7f00205e19c82215ff6e37da10da4a
2018-09-15 20:36:19 +00:00
const char* Name() const override { return kClassName(); }
static const char* kClassName() { return "ErrorHandlerListener"; }
Auto recovery from out of space errors (#4164) Summary: This commit implements automatic recovery from a Status::NoSpace() error during background operations such as write callback, flush and compaction. The broad design is as follows - 1. Compaction errors are treated as soft errors and don't put the database in read-only mode. A compaction is delayed until enough free disk space is available to accomodate the compaction outputs, which is estimated based on the input size. This means that users can continue to write, and we rely on the WriteController to delay or stop writes if the compaction debt becomes too high due to persistent low disk space condition 2. Errors during write callback and flush are treated as hard errors, i.e the database is put in read-only mode and goes back to read-write only fater certain recovery actions are taken. 3. Both types of recovery rely on the SstFileManagerImpl to poll for sufficient disk space. We assume that there is a 1-1 mapping between an SFM and the underlying OS storage container. For cases where multiple DBs are hosted on a single storage container, the user is expected to allocate a single SFM instance and use the same one for all the DBs. If no SFM is specified by the user, DBImpl::Open() will allocate one, but this will be one per DB and each DB will recover independently. The recovery implemented by SFM is as follows - a) On the first occurance of an out of space error during compaction, subsequent compactions will be delayed until the disk free space check indicates enough available space. The required space is computed as the sum of input sizes. b) The free space check requirement will be removed once the amount of free space is greater than the size reserved by in progress compactions when the first error occured c) If the out of space error is a hard error, a background thread in SFM will poll for sufficient headroom before triggering the recovery of the database and putting it in write-only mode. The headroom is calculated as the sum of the write_buffer_size of all the DB instances associated with the SFM 4. EventListener callbacks will be called at the start and completion of automatic recovery. Users can disable the auto recov ery in the start callback, and later initiate it manually by calling DB::Resume() Todo: 1. More extensive testing 2. Add disk full condition to db_stress (follow-on PR) Pull Request resolved: https://github.com/facebook/rocksdb/pull/4164 Differential Revision: D9846378 Pulled By: anand1976 fbshipit-source-id: 80ea875dbd7f00205e19c82215ff6e37da10da4a
2018-09-15 20:36:19 +00:00
void OnErrorRecoveryBegin(BackgroundErrorReason /*reason*/,
Status /*bg_error*/,
bool* auto_recovery) override {
Auto recovery from out of space errors (#4164) Summary: This commit implements automatic recovery from a Status::NoSpace() error during background operations such as write callback, flush and compaction. The broad design is as follows - 1. Compaction errors are treated as soft errors and don't put the database in read-only mode. A compaction is delayed until enough free disk space is available to accomodate the compaction outputs, which is estimated based on the input size. This means that users can continue to write, and we rely on the WriteController to delay or stop writes if the compaction debt becomes too high due to persistent low disk space condition 2. Errors during write callback and flush are treated as hard errors, i.e the database is put in read-only mode and goes back to read-write only fater certain recovery actions are taken. 3. Both types of recovery rely on the SstFileManagerImpl to poll for sufficient disk space. We assume that there is a 1-1 mapping between an SFM and the underlying OS storage container. For cases where multiple DBs are hosted on a single storage container, the user is expected to allocate a single SFM instance and use the same one for all the DBs. If no SFM is specified by the user, DBImpl::Open() will allocate one, but this will be one per DB and each DB will recover independently. The recovery implemented by SFM is as follows - a) On the first occurance of an out of space error during compaction, subsequent compactions will be delayed until the disk free space check indicates enough available space. The required space is computed as the sum of input sizes. b) The free space check requirement will be removed once the amount of free space is greater than the size reserved by in progress compactions when the first error occured c) If the out of space error is a hard error, a background thread in SFM will poll for sufficient headroom before triggering the recovery of the database and putting it in write-only mode. The headroom is calculated as the sum of the write_buffer_size of all the DB instances associated with the SFM 4. EventListener callbacks will be called at the start and completion of automatic recovery. Users can disable the auto recov ery in the start callback, and later initiate it manually by calling DB::Resume() Todo: 1. More extensive testing 2. Add disk full condition to db_stress (follow-on PR) Pull Request resolved: https://github.com/facebook/rocksdb/pull/4164 Differential Revision: D9846378 Pulled By: anand1976 fbshipit-source-id: 80ea875dbd7f00205e19c82215ff6e37da10da4a
2018-09-15 20:36:19 +00:00
if (*auto_recovery && no_auto_recovery_) {
*auto_recovery = false;
}
}
void OnErrorRecoveryCompleted(Status /*old_bg_error*/) override {
Auto recovery from out of space errors (#4164) Summary: This commit implements automatic recovery from a Status::NoSpace() error during background operations such as write callback, flush and compaction. The broad design is as follows - 1. Compaction errors are treated as soft errors and don't put the database in read-only mode. A compaction is delayed until enough free disk space is available to accomodate the compaction outputs, which is estimated based on the input size. This means that users can continue to write, and we rely on the WriteController to delay or stop writes if the compaction debt becomes too high due to persistent low disk space condition 2. Errors during write callback and flush are treated as hard errors, i.e the database is put in read-only mode and goes back to read-write only fater certain recovery actions are taken. 3. Both types of recovery rely on the SstFileManagerImpl to poll for sufficient disk space. We assume that there is a 1-1 mapping between an SFM and the underlying OS storage container. For cases where multiple DBs are hosted on a single storage container, the user is expected to allocate a single SFM instance and use the same one for all the DBs. If no SFM is specified by the user, DBImpl::Open() will allocate one, but this will be one per DB and each DB will recover independently. The recovery implemented by SFM is as follows - a) On the first occurance of an out of space error during compaction, subsequent compactions will be delayed until the disk free space check indicates enough available space. The required space is computed as the sum of input sizes. b) The free space check requirement will be removed once the amount of free space is greater than the size reserved by in progress compactions when the first error occured c) If the out of space error is a hard error, a background thread in SFM will poll for sufficient headroom before triggering the recovery of the database and putting it in write-only mode. The headroom is calculated as the sum of the write_buffer_size of all the DB instances associated with the SFM 4. EventListener callbacks will be called at the start and completion of automatic recovery. Users can disable the auto recov ery in the start callback, and later initiate it manually by calling DB::Resume() Todo: 1. More extensive testing 2. Add disk full condition to db_stress (follow-on PR) Pull Request resolved: https://github.com/facebook/rocksdb/pull/4164 Differential Revision: D9846378 Pulled By: anand1976 fbshipit-source-id: 80ea875dbd7f00205e19c82215ff6e37da10da4a
2018-09-15 20:36:19 +00:00
InstrumentedMutexLock l(&mutex_);
recovery_complete_ = true;
cv_.SignalAll();
}
bool WaitForRecovery(uint64_t abs_time_us) {
Auto recovery from out of space errors (#4164) Summary: This commit implements automatic recovery from a Status::NoSpace() error during background operations such as write callback, flush and compaction. The broad design is as follows - 1. Compaction errors are treated as soft errors and don't put the database in read-only mode. A compaction is delayed until enough free disk space is available to accomodate the compaction outputs, which is estimated based on the input size. This means that users can continue to write, and we rely on the WriteController to delay or stop writes if the compaction debt becomes too high due to persistent low disk space condition 2. Errors during write callback and flush are treated as hard errors, i.e the database is put in read-only mode and goes back to read-write only fater certain recovery actions are taken. 3. Both types of recovery rely on the SstFileManagerImpl to poll for sufficient disk space. We assume that there is a 1-1 mapping between an SFM and the underlying OS storage container. For cases where multiple DBs are hosted on a single storage container, the user is expected to allocate a single SFM instance and use the same one for all the DBs. If no SFM is specified by the user, DBImpl::Open() will allocate one, but this will be one per DB and each DB will recover independently. The recovery implemented by SFM is as follows - a) On the first occurance of an out of space error during compaction, subsequent compactions will be delayed until the disk free space check indicates enough available space. The required space is computed as the sum of input sizes. b) The free space check requirement will be removed once the amount of free space is greater than the size reserved by in progress compactions when the first error occured c) If the out of space error is a hard error, a background thread in SFM will poll for sufficient headroom before triggering the recovery of the database and putting it in write-only mode. The headroom is calculated as the sum of the write_buffer_size of all the DB instances associated with the SFM 4. EventListener callbacks will be called at the start and completion of automatic recovery. Users can disable the auto recov ery in the start callback, and later initiate it manually by calling DB::Resume() Todo: 1. More extensive testing 2. Add disk full condition to db_stress (follow-on PR) Pull Request resolved: https://github.com/facebook/rocksdb/pull/4164 Differential Revision: D9846378 Pulled By: anand1976 fbshipit-source-id: 80ea875dbd7f00205e19c82215ff6e37da10da4a
2018-09-15 20:36:19 +00:00
InstrumentedMutexLock l(&mutex_);
if (!recovery_complete_) {
cv_.TimedWait(abs_time_us);
Auto recovery from out of space errors (#4164) Summary: This commit implements automatic recovery from a Status::NoSpace() error during background operations such as write callback, flush and compaction. The broad design is as follows - 1. Compaction errors are treated as soft errors and don't put the database in read-only mode. A compaction is delayed until enough free disk space is available to accomodate the compaction outputs, which is estimated based on the input size. This means that users can continue to write, and we rely on the WriteController to delay or stop writes if the compaction debt becomes too high due to persistent low disk space condition 2. Errors during write callback and flush are treated as hard errors, i.e the database is put in read-only mode and goes back to read-write only fater certain recovery actions are taken. 3. Both types of recovery rely on the SstFileManagerImpl to poll for sufficient disk space. We assume that there is a 1-1 mapping between an SFM and the underlying OS storage container. For cases where multiple DBs are hosted on a single storage container, the user is expected to allocate a single SFM instance and use the same one for all the DBs. If no SFM is specified by the user, DBImpl::Open() will allocate one, but this will be one per DB and each DB will recover independently. The recovery implemented by SFM is as follows - a) On the first occurance of an out of space error during compaction, subsequent compactions will be delayed until the disk free space check indicates enough available space. The required space is computed as the sum of input sizes. b) The free space check requirement will be removed once the amount of free space is greater than the size reserved by in progress compactions when the first error occured c) If the out of space error is a hard error, a background thread in SFM will poll for sufficient headroom before triggering the recovery of the database and putting it in write-only mode. The headroom is calculated as the sum of the write_buffer_size of all the DB instances associated with the SFM 4. EventListener callbacks will be called at the start and completion of automatic recovery. Users can disable the auto recov ery in the start callback, and later initiate it manually by calling DB::Resume() Todo: 1. More extensive testing 2. Add disk full condition to db_stress (follow-on PR) Pull Request resolved: https://github.com/facebook/rocksdb/pull/4164 Differential Revision: D9846378 Pulled By: anand1976 fbshipit-source-id: 80ea875dbd7f00205e19c82215ff6e37da10da4a
2018-09-15 20:36:19 +00:00
}
if (recovery_complete_) {
recovery_complete_ = false;
return true;
}
return false;
}
void EnableAutoRecovery(bool enable = true) { no_auto_recovery_ = !enable; }
private:
InstrumentedMutex mutex_;
InstrumentedCondVar cv_;
bool no_auto_recovery_;
bool recovery_complete_;
};
std::shared_ptr<ErrorHandlerListener> listener_;
std::unique_ptr<TimestampEmulator> mock_app_clock_;
bool SanityCheck() {
if (FLAGS_compression_ratio > 1) {
fprintf(stderr, "compression_ratio should be between 0 and 1\n");
return false;
}
return true;
}
inline bool CompressSlice(const CompressionInfo& compression_info,
const Slice& input, std::string* compressed) {
constexpr uint32_t compress_format_version = 2;
return CompressData(input, compression_info, compress_format_version,
compressed);
}
void PrintHeader(const Options& options) {
PrintEnvironment();
fprintf(stdout,
"Keys: %d bytes each (+ %d bytes user-defined timestamp)\n",
FLAGS_key_size, FLAGS_user_timestamp_size);
auto avg_value_size = FLAGS_value_size;
if (FLAGS_value_size_distribution_type_e == kFixed) {
fprintf(stdout,
"Values: %d bytes each (%d bytes after compression)\n",
avg_value_size,
static_cast<int>(avg_value_size * FLAGS_compression_ratio + 0.5));
} else {
avg_value_size = (FLAGS_value_size_min + FLAGS_value_size_max) / 2;
fprintf(stdout,
"Values: %d avg bytes each (%d bytes after compression)\n",
avg_value_size,
static_cast<int>(avg_value_size * FLAGS_compression_ratio + 0.5));
fprintf(stdout, "Values Distribution: %s (min: %d, max: %d)\n",
FLAGS_value_size_distribution_type.c_str(), FLAGS_value_size_min,
FLAGS_value_size_max);
}
fprintf(stdout, "Entries: %" PRIu64 "\n", num_);
fprintf(stdout, "Prefix: %d bytes\n", FLAGS_prefix_size);
fprintf(stdout, "Keys per prefix: %" PRIu64 "\n", keys_per_prefix_);
fprintf(stdout, "RawSize: %.1f MB (estimated)\n",
((static_cast<int64_t>(FLAGS_key_size + avg_value_size) * num_) /
1048576.0));
fprintf(
stdout, "FileSize: %.1f MB (estimated)\n",
(((FLAGS_key_size + avg_value_size * FLAGS_compression_ratio) * num_) /
1048576.0));
fprintf(stdout, "Write rate: %" PRIu64 " bytes/second\n",
FLAGS_benchmark_write_rate_limit);
fprintf(stdout, "Read rate: %" PRIu64 " ops/second\n",
FLAGS_benchmark_read_rate_limit);
Adding NUMA support to db_bench tests Summary: Changes: - Adding numa_aware flag to db_bench.cc - Using numa.h library to bind memory and cpu of threads to a fixed NUMA node Result: There seems to be no significant change in the micros/op time with numa_aware enabled. I also tried this with other implementations, including a combination of pthread_setaffinity_np, sched_setaffinity and set_mempolicy methods. It'd be great if someone could point out where I'm going wrong and if we can achieve a better micors/op. Test Plan: Ran db_bench tests using following command: ./db_bench --db=/mnt/tmp --num_levels=6 --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --block_size=4096 --cache_size=17179869184 --cache_numshardbits=6 --compression_type=none --compression_ratio=1 --min_level_to_compress=-1 --disable_seek_compaction=1 --hard_rate_limit=2 --write_buffer_size=134217728 --max_write_buffer_number=2 --level0_file_num_compaction_trigger=8 --target_file_size_base=134217728 --max_bytes_for_level_base=1073741824 --disable_wal=0 --wal_dir=/mnt/tmp --sync=0 --disable_data_sync=1 --verify_checksum=1 --delete_obsolete_files_period_micros=314572800 --max_grandparent_overlap_factor=10 --max_background_compactions=4 --max_background_flushes=0 --level0_slowdown_writes_trigger=16 --level0_stop_writes_trigger=24 --statistics=0 --stats_per_interval=0 --stats_interval=1048576 --histogram=0 --use_plain_table=1 --open_files=-1 --mmap_read=1 --mmap_write=0 --memtablerep=prefix_hash --bloom_bits=10 --bloom_locality=1 --perf_level=0 --duration=300 --benchmarks=readwhilewriting --use_existing_db=1 --num=157286400 --threads=24 --writes_per_second=10240 --numa_aware=[False/True] The tests were run in private devserver with 24 cores and the db was prepopulated using filluniquerandom test. The tests resulted in 0.145 us/op with numa_aware=False and 0.161 us/op with numa_aware=True. Reviewers: sdong, yhchiang, ljin, igor Reviewed By: ljin, igor Subscribers: igor, leveldb Differential Revision: https://reviews.facebook.net/D19353
2014-07-07 17:53:31 +00:00
if (FLAGS_enable_numa) {
fprintf(stderr, "Running in NUMA enabled mode.\n");
#ifndef NUMA
fprintf(stderr, "NUMA is not defined in the system.\n");
exit(1);
#else
if (numa_available() == -1) {
fprintf(stderr, "NUMA is not supported by the system.\n");
exit(1);
}
#endif
}
auto compression = CompressionTypeToString(FLAGS_compression_type_e);
fprintf(stdout, "Compression: %s\n", compression.c_str());
fprintf(stdout, "Compression sampling rate: %" PRId64 "\n",
FLAGS_sample_for_compression);
if (options.memtable_factory != nullptr) {
fprintf(stdout, "Memtablerep: %s\n",
options.memtable_factory->GetId().c_str());
}
fprintf(stdout, "Perf Level: %d\n", FLAGS_perf_level);
PrintWarnings(compression.c_str());
fprintf(stdout, "------------------------------------------------\n");
}
void PrintWarnings(const char* compression) {
#if defined(__GNUC__) && !defined(__OPTIMIZE__)
fprintf(
stdout,
"WARNING: Optimization is disabled: benchmarks unnecessarily slow\n");
#endif
#ifndef NDEBUG
fprintf(stdout,
"WARNING: Assertions are enabled; benchmarks unnecessarily slow\n");
#endif
if (FLAGS_compression_type_e != ROCKSDB_NAMESPACE::kNoCompression) {
// The test string should not be too small.
const int len = FLAGS_block_size;
std::string input_str(len, 'y');
std::string compressed;
CompressionOptions opts;
Add `CompressionOptions::checksum` for enabling ZSTD checksum (#11666) Summary: Optionally enable zstd checksum flag (https://github.com/facebook/zstd/blob/d857369028d997c92ff1f1861a4d7f679a125464/lib/zstd.h#L428) to detect corruption during decompression. Main changes are in compression.h: * User can set CompressionOptions::checksum to true to enable this feature. * We enable this feature in ZSTD by setting the checksum flag in ZSTD compression context: `ZSTD_CCtx`. * Uses `ZSTD_compress2()` to do compression since it supports frame parameter like the checksum flag. Compression level is also set in compression context as a flag. * Error handling during decompression to propagate error message from ZSTD. * Updated microbench to test read performance impact. About compatibility, the current compression decoders should continue to work with the data created by the new compression API `ZSTD_compress2()`: https://github.com/facebook/zstd/issues/3711. Pull Request resolved: https://github.com/facebook/rocksdb/pull/11666 Test Plan: * Existing unit tests for zstd compression * Add unit test `DBTest2.ZSTDChecksum` to test the corruption case * Manually tested that compression levels, parallel compression, dictionary compression, index compression all work with the new ZSTD_compress2() API. * Manually tested with `sst_dump --command=recompress` that different compression levels and dictionary compression settings all work. * Manually tested compiling with older versions of ZSTD: v1.3.8, v1.1.0, v0.6.2. * Perf impact: from public benchmark data: http://fastcompression.blogspot.com/2019/03/presenting-xxh3.html for checksum and https://github.com/facebook/zstd#benchmarks, if decompression is 1700MB/s and checksum computation is 70000MB/s, checksum computation is an additional ~2.4% time for decompression. Compression is slower and checksumming should be less noticeable. * Microbench: ``` TEST_TMPDIR=/dev/shm ./branch_db_basic_bench --benchmark_filter=DBGet/comp_style:0/max_data:1048576/per_key_size:256/enable_statistics:0/negative_query:0/enable_filter:0/mmap:0/compression_type:7/compression_checksum:1/no_blockcache:1/iterations:10000/threads:1 --benchmark_repetitions=100 Min out of 100 runs: Main: 10390 10436 10456 10484 10499 10535 10544 10545 10565 10568 After this PR, checksum=false 10285 10397 10503 10508 10515 10557 10562 10635 10640 10660 After this PR, checksum=true 10827 10876 10925 10949 10971 11052 11061 11063 11100 11109 ``` * db_bench: ``` Write perf TEST_TMPDIR=/dev/shm/ ./db_bench_ichecksum --benchmarks=fillseq[-X10] --compression_type=zstd --num=10000000 --compression_checksum=.. [FillSeq checksum=0] fillseq [AVG 10 runs] : 281635 (± 31711) ops/sec; 31.2 (± 3.5) MB/sec fillseq [MEDIAN 10 runs] : 294027 ops/sec; 32.5 MB/sec [FillSeq checksum=1] fillseq [AVG 10 runs] : 286961 (± 34700) ops/sec; 31.7 (± 3.8) MB/sec fillseq [MEDIAN 10 runs] : 283278 ops/sec; 31.3 MB/sec Read perf TEST_TMPDIR=/dev/shm ./db_bench_ichecksum --benchmarks=readrandom[-X20] --num=100000000 --reads=1000000 --use_existing_db=true --readonly=1 [Readrandom checksum=1] readrandom [AVG 20 runs] : 360928 (± 3579) ops/sec; 4.0 (± 0.0) MB/sec readrandom [MEDIAN 20 runs] : 362468 ops/sec; 4.0 MB/sec [Readrandom checksum=0] readrandom [AVG 20 runs] : 380365 (± 2384) ops/sec; 4.2 (± 0.0) MB/sec readrandom [MEDIAN 20 runs] : 379800 ops/sec; 4.2 MB/sec Compression TEST_TMPDIR=/dev/shm ./db_bench_ichecksum --benchmarks=compress[-X20] --compression_type=zstd --num=100000000 --compression_checksum=1 checksum=1 compress [AVG 20 runs] : 54074 (± 634) ops/sec; 211.2 (± 2.5) MB/sec compress [MEDIAN 20 runs] : 54396 ops/sec; 212.5 MB/sec checksum=0 compress [AVG 20 runs] : 54598 (± 393) ops/sec; 213.3 (± 1.5) MB/sec compress [MEDIAN 20 runs] : 54592 ops/sec; 213.3 MB/sec Decompression: TEST_TMPDIR=/dev/shm ./db_bench_ichecksum --benchmarks=uncompress[-X20] --compression_type=zstd --compression_checksum=1 checksum = 0 uncompress [AVG 20 runs] : 167499 (± 962) ops/sec; 654.3 (± 3.8) MB/sec uncompress [MEDIAN 20 runs] : 167210 ops/sec; 653.2 MB/sec checksum = 1 uncompress [AVG 20 runs] : 167980 (± 924) ops/sec; 656.2 (± 3.6) MB/sec uncompress [MEDIAN 20 runs] : 168465 ops/sec; 658.1 MB/sec ``` Reviewed By: ajkr Differential Revision: D48019378 Pulled By: cbi42 fbshipit-source-id: 674120c6e1853c2ced1436ac8138559d0204feba
2023-08-18 22:01:59 +00:00
CompressionContext context(FLAGS_compression_type_e, opts);
CompressionInfo info(opts, context, CompressionDict::GetEmptyDict(),
FLAGS_compression_type_e,
FLAGS_sample_for_compression);
bool result = CompressSlice(info, Slice(input_str), &compressed);
if (!result) {
fprintf(stdout, "WARNING: %s compression is not enabled\n",
compression);
} else if (compressed.size() >= input_str.size()) {
fprintf(stdout, "WARNING: %s compression is not effective\n",
compression);
}
}
}
// Current the following isn't equivalent to OS_LINUX.
#if defined(__linux)
static Slice TrimSpace(Slice s) {
unsigned int start = 0;
while (start < s.size() && isspace(s[start])) {
start++;
}
unsigned int limit = static_cast<unsigned int>(s.size());
while (limit > start && isspace(s[limit - 1])) {
limit--;
}
return Slice(s.data() + start, limit - start);
}
#endif
void PrintEnvironment() {
fprintf(stderr, "RocksDB: version %s\n",
GetRocksVersionAsString(true).c_str());
#if defined(__linux) || defined(__APPLE__) || defined(__FreeBSD__)
time_t now = time(nullptr);
char buf[52];
// Lint complains about ctime() usage, so replace it with ctime_r(). The
// requirement is to provide a buffer which is at least 26 bytes.
fprintf(stderr, "Date: %s",
ctime_r(&now, buf)); // ctime_r() adds newline
#if defined(__linux)
FILE* cpuinfo = fopen("/proc/cpuinfo", "r");
if (cpuinfo != nullptr) {
char line[1000];
int num_cpus = 0;
std::string cpu_type;
std::string cache_size;
while (fgets(line, sizeof(line), cpuinfo) != nullptr) {
const char* sep = strchr(line, ':');
if (sep == nullptr) {
continue;
}
Slice key = TrimSpace(Slice(line, sep - 1 - line));
Slice val = TrimSpace(Slice(sep + 1));
if (key == "model name") {
++num_cpus;
cpu_type = val.ToString();
} else if (key == "cache size") {
cache_size = val.ToString();
}
}
fclose(cpuinfo);
fprintf(stderr, "CPU: %d * %s\n", num_cpus, cpu_type.c_str());
fprintf(stderr, "CPUCache: %s\n", cache_size.c_str());
}
#elif defined(__APPLE__)
struct host_basic_info h;
size_t hlen = HOST_BASIC_INFO_COUNT;
if (host_info(mach_host_self(), HOST_BASIC_INFO, (host_info_t)&h,
(uint32_t*)&hlen) == KERN_SUCCESS) {
std::string cpu_type;
std::string cache_size;
size_t hcache_size;
hlen = sizeof(hcache_size);
if (sysctlbyname("hw.cachelinesize", &hcache_size, &hlen, NULL, 0) == 0) {
cache_size = std::to_string(hcache_size);
}
switch (h.cpu_type) {
case CPU_TYPE_X86_64:
cpu_type = "x86_64";
break;
case CPU_TYPE_ARM64:
cpu_type = "arm64";
break;
default:
break;
}
fprintf(stderr, "CPU: %d * %s\n", h.max_cpus, cpu_type.c_str());
fprintf(stderr, "CPUCache: %s\n", cache_size.c_str());
}
#elif defined(__FreeBSD__)
int ncpus;
size_t len = sizeof(ncpus);
int mib[2] = {CTL_HW, HW_NCPU};
if (sysctl(mib, 2, &ncpus, &len, nullptr, 0) == 0) {
char cpu_type[16];
len = sizeof(cpu_type) - 1;
mib[1] = HW_MACHINE;
if (sysctl(mib, 2, cpu_type, &len, nullptr, 0) == 0) cpu_type[len] = 0;
fprintf(stderr, "CPU: %d * %s\n", ncpus, cpu_type);
// no programmatic way to get the cache line size except on PPC
}
#endif
#endif
}
Write a benchmark to emulate time series data Summary: Add a benchmark to `db_bench`. In this benchmark, a write thread will populate time series data in the format of 'id | timestamp', and multiple read threads will randomly retrieve all data from one id at a time. Test Plan: Run the benchmark: `num=134217728;bpl=536870912;mb=67108864;overlap=10;mcz=2;del=300000000;levels=6;ctrig=4;delay=8;stop=12;wbn=3;mbc=20;wbs=134217728;dds=0;sync=0;t=32;vs=800;bs=4096;cs=17179869184;of=500000;wps=0;si=10000000; kir=100000; dir=/data/users/jhli/test/; ./db_bench --benchmarks=timeseries --disable_seek_compaction=1 --mmap_read=0 --statistics=1 --histogram=1 --num=$num --threads=$t --value_size=$vs --block_size=$bs --cache_size=$cs --bloom_bits=10 --cache_numshardbits=6 --open_files=$of --verify_checksum=1 --db=$dir --sync=$sync --disable_wal=0 --compression_type=none --stats_interval=$si --compression_ratio=1 --disable_data_sync=$dds --write_buffer_size=$wbs --target_file_size_base=$mb --max_write_buffer_number=$wbn --max_background_compactions=$mbc --level0_file_num_compaction_trigger=$ctrig --level0_slowdown_writes_trigger=$delay --level0_stop_writes_trigger=$stop --num_levels=$levels --delete_obsolete_files_period_micros=$del --min_level_to_compress=$mcz --max_grandparent_overlap_factor=$overlap --stats_per_interval=1 --max_bytes_for_level_base=$bpl --use_existing_db=0 --key_id_range=$kir` Reviewers: andrewkr, sdong Reviewed By: sdong Subscribers: lgalanis, andrewkr, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D60651
2016-08-01 19:28:51 +00:00
static bool KeyExpired(const TimestampEmulator* timestamp_emulator,
const Slice& key) {
const char* pos = key.data();
pos += 8;
uint64_t timestamp = 0;
if (port::kLittleEndian) {
int bytes_to_fill = 8;
for (int i = 0; i < bytes_to_fill; ++i) {
timestamp |= (static_cast<uint64_t>(static_cast<unsigned char>(pos[i]))
<< ((bytes_to_fill - i - 1) << 3));
}
} else {
memcpy(&timestamp, pos, sizeof(timestamp));
}
return timestamp_emulator->Get() - timestamp > FLAGS_time_range;
}
class ExpiredTimeFilter : public CompactionFilter {
public:
explicit ExpiredTimeFilter(
const std::shared_ptr<TimestampEmulator>& timestamp_emulator)
: timestamp_emulator_(timestamp_emulator) {}
bool Filter(int /*level*/, const Slice& key,
const Slice& /*existing_value*/, std::string* /*new_value*/,
bool* /*value_changed*/) const override {
Write a benchmark to emulate time series data Summary: Add a benchmark to `db_bench`. In this benchmark, a write thread will populate time series data in the format of 'id | timestamp', and multiple read threads will randomly retrieve all data from one id at a time. Test Plan: Run the benchmark: `num=134217728;bpl=536870912;mb=67108864;overlap=10;mcz=2;del=300000000;levels=6;ctrig=4;delay=8;stop=12;wbn=3;mbc=20;wbs=134217728;dds=0;sync=0;t=32;vs=800;bs=4096;cs=17179869184;of=500000;wps=0;si=10000000; kir=100000; dir=/data/users/jhli/test/; ./db_bench --benchmarks=timeseries --disable_seek_compaction=1 --mmap_read=0 --statistics=1 --histogram=1 --num=$num --threads=$t --value_size=$vs --block_size=$bs --cache_size=$cs --bloom_bits=10 --cache_numshardbits=6 --open_files=$of --verify_checksum=1 --db=$dir --sync=$sync --disable_wal=0 --compression_type=none --stats_interval=$si --compression_ratio=1 --disable_data_sync=$dds --write_buffer_size=$wbs --target_file_size_base=$mb --max_write_buffer_number=$wbn --max_background_compactions=$mbc --level0_file_num_compaction_trigger=$ctrig --level0_slowdown_writes_trigger=$delay --level0_stop_writes_trigger=$stop --num_levels=$levels --delete_obsolete_files_period_micros=$del --min_level_to_compress=$mcz --max_grandparent_overlap_factor=$overlap --stats_per_interval=1 --max_bytes_for_level_base=$bpl --use_existing_db=0 --key_id_range=$kir` Reviewers: andrewkr, sdong Reviewed By: sdong Subscribers: lgalanis, andrewkr, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D60651
2016-08-01 19:28:51 +00:00
return KeyExpired(timestamp_emulator_.get(), key);
}
const char* Name() const override { return "ExpiredTimeFilter"; }
private:
std::shared_ptr<TimestampEmulator> timestamp_emulator_;
};
class KeepFilter : public CompactionFilter {
public:
bool Filter(int /*level*/, const Slice& /*key*/, const Slice& /*value*/,
std::string* /*new_value*/,
bool* /*value_changed*/) const override {
return false;
}
const char* Name() const override { return "KeepFilter"; }
};
static std::shared_ptr<MemoryAllocator> GetCacheAllocator() {
std::shared_ptr<MemoryAllocator> allocator;
if (FLAGS_use_cache_jemalloc_no_dump_allocator) {
JemallocAllocatorOptions jemalloc_options;
if (!NewJemallocNodumpAllocator(jemalloc_options, &allocator).ok()) {
fprintf(stderr, "JemallocNodumpAllocator not supported.\n");
exit(1);
}
} else if (FLAGS_use_cache_memkind_kmem_allocator) {
#ifdef MEMKIND
allocator = std::make_shared<MemkindKmemAllocator>();
#else
fprintf(stderr, "Memkind library is not linked with the binary.\n");
exit(1);
#endif
}
return allocator;
}
static int32_t GetCacheHashSeed() {
// For a fixed Cache seed, need a non-negative int32
return static_cast<int32_t>(*seed_base) & 0x7fffffff;
}
static std::shared_ptr<Cache> NewCache(int64_t capacity) {
Integrate CacheReservationManager with compressed secondary cache (#11449) Summary: This draft PR implements charging of reserved memory, for write buffers, table readers, and other purposes, proportionally to the block cache and the compressed secondary cache. The basic flow of memory reservation is maintained - clients use ```CacheReservationManager``` to request reservations, and ```CacheReservationManager``` inserts placeholder entries, i.e null value and non-zero charge, into the block cache. The ```CacheWithSecondaryAdapter``` wrapper uses its own instance of ```CacheReservationManager``` to keep track of reservations charged to the secondary cache, while the placeholder entries are inserted into the primary block cache. The design is as follows. When ```CacheWithSecondaryAdapter``` is constructed with the ```distribute_cache_res``` parameter set to true, it manages the entire memory budget across the primary and secondary cache. The secondary cache is assumed to be in memory, such as the ```CompressedSecondaryCache```. When a placeholder entry is inserted by a CacheReservationManager instance to reserve memory, the ```CacheWithSecondaryAdapter```ensures that the reservation is distributed proportionally across the primary/secondary caches. The primary block cache is initially sized to the sum of the primary cache budget + the secondary cache budget, as follows - |--------- Primary Cache Configured Capacity -----------| |---Secondary Cache Budget----|----Primary Cache Budget-----| A ```ConcurrentCacheReservationManager``` member in the ```CacheWithSecondaryAdapter```, ```pri_cache_res_```, is used to help with tracking the distribution of memory reservations. Initially, it accounts for the entire secondary cache budget as a reservation against the primary cache. This shrinks the usable capacity of the primary cache to the budget that the user originally desired. |--Reservation for Sec Cache--|-Pri Cache Usable Capacity---| When a reservation placeholder is inserted into the adapter, it is inserted directly into the primary cache. This means the entire charge of the placeholder is counted against the primary cache. To compensate and count a portion of it against the secondary cache, the secondary cache ```Deflate()``` method is called to shrink it. Since the ```Deflate()``` causes the secondary actual usage to shrink, it is reflected here by releasing an equal amount from the ```pri_cache_res_``` reservation. For example, if the pri/sec ratio is 50/50, this would be the state after placeholder insertion - |-Reservation for Sec Cache-|-Pri Cache Usable Capacity-|-R-| Likewise, when the user inserted placeholder is released, the secondary cache ```Inflate()``` method is called to grow it, and the ```pri_cache_res_``` reservation is increased by an equal amount. Other alternatives - 1. Another way of implementing this would have been to simply split the user reservation in ```CacheWithSecondaryAdapter``` into primary and secondary components. However, this would require allocating a structure to track the associated secondary cache reservation, which adds some complexity and overhead. 2. Yet another option is to implement the splitting directly in ```CacheReservationManager```. However, there are multiple instances of ```CacheReservationManager``` in a DB instance, making it complicated to keep track of them. The PR contains the following changes - 1. A new cache allocator, ```NewTieredVolatileCache()```, is defined for allocating a tiered primary block cache and compressed secondary cache. This internally allocates an instance of ```CacheWithSecondaryAdapter```. 3. New interfaces, ```Deflate()``` and ```Inflate()```, are added to the ```SecondaryCache``` interface. The default implementaion returns ```NotSupported``` with overrides in ```CompressedSecondaryCache```. 4. The ```CompressedSecondaryCache``` uses a ```ConcurrentCacheReservationManager``` instance to manage reservations done using ```Inflate()/Deflate()```. 5. The ```CacheWithSecondaryAdapter``` optionally distributes memory reservations across the primary and secondary caches. The primary cache is sized to the total memory budget (primary + secondary), and the capacity allocated to secondary cache is "reserved" against the primary cache. For any subsequent reservations, the primary cache pre-reserved capacity is adjusted. Benchmarks - Baseline ``` time ~/rocksdb_anand76/db_bench --db=/dev/shm/comp_cache_res/base --use_existing_db=true --benchmarks="readseq,readwhilewriting" --key_size=32 --value_size=1024 --num=20000000 --threads=32 --bloom_bits=10 --cache_size=30000000000 --use_compressed_secondary_cache=true --compressed_secondary_cache_size=5000000000 --duration=300 --cost_write_buffer_to_cache=true ``` ``` readseq : 3.301 micros/op 9694317 ops/sec 66.018 seconds 640000000 operations; 9763.0 MB/s readwhilewriting : 22.921 micros/op 1396058 ops/sec 300.021 seconds 418846968 operations; 1405.9 MB/s (13068999 of 13068999 found) real 6m31.052s user 152m5.660s sys 26m18.738s ``` With TieredVolatileCache ``` time ~/rocksdb_anand76/db_bench --db=/dev/shm/comp_cache_res/base --use_existing_db=true --benchmarks="readseq,readwhilewriting" --key_size=32 --value_size=1024 --num=20000000 --threads=32 --bloom_bits=10 --cache_size=30000000000 --use_compressed_secondary_cache=true --compressed_secondary_cache_size=5000000000 --duration=300 --cost_write_buffer_to_cache=true --use_tiered_volatile_cache=true ``` ``` readseq : 4.064 micros/op 7873915 ops/sec 81.281 seconds 640000000 operations; 7929.7 MB/s readwhilewriting : 20.944 micros/op 1527827 ops/sec 300.020 seconds 458378968 operations; 1538.6 MB/s (14296999 of 14296999 found) real 6m42.743s user 157m58.972s sys 33m16.671 ``` ``` readseq : 3.484 micros/op 9184967 ops/sec 69.679 seconds 640000000 operations; 9250.0 MB/s readwhilewriting : 21.261 micros/op 1505035 ops/sec 300.024 seconds 451545968 operations; 1515.7 MB/s (14101999 of 14101999 found) real 6m31.469s user 155m16.570s sys 27m47.834s ``` ToDo - 1. Add to db_stress Pull Request resolved: https://github.com/facebook/rocksdb/pull/11449 Reviewed By: pdillinger Differential Revision: D46197388 Pulled By: anand1976 fbshipit-source-id: 42d16f0254df683db4929db20d06ff26030e90df
2023-05-30 21:05:48 +00:00
CompressedSecondaryCacheOptions secondary_cache_opts;
TieredAdmissionPolicy adm_policy = TieredAdmissionPolicy::kAdmPolicyAuto;
Integrate CacheReservationManager with compressed secondary cache (#11449) Summary: This draft PR implements charging of reserved memory, for write buffers, table readers, and other purposes, proportionally to the block cache and the compressed secondary cache. The basic flow of memory reservation is maintained - clients use ```CacheReservationManager``` to request reservations, and ```CacheReservationManager``` inserts placeholder entries, i.e null value and non-zero charge, into the block cache. The ```CacheWithSecondaryAdapter``` wrapper uses its own instance of ```CacheReservationManager``` to keep track of reservations charged to the secondary cache, while the placeholder entries are inserted into the primary block cache. The design is as follows. When ```CacheWithSecondaryAdapter``` is constructed with the ```distribute_cache_res``` parameter set to true, it manages the entire memory budget across the primary and secondary cache. The secondary cache is assumed to be in memory, such as the ```CompressedSecondaryCache```. When a placeholder entry is inserted by a CacheReservationManager instance to reserve memory, the ```CacheWithSecondaryAdapter```ensures that the reservation is distributed proportionally across the primary/secondary caches. The primary block cache is initially sized to the sum of the primary cache budget + the secondary cache budget, as follows - |--------- Primary Cache Configured Capacity -----------| |---Secondary Cache Budget----|----Primary Cache Budget-----| A ```ConcurrentCacheReservationManager``` member in the ```CacheWithSecondaryAdapter```, ```pri_cache_res_```, is used to help with tracking the distribution of memory reservations. Initially, it accounts for the entire secondary cache budget as a reservation against the primary cache. This shrinks the usable capacity of the primary cache to the budget that the user originally desired. |--Reservation for Sec Cache--|-Pri Cache Usable Capacity---| When a reservation placeholder is inserted into the adapter, it is inserted directly into the primary cache. This means the entire charge of the placeholder is counted against the primary cache. To compensate and count a portion of it against the secondary cache, the secondary cache ```Deflate()``` method is called to shrink it. Since the ```Deflate()``` causes the secondary actual usage to shrink, it is reflected here by releasing an equal amount from the ```pri_cache_res_``` reservation. For example, if the pri/sec ratio is 50/50, this would be the state after placeholder insertion - |-Reservation for Sec Cache-|-Pri Cache Usable Capacity-|-R-| Likewise, when the user inserted placeholder is released, the secondary cache ```Inflate()``` method is called to grow it, and the ```pri_cache_res_``` reservation is increased by an equal amount. Other alternatives - 1. Another way of implementing this would have been to simply split the user reservation in ```CacheWithSecondaryAdapter``` into primary and secondary components. However, this would require allocating a structure to track the associated secondary cache reservation, which adds some complexity and overhead. 2. Yet another option is to implement the splitting directly in ```CacheReservationManager```. However, there are multiple instances of ```CacheReservationManager``` in a DB instance, making it complicated to keep track of them. The PR contains the following changes - 1. A new cache allocator, ```NewTieredVolatileCache()```, is defined for allocating a tiered primary block cache and compressed secondary cache. This internally allocates an instance of ```CacheWithSecondaryAdapter```. 3. New interfaces, ```Deflate()``` and ```Inflate()```, are added to the ```SecondaryCache``` interface. The default implementaion returns ```NotSupported``` with overrides in ```CompressedSecondaryCache```. 4. The ```CompressedSecondaryCache``` uses a ```ConcurrentCacheReservationManager``` instance to manage reservations done using ```Inflate()/Deflate()```. 5. The ```CacheWithSecondaryAdapter``` optionally distributes memory reservations across the primary and secondary caches. The primary cache is sized to the total memory budget (primary + secondary), and the capacity allocated to secondary cache is "reserved" against the primary cache. For any subsequent reservations, the primary cache pre-reserved capacity is adjusted. Benchmarks - Baseline ``` time ~/rocksdb_anand76/db_bench --db=/dev/shm/comp_cache_res/base --use_existing_db=true --benchmarks="readseq,readwhilewriting" --key_size=32 --value_size=1024 --num=20000000 --threads=32 --bloom_bits=10 --cache_size=30000000000 --use_compressed_secondary_cache=true --compressed_secondary_cache_size=5000000000 --duration=300 --cost_write_buffer_to_cache=true ``` ``` readseq : 3.301 micros/op 9694317 ops/sec 66.018 seconds 640000000 operations; 9763.0 MB/s readwhilewriting : 22.921 micros/op 1396058 ops/sec 300.021 seconds 418846968 operations; 1405.9 MB/s (13068999 of 13068999 found) real 6m31.052s user 152m5.660s sys 26m18.738s ``` With TieredVolatileCache ``` time ~/rocksdb_anand76/db_bench --db=/dev/shm/comp_cache_res/base --use_existing_db=true --benchmarks="readseq,readwhilewriting" --key_size=32 --value_size=1024 --num=20000000 --threads=32 --bloom_bits=10 --cache_size=30000000000 --use_compressed_secondary_cache=true --compressed_secondary_cache_size=5000000000 --duration=300 --cost_write_buffer_to_cache=true --use_tiered_volatile_cache=true ``` ``` readseq : 4.064 micros/op 7873915 ops/sec 81.281 seconds 640000000 operations; 7929.7 MB/s readwhilewriting : 20.944 micros/op 1527827 ops/sec 300.020 seconds 458378968 operations; 1538.6 MB/s (14296999 of 14296999 found) real 6m42.743s user 157m58.972s sys 33m16.671 ``` ``` readseq : 3.484 micros/op 9184967 ops/sec 69.679 seconds 640000000 operations; 9250.0 MB/s readwhilewriting : 21.261 micros/op 1505035 ops/sec 300.024 seconds 451545968 operations; 1515.7 MB/s (14101999 of 14101999 found) real 6m31.469s user 155m16.570s sys 27m47.834s ``` ToDo - 1. Add to db_stress Pull Request resolved: https://github.com/facebook/rocksdb/pull/11449 Reviewed By: pdillinger Differential Revision: D46197388 Pulled By: anand1976 fbshipit-source-id: 42d16f0254df683db4929db20d06ff26030e90df
2023-05-30 21:05:48 +00:00
bool use_tiered_cache = false;
if (capacity <= 0) {
return nullptr;
}
Integrate CacheReservationManager with compressed secondary cache (#11449) Summary: This draft PR implements charging of reserved memory, for write buffers, table readers, and other purposes, proportionally to the block cache and the compressed secondary cache. The basic flow of memory reservation is maintained - clients use ```CacheReservationManager``` to request reservations, and ```CacheReservationManager``` inserts placeholder entries, i.e null value and non-zero charge, into the block cache. The ```CacheWithSecondaryAdapter``` wrapper uses its own instance of ```CacheReservationManager``` to keep track of reservations charged to the secondary cache, while the placeholder entries are inserted into the primary block cache. The design is as follows. When ```CacheWithSecondaryAdapter``` is constructed with the ```distribute_cache_res``` parameter set to true, it manages the entire memory budget across the primary and secondary cache. The secondary cache is assumed to be in memory, such as the ```CompressedSecondaryCache```. When a placeholder entry is inserted by a CacheReservationManager instance to reserve memory, the ```CacheWithSecondaryAdapter```ensures that the reservation is distributed proportionally across the primary/secondary caches. The primary block cache is initially sized to the sum of the primary cache budget + the secondary cache budget, as follows - |--------- Primary Cache Configured Capacity -----------| |---Secondary Cache Budget----|----Primary Cache Budget-----| A ```ConcurrentCacheReservationManager``` member in the ```CacheWithSecondaryAdapter```, ```pri_cache_res_```, is used to help with tracking the distribution of memory reservations. Initially, it accounts for the entire secondary cache budget as a reservation against the primary cache. This shrinks the usable capacity of the primary cache to the budget that the user originally desired. |--Reservation for Sec Cache--|-Pri Cache Usable Capacity---| When a reservation placeholder is inserted into the adapter, it is inserted directly into the primary cache. This means the entire charge of the placeholder is counted against the primary cache. To compensate and count a portion of it against the secondary cache, the secondary cache ```Deflate()``` method is called to shrink it. Since the ```Deflate()``` causes the secondary actual usage to shrink, it is reflected here by releasing an equal amount from the ```pri_cache_res_``` reservation. For example, if the pri/sec ratio is 50/50, this would be the state after placeholder insertion - |-Reservation for Sec Cache-|-Pri Cache Usable Capacity-|-R-| Likewise, when the user inserted placeholder is released, the secondary cache ```Inflate()``` method is called to grow it, and the ```pri_cache_res_``` reservation is increased by an equal amount. Other alternatives - 1. Another way of implementing this would have been to simply split the user reservation in ```CacheWithSecondaryAdapter``` into primary and secondary components. However, this would require allocating a structure to track the associated secondary cache reservation, which adds some complexity and overhead. 2. Yet another option is to implement the splitting directly in ```CacheReservationManager```. However, there are multiple instances of ```CacheReservationManager``` in a DB instance, making it complicated to keep track of them. The PR contains the following changes - 1. A new cache allocator, ```NewTieredVolatileCache()```, is defined for allocating a tiered primary block cache and compressed secondary cache. This internally allocates an instance of ```CacheWithSecondaryAdapter```. 3. New interfaces, ```Deflate()``` and ```Inflate()```, are added to the ```SecondaryCache``` interface. The default implementaion returns ```NotSupported``` with overrides in ```CompressedSecondaryCache```. 4. The ```CompressedSecondaryCache``` uses a ```ConcurrentCacheReservationManager``` instance to manage reservations done using ```Inflate()/Deflate()```. 5. The ```CacheWithSecondaryAdapter``` optionally distributes memory reservations across the primary and secondary caches. The primary cache is sized to the total memory budget (primary + secondary), and the capacity allocated to secondary cache is "reserved" against the primary cache. For any subsequent reservations, the primary cache pre-reserved capacity is adjusted. Benchmarks - Baseline ``` time ~/rocksdb_anand76/db_bench --db=/dev/shm/comp_cache_res/base --use_existing_db=true --benchmarks="readseq,readwhilewriting" --key_size=32 --value_size=1024 --num=20000000 --threads=32 --bloom_bits=10 --cache_size=30000000000 --use_compressed_secondary_cache=true --compressed_secondary_cache_size=5000000000 --duration=300 --cost_write_buffer_to_cache=true ``` ``` readseq : 3.301 micros/op 9694317 ops/sec 66.018 seconds 640000000 operations; 9763.0 MB/s readwhilewriting : 22.921 micros/op 1396058 ops/sec 300.021 seconds 418846968 operations; 1405.9 MB/s (13068999 of 13068999 found) real 6m31.052s user 152m5.660s sys 26m18.738s ``` With TieredVolatileCache ``` time ~/rocksdb_anand76/db_bench --db=/dev/shm/comp_cache_res/base --use_existing_db=true --benchmarks="readseq,readwhilewriting" --key_size=32 --value_size=1024 --num=20000000 --threads=32 --bloom_bits=10 --cache_size=30000000000 --use_compressed_secondary_cache=true --compressed_secondary_cache_size=5000000000 --duration=300 --cost_write_buffer_to_cache=true --use_tiered_volatile_cache=true ``` ``` readseq : 4.064 micros/op 7873915 ops/sec 81.281 seconds 640000000 operations; 7929.7 MB/s readwhilewriting : 20.944 micros/op 1527827 ops/sec 300.020 seconds 458378968 operations; 1538.6 MB/s (14296999 of 14296999 found) real 6m42.743s user 157m58.972s sys 33m16.671 ``` ``` readseq : 3.484 micros/op 9184967 ops/sec 69.679 seconds 640000000 operations; 9250.0 MB/s readwhilewriting : 21.261 micros/op 1505035 ops/sec 300.024 seconds 451545968 operations; 1515.7 MB/s (14101999 of 14101999 found) real 6m31.469s user 155m16.570s sys 27m47.834s ``` ToDo - 1. Add to db_stress Pull Request resolved: https://github.com/facebook/rocksdb/pull/11449 Reviewed By: pdillinger Differential Revision: D46197388 Pulled By: anand1976 fbshipit-source-id: 42d16f0254df683db4929db20d06ff26030e90df
2023-05-30 21:05:48 +00:00
if (FLAGS_use_compressed_secondary_cache) {
secondary_cache_opts.capacity = FLAGS_compressed_secondary_cache_size;
secondary_cache_opts.num_shard_bits =
FLAGS_compressed_secondary_cache_numshardbits;
secondary_cache_opts.high_pri_pool_ratio =
FLAGS_compressed_secondary_cache_high_pri_pool_ratio;
secondary_cache_opts.low_pri_pool_ratio =
FLAGS_compressed_secondary_cache_low_pri_pool_ratio;
secondary_cache_opts.compression_type =
FLAGS_compressed_secondary_cache_compression_type_e;
secondary_cache_opts.compression_opts.level =
FLAGS_compressed_secondary_cache_compression_level;
Integrate CacheReservationManager with compressed secondary cache (#11449) Summary: This draft PR implements charging of reserved memory, for write buffers, table readers, and other purposes, proportionally to the block cache and the compressed secondary cache. The basic flow of memory reservation is maintained - clients use ```CacheReservationManager``` to request reservations, and ```CacheReservationManager``` inserts placeholder entries, i.e null value and non-zero charge, into the block cache. The ```CacheWithSecondaryAdapter``` wrapper uses its own instance of ```CacheReservationManager``` to keep track of reservations charged to the secondary cache, while the placeholder entries are inserted into the primary block cache. The design is as follows. When ```CacheWithSecondaryAdapter``` is constructed with the ```distribute_cache_res``` parameter set to true, it manages the entire memory budget across the primary and secondary cache. The secondary cache is assumed to be in memory, such as the ```CompressedSecondaryCache```. When a placeholder entry is inserted by a CacheReservationManager instance to reserve memory, the ```CacheWithSecondaryAdapter```ensures that the reservation is distributed proportionally across the primary/secondary caches. The primary block cache is initially sized to the sum of the primary cache budget + the secondary cache budget, as follows - |--------- Primary Cache Configured Capacity -----------| |---Secondary Cache Budget----|----Primary Cache Budget-----| A ```ConcurrentCacheReservationManager``` member in the ```CacheWithSecondaryAdapter```, ```pri_cache_res_```, is used to help with tracking the distribution of memory reservations. Initially, it accounts for the entire secondary cache budget as a reservation against the primary cache. This shrinks the usable capacity of the primary cache to the budget that the user originally desired. |--Reservation for Sec Cache--|-Pri Cache Usable Capacity---| When a reservation placeholder is inserted into the adapter, it is inserted directly into the primary cache. This means the entire charge of the placeholder is counted against the primary cache. To compensate and count a portion of it against the secondary cache, the secondary cache ```Deflate()``` method is called to shrink it. Since the ```Deflate()``` causes the secondary actual usage to shrink, it is reflected here by releasing an equal amount from the ```pri_cache_res_``` reservation. For example, if the pri/sec ratio is 50/50, this would be the state after placeholder insertion - |-Reservation for Sec Cache-|-Pri Cache Usable Capacity-|-R-| Likewise, when the user inserted placeholder is released, the secondary cache ```Inflate()``` method is called to grow it, and the ```pri_cache_res_``` reservation is increased by an equal amount. Other alternatives - 1. Another way of implementing this would have been to simply split the user reservation in ```CacheWithSecondaryAdapter``` into primary and secondary components. However, this would require allocating a structure to track the associated secondary cache reservation, which adds some complexity and overhead. 2. Yet another option is to implement the splitting directly in ```CacheReservationManager```. However, there are multiple instances of ```CacheReservationManager``` in a DB instance, making it complicated to keep track of them. The PR contains the following changes - 1. A new cache allocator, ```NewTieredVolatileCache()```, is defined for allocating a tiered primary block cache and compressed secondary cache. This internally allocates an instance of ```CacheWithSecondaryAdapter```. 3. New interfaces, ```Deflate()``` and ```Inflate()```, are added to the ```SecondaryCache``` interface. The default implementaion returns ```NotSupported``` with overrides in ```CompressedSecondaryCache```. 4. The ```CompressedSecondaryCache``` uses a ```ConcurrentCacheReservationManager``` instance to manage reservations done using ```Inflate()/Deflate()```. 5. The ```CacheWithSecondaryAdapter``` optionally distributes memory reservations across the primary and secondary caches. The primary cache is sized to the total memory budget (primary + secondary), and the capacity allocated to secondary cache is "reserved" against the primary cache. For any subsequent reservations, the primary cache pre-reserved capacity is adjusted. Benchmarks - Baseline ``` time ~/rocksdb_anand76/db_bench --db=/dev/shm/comp_cache_res/base --use_existing_db=true --benchmarks="readseq,readwhilewriting" --key_size=32 --value_size=1024 --num=20000000 --threads=32 --bloom_bits=10 --cache_size=30000000000 --use_compressed_secondary_cache=true --compressed_secondary_cache_size=5000000000 --duration=300 --cost_write_buffer_to_cache=true ``` ``` readseq : 3.301 micros/op 9694317 ops/sec 66.018 seconds 640000000 operations; 9763.0 MB/s readwhilewriting : 22.921 micros/op 1396058 ops/sec 300.021 seconds 418846968 operations; 1405.9 MB/s (13068999 of 13068999 found) real 6m31.052s user 152m5.660s sys 26m18.738s ``` With TieredVolatileCache ``` time ~/rocksdb_anand76/db_bench --db=/dev/shm/comp_cache_res/base --use_existing_db=true --benchmarks="readseq,readwhilewriting" --key_size=32 --value_size=1024 --num=20000000 --threads=32 --bloom_bits=10 --cache_size=30000000000 --use_compressed_secondary_cache=true --compressed_secondary_cache_size=5000000000 --duration=300 --cost_write_buffer_to_cache=true --use_tiered_volatile_cache=true ``` ``` readseq : 4.064 micros/op 7873915 ops/sec 81.281 seconds 640000000 operations; 7929.7 MB/s readwhilewriting : 20.944 micros/op 1527827 ops/sec 300.020 seconds 458378968 operations; 1538.6 MB/s (14296999 of 14296999 found) real 6m42.743s user 157m58.972s sys 33m16.671 ``` ``` readseq : 3.484 micros/op 9184967 ops/sec 69.679 seconds 640000000 operations; 9250.0 MB/s readwhilewriting : 21.261 micros/op 1505035 ops/sec 300.024 seconds 451545968 operations; 1515.7 MB/s (14101999 of 14101999 found) real 6m31.469s user 155m16.570s sys 27m47.834s ``` ToDo - 1. Add to db_stress Pull Request resolved: https://github.com/facebook/rocksdb/pull/11449 Reviewed By: pdillinger Differential Revision: D46197388 Pulled By: anand1976 fbshipit-source-id: 42d16f0254df683db4929db20d06ff26030e90df
2023-05-30 21:05:48 +00:00
secondary_cache_opts.compress_format_version =
FLAGS_compressed_secondary_cache_compress_format_version;
if (FLAGS_use_tiered_cache) {
Integrate CacheReservationManager with compressed secondary cache (#11449) Summary: This draft PR implements charging of reserved memory, for write buffers, table readers, and other purposes, proportionally to the block cache and the compressed secondary cache. The basic flow of memory reservation is maintained - clients use ```CacheReservationManager``` to request reservations, and ```CacheReservationManager``` inserts placeholder entries, i.e null value and non-zero charge, into the block cache. The ```CacheWithSecondaryAdapter``` wrapper uses its own instance of ```CacheReservationManager``` to keep track of reservations charged to the secondary cache, while the placeholder entries are inserted into the primary block cache. The design is as follows. When ```CacheWithSecondaryAdapter``` is constructed with the ```distribute_cache_res``` parameter set to true, it manages the entire memory budget across the primary and secondary cache. The secondary cache is assumed to be in memory, such as the ```CompressedSecondaryCache```. When a placeholder entry is inserted by a CacheReservationManager instance to reserve memory, the ```CacheWithSecondaryAdapter```ensures that the reservation is distributed proportionally across the primary/secondary caches. The primary block cache is initially sized to the sum of the primary cache budget + the secondary cache budget, as follows - |--------- Primary Cache Configured Capacity -----------| |---Secondary Cache Budget----|----Primary Cache Budget-----| A ```ConcurrentCacheReservationManager``` member in the ```CacheWithSecondaryAdapter```, ```pri_cache_res_```, is used to help with tracking the distribution of memory reservations. Initially, it accounts for the entire secondary cache budget as a reservation against the primary cache. This shrinks the usable capacity of the primary cache to the budget that the user originally desired. |--Reservation for Sec Cache--|-Pri Cache Usable Capacity---| When a reservation placeholder is inserted into the adapter, it is inserted directly into the primary cache. This means the entire charge of the placeholder is counted against the primary cache. To compensate and count a portion of it against the secondary cache, the secondary cache ```Deflate()``` method is called to shrink it. Since the ```Deflate()``` causes the secondary actual usage to shrink, it is reflected here by releasing an equal amount from the ```pri_cache_res_``` reservation. For example, if the pri/sec ratio is 50/50, this would be the state after placeholder insertion - |-Reservation for Sec Cache-|-Pri Cache Usable Capacity-|-R-| Likewise, when the user inserted placeholder is released, the secondary cache ```Inflate()``` method is called to grow it, and the ```pri_cache_res_``` reservation is increased by an equal amount. Other alternatives - 1. Another way of implementing this would have been to simply split the user reservation in ```CacheWithSecondaryAdapter``` into primary and secondary components. However, this would require allocating a structure to track the associated secondary cache reservation, which adds some complexity and overhead. 2. Yet another option is to implement the splitting directly in ```CacheReservationManager```. However, there are multiple instances of ```CacheReservationManager``` in a DB instance, making it complicated to keep track of them. The PR contains the following changes - 1. A new cache allocator, ```NewTieredVolatileCache()```, is defined for allocating a tiered primary block cache and compressed secondary cache. This internally allocates an instance of ```CacheWithSecondaryAdapter```. 3. New interfaces, ```Deflate()``` and ```Inflate()```, are added to the ```SecondaryCache``` interface. The default implementaion returns ```NotSupported``` with overrides in ```CompressedSecondaryCache```. 4. The ```CompressedSecondaryCache``` uses a ```ConcurrentCacheReservationManager``` instance to manage reservations done using ```Inflate()/Deflate()```. 5. The ```CacheWithSecondaryAdapter``` optionally distributes memory reservations across the primary and secondary caches. The primary cache is sized to the total memory budget (primary + secondary), and the capacity allocated to secondary cache is "reserved" against the primary cache. For any subsequent reservations, the primary cache pre-reserved capacity is adjusted. Benchmarks - Baseline ``` time ~/rocksdb_anand76/db_bench --db=/dev/shm/comp_cache_res/base --use_existing_db=true --benchmarks="readseq,readwhilewriting" --key_size=32 --value_size=1024 --num=20000000 --threads=32 --bloom_bits=10 --cache_size=30000000000 --use_compressed_secondary_cache=true --compressed_secondary_cache_size=5000000000 --duration=300 --cost_write_buffer_to_cache=true ``` ``` readseq : 3.301 micros/op 9694317 ops/sec 66.018 seconds 640000000 operations; 9763.0 MB/s readwhilewriting : 22.921 micros/op 1396058 ops/sec 300.021 seconds 418846968 operations; 1405.9 MB/s (13068999 of 13068999 found) real 6m31.052s user 152m5.660s sys 26m18.738s ``` With TieredVolatileCache ``` time ~/rocksdb_anand76/db_bench --db=/dev/shm/comp_cache_res/base --use_existing_db=true --benchmarks="readseq,readwhilewriting" --key_size=32 --value_size=1024 --num=20000000 --threads=32 --bloom_bits=10 --cache_size=30000000000 --use_compressed_secondary_cache=true --compressed_secondary_cache_size=5000000000 --duration=300 --cost_write_buffer_to_cache=true --use_tiered_volatile_cache=true ``` ``` readseq : 4.064 micros/op 7873915 ops/sec 81.281 seconds 640000000 operations; 7929.7 MB/s readwhilewriting : 20.944 micros/op 1527827 ops/sec 300.020 seconds 458378968 operations; 1538.6 MB/s (14296999 of 14296999 found) real 6m42.743s user 157m58.972s sys 33m16.671 ``` ``` readseq : 3.484 micros/op 9184967 ops/sec 69.679 seconds 640000000 operations; 9250.0 MB/s readwhilewriting : 21.261 micros/op 1505035 ops/sec 300.024 seconds 451545968 operations; 1515.7 MB/s (14101999 of 14101999 found) real 6m31.469s user 155m16.570s sys 27m47.834s ``` ToDo - 1. Add to db_stress Pull Request resolved: https://github.com/facebook/rocksdb/pull/11449 Reviewed By: pdillinger Differential Revision: D46197388 Pulled By: anand1976 fbshipit-source-id: 42d16f0254df683db4929db20d06ff26030e90df
2023-05-30 21:05:48 +00:00
use_tiered_cache = true;
adm_policy = StringToAdmissionPolicy(FLAGS_tiered_adm_policy.c_str());
}
}
if (!FLAGS_secondary_cache_uri.empty()) {
if (!use_tiered_cache && FLAGS_use_compressed_secondary_cache) {
fprintf(
stderr,
"Cannot specify both --secondary_cache_uri and "
"--use_compressed_secondary_cache when using a non-tiered cache\n");
exit(1);
}
Status s = SecondaryCache::CreateFromString(
ConfigOptions(), FLAGS_secondary_cache_uri, &secondary_cache);
if (secondary_cache == nullptr) {
fprintf(stderr,
"No secondary cache registered matching string: %s status=%s\n",
FLAGS_secondary_cache_uri.c_str(), s.ToString().c_str());
exit(1);
Integrate CacheReservationManager with compressed secondary cache (#11449) Summary: This draft PR implements charging of reserved memory, for write buffers, table readers, and other purposes, proportionally to the block cache and the compressed secondary cache. The basic flow of memory reservation is maintained - clients use ```CacheReservationManager``` to request reservations, and ```CacheReservationManager``` inserts placeholder entries, i.e null value and non-zero charge, into the block cache. The ```CacheWithSecondaryAdapter``` wrapper uses its own instance of ```CacheReservationManager``` to keep track of reservations charged to the secondary cache, while the placeholder entries are inserted into the primary block cache. The design is as follows. When ```CacheWithSecondaryAdapter``` is constructed with the ```distribute_cache_res``` parameter set to true, it manages the entire memory budget across the primary and secondary cache. The secondary cache is assumed to be in memory, such as the ```CompressedSecondaryCache```. When a placeholder entry is inserted by a CacheReservationManager instance to reserve memory, the ```CacheWithSecondaryAdapter```ensures that the reservation is distributed proportionally across the primary/secondary caches. The primary block cache is initially sized to the sum of the primary cache budget + the secondary cache budget, as follows - |--------- Primary Cache Configured Capacity -----------| |---Secondary Cache Budget----|----Primary Cache Budget-----| A ```ConcurrentCacheReservationManager``` member in the ```CacheWithSecondaryAdapter```, ```pri_cache_res_```, is used to help with tracking the distribution of memory reservations. Initially, it accounts for the entire secondary cache budget as a reservation against the primary cache. This shrinks the usable capacity of the primary cache to the budget that the user originally desired. |--Reservation for Sec Cache--|-Pri Cache Usable Capacity---| When a reservation placeholder is inserted into the adapter, it is inserted directly into the primary cache. This means the entire charge of the placeholder is counted against the primary cache. To compensate and count a portion of it against the secondary cache, the secondary cache ```Deflate()``` method is called to shrink it. Since the ```Deflate()``` causes the secondary actual usage to shrink, it is reflected here by releasing an equal amount from the ```pri_cache_res_``` reservation. For example, if the pri/sec ratio is 50/50, this would be the state after placeholder insertion - |-Reservation for Sec Cache-|-Pri Cache Usable Capacity-|-R-| Likewise, when the user inserted placeholder is released, the secondary cache ```Inflate()``` method is called to grow it, and the ```pri_cache_res_``` reservation is increased by an equal amount. Other alternatives - 1. Another way of implementing this would have been to simply split the user reservation in ```CacheWithSecondaryAdapter``` into primary and secondary components. However, this would require allocating a structure to track the associated secondary cache reservation, which adds some complexity and overhead. 2. Yet another option is to implement the splitting directly in ```CacheReservationManager```. However, there are multiple instances of ```CacheReservationManager``` in a DB instance, making it complicated to keep track of them. The PR contains the following changes - 1. A new cache allocator, ```NewTieredVolatileCache()```, is defined for allocating a tiered primary block cache and compressed secondary cache. This internally allocates an instance of ```CacheWithSecondaryAdapter```. 3. New interfaces, ```Deflate()``` and ```Inflate()```, are added to the ```SecondaryCache``` interface. The default implementaion returns ```NotSupported``` with overrides in ```CompressedSecondaryCache```. 4. The ```CompressedSecondaryCache``` uses a ```ConcurrentCacheReservationManager``` instance to manage reservations done using ```Inflate()/Deflate()```. 5. The ```CacheWithSecondaryAdapter``` optionally distributes memory reservations across the primary and secondary caches. The primary cache is sized to the total memory budget (primary + secondary), and the capacity allocated to secondary cache is "reserved" against the primary cache. For any subsequent reservations, the primary cache pre-reserved capacity is adjusted. Benchmarks - Baseline ``` time ~/rocksdb_anand76/db_bench --db=/dev/shm/comp_cache_res/base --use_existing_db=true --benchmarks="readseq,readwhilewriting" --key_size=32 --value_size=1024 --num=20000000 --threads=32 --bloom_bits=10 --cache_size=30000000000 --use_compressed_secondary_cache=true --compressed_secondary_cache_size=5000000000 --duration=300 --cost_write_buffer_to_cache=true ``` ``` readseq : 3.301 micros/op 9694317 ops/sec 66.018 seconds 640000000 operations; 9763.0 MB/s readwhilewriting : 22.921 micros/op 1396058 ops/sec 300.021 seconds 418846968 operations; 1405.9 MB/s (13068999 of 13068999 found) real 6m31.052s user 152m5.660s sys 26m18.738s ``` With TieredVolatileCache ``` time ~/rocksdb_anand76/db_bench --db=/dev/shm/comp_cache_res/base --use_existing_db=true --benchmarks="readseq,readwhilewriting" --key_size=32 --value_size=1024 --num=20000000 --threads=32 --bloom_bits=10 --cache_size=30000000000 --use_compressed_secondary_cache=true --compressed_secondary_cache_size=5000000000 --duration=300 --cost_write_buffer_to_cache=true --use_tiered_volatile_cache=true ``` ``` readseq : 4.064 micros/op 7873915 ops/sec 81.281 seconds 640000000 operations; 7929.7 MB/s readwhilewriting : 20.944 micros/op 1527827 ops/sec 300.020 seconds 458378968 operations; 1538.6 MB/s (14296999 of 14296999 found) real 6m42.743s user 157m58.972s sys 33m16.671 ``` ``` readseq : 3.484 micros/op 9184967 ops/sec 69.679 seconds 640000000 operations; 9250.0 MB/s readwhilewriting : 21.261 micros/op 1505035 ops/sec 300.024 seconds 451545968 operations; 1515.7 MB/s (14101999 of 14101999 found) real 6m31.469s user 155m16.570s sys 27m47.834s ``` ToDo - 1. Add to db_stress Pull Request resolved: https://github.com/facebook/rocksdb/pull/11449 Reviewed By: pdillinger Differential Revision: D46197388 Pulled By: anand1976 fbshipit-source-id: 42d16f0254df683db4929db20d06ff26030e90df
2023-05-30 21:05:48 +00:00
}
}
std::shared_ptr<Cache> block_cache;
if (FLAGS_cache_type == "clock_cache") {
Call experimental new clock cache HyperClockCache (#10684) Summary: This change establishes a distinctive name for the experimental new lock-free clock cache (originally developed by guidotag and revamped in PR https://github.com/facebook/rocksdb/issues/10626). A few reasons: * We want to make it clear that this is a fundamentally different implementation vs. the old clock cache, to avoid people saying "I already tried clock cache." * We want to highlight the key feature: it's fast (especially under parallel load) * Because it requires an estimated charge per entry, it is not drop-in API compatible with old clock cache. This estimate might always be required for highest performance, and giving it a distinct name should reduce confusion about the distinct API requirements. * We might develop a variant requiring the same estimate parameter but with LRU eviction. In that case, using the name HyperLRUCache should make things more clear. (FastLRUCache is just a prototype that might soon be removed.) Some API detail: * To reduce copy-pasting parameter lists, etc. as in LRUCache construction, I have a `MakeSharedCache()` function on `HyperClockCacheOptions` instead of `NewHyperClockCache()`. * Changes -cache_type=clock_cache to -cache_type=hyper_clock_cache for applicable tools. I think this is more consistent / sustainable for reasons already stated. For performance tests see https://github.com/facebook/rocksdb/pull/10626 Pull Request resolved: https://github.com/facebook/rocksdb/pull/10684 Test Plan: no interesting functional changes; tests updated Reviewed By: anand1976 Differential Revision: D39547800 Pulled By: pdillinger fbshipit-source-id: 5c0fe1b5cf3cb680ab369b928c8569682b9795bf
2022-09-16 19:47:29 +00:00
fprintf(stderr, "Old clock cache implementation has been removed.\n");
exit(1);
Placeholder for AutoHyperClockCache, more (#11692) Summary: * The plan is for AutoHyperClockCache to be selected when HyperClockCacheOptions::estimated_entry_charge == 0, and in that case to use a new configuration option min_avg_entry_charge for determining an extreme case maximum size for the hash table. For the placeholder, a hack is in place in HyperClockCacheOptions::MakeSharedCache() to make the unit tests happy despite the new options not really making sense with the current implementation. * Mostly updating and refactoring tests to test both the current HCC (internal name FixedHyperClockCache) and a placeholder for the new version (internal name AutoHyperClockCache). * Simplify some existing tests not to depend directly on cache type. * Type-parameterize the shard-level unit tests, which unfortunately requires more syntax like `this->` in places for disambiguation. * Added means of choosing auto_hyper_clock_cache to cache_bench, db_bench, and db_stress, including add to crash test. * Add another templated class BaseHyperClockCache to reduce future copy-paste * Added ReportProblems support to cache_bench * Added a DEBUG-level diagnostic to ReportProblems for the variance in load factor throughout the table, which will become more of a concern with linear hashing to be used in the Auto implementation. Example with current Fixed HCC: ``` 2023/08/10-13:41:41.602450 6ac36 [DEBUG] [che/clock_cache.cc:1507] Slot occupancy stats: Overall 49% (129008/262144), Min/Max/Window = 39%/60%/500, MaxRun{Pos/Neg} = 18/17 ``` In other words, with overall occupancy of 49%, the lowest across any 500 contiguous cells is 39% and highest 60%. Longest run of occupied is 18 and longest run of unoccupied is 17. This seems consistent with random samples from a uniform distribution. Pull Request resolved: https://github.com/facebook/rocksdb/pull/11692 Test Plan: Shouldn't be any meaningful changes yet to production code or to what is tested, but there is temporary redundancy in testing until the new implementation is plugged in. Reviewed By: jowlyzhang Differential Revision: D48247413 Pulled By: pdillinger fbshipit-source-id: 11541f996d97af403c2e43c92fb67ff22dd0b5da
2023-08-11 23:27:38 +00:00
} else if (EndsWith(FLAGS_cache_type, "hyper_clock_cache")) {
size_t estimated_entry_charge;
if (FLAGS_cache_type == "fixed_hyper_clock_cache" ||
FLAGS_cache_type == "hyper_clock_cache") {
estimated_entry_charge = FLAGS_block_size;
} else if (FLAGS_cache_type == "auto_hyper_clock_cache") {
estimated_entry_charge = 0;
} else {
fprintf(stderr, "Cache type not supported.");
exit(1);
}
HyperClockCacheOptions opts(FLAGS_cache_size, estimated_entry_charge,
FLAGS_cache_numshardbits);
opts.hash_seed = GetCacheHashSeed();
Integrate CacheReservationManager with compressed secondary cache (#11449) Summary: This draft PR implements charging of reserved memory, for write buffers, table readers, and other purposes, proportionally to the block cache and the compressed secondary cache. The basic flow of memory reservation is maintained - clients use ```CacheReservationManager``` to request reservations, and ```CacheReservationManager``` inserts placeholder entries, i.e null value and non-zero charge, into the block cache. The ```CacheWithSecondaryAdapter``` wrapper uses its own instance of ```CacheReservationManager``` to keep track of reservations charged to the secondary cache, while the placeholder entries are inserted into the primary block cache. The design is as follows. When ```CacheWithSecondaryAdapter``` is constructed with the ```distribute_cache_res``` parameter set to true, it manages the entire memory budget across the primary and secondary cache. The secondary cache is assumed to be in memory, such as the ```CompressedSecondaryCache```. When a placeholder entry is inserted by a CacheReservationManager instance to reserve memory, the ```CacheWithSecondaryAdapter```ensures that the reservation is distributed proportionally across the primary/secondary caches. The primary block cache is initially sized to the sum of the primary cache budget + the secondary cache budget, as follows - |--------- Primary Cache Configured Capacity -----------| |---Secondary Cache Budget----|----Primary Cache Budget-----| A ```ConcurrentCacheReservationManager``` member in the ```CacheWithSecondaryAdapter```, ```pri_cache_res_```, is used to help with tracking the distribution of memory reservations. Initially, it accounts for the entire secondary cache budget as a reservation against the primary cache. This shrinks the usable capacity of the primary cache to the budget that the user originally desired. |--Reservation for Sec Cache--|-Pri Cache Usable Capacity---| When a reservation placeholder is inserted into the adapter, it is inserted directly into the primary cache. This means the entire charge of the placeholder is counted against the primary cache. To compensate and count a portion of it against the secondary cache, the secondary cache ```Deflate()``` method is called to shrink it. Since the ```Deflate()``` causes the secondary actual usage to shrink, it is reflected here by releasing an equal amount from the ```pri_cache_res_``` reservation. For example, if the pri/sec ratio is 50/50, this would be the state after placeholder insertion - |-Reservation for Sec Cache-|-Pri Cache Usable Capacity-|-R-| Likewise, when the user inserted placeholder is released, the secondary cache ```Inflate()``` method is called to grow it, and the ```pri_cache_res_``` reservation is increased by an equal amount. Other alternatives - 1. Another way of implementing this would have been to simply split the user reservation in ```CacheWithSecondaryAdapter``` into primary and secondary components. However, this would require allocating a structure to track the associated secondary cache reservation, which adds some complexity and overhead. 2. Yet another option is to implement the splitting directly in ```CacheReservationManager```. However, there are multiple instances of ```CacheReservationManager``` in a DB instance, making it complicated to keep track of them. The PR contains the following changes - 1. A new cache allocator, ```NewTieredVolatileCache()```, is defined for allocating a tiered primary block cache and compressed secondary cache. This internally allocates an instance of ```CacheWithSecondaryAdapter```. 3. New interfaces, ```Deflate()``` and ```Inflate()```, are added to the ```SecondaryCache``` interface. The default implementaion returns ```NotSupported``` with overrides in ```CompressedSecondaryCache```. 4. The ```CompressedSecondaryCache``` uses a ```ConcurrentCacheReservationManager``` instance to manage reservations done using ```Inflate()/Deflate()```. 5. The ```CacheWithSecondaryAdapter``` optionally distributes memory reservations across the primary and secondary caches. The primary cache is sized to the total memory budget (primary + secondary), and the capacity allocated to secondary cache is "reserved" against the primary cache. For any subsequent reservations, the primary cache pre-reserved capacity is adjusted. Benchmarks - Baseline ``` time ~/rocksdb_anand76/db_bench --db=/dev/shm/comp_cache_res/base --use_existing_db=true --benchmarks="readseq,readwhilewriting" --key_size=32 --value_size=1024 --num=20000000 --threads=32 --bloom_bits=10 --cache_size=30000000000 --use_compressed_secondary_cache=true --compressed_secondary_cache_size=5000000000 --duration=300 --cost_write_buffer_to_cache=true ``` ``` readseq : 3.301 micros/op 9694317 ops/sec 66.018 seconds 640000000 operations; 9763.0 MB/s readwhilewriting : 22.921 micros/op 1396058 ops/sec 300.021 seconds 418846968 operations; 1405.9 MB/s (13068999 of 13068999 found) real 6m31.052s user 152m5.660s sys 26m18.738s ``` With TieredVolatileCache ``` time ~/rocksdb_anand76/db_bench --db=/dev/shm/comp_cache_res/base --use_existing_db=true --benchmarks="readseq,readwhilewriting" --key_size=32 --value_size=1024 --num=20000000 --threads=32 --bloom_bits=10 --cache_size=30000000000 --use_compressed_secondary_cache=true --compressed_secondary_cache_size=5000000000 --duration=300 --cost_write_buffer_to_cache=true --use_tiered_volatile_cache=true ``` ``` readseq : 4.064 micros/op 7873915 ops/sec 81.281 seconds 640000000 operations; 7929.7 MB/s readwhilewriting : 20.944 micros/op 1527827 ops/sec 300.020 seconds 458378968 operations; 1538.6 MB/s (14296999 of 14296999 found) real 6m42.743s user 157m58.972s sys 33m16.671 ``` ``` readseq : 3.484 micros/op 9184967 ops/sec 69.679 seconds 640000000 operations; 9250.0 MB/s readwhilewriting : 21.261 micros/op 1505035 ops/sec 300.024 seconds 451545968 operations; 1515.7 MB/s (14101999 of 14101999 found) real 6m31.469s user 155m16.570s sys 27m47.834s ``` ToDo - 1. Add to db_stress Pull Request resolved: https://github.com/facebook/rocksdb/pull/11449 Reviewed By: pdillinger Differential Revision: D46197388 Pulled By: anand1976 fbshipit-source-id: 42d16f0254df683db4929db20d06ff26030e90df
2023-05-30 21:05:48 +00:00
if (use_tiered_cache) {
Support compressed and local flash secondary cache stacking (#11812) Summary: This PR implements support for a three tier cache - primary block cache, compressed secondary cache, and a nvm (local flash) secondary cache. This allows more effective utilization of the nvm cache, and minimizes the number of reads from local flash by caching compressed blocks in the compressed secondary cache. The basic design is as follows - 1. A new secondary cache implementation, ```TieredSecondaryCache```, is introduced. It keeps the compressed and nvm secondary caches and manages the movement of blocks between them and the primary block cache. To setup a three tier cache, we allocate a ```CacheWithSecondaryAdapter```, with a ```TieredSecondaryCache``` instance as the secondary cache. 2. The table reader passes both the uncompressed and compressed block to ```FullTypedCacheInterface::InsertFull```, allowing the block cache to optionally store the compressed block. 3. When there's a miss, the block object is constructed and inserted in the primary cache, and the compressed block is inserted into the nvm cache by calling ```InsertSaved```. This avoids the overhead of recompressing the block, as well as avoiding putting more memory pressure on the compressed secondary cache. 4. When there's a hit in the nvm cache, we attempt to insert the block in the compressed secondary cache and the primary cache, subject to the admission policy of those caches (i.e admit on second access). Blocks/items evicted from any tier are simply discarded. We can easily implement additional admission policies if desired. Todo (In a subsequent PR): 1. Add to db_bench and run benchmarks 2. Add to db_stress Pull Request resolved: https://github.com/facebook/rocksdb/pull/11812 Reviewed By: pdillinger Differential Revision: D49461842 Pulled By: anand1976 fbshipit-source-id: b40ac1330ef7cd8c12efa0a3ca75128e602e3a0b
2023-09-22 03:30:53 +00:00
TieredCacheOptions tiered_opts;
Placeholder for AutoHyperClockCache, more (#11692) Summary: * The plan is for AutoHyperClockCache to be selected when HyperClockCacheOptions::estimated_entry_charge == 0, and in that case to use a new configuration option min_avg_entry_charge for determining an extreme case maximum size for the hash table. For the placeholder, a hack is in place in HyperClockCacheOptions::MakeSharedCache() to make the unit tests happy despite the new options not really making sense with the current implementation. * Mostly updating and refactoring tests to test both the current HCC (internal name FixedHyperClockCache) and a placeholder for the new version (internal name AutoHyperClockCache). * Simplify some existing tests not to depend directly on cache type. * Type-parameterize the shard-level unit tests, which unfortunately requires more syntax like `this->` in places for disambiguation. * Added means of choosing auto_hyper_clock_cache to cache_bench, db_bench, and db_stress, including add to crash test. * Add another templated class BaseHyperClockCache to reduce future copy-paste * Added ReportProblems support to cache_bench * Added a DEBUG-level diagnostic to ReportProblems for the variance in load factor throughout the table, which will become more of a concern with linear hashing to be used in the Auto implementation. Example with current Fixed HCC: ``` 2023/08/10-13:41:41.602450 6ac36 [DEBUG] [che/clock_cache.cc:1507] Slot occupancy stats: Overall 49% (129008/262144), Min/Max/Window = 39%/60%/500, MaxRun{Pos/Neg} = 18/17 ``` In other words, with overall occupancy of 49%, the lowest across any 500 contiguous cells is 39% and highest 60%. Longest run of occupied is 18 and longest run of unoccupied is 17. This seems consistent with random samples from a uniform distribution. Pull Request resolved: https://github.com/facebook/rocksdb/pull/11692 Test Plan: Shouldn't be any meaningful changes yet to production code or to what is tested, but there is temporary redundancy in testing until the new implementation is plugged in. Reviewed By: jowlyzhang Differential Revision: D48247413 Pulled By: pdillinger fbshipit-source-id: 11541f996d97af403c2e43c92fb67ff22dd0b5da
2023-08-11 23:27:38 +00:00
tiered_opts.cache_type = PrimaryCacheType::kCacheTypeHCC;
tiered_opts.cache_opts = &opts;
tiered_opts.total_capacity =
opts.capacity + secondary_cache_opts.capacity;
tiered_opts.compressed_secondary_ratio =
secondary_cache_opts.capacity * 1.0 / tiered_opts.total_capacity;
Placeholder for AutoHyperClockCache, more (#11692) Summary: * The plan is for AutoHyperClockCache to be selected when HyperClockCacheOptions::estimated_entry_charge == 0, and in that case to use a new configuration option min_avg_entry_charge for determining an extreme case maximum size for the hash table. For the placeholder, a hack is in place in HyperClockCacheOptions::MakeSharedCache() to make the unit tests happy despite the new options not really making sense with the current implementation. * Mostly updating and refactoring tests to test both the current HCC (internal name FixedHyperClockCache) and a placeholder for the new version (internal name AutoHyperClockCache). * Simplify some existing tests not to depend directly on cache type. * Type-parameterize the shard-level unit tests, which unfortunately requires more syntax like `this->` in places for disambiguation. * Added means of choosing auto_hyper_clock_cache to cache_bench, db_bench, and db_stress, including add to crash test. * Add another templated class BaseHyperClockCache to reduce future copy-paste * Added ReportProblems support to cache_bench * Added a DEBUG-level diagnostic to ReportProblems for the variance in load factor throughout the table, which will become more of a concern with linear hashing to be used in the Auto implementation. Example with current Fixed HCC: ``` 2023/08/10-13:41:41.602450 6ac36 [DEBUG] [che/clock_cache.cc:1507] Slot occupancy stats: Overall 49% (129008/262144), Min/Max/Window = 39%/60%/500, MaxRun{Pos/Neg} = 18/17 ``` In other words, with overall occupancy of 49%, the lowest across any 500 contiguous cells is 39% and highest 60%. Longest run of occupied is 18 and longest run of unoccupied is 17. This seems consistent with random samples from a uniform distribution. Pull Request resolved: https://github.com/facebook/rocksdb/pull/11692 Test Plan: Shouldn't be any meaningful changes yet to production code or to what is tested, but there is temporary redundancy in testing until the new implementation is plugged in. Reviewed By: jowlyzhang Differential Revision: D48247413 Pulled By: pdillinger fbshipit-source-id: 11541f996d97af403c2e43c92fb67ff22dd0b5da
2023-08-11 23:27:38 +00:00
tiered_opts.comp_cache_opts = secondary_cache_opts;
tiered_opts.nvm_sec_cache = secondary_cache;
tiered_opts.adm_policy = adm_policy;
block_cache = NewTieredCache(tiered_opts);
Integrate CacheReservationManager with compressed secondary cache (#11449) Summary: This draft PR implements charging of reserved memory, for write buffers, table readers, and other purposes, proportionally to the block cache and the compressed secondary cache. The basic flow of memory reservation is maintained - clients use ```CacheReservationManager``` to request reservations, and ```CacheReservationManager``` inserts placeholder entries, i.e null value and non-zero charge, into the block cache. The ```CacheWithSecondaryAdapter``` wrapper uses its own instance of ```CacheReservationManager``` to keep track of reservations charged to the secondary cache, while the placeholder entries are inserted into the primary block cache. The design is as follows. When ```CacheWithSecondaryAdapter``` is constructed with the ```distribute_cache_res``` parameter set to true, it manages the entire memory budget across the primary and secondary cache. The secondary cache is assumed to be in memory, such as the ```CompressedSecondaryCache```. When a placeholder entry is inserted by a CacheReservationManager instance to reserve memory, the ```CacheWithSecondaryAdapter```ensures that the reservation is distributed proportionally across the primary/secondary caches. The primary block cache is initially sized to the sum of the primary cache budget + the secondary cache budget, as follows - |--------- Primary Cache Configured Capacity -----------| |---Secondary Cache Budget----|----Primary Cache Budget-----| A ```ConcurrentCacheReservationManager``` member in the ```CacheWithSecondaryAdapter```, ```pri_cache_res_```, is used to help with tracking the distribution of memory reservations. Initially, it accounts for the entire secondary cache budget as a reservation against the primary cache. This shrinks the usable capacity of the primary cache to the budget that the user originally desired. |--Reservation for Sec Cache--|-Pri Cache Usable Capacity---| When a reservation placeholder is inserted into the adapter, it is inserted directly into the primary cache. This means the entire charge of the placeholder is counted against the primary cache. To compensate and count a portion of it against the secondary cache, the secondary cache ```Deflate()``` method is called to shrink it. Since the ```Deflate()``` causes the secondary actual usage to shrink, it is reflected here by releasing an equal amount from the ```pri_cache_res_``` reservation. For example, if the pri/sec ratio is 50/50, this would be the state after placeholder insertion - |-Reservation for Sec Cache-|-Pri Cache Usable Capacity-|-R-| Likewise, when the user inserted placeholder is released, the secondary cache ```Inflate()``` method is called to grow it, and the ```pri_cache_res_``` reservation is increased by an equal amount. Other alternatives - 1. Another way of implementing this would have been to simply split the user reservation in ```CacheWithSecondaryAdapter``` into primary and secondary components. However, this would require allocating a structure to track the associated secondary cache reservation, which adds some complexity and overhead. 2. Yet another option is to implement the splitting directly in ```CacheReservationManager```. However, there are multiple instances of ```CacheReservationManager``` in a DB instance, making it complicated to keep track of them. The PR contains the following changes - 1. A new cache allocator, ```NewTieredVolatileCache()```, is defined for allocating a tiered primary block cache and compressed secondary cache. This internally allocates an instance of ```CacheWithSecondaryAdapter```. 3. New interfaces, ```Deflate()``` and ```Inflate()```, are added to the ```SecondaryCache``` interface. The default implementaion returns ```NotSupported``` with overrides in ```CompressedSecondaryCache```. 4. The ```CompressedSecondaryCache``` uses a ```ConcurrentCacheReservationManager``` instance to manage reservations done using ```Inflate()/Deflate()```. 5. The ```CacheWithSecondaryAdapter``` optionally distributes memory reservations across the primary and secondary caches. The primary cache is sized to the total memory budget (primary + secondary), and the capacity allocated to secondary cache is "reserved" against the primary cache. For any subsequent reservations, the primary cache pre-reserved capacity is adjusted. Benchmarks - Baseline ``` time ~/rocksdb_anand76/db_bench --db=/dev/shm/comp_cache_res/base --use_existing_db=true --benchmarks="readseq,readwhilewriting" --key_size=32 --value_size=1024 --num=20000000 --threads=32 --bloom_bits=10 --cache_size=30000000000 --use_compressed_secondary_cache=true --compressed_secondary_cache_size=5000000000 --duration=300 --cost_write_buffer_to_cache=true ``` ``` readseq : 3.301 micros/op 9694317 ops/sec 66.018 seconds 640000000 operations; 9763.0 MB/s readwhilewriting : 22.921 micros/op 1396058 ops/sec 300.021 seconds 418846968 operations; 1405.9 MB/s (13068999 of 13068999 found) real 6m31.052s user 152m5.660s sys 26m18.738s ``` With TieredVolatileCache ``` time ~/rocksdb_anand76/db_bench --db=/dev/shm/comp_cache_res/base --use_existing_db=true --benchmarks="readseq,readwhilewriting" --key_size=32 --value_size=1024 --num=20000000 --threads=32 --bloom_bits=10 --cache_size=30000000000 --use_compressed_secondary_cache=true --compressed_secondary_cache_size=5000000000 --duration=300 --cost_write_buffer_to_cache=true --use_tiered_volatile_cache=true ``` ``` readseq : 4.064 micros/op 7873915 ops/sec 81.281 seconds 640000000 operations; 7929.7 MB/s readwhilewriting : 20.944 micros/op 1527827 ops/sec 300.020 seconds 458378968 operations; 1538.6 MB/s (14296999 of 14296999 found) real 6m42.743s user 157m58.972s sys 33m16.671 ``` ``` readseq : 3.484 micros/op 9184967 ops/sec 69.679 seconds 640000000 operations; 9250.0 MB/s readwhilewriting : 21.261 micros/op 1505035 ops/sec 300.024 seconds 451545968 operations; 1515.7 MB/s (14101999 of 14101999 found) real 6m31.469s user 155m16.570s sys 27m47.834s ``` ToDo - 1. Add to db_stress Pull Request resolved: https://github.com/facebook/rocksdb/pull/11449 Reviewed By: pdillinger Differential Revision: D46197388 Pulled By: anand1976 fbshipit-source-id: 42d16f0254df683db4929db20d06ff26030e90df
2023-05-30 21:05:48 +00:00
} else {
if (!FLAGS_secondary_cache_uri.empty()) {
opts.secondary_cache = secondary_cache;
} else if (FLAGS_use_compressed_secondary_cache) {
opts.secondary_cache =
NewCompressedSecondaryCache(secondary_cache_opts);
}
block_cache = opts.MakeSharedCache();
Integrate CacheReservationManager with compressed secondary cache (#11449) Summary: This draft PR implements charging of reserved memory, for write buffers, table readers, and other purposes, proportionally to the block cache and the compressed secondary cache. The basic flow of memory reservation is maintained - clients use ```CacheReservationManager``` to request reservations, and ```CacheReservationManager``` inserts placeholder entries, i.e null value and non-zero charge, into the block cache. The ```CacheWithSecondaryAdapter``` wrapper uses its own instance of ```CacheReservationManager``` to keep track of reservations charged to the secondary cache, while the placeholder entries are inserted into the primary block cache. The design is as follows. When ```CacheWithSecondaryAdapter``` is constructed with the ```distribute_cache_res``` parameter set to true, it manages the entire memory budget across the primary and secondary cache. The secondary cache is assumed to be in memory, such as the ```CompressedSecondaryCache```. When a placeholder entry is inserted by a CacheReservationManager instance to reserve memory, the ```CacheWithSecondaryAdapter```ensures that the reservation is distributed proportionally across the primary/secondary caches. The primary block cache is initially sized to the sum of the primary cache budget + the secondary cache budget, as follows - |--------- Primary Cache Configured Capacity -----------| |---Secondary Cache Budget----|----Primary Cache Budget-----| A ```ConcurrentCacheReservationManager``` member in the ```CacheWithSecondaryAdapter```, ```pri_cache_res_```, is used to help with tracking the distribution of memory reservations. Initially, it accounts for the entire secondary cache budget as a reservation against the primary cache. This shrinks the usable capacity of the primary cache to the budget that the user originally desired. |--Reservation for Sec Cache--|-Pri Cache Usable Capacity---| When a reservation placeholder is inserted into the adapter, it is inserted directly into the primary cache. This means the entire charge of the placeholder is counted against the primary cache. To compensate and count a portion of it against the secondary cache, the secondary cache ```Deflate()``` method is called to shrink it. Since the ```Deflate()``` causes the secondary actual usage to shrink, it is reflected here by releasing an equal amount from the ```pri_cache_res_``` reservation. For example, if the pri/sec ratio is 50/50, this would be the state after placeholder insertion - |-Reservation for Sec Cache-|-Pri Cache Usable Capacity-|-R-| Likewise, when the user inserted placeholder is released, the secondary cache ```Inflate()``` method is called to grow it, and the ```pri_cache_res_``` reservation is increased by an equal amount. Other alternatives - 1. Another way of implementing this would have been to simply split the user reservation in ```CacheWithSecondaryAdapter``` into primary and secondary components. However, this would require allocating a structure to track the associated secondary cache reservation, which adds some complexity and overhead. 2. Yet another option is to implement the splitting directly in ```CacheReservationManager```. However, there are multiple instances of ```CacheReservationManager``` in a DB instance, making it complicated to keep track of them. The PR contains the following changes - 1. A new cache allocator, ```NewTieredVolatileCache()```, is defined for allocating a tiered primary block cache and compressed secondary cache. This internally allocates an instance of ```CacheWithSecondaryAdapter```. 3. New interfaces, ```Deflate()``` and ```Inflate()```, are added to the ```SecondaryCache``` interface. The default implementaion returns ```NotSupported``` with overrides in ```CompressedSecondaryCache```. 4. The ```CompressedSecondaryCache``` uses a ```ConcurrentCacheReservationManager``` instance to manage reservations done using ```Inflate()/Deflate()```. 5. The ```CacheWithSecondaryAdapter``` optionally distributes memory reservations across the primary and secondary caches. The primary cache is sized to the total memory budget (primary + secondary), and the capacity allocated to secondary cache is "reserved" against the primary cache. For any subsequent reservations, the primary cache pre-reserved capacity is adjusted. Benchmarks - Baseline ``` time ~/rocksdb_anand76/db_bench --db=/dev/shm/comp_cache_res/base --use_existing_db=true --benchmarks="readseq,readwhilewriting" --key_size=32 --value_size=1024 --num=20000000 --threads=32 --bloom_bits=10 --cache_size=30000000000 --use_compressed_secondary_cache=true --compressed_secondary_cache_size=5000000000 --duration=300 --cost_write_buffer_to_cache=true ``` ``` readseq : 3.301 micros/op 9694317 ops/sec 66.018 seconds 640000000 operations; 9763.0 MB/s readwhilewriting : 22.921 micros/op 1396058 ops/sec 300.021 seconds 418846968 operations; 1405.9 MB/s (13068999 of 13068999 found) real 6m31.052s user 152m5.660s sys 26m18.738s ``` With TieredVolatileCache ``` time ~/rocksdb_anand76/db_bench --db=/dev/shm/comp_cache_res/base --use_existing_db=true --benchmarks="readseq,readwhilewriting" --key_size=32 --value_size=1024 --num=20000000 --threads=32 --bloom_bits=10 --cache_size=30000000000 --use_compressed_secondary_cache=true --compressed_secondary_cache_size=5000000000 --duration=300 --cost_write_buffer_to_cache=true --use_tiered_volatile_cache=true ``` ``` readseq : 4.064 micros/op 7873915 ops/sec 81.281 seconds 640000000 operations; 7929.7 MB/s readwhilewriting : 20.944 micros/op 1527827 ops/sec 300.020 seconds 458378968 operations; 1538.6 MB/s (14296999 of 14296999 found) real 6m42.743s user 157m58.972s sys 33m16.671 ``` ``` readseq : 3.484 micros/op 9184967 ops/sec 69.679 seconds 640000000 operations; 9250.0 MB/s readwhilewriting : 21.261 micros/op 1505035 ops/sec 300.024 seconds 451545968 operations; 1515.7 MB/s (14101999 of 14101999 found) real 6m31.469s user 155m16.570s sys 27m47.834s ``` ToDo - 1. Add to db_stress Pull Request resolved: https://github.com/facebook/rocksdb/pull/11449 Reviewed By: pdillinger Differential Revision: D46197388 Pulled By: anand1976 fbshipit-source-id: 42d16f0254df683db4929db20d06ff26030e90df
2023-05-30 21:05:48 +00:00
}
} else if (FLAGS_cache_type == "lru_cache") {
LRUCacheOptions opts(
static_cast<size_t>(capacity), FLAGS_cache_numshardbits,
false /*strict_capacity_limit*/, FLAGS_cache_high_pri_pool_ratio,
GetCacheAllocator(), kDefaultToAdaptiveMutex,
kDefaultCacheMetadataChargePolicy, FLAGS_cache_low_pri_pool_ratio);
opts.hash_seed = GetCacheHashSeed();
Integrate CacheReservationManager with compressed secondary cache (#11449) Summary: This draft PR implements charging of reserved memory, for write buffers, table readers, and other purposes, proportionally to the block cache and the compressed secondary cache. The basic flow of memory reservation is maintained - clients use ```CacheReservationManager``` to request reservations, and ```CacheReservationManager``` inserts placeholder entries, i.e null value and non-zero charge, into the block cache. The ```CacheWithSecondaryAdapter``` wrapper uses its own instance of ```CacheReservationManager``` to keep track of reservations charged to the secondary cache, while the placeholder entries are inserted into the primary block cache. The design is as follows. When ```CacheWithSecondaryAdapter``` is constructed with the ```distribute_cache_res``` parameter set to true, it manages the entire memory budget across the primary and secondary cache. The secondary cache is assumed to be in memory, such as the ```CompressedSecondaryCache```. When a placeholder entry is inserted by a CacheReservationManager instance to reserve memory, the ```CacheWithSecondaryAdapter```ensures that the reservation is distributed proportionally across the primary/secondary caches. The primary block cache is initially sized to the sum of the primary cache budget + the secondary cache budget, as follows - |--------- Primary Cache Configured Capacity -----------| |---Secondary Cache Budget----|----Primary Cache Budget-----| A ```ConcurrentCacheReservationManager``` member in the ```CacheWithSecondaryAdapter```, ```pri_cache_res_```, is used to help with tracking the distribution of memory reservations. Initially, it accounts for the entire secondary cache budget as a reservation against the primary cache. This shrinks the usable capacity of the primary cache to the budget that the user originally desired. |--Reservation for Sec Cache--|-Pri Cache Usable Capacity---| When a reservation placeholder is inserted into the adapter, it is inserted directly into the primary cache. This means the entire charge of the placeholder is counted against the primary cache. To compensate and count a portion of it against the secondary cache, the secondary cache ```Deflate()``` method is called to shrink it. Since the ```Deflate()``` causes the secondary actual usage to shrink, it is reflected here by releasing an equal amount from the ```pri_cache_res_``` reservation. For example, if the pri/sec ratio is 50/50, this would be the state after placeholder insertion - |-Reservation for Sec Cache-|-Pri Cache Usable Capacity-|-R-| Likewise, when the user inserted placeholder is released, the secondary cache ```Inflate()``` method is called to grow it, and the ```pri_cache_res_``` reservation is increased by an equal amount. Other alternatives - 1. Another way of implementing this would have been to simply split the user reservation in ```CacheWithSecondaryAdapter``` into primary and secondary components. However, this would require allocating a structure to track the associated secondary cache reservation, which adds some complexity and overhead. 2. Yet another option is to implement the splitting directly in ```CacheReservationManager```. However, there are multiple instances of ```CacheReservationManager``` in a DB instance, making it complicated to keep track of them. The PR contains the following changes - 1. A new cache allocator, ```NewTieredVolatileCache()```, is defined for allocating a tiered primary block cache and compressed secondary cache. This internally allocates an instance of ```CacheWithSecondaryAdapter```. 3. New interfaces, ```Deflate()``` and ```Inflate()```, are added to the ```SecondaryCache``` interface. The default implementaion returns ```NotSupported``` with overrides in ```CompressedSecondaryCache```. 4. The ```CompressedSecondaryCache``` uses a ```ConcurrentCacheReservationManager``` instance to manage reservations done using ```Inflate()/Deflate()```. 5. The ```CacheWithSecondaryAdapter``` optionally distributes memory reservations across the primary and secondary caches. The primary cache is sized to the total memory budget (primary + secondary), and the capacity allocated to secondary cache is "reserved" against the primary cache. For any subsequent reservations, the primary cache pre-reserved capacity is adjusted. Benchmarks - Baseline ``` time ~/rocksdb_anand76/db_bench --db=/dev/shm/comp_cache_res/base --use_existing_db=true --benchmarks="readseq,readwhilewriting" --key_size=32 --value_size=1024 --num=20000000 --threads=32 --bloom_bits=10 --cache_size=30000000000 --use_compressed_secondary_cache=true --compressed_secondary_cache_size=5000000000 --duration=300 --cost_write_buffer_to_cache=true ``` ``` readseq : 3.301 micros/op 9694317 ops/sec 66.018 seconds 640000000 operations; 9763.0 MB/s readwhilewriting : 22.921 micros/op 1396058 ops/sec 300.021 seconds 418846968 operations; 1405.9 MB/s (13068999 of 13068999 found) real 6m31.052s user 152m5.660s sys 26m18.738s ``` With TieredVolatileCache ``` time ~/rocksdb_anand76/db_bench --db=/dev/shm/comp_cache_res/base --use_existing_db=true --benchmarks="readseq,readwhilewriting" --key_size=32 --value_size=1024 --num=20000000 --threads=32 --bloom_bits=10 --cache_size=30000000000 --use_compressed_secondary_cache=true --compressed_secondary_cache_size=5000000000 --duration=300 --cost_write_buffer_to_cache=true --use_tiered_volatile_cache=true ``` ``` readseq : 4.064 micros/op 7873915 ops/sec 81.281 seconds 640000000 operations; 7929.7 MB/s readwhilewriting : 20.944 micros/op 1527827 ops/sec 300.020 seconds 458378968 operations; 1538.6 MB/s (14296999 of 14296999 found) real 6m42.743s user 157m58.972s sys 33m16.671 ``` ``` readseq : 3.484 micros/op 9184967 ops/sec 69.679 seconds 640000000 operations; 9250.0 MB/s readwhilewriting : 21.261 micros/op 1505035 ops/sec 300.024 seconds 451545968 operations; 1515.7 MB/s (14101999 of 14101999 found) real 6m31.469s user 155m16.570s sys 27m47.834s ``` ToDo - 1. Add to db_stress Pull Request resolved: https://github.com/facebook/rocksdb/pull/11449 Reviewed By: pdillinger Differential Revision: D46197388 Pulled By: anand1976 fbshipit-source-id: 42d16f0254df683db4929db20d06ff26030e90df
2023-05-30 21:05:48 +00:00
if (use_tiered_cache) {
Support compressed and local flash secondary cache stacking (#11812) Summary: This PR implements support for a three tier cache - primary block cache, compressed secondary cache, and a nvm (local flash) secondary cache. This allows more effective utilization of the nvm cache, and minimizes the number of reads from local flash by caching compressed blocks in the compressed secondary cache. The basic design is as follows - 1. A new secondary cache implementation, ```TieredSecondaryCache```, is introduced. It keeps the compressed and nvm secondary caches and manages the movement of blocks between them and the primary block cache. To setup a three tier cache, we allocate a ```CacheWithSecondaryAdapter```, with a ```TieredSecondaryCache``` instance as the secondary cache. 2. The table reader passes both the uncompressed and compressed block to ```FullTypedCacheInterface::InsertFull```, allowing the block cache to optionally store the compressed block. 3. When there's a miss, the block object is constructed and inserted in the primary cache, and the compressed block is inserted into the nvm cache by calling ```InsertSaved```. This avoids the overhead of recompressing the block, as well as avoiding putting more memory pressure on the compressed secondary cache. 4. When there's a hit in the nvm cache, we attempt to insert the block in the compressed secondary cache and the primary cache, subject to the admission policy of those caches (i.e admit on second access). Blocks/items evicted from any tier are simply discarded. We can easily implement additional admission policies if desired. Todo (In a subsequent PR): 1. Add to db_bench and run benchmarks 2. Add to db_stress Pull Request resolved: https://github.com/facebook/rocksdb/pull/11812 Reviewed By: pdillinger Differential Revision: D49461842 Pulled By: anand1976 fbshipit-source-id: b40ac1330ef7cd8c12efa0a3ca75128e602e3a0b
2023-09-22 03:30:53 +00:00
TieredCacheOptions tiered_opts;
Integrate CacheReservationManager with compressed secondary cache (#11449) Summary: This draft PR implements charging of reserved memory, for write buffers, table readers, and other purposes, proportionally to the block cache and the compressed secondary cache. The basic flow of memory reservation is maintained - clients use ```CacheReservationManager``` to request reservations, and ```CacheReservationManager``` inserts placeholder entries, i.e null value and non-zero charge, into the block cache. The ```CacheWithSecondaryAdapter``` wrapper uses its own instance of ```CacheReservationManager``` to keep track of reservations charged to the secondary cache, while the placeholder entries are inserted into the primary block cache. The design is as follows. When ```CacheWithSecondaryAdapter``` is constructed with the ```distribute_cache_res``` parameter set to true, it manages the entire memory budget across the primary and secondary cache. The secondary cache is assumed to be in memory, such as the ```CompressedSecondaryCache```. When a placeholder entry is inserted by a CacheReservationManager instance to reserve memory, the ```CacheWithSecondaryAdapter```ensures that the reservation is distributed proportionally across the primary/secondary caches. The primary block cache is initially sized to the sum of the primary cache budget + the secondary cache budget, as follows - |--------- Primary Cache Configured Capacity -----------| |---Secondary Cache Budget----|----Primary Cache Budget-----| A ```ConcurrentCacheReservationManager``` member in the ```CacheWithSecondaryAdapter```, ```pri_cache_res_```, is used to help with tracking the distribution of memory reservations. Initially, it accounts for the entire secondary cache budget as a reservation against the primary cache. This shrinks the usable capacity of the primary cache to the budget that the user originally desired. |--Reservation for Sec Cache--|-Pri Cache Usable Capacity---| When a reservation placeholder is inserted into the adapter, it is inserted directly into the primary cache. This means the entire charge of the placeholder is counted against the primary cache. To compensate and count a portion of it against the secondary cache, the secondary cache ```Deflate()``` method is called to shrink it. Since the ```Deflate()``` causes the secondary actual usage to shrink, it is reflected here by releasing an equal amount from the ```pri_cache_res_``` reservation. For example, if the pri/sec ratio is 50/50, this would be the state after placeholder insertion - |-Reservation for Sec Cache-|-Pri Cache Usable Capacity-|-R-| Likewise, when the user inserted placeholder is released, the secondary cache ```Inflate()``` method is called to grow it, and the ```pri_cache_res_``` reservation is increased by an equal amount. Other alternatives - 1. Another way of implementing this would have been to simply split the user reservation in ```CacheWithSecondaryAdapter``` into primary and secondary components. However, this would require allocating a structure to track the associated secondary cache reservation, which adds some complexity and overhead. 2. Yet another option is to implement the splitting directly in ```CacheReservationManager```. However, there are multiple instances of ```CacheReservationManager``` in a DB instance, making it complicated to keep track of them. The PR contains the following changes - 1. A new cache allocator, ```NewTieredVolatileCache()```, is defined for allocating a tiered primary block cache and compressed secondary cache. This internally allocates an instance of ```CacheWithSecondaryAdapter```. 3. New interfaces, ```Deflate()``` and ```Inflate()```, are added to the ```SecondaryCache``` interface. The default implementaion returns ```NotSupported``` with overrides in ```CompressedSecondaryCache```. 4. The ```CompressedSecondaryCache``` uses a ```ConcurrentCacheReservationManager``` instance to manage reservations done using ```Inflate()/Deflate()```. 5. The ```CacheWithSecondaryAdapter``` optionally distributes memory reservations across the primary and secondary caches. The primary cache is sized to the total memory budget (primary + secondary), and the capacity allocated to secondary cache is "reserved" against the primary cache. For any subsequent reservations, the primary cache pre-reserved capacity is adjusted. Benchmarks - Baseline ``` time ~/rocksdb_anand76/db_bench --db=/dev/shm/comp_cache_res/base --use_existing_db=true --benchmarks="readseq,readwhilewriting" --key_size=32 --value_size=1024 --num=20000000 --threads=32 --bloom_bits=10 --cache_size=30000000000 --use_compressed_secondary_cache=true --compressed_secondary_cache_size=5000000000 --duration=300 --cost_write_buffer_to_cache=true ``` ``` readseq : 3.301 micros/op 9694317 ops/sec 66.018 seconds 640000000 operations; 9763.0 MB/s readwhilewriting : 22.921 micros/op 1396058 ops/sec 300.021 seconds 418846968 operations; 1405.9 MB/s (13068999 of 13068999 found) real 6m31.052s user 152m5.660s sys 26m18.738s ``` With TieredVolatileCache ``` time ~/rocksdb_anand76/db_bench --db=/dev/shm/comp_cache_res/base --use_existing_db=true --benchmarks="readseq,readwhilewriting" --key_size=32 --value_size=1024 --num=20000000 --threads=32 --bloom_bits=10 --cache_size=30000000000 --use_compressed_secondary_cache=true --compressed_secondary_cache_size=5000000000 --duration=300 --cost_write_buffer_to_cache=true --use_tiered_volatile_cache=true ``` ``` readseq : 4.064 micros/op 7873915 ops/sec 81.281 seconds 640000000 operations; 7929.7 MB/s readwhilewriting : 20.944 micros/op 1527827 ops/sec 300.020 seconds 458378968 operations; 1538.6 MB/s (14296999 of 14296999 found) real 6m42.743s user 157m58.972s sys 33m16.671 ``` ``` readseq : 3.484 micros/op 9184967 ops/sec 69.679 seconds 640000000 operations; 9250.0 MB/s readwhilewriting : 21.261 micros/op 1505035 ops/sec 300.024 seconds 451545968 operations; 1515.7 MB/s (14101999 of 14101999 found) real 6m31.469s user 155m16.570s sys 27m47.834s ``` ToDo - 1. Add to db_stress Pull Request resolved: https://github.com/facebook/rocksdb/pull/11449 Reviewed By: pdillinger Differential Revision: D46197388 Pulled By: anand1976 fbshipit-source-id: 42d16f0254df683db4929db20d06ff26030e90df
2023-05-30 21:05:48 +00:00
tiered_opts.cache_type = PrimaryCacheType::kCacheTypeLRU;
tiered_opts.cache_opts = &opts;
tiered_opts.total_capacity =
opts.capacity + secondary_cache_opts.capacity;
tiered_opts.compressed_secondary_ratio =
secondary_cache_opts.capacity * 1.0 / tiered_opts.total_capacity;
Integrate CacheReservationManager with compressed secondary cache (#11449) Summary: This draft PR implements charging of reserved memory, for write buffers, table readers, and other purposes, proportionally to the block cache and the compressed secondary cache. The basic flow of memory reservation is maintained - clients use ```CacheReservationManager``` to request reservations, and ```CacheReservationManager``` inserts placeholder entries, i.e null value and non-zero charge, into the block cache. The ```CacheWithSecondaryAdapter``` wrapper uses its own instance of ```CacheReservationManager``` to keep track of reservations charged to the secondary cache, while the placeholder entries are inserted into the primary block cache. The design is as follows. When ```CacheWithSecondaryAdapter``` is constructed with the ```distribute_cache_res``` parameter set to true, it manages the entire memory budget across the primary and secondary cache. The secondary cache is assumed to be in memory, such as the ```CompressedSecondaryCache```. When a placeholder entry is inserted by a CacheReservationManager instance to reserve memory, the ```CacheWithSecondaryAdapter```ensures that the reservation is distributed proportionally across the primary/secondary caches. The primary block cache is initially sized to the sum of the primary cache budget + the secondary cache budget, as follows - |--------- Primary Cache Configured Capacity -----------| |---Secondary Cache Budget----|----Primary Cache Budget-----| A ```ConcurrentCacheReservationManager``` member in the ```CacheWithSecondaryAdapter```, ```pri_cache_res_```, is used to help with tracking the distribution of memory reservations. Initially, it accounts for the entire secondary cache budget as a reservation against the primary cache. This shrinks the usable capacity of the primary cache to the budget that the user originally desired. |--Reservation for Sec Cache--|-Pri Cache Usable Capacity---| When a reservation placeholder is inserted into the adapter, it is inserted directly into the primary cache. This means the entire charge of the placeholder is counted against the primary cache. To compensate and count a portion of it against the secondary cache, the secondary cache ```Deflate()``` method is called to shrink it. Since the ```Deflate()``` causes the secondary actual usage to shrink, it is reflected here by releasing an equal amount from the ```pri_cache_res_``` reservation. For example, if the pri/sec ratio is 50/50, this would be the state after placeholder insertion - |-Reservation for Sec Cache-|-Pri Cache Usable Capacity-|-R-| Likewise, when the user inserted placeholder is released, the secondary cache ```Inflate()``` method is called to grow it, and the ```pri_cache_res_``` reservation is increased by an equal amount. Other alternatives - 1. Another way of implementing this would have been to simply split the user reservation in ```CacheWithSecondaryAdapter``` into primary and secondary components. However, this would require allocating a structure to track the associated secondary cache reservation, which adds some complexity and overhead. 2. Yet another option is to implement the splitting directly in ```CacheReservationManager```. However, there are multiple instances of ```CacheReservationManager``` in a DB instance, making it complicated to keep track of them. The PR contains the following changes - 1. A new cache allocator, ```NewTieredVolatileCache()```, is defined for allocating a tiered primary block cache and compressed secondary cache. This internally allocates an instance of ```CacheWithSecondaryAdapter```. 3. New interfaces, ```Deflate()``` and ```Inflate()```, are added to the ```SecondaryCache``` interface. The default implementaion returns ```NotSupported``` with overrides in ```CompressedSecondaryCache```. 4. The ```CompressedSecondaryCache``` uses a ```ConcurrentCacheReservationManager``` instance to manage reservations done using ```Inflate()/Deflate()```. 5. The ```CacheWithSecondaryAdapter``` optionally distributes memory reservations across the primary and secondary caches. The primary cache is sized to the total memory budget (primary + secondary), and the capacity allocated to secondary cache is "reserved" against the primary cache. For any subsequent reservations, the primary cache pre-reserved capacity is adjusted. Benchmarks - Baseline ``` time ~/rocksdb_anand76/db_bench --db=/dev/shm/comp_cache_res/base --use_existing_db=true --benchmarks="readseq,readwhilewriting" --key_size=32 --value_size=1024 --num=20000000 --threads=32 --bloom_bits=10 --cache_size=30000000000 --use_compressed_secondary_cache=true --compressed_secondary_cache_size=5000000000 --duration=300 --cost_write_buffer_to_cache=true ``` ``` readseq : 3.301 micros/op 9694317 ops/sec 66.018 seconds 640000000 operations; 9763.0 MB/s readwhilewriting : 22.921 micros/op 1396058 ops/sec 300.021 seconds 418846968 operations; 1405.9 MB/s (13068999 of 13068999 found) real 6m31.052s user 152m5.660s sys 26m18.738s ``` With TieredVolatileCache ``` time ~/rocksdb_anand76/db_bench --db=/dev/shm/comp_cache_res/base --use_existing_db=true --benchmarks="readseq,readwhilewriting" --key_size=32 --value_size=1024 --num=20000000 --threads=32 --bloom_bits=10 --cache_size=30000000000 --use_compressed_secondary_cache=true --compressed_secondary_cache_size=5000000000 --duration=300 --cost_write_buffer_to_cache=true --use_tiered_volatile_cache=true ``` ``` readseq : 4.064 micros/op 7873915 ops/sec 81.281 seconds 640000000 operations; 7929.7 MB/s readwhilewriting : 20.944 micros/op 1527827 ops/sec 300.020 seconds 458378968 operations; 1538.6 MB/s (14296999 of 14296999 found) real 6m42.743s user 157m58.972s sys 33m16.671 ``` ``` readseq : 3.484 micros/op 9184967 ops/sec 69.679 seconds 640000000 operations; 9250.0 MB/s readwhilewriting : 21.261 micros/op 1505035 ops/sec 300.024 seconds 451545968 operations; 1515.7 MB/s (14101999 of 14101999 found) real 6m31.469s user 155m16.570s sys 27m47.834s ``` ToDo - 1. Add to db_stress Pull Request resolved: https://github.com/facebook/rocksdb/pull/11449 Reviewed By: pdillinger Differential Revision: D46197388 Pulled By: anand1976 fbshipit-source-id: 42d16f0254df683db4929db20d06ff26030e90df
2023-05-30 21:05:48 +00:00
tiered_opts.comp_cache_opts = secondary_cache_opts;
tiered_opts.nvm_sec_cache = secondary_cache;
tiered_opts.adm_policy = adm_policy;
block_cache = NewTieredCache(tiered_opts);
Integrate CacheReservationManager with compressed secondary cache (#11449) Summary: This draft PR implements charging of reserved memory, for write buffers, table readers, and other purposes, proportionally to the block cache and the compressed secondary cache. The basic flow of memory reservation is maintained - clients use ```CacheReservationManager``` to request reservations, and ```CacheReservationManager``` inserts placeholder entries, i.e null value and non-zero charge, into the block cache. The ```CacheWithSecondaryAdapter``` wrapper uses its own instance of ```CacheReservationManager``` to keep track of reservations charged to the secondary cache, while the placeholder entries are inserted into the primary block cache. The design is as follows. When ```CacheWithSecondaryAdapter``` is constructed with the ```distribute_cache_res``` parameter set to true, it manages the entire memory budget across the primary and secondary cache. The secondary cache is assumed to be in memory, such as the ```CompressedSecondaryCache```. When a placeholder entry is inserted by a CacheReservationManager instance to reserve memory, the ```CacheWithSecondaryAdapter```ensures that the reservation is distributed proportionally across the primary/secondary caches. The primary block cache is initially sized to the sum of the primary cache budget + the secondary cache budget, as follows - |--------- Primary Cache Configured Capacity -----------| |---Secondary Cache Budget----|----Primary Cache Budget-----| A ```ConcurrentCacheReservationManager``` member in the ```CacheWithSecondaryAdapter```, ```pri_cache_res_```, is used to help with tracking the distribution of memory reservations. Initially, it accounts for the entire secondary cache budget as a reservation against the primary cache. This shrinks the usable capacity of the primary cache to the budget that the user originally desired. |--Reservation for Sec Cache--|-Pri Cache Usable Capacity---| When a reservation placeholder is inserted into the adapter, it is inserted directly into the primary cache. This means the entire charge of the placeholder is counted against the primary cache. To compensate and count a portion of it against the secondary cache, the secondary cache ```Deflate()``` method is called to shrink it. Since the ```Deflate()``` causes the secondary actual usage to shrink, it is reflected here by releasing an equal amount from the ```pri_cache_res_``` reservation. For example, if the pri/sec ratio is 50/50, this would be the state after placeholder insertion - |-Reservation for Sec Cache-|-Pri Cache Usable Capacity-|-R-| Likewise, when the user inserted placeholder is released, the secondary cache ```Inflate()``` method is called to grow it, and the ```pri_cache_res_``` reservation is increased by an equal amount. Other alternatives - 1. Another way of implementing this would have been to simply split the user reservation in ```CacheWithSecondaryAdapter``` into primary and secondary components. However, this would require allocating a structure to track the associated secondary cache reservation, which adds some complexity and overhead. 2. Yet another option is to implement the splitting directly in ```CacheReservationManager```. However, there are multiple instances of ```CacheReservationManager``` in a DB instance, making it complicated to keep track of them. The PR contains the following changes - 1. A new cache allocator, ```NewTieredVolatileCache()```, is defined for allocating a tiered primary block cache and compressed secondary cache. This internally allocates an instance of ```CacheWithSecondaryAdapter```. 3. New interfaces, ```Deflate()``` and ```Inflate()```, are added to the ```SecondaryCache``` interface. The default implementaion returns ```NotSupported``` with overrides in ```CompressedSecondaryCache```. 4. The ```CompressedSecondaryCache``` uses a ```ConcurrentCacheReservationManager``` instance to manage reservations done using ```Inflate()/Deflate()```. 5. The ```CacheWithSecondaryAdapter``` optionally distributes memory reservations across the primary and secondary caches. The primary cache is sized to the total memory budget (primary + secondary), and the capacity allocated to secondary cache is "reserved" against the primary cache. For any subsequent reservations, the primary cache pre-reserved capacity is adjusted. Benchmarks - Baseline ``` time ~/rocksdb_anand76/db_bench --db=/dev/shm/comp_cache_res/base --use_existing_db=true --benchmarks="readseq,readwhilewriting" --key_size=32 --value_size=1024 --num=20000000 --threads=32 --bloom_bits=10 --cache_size=30000000000 --use_compressed_secondary_cache=true --compressed_secondary_cache_size=5000000000 --duration=300 --cost_write_buffer_to_cache=true ``` ``` readseq : 3.301 micros/op 9694317 ops/sec 66.018 seconds 640000000 operations; 9763.0 MB/s readwhilewriting : 22.921 micros/op 1396058 ops/sec 300.021 seconds 418846968 operations; 1405.9 MB/s (13068999 of 13068999 found) real 6m31.052s user 152m5.660s sys 26m18.738s ``` With TieredVolatileCache ``` time ~/rocksdb_anand76/db_bench --db=/dev/shm/comp_cache_res/base --use_existing_db=true --benchmarks="readseq,readwhilewriting" --key_size=32 --value_size=1024 --num=20000000 --threads=32 --bloom_bits=10 --cache_size=30000000000 --use_compressed_secondary_cache=true --compressed_secondary_cache_size=5000000000 --duration=300 --cost_write_buffer_to_cache=true --use_tiered_volatile_cache=true ``` ``` readseq : 4.064 micros/op 7873915 ops/sec 81.281 seconds 640000000 operations; 7929.7 MB/s readwhilewriting : 20.944 micros/op 1527827 ops/sec 300.020 seconds 458378968 operations; 1538.6 MB/s (14296999 of 14296999 found) real 6m42.743s user 157m58.972s sys 33m16.671 ``` ``` readseq : 3.484 micros/op 9184967 ops/sec 69.679 seconds 640000000 operations; 9250.0 MB/s readwhilewriting : 21.261 micros/op 1505035 ops/sec 300.024 seconds 451545968 operations; 1515.7 MB/s (14101999 of 14101999 found) real 6m31.469s user 155m16.570s sys 27m47.834s ``` ToDo - 1. Add to db_stress Pull Request resolved: https://github.com/facebook/rocksdb/pull/11449 Reviewed By: pdillinger Differential Revision: D46197388 Pulled By: anand1976 fbshipit-source-id: 42d16f0254df683db4929db20d06ff26030e90df
2023-05-30 21:05:48 +00:00
} else {
if (!FLAGS_secondary_cache_uri.empty()) {
opts.secondary_cache = secondary_cache;
} else if (FLAGS_use_compressed_secondary_cache) {
opts.secondary_cache =
NewCompressedSecondaryCache(secondary_cache_opts);
}
block_cache = opts.MakeSharedCache();
Integrate CacheReservationManager with compressed secondary cache (#11449) Summary: This draft PR implements charging of reserved memory, for write buffers, table readers, and other purposes, proportionally to the block cache and the compressed secondary cache. The basic flow of memory reservation is maintained - clients use ```CacheReservationManager``` to request reservations, and ```CacheReservationManager``` inserts placeholder entries, i.e null value and non-zero charge, into the block cache. The ```CacheWithSecondaryAdapter``` wrapper uses its own instance of ```CacheReservationManager``` to keep track of reservations charged to the secondary cache, while the placeholder entries are inserted into the primary block cache. The design is as follows. When ```CacheWithSecondaryAdapter``` is constructed with the ```distribute_cache_res``` parameter set to true, it manages the entire memory budget across the primary and secondary cache. The secondary cache is assumed to be in memory, such as the ```CompressedSecondaryCache```. When a placeholder entry is inserted by a CacheReservationManager instance to reserve memory, the ```CacheWithSecondaryAdapter```ensures that the reservation is distributed proportionally across the primary/secondary caches. The primary block cache is initially sized to the sum of the primary cache budget + the secondary cache budget, as follows - |--------- Primary Cache Configured Capacity -----------| |---Secondary Cache Budget----|----Primary Cache Budget-----| A ```ConcurrentCacheReservationManager``` member in the ```CacheWithSecondaryAdapter```, ```pri_cache_res_```, is used to help with tracking the distribution of memory reservations. Initially, it accounts for the entire secondary cache budget as a reservation against the primary cache. This shrinks the usable capacity of the primary cache to the budget that the user originally desired. |--Reservation for Sec Cache--|-Pri Cache Usable Capacity---| When a reservation placeholder is inserted into the adapter, it is inserted directly into the primary cache. This means the entire charge of the placeholder is counted against the primary cache. To compensate and count a portion of it against the secondary cache, the secondary cache ```Deflate()``` method is called to shrink it. Since the ```Deflate()``` causes the secondary actual usage to shrink, it is reflected here by releasing an equal amount from the ```pri_cache_res_``` reservation. For example, if the pri/sec ratio is 50/50, this would be the state after placeholder insertion - |-Reservation for Sec Cache-|-Pri Cache Usable Capacity-|-R-| Likewise, when the user inserted placeholder is released, the secondary cache ```Inflate()``` method is called to grow it, and the ```pri_cache_res_``` reservation is increased by an equal amount. Other alternatives - 1. Another way of implementing this would have been to simply split the user reservation in ```CacheWithSecondaryAdapter``` into primary and secondary components. However, this would require allocating a structure to track the associated secondary cache reservation, which adds some complexity and overhead. 2. Yet another option is to implement the splitting directly in ```CacheReservationManager```. However, there are multiple instances of ```CacheReservationManager``` in a DB instance, making it complicated to keep track of them. The PR contains the following changes - 1. A new cache allocator, ```NewTieredVolatileCache()```, is defined for allocating a tiered primary block cache and compressed secondary cache. This internally allocates an instance of ```CacheWithSecondaryAdapter```. 3. New interfaces, ```Deflate()``` and ```Inflate()```, are added to the ```SecondaryCache``` interface. The default implementaion returns ```NotSupported``` with overrides in ```CompressedSecondaryCache```. 4. The ```CompressedSecondaryCache``` uses a ```ConcurrentCacheReservationManager``` instance to manage reservations done using ```Inflate()/Deflate()```. 5. The ```CacheWithSecondaryAdapter``` optionally distributes memory reservations across the primary and secondary caches. The primary cache is sized to the total memory budget (primary + secondary), and the capacity allocated to secondary cache is "reserved" against the primary cache. For any subsequent reservations, the primary cache pre-reserved capacity is adjusted. Benchmarks - Baseline ``` time ~/rocksdb_anand76/db_bench --db=/dev/shm/comp_cache_res/base --use_existing_db=true --benchmarks="readseq,readwhilewriting" --key_size=32 --value_size=1024 --num=20000000 --threads=32 --bloom_bits=10 --cache_size=30000000000 --use_compressed_secondary_cache=true --compressed_secondary_cache_size=5000000000 --duration=300 --cost_write_buffer_to_cache=true ``` ``` readseq : 3.301 micros/op 9694317 ops/sec 66.018 seconds 640000000 operations; 9763.0 MB/s readwhilewriting : 22.921 micros/op 1396058 ops/sec 300.021 seconds 418846968 operations; 1405.9 MB/s (13068999 of 13068999 found) real 6m31.052s user 152m5.660s sys 26m18.738s ``` With TieredVolatileCache ``` time ~/rocksdb_anand76/db_bench --db=/dev/shm/comp_cache_res/base --use_existing_db=true --benchmarks="readseq,readwhilewriting" --key_size=32 --value_size=1024 --num=20000000 --threads=32 --bloom_bits=10 --cache_size=30000000000 --use_compressed_secondary_cache=true --compressed_secondary_cache_size=5000000000 --duration=300 --cost_write_buffer_to_cache=true --use_tiered_volatile_cache=true ``` ``` readseq : 4.064 micros/op 7873915 ops/sec 81.281 seconds 640000000 operations; 7929.7 MB/s readwhilewriting : 20.944 micros/op 1527827 ops/sec 300.020 seconds 458378968 operations; 1538.6 MB/s (14296999 of 14296999 found) real 6m42.743s user 157m58.972s sys 33m16.671 ``` ``` readseq : 3.484 micros/op 9184967 ops/sec 69.679 seconds 640000000 operations; 9250.0 MB/s readwhilewriting : 21.261 micros/op 1505035 ops/sec 300.024 seconds 451545968 operations; 1515.7 MB/s (14101999 of 14101999 found) real 6m31.469s user 155m16.570s sys 27m47.834s ``` ToDo - 1. Add to db_stress Pull Request resolved: https://github.com/facebook/rocksdb/pull/11449 Reviewed By: pdillinger Differential Revision: D46197388 Pulled By: anand1976 fbshipit-source-id: 42d16f0254df683db4929db20d06ff26030e90df
2023-05-30 21:05:48 +00:00
}
} else {
fprintf(stderr, "Cache type not supported.");
exit(1);
}
if (!block_cache) {
fprintf(stderr, "Unable to allocate block cache\n");
exit(1);
}
return block_cache;
}
public:
Benchmark()
: cache_(NewCache(FLAGS_cache_size)),
compressed_cache_(NewCache(FLAGS_compressed_cache_size)),
Fix auto_prefix_mode performance with partitioned filters (#10012) Summary: Essentially refactored the RangeMayExist implementation in FullFilterBlockReader to FilterBlockReaderCommon so that it applies to partitioned filters as well. (The function is not called for the block-based filter case.) RangeMayExist is essentially a series of checks around a possible PrefixMayExist, and I'm confident those checks should be the same for partitioned as for full filters. (I think it's likely that bugs remain in those checks, but this change is overall a simplifying one.) Added auto_prefix_mode support to db_bench Other small fixes as well Fixes https://github.com/facebook/rocksdb/issues/10003 Pull Request resolved: https://github.com/facebook/rocksdb/pull/10012 Test Plan: Expanded unit test that uses statistics to check for filter optimization, fails without the production code changes here Performance: populate two DBs with ``` TEST_TMPDIR=/dev/shm/rocksdb_nonpartitioned ./db_bench -benchmarks=fillrandom -num=10000000 -disable_wal=1 -write_buffer_size=30000000 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=8 TEST_TMPDIR=/dev/shm/rocksdb_partitioned ./db_bench -benchmarks=fillrandom -num=10000000 -disable_wal=1 -write_buffer_size=30000000 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=8 -partition_index_and_filters ``` Observe no measurable change in non-partitioned performance ``` TEST_TMPDIR=/dev/shm/rocksdb_nonpartitioned ./db_bench -benchmarks=seekrandom[-X1000] -num=10000000 -readonly -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=8 -auto_prefix_mode -cache_index_and_filter_blocks=1 -cache_size=1000000000 -duration 20 ``` Before: seekrandom [AVG 15 runs] : 11798 (± 331) ops/sec After: seekrandom [AVG 15 runs] : 11724 (± 315) ops/sec Observe big improvement with partitioned (also supported by bloom use statistics) ``` TEST_TMPDIR=/dev/shm/rocksdb_partitioned ./db_bench -benchmarks=seekrandom[-X1000] -num=10000000 -readonly -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=8 -partition_index_and_filters -auto_prefix_mode -cache_index_and_filter_blocks=1 -cache_size=1000000000 -duration 20 ``` Before: seekrandom [AVG 12 runs] : 2942 (± 57) ops/sec After: seekrandom [AVG 12 runs] : 7489 (± 184) ops/sec Reviewed By: siying Differential Revision: D36469796 Pulled By: pdillinger fbshipit-source-id: bcf1e2a68d347b32adb2b27384f945434e7a266d
2022-05-19 20:09:03 +00:00
prefix_extractor_(FLAGS_prefix_size != 0
? NewFixedPrefixTransform(FLAGS_prefix_size)
: nullptr),
num_(FLAGS_num),
key_size_(FLAGS_key_size),
user_timestamp_size_(FLAGS_user_timestamp_size),
prefix_size_(FLAGS_prefix_size),
Avoid seed reuse when --benchmarks has more than one test (#9733) Summary: When --benchmarks has more than one test then the threads in one benchmark will use the same set of seeds as the threads in the previous benchmark. This diff fixe that. This fixes https://github.com/facebook/rocksdb/issues/9632 Pull Request resolved: https://github.com/facebook/rocksdb/pull/9733 Test Plan: For this command line the block cache is 8GB, so it caches at most 1024 8KB blocks. Note that without this diff the second run of readrandom has a much better response time because seed reuse means the second run reads the same 1000 blocks as the first run and they are cached at that point. But with this diff that does not happen. ./db_bench --benchmarks=fillseq,flush,compact0,waitforcompaction,levelstats,readrandom,readrandom --compression_type=zlib --num=10000000 --reads=1000 --block_size=8192 ... ``` Level Files Size(MB) -------------------- 0 0 0 1 11 238 2 9 253 3 0 0 4 0 0 5 0 0 6 0 0 ``` --- perf results without this diff DB path: [/tmp/rocksdbtest-2260/dbbench] readrandom : 46.212 micros/op 21618 ops/sec; 2.4 MB/s (1000 of 1000 found) DB path: [/tmp/rocksdbtest-2260/dbbench] readrandom : 21.963 micros/op 45450 ops/sec; 5.0 MB/s (1000 of 1000 found) --- perf results with this diff DB path: [/tmp/rocksdbtest-2260/dbbench] readrandom : 47.213 micros/op 21126 ops/sec; 2.3 MB/s (1000 of 1000 found) DB path: [/tmp/rocksdbtest-2260/dbbench] readrandom : 42.880 micros/op 23299 ops/sec; 2.6 MB/s (1000 of 1000 found) Reviewed By: jay-zhuang Differential Revision: D35089763 Pulled By: mdcallag fbshipit-source-id: 1b50143a07afe876b8c8e5fa50dd94a8ce57fc6b
2022-03-24 15:57:48 +00:00
total_thread_count_(0),
keys_per_prefix_(FLAGS_keys_per_prefix),
entries_per_batch_(1),
reads_(FLAGS_reads < 0 ? FLAGS_num : FLAGS_reads),
read_random_exp_range_(0.0),
writes_(FLAGS_writes < 0 ? FLAGS_num : FLAGS_writes),
readwrites_(
(FLAGS_writes < 0 && FLAGS_reads < 0)
? FLAGS_num
: ((FLAGS_writes > FLAGS_reads) ? FLAGS_writes : FLAGS_reads)),
merge_keys_(FLAGS_merge_keys < 0 ? FLAGS_num : FLAGS_merge_keys),
report_file_operations_(FLAGS_report_file_operations),
use_blob_db_(FLAGS_use_blob_db), // Stacked BlobDB
read_operands_(false) {
add simulator Cache as class SimCache/SimLRUCache(with test) Summary: add class SimCache(base class with instrumentation api) and SimLRUCache(derived class with detailed implementation) which is used as an instrumented block cache that can predict hit rate for different cache size Test Plan: Add a test case in `db_block_cache_test.cc` called `SimCacheTest` to test basic logic of SimCache. Also add option `-simcache_size` in db_bench. if set with a value other than -1, then the benchmark will use this value as the size of the simulator cache and finally output the simulation result. ``` [gzh@dev9927.prn1 ~/local/rocksdb] ./db_bench -benchmarks "fillseq,readrandom" -cache_size 1000000 -simcache_size 1000000 RocksDB: version 4.8 Date: Tue May 17 16:56:16 2016 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Compression: Snappy Memtablerep: skip_list Perf Level: 0 WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ DB path: [/tmp/rocksdbtest-112628/dbbench] fillseq : 6.809 micros/op 146874 ops/sec; 16.2 MB/s DB path: [/tmp/rocksdbtest-112628/dbbench] readrandom : 6.343 micros/op 157665 ops/sec; 17.4 MB/s (1000000 of 1000000 found) SIMULATOR CACHE STATISTICS: SimCache LOOKUPs: 986559 SimCache HITs: 264760 SimCache HITRATE: 26.84% [gzh@dev9927.prn1 ~/local/rocksdb] ./db_bench -benchmarks "fillseq,readrandom" -cache_size 1000000 -simcache_size 10000000 RocksDB: version 4.8 Date: Tue May 17 16:57:10 2016 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Compression: Snappy Memtablerep: skip_list Perf Level: 0 WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ DB path: [/tmp/rocksdbtest-112628/dbbench] fillseq : 5.066 micros/op 197394 ops/sec; 21.8 MB/s DB path: [/tmp/rocksdbtest-112628/dbbench] readrandom : 6.457 micros/op 154870 ops/sec; 17.1 MB/s (1000000 of 1000000 found) SIMULATOR CACHE STATISTICS: SimCache LOOKUPs: 1059764 SimCache HITs: 374501 SimCache HITRATE: 35.34% [gzh@dev9927.prn1 ~/local/rocksdb] ./db_bench -benchmarks "fillseq,readrandom" -cache_size 1000000 -simcache_size 100000000 RocksDB: version 4.8 Date: Tue May 17 16:57:32 2016 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Compression: Snappy Memtablerep: skip_list Perf Level: 0 WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ DB path: [/tmp/rocksdbtest-112628/dbbench] fillseq : 5.632 micros/op 177572 ops/sec; 19.6 MB/s DB path: [/tmp/rocksdbtest-112628/dbbench] readrandom : 6.892 micros/op 145094 ops/sec; 16.1 MB/s (1000000 of 1000000 found) SIMULATOR CACHE STATISTICS: SimCache LOOKUPs: 1150767 SimCache HITs: 1034535 SimCache HITRATE: 89.90% ``` Reviewers: IslamAbdelRahman, andrewkr, sdong Reviewed By: sdong Subscribers: MarkCallaghan, andrewkr, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D57999
2016-05-24 06:35:23 +00:00
// use simcache instead of cache
if (FLAGS_simcache_size >= 0) {
if (FLAGS_cache_numshardbits >= 1) {
cache_ =
NewSimCache(cache_, FLAGS_simcache_size, FLAGS_cache_numshardbits);
} else {
cache_ = NewSimCache(cache_, FLAGS_simcache_size, 0);
}
}
if (report_file_operations_) {
FLAGS_env = new CompositeEnvWrapper(
FLAGS_env,
std::make_shared<CountedFileSystem>(FLAGS_env->GetFileSystem()));
}
if (FLAGS_prefix_size > FLAGS_key_size) {
fprintf(stderr, "prefix size is larger than key size");
exit(1);
}
std::vector<std::string> files;
FLAGS_env->GetChildren(FLAGS_db, &files);
for (size_t i = 0; i < files.size(); i++) {
if (Slice(files[i]).starts_with("heap-")) {
FLAGS_env->DeleteFile(FLAGS_db + "/" + files[i]);
}
}
if (!FLAGS_use_existing_db) {
Options options;
options.env = FLAGS_env;
if (!FLAGS_wal_dir.empty()) {
options.wal_dir = FLAGS_wal_dir;
}
if (use_blob_db_) {
// Stacked BlobDB
blob_db::DestroyBlobDB(FLAGS_db, options, blob_db::BlobDBOptions());
}
DestroyDB(FLAGS_db, options);
if (!FLAGS_wal_dir.empty()) {
FLAGS_env->DeleteDir(FLAGS_wal_dir);
}
if (FLAGS_num_multi_db > 1) {
FLAGS_env->CreateDir(FLAGS_db);
if (!FLAGS_wal_dir.empty()) {
FLAGS_env->CreateDir(FLAGS_wal_dir);
}
}
}
Auto recovery from out of space errors (#4164) Summary: This commit implements automatic recovery from a Status::NoSpace() error during background operations such as write callback, flush and compaction. The broad design is as follows - 1. Compaction errors are treated as soft errors and don't put the database in read-only mode. A compaction is delayed until enough free disk space is available to accomodate the compaction outputs, which is estimated based on the input size. This means that users can continue to write, and we rely on the WriteController to delay or stop writes if the compaction debt becomes too high due to persistent low disk space condition 2. Errors during write callback and flush are treated as hard errors, i.e the database is put in read-only mode and goes back to read-write only fater certain recovery actions are taken. 3. Both types of recovery rely on the SstFileManagerImpl to poll for sufficient disk space. We assume that there is a 1-1 mapping between an SFM and the underlying OS storage container. For cases where multiple DBs are hosted on a single storage container, the user is expected to allocate a single SFM instance and use the same one for all the DBs. If no SFM is specified by the user, DBImpl::Open() will allocate one, but this will be one per DB and each DB will recover independently. The recovery implemented by SFM is as follows - a) On the first occurance of an out of space error during compaction, subsequent compactions will be delayed until the disk free space check indicates enough available space. The required space is computed as the sum of input sizes. b) The free space check requirement will be removed once the amount of free space is greater than the size reserved by in progress compactions when the first error occured c) If the out of space error is a hard error, a background thread in SFM will poll for sufficient headroom before triggering the recovery of the database and putting it in write-only mode. The headroom is calculated as the sum of the write_buffer_size of all the DB instances associated with the SFM 4. EventListener callbacks will be called at the start and completion of automatic recovery. Users can disable the auto recov ery in the start callback, and later initiate it manually by calling DB::Resume() Todo: 1. More extensive testing 2. Add disk full condition to db_stress (follow-on PR) Pull Request resolved: https://github.com/facebook/rocksdb/pull/4164 Differential Revision: D9846378 Pulled By: anand1976 fbshipit-source-id: 80ea875dbd7f00205e19c82215ff6e37da10da4a
2018-09-15 20:36:19 +00:00
listener_.reset(new ErrorHandlerListener());
if (user_timestamp_size_ > 0) {
mock_app_clock_.reset(new TimestampEmulator());
}
}
void DeleteDBs() {
db_.DeleteDBs();
for (const DBWithColumnFamilies& dbwcf : multi_dbs_) {
delete dbwcf.db;
}
}
~Benchmark() {
DeleteDBs();
2015-03-13 23:41:00 +00:00
if (cache_.get() != nullptr) {
Use deleters to label cache entries and collect stats (#8297) Summary: This change gathers and publishes statistics about the kinds of items in block cache. This is especially important for profiling relative usage of cache by index vs. filter vs. data blocks. It works by iterating over the cache during periodic stats dump (InternalStats, stats_dump_period_sec) or on demand when DB::Get(Map)Property(kBlockCacheEntryStats), except that for efficiency and sharing among column families, saved data from the last scan is used when the data is not considered too old. The new information can be seen in info LOG, for example: Block cache LRUCache@0x7fca62229330 capacity: 95.37 MB collections: 8 last_copies: 0 last_secs: 0.00178 secs_since: 0 Block cache entry stats(count,size,portion): DataBlock(7092,28.24 MB,29.6136%) FilterBlock(215,867.90 KB,0.888728%) FilterMetaBlock(2,5.31 KB,0.00544%) IndexBlock(217,180.11 KB,0.184432%) WriteBuffer(1,256.00 KB,0.262144%) Misc(1,0.00 KB,0%) And also through DB::GetProperty and GetMapProperty (here using ldb just for demonstration): $ ./ldb --db=/dev/shm/dbbench/ get_property rocksdb.block-cache-entry-stats rocksdb.block-cache-entry-stats.bytes.data-block: 0 rocksdb.block-cache-entry-stats.bytes.deprecated-filter-block: 0 rocksdb.block-cache-entry-stats.bytes.filter-block: 0 rocksdb.block-cache-entry-stats.bytes.filter-meta-block: 0 rocksdb.block-cache-entry-stats.bytes.index-block: 178992 rocksdb.block-cache-entry-stats.bytes.misc: 0 rocksdb.block-cache-entry-stats.bytes.other-block: 0 rocksdb.block-cache-entry-stats.bytes.write-buffer: 0 rocksdb.block-cache-entry-stats.capacity: 8388608 rocksdb.block-cache-entry-stats.count.data-block: 0 rocksdb.block-cache-entry-stats.count.deprecated-filter-block: 0 rocksdb.block-cache-entry-stats.count.filter-block: 0 rocksdb.block-cache-entry-stats.count.filter-meta-block: 0 rocksdb.block-cache-entry-stats.count.index-block: 215 rocksdb.block-cache-entry-stats.count.misc: 1 rocksdb.block-cache-entry-stats.count.other-block: 0 rocksdb.block-cache-entry-stats.count.write-buffer: 0 rocksdb.block-cache-entry-stats.id: LRUCache@0x7f3636661290 rocksdb.block-cache-entry-stats.percent.data-block: 0.000000 rocksdb.block-cache-entry-stats.percent.deprecated-filter-block: 0.000000 rocksdb.block-cache-entry-stats.percent.filter-block: 0.000000 rocksdb.block-cache-entry-stats.percent.filter-meta-block: 0.000000 rocksdb.block-cache-entry-stats.percent.index-block: 2.133751 rocksdb.block-cache-entry-stats.percent.misc: 0.000000 rocksdb.block-cache-entry-stats.percent.other-block: 0.000000 rocksdb.block-cache-entry-stats.percent.write-buffer: 0.000000 rocksdb.block-cache-entry-stats.secs_for_last_collection: 0.000052 rocksdb.block-cache-entry-stats.secs_since_last_collection: 0 Solution detail - We need some way to flag what kind of blocks each entry belongs to, preferably without changing the Cache API. One of the complications is that Cache is a general interface that could have other users that don't adhere to whichever convention we decide on for keys and values. Or we would pay for an extra field in the Handle that would only be used for this purpose. This change uses a back-door approach, the deleter, to indicate the "role" of a Cache entry (in addition to the value type, implicitly). This has the added benefit of ensuring proper code origin whenever we recognize a particular role for a cache entry; if the entry came from some other part of the code, it will use an unrecognized deleter, which we simply attribute to the "Misc" role. An internal API makes for simple instantiation and automatic registration of Cache deleters for a given value type and "role". Another internal API, CacheEntryStatsCollector, solves the problem of caching the results of a scan and sharing them, to ensure scans are neither excessive nor redundant so as not to harm Cache performance. Because code is added to BlocklikeTraits, it is pulled out of block_based_table_reader.cc into its own file. This is a reformulation of https://github.com/facebook/rocksdb/issues/8276, without the type checking option (could still be added), and with actual stat gathering. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8297 Test Plan: manual testing with db_bench, and a couple of basic unit tests Reviewed By: ltamasi Differential Revision: D28488721 Pulled By: pdillinger fbshipit-source-id: 472f524a9691b5afb107934be2d41d84f2b129fb
2021-05-19 23:45:51 +00:00
// Clear cache reference first
open_options_.write_buffer_manager.reset();
2015-03-13 23:41:00 +00:00
// this will leak, but we're shutting down so nobody cares
cache_->DisownData();
}
}
Slice AllocateKey(std::unique_ptr<const char[]>* key_guard) {
char* data = new char[key_size_];
const char* const_data = data;
key_guard->reset(const_data);
return Slice(key_guard->get(), key_size_);
}
// Generate key according to the given specification and random number.
// The resulting key will have the following format:
// - If keys_per_prefix_ is positive, extra trailing bytes are either cut
// off or padded with '0'.
// The prefix value is derived from key value.
// ----------------------------
// | prefix 00000 | key 00000 |
// ----------------------------
//
// - If keys_per_prefix_ is 0, the key is simply a binary representation of
// random number followed by trailing '0's
// ----------------------------
// | key 00000 |
// ----------------------------
void GenerateKeyFromInt(uint64_t v, int64_t num_keys, Slice* key) {
if (!keys_.empty()) {
assert(FLAGS_use_existing_keys);
assert(keys_.size() == static_cast<size_t>(num_keys));
assert(v < static_cast<uint64_t>(num_keys));
*key = keys_[v];
return;
}
char* start = const_cast<char*>(key->data());
char* pos = start;
if (keys_per_prefix_ > 0) {
int64_t num_prefix = num_keys / keys_per_prefix_;
int64_t prefix = v % num_prefix;
int bytes_to_fill = std::min(prefix_size_, 8);
if (port::kLittleEndian) {
for (int i = 0; i < bytes_to_fill; ++i) {
pos[i] = (prefix >> ((bytes_to_fill - i - 1) << 3)) & 0xFF;
}
} else {
memcpy(pos, static_cast<void*>(&prefix), bytes_to_fill);
}
if (prefix_size_ > 8) {
// fill the rest with 0s
memset(pos + 8, '0', prefix_size_ - 8);
}
pos += prefix_size_;
}
int bytes_to_fill = std::min(key_size_ - static_cast<int>(pos - start), 8);
if (port::kLittleEndian) {
for (int i = 0; i < bytes_to_fill; ++i) {
pos[i] = (v >> ((bytes_to_fill - i - 1) << 3)) & 0xFF;
}
} else {
memcpy(pos, static_cast<void*>(&v), bytes_to_fill);
}
pos += bytes_to_fill;
if (key_size_ > pos - start) {
memset(pos, '0', key_size_ - (pos - start));
}
}
void GenerateKeyFromIntForSeek(uint64_t v, int64_t num_keys, Slice* key) {
GenerateKeyFromInt(v, num_keys, key);
if (FLAGS_seek_missing_prefix) {
assert(prefix_size_ > 8);
char* key_ptr = const_cast<char*>(key->data());
// This rely on GenerateKeyFromInt filling paddings with '0's.
// Putting a '1' will create a non-existing prefix.
key_ptr[8] = '1';
}
}
std::string GetPathForMultiple(std::string base_name, size_t id) {
if (!base_name.empty()) {
#ifndef OS_WIN
if (base_name.back() != '/') {
base_name += '/';
}
#else
if (base_name.back() != '\\') {
base_name += '\\';
}
#endif
}
return base_name + std::to_string(id);
}
void VerifyDBFromDB(std::string& truth_db_name) {
DBWithColumnFamilies truth_db;
auto s = DB::OpenForReadOnly(open_options_, truth_db_name, &truth_db.db);
if (!s.ok()) {
fprintf(stderr, "open error: %s\n", s.ToString().c_str());
exit(1);
}
ReadOptions ro;
ro.total_order_seek = true;
std::unique_ptr<Iterator> truth_iter(truth_db.db->NewIterator(ro));
std::unique_ptr<Iterator> db_iter(db_.db->NewIterator(ro));
// Verify that all the key/values in truth_db are retrivable in db with
// ::Get
fprintf(stderr, "Verifying db >= truth_db with ::Get...\n");
for (truth_iter->SeekToFirst(); truth_iter->Valid(); truth_iter->Next()) {
std::string value;
s = db_.db->Get(ro, truth_iter->key(), &value);
assert(s.ok());
// TODO(myabandeh): provide debugging hints
assert(Slice(value) == truth_iter->value());
}
// Verify that the db iterator does not give any extra key/value
fprintf(stderr, "Verifying db == truth_db...\n");
for (db_iter->SeekToFirst(), truth_iter->SeekToFirst(); db_iter->Valid();
db_iter->Next(), truth_iter->Next()) {
assert(truth_iter->Valid());
assert(truth_iter->value() == db_iter->value());
}
// No more key should be left unchecked in truth_db
assert(!truth_iter->Valid());
fprintf(stderr, "...Verified\n");
}
void ErrorExit() {
DeleteDBs();
exit(1);
}
void Run() {
if (!SanityCheck()) {
ErrorExit();
}
Open(&open_options_);
PrintHeader(open_options_);
std::stringstream benchmark_stream(FLAGS_benchmarks);
std::string name;
Write a benchmark to emulate time series data Summary: Add a benchmark to `db_bench`. In this benchmark, a write thread will populate time series data in the format of 'id | timestamp', and multiple read threads will randomly retrieve all data from one id at a time. Test Plan: Run the benchmark: `num=134217728;bpl=536870912;mb=67108864;overlap=10;mcz=2;del=300000000;levels=6;ctrig=4;delay=8;stop=12;wbn=3;mbc=20;wbs=134217728;dds=0;sync=0;t=32;vs=800;bs=4096;cs=17179869184;of=500000;wps=0;si=10000000; kir=100000; dir=/data/users/jhli/test/; ./db_bench --benchmarks=timeseries --disable_seek_compaction=1 --mmap_read=0 --statistics=1 --histogram=1 --num=$num --threads=$t --value_size=$vs --block_size=$bs --cache_size=$cs --bloom_bits=10 --cache_numshardbits=6 --open_files=$of --verify_checksum=1 --db=$dir --sync=$sync --disable_wal=0 --compression_type=none --stats_interval=$si --compression_ratio=1 --disable_data_sync=$dds --write_buffer_size=$wbs --target_file_size_base=$mb --max_write_buffer_number=$wbn --max_background_compactions=$mbc --level0_file_num_compaction_trigger=$ctrig --level0_slowdown_writes_trigger=$delay --level0_stop_writes_trigger=$stop --num_levels=$levels --delete_obsolete_files_period_micros=$del --min_level_to_compress=$mcz --max_grandparent_overlap_factor=$overlap --stats_per_interval=1 --max_bytes_for_level_base=$bpl --use_existing_db=0 --key_id_range=$kir` Reviewers: andrewkr, sdong Reviewed By: sdong Subscribers: lgalanis, andrewkr, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D60651
2016-08-01 19:28:51 +00:00
std::unique_ptr<ExpiredTimeFilter> filter;
while (std::getline(benchmark_stream, name, ',')) {
// Sanitize parameters
num_ = FLAGS_num;
reads_ = (FLAGS_reads < 0 ? FLAGS_num : FLAGS_reads);
writes_ = (FLAGS_writes < 0 ? FLAGS_num : FLAGS_writes);
deletes_ = (FLAGS_deletes < 0 ? FLAGS_num : FLAGS_deletes);
value_size = FLAGS_value_size;
key_size_ = FLAGS_key_size;
entries_per_batch_ = FLAGS_batch_size;
writes_before_delete_range_ = FLAGS_writes_before_delete_range;
writes_per_range_tombstone_ = FLAGS_writes_per_range_tombstone;
range_tombstone_width_ = FLAGS_range_tombstone_width;
max_num_range_tombstones_ = FLAGS_max_num_range_tombstones;
write_options_ = WriteOptions();
read_random_exp_range_ = FLAGS_read_random_exp_range;
if (FLAGS_sync) {
write_options_.sync = true;
}
write_options_.disableWAL = FLAGS_disable_wal;
Rate-limit automatic WAL flush after each user write (#9607) Summary: **Context:** WAL flush is currently not rate-limited by `Options::rate_limiter`. This PR is to provide rate-limiting to auto WAL flush, the one that automatically happen after each user write operation (i.e, `Options::manual_wal_flush == false`), by adding `WriteOptions::rate_limiter_options`. Note that we are NOT rate-limiting WAL flush that do NOT automatically happen after each user write, such as `Options::manual_wal_flush == true + manual FlushWAL()` (rate-limiting multiple WAL flushes), for the benefits of: - being consistent with [ReadOptions::rate_limiter_priority](https://github.com/facebook/rocksdb/blob/7.0.fb/include/rocksdb/options.h#L515) - being able to turn off some WAL flush's rate-limiting but not all (e.g, turn off specific the WAL flush of a critical user write like a service's heartbeat) `WriteOptions::rate_limiter_options` only accept `Env::IO_USER` and `Env::IO_TOTAL` currently due to an implementation constraint. - The constraint is that we currently queue parallel writes (including WAL writes) based on FIFO policy which does not factor rate limiter priority into this layer's scheduling. If we allow lower priorities such as `Env::IO_HIGH/MID/LOW` and such writes specified with lower priorities occurs before ones specified with higher priorities (even just by a tiny bit in arrival time), the former would have blocked the latter, leading to a "priority inversion" issue and contradictory to what we promise for rate-limiting priority. Therefore we only allow `Env::IO_USER` and `Env::IO_TOTAL` right now before improving that scheduling. A pre-requisite to this feature is to support operation-level rate limiting in `WritableFileWriter`, which is also included in this PR. **Summary:** - Renamed test suite `DBRateLimiterTest to DBRateLimiterOnReadTest` for adding a new test suite - Accept `rate_limiter_priority` in `WritableFileWriter`'s private and public write functions - Passed `WriteOptions::rate_limiter_options` to `WritableFileWriter` in the path of automatic WAL flush. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9607 Test Plan: - Added new unit test to verify existing flush/compaction rate-limiting does not break, since `DBTest, RateLimitingTest` is disabled and current db-level rate-limiting tests focus on read only (e.g, `db_rate_limiter_test`, `DBTest2, RateLimitedCompactionReads`). - Added new unit test `DBRateLimiterOnWriteWALTest, AutoWalFlush` - `strace -ftt -e trace=write ./db_bench -benchmarks=fillseq -db=/dev/shm/testdb -rate_limit_auto_wal_flush=1 -rate_limiter_bytes_per_sec=15 -rate_limiter_refill_period_us=1000000 -write_buffer_size=100000000 -disable_auto_compactions=1 -num=100` - verified that WAL flush(i.e, system-call _write_) were chunked into 15 bytes and each _write_ was roughly 1 second apart - verified the chunking disappeared when `-rate_limit_auto_wal_flush=0` - crash test: `python3 tools/db_crashtest.py blackbox --disable_wal=0 --rate_limit_auto_wal_flush=1 --rate_limiter_bytes_per_sec=10485760 --interval=10` killed as normal **Benchmarked on flush/compaction to ensure no performance regression:** - compaction with rate-limiting (see table 1, avg over 1280-run): pre-change: **915635 micros/op**; post-change: **907350 micros/op (improved by 0.106%)** ``` #!/bin/bash TEST_TMPDIR=/dev/shm/testdb START=1 NUM_DATA_ENTRY=8 N=10 rm -f compact_bmk_output.txt compact_bmk_output_2.txt dont_care_output.txt for i in $(eval echo "{$START..$NUM_DATA_ENTRY}") do NUM_RUN=$(($N*(2**($i-1)))) for j in $(eval echo "{$START..$NUM_RUN}") do ./db_bench --benchmarks=fillrandom -db=$TEST_TMPDIR -disable_auto_compactions=1 -write_buffer_size=6710886 > dont_care_output.txt && ./db_bench --benchmarks=compact -use_existing_db=1 -db=$TEST_TMPDIR -level0_file_num_compaction_trigger=1 -rate_limiter_bytes_per_sec=100000000 | egrep 'compact' done > compact_bmk_output.txt && awk -v NUM_RUN=$NUM_RUN '{sum+=$3;sum_sqrt+=$3^2}END{print sum/NUM_RUN, sqrt(sum_sqrt/NUM_RUN-(sum/NUM_RUN)^2)}' compact_bmk_output.txt >> compact_bmk_output_2.txt done ``` - compaction w/o rate-limiting (see table 2, avg over 640-run): pre-change: **822197 micros/op**; post-change: **823148 micros/op (regressed by 0.12%)** ``` Same as above script, except that -rate_limiter_bytes_per_sec=0 ``` - flush with rate-limiting (see table 3, avg over 320-run, run on the [patch](https://github.com/hx235/rocksdb/commit/ee5c6023a9f6533fab9afdc681568daa21da4953) to augment current db_bench ): pre-change: **745752 micros/op**; post-change: **745331 micros/op (regressed by 0.06 %)** ``` #!/bin/bash TEST_TMPDIR=/dev/shm/testdb START=1 NUM_DATA_ENTRY=8 N=10 rm -f flush_bmk_output.txt flush_bmk_output_2.txt for i in $(eval echo "{$START..$NUM_DATA_ENTRY}") do NUM_RUN=$(($N*(2**($i-1)))) for j in $(eval echo "{$START..$NUM_RUN}") do ./db_bench -db=$TEST_TMPDIR -write_buffer_size=1048576000 -num=1000000 -rate_limiter_bytes_per_sec=100000000 -benchmarks=fillseq,flush | egrep 'flush' done > flush_bmk_output.txt && awk -v NUM_RUN=$NUM_RUN '{sum+=$3;sum_sqrt+=$3^2}END{print sum/NUM_RUN, sqrt(sum_sqrt/NUM_RUN-(sum/NUM_RUN)^2)}' flush_bmk_output.txt >> flush_bmk_output_2.txt done ``` - flush w/o rate-limiting (see table 4, avg over 320-run, run on the [patch](https://github.com/hx235/rocksdb/commit/ee5c6023a9f6533fab9afdc681568daa21da4953) to augment current db_bench): pre-change: **487512 micros/op**, post-change: **485856 micors/ops (improved by 0.34%)** ``` Same as above script, except that -rate_limiter_bytes_per_sec=0 ``` | table 1 - compact with rate-limiting| #-run | (pre-change) avg micros/op | std micros/op | (post-change) avg micros/op | std micros/op | change in avg micros/op (%) -- | -- | -- | -- | -- | -- 10 | 896978 | 16046.9 | 901242 | 15670.9 | 0.475373978 20 | 893718 | 15813 | 886505 | 17544.7 | -0.8070778478 40 | 900426 | 23882.2 | 894958 | 15104.5 | -0.6072681153 80 | 906635 | 21761.5 | 903332 | 23948.3 | -0.3643141948 160 | 898632 | 21098.9 | 907583 | 21145 | 0.9960695813 3.20E+02 | 905252 | 22785.5 | 908106 | 25325.5 | 0.3152713278 6.40E+02 | 905213 | 23598.6 | 906741 | 21370.5 | 0.1688000504 **1.28E+03** | **908316** | **23533.1** | **907350** | **24626.8** | **-0.1063506533** average over #-run | 901896.25 | 21064.9625 | 901977.125 | 20592.025 | 0.008967217682 | table 2 - compact w/o rate-limiting| #-run | (pre-change) avg micros/op | std micros/op | (post-change) avg micros/op | std micros/op | change in avg micros/op (%) -- | -- | -- | -- | -- | -- 10 | 811211 | 26996.7 | 807586 | 28456.4 | -0.4468627768 20 | 815465 | 14803.7 | 814608 | 28719.7 | -0.105093413 40 | 809203 | 26187.1 | 797835 | 25492.1 | -1.404839082 80 | 822088 | 28765.3 | 822192 | 32840.4 | 0.01265071379 160 | 821719 | 36344.7 | 821664 | 29544.9 | -0.006693285661 3.20E+02 | 820921 | 27756.4 | 821403 | 28347.7 | 0.05871454135 **6.40E+02** | **822197** | **28960.6** | **823148** | **30055.1** | **0.1156657103** average over #-run | 8.18E+05 | 2.71E+04 | 8.15E+05 | 2.91E+04 | -0.25 | table 3 - flush with rate-limiting| #-run | (pre-change) avg micros/op | std micros/op | (post-change) avg micros/op | std micros/op | change in avg micros/op (%) -- | -- | -- | -- | -- | -- 10 | 741721 | 11770.8 | 740345 | 5949.76 | -0.1855144994 20 | 735169 | 3561.83 | 743199 | 9755.77 | 1.09226586 40 | 743368 | 8891.03 | 742102 | 8683.22 | -0.1703059588 80 | 742129 | 8148.51 | 743417 | 9631.58| 0.1735547324 160 | 749045 | 9757.21 | 746256 | 9191.86 | -0.3723407806 **3.20E+02** | **745752** | **9819.65** | **745331** | **9840.62** | **-0.0564530836** 6.40E+02 | 749006 | 11080.5 | 748173 | 10578.7 | -0.1112140624 average over #-run | 743741.4286 | 9004.218571 | 744117.5714 | 9090.215714 | 0.05057441238 | table 4 - flush w/o rate-limiting| #-run | (pre-change) avg micros/op | std micros/op | (post-change) avg micros/op | std micros/op | change in avg micros/op (%) -- | -- | -- | -- | -- | -- 10 | 477283 | 24719.6 | 473864 | 12379 | -0.7163464863 20 | 486743 | 20175.2 | 502296 | 23931.3 | 3.195320734 40 | 482846 | 15309.2 | 489820 | 22259.5 | 1.444352858 80 | 491490 | 21883.1 | 490071 | 23085.7 | -0.2887139108 160 | 493347 | 28074.3 | 483609 | 21211.7 | -1.973864238 **3.20E+02** | **487512** | **21401.5** | **485856** | **22195.2** | **-0.3396839462** 6.40E+02 | 490307 | 25418.6 | 485435 | 22405.2 | -0.9936631539 average over #-run | 4.87E+05 | 2.24E+04 | 4.87E+05 | 2.11E+04 | 0.00E+00 Reviewed By: ajkr Differential Revision: D34442441 Pulled By: hx235 fbshipit-source-id: 4790f13e1e5c0a95ae1d1cc93ffcf69dc6e78bdd
2022-03-08 21:19:39 +00:00
write_options_.rate_limiter_priority =
FLAGS_rate_limit_auto_wal_flush ? Env::IO_USER : Env::IO_TOTAL;
Ignore `total_order_seek` in DB::Get (#9427) Summary: Apparently setting total_order_seek=true for DB::Get was intended to allow accurate read semantics if the current prefix extractor doesn't match what was used to generate SST files on disk. But since prefix_extractor was made a mutable option in 5.14.0, we have been able to detect this case and provide the correct semantics regardless of the total_order_seek option. Since that time, the option has only made Get() slower in a reasonably common case: prefix_extractor unchanged and whole_key_filtering=false. So this change primarily removes unnecessary effect of total_order_seek on Get. Also cleans up some related comments. Also adds a -total_order_seek option to db_bench and canonicalizes handling of ReadOptions in db_bench so that command line options have the expected association with library features. (There is potential for change in regression test behavior, but the old behavior is likely indefensible, or some other inconsistency would need to be fixed.) TODO in follow-up work: there should be no reason for Get() to depend on current prefix extractor at all. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9427 Test Plan: Unit tests updated. Performance (using db_bench update) Create DB with `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -benchmarks=fillrandom -num=10000000 -disable_wal=1 -write_buffer_size=10000000 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12 -whole_key_filtering=0` Test with and without `-total_order_seek` on `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -use_existing_db -readonly -benchmarks=readrandom -num=10000000 -duration=40 -disable_wal=1 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12` Before this change, total_order_seek=false: 25188 ops/sec Before this change, total_order_seek=true: 1222 ops/sec (~20x slower) After this change, total_order_seek=false: 24570 ops/sec After this change, total_order_seek=true: 25012 ops/sec (indistinguishable) Reviewed By: siying Differential Revision: D33753458 Pulled By: pdillinger fbshipit-source-id: bf892f34907a5e407d9c40bd4d42f0adbcbe0014
2022-02-01 03:45:17 +00:00
read_options_ = ReadOptions(FLAGS_verify_checksum, true);
read_options_.total_order_seek = FLAGS_total_order_seek;
read_options_.prefix_same_as_start = FLAGS_prefix_same_as_start;
Add rate limiter priority to ReadOptions (#9424) Summary: Users can set the priority for file reads associated with their operation by setting `ReadOptions::rate_limiter_priority` to something other than `Env::IO_TOTAL`. Rate limiting `VerifyChecksum()` and `VerifyFileChecksums()` is the motivation for this PR, so it also includes benchmarks and minor bug fixes to get that working. `RandomAccessFileReader::Read()` already had support for rate limiting compaction reads. I changed that rate limiting to be non-specific to compaction, but rather performed according to the passed in `Env::IOPriority`. Now the compaction read rate limiting is supported by setting `rate_limiter_priority = Env::IO_LOW` on its `ReadOptions`. There is no default value for the new `Env::IOPriority` parameter to `RandomAccessFileReader::Read()`. That means this PR goes through all callers (in some cases multiple layers up the call stack) to find a `ReadOptions` to provide the priority. There are TODOs for cases I believe it would be good to let user control the priority some day (e.g., file footer reads), and no TODO in cases I believe it doesn't matter (e.g., trace file reads). The API doc only lists the missing cases where a file read associated with a provided `ReadOptions` cannot be rate limited. For cases like file ingestion checksum calculation, there is no API to provide `ReadOptions` or `Env::IOPriority`, so I didn't count that as missing. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9424 Test Plan: - new unit tests - new benchmarks on ~50MB database with 1MB/s read rate limit and 100ms refill interval; verified with strace reads are chunked (at 0.1MB per chunk) and spaced roughly 100ms apart. - setup command: `./db_bench -benchmarks=fillrandom,compact -db=/tmp/testdb -target_file_size_base=1048576 -disable_auto_compactions=true -file_checksum=true` - benchmarks command: `strace -ttfe pread64 ./db_bench -benchmarks=verifychecksum,verifyfilechecksums -use_existing_db=true -db=/tmp/testdb -rate_limiter_bytes_per_sec=1048576 -rate_limit_bg_reads=1 -rate_limit_user_ops=true -file_checksum=true` - crash test using IO_USER priority on non-validation reads with https://github.com/facebook/rocksdb/issues/9567 reverted: `python3 tools/db_crashtest.py blackbox --max_key=1000000 --write_buffer_size=524288 --target_file_size_base=524288 --level_compaction_dynamic_level_bytes=true --duration=3600 --rate_limit_bg_reads=true --rate_limit_user_ops=true --rate_limiter_bytes_per_sec=10485760 --interval=10` Reviewed By: hx235 Differential Revision: D33747386 Pulled By: ajkr fbshipit-source-id: a2d985e97912fba8c54763798e04f006ccc56e0c
2022-02-17 07:17:03 +00:00
read_options_.rate_limiter_priority =
FLAGS_rate_limit_user_ops ? Env::IO_USER : Env::IO_TOTAL;
Ignore `total_order_seek` in DB::Get (#9427) Summary: Apparently setting total_order_seek=true for DB::Get was intended to allow accurate read semantics if the current prefix extractor doesn't match what was used to generate SST files on disk. But since prefix_extractor was made a mutable option in 5.14.0, we have been able to detect this case and provide the correct semantics regardless of the total_order_seek option. Since that time, the option has only made Get() slower in a reasonably common case: prefix_extractor unchanged and whole_key_filtering=false. So this change primarily removes unnecessary effect of total_order_seek on Get. Also cleans up some related comments. Also adds a -total_order_seek option to db_bench and canonicalizes handling of ReadOptions in db_bench so that command line options have the expected association with library features. (There is potential for change in regression test behavior, but the old behavior is likely indefensible, or some other inconsistency would need to be fixed.) TODO in follow-up work: there should be no reason for Get() to depend on current prefix extractor at all. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9427 Test Plan: Unit tests updated. Performance (using db_bench update) Create DB with `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -benchmarks=fillrandom -num=10000000 -disable_wal=1 -write_buffer_size=10000000 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12 -whole_key_filtering=0` Test with and without `-total_order_seek` on `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -use_existing_db -readonly -benchmarks=readrandom -num=10000000 -duration=40 -disable_wal=1 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12` Before this change, total_order_seek=false: 25188 ops/sec Before this change, total_order_seek=true: 1222 ops/sec (~20x slower) After this change, total_order_seek=false: 24570 ops/sec After this change, total_order_seek=true: 25012 ops/sec (indistinguishable) Reviewed By: siying Differential Revision: D33753458 Pulled By: pdillinger fbshipit-source-id: bf892f34907a5e407d9c40bd4d42f0adbcbe0014
2022-02-01 03:45:17 +00:00
read_options_.tailing = FLAGS_use_tailing_iterator;
read_options_.readahead_size = FLAGS_readahead_size;
read_options_.adaptive_readahead = FLAGS_adaptive_readahead;
read_options_.async_io = FLAGS_async_io;
MultiGet async IO across multiple levels (#10535) Summary: This PR exploits parallelism in MultiGet across levels. It applies only to the coroutine version of MultiGet. Previously, MultiGet file reads from SST files in the same level were parallelized. With this PR, MultiGet batches with keys distributed across multiple levels are read in parallel. This is accomplished by splitting the keys not present in a level (determined by bloom filtering) into a separate batch, and processing the new batch in parallel with the original batch. Pull Request resolved: https://github.com/facebook/rocksdb/pull/10535 Test Plan: 1. Ensure existing MultiGet unit tests pass, updating them as necessary 2. New unit tests - TODO 3. Run stress test - TODO No noticeable regression (<1%) without async IO - Without PR: `multireadrandom : 7.261 micros/op 1101724 ops/sec 60.007 seconds 66110936 operations; 571.6 MB/s (8168992 of 8168992 found)` With PR: `multireadrandom : 7.305 micros/op 1095167 ops/sec 60.007 seconds 65717936 operations; 568.2 MB/s (8271992 of 8271992 found)` For a fully cached DB, but with async IO option on, no regression observed (<1%) - Without PR: `multireadrandom : 5.201 micros/op 1538027 ops/sec 60.005 seconds 92288936 operations; 797.9 MB/s (11540992 of 11540992 found) ` With PR: `multireadrandom : 5.249 micros/op 1524097 ops/sec 60.005 seconds 91452936 operations; 790.7 MB/s (11649992 of 11649992 found) ` Reviewed By: akankshamahajan15 Differential Revision: D38774009 Pulled By: anand1976 fbshipit-source-id: c955e259749f1c091590ade73105b3ee46cd0007
2022-08-19 23:52:52 +00:00
read_options_.optimize_multiget_for_io = FLAGS_optimize_multiget_for_io;
read_options_.auto_readahead_size = FLAGS_auto_readahead_size;
void (Benchmark::*method)(ThreadState*) = nullptr;
void (Benchmark::*post_process_method)() = nullptr;
bool fresh_db = false;
int num_threads = FLAGS_threads;
[db_bench] Support single benchmark arguments (Repeat for X times, Warm up for X times), Support CombinedStats (AVG / MEDIAN) Summary: This diff allow us to run a single benchmark X times and warm it up for Y times. and see the AVG & MEDIAN throughput of these X runs for example ``` $ ./db_bench --benchmarks="fillseq,readseq[X5-W2]" Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags RocksDB: version 4.12 Date: Wed Aug 24 10:45:26 2016 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Compression: Snappy Memtablerep: skip_list Perf Level: 1 WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags DB path: [/tmp/rocksdbtest-8616/dbbench] fillseq : 4.695 micros/op 212971 ops/sec; 23.6 MB/s DB path: [/tmp/rocksdbtest-8616/dbbench] Warming up benchmark by running 2 times readseq : 0.214 micros/op 4677005 ops/sec; 517.4 MB/s readseq : 0.212 micros/op 4706834 ops/sec; 520.7 MB/s Running benchmark for 5 times readseq : 0.218 micros/op 4588187 ops/sec; 507.6 MB/s readseq : 0.208 micros/op 4816538 ops/sec; 532.8 MB/s readseq : 0.213 micros/op 4685376 ops/sec; 518.3 MB/s readseq : 0.214 micros/op 4676787 ops/sec; 517.4 MB/s readseq : 0.217 micros/op 4618532 ops/sec; 510.9 MB/s readseq [AVG 5 runs] : 4677084 ops/sec; 517.4 MB/sec readseq [MEDIAN 5 runs] : 4676787 ops/sec; 517.4 MB/sec ``` Test Plan: run db_bench Reviewers: sdong, andrewkr, yhchiang Reviewed By: yhchiang Subscribers: andrewkr, dhruba Differential Revision: https://reviews.facebook.net/D62235
2016-08-25 19:57:35 +00:00
int num_repeat = 1;
int num_warmup = 0;
if (!name.empty() && *name.rbegin() == ']') {
auto it = name.find('[');
if (it == std::string::npos) {
fprintf(stderr, "unknown benchmark arguments '%s'\n", name.c_str());
ErrorExit();
[db_bench] Support single benchmark arguments (Repeat for X times, Warm up for X times), Support CombinedStats (AVG / MEDIAN) Summary: This diff allow us to run a single benchmark X times and warm it up for Y times. and see the AVG & MEDIAN throughput of these X runs for example ``` $ ./db_bench --benchmarks="fillseq,readseq[X5-W2]" Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags RocksDB: version 4.12 Date: Wed Aug 24 10:45:26 2016 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Compression: Snappy Memtablerep: skip_list Perf Level: 1 WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags DB path: [/tmp/rocksdbtest-8616/dbbench] fillseq : 4.695 micros/op 212971 ops/sec; 23.6 MB/s DB path: [/tmp/rocksdbtest-8616/dbbench] Warming up benchmark by running 2 times readseq : 0.214 micros/op 4677005 ops/sec; 517.4 MB/s readseq : 0.212 micros/op 4706834 ops/sec; 520.7 MB/s Running benchmark for 5 times readseq : 0.218 micros/op 4588187 ops/sec; 507.6 MB/s readseq : 0.208 micros/op 4816538 ops/sec; 532.8 MB/s readseq : 0.213 micros/op 4685376 ops/sec; 518.3 MB/s readseq : 0.214 micros/op 4676787 ops/sec; 517.4 MB/s readseq : 0.217 micros/op 4618532 ops/sec; 510.9 MB/s readseq [AVG 5 runs] : 4677084 ops/sec; 517.4 MB/sec readseq [MEDIAN 5 runs] : 4676787 ops/sec; 517.4 MB/sec ``` Test Plan: run db_bench Reviewers: sdong, andrewkr, yhchiang Reviewed By: yhchiang Subscribers: andrewkr, dhruba Differential Revision: https://reviews.facebook.net/D62235
2016-08-25 19:57:35 +00:00
}
std::string args = name.substr(it + 1);
args.resize(args.size() - 1);
name.resize(it);
std::string bench_arg;
std::stringstream args_stream(args);
while (std::getline(args_stream, bench_arg, '-')) {
if (bench_arg.empty()) {
continue;
}
if (bench_arg[0] == 'X') {
// Repeat the benchmark n times
std::string num_str = bench_arg.substr(1);
num_repeat = std::stoi(num_str);
} else if (bench_arg[0] == 'W') {
// Warm up the benchmark for n times
std::string num_str = bench_arg.substr(1);
num_warmup = std::stoi(num_str);
}
}
}
// Both fillseqdeterministic and filluniquerandomdeterministic
// fill the levels except the max level with UNIQUE_RANDOM
// and fill the max level with fillseq and filluniquerandom, respectively
if (name == "fillseqdeterministic" ||
name == "filluniquerandomdeterministic") {
if (!FLAGS_disable_auto_compactions) {
fprintf(stderr,
"Please disable_auto_compactions in FillDeterministic "
"benchmark\n");
ErrorExit();
}
if (num_threads > 1) {
fprintf(stderr,
"filldeterministic multithreaded not supported"
", use 1 thread\n");
num_threads = 1;
}
fresh_db = true;
if (name == "fillseqdeterministic") {
method = &Benchmark::WriteSeqDeterministic;
} else {
method = &Benchmark::WriteUniqueRandomDeterministic;
}
} else if (name == "fillseq") {
fresh_db = true;
method = &Benchmark::WriteSeq;
} else if (name == "fillbatch") {
fresh_db = true;
entries_per_batch_ = 1000;
method = &Benchmark::WriteSeq;
} else if (name == "fillrandom") {
fresh_db = true;
method = &Benchmark::WriteRandom;
Create fillanddeleteuniquerandom benchmark (db_bench), with new option flags. (#8593) Summary: Introduction of a new `fillanddeleteuniquerandom` benchmark (`db_bench`) with 5 new option flags to simulate a benchmark where the following sequence is repeated multiple times: "A set of keys S1 is inserted ('`disposable entries`'), then after some delay another set of keys S2 is inserted ('`persistent entries`') and the first set of keys S1 is deleted. S2 artificially represents the insertion of hypothetical results from some undefined computation done on the first set of keys S1. The next sequence can start as soon as the last disposable entry in the set S1 of this sequence is inserted, if the `delay` is non negligible." New flags: - `disposable_entries_delete_delay`: minimum delay in microseconds between insertion of the last `disposable` entry, and the start of the insertion of the first `persistent` entry. - `disposable_entries_batch_size`: number of `disposable` entries inserted at the beginning of each sequence. - `disposable_entries_value_size`: size of the random `value` string for the `disposable` entries. - `persistent_entries_batch_size`: number of `persistent` entries inserted at the end of each sequence, right before the deletion of the `disposable` entries starts. - `persistent_entries_value_size`: size of the random value string for the `persistent` entries. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8593 Reviewed By: pdillinger Differential Revision: D29974436 Pulled By: bjlemaire fbshipit-source-id: f578033e5b45e8268ba6fa6f38f4770c2e6e801d
2021-07-29 21:57:03 +00:00
} else if (name == "filluniquerandom" ||
name == "fillanddeleteuniquerandom") {
fresh_db = true;
if (num_threads > 1) {
fprintf(stderr,
Create fillanddeleteuniquerandom benchmark (db_bench), with new option flags. (#8593) Summary: Introduction of a new `fillanddeleteuniquerandom` benchmark (`db_bench`) with 5 new option flags to simulate a benchmark where the following sequence is repeated multiple times: "A set of keys S1 is inserted ('`disposable entries`'), then after some delay another set of keys S2 is inserted ('`persistent entries`') and the first set of keys S1 is deleted. S2 artificially represents the insertion of hypothetical results from some undefined computation done on the first set of keys S1. The next sequence can start as soon as the last disposable entry in the set S1 of this sequence is inserted, if the `delay` is non negligible." New flags: - `disposable_entries_delete_delay`: minimum delay in microseconds between insertion of the last `disposable` entry, and the start of the insertion of the first `persistent` entry. - `disposable_entries_batch_size`: number of `disposable` entries inserted at the beginning of each sequence. - `disposable_entries_value_size`: size of the random `value` string for the `disposable` entries. - `persistent_entries_batch_size`: number of `persistent` entries inserted at the end of each sequence, right before the deletion of the `disposable` entries starts. - `persistent_entries_value_size`: size of the random value string for the `persistent` entries. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8593 Reviewed By: pdillinger Differential Revision: D29974436 Pulled By: bjlemaire fbshipit-source-id: f578033e5b45e8268ba6fa6f38f4770c2e6e801d
2021-07-29 21:57:03 +00:00
"filluniquerandom and fillanddeleteuniquerandom "
"multithreaded not supported, use 1 thread");
num_threads = 1;
}
method = &Benchmark::WriteUniqueRandom;
} else if (name == "overwrite") {
method = &Benchmark::WriteRandom;
} else if (name == "fillsync") {
fresh_db = true;
num_ /= 1000;
write_options_.sync = true;
method = &Benchmark::WriteRandom;
} else if (name == "fill100K") {
fresh_db = true;
num_ /= 1000;
value_size = 100 * 1000;
method = &Benchmark::WriteRandom;
} else if (name == "readseq") {
method = &Benchmark::ReadSequential;
} else if (name == "readtorowcache") {
if (!FLAGS_use_existing_keys || !FLAGS_row_cache_size) {
fprintf(stderr,
"Please set use_existing_keys to true and specify a "
"row cache size in readtorowcache benchmark\n");
ErrorExit();
}
method = &Benchmark::ReadToRowCache;
} else if (name == "readtocache") {
method = &Benchmark::ReadSequential;
num_threads = 1;
reads_ = num_;
} else if (name == "readreverse") {
method = &Benchmark::ReadReverse;
} else if (name == "readrandom") {
Introduce a new MultiGet batching implementation (#5011) Summary: This PR introduces a new MultiGet() API, with the underlying implementation grouping keys based on SST file and batching lookups in a file. The reason for the new API is twofold - the definition allows callers to allocate storage for status and values on stack instead of std::vector, as well as return values as PinnableSlices in order to avoid copying, and it keeps the original MultiGet() implementation intact while we experiment with batching. Batching is useful when there is some spatial locality to the keys being queries, as well as larger batch sizes. The main benefits are due to - 1. Fewer function calls, especially to BlockBasedTableReader::MultiGet() and FullFilterBlockReader::KeysMayMatch() 2. Bloom filter cachelines can be prefetched, hiding the cache miss latency The next step is to optimize the binary searches in the level_storage_info, index blocks and data blocks, since we could reduce the number of key comparisons if the keys are relatively close to each other. The batching optimizations also need to be extended to other formats, such as PlainTable and filter formats. This also needs to be added to db_stress. Benchmark results from db_bench for various batch size/locality of reference combinations are given below. Locality was simulated by offsetting the keys in a batch by a stride length. Each SST file is about 8.6MB uncompressed and key/value size is 16/100 uncompressed. To focus on the cpu benefit of batching, the runs were single threaded and bound to the same cpu to eliminate interference from other system events. The results show a 10-25% improvement in micros/op from smaller to larger batch sizes (4 - 32). Batch Sizes 1 | 2 | 4 | 8 | 16 | 32 Random pattern (Stride length 0) 4.158 | 4.109 | 4.026 | 4.05 | 4.1 | 4.074 - Get 4.438 | 4.302 | 4.165 | 4.122 | 4.096 | 4.075 - MultiGet (no batching) 4.461 | 4.256 | 4.277 | 4.11 | 4.182 | 4.14 - MultiGet (w/ batching) Good locality (Stride length 16) 4.048 | 3.659 | 3.248 | 2.99 | 2.84 | 2.753 4.429 | 3.728 | 3.406 | 3.053 | 2.911 | 2.781 4.452 | 3.45 | 2.833 | 2.451 | 2.233 | 2.135 Good locality (Stride length 256) 4.066 | 3.786 | 3.581 | 3.447 | 3.415 | 3.232 4.406 | 4.005 | 3.644 | 3.49 | 3.381 | 3.268 4.393 | 3.649 | 3.186 | 2.882 | 2.676 | 2.62 Medium locality (Stride length 4096) 4.012 | 3.922 | 3.768 | 3.61 | 3.582 | 3.555 4.364 | 4.057 | 3.791 | 3.65 | 3.57 | 3.465 4.479 | 3.758 | 3.316 | 3.077 | 2.959 | 2.891 dbbench command used (on a DB with 4 levels, 12 million keys)- TEST_TMPDIR=/dev/shm numactl -C 10 ./db_bench.tmp -use_existing_db=true -benchmarks="readseq,multireadrandom" -write_buffer_size=4194304 -target_file_size_base=4194304 -max_bytes_for_level_base=16777216 -num=12000000 -reads=12000000 -duration=90 -threads=1 -compression_type=none -cache_size=4194304000 -batch_size=32 -disable_auto_compactions=true -bloom_bits=10 -cache_index_and_filter_blocks=true -pin_l0_filter_and_index_blocks_in_cache=true -multiread_batched=true -multiread_stride=4 Pull Request resolved: https://github.com/facebook/rocksdb/pull/5011 Differential Revision: D14348703 Pulled By: anand1976 fbshipit-source-id: 774406dab3776d979c809522a67bedac6c17f84b
2019-04-11 21:24:09 +00:00
if (FLAGS_multiread_stride) {
fprintf(stderr, "entries_per_batch = %" PRIi64 "\n",
entries_per_batch_);
}
method = &Benchmark::ReadRandom;
} else if (name == "readrandomfast") {
method = &Benchmark::ReadRandomFast;
} else if (name == "multireadrandom") {
fprintf(stderr, "entries_per_batch = %" PRIi64 "\n",
entries_per_batch_);
method = &Benchmark::MultiReadRandom;
} else if (name == "multireadwhilewriting") {
fprintf(stderr, "entries_per_batch = %" PRIi64 "\n",
entries_per_batch_);
num_threads++;
method = &Benchmark::MultiReadWhileWriting;
For ApproximateSizes, pro-rate table metadata size over data blocks (#6784) Summary: The implementation of GetApproximateSizes was inconsistent in its treatment of the size of non-data blocks of SST files, sometimes including and sometimes now. This was at its worst with large portion of table file used by filters and querying a small range that crossed a table boundary: the size estimate would include large filter size. It's conceivable that someone might want only to know the size in terms of data blocks, but I believe that's unlikely enough to ignore for now. Similarly, there's no evidence the internal function AppoximateOffsetOf is used for anything other than a one-sided ApproximateSize, so I intend to refactor to remove redundancy in a follow-up commit. So to fix this, GetApproximateSizes (and implementation details ApproximateSize and ApproximateOffsetOf) now consistently include in their returned sizes a portion of table file metadata (incl filters and indexes) based on the size portion of the data blocks in range. In other words, if a key range covers data blocks that are X% by size of all the table's data blocks, returned approximate size is X% of the total file size. It would technically be more accurate to attribute metadata based on number of keys, but that's not computationally efficient with data available and rarely a meaningful difference. Also includes miscellaneous comment improvements / clarifications. Also included is a new approximatesizerandom benchmark for db_bench. No significant performance difference seen with this change, whether ~700 ops/sec with cache_index_and_filter_blocks and small cache or ~150k ops/sec without cache_index_and_filter_blocks. Pull Request resolved: https://github.com/facebook/rocksdb/pull/6784 Test Plan: Test added to DBTest.ApproximateSizesFilesWithErrorMargin. Old code running new test... [ RUN ] DBTest.ApproximateSizesFilesWithErrorMargin db/db_test.cc:1562: Failure Expected: (size) <= (11 * 100), actual: 9478 vs 1100 Other tests updated to reflect consistent accounting of metadata. Reviewed By: siying Differential Revision: D21334706 Pulled By: pdillinger fbshipit-source-id: 6f86870e45213334fedbe9c73b4ebb1d8d611185
2020-06-02 19:27:59 +00:00
} else if (name == "approximatesizerandom") {
fprintf(stderr, "entries_per_batch = %" PRIi64 "\n",
entries_per_batch_);
method = &Benchmark::ApproximateSizeRandom;
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 18:40:44 +00:00
} else if (name == "mixgraph") {
method = &Benchmark::MixGraph;
} else if (name == "readmissing") {
++key_size_;
method = &Benchmark::ReadRandom;
} else if (name == "newiterator") {
method = &Benchmark::IteratorCreation;
} else if (name == "newiteratorwhilewriting") {
num_threads++; // Add extra thread for writing
method = &Benchmark::IteratorCreationWhileWriting;
} else if (name == "seekrandom") {
method = &Benchmark::SeekRandom;
} else if (name == "seekrandomwhilewriting") {
num_threads++; // Add extra thread for writing
method = &Benchmark::SeekRandomWhileWriting;
} else if (name == "seekrandomwhilemerging") {
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
2015-03-30 18:28:25 +00:00
num_threads++; // Add extra thread for merging
method = &Benchmark::SeekRandomWhileMerging;
} else if (name == "readrandomsmall") {
reads_ /= 1000;
method = &Benchmark::ReadRandom;
} else if (name == "deleteseq") {
method = &Benchmark::DeleteSeq;
} else if (name == "deleterandom") {
method = &Benchmark::DeleteRandom;
} else if (name == "readwhilewriting") {
num_threads++; // Add extra thread for writing
method = &Benchmark::ReadWhileWriting;
} else if (name == "readwhilemerging") {
num_threads++; // Add extra thread for writing
method = &Benchmark::ReadWhileMerging;
} else if (name == "readwhilescanning") {
num_threads++; // Add extra thread for scaning
method = &Benchmark::ReadWhileScanning;
} else if (name == "readrandomwriterandom") {
method = &Benchmark::ReadRandomWriteRandom;
} else if (name == "readrandommergerandom") {
if (FLAGS_merge_operator.empty()) {
fprintf(stdout, "%-12s : skipped (--merge_operator is unknown)\n",
name.c_str());
ErrorExit();
}
method = &Benchmark::ReadRandomMergeRandom;
} else if (name == "updaterandom") {
method = &Benchmark::UpdateRandom;
} else if (name == "xorupdaterandom") {
method = &Benchmark::XORUpdateRandom;
} else if (name == "appendrandom") {
method = &Benchmark::AppendRandom;
} else if (name == "mergerandom") {
if (FLAGS_merge_operator.empty()) {
fprintf(stdout, "%-12s : skipped (--merge_operator is unknown)\n",
name.c_str());
exit(1);
}
method = &Benchmark::MergeRandom;
} else if (name == "randomwithverify") {
method = &Benchmark::RandomWithVerify;
} else if (name == "fillseekseq") {
SkipListRep::LookaheadIterator Summary: This diff introduces the `lookahead` argument to `SkipListFactory()`. This is an optimization for the tailing use case which includes many seeks. E.g. consider the following operations on a skip list iterator: Seek(x), Next(), Next(), Seek(x+2), Next(), Seek(x+3), Next(), Next(), ... If `lookahead` is positive, `SkipListRep` will return an iterator which also keeps track of the previously visited node. Seek() then first does a linear search starting from that node (up to `lookahead` steps). As in the tailing example above, this may require fewer than ~log(n) comparisons as with regular skip list search. Test Plan: Added a new benchmark (`fillseekseq`) which simulates the usage pattern. It first writes N records (with consecutive keys), then measures how much time it takes to read them by calling `Seek()` and `Next()`. $ time ./db_bench -num 10000000 -benchmarks fillseekseq -prefix_size 1 \ -key_size 8 -write_buffer_size $[1024*1024*1024] -value_size 50 \ -seekseq_next 2 -skip_list_lookahead=0 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.389 micros/op 2569047 ops/sec; real 0m21.806s user 0m12.106s sys 0m9.672s $ time ./db_bench [...] -skip_list_lookahead=2 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.153 micros/op 6540684 ops/sec; real 0m19.469s user 0m10.192s sys 0m9.252s Reviewers: ljin, sdong, igor Reviewed By: igor Subscribers: dhruba, leveldb, march, lovro Differential Revision: https://reviews.facebook.net/D23997
2014-09-23 22:52:28 +00:00
method = &Benchmark::WriteSeqSeekSeq;
} else if (name == "compact") {
method = &Benchmark::Compact;
} else if (name == "compactall") {
CompactAll();
} else if (name == "compact0") {
CompactLevel(0);
} else if (name == "compact1") {
CompactLevel(1);
} else if (name == "waitforcompaction") {
WaitForCompaction();
} else if (name == "flush") {
Flush();
} else if (name == "crc32c") {
method = &Benchmark::Crc32c;
} else if (name == "xxhash") {
method = &Benchmark::xxHash;
Implement XXH3 block checksum type (#9069) Summary: XXH3 - latest hash function that is extremely fast on large data, easily faster than crc32c on most any x86_64 hardware. In integrating this hash function, I have handled the compression type byte in a non-standard way to avoid using the streaming API (extra data movement and active code size because of hash function complexity). This approach got a thumbs-up from Yann Collet. Existing functionality change: * reject bad ChecksumType in options with InvalidArgument This change split off from https://github.com/facebook/rocksdb/issues/9058 because context-aware checksum is likely to be handled through different configuration than ChecksumType. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9069 Test Plan: tests updated, and substantially expanded. Unit tests now check that we don't accidentally change the values generated by the checksum algorithms ("schema test") and that we properly handle invalid/unrecognized checksum types in options or in file footer. DBTestBase::ChangeOptions (etc.) updated from two to one configuration changing from default CRC32c ChecksumType. The point of this test code is to detect possible interactions among features, and the likelihood of some bad interaction being detected by including configurations other than XXH3 and CRC32c--and then not detected by stress/crash test--is extremely low. Stress/crash test also updated (manual run long enough to see it accepts new checksum type). db_bench also updated for microbenchmarking checksums. ### Performance microbenchmark (PORTABLE=0 DEBUG_LEVEL=0, Broadwell processor) ./db_bench -benchmarks=crc32c,xxhash,xxhash64,xxh3,crc32c,xxhash,xxhash64,xxh3,crc32c,xxhash,xxhash64,xxh3 crc32c : 0.200 micros/op 5005220 ops/sec; 19551.6 MB/s (4096 per op) xxhash : 0.807 micros/op 1238408 ops/sec; 4837.5 MB/s (4096 per op) xxhash64 : 0.421 micros/op 2376514 ops/sec; 9283.3 MB/s (4096 per op) xxh3 : 0.171 micros/op 5858391 ops/sec; 22884.3 MB/s (4096 per op) crc32c : 0.206 micros/op 4859566 ops/sec; 18982.7 MB/s (4096 per op) xxhash : 0.793 micros/op 1260850 ops/sec; 4925.2 MB/s (4096 per op) xxhash64 : 0.410 micros/op 2439182 ops/sec; 9528.1 MB/s (4096 per op) xxh3 : 0.161 micros/op 6202872 ops/sec; 24230.0 MB/s (4096 per op) crc32c : 0.203 micros/op 4924686 ops/sec; 19237.1 MB/s (4096 per op) xxhash : 0.839 micros/op 1192388 ops/sec; 4657.8 MB/s (4096 per op) xxhash64 : 0.424 micros/op 2357391 ops/sec; 9208.6 MB/s (4096 per op) xxh3 : 0.162 micros/op 6182678 ops/sec; 24151.1 MB/s (4096 per op) As you can see, especially once warmed up, xxh3 is fastest. ### Performance macrobenchmark (PORTABLE=0 DEBUG_LEVEL=0, Broadwell processor) Test for I in `seq 1 50`; do for CHK in 0 1 2 3 4; do TEST_TMPDIR=/dev/shm/rocksdb$CHK ./db_bench -benchmarks=fillseq -memtablerep=vector -allow_concurrent_memtable_write=false -num=30000000 -checksum_type=$CHK 2>&1 | grep 'micros/op' | tee -a results-$CHK & done; wait; done Results (ops/sec) for FILE in results*; do echo -n "$FILE "; awk '{ s += $5; c++; } END { print 1.0 * s / c; }' < $FILE; done results-0 252118 # kNoChecksum results-1 251588 # kCRC32c results-2 251863 # kxxHash results-3 252016 # kxxHash64 results-4 252038 # kXXH3 Reviewed By: mrambacher Differential Revision: D31905249 Pulled By: pdillinger fbshipit-source-id: cb9b998ebe2523fc7c400eedf62124a78bf4b4d1
2021-10-29 05:13:47 +00:00
} else if (name == "xxhash64") {
method = &Benchmark::xxHash64;
} else if (name == "xxh3") {
method = &Benchmark::xxh3;
} else if (name == "acquireload") {
method = &Benchmark::AcquireLoad;
} else if (name == "compress") {
2014-02-08 02:12:30 +00:00
method = &Benchmark::Compress;
} else if (name == "uncompress") {
2014-02-08 02:12:30 +00:00
method = &Benchmark::Uncompress;
} else if (name == "randomtransaction") {
method = &Benchmark::RandomTransaction;
post_process_method = &Benchmark::RandomTransactionVerify;
Support for SingleDelete() Summary: This patch fixes #7460559. It introduces SingleDelete as a new database operation. This operation can be used to delete keys that were never overwritten (no put following another put of the same key). If an overwritten key is single deleted the behavior is undefined. Single deletion of a non-existent key has no effect but multiple consecutive single deletions are not allowed (see limitations). In contrast to the conventional Delete() operation, the deletion entry is removed along with the value when the two are lined up in a compaction. Note: The semantics are similar to @igor's prototype that allowed to have this behavior on the granularity of a column family ( https://reviews.facebook.net/D42093 ). This new patch, however, is more aggressive when it comes to removing tombstones: It removes the SingleDelete together with the value whenever there is no snapshot between them while the older patch only did this when the sequence number of the deletion was older than the earliest snapshot. Most of the complex additions are in the Compaction Iterator, all other changes should be relatively straightforward. The patch also includes basic support for single deletions in db_stress and db_bench. Limitations: - Not compatible with cuckoo hash tables - Single deletions cannot be used in combination with merges and normal deletions on the same key (other keys are not affected by this) - Consecutive single deletions are currently not allowed (and older version of this patch supported this so it could be resurrected if needed) Test Plan: make all check Reviewers: yhchiang, sdong, rven, anthony, yoshinorim, igor Reviewed By: igor Subscribers: maykov, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D43179
2015-09-17 18:42:56 +00:00
} else if (name == "randomreplacekeys") {
fresh_db = true;
method = &Benchmark::RandomReplaceKeys;
Write a benchmark to emulate time series data Summary: Add a benchmark to `db_bench`. In this benchmark, a write thread will populate time series data in the format of 'id | timestamp', and multiple read threads will randomly retrieve all data from one id at a time. Test Plan: Run the benchmark: `num=134217728;bpl=536870912;mb=67108864;overlap=10;mcz=2;del=300000000;levels=6;ctrig=4;delay=8;stop=12;wbn=3;mbc=20;wbs=134217728;dds=0;sync=0;t=32;vs=800;bs=4096;cs=17179869184;of=500000;wps=0;si=10000000; kir=100000; dir=/data/users/jhli/test/; ./db_bench --benchmarks=timeseries --disable_seek_compaction=1 --mmap_read=0 --statistics=1 --histogram=1 --num=$num --threads=$t --value_size=$vs --block_size=$bs --cache_size=$cs --bloom_bits=10 --cache_numshardbits=6 --open_files=$of --verify_checksum=1 --db=$dir --sync=$sync --disable_wal=0 --compression_type=none --stats_interval=$si --compression_ratio=1 --disable_data_sync=$dds --write_buffer_size=$wbs --target_file_size_base=$mb --max_write_buffer_number=$wbn --max_background_compactions=$mbc --level0_file_num_compaction_trigger=$ctrig --level0_slowdown_writes_trigger=$delay --level0_stop_writes_trigger=$stop --num_levels=$levels --delete_obsolete_files_period_micros=$del --min_level_to_compress=$mcz --max_grandparent_overlap_factor=$overlap --stats_per_interval=1 --max_bytes_for_level_base=$bpl --use_existing_db=0 --key_id_range=$kir` Reviewers: andrewkr, sdong Reviewed By: sdong Subscribers: lgalanis, andrewkr, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D60651
2016-08-01 19:28:51 +00:00
} else if (name == "timeseries") {
timestamp_emulator_.reset(new TimestampEmulator());
if (FLAGS_expire_style == "compaction_filter") {
filter.reset(new ExpiredTimeFilter(timestamp_emulator_));
fprintf(stdout, "Compaction filter is used to remove expired data");
open_options_.compaction_filter = filter.get();
}
fresh_db = true;
method = &Benchmark::TimeSeries;
Revamp, optimize new experimental clock cache (#10626) Summary: * Consolidates most metadata into a single word per slot so that more can be accomplished with a single atomic update. In the common case, Lookup was previously about 4 atomic updates, now just 1 atomic update. Common case Release was previously 1 atomic read + 1 atomic update, now just 1 atomic update. * Eliminate spins / waits / yields, which likely threaten some "lock free" benefits. Compare-exchange loops are only used in explicit Erase, and strict_capacity_limit=true Insert. Eviction uses opportunistic compare- exchange. * Relaxes some aggressiveness and guarantees. For example, * Duplicate Inserts will sometimes go undetected and the shadow duplicate will age out with eviction. * In many cases, the older Inserted value for a given cache key will be kept (i.e. Insert does not support overwrite). * Entries explicitly erased (rather than evicted) might not be freed immediately in some rare cases. * With strict_capacity_limit=false, capacity limit is not tracked/enforced as precisely as LRUCache, but is self-correcting and should only deviate by a very small number of extra or fewer entries. * Use smaller "computed default" number of cache shards in many cases, because benefits to larger usage tracking / eviction pools outweigh the small cost of more lock-free atomic contention. The improvement in CPU and I/O is dramatic in some limit-memory cases. * Even without the sharding change, the eviction algorithm is likely more effective than LRU overall because it's more stateful, even though the "hot path" state tracking for it is essentially free with ref counting. It is like a generalized CLOCK with aging (see code comments). I don't have performance numbers showing a specific improvement, but in theory, for a Poisson access pattern to each block, keeping some state allows better estimation of time to next access (Poisson interval) than strict LRU. The bounded randomness in CLOCK can also reduce "cliff" effect for repeated range scans approaching and exceeding cache size. ## Hot path algorithm comparison Rough descriptions, focusing on number and kind of atomic operations: * Old `Lookup()` (2-5 atomic updates per probe): ``` Loop: Increment internal ref count at slot If possible hit: Check flags atomic (and non-atomic fields) If cache hit: Three distinct updates to 'flags' atomic Increment refs for internal-to-external Return Decrement internal ref count while atomic read 'displacements' > 0 ``` * New `Lookup()` (1-2 atomic updates per probe): ``` Loop: Increment acquire counter in meta word (optimistic) If visible entry (already read meta word): If match (read non-atomic fields): Return Else: Decrement acquire counter in meta word Else if invisible entry (rare, already read meta word): Decrement acquire counter in meta word while atomic read 'displacements' > 0 ``` * Old `Release()` (1 atomic update, conditional on atomic read, rarely more): ``` Read atomic ref count If last reference and invisible (rare): Use CAS etc. to remove Return Else: Decrement ref count ``` * New `Release()` (1 unconditional atomic update, rarely more): ``` Increment release counter in meta word If last reference and invisible (rare): Use CAS etc. to remove Return ``` ## Performance test setup Build DB with ``` TEST_TMPDIR=/dev/shm ./db_bench -benchmarks=fillrandom -num=30000000 -disable_wal=1 -bloom_bits=16 ``` Test with ``` TEST_TMPDIR=/dev/shm ./db_bench -benchmarks=readrandom -readonly -num=30000000 -bloom_bits=16 -cache_index_and_filter_blocks=1 -cache_size=${CACHE_MB}000000 -duration 60 -threads=$THREADS -statistics ``` Numbers on a single socket Skylake Xeon system with 48 hardware threads, DEBUG_LEVEL=0 PORTABLE=0. Very similar story on a dual socket system with 80 hardware threads. Using (every 2nd) Fibonacci MB cache sizes to sample the territory between powers of two. Configurations: base: LRUCache before this change, but with db_bench change to default cache_numshardbits=-1 (instead of fixed at 6) folly: LRUCache before this change, with folly enabled (distributed mutex) but on an old compiler (sorry) gt_clock: experimental ClockCache before this change new_clock: experimental ClockCache with this change ## Performance test results First test "hot path" read performance, with block cache large enough for whole DB: 4181MB 1thread base -> kops/s: 47.761 4181MB 1thread folly -> kops/s: 45.877 4181MB 1thread gt_clock -> kops/s: 51.092 4181MB 1thread new_clock -> kops/s: 53.944 4181MB 16thread base -> kops/s: 284.567 4181MB 16thread folly -> kops/s: 249.015 4181MB 16thread gt_clock -> kops/s: 743.762 4181MB 16thread new_clock -> kops/s: 861.821 4181MB 24thread base -> kops/s: 303.415 4181MB 24thread folly -> kops/s: 266.548 4181MB 24thread gt_clock -> kops/s: 975.706 4181MB 24thread new_clock -> kops/s: 1205.64 (~= 24 * 53.944) 4181MB 32thread base -> kops/s: 311.251 4181MB 32thread folly -> kops/s: 274.952 4181MB 32thread gt_clock -> kops/s: 1045.98 4181MB 32thread new_clock -> kops/s: 1370.38 4181MB 48thread base -> kops/s: 310.504 4181MB 48thread folly -> kops/s: 268.322 4181MB 48thread gt_clock -> kops/s: 1195.65 4181MB 48thread new_clock -> kops/s: 1604.85 (~= 24 * 1.25 * 53.944) 4181MB 64thread base -> kops/s: 307.839 4181MB 64thread folly -> kops/s: 272.172 4181MB 64thread gt_clock -> kops/s: 1204.47 4181MB 64thread new_clock -> kops/s: 1615.37 4181MB 128thread base -> kops/s: 310.934 4181MB 128thread folly -> kops/s: 267.468 4181MB 128thread gt_clock -> kops/s: 1188.75 4181MB 128thread new_clock -> kops/s: 1595.46 Whether we have just one thread on a quiet system or an overload of threads, the new version wins every time in thousand-ops per second, sometimes dramatically so. Mutex-based implementation quickly becomes contention-limited. New clock cache shows essentially perfect scaling up to number of physical cores (24), and then each hyperthreaded core adding about 1/4 the throughput of an additional physical core (see 48 thread case). Block cache miss rates (omitted above) are negligible across the board. With partitioned instead of full filters, the maximum speed-up vs. base is more like 2.5x rather than 5x. Now test a large block cache with low miss ratio, but some eviction is required: 1597MB 1thread base -> kops/s: 46.603 io_bytes/op: 1584.63 miss_ratio: 0.0201066 max_rss_mb: 1589.23 1597MB 1thread folly -> kops/s: 45.079 io_bytes/op: 1530.03 miss_ratio: 0.019872 max_rss_mb: 1550.43 1597MB 1thread gt_clock -> kops/s: 48.711 io_bytes/op: 1566.63 miss_ratio: 0.0198923 max_rss_mb: 1691.4 1597MB 1thread new_clock -> kops/s: 51.531 io_bytes/op: 1589.07 miss_ratio: 0.0201969 max_rss_mb: 1583.56 1597MB 32thread base -> kops/s: 301.174 io_bytes/op: 1439.52 miss_ratio: 0.0184218 max_rss_mb: 1656.59 1597MB 32thread folly -> kops/s: 273.09 io_bytes/op: 1375.12 miss_ratio: 0.0180002 max_rss_mb: 1586.8 1597MB 32thread gt_clock -> kops/s: 904.497 io_bytes/op: 1411.29 miss_ratio: 0.0179934 max_rss_mb: 1775.89 1597MB 32thread new_clock -> kops/s: 1182.59 io_bytes/op: 1440.77 miss_ratio: 0.0185449 max_rss_mb: 1636.45 1597MB 128thread base -> kops/s: 309.91 io_bytes/op: 1438.25 miss_ratio: 0.018399 max_rss_mb: 1689.98 1597MB 128thread folly -> kops/s: 267.605 io_bytes/op: 1394.16 miss_ratio: 0.0180286 max_rss_mb: 1631.91 1597MB 128thread gt_clock -> kops/s: 691.518 io_bytes/op: 9056.73 miss_ratio: 0.0186572 max_rss_mb: 1982.26 1597MB 128thread new_clock -> kops/s: 1406.12 io_bytes/op: 1440.82 miss_ratio: 0.0185463 max_rss_mb: 1685.63 610MB 1thread base -> kops/s: 45.511 io_bytes/op: 2279.61 miss_ratio: 0.0290528 max_rss_mb: 615.137 610MB 1thread folly -> kops/s: 43.386 io_bytes/op: 2217.29 miss_ratio: 0.0289282 max_rss_mb: 600.996 610MB 1thread gt_clock -> kops/s: 46.207 io_bytes/op: 2275.51 miss_ratio: 0.0290057 max_rss_mb: 637.934 610MB 1thread new_clock -> kops/s: 48.879 io_bytes/op: 2283.1 miss_ratio: 0.0291253 max_rss_mb: 613.5 610MB 32thread base -> kops/s: 306.59 io_bytes/op: 2250 miss_ratio: 0.0288721 max_rss_mb: 683.402 610MB 32thread folly -> kops/s: 269.176 io_bytes/op: 2187.86 miss_ratio: 0.0286938 max_rss_mb: 628.742 610MB 32thread gt_clock -> kops/s: 855.097 io_bytes/op: 2279.26 miss_ratio: 0.0288009 max_rss_mb: 733.062 610MB 32thread new_clock -> kops/s: 1121.47 io_bytes/op: 2244.29 miss_ratio: 0.0289046 max_rss_mb: 666.453 610MB 128thread base -> kops/s: 305.079 io_bytes/op: 2252.43 miss_ratio: 0.0288884 max_rss_mb: 723.457 610MB 128thread folly -> kops/s: 269.583 io_bytes/op: 2204.58 miss_ratio: 0.0287001 max_rss_mb: 676.426 610MB 128thread gt_clock -> kops/s: 53.298 io_bytes/op: 8128.98 miss_ratio: 0.0292452 max_rss_mb: 956.273 610MB 128thread new_clock -> kops/s: 1301.09 io_bytes/op: 2246.04 miss_ratio: 0.0289171 max_rss_mb: 788.812 The new version is still winning every time, sometimes dramatically so, and we can tell from the maximum resident memory numbers (which contain some noise, by the way) that the new cache is not cheating on memory usage. IMPORTANT: The previous generation experimental clock cache appears to hit a serious bottleneck in the higher thread count configurations, presumably due to some of its waiting functionality. (The same bottleneck is not seen with partitioned index+filters.) Now we consider even smaller cache sizes, with higher miss ratios, eviction work, etc. 233MB 1thread base -> kops/s: 10.557 io_bytes/op: 227040 miss_ratio: 0.0403105 max_rss_mb: 247.371 233MB 1thread folly -> kops/s: 15.348 io_bytes/op: 112007 miss_ratio: 0.0372238 max_rss_mb: 245.293 233MB 1thread gt_clock -> kops/s: 6.365 io_bytes/op: 244854 miss_ratio: 0.0413873 max_rss_mb: 259.844 233MB 1thread new_clock -> kops/s: 47.501 io_bytes/op: 2591.93 miss_ratio: 0.0330989 max_rss_mb: 242.461 233MB 32thread base -> kops/s: 96.498 io_bytes/op: 363379 miss_ratio: 0.0459966 max_rss_mb: 479.227 233MB 32thread folly -> kops/s: 109.95 io_bytes/op: 314799 miss_ratio: 0.0450032 max_rss_mb: 400.738 233MB 32thread gt_clock -> kops/s: 2.353 io_bytes/op: 385397 miss_ratio: 0.048445 max_rss_mb: 500.688 233MB 32thread new_clock -> kops/s: 1088.95 io_bytes/op: 2567.02 miss_ratio: 0.0330593 max_rss_mb: 303.402 233MB 128thread base -> kops/s: 84.302 io_bytes/op: 378020 miss_ratio: 0.0466558 max_rss_mb: 1051.84 233MB 128thread folly -> kops/s: 89.921 io_bytes/op: 338242 miss_ratio: 0.0460309 max_rss_mb: 812.785 233MB 128thread gt_clock -> kops/s: 2.588 io_bytes/op: 462833 miss_ratio: 0.0509158 max_rss_mb: 1109.94 233MB 128thread new_clock -> kops/s: 1299.26 io_bytes/op: 2565.94 miss_ratio: 0.0330531 max_rss_mb: 361.016 89MB 1thread base -> kops/s: 0.574 io_bytes/op: 5.35977e+06 miss_ratio: 0.274427 max_rss_mb: 91.3086 89MB 1thread folly -> kops/s: 0.578 io_bytes/op: 5.16549e+06 miss_ratio: 0.27276 max_rss_mb: 96.8984 89MB 1thread gt_clock -> kops/s: 0.512 io_bytes/op: 4.13111e+06 miss_ratio: 0.242817 max_rss_mb: 119.441 89MB 1thread new_clock -> kops/s: 48.172 io_bytes/op: 2709.76 miss_ratio: 0.0346162 max_rss_mb: 100.754 89MB 32thread base -> kops/s: 5.779 io_bytes/op: 6.14192e+06 miss_ratio: 0.320399 max_rss_mb: 311.812 89MB 32thread folly -> kops/s: 5.601 io_bytes/op: 5.83838e+06 miss_ratio: 0.313123 max_rss_mb: 252.418 89MB 32thread gt_clock -> kops/s: 0.77 io_bytes/op: 3.99236e+06 miss_ratio: 0.236296 max_rss_mb: 396.422 89MB 32thread new_clock -> kops/s: 1064.97 io_bytes/op: 2687.23 miss_ratio: 0.0346134 max_rss_mb: 155.293 89MB 128thread base -> kops/s: 4.959 io_bytes/op: 6.20297e+06 miss_ratio: 0.323945 max_rss_mb: 823.43 89MB 128thread folly -> kops/s: 4.962 io_bytes/op: 5.9601e+06 miss_ratio: 0.319857 max_rss_mb: 626.824 89MB 128thread gt_clock -> kops/s: 1.009 io_bytes/op: 4.1083e+06 miss_ratio: 0.242512 max_rss_mb: 1095.32 89MB 128thread new_clock -> kops/s: 1224.39 io_bytes/op: 2688.2 miss_ratio: 0.0346207 max_rss_mb: 218.223 ^ Now something interesting has happened: the new clock cache has gained a dramatic lead in the single-threaded case, and this is because the cache is so small, and full filters are so big, that dividing the cache into 64 shards leads to significant (random) imbalances in cache shards and excessive churn in imbalanced shards. This new clock cache only uses two shards for this configuration, and that helps to ensure that entries are part of a sufficiently big pool that their eviction order resembles the single-shard order. (This effect is not seen with partitioned index+filters.) Even smaller cache size: 34MB 1thread base -> kops/s: 0.198 io_bytes/op: 1.65342e+07 miss_ratio: 0.939466 max_rss_mb: 48.6914 34MB 1thread folly -> kops/s: 0.201 io_bytes/op: 1.63416e+07 miss_ratio: 0.939081 max_rss_mb: 45.3281 34MB 1thread gt_clock -> kops/s: 0.448 io_bytes/op: 4.43957e+06 miss_ratio: 0.266749 max_rss_mb: 100.523 34MB 1thread new_clock -> kops/s: 1.055 io_bytes/op: 1.85439e+06 miss_ratio: 0.107512 max_rss_mb: 75.3125 34MB 32thread base -> kops/s: 3.346 io_bytes/op: 1.64852e+07 miss_ratio: 0.93596 max_rss_mb: 180.48 34MB 32thread folly -> kops/s: 3.431 io_bytes/op: 1.62857e+07 miss_ratio: 0.935693 max_rss_mb: 137.531 34MB 32thread gt_clock -> kops/s: 1.47 io_bytes/op: 4.89704e+06 miss_ratio: 0.295081 max_rss_mb: 392.465 34MB 32thread new_clock -> kops/s: 8.19 io_bytes/op: 3.70456e+06 miss_ratio: 0.20826 max_rss_mb: 519.793 34MB 128thread base -> kops/s: 2.293 io_bytes/op: 1.64351e+07 miss_ratio: 0.931866 max_rss_mb: 449.484 34MB 128thread folly -> kops/s: 2.34 io_bytes/op: 1.6219e+07 miss_ratio: 0.932023 max_rss_mb: 396.457 34MB 128thread gt_clock -> kops/s: 1.798 io_bytes/op: 5.4241e+06 miss_ratio: 0.324881 max_rss_mb: 1104.41 34MB 128thread new_clock -> kops/s: 10.519 io_bytes/op: 2.39354e+06 miss_ratio: 0.136147 max_rss_mb: 1050.52 As the miss ratio gets higher (say, above 10%), the CPU time spent in eviction starts to erode the advantage of using fewer shards (13% miss rate much lower than 94%). LRU's O(1) eviction time can eventually pay off when there's enough block cache churn: 13MB 1thread base -> kops/s: 0.195 io_bytes/op: 1.65732e+07 miss_ratio: 0.946604 max_rss_mb: 45.6328 13MB 1thread folly -> kops/s: 0.197 io_bytes/op: 1.63793e+07 miss_ratio: 0.94661 max_rss_mb: 33.8633 13MB 1thread gt_clock -> kops/s: 0.519 io_bytes/op: 4.43316e+06 miss_ratio: 0.269379 max_rss_mb: 100.684 13MB 1thread new_clock -> kops/s: 0.176 io_bytes/op: 1.54148e+07 miss_ratio: 0.91545 max_rss_mb: 66.2383 13MB 32thread base -> kops/s: 3.266 io_bytes/op: 1.65544e+07 miss_ratio: 0.943386 max_rss_mb: 132.492 13MB 32thread folly -> kops/s: 3.396 io_bytes/op: 1.63142e+07 miss_ratio: 0.943243 max_rss_mb: 101.863 13MB 32thread gt_clock -> kops/s: 2.758 io_bytes/op: 5.13714e+06 miss_ratio: 0.310652 max_rss_mb: 396.121 13MB 32thread new_clock -> kops/s: 3.11 io_bytes/op: 1.23419e+07 miss_ratio: 0.708425 max_rss_mb: 321.758 13MB 128thread base -> kops/s: 2.31 io_bytes/op: 1.64823e+07 miss_ratio: 0.939543 max_rss_mb: 425.539 13MB 128thread folly -> kops/s: 2.339 io_bytes/op: 1.6242e+07 miss_ratio: 0.939966 max_rss_mb: 346.098 13MB 128thread gt_clock -> kops/s: 3.223 io_bytes/op: 5.76928e+06 miss_ratio: 0.345899 max_rss_mb: 1087.77 13MB 128thread new_clock -> kops/s: 2.984 io_bytes/op: 1.05341e+07 miss_ratio: 0.606198 max_rss_mb: 898.27 gt_clock is clearly blowing way past its memory budget for lower miss rates and best throughput. new_clock also seems to be exceeding budgets, and this warrants more investigation but is not the use case we are targeting with the new cache. With partitioned index+filter, the miss ratio is much better, and although still high enough that the eviction CPU time is definitely offsetting mutex contention: 13MB 1thread base -> kops/s: 16.326 io_bytes/op: 23743.9 miss_ratio: 0.205362 max_rss_mb: 65.2852 13MB 1thread folly -> kops/s: 15.574 io_bytes/op: 19415 miss_ratio: 0.184157 max_rss_mb: 56.3516 13MB 1thread gt_clock -> kops/s: 14.459 io_bytes/op: 22873 miss_ratio: 0.198355 max_rss_mb: 63.9688 13MB 1thread new_clock -> kops/s: 16.34 io_bytes/op: 24386.5 miss_ratio: 0.210512 max_rss_mb: 61.707 13MB 128thread base -> kops/s: 289.786 io_bytes/op: 23710.9 miss_ratio: 0.205056 max_rss_mb: 103.57 13MB 128thread folly -> kops/s: 185.282 io_bytes/op: 19433.1 miss_ratio: 0.184275 max_rss_mb: 116.219 13MB 128thread gt_clock -> kops/s: 354.451 io_bytes/op: 23150.6 miss_ratio: 0.200495 max_rss_mb: 102.871 13MB 128thread new_clock -> kops/s: 295.359 io_bytes/op: 24626.4 miss_ratio: 0.212452 max_rss_mb: 121.109 Pull Request resolved: https://github.com/facebook/rocksdb/pull/10626 Test Plan: updated unit tests, stress/crash test runs including with TSAN, ASAN, UBSAN Reviewed By: anand1976 Differential Revision: D39368406 Pulled By: pdillinger fbshipit-source-id: 5afc44da4c656f8f751b44552bbf27bd3ca6fef9
2022-09-16 07:24:11 +00:00
} else if (name == "block_cache_entry_stats") {
// DB::Properties::kBlockCacheEntryStats
PrintStats("rocksdb.block-cache-entry-stats");
Automatic table sizing for HyperClockCache (AutoHCC) (#11738) Summary: This change add an experimental next-generation HyperClockCache (HCC) with automatic sizing of the underlying hash table. Both the existing version (stable) and the new version (experimental for now) of HCC are available depending on whether an estimated average entry charge is provided in HyperClockCacheOptions. Internally, we call the two implementations AutoHyperClockCache (new) and FixedHyperClockCache (existing). The performance characteristics and much of the underlying logic are similar enough that AutoHCC is likely to make FixedHCC obsolete, and so it's best considered an evolution of the same technology or solution rather than an alternative. More specifically, both implementations share essentially the same logic for managing the state of individual entries in the cache, including metadata for reference counting and counting clocks for eviction. This metadata, which I like to call the "low-level HCC protocol," includes a read-write lock on entries, but relaxed consistency requirements on the cache (e.g. allowing rare duplication) means high-level cache operations never wait for these low-level per-entry locks. FixedHCC is fully wait-free. AutoHCC is different in how entries are indexed into an efficient hash table. AutoHCC is "essentially wait-free" as there is no pattern of typical high-level operations on a large cache that can lead to one thread waiting on another to complete some work, though it can happen in some unusual/unlucky cases, or atypical uses such as erasing specific cache keys. Table growth and entry reclamation is more complex in AutoHCC compared to FixedHCC, so uses some localized locking to manage that. AutoHCC uses linear hashing to grow the table as needed, with low latency and to a precise size. AutoHCC depends on anonymous mmap support from the OS (currently verified working on Linux, MacOS, and Windows) to allow the array underlying a hash table to grow in place without wasting resident memory on space reserved but unused. AutoHCC uses a form of chaining while FixedHCC uses open addressing and double hashing. More specifics: * In developing this PR, a rare availability bug (minor) was noticed in the existing HCC implementation of Release()+erase_if_last_ref, which is now inherited into AutoHCC. Fixing this without a performance regression will not be simple, so is left for follow-up work. * Some existing unit tests required adjustment of operational parameters or conditions to work with the new behaviors of AutoHCC. A number of bugs were found and fixed in the validation process, including getting unit tests in good working order. * Added an option to cache_bench, `-degenerate_hash_bits` for correctness stress testing described below. For this, the tool uses the reverse-engineered hash function for HCC to generate keys in which the specified number of hash bits, in critical positions, have a fixed value. Essentially each degenerate hash bit will half the number of chain heads utilized and double the average chain length. Pull Request resolved: https://github.com/facebook/rocksdb/pull/11738 Test Plan: unit tests updated, and already added to db crash test. Also ## Correctness The code includes generous assertions to check for unexpected states, especially at destruction time, so should be able to detect critical concurrency bugs. Less serious "availability bugs" in which cache data is hidden or cleanly lost are more difficult to detect, but also less scary for data correctness (as long as performance is good and the design is sound). In average operation, the structure is extremely low stress and low contention (see next section) so stressing the corner case logic requires artificially stressing the operating conditions. First, we keep the structure small to increase the number of threads hitting the same chain or entry, and just one cache shard. Second, we artificially degrade the hashing so that chains are much longer than typical, using the new `-degenerate_hash_bits` option to cache_bench. Third, we re-create the structure from scratch frequently in order to exercise the Grow logic repeatedly and to get the benefit of the consistency checks in the structure's destructor in debug builds. For cache_bench this also means disabling the single-threaded "populate cache" step (normally used for steady state performance testing). And of course use many more threads than cores to have many preemptions. An effective test for working out bugs was this (using debug build of course): ``` while ./cache_bench -cache_type=auto_hyper_clock_cache -histograms=0 -cache_size=8000000 -threads=100 -populate_cache=0 -ops_per_thread=10000 -degenerate_hash_bits=6 -num_shard_bits=0; do :; done ``` Or even smaller cases. This setup has around 27 utilized chains, with around 35 entries each, and yield-waits more than 1 million times per second (very high contention; see next section). I have let this run for hours searching for any lingering issues. I've also run cache_bench under ASAN, UBSAN, and TSAN. ## Essentially wait free There is a counter for number of yield() calls when one thread is waiting on another. When we pre-populate the structure in a single thread, ``` ./cache_bench -cache_type=auto_hyper_clock_cache -histograms=0 -populate_cache=1 -ops_per_thread=200000 2>&1 | grep Yield ``` We see something on the order of 1 yield call per second across 16 threads, even when we load the system other other jobs (parallel compilation). With -populate_cache=0, there are more yield opportunities with parallel table growth. On an otherwise unloaded system, we still see very small (single digit) yield counts, with a chance of getting into the thousands, and getting into 10s of thousands per second during table growth phase if the system is loaded with other jobs. However, I am not worried about this if performance is still good (see next section). ## Overall performance Although cache_bench initially suggested performance very close to FixedHCC, there was a very noticeable performance hit under a db_bench setup like used in validating https://github.com/facebook/rocksdb/issues/10626. Much of the difference has been reduced by optimizing Lookup with a "naive" pass that will almost always find entries quickly, and only falling back to the careful Lookup algorithm when not found in the first pass. Setups (chosen to be sensitive to block cache performance), and compiled with USE_CLANG=1 JEMALLOC=1 PORTABLE=0 DEBUG_LEVEL=0: ``` TEST_TMPDIR=/dev/shm base/db_bench -benchmarks=fillrandom -num=30000000 -disable_wal=1 -bloom_bits=16 ``` ### No regression on FixedHCC Running before & after builds at the same time on a 48 core machine. ``` TEST_TMPDIR=/dev/shm /usr/bin/time ./db_bench -benchmarks=readrandom[-X10],block_cache_entry_stats,cache_report_problems -readonly -num=30000000 -bloom_bits=16 -cache_index_and_filter_blocks=1 -cache_size=610000000 -duration 20 -threads=24 -cache_type=fixed_hyper_clock_cache -seed=1234 ``` Before: readrandom [AVG 10 runs] : 847234 (± 8150) ops/sec; 59.2 (± 0.6) MB/sec 703MB max RSS After: readrandom [AVG 10 runs] : 851021 (± 7929) ops/sec; 59.5 (± 0.6) MB/sec 706MB max RSS Probably no material difference. ### Single-threaded performance Using `[-X2]` and `-threads=1` and `-duration=30`, running all three at the same time: lru_cache: 55100 ops/sec, then 55862 ops/sec (627MB max RSS) fixed_hyper_clock_cache: 60496 ops/sec, then 61231 ops/sec (626MB max RSS) auto_hyper_clock_cache: 47560 ops/sec, then 56081 ops/sec (626MB max RSS) So AutoHCC has more ramp-up cost in the first pass as the cache grows to the appropriate size. (In single-threaded operation, the parallelizability and per-op low latency of table growth is overall slower.) However, once up to size, its performance is comparable to LRUCache. FixedHCC's lean operations still win overall when a good estimate is available. If we look at HCC table stats, we can see that this configuration is not favorable to AutoHCC (and I have verified that other memory sizes do not yield substantially different results, until shards are under-sized for the full filters): FixedHCC: Slot occupancy stats: Overall 47% (124991/262144), Min/Max/Window = 28%/64%/500, MaxRun{Pos/Neg} = 17/22 AutoHCC: Slot occupancy stats: Overall 59% (125781/209682), Min/Max/Window = 43%/82%/500, MaxRun{Pos/Neg} = 76/16 Head occupancy stats: Overall 43% (92259/209682), Min/Max/Window = 24%/74%/500, MaxRun{Pos/Neg} = 19/26 Entries at home count: 53350 FixedHCC configuration is relatively good for speed, and not ideal for space utilization. As is typical, AutoHCC has tighter control on metadata usage (209682 x 64 bytes rather than 262144 x 64 bytes), and the higher load factor is slightly worse for speed. LRUCache also has more metadata usage, at 199680 x 96 bytes of tracked metadata (plus roughly another 10% of that untracked in the head pointers), and that metadata is subject to fragmentation. ### Parallel performance, high hit rate Now using `[-X10]` and `-threads=10`, all three at the same time lru_cache: [AVG 10 runs] : 263629 (± 1425) ops/sec; 18.4 (± 0.1) MB/sec 655MB max RSS, 97.1% cache hit rate fixed_hyper_clock_cache: [AVG 10 runs] : 479590 (± 8114) ops/sec; 33.5 (± 0.6) MB/sec 651MB max RSS, 97.1% cache hit rate auto_hyper_clock_cache: [AVG 10 runs] : 418687 (± 5915) ops/sec; 29.3 (± 0.4) MB/sec 657MB max RSS, 97.1% cache hit rate Even with just 10-way parallelism for each cache (though 30+/48 cores busy overall), LRUCache is already showing performance degradation, while AutoHCC is in the neighborhood of FixedHCC. And that brings us to the question of how AutoHCC holds up under extreme parallelism, so now independent runs with `-threads=100` (overloading 48 cores). lru_cache: 438613 ops/sec, 827MB max RSS fixed_hyper_clock_cache: 1651310 ops/sec, 812MB max RSS auto_hyper_clock_cache: 1505875 ops/sec, 821MB max RSS (Yield count: 1089 over 30s) Clearly, AutoHCC holds up extremely well under extreme parallelism, even closing some of the modest performance gap with FixedHCC. ### Parallel performance, low hit rate To get down to roughly 50% cache hit rate, we use `-cache_index_and_filter_blocks=0 -cache_size=1650000000` with `-threads=10`. Here the extra cost of running counting clock eviction, especially on the chains of AutoHCC, are evident, especially with the lower contention of cache_index_and_filter_blocks=0: lru_cache: 725231 ops/sec, 1770MB max RSS, 51.3% hit rate fixed_hyper_clock_cache: 638620 ops/sec, 1765MB max RSS, 50.2% hit rate auto_hyper_clock_cache: 541018 ops/sec, 1777MB max RSS, 50.8% hit rate Reviewed By: jowlyzhang Differential Revision: D48784755 Pulled By: pdillinger fbshipit-source-id: e79813dc087474ac427637dd282a14fa3011a6e4
2023-09-01 22:44:38 +00:00
} else if (name == "cache_report_problems") {
CacheReportProblems();
} else if (name == "stats") {
PrintStats("rocksdb.stats");
} else if (name == "resetstats") {
ResetStats();
} else if (name == "verify") {
VerifyDBFromDB(FLAGS_truth_db);
} else if (name == "levelstats") {
PrintStats("rocksdb.levelstats");
} else if (name == "memstats") {
std::vector<std::string> keys{"rocksdb.num-immutable-mem-table",
"rocksdb.cur-size-active-mem-table",
"rocksdb.cur-size-all-mem-tables",
"rocksdb.size-all-mem-tables",
"rocksdb.num-entries-active-mem-table",
"rocksdb.num-entries-imm-mem-tables"};
PrintStats(keys);
} else if (name == "sstables") {
PrintStats("rocksdb.sstables");
} else if (name == "stats_history") {
PrintStatsHistory();
} else if (name == "replay") {
if (num_threads > 1) {
fprintf(stderr, "Multi-threaded replay is not yet supported\n");
ErrorExit();
}
if (FLAGS_trace_file == "") {
fprintf(stderr, "Please set --trace_file to be replayed from\n");
ErrorExit();
}
method = &Benchmark::Replay;
New API to get all merge operands for a Key (#5604) Summary: This is a new API added to db.h to allow for fetching all merge operands associated with a Key. The main motivation for this API is to support use cases where doing a full online merge is not necessary as it is performance sensitive. Example use-cases: 1. Update subset of columns and read subset of columns - Imagine a SQL Table, a row is encoded as a K/V pair (as it is done in MyRocks). If there are many columns and users only updated one of them, we can use merge operator to reduce write amplification. While users only read one or two columns in the read query, this feature can avoid a full merging of the whole row, and save some CPU. 2. Updating very few attributes in a value which is a JSON-like document - Updating one attribute can be done efficiently using merge operator, while reading back one attribute can be done more efficiently if we don't need to do a full merge. ---------------------------------------------------------------------------------------------------- API : Status GetMergeOperands( const ReadOptions& options, ColumnFamilyHandle* column_family, const Slice& key, PinnableSlice* merge_operands, GetMergeOperandsOptions* get_merge_operands_options, int* number_of_operands) Example usage : int size = 100; int number_of_operands = 0; std::vector<PinnableSlice> values(size); GetMergeOperandsOptions merge_operands_info; db_->GetMergeOperands(ReadOptions(), db_->DefaultColumnFamily(), "k1", values.data(), merge_operands_info, &number_of_operands); Description : Returns all the merge operands corresponding to the key. If the number of merge operands in DB is greater than merge_operands_options.expected_max_number_of_operands no merge operands are returned and status is Incomplete. Merge operands returned are in the order of insertion. merge_operands-> Points to an array of at-least merge_operands_options.expected_max_number_of_operands and the caller is responsible for allocating it. If the status returned is Incomplete then number_of_operands will contain the total number of merge operands found in DB for key. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5604 Test Plan: Added unit test and perf test in db_bench that can be run using the command: ./db_bench -benchmarks=getmergeoperands --merge_operator=sortlist Differential Revision: D16657366 Pulled By: vjnadimpalli fbshipit-source-id: 0faadd752351745224ee12d4ae9ef3cb529951bf
2019-08-06 21:22:34 +00:00
} else if (name == "getmergeoperands") {
method = &Benchmark::GetMergeOperands;
Add rate limiter priority to ReadOptions (#9424) Summary: Users can set the priority for file reads associated with their operation by setting `ReadOptions::rate_limiter_priority` to something other than `Env::IO_TOTAL`. Rate limiting `VerifyChecksum()` and `VerifyFileChecksums()` is the motivation for this PR, so it also includes benchmarks and minor bug fixes to get that working. `RandomAccessFileReader::Read()` already had support for rate limiting compaction reads. I changed that rate limiting to be non-specific to compaction, but rather performed according to the passed in `Env::IOPriority`. Now the compaction read rate limiting is supported by setting `rate_limiter_priority = Env::IO_LOW` on its `ReadOptions`. There is no default value for the new `Env::IOPriority` parameter to `RandomAccessFileReader::Read()`. That means this PR goes through all callers (in some cases multiple layers up the call stack) to find a `ReadOptions` to provide the priority. There are TODOs for cases I believe it would be good to let user control the priority some day (e.g., file footer reads), and no TODO in cases I believe it doesn't matter (e.g., trace file reads). The API doc only lists the missing cases where a file read associated with a provided `ReadOptions` cannot be rate limited. For cases like file ingestion checksum calculation, there is no API to provide `ReadOptions` or `Env::IOPriority`, so I didn't count that as missing. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9424 Test Plan: - new unit tests - new benchmarks on ~50MB database with 1MB/s read rate limit and 100ms refill interval; verified with strace reads are chunked (at 0.1MB per chunk) and spaced roughly 100ms apart. - setup command: `./db_bench -benchmarks=fillrandom,compact -db=/tmp/testdb -target_file_size_base=1048576 -disable_auto_compactions=true -file_checksum=true` - benchmarks command: `strace -ttfe pread64 ./db_bench -benchmarks=verifychecksum,verifyfilechecksums -use_existing_db=true -db=/tmp/testdb -rate_limiter_bytes_per_sec=1048576 -rate_limit_bg_reads=1 -rate_limit_user_ops=true -file_checksum=true` - crash test using IO_USER priority on non-validation reads with https://github.com/facebook/rocksdb/issues/9567 reverted: `python3 tools/db_crashtest.py blackbox --max_key=1000000 --write_buffer_size=524288 --target_file_size_base=524288 --level_compaction_dynamic_level_bytes=true --duration=3600 --rate_limit_bg_reads=true --rate_limit_user_ops=true --rate_limiter_bytes_per_sec=10485760 --interval=10` Reviewed By: hx235 Differential Revision: D33747386 Pulled By: ajkr fbshipit-source-id: a2d985e97912fba8c54763798e04f006ccc56e0c
2022-02-17 07:17:03 +00:00
} else if (name == "verifychecksum") {
method = &Benchmark::VerifyChecksum;
} else if (name == "verifyfilechecksums") {
method = &Benchmark::VerifyFileChecksums;
} else if (name == "readrandomoperands") {
read_operands_ = true;
method = &Benchmark::ReadRandom;
Support read rate-limiting in SequentialFileReader (#9973) Summary: Added rate limiter and read rate-limiting support to SequentialFileReader. I've updated call sites to SequentialFileReader::Read with appropriate IO priority (or left a TODO and specified IO_TOTAL for now). The PR is separated into four commits: the first one added the rate-limiting support, but with some fixes in the unit test since the number of request bytes from rate limiter in SequentialFileReader are not accurate (there is overcharge at EOF). The second commit fixed this by allowing SequentialFileReader to check file size and determine how many bytes are left in the file to read. The third commit added benchmark related code. The fourth commit moved the logic of using file size to avoid overcharging the rate limiter into backup engine (the main user of SequentialFileReader). Pull Request resolved: https://github.com/facebook/rocksdb/pull/9973 Test Plan: - `make check`, backup_engine_test covers usage of SequentialFileReader with rate limiter. - Run db_bench to check if rate limiting is throttling as expected: Verified that reads and writes are together throttled at 2MB/s, and at 0.2MB chunks that are 100ms apart. - Set up: `./db_bench --benchmarks=fillrandom -db=/dev/shm/test_rocksdb` - Benchmark: ``` strace -ttfe read,write ./db_bench --benchmarks=backup -db=/dev/shm/test_rocksdb --backup_rate_limit=2097152 --use_existing_db strace -ttfe read,write ./db_bench --benchmarks=restore -db=/dev/shm/test_rocksdb --restore_rate_limit=2097152 --use_existing_db ``` - db bench on backup and restore to ensure no performance regression. - backup (avg over 50 runs): pre-change: 1.90443e+06 micros/op; post-change: 1.8993e+06 micros/op (improve by 0.2%) - restore (avg over 50 runs): pre-change: 1.79105e+06 micros/op; post-change: 1.78192e+06 micros/op (improve by 0.5%) ``` # Set up ./db_bench --benchmarks=fillrandom -db=/tmp/test_rocksdb -num=10000000 # benchmark TEST_TMPDIR=/tmp/test_rocksdb NUM_RUN=50 for ((j=0;j<$NUM_RUN;j++)) do ./db_bench -db=$TEST_TMPDIR -num=10000000 -benchmarks=backup -use_existing_db | egrep 'backup' # Restore #./db_bench -db=$TEST_TMPDIR -num=10000000 -benchmarks=restore -use_existing_db done > rate_limit.txt && awk -v NUM_RUN=$NUM_RUN '{sum+=$3;sum_sqrt+=$3^2}END{print sum/NUM_RUN, sqrt(sum_sqrt/NUM_RUN-(sum/NUM_RUN)^2)}' rate_limit.txt >> rate_limit_2.txt ``` Reviewed By: hx235 Differential Revision: D36327418 Pulled By: cbi42 fbshipit-source-id: e75d4307cff815945482df5ba630c1e88d064691
2022-05-24 17:28:57 +00:00
} else if (name == "backup") {
method = &Benchmark::Backup;
} else if (name == "restore") {
method = &Benchmark::Restore;
} else if (!name.empty()) { // No error message for empty name
fprintf(stderr, "unknown benchmark '%s'\n", name.c_str());
ErrorExit();
}
if (fresh_db) {
if (FLAGS_use_existing_db) {
fprintf(stdout, "%-12s : skipped (--use_existing_db is true)\n",
name.c_str());
method = nullptr;
} else {
if (db_.db != nullptr) {
db_.DeleteDBs();
DestroyDB(FLAGS_db, open_options_);
}
Options options = open_options_;
for (size_t i = 0; i < multi_dbs_.size(); i++) {
delete multi_dbs_[i].db;
if (!open_options_.wal_dir.empty()) {
options.wal_dir = GetPathForMultiple(open_options_.wal_dir, i);
}
DestroyDB(GetPathForMultiple(FLAGS_db, i), options);
}
multi_dbs_.clear();
}
Open(&open_options_); // use open_options for the last accessed
}
if (method != nullptr) {
fprintf(stdout, "DB path: [%s]\n", FLAGS_db.c_str());
Support read rate-limiting in SequentialFileReader (#9973) Summary: Added rate limiter and read rate-limiting support to SequentialFileReader. I've updated call sites to SequentialFileReader::Read with appropriate IO priority (or left a TODO and specified IO_TOTAL for now). The PR is separated into four commits: the first one added the rate-limiting support, but with some fixes in the unit test since the number of request bytes from rate limiter in SequentialFileReader are not accurate (there is overcharge at EOF). The second commit fixed this by allowing SequentialFileReader to check file size and determine how many bytes are left in the file to read. The third commit added benchmark related code. The fourth commit moved the logic of using file size to avoid overcharging the rate limiter into backup engine (the main user of SequentialFileReader). Pull Request resolved: https://github.com/facebook/rocksdb/pull/9973 Test Plan: - `make check`, backup_engine_test covers usage of SequentialFileReader with rate limiter. - Run db_bench to check if rate limiting is throttling as expected: Verified that reads and writes are together throttled at 2MB/s, and at 0.2MB chunks that are 100ms apart. - Set up: `./db_bench --benchmarks=fillrandom -db=/dev/shm/test_rocksdb` - Benchmark: ``` strace -ttfe read,write ./db_bench --benchmarks=backup -db=/dev/shm/test_rocksdb --backup_rate_limit=2097152 --use_existing_db strace -ttfe read,write ./db_bench --benchmarks=restore -db=/dev/shm/test_rocksdb --restore_rate_limit=2097152 --use_existing_db ``` - db bench on backup and restore to ensure no performance regression. - backup (avg over 50 runs): pre-change: 1.90443e+06 micros/op; post-change: 1.8993e+06 micros/op (improve by 0.2%) - restore (avg over 50 runs): pre-change: 1.79105e+06 micros/op; post-change: 1.78192e+06 micros/op (improve by 0.5%) ``` # Set up ./db_bench --benchmarks=fillrandom -db=/tmp/test_rocksdb -num=10000000 # benchmark TEST_TMPDIR=/tmp/test_rocksdb NUM_RUN=50 for ((j=0;j<$NUM_RUN;j++)) do ./db_bench -db=$TEST_TMPDIR -num=10000000 -benchmarks=backup -use_existing_db | egrep 'backup' # Restore #./db_bench -db=$TEST_TMPDIR -num=10000000 -benchmarks=restore -use_existing_db done > rate_limit.txt && awk -v NUM_RUN=$NUM_RUN '{sum+=$3;sum_sqrt+=$3^2}END{print sum/NUM_RUN, sqrt(sum_sqrt/NUM_RUN-(sum/NUM_RUN)^2)}' rate_limit.txt >> rate_limit_2.txt ``` Reviewed By: hx235 Differential Revision: D36327418 Pulled By: cbi42 fbshipit-source-id: e75d4307cff815945482df5ba630c1e88d064691
2022-05-24 17:28:57 +00:00
if (name == "backup") {
std::cout << "Backup path: [" << FLAGS_backup_dir << "]" << std::endl;
} else if (name == "restore") {
std::cout << "Backup path: [" << FLAGS_backup_dir << "]" << std::endl;
std::cout << "Restore path: [" << FLAGS_restore_dir << "]"
<< std::endl;
}
// A trace_file option can be provided both for trace and replay
// operations. But db_bench does not support tracing and replaying at
// the same time, for now. So, start tracing only when it is not a
// replay.
if (FLAGS_trace_file != "" && name != "replay") {
std::unique_ptr<TraceWriter> trace_writer;
Status s = NewFileTraceWriter(FLAGS_env, EnvOptions(),
FLAGS_trace_file, &trace_writer);
if (!s.ok()) {
fprintf(stderr, "Encountered an error starting a trace, %s\n",
s.ToString().c_str());
ErrorExit();
}
s = db_.db->StartTrace(trace_options_, std::move(trace_writer));
if (!s.ok()) {
fprintf(stderr, "Encountered an error starting a trace, %s\n",
s.ToString().c_str());
ErrorExit();
}
fprintf(stdout, "Tracing the workload to: [%s]\n",
FLAGS_trace_file.c_str());
}
// Start block cache tracing.
if (!FLAGS_block_cache_trace_file.empty()) {
// Sanity checks.
if (FLAGS_block_cache_trace_sampling_frequency <= 0) {
fprintf(stderr,
"Block cache trace sampling frequency must be higher than "
"0.\n");
ErrorExit();
}
if (FLAGS_block_cache_trace_max_trace_file_size_in_bytes <= 0) {
fprintf(stderr,
"The maximum file size for block cache tracing must be "
"higher than 0.\n");
ErrorExit();
}
block_cache_trace_options_.max_trace_file_size =
FLAGS_block_cache_trace_max_trace_file_size_in_bytes;
block_cache_trace_options_.sampling_frequency =
FLAGS_block_cache_trace_sampling_frequency;
std::unique_ptr<TraceWriter> block_cache_trace_writer;
Status s = NewFileTraceWriter(FLAGS_env, EnvOptions(),
FLAGS_block_cache_trace_file,
&block_cache_trace_writer);
if (!s.ok()) {
fprintf(stderr,
"Encountered an error when creating trace writer, %s\n",
s.ToString().c_str());
ErrorExit();
}
s = db_.db->StartBlockCacheTrace(block_cache_trace_options_,
std::move(block_cache_trace_writer));
if (!s.ok()) {
fprintf(
stderr,
"Encountered an error when starting block cache tracing, %s\n",
s.ToString().c_str());
ErrorExit();
}
fprintf(stdout, "Tracing block cache accesses to: [%s]\n",
FLAGS_block_cache_trace_file.c_str());
}
[db_bench] Support single benchmark arguments (Repeat for X times, Warm up for X times), Support CombinedStats (AVG / MEDIAN) Summary: This diff allow us to run a single benchmark X times and warm it up for Y times. and see the AVG & MEDIAN throughput of these X runs for example ``` $ ./db_bench --benchmarks="fillseq,readseq[X5-W2]" Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags RocksDB: version 4.12 Date: Wed Aug 24 10:45:26 2016 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Compression: Snappy Memtablerep: skip_list Perf Level: 1 WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags DB path: [/tmp/rocksdbtest-8616/dbbench] fillseq : 4.695 micros/op 212971 ops/sec; 23.6 MB/s DB path: [/tmp/rocksdbtest-8616/dbbench] Warming up benchmark by running 2 times readseq : 0.214 micros/op 4677005 ops/sec; 517.4 MB/s readseq : 0.212 micros/op 4706834 ops/sec; 520.7 MB/s Running benchmark for 5 times readseq : 0.218 micros/op 4588187 ops/sec; 507.6 MB/s readseq : 0.208 micros/op 4816538 ops/sec; 532.8 MB/s readseq : 0.213 micros/op 4685376 ops/sec; 518.3 MB/s readseq : 0.214 micros/op 4676787 ops/sec; 517.4 MB/s readseq : 0.217 micros/op 4618532 ops/sec; 510.9 MB/s readseq [AVG 5 runs] : 4677084 ops/sec; 517.4 MB/sec readseq [MEDIAN 5 runs] : 4676787 ops/sec; 517.4 MB/sec ``` Test Plan: run db_bench Reviewers: sdong, andrewkr, yhchiang Reviewed By: yhchiang Subscribers: andrewkr, dhruba Differential Revision: https://reviews.facebook.net/D62235
2016-08-25 19:57:35 +00:00
if (num_warmup > 0) {
printf("Warming up benchmark by running %d times\n", num_warmup);
}
for (int i = 0; i < num_warmup; i++) {
RunBenchmark(num_threads, name, method);
}
if (num_repeat > 1) {
printf("Running benchmark for %d times\n", num_repeat);
}
CombinedStats combined_stats;
for (int i = 0; i < num_repeat; i++) {
Stats stats = RunBenchmark(num_threads, name, method);
combined_stats.AddStats(stats);
Add 95% confidence intervals to db_bench output (#9882) Summary: Enhancing `db_bench` output with 95% statistical confidence intervals for better performance evaluation. The goal is to unambiguously separate random variance when running benchmark over multiple iterations. Output enhanced with confidence intervals exposed in brackets: ``` $ ./db_bench --benchmarks=fillseq[-X10] Running benchmark for 10 times fillseq : 4.961 micros/op 201578 ops/sec; 22.3 MB/s fillseq : 5.030 micros/op 198824 ops/sec; 22.0 MB/s fillseq [AVG 2 runs] : 200201 (± 2698) ops/sec; 22.1 (± 0.3) MB/sec fillseq : 4.963 micros/op 201471 ops/sec; 22.3 MB/s fillseq [AVG 3 runs] : 200624 (± 1765) ops/sec; 22.2 (± 0.2) MB/sec fillseq : 5.035 micros/op 198625 ops/sec; 22.0 MB/s fillseq [AVG 4 runs] : 200124 (± 1586) ops/sec; 22.1 (± 0.2) MB/sec fillseq : 4.979 micros/op 200861 ops/sec; 22.2 MB/s fillseq [AVG 5 runs] : 200272 (± 1262) ops/sec; 22.2 (± 0.1) MB/sec fillseq : 4.893 micros/op 204367 ops/sec; 22.6 MB/s fillseq [AVG 6 runs] : 200954 (± 1688) ops/sec; 22.2 (± 0.2) MB/sec fillseq : 4.914 micros/op 203502 ops/sec; 22.5 MB/s fillseq [AVG 7 runs] : 201318 (± 1595) ops/sec; 22.3 (± 0.2) MB/sec fillseq : 4.998 micros/op 200074 ops/sec; 22.1 MB/s fillseq [AVG 8 runs] : 201163 (± 1415) ops/sec; 22.3 (± 0.2) MB/sec fillseq : 4.946 micros/op 202188 ops/sec; 22.4 MB/s fillseq [AVG 9 runs] : 201277 (± 1267) ops/sec; 22.3 (± 0.1) MB/sec fillseq : 5.093 micros/op 196331 ops/sec; 21.7 MB/s fillseq [AVG 10 runs] : 200782 (± 1491) ops/sec; 22.2 (± 0.2) MB/sec fillseq [AVG 10 runs] : 200782 (± 1491) ops/sec; 22.2 (± 0.2) MB/sec fillseq [MEDIAN 10 runs] : 201166 ops/sec; 22.3 MB/s ``` For more explicit interval representation, use `--confidence_interval_only` flag: ``` $ ./db_bench --benchmarks=fillseq[-X10] --confidence_interval_only Running benchmark for 10 times fillseq : 4.935 micros/op 202648 ops/sec; 22.4 MB/s fillseq : 5.078 micros/op 196943 ops/sec; 21.8 MB/s fillseq [CI95 2 runs] : (194205, 205385) ops/sec; (21.5, 22.7) MB/sec fillseq : 5.159 micros/op 193816 ops/sec; 21.4 MB/s fillseq [CI95 3 runs] : (192735, 202869) ops/sec; (21.3, 22.4) MB/sec fillseq : 4.947 micros/op 202158 ops/sec; 22.4 MB/s fillseq [CI95 4 runs] : (194721, 203061) ops/sec; (21.5, 22.5) MB/sec fillseq : 4.908 micros/op 203756 ops/sec; 22.5 MB/s fillseq [CI95 5 runs] : (196113, 203615) ops/sec; (21.7, 22.5) MB/sec fillseq : 5.063 micros/op 197528 ops/sec; 21.9 MB/s fillseq [CI95 6 runs] : (196319, 202631) ops/sec; (21.7, 22.4) MB/sec fillseq : 5.214 micros/op 191799 ops/sec; 21.2 MB/s fillseq [CI95 7 runs] : (194953, 201803) ops/sec; (21.6, 22.3) MB/sec fillseq : 5.260 micros/op 190095 ops/sec; 21.0 MB/s fillseq [CI95 8 runs] : (193749, 200937) ops/sec; (21.4, 22.2) MB/sec fillseq : 5.076 micros/op 196992 ops/sec; 21.8 MB/s fillseq [CI95 9 runs] : (194134, 200474) ops/sec; (21.5, 22.2) MB/sec fillseq : 5.388 micros/op 185603 ops/sec; 20.5 MB/s fillseq [CI95 10 runs] : (192487, 199781) ops/sec; (21.3, 22.1) MB/sec fillseq [AVG 10 runs] : 196134 (± 3647) ops/sec; 21.7 (± 0.4) MB/sec fillseq [MEDIAN 10 runs] : 196968 ops/sec; 21.8 MB/sec ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/9882 Reviewed By: pdillinger Differential Revision: D35796148 Pulled By: vanekjar fbshipit-source-id: 8313712d16728ff982b8aff28195ee56622385b8
2022-04-25 21:49:54 +00:00
if (FLAGS_confidence_interval_only) {
combined_stats.ReportWithConfidenceIntervals(name);
} else {
combined_stats.Report(name);
}
[db_bench] Support single benchmark arguments (Repeat for X times, Warm up for X times), Support CombinedStats (AVG / MEDIAN) Summary: This diff allow us to run a single benchmark X times and warm it up for Y times. and see the AVG & MEDIAN throughput of these X runs for example ``` $ ./db_bench --benchmarks="fillseq,readseq[X5-W2]" Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags RocksDB: version 4.12 Date: Wed Aug 24 10:45:26 2016 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Compression: Snappy Memtablerep: skip_list Perf Level: 1 WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags DB path: [/tmp/rocksdbtest-8616/dbbench] fillseq : 4.695 micros/op 212971 ops/sec; 23.6 MB/s DB path: [/tmp/rocksdbtest-8616/dbbench] Warming up benchmark by running 2 times readseq : 0.214 micros/op 4677005 ops/sec; 517.4 MB/s readseq : 0.212 micros/op 4706834 ops/sec; 520.7 MB/s Running benchmark for 5 times readseq : 0.218 micros/op 4588187 ops/sec; 507.6 MB/s readseq : 0.208 micros/op 4816538 ops/sec; 532.8 MB/s readseq : 0.213 micros/op 4685376 ops/sec; 518.3 MB/s readseq : 0.214 micros/op 4676787 ops/sec; 517.4 MB/s readseq : 0.217 micros/op 4618532 ops/sec; 510.9 MB/s readseq [AVG 5 runs] : 4677084 ops/sec; 517.4 MB/sec readseq [MEDIAN 5 runs] : 4676787 ops/sec; 517.4 MB/sec ``` Test Plan: run db_bench Reviewers: sdong, andrewkr, yhchiang Reviewed By: yhchiang Subscribers: andrewkr, dhruba Differential Revision: https://reviews.facebook.net/D62235
2016-08-25 19:57:35 +00:00
}
if (num_repeat > 1) {
Add 95% confidence intervals to db_bench output (#9882) Summary: Enhancing `db_bench` output with 95% statistical confidence intervals for better performance evaluation. The goal is to unambiguously separate random variance when running benchmark over multiple iterations. Output enhanced with confidence intervals exposed in brackets: ``` $ ./db_bench --benchmarks=fillseq[-X10] Running benchmark for 10 times fillseq : 4.961 micros/op 201578 ops/sec; 22.3 MB/s fillseq : 5.030 micros/op 198824 ops/sec; 22.0 MB/s fillseq [AVG 2 runs] : 200201 (± 2698) ops/sec; 22.1 (± 0.3) MB/sec fillseq : 4.963 micros/op 201471 ops/sec; 22.3 MB/s fillseq [AVG 3 runs] : 200624 (± 1765) ops/sec; 22.2 (± 0.2) MB/sec fillseq : 5.035 micros/op 198625 ops/sec; 22.0 MB/s fillseq [AVG 4 runs] : 200124 (± 1586) ops/sec; 22.1 (± 0.2) MB/sec fillseq : 4.979 micros/op 200861 ops/sec; 22.2 MB/s fillseq [AVG 5 runs] : 200272 (± 1262) ops/sec; 22.2 (± 0.1) MB/sec fillseq : 4.893 micros/op 204367 ops/sec; 22.6 MB/s fillseq [AVG 6 runs] : 200954 (± 1688) ops/sec; 22.2 (± 0.2) MB/sec fillseq : 4.914 micros/op 203502 ops/sec; 22.5 MB/s fillseq [AVG 7 runs] : 201318 (± 1595) ops/sec; 22.3 (± 0.2) MB/sec fillseq : 4.998 micros/op 200074 ops/sec; 22.1 MB/s fillseq [AVG 8 runs] : 201163 (± 1415) ops/sec; 22.3 (± 0.2) MB/sec fillseq : 4.946 micros/op 202188 ops/sec; 22.4 MB/s fillseq [AVG 9 runs] : 201277 (± 1267) ops/sec; 22.3 (± 0.1) MB/sec fillseq : 5.093 micros/op 196331 ops/sec; 21.7 MB/s fillseq [AVG 10 runs] : 200782 (± 1491) ops/sec; 22.2 (± 0.2) MB/sec fillseq [AVG 10 runs] : 200782 (± 1491) ops/sec; 22.2 (± 0.2) MB/sec fillseq [MEDIAN 10 runs] : 201166 ops/sec; 22.3 MB/s ``` For more explicit interval representation, use `--confidence_interval_only` flag: ``` $ ./db_bench --benchmarks=fillseq[-X10] --confidence_interval_only Running benchmark for 10 times fillseq : 4.935 micros/op 202648 ops/sec; 22.4 MB/s fillseq : 5.078 micros/op 196943 ops/sec; 21.8 MB/s fillseq [CI95 2 runs] : (194205, 205385) ops/sec; (21.5, 22.7) MB/sec fillseq : 5.159 micros/op 193816 ops/sec; 21.4 MB/s fillseq [CI95 3 runs] : (192735, 202869) ops/sec; (21.3, 22.4) MB/sec fillseq : 4.947 micros/op 202158 ops/sec; 22.4 MB/s fillseq [CI95 4 runs] : (194721, 203061) ops/sec; (21.5, 22.5) MB/sec fillseq : 4.908 micros/op 203756 ops/sec; 22.5 MB/s fillseq [CI95 5 runs] : (196113, 203615) ops/sec; (21.7, 22.5) MB/sec fillseq : 5.063 micros/op 197528 ops/sec; 21.9 MB/s fillseq [CI95 6 runs] : (196319, 202631) ops/sec; (21.7, 22.4) MB/sec fillseq : 5.214 micros/op 191799 ops/sec; 21.2 MB/s fillseq [CI95 7 runs] : (194953, 201803) ops/sec; (21.6, 22.3) MB/sec fillseq : 5.260 micros/op 190095 ops/sec; 21.0 MB/s fillseq [CI95 8 runs] : (193749, 200937) ops/sec; (21.4, 22.2) MB/sec fillseq : 5.076 micros/op 196992 ops/sec; 21.8 MB/s fillseq [CI95 9 runs] : (194134, 200474) ops/sec; (21.5, 22.2) MB/sec fillseq : 5.388 micros/op 185603 ops/sec; 20.5 MB/s fillseq [CI95 10 runs] : (192487, 199781) ops/sec; (21.3, 22.1) MB/sec fillseq [AVG 10 runs] : 196134 (± 3647) ops/sec; 21.7 (± 0.4) MB/sec fillseq [MEDIAN 10 runs] : 196968 ops/sec; 21.8 MB/sec ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/9882 Reviewed By: pdillinger Differential Revision: D35796148 Pulled By: vanekjar fbshipit-source-id: 8313712d16728ff982b8aff28195ee56622385b8
2022-04-25 21:49:54 +00:00
combined_stats.ReportFinal(name);
[db_bench] Support single benchmark arguments (Repeat for X times, Warm up for X times), Support CombinedStats (AVG / MEDIAN) Summary: This diff allow us to run a single benchmark X times and warm it up for Y times. and see the AVG & MEDIAN throughput of these X runs for example ``` $ ./db_bench --benchmarks="fillseq,readseq[X5-W2]" Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags RocksDB: version 4.12 Date: Wed Aug 24 10:45:26 2016 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Compression: Snappy Memtablerep: skip_list Perf Level: 1 WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags DB path: [/tmp/rocksdbtest-8616/dbbench] fillseq : 4.695 micros/op 212971 ops/sec; 23.6 MB/s DB path: [/tmp/rocksdbtest-8616/dbbench] Warming up benchmark by running 2 times readseq : 0.214 micros/op 4677005 ops/sec; 517.4 MB/s readseq : 0.212 micros/op 4706834 ops/sec; 520.7 MB/s Running benchmark for 5 times readseq : 0.218 micros/op 4588187 ops/sec; 507.6 MB/s readseq : 0.208 micros/op 4816538 ops/sec; 532.8 MB/s readseq : 0.213 micros/op 4685376 ops/sec; 518.3 MB/s readseq : 0.214 micros/op 4676787 ops/sec; 517.4 MB/s readseq : 0.217 micros/op 4618532 ops/sec; 510.9 MB/s readseq [AVG 5 runs] : 4677084 ops/sec; 517.4 MB/sec readseq [MEDIAN 5 runs] : 4676787 ops/sec; 517.4 MB/sec ``` Test Plan: run db_bench Reviewers: sdong, andrewkr, yhchiang Reviewed By: yhchiang Subscribers: andrewkr, dhruba Differential Revision: https://reviews.facebook.net/D62235
2016-08-25 19:57:35 +00:00
}
}
if (post_process_method != nullptr) {
(this->*post_process_method)();
}
}
if (secondary_update_thread_) {
secondary_update_stopped_.store(1, std::memory_order_relaxed);
secondary_update_thread_->join();
secondary_update_thread_.reset();
}
if (name != "replay" && FLAGS_trace_file != "") {
Status s = db_.db->EndTrace();
if (!s.ok()) {
fprintf(stderr, "Encountered an error ending the trace, %s\n",
s.ToString().c_str());
}
}
if (!FLAGS_block_cache_trace_file.empty()) {
Status s = db_.db->EndBlockCacheTrace();
if (!s.ok()) {
fprintf(stderr,
"Encountered an error ending the block cache tracing, %s\n",
s.ToString().c_str());
}
}
if (FLAGS_statistics) {
fprintf(stdout, "STATISTICS:\n%s\n", dbstats->ToString().c_str());
}
if (FLAGS_simcache_size >= 0) {
fprintf(
stdout, "SIMULATOR CACHE STATISTICS:\n%s\n",
static_cast_with_check<SimCache>(cache_.get())->ToString().c_str());
add simulator Cache as class SimCache/SimLRUCache(with test) Summary: add class SimCache(base class with instrumentation api) and SimLRUCache(derived class with detailed implementation) which is used as an instrumented block cache that can predict hit rate for different cache size Test Plan: Add a test case in `db_block_cache_test.cc` called `SimCacheTest` to test basic logic of SimCache. Also add option `-simcache_size` in db_bench. if set with a value other than -1, then the benchmark will use this value as the size of the simulator cache and finally output the simulation result. ``` [gzh@dev9927.prn1 ~/local/rocksdb] ./db_bench -benchmarks "fillseq,readrandom" -cache_size 1000000 -simcache_size 1000000 RocksDB: version 4.8 Date: Tue May 17 16:56:16 2016 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Compression: Snappy Memtablerep: skip_list Perf Level: 0 WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ DB path: [/tmp/rocksdbtest-112628/dbbench] fillseq : 6.809 micros/op 146874 ops/sec; 16.2 MB/s DB path: [/tmp/rocksdbtest-112628/dbbench] readrandom : 6.343 micros/op 157665 ops/sec; 17.4 MB/s (1000000 of 1000000 found) SIMULATOR CACHE STATISTICS: SimCache LOOKUPs: 986559 SimCache HITs: 264760 SimCache HITRATE: 26.84% [gzh@dev9927.prn1 ~/local/rocksdb] ./db_bench -benchmarks "fillseq,readrandom" -cache_size 1000000 -simcache_size 10000000 RocksDB: version 4.8 Date: Tue May 17 16:57:10 2016 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Compression: Snappy Memtablerep: skip_list Perf Level: 0 WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ DB path: [/tmp/rocksdbtest-112628/dbbench] fillseq : 5.066 micros/op 197394 ops/sec; 21.8 MB/s DB path: [/tmp/rocksdbtest-112628/dbbench] readrandom : 6.457 micros/op 154870 ops/sec; 17.1 MB/s (1000000 of 1000000 found) SIMULATOR CACHE STATISTICS: SimCache LOOKUPs: 1059764 SimCache HITs: 374501 SimCache HITRATE: 35.34% [gzh@dev9927.prn1 ~/local/rocksdb] ./db_bench -benchmarks "fillseq,readrandom" -cache_size 1000000 -simcache_size 100000000 RocksDB: version 4.8 Date: Tue May 17 16:57:32 2016 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Compression: Snappy Memtablerep: skip_list Perf Level: 0 WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ DB path: [/tmp/rocksdbtest-112628/dbbench] fillseq : 5.632 micros/op 177572 ops/sec; 19.6 MB/s DB path: [/tmp/rocksdbtest-112628/dbbench] readrandom : 6.892 micros/op 145094 ops/sec; 16.1 MB/s (1000000 of 1000000 found) SIMULATOR CACHE STATISTICS: SimCache LOOKUPs: 1150767 SimCache HITs: 1034535 SimCache HITRATE: 89.90% ``` Reviewers: IslamAbdelRahman, andrewkr, sdong Reviewed By: sdong Subscribers: MarkCallaghan, andrewkr, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D57999
2016-05-24 06:35:23 +00:00
}
if (FLAGS_use_secondary_db) {
fprintf(stdout, "Secondary instance updated %" PRIu64 " times.\n",
secondary_db_updates_);
}
}
private:
Write a benchmark to emulate time series data Summary: Add a benchmark to `db_bench`. In this benchmark, a write thread will populate time series data in the format of 'id | timestamp', and multiple read threads will randomly retrieve all data from one id at a time. Test Plan: Run the benchmark: `num=134217728;bpl=536870912;mb=67108864;overlap=10;mcz=2;del=300000000;levels=6;ctrig=4;delay=8;stop=12;wbn=3;mbc=20;wbs=134217728;dds=0;sync=0;t=32;vs=800;bs=4096;cs=17179869184;of=500000;wps=0;si=10000000; kir=100000; dir=/data/users/jhli/test/; ./db_bench --benchmarks=timeseries --disable_seek_compaction=1 --mmap_read=0 --statistics=1 --histogram=1 --num=$num --threads=$t --value_size=$vs --block_size=$bs --cache_size=$cs --bloom_bits=10 --cache_numshardbits=6 --open_files=$of --verify_checksum=1 --db=$dir --sync=$sync --disable_wal=0 --compression_type=none --stats_interval=$si --compression_ratio=1 --disable_data_sync=$dds --write_buffer_size=$wbs --target_file_size_base=$mb --max_write_buffer_number=$wbn --max_background_compactions=$mbc --level0_file_num_compaction_trigger=$ctrig --level0_slowdown_writes_trigger=$delay --level0_stop_writes_trigger=$stop --num_levels=$levels --delete_obsolete_files_period_micros=$del --min_level_to_compress=$mcz --max_grandparent_overlap_factor=$overlap --stats_per_interval=1 --max_bytes_for_level_base=$bpl --use_existing_db=0 --key_id_range=$kir` Reviewers: andrewkr, sdong Reviewed By: sdong Subscribers: lgalanis, andrewkr, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D60651
2016-08-01 19:28:51 +00:00
std::shared_ptr<TimestampEmulator> timestamp_emulator_;
std::unique_ptr<port::Thread> secondary_update_thread_;
std::atomic<int> secondary_update_stopped_{0};
uint64_t secondary_db_updates_ = 0;
struct ThreadArg {
Benchmark* bm;
SharedState* shared;
ThreadState* thread;
void (Benchmark::*method)(ThreadState*);
};
static void ThreadBody(void* v) {
Prefer static_cast in place of most reinterpret_cast (#12308) Summary: The following are risks associated with pointer-to-pointer reinterpret_cast: * Can produce the "wrong result" (crash or memory corruption). IIRC, in theory this can happen for any up-cast or down-cast for a non-standard-layout type, though in practice would only happen for multiple inheritance cases (where the base class pointer might be "inside" the derived object). We don't use multiple inheritance a lot, but we do. * Can mask useful compiler errors upon code change, including converting between unrelated pointer types that you are expecting to be related, and converting between pointer and scalar types unintentionally. I can only think of some obscure cases where static_cast could be troublesome when it compiles as a replacement: * Going through `void*` could plausibly cause unnecessary or broken pointer arithmetic. Suppose we have `struct Derived: public Base1, public Base2`. If we have `Derived*` -> `void*` -> `Base2*` -> `Derived*` through reinterpret casts, this could plausibly work (though technical UB) assuming the `Base2*` is not dereferenced. Changing to static cast could introduce breaking pointer arithmetic. * Unnecessary (but safe) pointer arithmetic could arise in a case like `Derived*` -> `Base2*` -> `Derived*` where before the Base2 pointer might not have been dereferenced. This could potentially affect performance. With some light scripting, I tried replacing pointer-to-pointer reinterpret_casts with static_cast and kept the cases that still compile. Most occurrences of reinterpret_cast have successfully been changed (except for java/ and third-party/). 294 changed, 257 remain. A couple of related interventions included here: * Previously Cache::Handle was not actually derived from in the implementations and just used as a `void*` stand-in with reinterpret_cast. Now there is a relationship to allow static_cast. In theory, this could introduce pointer arithmetic (as described above) but is unlikely without multiple inheritance AND non-empty Cache::Handle. * Remove some unnecessary casts to void* as this is allowed to be implicit (for better or worse). Most of the remaining reinterpret_casts are for converting to/from raw bytes of objects. We could consider better idioms for these patterns in follow-up work. I wish there were a way to implement a template variant of static_cast that would only compile if no pointer arithmetic is generated, but best I can tell, this is not possible. AFAIK the best you could do is a dynamic check that the void* conversion after the static cast is unchanged. Pull Request resolved: https://github.com/facebook/rocksdb/pull/12308 Test Plan: existing tests, CI Reviewed By: ltamasi Differential Revision: D53204947 Pulled By: pdillinger fbshipit-source-id: 9de23e618263b0d5b9820f4e15966876888a16e2
2024-02-07 18:44:11 +00:00
ThreadArg* arg = static_cast<ThreadArg*>(v);
SharedState* shared = arg->shared;
ThreadState* thread = arg->thread;
{
MutexLock l(&shared->mu);
shared->num_initialized++;
if (shared->num_initialized >= shared->total) {
shared->cv.SignalAll();
}
while (!shared->start) {
shared->cv.Wait();
}
}
SetPerfLevel(static_cast<PerfLevel>(shared->perf_level));
perf_context.EnablePerLevelPerfContext();
thread->stats.Start(thread->tid);
(arg->bm->*(arg->method))(thread);
if (FLAGS_perf_level > ROCKSDB_NAMESPACE::PerfLevel::kDisable) {
thread->stats.AddMessage(std::string("PERF_CONTEXT:\n") +
get_perf_context()->ToString());
}
thread->stats.Stop();
{
MutexLock l(&shared->mu);
shared->num_done++;
if (shared->num_done >= shared->total) {
shared->cv.SignalAll();
}
}
}
[db_bench] Support single benchmark arguments (Repeat for X times, Warm up for X times), Support CombinedStats (AVG / MEDIAN) Summary: This diff allow us to run a single benchmark X times and warm it up for Y times. and see the AVG & MEDIAN throughput of these X runs for example ``` $ ./db_bench --benchmarks="fillseq,readseq[X5-W2]" Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags RocksDB: version 4.12 Date: Wed Aug 24 10:45:26 2016 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Compression: Snappy Memtablerep: skip_list Perf Level: 1 WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags DB path: [/tmp/rocksdbtest-8616/dbbench] fillseq : 4.695 micros/op 212971 ops/sec; 23.6 MB/s DB path: [/tmp/rocksdbtest-8616/dbbench] Warming up benchmark by running 2 times readseq : 0.214 micros/op 4677005 ops/sec; 517.4 MB/s readseq : 0.212 micros/op 4706834 ops/sec; 520.7 MB/s Running benchmark for 5 times readseq : 0.218 micros/op 4588187 ops/sec; 507.6 MB/s readseq : 0.208 micros/op 4816538 ops/sec; 532.8 MB/s readseq : 0.213 micros/op 4685376 ops/sec; 518.3 MB/s readseq : 0.214 micros/op 4676787 ops/sec; 517.4 MB/s readseq : 0.217 micros/op 4618532 ops/sec; 510.9 MB/s readseq [AVG 5 runs] : 4677084 ops/sec; 517.4 MB/sec readseq [MEDIAN 5 runs] : 4676787 ops/sec; 517.4 MB/sec ``` Test Plan: run db_bench Reviewers: sdong, andrewkr, yhchiang Reviewed By: yhchiang Subscribers: andrewkr, dhruba Differential Revision: https://reviews.facebook.net/D62235
2016-08-25 19:57:35 +00:00
Stats RunBenchmark(int n, Slice name,
void (Benchmark::*method)(ThreadState*)) {
SharedState shared;
shared.total = n;
shared.num_initialized = 0;
shared.num_done = 0;
shared.start = false;
if (FLAGS_benchmark_write_rate_limit > 0) {
shared.write_rate_limiter.reset(
NewGenericRateLimiter(FLAGS_benchmark_write_rate_limit));
}
if (FLAGS_benchmark_read_rate_limit > 0) {
shared.read_rate_limiter.reset(NewGenericRateLimiter(
FLAGS_benchmark_read_rate_limit, 100000 /* refill_period_us */,
10 /* fairness */, RateLimiter::Mode::kReadsOnly));
}
std::unique_ptr<ReporterAgent> reporter_agent;
if (FLAGS_report_interval_seconds > 0) {
reporter_agent.reset(new ReporterAgent(FLAGS_env, FLAGS_report_file,
FLAGS_report_interval_seconds));
}
ThreadArg* arg = new ThreadArg[n];
Adding NUMA support to db_bench tests Summary: Changes: - Adding numa_aware flag to db_bench.cc - Using numa.h library to bind memory and cpu of threads to a fixed NUMA node Result: There seems to be no significant change in the micros/op time with numa_aware enabled. I also tried this with other implementations, including a combination of pthread_setaffinity_np, sched_setaffinity and set_mempolicy methods. It'd be great if someone could point out where I'm going wrong and if we can achieve a better micors/op. Test Plan: Ran db_bench tests using following command: ./db_bench --db=/mnt/tmp --num_levels=6 --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --block_size=4096 --cache_size=17179869184 --cache_numshardbits=6 --compression_type=none --compression_ratio=1 --min_level_to_compress=-1 --disable_seek_compaction=1 --hard_rate_limit=2 --write_buffer_size=134217728 --max_write_buffer_number=2 --level0_file_num_compaction_trigger=8 --target_file_size_base=134217728 --max_bytes_for_level_base=1073741824 --disable_wal=0 --wal_dir=/mnt/tmp --sync=0 --disable_data_sync=1 --verify_checksum=1 --delete_obsolete_files_period_micros=314572800 --max_grandparent_overlap_factor=10 --max_background_compactions=4 --max_background_flushes=0 --level0_slowdown_writes_trigger=16 --level0_stop_writes_trigger=24 --statistics=0 --stats_per_interval=0 --stats_interval=1048576 --histogram=0 --use_plain_table=1 --open_files=-1 --mmap_read=1 --mmap_write=0 --memtablerep=prefix_hash --bloom_bits=10 --bloom_locality=1 --perf_level=0 --duration=300 --benchmarks=readwhilewriting --use_existing_db=1 --num=157286400 --threads=24 --writes_per_second=10240 --numa_aware=[False/True] The tests were run in private devserver with 24 cores and the db was prepopulated using filluniquerandom test. The tests resulted in 0.145 us/op with numa_aware=False and 0.161 us/op with numa_aware=True. Reviewers: sdong, yhchiang, ljin, igor Reviewed By: ljin, igor Subscribers: igor, leveldb Differential Revision: https://reviews.facebook.net/D19353
2014-07-07 17:53:31 +00:00
for (int i = 0; i < n; i++) {
Adding NUMA support to db_bench tests Summary: Changes: - Adding numa_aware flag to db_bench.cc - Using numa.h library to bind memory and cpu of threads to a fixed NUMA node Result: There seems to be no significant change in the micros/op time with numa_aware enabled. I also tried this with other implementations, including a combination of pthread_setaffinity_np, sched_setaffinity and set_mempolicy methods. It'd be great if someone could point out where I'm going wrong and if we can achieve a better micors/op. Test Plan: Ran db_bench tests using following command: ./db_bench --db=/mnt/tmp --num_levels=6 --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --block_size=4096 --cache_size=17179869184 --cache_numshardbits=6 --compression_type=none --compression_ratio=1 --min_level_to_compress=-1 --disable_seek_compaction=1 --hard_rate_limit=2 --write_buffer_size=134217728 --max_write_buffer_number=2 --level0_file_num_compaction_trigger=8 --target_file_size_base=134217728 --max_bytes_for_level_base=1073741824 --disable_wal=0 --wal_dir=/mnt/tmp --sync=0 --disable_data_sync=1 --verify_checksum=1 --delete_obsolete_files_period_micros=314572800 --max_grandparent_overlap_factor=10 --max_background_compactions=4 --max_background_flushes=0 --level0_slowdown_writes_trigger=16 --level0_stop_writes_trigger=24 --statistics=0 --stats_per_interval=0 --stats_interval=1048576 --histogram=0 --use_plain_table=1 --open_files=-1 --mmap_read=1 --mmap_write=0 --memtablerep=prefix_hash --bloom_bits=10 --bloom_locality=1 --perf_level=0 --duration=300 --benchmarks=readwhilewriting --use_existing_db=1 --num=157286400 --threads=24 --writes_per_second=10240 --numa_aware=[False/True] The tests were run in private devserver with 24 cores and the db was prepopulated using filluniquerandom test. The tests resulted in 0.145 us/op with numa_aware=False and 0.161 us/op with numa_aware=True. Reviewers: sdong, yhchiang, ljin, igor Reviewed By: ljin, igor Subscribers: igor, leveldb Differential Revision: https://reviews.facebook.net/D19353
2014-07-07 17:53:31 +00:00
#ifdef NUMA
if (FLAGS_enable_numa) {
// Performs a local allocation of memory to threads in numa node.
int n_nodes = numa_num_task_nodes(); // Number of nodes in NUMA.
numa_exit_on_error = 1;
int numa_node = i % n_nodes;
bitmask* nodes = numa_allocate_nodemask();
numa_bitmask_clearall(nodes);
numa_bitmask_setbit(nodes, numa_node);
// numa_bind() call binds the process to the node and these
// properties are passed on to the thread that is created in
// StartThread method called later in the loop.
numa_bind(nodes);
numa_set_strict(1);
numa_free_nodemask(nodes);
}
#endif
arg[i].bm = this;
arg[i].method = method;
arg[i].shared = &shared;
Avoid seed reuse when --benchmarks has more than one test (#9733) Summary: When --benchmarks has more than one test then the threads in one benchmark will use the same set of seeds as the threads in the previous benchmark. This diff fixe that. This fixes https://github.com/facebook/rocksdb/issues/9632 Pull Request resolved: https://github.com/facebook/rocksdb/pull/9733 Test Plan: For this command line the block cache is 8GB, so it caches at most 1024 8KB blocks. Note that without this diff the second run of readrandom has a much better response time because seed reuse means the second run reads the same 1000 blocks as the first run and they are cached at that point. But with this diff that does not happen. ./db_bench --benchmarks=fillseq,flush,compact0,waitforcompaction,levelstats,readrandom,readrandom --compression_type=zlib --num=10000000 --reads=1000 --block_size=8192 ... ``` Level Files Size(MB) -------------------- 0 0 0 1 11 238 2 9 253 3 0 0 4 0 0 5 0 0 6 0 0 ``` --- perf results without this diff DB path: [/tmp/rocksdbtest-2260/dbbench] readrandom : 46.212 micros/op 21618 ops/sec; 2.4 MB/s (1000 of 1000 found) DB path: [/tmp/rocksdbtest-2260/dbbench] readrandom : 21.963 micros/op 45450 ops/sec; 5.0 MB/s (1000 of 1000 found) --- perf results with this diff DB path: [/tmp/rocksdbtest-2260/dbbench] readrandom : 47.213 micros/op 21126 ops/sec; 2.3 MB/s (1000 of 1000 found) DB path: [/tmp/rocksdbtest-2260/dbbench] readrandom : 42.880 micros/op 23299 ops/sec; 2.6 MB/s (1000 of 1000 found) Reviewed By: jay-zhuang Differential Revision: D35089763 Pulled By: mdcallag fbshipit-source-id: 1b50143a07afe876b8c8e5fa50dd94a8ce57fc6b
2022-03-24 15:57:48 +00:00
total_thread_count_++;
arg[i].thread = new ThreadState(i, total_thread_count_);
arg[i].thread->stats.SetReporterAgent(reporter_agent.get());
arg[i].thread->shared = &shared;
FLAGS_env->StartThread(ThreadBody, &arg[i]);
}
shared.mu.Lock();
while (shared.num_initialized < n) {
shared.cv.Wait();
}
shared.start = true;
shared.cv.SignalAll();
while (shared.num_done < n) {
shared.cv.Wait();
}
shared.mu.Unlock();
// Stats for some threads can be excluded.
Stats merge_stats;
for (int i = 0; i < n; i++) {
merge_stats.Merge(arg[i].thread->stats);
}
merge_stats.Report(name);
for (int i = 0; i < n; i++) {
delete arg[i].thread;
}
delete[] arg;
[db_bench] Support single benchmark arguments (Repeat for X times, Warm up for X times), Support CombinedStats (AVG / MEDIAN) Summary: This diff allow us to run a single benchmark X times and warm it up for Y times. and see the AVG & MEDIAN throughput of these X runs for example ``` $ ./db_bench --benchmarks="fillseq,readseq[X5-W2]" Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags RocksDB: version 4.12 Date: Wed Aug 24 10:45:26 2016 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Compression: Snappy Memtablerep: skip_list Perf Level: 1 WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags DB path: [/tmp/rocksdbtest-8616/dbbench] fillseq : 4.695 micros/op 212971 ops/sec; 23.6 MB/s DB path: [/tmp/rocksdbtest-8616/dbbench] Warming up benchmark by running 2 times readseq : 0.214 micros/op 4677005 ops/sec; 517.4 MB/s readseq : 0.212 micros/op 4706834 ops/sec; 520.7 MB/s Running benchmark for 5 times readseq : 0.218 micros/op 4588187 ops/sec; 507.6 MB/s readseq : 0.208 micros/op 4816538 ops/sec; 532.8 MB/s readseq : 0.213 micros/op 4685376 ops/sec; 518.3 MB/s readseq : 0.214 micros/op 4676787 ops/sec; 517.4 MB/s readseq : 0.217 micros/op 4618532 ops/sec; 510.9 MB/s readseq [AVG 5 runs] : 4677084 ops/sec; 517.4 MB/sec readseq [MEDIAN 5 runs] : 4676787 ops/sec; 517.4 MB/sec ``` Test Plan: run db_bench Reviewers: sdong, andrewkr, yhchiang Reviewed By: yhchiang Subscribers: andrewkr, dhruba Differential Revision: https://reviews.facebook.net/D62235
2016-08-25 19:57:35 +00:00
return merge_stats;
}
Implement XXH3 block checksum type (#9069) Summary: XXH3 - latest hash function that is extremely fast on large data, easily faster than crc32c on most any x86_64 hardware. In integrating this hash function, I have handled the compression type byte in a non-standard way to avoid using the streaming API (extra data movement and active code size because of hash function complexity). This approach got a thumbs-up from Yann Collet. Existing functionality change: * reject bad ChecksumType in options with InvalidArgument This change split off from https://github.com/facebook/rocksdb/issues/9058 because context-aware checksum is likely to be handled through different configuration than ChecksumType. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9069 Test Plan: tests updated, and substantially expanded. Unit tests now check that we don't accidentally change the values generated by the checksum algorithms ("schema test") and that we properly handle invalid/unrecognized checksum types in options or in file footer. DBTestBase::ChangeOptions (etc.) updated from two to one configuration changing from default CRC32c ChecksumType. The point of this test code is to detect possible interactions among features, and the likelihood of some bad interaction being detected by including configurations other than XXH3 and CRC32c--and then not detected by stress/crash test--is extremely low. Stress/crash test also updated (manual run long enough to see it accepts new checksum type). db_bench also updated for microbenchmarking checksums. ### Performance microbenchmark (PORTABLE=0 DEBUG_LEVEL=0, Broadwell processor) ./db_bench -benchmarks=crc32c,xxhash,xxhash64,xxh3,crc32c,xxhash,xxhash64,xxh3,crc32c,xxhash,xxhash64,xxh3 crc32c : 0.200 micros/op 5005220 ops/sec; 19551.6 MB/s (4096 per op) xxhash : 0.807 micros/op 1238408 ops/sec; 4837.5 MB/s (4096 per op) xxhash64 : 0.421 micros/op 2376514 ops/sec; 9283.3 MB/s (4096 per op) xxh3 : 0.171 micros/op 5858391 ops/sec; 22884.3 MB/s (4096 per op) crc32c : 0.206 micros/op 4859566 ops/sec; 18982.7 MB/s (4096 per op) xxhash : 0.793 micros/op 1260850 ops/sec; 4925.2 MB/s (4096 per op) xxhash64 : 0.410 micros/op 2439182 ops/sec; 9528.1 MB/s (4096 per op) xxh3 : 0.161 micros/op 6202872 ops/sec; 24230.0 MB/s (4096 per op) crc32c : 0.203 micros/op 4924686 ops/sec; 19237.1 MB/s (4096 per op) xxhash : 0.839 micros/op 1192388 ops/sec; 4657.8 MB/s (4096 per op) xxhash64 : 0.424 micros/op 2357391 ops/sec; 9208.6 MB/s (4096 per op) xxh3 : 0.162 micros/op 6182678 ops/sec; 24151.1 MB/s (4096 per op) As you can see, especially once warmed up, xxh3 is fastest. ### Performance macrobenchmark (PORTABLE=0 DEBUG_LEVEL=0, Broadwell processor) Test for I in `seq 1 50`; do for CHK in 0 1 2 3 4; do TEST_TMPDIR=/dev/shm/rocksdb$CHK ./db_bench -benchmarks=fillseq -memtablerep=vector -allow_concurrent_memtable_write=false -num=30000000 -checksum_type=$CHK 2>&1 | grep 'micros/op' | tee -a results-$CHK & done; wait; done Results (ops/sec) for FILE in results*; do echo -n "$FILE "; awk '{ s += $5; c++; } END { print 1.0 * s / c; }' < $FILE; done results-0 252118 # kNoChecksum results-1 251588 # kCRC32c results-2 251863 # kxxHash results-3 252016 # kxxHash64 results-4 252038 # kXXH3 Reviewed By: mrambacher Differential Revision: D31905249 Pulled By: pdillinger fbshipit-source-id: cb9b998ebe2523fc7c400eedf62124a78bf4b4d1
2021-10-29 05:13:47 +00:00
template <OperationType kOpType, typename FnType, typename... Args>
static inline void ChecksumBenchmark(FnType fn, ThreadState* thread,
Args... args) {
const int size = FLAGS_block_size; // use --block_size option for db_bench
std::string labels = "(" + std::to_string(FLAGS_block_size) + " per op)";
Port 3 way SSE4.2 crc32c implementation from Folly Summary: **# Summary** RocksDB uses SSE crc32 intrinsics to calculate the crc32 values but it does it in single way fashion (not pipelined on single CPU core). Intel's whitepaper () published an algorithm that uses 3-way pipelining for the crc32 intrinsics, then use pclmulqdq intrinsic to combine the values. Because pclmulqdq has overhead on its own, this algorithm will show perf gains on buffers larger than 216 bytes, which makes RocksDB a perfect user, since most of the buffers RocksDB call crc32c on is over 4KB. Initial db_bench show tremendous CPU gain. This change uses the 3-way SSE algorithm by default. The old SSE algorithm is now behind a compiler tag NO_THREEWAY_CRC32C. If user compiles the code with NO_THREEWAY_CRC32C=1 then the old SSE Crc32c algorithm would be used. If the server does not have SSE4.2 at the run time the slow way (Non SSE) will be used. **# Performance Test Results** We ran the FillRandom and ReadRandom benchmarks in db_bench. ReadRandom is the point of interest here since it calculates the CRC32 for the in-mem buffers. We did 3 runs for each algorithm. Before this change the CRC32 value computation takes about 11.5% of total CPU cost, and with the new 3-way algorithm it reduced to around 4.5%. The overall throughput also improved from 25.53MB/s to 27.63MB/s. 1) ReadRandom in db_bench overall metrics PER RUN Algorithm | run | micros/op | ops/sec |Throughput (MB/s) 3-way | 1 | 4.143 | 241387 | 26.7 3-way | 2 | 3.775 | 264872 | 29.3 3-way | 3 | 4.116 | 242929 | 26.9 FastCrc32c|1 | 4.037 | 247727 | 27.4 FastCrc32c|2 | 4.648 | 215166 | 23.8 FastCrc32c|3 | 4.352 | 229799 | 25.4 AVG Algorithm | Average of micros/op | Average of ops/sec | Average of Throughput (MB/s) 3-way | 4.01 | 249,729 | 27.63 FastCrc32c | 4.35 | 230,897 | 25.53 2) Crc32c computation CPU cost (inclusive samples percentage) PER RUN Implementation | run |  TotalSamples | Crc32c percentage 3-way   | 1    |  4,572,250,000 | 4.37% 3-way   | 2    |  3,779,250,000 | 4.62% 3-way   | 3    |  4,129,500,000 | 4.48% FastCrc32c     | 1    |  4,663,500,000 | 11.24% FastCrc32c     | 2    |  4,047,500,000 | 12.34% FastCrc32c     | 3    |  4,366,750,000 | 11.68% **# Test Plan** make -j64 corruption_test && ./corruption_test By default it uses 3-way SSE algorithm NO_THREEWAY_CRC32C=1 make -j64 corruption_test && ./corruption_test make clean && DEBUG_LEVEL=0 make -j64 db_bench make clean && DEBUG_LEVEL=0 NO_THREEWAY_CRC32C=1 make -j64 db_bench Closes https://github.com/facebook/rocksdb/pull/3173 Differential Revision: D6330882 Pulled By: yingsu00 fbshipit-source-id: 8ec3d89719533b63b536a736663ca6f0dd4482e9
2017-12-20 02:20:50 +00:00
const char* label = labels.c_str();
std::string data(size, 'x');
Implement XXH3 block checksum type (#9069) Summary: XXH3 - latest hash function that is extremely fast on large data, easily faster than crc32c on most any x86_64 hardware. In integrating this hash function, I have handled the compression type byte in a non-standard way to avoid using the streaming API (extra data movement and active code size because of hash function complexity). This approach got a thumbs-up from Yann Collet. Existing functionality change: * reject bad ChecksumType in options with InvalidArgument This change split off from https://github.com/facebook/rocksdb/issues/9058 because context-aware checksum is likely to be handled through different configuration than ChecksumType. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9069 Test Plan: tests updated, and substantially expanded. Unit tests now check that we don't accidentally change the values generated by the checksum algorithms ("schema test") and that we properly handle invalid/unrecognized checksum types in options or in file footer. DBTestBase::ChangeOptions (etc.) updated from two to one configuration changing from default CRC32c ChecksumType. The point of this test code is to detect possible interactions among features, and the likelihood of some bad interaction being detected by including configurations other than XXH3 and CRC32c--and then not detected by stress/crash test--is extremely low. Stress/crash test also updated (manual run long enough to see it accepts new checksum type). db_bench also updated for microbenchmarking checksums. ### Performance microbenchmark (PORTABLE=0 DEBUG_LEVEL=0, Broadwell processor) ./db_bench -benchmarks=crc32c,xxhash,xxhash64,xxh3,crc32c,xxhash,xxhash64,xxh3,crc32c,xxhash,xxhash64,xxh3 crc32c : 0.200 micros/op 5005220 ops/sec; 19551.6 MB/s (4096 per op) xxhash : 0.807 micros/op 1238408 ops/sec; 4837.5 MB/s (4096 per op) xxhash64 : 0.421 micros/op 2376514 ops/sec; 9283.3 MB/s (4096 per op) xxh3 : 0.171 micros/op 5858391 ops/sec; 22884.3 MB/s (4096 per op) crc32c : 0.206 micros/op 4859566 ops/sec; 18982.7 MB/s (4096 per op) xxhash : 0.793 micros/op 1260850 ops/sec; 4925.2 MB/s (4096 per op) xxhash64 : 0.410 micros/op 2439182 ops/sec; 9528.1 MB/s (4096 per op) xxh3 : 0.161 micros/op 6202872 ops/sec; 24230.0 MB/s (4096 per op) crc32c : 0.203 micros/op 4924686 ops/sec; 19237.1 MB/s (4096 per op) xxhash : 0.839 micros/op 1192388 ops/sec; 4657.8 MB/s (4096 per op) xxhash64 : 0.424 micros/op 2357391 ops/sec; 9208.6 MB/s (4096 per op) xxh3 : 0.162 micros/op 6182678 ops/sec; 24151.1 MB/s (4096 per op) As you can see, especially once warmed up, xxh3 is fastest. ### Performance macrobenchmark (PORTABLE=0 DEBUG_LEVEL=0, Broadwell processor) Test for I in `seq 1 50`; do for CHK in 0 1 2 3 4; do TEST_TMPDIR=/dev/shm/rocksdb$CHK ./db_bench -benchmarks=fillseq -memtablerep=vector -allow_concurrent_memtable_write=false -num=30000000 -checksum_type=$CHK 2>&1 | grep 'micros/op' | tee -a results-$CHK & done; wait; done Results (ops/sec) for FILE in results*; do echo -n "$FILE "; awk '{ s += $5; c++; } END { print 1.0 * s / c; }' < $FILE; done results-0 252118 # kNoChecksum results-1 251588 # kCRC32c results-2 251863 # kxxHash results-3 252016 # kxxHash64 results-4 252038 # kXXH3 Reviewed By: mrambacher Differential Revision: D31905249 Pulled By: pdillinger fbshipit-source-id: cb9b998ebe2523fc7c400eedf62124a78bf4b4d1
2021-10-29 05:13:47 +00:00
uint64_t bytes = 0;
uint32_t val = 0;
while (bytes < 5000U * uint64_t{1048576}) { // ~5GB
val += static_cast<uint32_t>(fn(data.data(), size, args...));
thread->stats.FinishedOps(nullptr, nullptr, 1, kOpType);
bytes += size;
}
// Print so result is not dead
Implement XXH3 block checksum type (#9069) Summary: XXH3 - latest hash function that is extremely fast on large data, easily faster than crc32c on most any x86_64 hardware. In integrating this hash function, I have handled the compression type byte in a non-standard way to avoid using the streaming API (extra data movement and active code size because of hash function complexity). This approach got a thumbs-up from Yann Collet. Existing functionality change: * reject bad ChecksumType in options with InvalidArgument This change split off from https://github.com/facebook/rocksdb/issues/9058 because context-aware checksum is likely to be handled through different configuration than ChecksumType. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9069 Test Plan: tests updated, and substantially expanded. Unit tests now check that we don't accidentally change the values generated by the checksum algorithms ("schema test") and that we properly handle invalid/unrecognized checksum types in options or in file footer. DBTestBase::ChangeOptions (etc.) updated from two to one configuration changing from default CRC32c ChecksumType. The point of this test code is to detect possible interactions among features, and the likelihood of some bad interaction being detected by including configurations other than XXH3 and CRC32c--and then not detected by stress/crash test--is extremely low. Stress/crash test also updated (manual run long enough to see it accepts new checksum type). db_bench also updated for microbenchmarking checksums. ### Performance microbenchmark (PORTABLE=0 DEBUG_LEVEL=0, Broadwell processor) ./db_bench -benchmarks=crc32c,xxhash,xxhash64,xxh3,crc32c,xxhash,xxhash64,xxh3,crc32c,xxhash,xxhash64,xxh3 crc32c : 0.200 micros/op 5005220 ops/sec; 19551.6 MB/s (4096 per op) xxhash : 0.807 micros/op 1238408 ops/sec; 4837.5 MB/s (4096 per op) xxhash64 : 0.421 micros/op 2376514 ops/sec; 9283.3 MB/s (4096 per op) xxh3 : 0.171 micros/op 5858391 ops/sec; 22884.3 MB/s (4096 per op) crc32c : 0.206 micros/op 4859566 ops/sec; 18982.7 MB/s (4096 per op) xxhash : 0.793 micros/op 1260850 ops/sec; 4925.2 MB/s (4096 per op) xxhash64 : 0.410 micros/op 2439182 ops/sec; 9528.1 MB/s (4096 per op) xxh3 : 0.161 micros/op 6202872 ops/sec; 24230.0 MB/s (4096 per op) crc32c : 0.203 micros/op 4924686 ops/sec; 19237.1 MB/s (4096 per op) xxhash : 0.839 micros/op 1192388 ops/sec; 4657.8 MB/s (4096 per op) xxhash64 : 0.424 micros/op 2357391 ops/sec; 9208.6 MB/s (4096 per op) xxh3 : 0.162 micros/op 6182678 ops/sec; 24151.1 MB/s (4096 per op) As you can see, especially once warmed up, xxh3 is fastest. ### Performance macrobenchmark (PORTABLE=0 DEBUG_LEVEL=0, Broadwell processor) Test for I in `seq 1 50`; do for CHK in 0 1 2 3 4; do TEST_TMPDIR=/dev/shm/rocksdb$CHK ./db_bench -benchmarks=fillseq -memtablerep=vector -allow_concurrent_memtable_write=false -num=30000000 -checksum_type=$CHK 2>&1 | grep 'micros/op' | tee -a results-$CHK & done; wait; done Results (ops/sec) for FILE in results*; do echo -n "$FILE "; awk '{ s += $5; c++; } END { print 1.0 * s / c; }' < $FILE; done results-0 252118 # kNoChecksum results-1 251588 # kCRC32c results-2 251863 # kxxHash results-3 252016 # kxxHash64 results-4 252038 # kXXH3 Reviewed By: mrambacher Differential Revision: D31905249 Pulled By: pdillinger fbshipit-source-id: cb9b998ebe2523fc7c400eedf62124a78bf4b4d1
2021-10-29 05:13:47 +00:00
fprintf(stderr, "... val=0x%x\r", static_cast<unsigned int>(val));
thread->stats.AddBytes(bytes);
thread->stats.AddMessage(label);
}
Implement XXH3 block checksum type (#9069) Summary: XXH3 - latest hash function that is extremely fast on large data, easily faster than crc32c on most any x86_64 hardware. In integrating this hash function, I have handled the compression type byte in a non-standard way to avoid using the streaming API (extra data movement and active code size because of hash function complexity). This approach got a thumbs-up from Yann Collet. Existing functionality change: * reject bad ChecksumType in options with InvalidArgument This change split off from https://github.com/facebook/rocksdb/issues/9058 because context-aware checksum is likely to be handled through different configuration than ChecksumType. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9069 Test Plan: tests updated, and substantially expanded. Unit tests now check that we don't accidentally change the values generated by the checksum algorithms ("schema test") and that we properly handle invalid/unrecognized checksum types in options or in file footer. DBTestBase::ChangeOptions (etc.) updated from two to one configuration changing from default CRC32c ChecksumType. The point of this test code is to detect possible interactions among features, and the likelihood of some bad interaction being detected by including configurations other than XXH3 and CRC32c--and then not detected by stress/crash test--is extremely low. Stress/crash test also updated (manual run long enough to see it accepts new checksum type). db_bench also updated for microbenchmarking checksums. ### Performance microbenchmark (PORTABLE=0 DEBUG_LEVEL=0, Broadwell processor) ./db_bench -benchmarks=crc32c,xxhash,xxhash64,xxh3,crc32c,xxhash,xxhash64,xxh3,crc32c,xxhash,xxhash64,xxh3 crc32c : 0.200 micros/op 5005220 ops/sec; 19551.6 MB/s (4096 per op) xxhash : 0.807 micros/op 1238408 ops/sec; 4837.5 MB/s (4096 per op) xxhash64 : 0.421 micros/op 2376514 ops/sec; 9283.3 MB/s (4096 per op) xxh3 : 0.171 micros/op 5858391 ops/sec; 22884.3 MB/s (4096 per op) crc32c : 0.206 micros/op 4859566 ops/sec; 18982.7 MB/s (4096 per op) xxhash : 0.793 micros/op 1260850 ops/sec; 4925.2 MB/s (4096 per op) xxhash64 : 0.410 micros/op 2439182 ops/sec; 9528.1 MB/s (4096 per op) xxh3 : 0.161 micros/op 6202872 ops/sec; 24230.0 MB/s (4096 per op) crc32c : 0.203 micros/op 4924686 ops/sec; 19237.1 MB/s (4096 per op) xxhash : 0.839 micros/op 1192388 ops/sec; 4657.8 MB/s (4096 per op) xxhash64 : 0.424 micros/op 2357391 ops/sec; 9208.6 MB/s (4096 per op) xxh3 : 0.162 micros/op 6182678 ops/sec; 24151.1 MB/s (4096 per op) As you can see, especially once warmed up, xxh3 is fastest. ### Performance macrobenchmark (PORTABLE=0 DEBUG_LEVEL=0, Broadwell processor) Test for I in `seq 1 50`; do for CHK in 0 1 2 3 4; do TEST_TMPDIR=/dev/shm/rocksdb$CHK ./db_bench -benchmarks=fillseq -memtablerep=vector -allow_concurrent_memtable_write=false -num=30000000 -checksum_type=$CHK 2>&1 | grep 'micros/op' | tee -a results-$CHK & done; wait; done Results (ops/sec) for FILE in results*; do echo -n "$FILE "; awk '{ s += $5; c++; } END { print 1.0 * s / c; }' < $FILE; done results-0 252118 # kNoChecksum results-1 251588 # kCRC32c results-2 251863 # kxxHash results-3 252016 # kxxHash64 results-4 252038 # kXXH3 Reviewed By: mrambacher Differential Revision: D31905249 Pulled By: pdillinger fbshipit-source-id: cb9b998ebe2523fc7c400eedf62124a78bf4b4d1
2021-10-29 05:13:47 +00:00
void Crc32c(ThreadState* thread) {
ChecksumBenchmark<kCrc>(crc32c::Value, thread);
}
void xxHash(ThreadState* thread) {
Implement XXH3 block checksum type (#9069) Summary: XXH3 - latest hash function that is extremely fast on large data, easily faster than crc32c on most any x86_64 hardware. In integrating this hash function, I have handled the compression type byte in a non-standard way to avoid using the streaming API (extra data movement and active code size because of hash function complexity). This approach got a thumbs-up from Yann Collet. Existing functionality change: * reject bad ChecksumType in options with InvalidArgument This change split off from https://github.com/facebook/rocksdb/issues/9058 because context-aware checksum is likely to be handled through different configuration than ChecksumType. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9069 Test Plan: tests updated, and substantially expanded. Unit tests now check that we don't accidentally change the values generated by the checksum algorithms ("schema test") and that we properly handle invalid/unrecognized checksum types in options or in file footer. DBTestBase::ChangeOptions (etc.) updated from two to one configuration changing from default CRC32c ChecksumType. The point of this test code is to detect possible interactions among features, and the likelihood of some bad interaction being detected by including configurations other than XXH3 and CRC32c--and then not detected by stress/crash test--is extremely low. Stress/crash test also updated (manual run long enough to see it accepts new checksum type). db_bench also updated for microbenchmarking checksums. ### Performance microbenchmark (PORTABLE=0 DEBUG_LEVEL=0, Broadwell processor) ./db_bench -benchmarks=crc32c,xxhash,xxhash64,xxh3,crc32c,xxhash,xxhash64,xxh3,crc32c,xxhash,xxhash64,xxh3 crc32c : 0.200 micros/op 5005220 ops/sec; 19551.6 MB/s (4096 per op) xxhash : 0.807 micros/op 1238408 ops/sec; 4837.5 MB/s (4096 per op) xxhash64 : 0.421 micros/op 2376514 ops/sec; 9283.3 MB/s (4096 per op) xxh3 : 0.171 micros/op 5858391 ops/sec; 22884.3 MB/s (4096 per op) crc32c : 0.206 micros/op 4859566 ops/sec; 18982.7 MB/s (4096 per op) xxhash : 0.793 micros/op 1260850 ops/sec; 4925.2 MB/s (4096 per op) xxhash64 : 0.410 micros/op 2439182 ops/sec; 9528.1 MB/s (4096 per op) xxh3 : 0.161 micros/op 6202872 ops/sec; 24230.0 MB/s (4096 per op) crc32c : 0.203 micros/op 4924686 ops/sec; 19237.1 MB/s (4096 per op) xxhash : 0.839 micros/op 1192388 ops/sec; 4657.8 MB/s (4096 per op) xxhash64 : 0.424 micros/op 2357391 ops/sec; 9208.6 MB/s (4096 per op) xxh3 : 0.162 micros/op 6182678 ops/sec; 24151.1 MB/s (4096 per op) As you can see, especially once warmed up, xxh3 is fastest. ### Performance macrobenchmark (PORTABLE=0 DEBUG_LEVEL=0, Broadwell processor) Test for I in `seq 1 50`; do for CHK in 0 1 2 3 4; do TEST_TMPDIR=/dev/shm/rocksdb$CHK ./db_bench -benchmarks=fillseq -memtablerep=vector -allow_concurrent_memtable_write=false -num=30000000 -checksum_type=$CHK 2>&1 | grep 'micros/op' | tee -a results-$CHK & done; wait; done Results (ops/sec) for FILE in results*; do echo -n "$FILE "; awk '{ s += $5; c++; } END { print 1.0 * s / c; }' < $FILE; done results-0 252118 # kNoChecksum results-1 251588 # kCRC32c results-2 251863 # kxxHash results-3 252016 # kxxHash64 results-4 252038 # kXXH3 Reviewed By: mrambacher Differential Revision: D31905249 Pulled By: pdillinger fbshipit-source-id: cb9b998ebe2523fc7c400eedf62124a78bf4b4d1
2021-10-29 05:13:47 +00:00
ChecksumBenchmark<kHash>(XXH32, thread, /*seed*/ 0);
}
Implement XXH3 block checksum type (#9069) Summary: XXH3 - latest hash function that is extremely fast on large data, easily faster than crc32c on most any x86_64 hardware. In integrating this hash function, I have handled the compression type byte in a non-standard way to avoid using the streaming API (extra data movement and active code size because of hash function complexity). This approach got a thumbs-up from Yann Collet. Existing functionality change: * reject bad ChecksumType in options with InvalidArgument This change split off from https://github.com/facebook/rocksdb/issues/9058 because context-aware checksum is likely to be handled through different configuration than ChecksumType. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9069 Test Plan: tests updated, and substantially expanded. Unit tests now check that we don't accidentally change the values generated by the checksum algorithms ("schema test") and that we properly handle invalid/unrecognized checksum types in options or in file footer. DBTestBase::ChangeOptions (etc.) updated from two to one configuration changing from default CRC32c ChecksumType. The point of this test code is to detect possible interactions among features, and the likelihood of some bad interaction being detected by including configurations other than XXH3 and CRC32c--and then not detected by stress/crash test--is extremely low. Stress/crash test also updated (manual run long enough to see it accepts new checksum type). db_bench also updated for microbenchmarking checksums. ### Performance microbenchmark (PORTABLE=0 DEBUG_LEVEL=0, Broadwell processor) ./db_bench -benchmarks=crc32c,xxhash,xxhash64,xxh3,crc32c,xxhash,xxhash64,xxh3,crc32c,xxhash,xxhash64,xxh3 crc32c : 0.200 micros/op 5005220 ops/sec; 19551.6 MB/s (4096 per op) xxhash : 0.807 micros/op 1238408 ops/sec; 4837.5 MB/s (4096 per op) xxhash64 : 0.421 micros/op 2376514 ops/sec; 9283.3 MB/s (4096 per op) xxh3 : 0.171 micros/op 5858391 ops/sec; 22884.3 MB/s (4096 per op) crc32c : 0.206 micros/op 4859566 ops/sec; 18982.7 MB/s (4096 per op) xxhash : 0.793 micros/op 1260850 ops/sec; 4925.2 MB/s (4096 per op) xxhash64 : 0.410 micros/op 2439182 ops/sec; 9528.1 MB/s (4096 per op) xxh3 : 0.161 micros/op 6202872 ops/sec; 24230.0 MB/s (4096 per op) crc32c : 0.203 micros/op 4924686 ops/sec; 19237.1 MB/s (4096 per op) xxhash : 0.839 micros/op 1192388 ops/sec; 4657.8 MB/s (4096 per op) xxhash64 : 0.424 micros/op 2357391 ops/sec; 9208.6 MB/s (4096 per op) xxh3 : 0.162 micros/op 6182678 ops/sec; 24151.1 MB/s (4096 per op) As you can see, especially once warmed up, xxh3 is fastest. ### Performance macrobenchmark (PORTABLE=0 DEBUG_LEVEL=0, Broadwell processor) Test for I in `seq 1 50`; do for CHK in 0 1 2 3 4; do TEST_TMPDIR=/dev/shm/rocksdb$CHK ./db_bench -benchmarks=fillseq -memtablerep=vector -allow_concurrent_memtable_write=false -num=30000000 -checksum_type=$CHK 2>&1 | grep 'micros/op' | tee -a results-$CHK & done; wait; done Results (ops/sec) for FILE in results*; do echo -n "$FILE "; awk '{ s += $5; c++; } END { print 1.0 * s / c; }' < $FILE; done results-0 252118 # kNoChecksum results-1 251588 # kCRC32c results-2 251863 # kxxHash results-3 252016 # kxxHash64 results-4 252038 # kXXH3 Reviewed By: mrambacher Differential Revision: D31905249 Pulled By: pdillinger fbshipit-source-id: cb9b998ebe2523fc7c400eedf62124a78bf4b4d1
2021-10-29 05:13:47 +00:00
void xxHash64(ThreadState* thread) {
ChecksumBenchmark<kHash>(XXH64, thread, /*seed*/ 0);
}
void xxh3(ThreadState* thread) {
ChecksumBenchmark<kHash>(XXH3_64bits, thread);
}
void AcquireLoad(ThreadState* thread) {
int dummy;
2014-10-27 22:41:05 +00:00
std::atomic<void*> ap(&dummy);
int count = 0;
void* ptr = nullptr;
thread->stats.AddMessage("(each op is 1000 loads)");
while (count < 100000) {
for (int i = 0; i < 1000; i++) {
ptr = ap.load(std::memory_order_acquire);
}
count++;
thread->stats.FinishedOps(nullptr, nullptr, 1, kOthers);
}
if (ptr == nullptr) {
exit(1); // Disable unused variable warning.
}
}
void Compress(ThreadState* thread) {
RandomGenerator gen;
Slice input = gen.Generate(FLAGS_block_size);
int64_t bytes = 0;
int64_t produced = 0;
bool ok = true;
std::string compressed;
CompressionOptions opts;
Add `CompressionOptions::checksum` for enabling ZSTD checksum (#11666) Summary: Optionally enable zstd checksum flag (https://github.com/facebook/zstd/blob/d857369028d997c92ff1f1861a4d7f679a125464/lib/zstd.h#L428) to detect corruption during decompression. Main changes are in compression.h: * User can set CompressionOptions::checksum to true to enable this feature. * We enable this feature in ZSTD by setting the checksum flag in ZSTD compression context: `ZSTD_CCtx`. * Uses `ZSTD_compress2()` to do compression since it supports frame parameter like the checksum flag. Compression level is also set in compression context as a flag. * Error handling during decompression to propagate error message from ZSTD. * Updated microbench to test read performance impact. About compatibility, the current compression decoders should continue to work with the data created by the new compression API `ZSTD_compress2()`: https://github.com/facebook/zstd/issues/3711. Pull Request resolved: https://github.com/facebook/rocksdb/pull/11666 Test Plan: * Existing unit tests for zstd compression * Add unit test `DBTest2.ZSTDChecksum` to test the corruption case * Manually tested that compression levels, parallel compression, dictionary compression, index compression all work with the new ZSTD_compress2() API. * Manually tested with `sst_dump --command=recompress` that different compression levels and dictionary compression settings all work. * Manually tested compiling with older versions of ZSTD: v1.3.8, v1.1.0, v0.6.2. * Perf impact: from public benchmark data: http://fastcompression.blogspot.com/2019/03/presenting-xxh3.html for checksum and https://github.com/facebook/zstd#benchmarks, if decompression is 1700MB/s and checksum computation is 70000MB/s, checksum computation is an additional ~2.4% time for decompression. Compression is slower and checksumming should be less noticeable. * Microbench: ``` TEST_TMPDIR=/dev/shm ./branch_db_basic_bench --benchmark_filter=DBGet/comp_style:0/max_data:1048576/per_key_size:256/enable_statistics:0/negative_query:0/enable_filter:0/mmap:0/compression_type:7/compression_checksum:1/no_blockcache:1/iterations:10000/threads:1 --benchmark_repetitions=100 Min out of 100 runs: Main: 10390 10436 10456 10484 10499 10535 10544 10545 10565 10568 After this PR, checksum=false 10285 10397 10503 10508 10515 10557 10562 10635 10640 10660 After this PR, checksum=true 10827 10876 10925 10949 10971 11052 11061 11063 11100 11109 ``` * db_bench: ``` Write perf TEST_TMPDIR=/dev/shm/ ./db_bench_ichecksum --benchmarks=fillseq[-X10] --compression_type=zstd --num=10000000 --compression_checksum=.. [FillSeq checksum=0] fillseq [AVG 10 runs] : 281635 (± 31711) ops/sec; 31.2 (± 3.5) MB/sec fillseq [MEDIAN 10 runs] : 294027 ops/sec; 32.5 MB/sec [FillSeq checksum=1] fillseq [AVG 10 runs] : 286961 (± 34700) ops/sec; 31.7 (± 3.8) MB/sec fillseq [MEDIAN 10 runs] : 283278 ops/sec; 31.3 MB/sec Read perf TEST_TMPDIR=/dev/shm ./db_bench_ichecksum --benchmarks=readrandom[-X20] --num=100000000 --reads=1000000 --use_existing_db=true --readonly=1 [Readrandom checksum=1] readrandom [AVG 20 runs] : 360928 (± 3579) ops/sec; 4.0 (± 0.0) MB/sec readrandom [MEDIAN 20 runs] : 362468 ops/sec; 4.0 MB/sec [Readrandom checksum=0] readrandom [AVG 20 runs] : 380365 (± 2384) ops/sec; 4.2 (± 0.0) MB/sec readrandom [MEDIAN 20 runs] : 379800 ops/sec; 4.2 MB/sec Compression TEST_TMPDIR=/dev/shm ./db_bench_ichecksum --benchmarks=compress[-X20] --compression_type=zstd --num=100000000 --compression_checksum=1 checksum=1 compress [AVG 20 runs] : 54074 (± 634) ops/sec; 211.2 (± 2.5) MB/sec compress [MEDIAN 20 runs] : 54396 ops/sec; 212.5 MB/sec checksum=0 compress [AVG 20 runs] : 54598 (± 393) ops/sec; 213.3 (± 1.5) MB/sec compress [MEDIAN 20 runs] : 54592 ops/sec; 213.3 MB/sec Decompression: TEST_TMPDIR=/dev/shm ./db_bench_ichecksum --benchmarks=uncompress[-X20] --compression_type=zstd --compression_checksum=1 checksum = 0 uncompress [AVG 20 runs] : 167499 (± 962) ops/sec; 654.3 (± 3.8) MB/sec uncompress [MEDIAN 20 runs] : 167210 ops/sec; 653.2 MB/sec checksum = 1 uncompress [AVG 20 runs] : 167980 (± 924) ops/sec; 656.2 (± 3.6) MB/sec uncompress [MEDIAN 20 runs] : 168465 ops/sec; 658.1 MB/sec ``` Reviewed By: ajkr Differential Revision: D48019378 Pulled By: cbi42 fbshipit-source-id: 674120c6e1853c2ced1436ac8138559d0204feba
2023-08-18 22:01:59 +00:00
opts.level = FLAGS_compression_level;
CompressionContext context(FLAGS_compression_type_e, opts);
CompressionInfo info(opts, context, CompressionDict::GetEmptyDict(),
FLAGS_compression_type_e,
FLAGS_sample_for_compression);
2014-02-08 02:12:30 +00:00
// Compress 1G
while (ok && bytes < int64_t(1) << 30) {
compressed.clear();
ok = CompressSlice(info, input, &compressed);
produced += compressed.size();
bytes += input.size();
thread->stats.FinishedOps(nullptr, nullptr, 1, kCompress);
}
if (!ok) {
2014-02-08 02:12:30 +00:00
thread->stats.AddMessage("(compression failure)");
} else {
char buf[340];
snprintf(buf, sizeof(buf), "(output: %.1f%%)",
(produced * 100.0) / bytes);
thread->stats.AddMessage(buf);
thread->stats.AddBytes(bytes);
}
}
void Uncompress(ThreadState* thread) {
RandomGenerator gen;
Slice input = gen.Generate(FLAGS_block_size);
std::string compressed;
2014-02-08 02:12:30 +00:00
CompressionOptions compression_opts;
Add `CompressionOptions::checksum` for enabling ZSTD checksum (#11666) Summary: Optionally enable zstd checksum flag (https://github.com/facebook/zstd/blob/d857369028d997c92ff1f1861a4d7f679a125464/lib/zstd.h#L428) to detect corruption during decompression. Main changes are in compression.h: * User can set CompressionOptions::checksum to true to enable this feature. * We enable this feature in ZSTD by setting the checksum flag in ZSTD compression context: `ZSTD_CCtx`. * Uses `ZSTD_compress2()` to do compression since it supports frame parameter like the checksum flag. Compression level is also set in compression context as a flag. * Error handling during decompression to propagate error message from ZSTD. * Updated microbench to test read performance impact. About compatibility, the current compression decoders should continue to work with the data created by the new compression API `ZSTD_compress2()`: https://github.com/facebook/zstd/issues/3711. Pull Request resolved: https://github.com/facebook/rocksdb/pull/11666 Test Plan: * Existing unit tests for zstd compression * Add unit test `DBTest2.ZSTDChecksum` to test the corruption case * Manually tested that compression levels, parallel compression, dictionary compression, index compression all work with the new ZSTD_compress2() API. * Manually tested with `sst_dump --command=recompress` that different compression levels and dictionary compression settings all work. * Manually tested compiling with older versions of ZSTD: v1.3.8, v1.1.0, v0.6.2. * Perf impact: from public benchmark data: http://fastcompression.blogspot.com/2019/03/presenting-xxh3.html for checksum and https://github.com/facebook/zstd#benchmarks, if decompression is 1700MB/s and checksum computation is 70000MB/s, checksum computation is an additional ~2.4% time for decompression. Compression is slower and checksumming should be less noticeable. * Microbench: ``` TEST_TMPDIR=/dev/shm ./branch_db_basic_bench --benchmark_filter=DBGet/comp_style:0/max_data:1048576/per_key_size:256/enable_statistics:0/negative_query:0/enable_filter:0/mmap:0/compression_type:7/compression_checksum:1/no_blockcache:1/iterations:10000/threads:1 --benchmark_repetitions=100 Min out of 100 runs: Main: 10390 10436 10456 10484 10499 10535 10544 10545 10565 10568 After this PR, checksum=false 10285 10397 10503 10508 10515 10557 10562 10635 10640 10660 After this PR, checksum=true 10827 10876 10925 10949 10971 11052 11061 11063 11100 11109 ``` * db_bench: ``` Write perf TEST_TMPDIR=/dev/shm/ ./db_bench_ichecksum --benchmarks=fillseq[-X10] --compression_type=zstd --num=10000000 --compression_checksum=.. [FillSeq checksum=0] fillseq [AVG 10 runs] : 281635 (± 31711) ops/sec; 31.2 (± 3.5) MB/sec fillseq [MEDIAN 10 runs] : 294027 ops/sec; 32.5 MB/sec [FillSeq checksum=1] fillseq [AVG 10 runs] : 286961 (± 34700) ops/sec; 31.7 (± 3.8) MB/sec fillseq [MEDIAN 10 runs] : 283278 ops/sec; 31.3 MB/sec Read perf TEST_TMPDIR=/dev/shm ./db_bench_ichecksum --benchmarks=readrandom[-X20] --num=100000000 --reads=1000000 --use_existing_db=true --readonly=1 [Readrandom checksum=1] readrandom [AVG 20 runs] : 360928 (± 3579) ops/sec; 4.0 (± 0.0) MB/sec readrandom [MEDIAN 20 runs] : 362468 ops/sec; 4.0 MB/sec [Readrandom checksum=0] readrandom [AVG 20 runs] : 380365 (± 2384) ops/sec; 4.2 (± 0.0) MB/sec readrandom [MEDIAN 20 runs] : 379800 ops/sec; 4.2 MB/sec Compression TEST_TMPDIR=/dev/shm ./db_bench_ichecksum --benchmarks=compress[-X20] --compression_type=zstd --num=100000000 --compression_checksum=1 checksum=1 compress [AVG 20 runs] : 54074 (± 634) ops/sec; 211.2 (± 2.5) MB/sec compress [MEDIAN 20 runs] : 54396 ops/sec; 212.5 MB/sec checksum=0 compress [AVG 20 runs] : 54598 (± 393) ops/sec; 213.3 (± 1.5) MB/sec compress [MEDIAN 20 runs] : 54592 ops/sec; 213.3 MB/sec Decompression: TEST_TMPDIR=/dev/shm ./db_bench_ichecksum --benchmarks=uncompress[-X20] --compression_type=zstd --compression_checksum=1 checksum = 0 uncompress [AVG 20 runs] : 167499 (± 962) ops/sec; 654.3 (± 3.8) MB/sec uncompress [MEDIAN 20 runs] : 167210 ops/sec; 653.2 MB/sec checksum = 1 uncompress [AVG 20 runs] : 167980 (± 924) ops/sec; 656.2 (± 3.6) MB/sec uncompress [MEDIAN 20 runs] : 168465 ops/sec; 658.1 MB/sec ``` Reviewed By: ajkr Differential Revision: D48019378 Pulled By: cbi42 fbshipit-source-id: 674120c6e1853c2ced1436ac8138559d0204feba
2023-08-18 22:01:59 +00:00
compression_opts.level = FLAGS_compression_level;
CompressionContext compression_ctx(FLAGS_compression_type_e,
compression_opts);
CompressionInfo compression_info(
compression_opts, compression_ctx, CompressionDict::GetEmptyDict(),
FLAGS_compression_type_e, FLAGS_sample_for_compression);
UncompressionContext uncompression_ctx(FLAGS_compression_type_e);
UncompressionInfo uncompression_info(uncompression_ctx,
UncompressionDict::GetEmptyDict(),
FLAGS_compression_type_e);
bool ok = CompressSlice(compression_info, input, &compressed);
int64_t bytes = 0;
size_t uncompressed_size = 0;
2014-02-08 02:12:30 +00:00
while (ok && bytes < 1024 * 1048576) {
constexpr uint32_t compress_format_version = 2;
CacheAllocationPtr uncompressed = UncompressData(
uncompression_info, compressed.data(), compressed.size(),
&uncompressed_size, compress_format_version);
ok = uncompressed.get() != nullptr;
bytes += input.size();
thread->stats.FinishedOps(nullptr, nullptr, 1, kUncompress);
}
if (!ok) {
2014-02-08 02:12:30 +00:00
thread->stats.AddMessage("(compression failure)");
} else {
thread->stats.AddBytes(bytes);
}
}
// Returns true if the options is initialized from the specified
// options file.
bool InitializeOptionsFromFile(Options* opts) {
printf("Initializing RocksDB Options from the specified file\n");
DBOptions db_opts;
std::vector<ColumnFamilyDescriptor> cf_descs;
if (FLAGS_options_file != "") {
ConfigOptions config_opts;
config_opts.ignore_unknown_options = false;
config_opts.input_strings_escaped = true;
config_opts.env = FLAGS_env;
auto s = LoadOptionsFromFile(config_opts, FLAGS_options_file, &db_opts,
&cf_descs);
db_opts.env = FLAGS_env;
if (s.ok()) {
*opts = Options(db_opts, cf_descs[0].options);
return true;
}
fprintf(stderr, "Unable to load options file %s --- %s\n",
FLAGS_options_file.c_str(), s.ToString().c_str());
exit(1);
}
return false;
}
void InitializeOptionsFromFlags(Options* opts) {
printf("Initializing RocksDB Options from command-line flags\n");
Options& options = *opts;
ConfigOptions config_options(options);
config_options.ignore_unsupported_options = false;
assert(db_.db == nullptr);
options.env = FLAGS_env;
options.wal_dir = FLAGS_wal_dir;
options.dump_malloc_stats = FLAGS_dump_malloc_stats;
options.stats_dump_period_sec =
static_cast<unsigned int>(FLAGS_stats_dump_period_sec);
options.stats_persist_period_sec =
static_cast<unsigned int>(FLAGS_stats_persist_period_sec);
options.persist_stats_to_disk = FLAGS_persist_stats_to_disk;
options.stats_history_buffer_size =
static_cast<size_t>(FLAGS_stats_history_buffer_size);
options.avoid_flush_during_recovery = FLAGS_avoid_flush_during_recovery;
options.compression_opts.level = FLAGS_compression_level;
options.compression_opts.max_dict_bytes = FLAGS_compression_max_dict_bytes;
options.compression_opts.zstd_max_train_bytes =
FLAGS_compression_zstd_max_train_bytes;
options.compression_opts.parallel_threads =
FLAGS_compression_parallel_threads;
options.compression_opts.max_dict_buffer_bytes =
FLAGS_compression_max_dict_buffer_bytes;
Support using ZDICT_finalizeDictionary to generate zstd dictionary (#9857) Summary: An untrained dictionary is currently simply the concatenation of several samples. The ZSTD API, ZDICT_finalizeDictionary(), can improve such a dictionary's effectiveness at low cost. This PR changes how dictionary is created by calling the ZSTD ZDICT_finalizeDictionary() API instead of creating raw content dictionary (when max_dict_buffer_bytes > 0), and pass in all buffered uncompressed data blocks as samples. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9857 Test Plan: #### db_bench test for cpu/memory of compression+decompression and space saving on synthetic data: Set up: change the parameter [here](https://github.com/facebook/rocksdb/blob/fb9a167a55e0970b1ef6f67c1600c8d9c4c6114f/tools/db_bench_tool.cc#L1766) to 16384 to make synthetic data more compressible. ``` # linked local ZSTD with version 1.5.2 # DEBUG_LEVEL=0 ROCKSDB_NO_FBCODE=1 ROCKSDB_DISABLE_ZSTD=1 EXTRA_CXXFLAGS="-DZSTD_STATIC_LINKING_ONLY -DZSTD -I/data/users/changyubi/install/include/" EXTRA_LDFLAGS="-L/data/users/changyubi/install/lib/ -l:libzstd.a" make -j32 db_bench dict_bytes=16384 train_bytes=1048576 echo "========== No Dictionary ==========" TEST_TMPDIR=/dev/shm ./db_bench -benchmarks=filluniquerandom,compact -num=10000000 -compression_type=zstd -compression_max_dict_bytes=0 -block_size=4096 -max_background_jobs=24 -memtablerep=vector -allow_concurrent_memtable_write=false -disable_wal=true -max_write_buffer_number=8 >/dev/null 2>&1 TEST_TMPDIR=/dev/shm /usr/bin/time ./db_bench -use_existing_db=true -benchmarks=compact -compression_type=zstd -compression_max_dict_bytes=0 -block_size=4096 2>&1 | grep elapsed du -hc /dev/shm/dbbench/*sst | grep total echo "========== Raw Content Dictionary ==========" TEST_TMPDIR=/dev/shm ./db_bench_main -benchmarks=filluniquerandom,compact -num=10000000 -compression_type=zstd -compression_max_dict_bytes=$dict_bytes -block_size=4096 -max_background_jobs=24 -memtablerep=vector -allow_concurrent_memtable_write=false -disable_wal=true -max_write_buffer_number=8 >/dev/null 2>&1 TEST_TMPDIR=/dev/shm /usr/bin/time ./db_bench_main -use_existing_db=true -benchmarks=compact -compression_type=zstd -compression_max_dict_bytes=$dict_bytes -block_size=4096 2>&1 | grep elapsed du -hc /dev/shm/dbbench/*sst | grep total echo "========== FinalizeDictionary ==========" TEST_TMPDIR=/dev/shm ./db_bench -benchmarks=filluniquerandom,compact -num=10000000 -compression_type=zstd -compression_max_dict_bytes=$dict_bytes -compression_zstd_max_train_bytes=$train_bytes -compression_use_zstd_dict_trainer=false -block_size=4096 -max_background_jobs=24 -memtablerep=vector -allow_concurrent_memtable_write=false -disable_wal=true -max_write_buffer_number=8 >/dev/null 2>&1 TEST_TMPDIR=/dev/shm /usr/bin/time ./db_bench -use_existing_db=true -benchmarks=compact -compression_type=zstd -compression_max_dict_bytes=$dict_bytes -compression_zstd_max_train_bytes=$train_bytes -compression_use_zstd_dict_trainer=false -block_size=4096 2>&1 | grep elapsed du -hc /dev/shm/dbbench/*sst | grep total echo "========== TrainDictionary ==========" TEST_TMPDIR=/dev/shm ./db_bench -benchmarks=filluniquerandom,compact -num=10000000 -compression_type=zstd -compression_max_dict_bytes=$dict_bytes -compression_zstd_max_train_bytes=$train_bytes -block_size=4096 -max_background_jobs=24 -memtablerep=vector -allow_concurrent_memtable_write=false -disable_wal=true -max_write_buffer_number=8 >/dev/null 2>&1 TEST_TMPDIR=/dev/shm /usr/bin/time ./db_bench -use_existing_db=true -benchmarks=compact -compression_type=zstd -compression_max_dict_bytes=$dict_bytes -compression_zstd_max_train_bytes=$train_bytes -block_size=4096 2>&1 | grep elapsed du -hc /dev/shm/dbbench/*sst | grep total # Result: TrainDictionary is much better on space saving, but FinalizeDictionary seems to use less memory. # before compression data size: 1.2GB dict_bytes=16384 max_dict_buffer_bytes = 1048576 space cpu/memory No Dictionary 468M 14.93user 1.00system 0:15.92elapsed 100%CPU (0avgtext+0avgdata 23904maxresident)k Raw Dictionary 251M 15.81user 0.80system 0:16.56elapsed 100%CPU (0avgtext+0avgdata 156808maxresident)k FinalizeDictionary 236M 11.93user 0.64system 0:12.56elapsed 100%CPU (0avgtext+0avgdata 89548maxresident)k TrainDictionary 84M 7.29user 0.45system 0:07.75elapsed 100%CPU (0avgtext+0avgdata 97288maxresident)k ``` #### Benchmark on 10 sample SST files for spacing saving and CPU time on compression: FinalizeDictionary is comparable to TrainDictionary in terms of space saving, and takes less time in compression. ``` dict_bytes=16384 train_bytes=1048576 for sst_file in `ls ../temp/myrock-sst/` do echo "********** $sst_file **********" echo "========== No Dictionary ==========" ./sst_dump --file="../temp/myrock-sst/$sst_file" --command=recompress --compression_level_from=6 --compression_level_to=6 --compression_types=kZSTD echo "========== Raw Content Dictionary ==========" ./sst_dump --file="../temp/myrock-sst/$sst_file" --command=recompress --compression_level_from=6 --compression_level_to=6 --compression_types=kZSTD --compression_max_dict_bytes=$dict_bytes echo "========== FinalizeDictionary ==========" ./sst_dump --file="../temp/myrock-sst/$sst_file" --command=recompress --compression_level_from=6 --compression_level_to=6 --compression_types=kZSTD --compression_max_dict_bytes=$dict_bytes --compression_zstd_max_train_bytes=$train_bytes --compression_use_zstd_finalize_dict echo "========== TrainDictionary ==========" ./sst_dump --file="../temp/myrock-sst/$sst_file" --command=recompress --compression_level_from=6 --compression_level_to=6 --compression_types=kZSTD --compression_max_dict_bytes=$dict_bytes --compression_zstd_max_train_bytes=$train_bytes done 010240.sst (Size/Time) 011029.sst 013184.sst 021552.sst 185054.sst 185137.sst 191666.sst 7560381.sst 7604174.sst 7635312.sst No Dictionary 28165569 / 2614419 32899411 / 2976832 32977848 / 3055542 31966329 / 2004590 33614351 / 1755877 33429029 / 1717042 33611933 / 1776936 33634045 / 2771417 33789721 / 2205414 33592194 / 388254 Raw Content Dictionary 28019950 / 2697961 33748665 / 3572422 33896373 / 3534701 26418431 / 2259658 28560825 / 1839168 28455030 / 1846039 28494319 / 1861349 32391599 / 3095649 33772142 / 2407843 33592230 / 474523 FinalizeDictionary 27896012 / 2650029 33763886 / 3719427 33904283 / 3552793 26008225 / 2198033 28111872 / 1869530 28014374 / 1789771 28047706 / 1848300 32296254 / 3204027 33698698 / 2381468 33592344 / 517433 TrainDictionary 28046089 / 2740037 33706480 / 3679019 33885741 / 3629351 25087123 / 2204558 27194353 / 1970207 27234229 / 1896811 27166710 / 1903119 32011041 / 3322315 32730692 / 2406146 33608631 / 570593 ``` #### Decompression/Read test: With FinalizeDictionary/TrainDictionary, some data structure used for decompression are in stored in dictionary, so they are expected to be faster in terms of decompression/reads. ``` dict_bytes=16384 train_bytes=1048576 echo "No Dictionary" TEST_TMPDIR=/dev/shm/ ./db_bench -benchmarks=filluniquerandom,compact -compression_type=zstd -compression_max_dict_bytes=0 > /dev/null 2>&1 TEST_TMPDIR=/dev/shm/ ./db_bench -use_existing_db=true -benchmarks=readrandom -cache_size=0 -compression_type=zstd -compression_max_dict_bytes=0 2>&1 | grep MB/s echo "Raw Dictionary" TEST_TMPDIR=/dev/shm/ ./db_bench -benchmarks=filluniquerandom,compact -compression_type=zstd -compression_max_dict_bytes=$dict_bytes > /dev/null 2>&1 TEST_TMPDIR=/dev/shm/ ./db_bench -use_existing_db=true -benchmarks=readrandom -cache_size=0 -compression_type=zstd -compression_max_dict_bytes=$dict_bytes 2>&1 | grep MB/s echo "FinalizeDict" TEST_TMPDIR=/dev/shm/ ./db_bench -benchmarks=filluniquerandom,compact -compression_type=zstd -compression_max_dict_bytes=$dict_bytes -compression_zstd_max_train_bytes=$train_bytes -compression_use_zstd_dict_trainer=false > /dev/null 2>&1 TEST_TMPDIR=/dev/shm/ ./db_bench -use_existing_db=true -benchmarks=readrandom -cache_size=0 -compression_type=zstd -compression_max_dict_bytes=$dict_bytes -compression_zstd_max_train_bytes=$train_bytes -compression_use_zstd_dict_trainer=false 2>&1 | grep MB/s echo "Train Dictionary" TEST_TMPDIR=/dev/shm/ ./db_bench -benchmarks=filluniquerandom,compact -compression_type=zstd -compression_max_dict_bytes=$dict_bytes -compression_zstd_max_train_bytes=$train_bytes > /dev/null 2>&1 TEST_TMPDIR=/dev/shm/ ./db_bench -use_existing_db=true -benchmarks=readrandom -cache_size=0 -compression_type=zstd -compression_max_dict_bytes=$dict_bytes -compression_zstd_max_train_bytes=$train_bytes 2>&1 | grep MB/s No Dictionary readrandom : 12.183 micros/op 82082 ops/sec 12.183 seconds 1000000 operations; 9.1 MB/s (1000000 of 1000000 found) Raw Dictionary readrandom : 12.314 micros/op 81205 ops/sec 12.314 seconds 1000000 operations; 9.0 MB/s (1000000 of 1000000 found) FinalizeDict readrandom : 9.787 micros/op 102180 ops/sec 9.787 seconds 1000000 operations; 11.3 MB/s (1000000 of 1000000 found) Train Dictionary readrandom : 9.698 micros/op 103108 ops/sec 9.699 seconds 1000000 operations; 11.4 MB/s (1000000 of 1000000 found) ``` Reviewed By: ajkr Differential Revision: D35720026 Pulled By: cbi42 fbshipit-source-id: 24d230fdff0fd28a1bb650658798f00dfcfb2a1f
2022-05-20 19:09:09 +00:00
options.compression_opts.use_zstd_dict_trainer =
FLAGS_compression_use_zstd_dict_trainer;
options.max_open_files = FLAGS_open_files;
if (FLAGS_cost_write_buffer_to_cache || FLAGS_db_write_buffer_size != 0) {
options.write_buffer_manager.reset(
new WriteBufferManager(FLAGS_db_write_buffer_size, cache_));
}
options.arena_block_size = FLAGS_arena_block_size;
options.write_buffer_size = FLAGS_write_buffer_size;
options.max_write_buffer_number = FLAGS_max_write_buffer_number;
options.min_write_buffer_number_to_merge =
FLAGS_min_write_buffer_number_to_merge;
Support saving history in memtable_list Summary: For transactions, we are using the memtables to validate that there are no write conflicts. But after flushing, we don't have any memtables, and transactions could fail to commit. So we want to someone keep around some extra history to use for conflict checking. In addition, we want to provide a way to increase the size of this history if too many transactions fail to commit. After chatting with people, it seems like everyone prefers just using Memtables to store this history (instead of a separate history structure). It seems like the best place for this is abstracted inside the memtable_list. I decide to create a separate list in MemtableListVersion as using the same list complicated the flush/installalflushresults logic too much. This diff adds a new parameter to control how much memtable history to keep around after flushing. However, it sounds like people aren't too fond of adding new parameters. So I am making the default size of flushed+not-flushed memtables be set to max_write_buffers. This should not change the maximum amount of memory used, but make it more likely we're using closer the the limit. (We are now postponing deleting flushed memtables until the max_write_buffer limit is reached). So while we might use more memory on average, we are still obeying the limit set (and you could argue it's better to go ahead and use up memory now instead of waiting for a write stall to happen to test this limit). However, if people are opposed to this default behavior, we can easily set it to 0 and require this parameter be set in order to use transactions. Test Plan: Added a xfunc test to play around with setting different values of this parameter in all tests. Added testing in memtablelist_test and planning on adding more testing here. Reviewers: sdong, rven, igor Reviewed By: igor Subscribers: dhruba, leveldb Differential Revision: https://reviews.facebook.net/D37443
2015-05-28 23:34:24 +00:00
options.max_write_buffer_number_to_maintain =
FLAGS_max_write_buffer_number_to_maintain;
Refactor trimming logic for immutable memtables (#5022) Summary: MyRocks currently sets `max_write_buffer_number_to_maintain` in order to maintain enough history for transaction conflict checking. The effectiveness of this approach depends on the size of memtables. When memtables are small, it may not keep enough history; when memtables are large, this may consume too much memory. We are proposing a new way to configure memtable list history: by limiting the memory usage of immutable memtables. The new option is `max_write_buffer_size_to_maintain` and it will take precedence over the old `max_write_buffer_number_to_maintain` if they are both set to non-zero values. The new option accounts for the total memory usage of flushed immutable memtables and mutable memtable. When the total usage exceeds the limit, RocksDB may start dropping immutable memtables (which is also called trimming history), starting from the oldest one. The semantics of the old option actually works both as an upper bound and lower bound. History trimming will start if number of immutable memtables exceeds the limit, but it will never go below (limit-1) due to history trimming. In order the mimic the behavior with the new option, history trimming will stop if dropping the next immutable memtable causes the total memory usage go below the size limit. For example, assuming the size limit is set to 64MB, and there are 3 immutable memtables with sizes of 20, 30, 30. Although the total memory usage is 80MB > 64MB, dropping the oldest memtable will reduce the memory usage to 60MB < 64MB, so in this case no memtable will be dropped. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5022 Differential Revision: D14394062 Pulled By: miasantreble fbshipit-source-id: 60457a509c6af89d0993f988c9b5c2aa9e45f5c5
2019-08-23 20:54:09 +00:00
options.max_write_buffer_size_to_maintain =
FLAGS_max_write_buffer_size_to_maintain;
options.max_background_jobs = FLAGS_max_background_jobs;
options.max_background_compactions = FLAGS_max_background_compactions;
options.max_subcompactions = static_cast<uint32_t>(FLAGS_subcompactions);
options.max_background_flushes = FLAGS_max_background_flushes;
options.compaction_style = FLAGS_compaction_style_e;
options.compaction_pri = FLAGS_compaction_pri_e;
options.allow_mmap_reads = FLAGS_mmap_read;
options.allow_mmap_writes = FLAGS_mmap_write;
options.use_direct_reads = FLAGS_use_direct_reads;
options.use_direct_io_for_flush_and_compaction =
FLAGS_use_direct_io_for_flush_and_compaction;
options.manual_wal_flush = FLAGS_manual_wal_flush;
options.wal_compression = FLAGS_wal_compression_e;
options.ttl = FLAGS_fifo_compaction_ttl;
options.compaction_options_fifo = CompactionOptionsFIFO(
FLAGS_fifo_compaction_max_table_files_size_mb * 1024 * 1024,
FLAGS_fifo_compaction_allow_compaction);
options.compaction_options_fifo.age_for_warm = FLAGS_fifo_age_for_warm;
Fix auto_prefix_mode performance with partitioned filters (#10012) Summary: Essentially refactored the RangeMayExist implementation in FullFilterBlockReader to FilterBlockReaderCommon so that it applies to partitioned filters as well. (The function is not called for the block-based filter case.) RangeMayExist is essentially a series of checks around a possible PrefixMayExist, and I'm confident those checks should be the same for partitioned as for full filters. (I think it's likely that bugs remain in those checks, but this change is overall a simplifying one.) Added auto_prefix_mode support to db_bench Other small fixes as well Fixes https://github.com/facebook/rocksdb/issues/10003 Pull Request resolved: https://github.com/facebook/rocksdb/pull/10012 Test Plan: Expanded unit test that uses statistics to check for filter optimization, fails without the production code changes here Performance: populate two DBs with ``` TEST_TMPDIR=/dev/shm/rocksdb_nonpartitioned ./db_bench -benchmarks=fillrandom -num=10000000 -disable_wal=1 -write_buffer_size=30000000 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=8 TEST_TMPDIR=/dev/shm/rocksdb_partitioned ./db_bench -benchmarks=fillrandom -num=10000000 -disable_wal=1 -write_buffer_size=30000000 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=8 -partition_index_and_filters ``` Observe no measurable change in non-partitioned performance ``` TEST_TMPDIR=/dev/shm/rocksdb_nonpartitioned ./db_bench -benchmarks=seekrandom[-X1000] -num=10000000 -readonly -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=8 -auto_prefix_mode -cache_index_and_filter_blocks=1 -cache_size=1000000000 -duration 20 ``` Before: seekrandom [AVG 15 runs] : 11798 (± 331) ops/sec After: seekrandom [AVG 15 runs] : 11724 (± 315) ops/sec Observe big improvement with partitioned (also supported by bloom use statistics) ``` TEST_TMPDIR=/dev/shm/rocksdb_partitioned ./db_bench -benchmarks=seekrandom[-X1000] -num=10000000 -readonly -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=8 -partition_index_and_filters -auto_prefix_mode -cache_index_and_filter_blocks=1 -cache_size=1000000000 -duration 20 ``` Before: seekrandom [AVG 12 runs] : 2942 (± 57) ops/sec After: seekrandom [AVG 12 runs] : 7489 (± 184) ops/sec Reviewed By: siying Differential Revision: D36469796 Pulled By: pdillinger fbshipit-source-id: bcf1e2a68d347b32adb2b27384f945434e7a266d
2022-05-19 20:09:03 +00:00
options.prefix_extractor = prefix_extractor_;
if (FLAGS_use_uint64_comparator) {
options.comparator = test::Uint64Comparator();
if (FLAGS_key_size != 8) {
fprintf(stderr, "Using Uint64 comparator but key size is not 8.\n");
exit(1);
}
}
if (FLAGS_use_stderr_info_logger) {
Automatic table sizing for HyperClockCache (AutoHCC) (#11738) Summary: This change add an experimental next-generation HyperClockCache (HCC) with automatic sizing of the underlying hash table. Both the existing version (stable) and the new version (experimental for now) of HCC are available depending on whether an estimated average entry charge is provided in HyperClockCacheOptions. Internally, we call the two implementations AutoHyperClockCache (new) and FixedHyperClockCache (existing). The performance characteristics and much of the underlying logic are similar enough that AutoHCC is likely to make FixedHCC obsolete, and so it's best considered an evolution of the same technology or solution rather than an alternative. More specifically, both implementations share essentially the same logic for managing the state of individual entries in the cache, including metadata for reference counting and counting clocks for eviction. This metadata, which I like to call the "low-level HCC protocol," includes a read-write lock on entries, but relaxed consistency requirements on the cache (e.g. allowing rare duplication) means high-level cache operations never wait for these low-level per-entry locks. FixedHCC is fully wait-free. AutoHCC is different in how entries are indexed into an efficient hash table. AutoHCC is "essentially wait-free" as there is no pattern of typical high-level operations on a large cache that can lead to one thread waiting on another to complete some work, though it can happen in some unusual/unlucky cases, or atypical uses such as erasing specific cache keys. Table growth and entry reclamation is more complex in AutoHCC compared to FixedHCC, so uses some localized locking to manage that. AutoHCC uses linear hashing to grow the table as needed, with low latency and to a precise size. AutoHCC depends on anonymous mmap support from the OS (currently verified working on Linux, MacOS, and Windows) to allow the array underlying a hash table to grow in place without wasting resident memory on space reserved but unused. AutoHCC uses a form of chaining while FixedHCC uses open addressing and double hashing. More specifics: * In developing this PR, a rare availability bug (minor) was noticed in the existing HCC implementation of Release()+erase_if_last_ref, which is now inherited into AutoHCC. Fixing this without a performance regression will not be simple, so is left for follow-up work. * Some existing unit tests required adjustment of operational parameters or conditions to work with the new behaviors of AutoHCC. A number of bugs were found and fixed in the validation process, including getting unit tests in good working order. * Added an option to cache_bench, `-degenerate_hash_bits` for correctness stress testing described below. For this, the tool uses the reverse-engineered hash function for HCC to generate keys in which the specified number of hash bits, in critical positions, have a fixed value. Essentially each degenerate hash bit will half the number of chain heads utilized and double the average chain length. Pull Request resolved: https://github.com/facebook/rocksdb/pull/11738 Test Plan: unit tests updated, and already added to db crash test. Also ## Correctness The code includes generous assertions to check for unexpected states, especially at destruction time, so should be able to detect critical concurrency bugs. Less serious "availability bugs" in which cache data is hidden or cleanly lost are more difficult to detect, but also less scary for data correctness (as long as performance is good and the design is sound). In average operation, the structure is extremely low stress and low contention (see next section) so stressing the corner case logic requires artificially stressing the operating conditions. First, we keep the structure small to increase the number of threads hitting the same chain or entry, and just one cache shard. Second, we artificially degrade the hashing so that chains are much longer than typical, using the new `-degenerate_hash_bits` option to cache_bench. Third, we re-create the structure from scratch frequently in order to exercise the Grow logic repeatedly and to get the benefit of the consistency checks in the structure's destructor in debug builds. For cache_bench this also means disabling the single-threaded "populate cache" step (normally used for steady state performance testing). And of course use many more threads than cores to have many preemptions. An effective test for working out bugs was this (using debug build of course): ``` while ./cache_bench -cache_type=auto_hyper_clock_cache -histograms=0 -cache_size=8000000 -threads=100 -populate_cache=0 -ops_per_thread=10000 -degenerate_hash_bits=6 -num_shard_bits=0; do :; done ``` Or even smaller cases. This setup has around 27 utilized chains, with around 35 entries each, and yield-waits more than 1 million times per second (very high contention; see next section). I have let this run for hours searching for any lingering issues. I've also run cache_bench under ASAN, UBSAN, and TSAN. ## Essentially wait free There is a counter for number of yield() calls when one thread is waiting on another. When we pre-populate the structure in a single thread, ``` ./cache_bench -cache_type=auto_hyper_clock_cache -histograms=0 -populate_cache=1 -ops_per_thread=200000 2>&1 | grep Yield ``` We see something on the order of 1 yield call per second across 16 threads, even when we load the system other other jobs (parallel compilation). With -populate_cache=0, there are more yield opportunities with parallel table growth. On an otherwise unloaded system, we still see very small (single digit) yield counts, with a chance of getting into the thousands, and getting into 10s of thousands per second during table growth phase if the system is loaded with other jobs. However, I am not worried about this if performance is still good (see next section). ## Overall performance Although cache_bench initially suggested performance very close to FixedHCC, there was a very noticeable performance hit under a db_bench setup like used in validating https://github.com/facebook/rocksdb/issues/10626. Much of the difference has been reduced by optimizing Lookup with a "naive" pass that will almost always find entries quickly, and only falling back to the careful Lookup algorithm when not found in the first pass. Setups (chosen to be sensitive to block cache performance), and compiled with USE_CLANG=1 JEMALLOC=1 PORTABLE=0 DEBUG_LEVEL=0: ``` TEST_TMPDIR=/dev/shm base/db_bench -benchmarks=fillrandom -num=30000000 -disable_wal=1 -bloom_bits=16 ``` ### No regression on FixedHCC Running before & after builds at the same time on a 48 core machine. ``` TEST_TMPDIR=/dev/shm /usr/bin/time ./db_bench -benchmarks=readrandom[-X10],block_cache_entry_stats,cache_report_problems -readonly -num=30000000 -bloom_bits=16 -cache_index_and_filter_blocks=1 -cache_size=610000000 -duration 20 -threads=24 -cache_type=fixed_hyper_clock_cache -seed=1234 ``` Before: readrandom [AVG 10 runs] : 847234 (± 8150) ops/sec; 59.2 (± 0.6) MB/sec 703MB max RSS After: readrandom [AVG 10 runs] : 851021 (± 7929) ops/sec; 59.5 (± 0.6) MB/sec 706MB max RSS Probably no material difference. ### Single-threaded performance Using `[-X2]` and `-threads=1` and `-duration=30`, running all three at the same time: lru_cache: 55100 ops/sec, then 55862 ops/sec (627MB max RSS) fixed_hyper_clock_cache: 60496 ops/sec, then 61231 ops/sec (626MB max RSS) auto_hyper_clock_cache: 47560 ops/sec, then 56081 ops/sec (626MB max RSS) So AutoHCC has more ramp-up cost in the first pass as the cache grows to the appropriate size. (In single-threaded operation, the parallelizability and per-op low latency of table growth is overall slower.) However, once up to size, its performance is comparable to LRUCache. FixedHCC's lean operations still win overall when a good estimate is available. If we look at HCC table stats, we can see that this configuration is not favorable to AutoHCC (and I have verified that other memory sizes do not yield substantially different results, until shards are under-sized for the full filters): FixedHCC: Slot occupancy stats: Overall 47% (124991/262144), Min/Max/Window = 28%/64%/500, MaxRun{Pos/Neg} = 17/22 AutoHCC: Slot occupancy stats: Overall 59% (125781/209682), Min/Max/Window = 43%/82%/500, MaxRun{Pos/Neg} = 76/16 Head occupancy stats: Overall 43% (92259/209682), Min/Max/Window = 24%/74%/500, MaxRun{Pos/Neg} = 19/26 Entries at home count: 53350 FixedHCC configuration is relatively good for speed, and not ideal for space utilization. As is typical, AutoHCC has tighter control on metadata usage (209682 x 64 bytes rather than 262144 x 64 bytes), and the higher load factor is slightly worse for speed. LRUCache also has more metadata usage, at 199680 x 96 bytes of tracked metadata (plus roughly another 10% of that untracked in the head pointers), and that metadata is subject to fragmentation. ### Parallel performance, high hit rate Now using `[-X10]` and `-threads=10`, all three at the same time lru_cache: [AVG 10 runs] : 263629 (± 1425) ops/sec; 18.4 (± 0.1) MB/sec 655MB max RSS, 97.1% cache hit rate fixed_hyper_clock_cache: [AVG 10 runs] : 479590 (± 8114) ops/sec; 33.5 (± 0.6) MB/sec 651MB max RSS, 97.1% cache hit rate auto_hyper_clock_cache: [AVG 10 runs] : 418687 (± 5915) ops/sec; 29.3 (± 0.4) MB/sec 657MB max RSS, 97.1% cache hit rate Even with just 10-way parallelism for each cache (though 30+/48 cores busy overall), LRUCache is already showing performance degradation, while AutoHCC is in the neighborhood of FixedHCC. And that brings us to the question of how AutoHCC holds up under extreme parallelism, so now independent runs with `-threads=100` (overloading 48 cores). lru_cache: 438613 ops/sec, 827MB max RSS fixed_hyper_clock_cache: 1651310 ops/sec, 812MB max RSS auto_hyper_clock_cache: 1505875 ops/sec, 821MB max RSS (Yield count: 1089 over 30s) Clearly, AutoHCC holds up extremely well under extreme parallelism, even closing some of the modest performance gap with FixedHCC. ### Parallel performance, low hit rate To get down to roughly 50% cache hit rate, we use `-cache_index_and_filter_blocks=0 -cache_size=1650000000` with `-threads=10`. Here the extra cost of running counting clock eviction, especially on the chains of AutoHCC, are evident, especially with the lower contention of cache_index_and_filter_blocks=0: lru_cache: 725231 ops/sec, 1770MB max RSS, 51.3% hit rate fixed_hyper_clock_cache: 638620 ops/sec, 1765MB max RSS, 50.2% hit rate auto_hyper_clock_cache: 541018 ops/sec, 1777MB max RSS, 50.8% hit rate Reviewed By: jowlyzhang Differential Revision: D48784755 Pulled By: pdillinger fbshipit-source-id: e79813dc087474ac427637dd282a14fa3011a6e4
2023-09-01 22:44:38 +00:00
options.info_log = std::make_shared<StderrLogger>();
}
options.memtable_huge_page_size = FLAGS_memtable_use_huge_page ? 2048 : 0;
options.memtable_prefix_bloom_size_ratio = FLAGS_memtable_bloom_size_ratio;
options.memtable_whole_key_filtering = FLAGS_memtable_whole_key_filtering;
if (FLAGS_memtable_insert_with_hint_prefix_size > 0) {
options.memtable_insert_with_hint_prefix_extractor.reset(
NewCappedPrefixTransform(
FLAGS_memtable_insert_with_hint_prefix_size));
}
options.bloom_locality = FLAGS_bloom_locality;
options.max_file_opening_threads = FLAGS_file_opening_threads;
options.compaction_readahead_size = FLAGS_compaction_readahead_size;
options.log_readahead_size = FLAGS_log_readahead_size;
options.random_access_max_buffer_size = FLAGS_random_access_max_buffer_size;
options.writable_file_max_buffer_size = FLAGS_writable_file_max_buffer_size;
options.use_fsync = FLAGS_use_fsync;
options.num_levels = FLAGS_num_levels;
options.target_file_size_base = FLAGS_target_file_size_base;
options.target_file_size_multiplier = FLAGS_target_file_size_multiplier;
options.max_bytes_for_level_base = FLAGS_max_bytes_for_level_base;
options.level_compaction_dynamic_level_bytes =
FLAGS_level_compaction_dynamic_level_bytes;
options.max_bytes_for_level_multiplier =
FLAGS_max_bytes_for_level_multiplier;
Status s =
CreateMemTableRepFactory(config_options, &options.memtable_factory);
if (!s.ok()) {
fprintf(stderr, "Could not create memtable factory: %s\n",
s.ToString().c_str());
exit(1);
} else if ((FLAGS_prefix_size == 0) &&
(options.memtable_factory->IsInstanceOf("prefix_hash") ||
options.memtable_factory->IsInstanceOf("hash_linkedlist"))) {
fprintf(stderr,
"prefix_size should be non-zero if PrefixHash or "
"HashLinkedList memtablerep is used\n");
exit(1);
}
if (FLAGS_use_plain_table) {
if (!options.memtable_factory->IsInstanceOf("prefix_hash") &&
!options.memtable_factory->IsInstanceOf("hash_linkedlist")) {
fprintf(stderr, "Warning: plain table is used with %s\n",
options.memtable_factory->Name());
}
int bloom_bits_per_key = FLAGS_bloom_bits;
if (bloom_bits_per_key < 0) {
bloom_bits_per_key = PlainTableOptions().bloom_bits_per_key;
}
PlainTableOptions plain_table_options;
plain_table_options.user_key_len = FLAGS_key_size;
plain_table_options.bloom_bits_per_key = bloom_bits_per_key;
plain_table_options.hash_table_ratio = 0.75;
options.table_factory = std::shared_ptr<TableFactory>(
NewPlainTableFactory(plain_table_options));
} else if (FLAGS_use_cuckoo_table) {
if (FLAGS_cuckoo_hash_ratio > 1 || FLAGS_cuckoo_hash_ratio < 0) {
fprintf(stderr, "Invalid cuckoo_hash_ratio\n");
exit(1);
}
if (!FLAGS_mmap_read) {
fprintf(stderr, "cuckoo table format requires mmap read to operate\n");
exit(1);
}
ROCKSDB_NAMESPACE::CuckooTableOptions table_options;
CuckooTable: add one option to allow identity function for the first hash function Summary: MurmurHash becomes expensive when we do millions Get() a second in one thread. Add this option to allow the first hash function to use identity function as hash function. It results in QPS increase from 3.7M/s to ~4.3M/s. I did not observe improvement for end to end RocksDB performance. This may be caused by other bottlenecks that I will address in a separate diff. Test Plan: ``` [ljin@dev1964 rocksdb] ./cuckoo_table_reader_test --enable_perf --file_dir=/dev/shm --write --identity_as_first_hash=0 ==== Test CuckooReaderTest.WhenKeyExists ==== Test CuckooReaderTest.WhenKeyExistsWithUint64Comparator ==== Test CuckooReaderTest.CheckIterator ==== Test CuckooReaderTest.CheckIteratorUint64 ==== Test CuckooReaderTest.WhenKeyNotFound ==== Test CuckooReaderTest.TestReadPerformance With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.272us (3.7 Mqps) with batch size of 0, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.138us (7.2 Mqps) with batch size of 10, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.142us (7.1 Mqps) with batch size of 25, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.142us (7.0 Mqps) with batch size of 50, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.144us (6.9 Mqps) with batch size of 100, # of found keys 125829120 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.201us (5.0 Mqps) with batch size of 0, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.121us (8.3 Mqps) with batch size of 10, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.123us (8.1 Mqps) with batch size of 25, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.121us (8.3 Mqps) with batch size of 50, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.112us (8.9 Mqps) with batch size of 100, # of found keys 104857600 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.251us (4.0 Mqps) with batch size of 0, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.107us (9.4 Mqps) with batch size of 10, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.099us (10.1 Mqps) with batch size of 25, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.100us (10.0 Mqps) with batch size of 50, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.116us (8.6 Mqps) with batch size of 100, # of found keys 83886080 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.189us (5.3 Mqps) with batch size of 0, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.095us (10.5 Mqps) with batch size of 10, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.096us (10.4 Mqps) with batch size of 25, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.098us (10.2 Mqps) with batch size of 50, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.105us (9.5 Mqps) with batch size of 100, # of found keys 73400320 [ljin@dev1964 rocksdb] ./cuckoo_table_reader_test --enable_perf --file_dir=/dev/shm --write --identity_as_first_hash=1 ==== Test CuckooReaderTest.WhenKeyExists ==== Test CuckooReaderTest.WhenKeyExistsWithUint64Comparator ==== Test CuckooReaderTest.CheckIterator ==== Test CuckooReaderTest.CheckIteratorUint64 ==== Test CuckooReaderTest.WhenKeyNotFound ==== Test CuckooReaderTest.TestReadPerformance With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.230us (4.3 Mqps) with batch size of 0, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.086us (11.7 Mqps) with batch size of 10, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.088us (11.3 Mqps) with batch size of 25, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.083us (12.1 Mqps) with batch size of 50, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.083us (12.1 Mqps) with batch size of 100, # of found keys 125829120 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.159us (6.3 Mqps) with batch size of 0, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.078us (12.8 Mqps) with batch size of 10, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.080us (12.6 Mqps) with batch size of 25, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.080us (12.5 Mqps) with batch size of 50, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.082us (12.2 Mqps) with batch size of 100, # of found keys 104857600 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.154us (6.5 Mqps) with batch size of 0, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.077us (13.0 Mqps) with batch size of 10, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.077us (12.9 Mqps) with batch size of 25, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.078us (12.8 Mqps) with batch size of 50, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.079us (12.6 Mqps) with batch size of 100, # of found keys 83886080 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.218us (4.6 Mqps) with batch size of 0, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.083us (12.0 Mqps) with batch size of 10, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.085us (11.7 Mqps) with batch size of 25, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.086us (11.6 Mqps) with batch size of 50, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.078us (12.8 Mqps) with batch size of 100, # of found keys 73400320 ``` Reviewers: sdong, igor, yhchiang Reviewed By: igor Subscribers: leveldb Differential Revision: https://reviews.facebook.net/D23451
2014-09-18 18:00:48 +00:00
table_options.hash_table_ratio = FLAGS_cuckoo_hash_ratio;
table_options.identity_as_first_hash = FLAGS_identity_as_first_hash;
options.table_factory =
std::shared_ptr<TableFactory>(NewCuckooTableFactory(table_options));
} else {
BlockBasedTableOptions block_based_options;
Implement XXH3 block checksum type (#9069) Summary: XXH3 - latest hash function that is extremely fast on large data, easily faster than crc32c on most any x86_64 hardware. In integrating this hash function, I have handled the compression type byte in a non-standard way to avoid using the streaming API (extra data movement and active code size because of hash function complexity). This approach got a thumbs-up from Yann Collet. Existing functionality change: * reject bad ChecksumType in options with InvalidArgument This change split off from https://github.com/facebook/rocksdb/issues/9058 because context-aware checksum is likely to be handled through different configuration than ChecksumType. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9069 Test Plan: tests updated, and substantially expanded. Unit tests now check that we don't accidentally change the values generated by the checksum algorithms ("schema test") and that we properly handle invalid/unrecognized checksum types in options or in file footer. DBTestBase::ChangeOptions (etc.) updated from two to one configuration changing from default CRC32c ChecksumType. The point of this test code is to detect possible interactions among features, and the likelihood of some bad interaction being detected by including configurations other than XXH3 and CRC32c--and then not detected by stress/crash test--is extremely low. Stress/crash test also updated (manual run long enough to see it accepts new checksum type). db_bench also updated for microbenchmarking checksums. ### Performance microbenchmark (PORTABLE=0 DEBUG_LEVEL=0, Broadwell processor) ./db_bench -benchmarks=crc32c,xxhash,xxhash64,xxh3,crc32c,xxhash,xxhash64,xxh3,crc32c,xxhash,xxhash64,xxh3 crc32c : 0.200 micros/op 5005220 ops/sec; 19551.6 MB/s (4096 per op) xxhash : 0.807 micros/op 1238408 ops/sec; 4837.5 MB/s (4096 per op) xxhash64 : 0.421 micros/op 2376514 ops/sec; 9283.3 MB/s (4096 per op) xxh3 : 0.171 micros/op 5858391 ops/sec; 22884.3 MB/s (4096 per op) crc32c : 0.206 micros/op 4859566 ops/sec; 18982.7 MB/s (4096 per op) xxhash : 0.793 micros/op 1260850 ops/sec; 4925.2 MB/s (4096 per op) xxhash64 : 0.410 micros/op 2439182 ops/sec; 9528.1 MB/s (4096 per op) xxh3 : 0.161 micros/op 6202872 ops/sec; 24230.0 MB/s (4096 per op) crc32c : 0.203 micros/op 4924686 ops/sec; 19237.1 MB/s (4096 per op) xxhash : 0.839 micros/op 1192388 ops/sec; 4657.8 MB/s (4096 per op) xxhash64 : 0.424 micros/op 2357391 ops/sec; 9208.6 MB/s (4096 per op) xxh3 : 0.162 micros/op 6182678 ops/sec; 24151.1 MB/s (4096 per op) As you can see, especially once warmed up, xxh3 is fastest. ### Performance macrobenchmark (PORTABLE=0 DEBUG_LEVEL=0, Broadwell processor) Test for I in `seq 1 50`; do for CHK in 0 1 2 3 4; do TEST_TMPDIR=/dev/shm/rocksdb$CHK ./db_bench -benchmarks=fillseq -memtablerep=vector -allow_concurrent_memtable_write=false -num=30000000 -checksum_type=$CHK 2>&1 | grep 'micros/op' | tee -a results-$CHK & done; wait; done Results (ops/sec) for FILE in results*; do echo -n "$FILE "; awk '{ s += $5; c++; } END { print 1.0 * s / c; }' < $FILE; done results-0 252118 # kNoChecksum results-1 251588 # kCRC32c results-2 251863 # kxxHash results-3 252016 # kxxHash64 results-4 252038 # kXXH3 Reviewed By: mrambacher Differential Revision: D31905249 Pulled By: pdillinger fbshipit-source-id: cb9b998ebe2523fc7c400eedf62124a78bf4b4d1
2021-10-29 05:13:47 +00:00
block_based_options.checksum =
static_cast<ChecksumType>(FLAGS_checksum_type);
if (FLAGS_use_hash_search) {
if (FLAGS_prefix_size == 0) {
fprintf(stderr,
"prefix_size not assigned when enable use_hash_search \n");
exit(1);
}
block_based_options.index_type = BlockBasedTableOptions::kHashSearch;
} else {
block_based_options.index_type = BlockBasedTableOptions::kBinarySearch;
}
if (FLAGS_partition_index_and_filters || FLAGS_partition_index) {
if (FLAGS_index_with_first_key) {
fprintf(stderr,
"--index_with_first_key is not compatible with"
" partition index.");
}
if (FLAGS_use_hash_search) {
fprintf(stderr,
"use_hash_search is incompatible with "
"partition index and is ignored");
}
block_based_options.index_type =
BlockBasedTableOptions::kTwoLevelIndexSearch;
block_based_options.metadata_block_size = FLAGS_metadata_block_size;
if (FLAGS_partition_index_and_filters) {
block_based_options.partition_filters = true;
}
} else if (FLAGS_index_with_first_key) {
block_based_options.index_type =
BlockBasedTableOptions::kBinarySearchWithFirstKey;
}
BlockBasedTableOptions::IndexShorteningMode index_shortening =
block_based_options.index_shortening;
switch (FLAGS_index_shortening_mode) {
case 0:
index_shortening =
BlockBasedTableOptions::IndexShorteningMode::kNoShortening;
break;
case 1:
index_shortening =
BlockBasedTableOptions::IndexShorteningMode::kShortenSeparators;
break;
case 2:
index_shortening = BlockBasedTableOptions::IndexShorteningMode::
kShortenSeparatorsAndSuccessor;
break;
default:
fprintf(stderr, "Unknown key shortening mode\n");
}
Minimize memory internal fragmentation for Bloom filters (#6427) Summary: New experimental option BBTO::optimize_filters_for_memory builds filters that maximize their use of "usable size" from malloc_usable_size, which is also used to compute block cache charges. Rather than always "rounding up," we track state in the BloomFilterPolicy object to mix essentially "rounding down" and "rounding up" so that the average FP rate of all generated filters is the same as without the option. (YMMV as heavily accessed filters might be unluckily lower accuracy.) Thus, the option near-minimizes what the block cache considers as "memory used" for a given target Bloom filter false positive rate and Bloom filter implementation. There are no forward or backward compatibility issues with this change, though it only works on the format_version=5 Bloom filter. With Jemalloc, we see about 10% reduction in memory footprint (and block cache charge) for Bloom filters, but 1-2% increase in storage footprint, due to encoding efficiency losses (FP rate is non-linear with bits/key). Why not weighted random round up/down rather than state tracking? By only requiring malloc_usable_size, we don't actually know what the next larger and next smaller usable sizes for the allocator are. We pick a requested size, accept and use whatever usable size it has, and use the difference to inform our next choice. This allows us to narrow in on the right balance without tracking/predicting usable sizes. Why not weight history of generated filter false positive rates by number of keys? This could lead to excess skew in small filters after generating a large filter. Results from filter_bench with jemalloc (irrelevant details omitted): (normal keys/filter, but high variance) $ ./filter_bench -quick -impl=2 -average_keys_per_filter=30000 -vary_key_count_ratio=0.9 Build avg ns/key: 29.6278 Number of filters: 5516 Total size (MB): 200.046 Reported total allocated memory (MB): 220.597 Reported internal fragmentation: 10.2732% Bits/key stored: 10.0097 Average FP rate %: 0.965228 $ ./filter_bench -quick -impl=2 -average_keys_per_filter=30000 -vary_key_count_ratio=0.9 -optimize_filters_for_memory Build avg ns/key: 30.5104 Number of filters: 5464 Total size (MB): 200.015 Reported total allocated memory (MB): 200.322 Reported internal fragmentation: 0.153709% Bits/key stored: 10.1011 Average FP rate %: 0.966313 (very few keys / filter, optimization not as effective due to ~59 byte internal fragmentation in blocked Bloom filter representation) $ ./filter_bench -quick -impl=2 -average_keys_per_filter=1000 -vary_key_count_ratio=0.9 Build avg ns/key: 29.5649 Number of filters: 162950 Total size (MB): 200.001 Reported total allocated memory (MB): 224.624 Reported internal fragmentation: 12.3117% Bits/key stored: 10.2951 Average FP rate %: 0.821534 $ ./filter_bench -quick -impl=2 -average_keys_per_filter=1000 -vary_key_count_ratio=0.9 -optimize_filters_for_memory Build avg ns/key: 31.8057 Number of filters: 159849 Total size (MB): 200 Reported total allocated memory (MB): 208.846 Reported internal fragmentation: 4.42297% Bits/key stored: 10.4948 Average FP rate %: 0.811006 (high keys/filter) $ ./filter_bench -quick -impl=2 -average_keys_per_filter=1000000 -vary_key_count_ratio=0.9 Build avg ns/key: 29.7017 Number of filters: 164 Total size (MB): 200.352 Reported total allocated memory (MB): 221.5 Reported internal fragmentation: 10.5552% Bits/key stored: 10.0003 Average FP rate %: 0.969358 $ ./filter_bench -quick -impl=2 -average_keys_per_filter=1000000 -vary_key_count_ratio=0.9 -optimize_filters_for_memory Build avg ns/key: 30.7131 Number of filters: 160 Total size (MB): 200.928 Reported total allocated memory (MB): 200.938 Reported internal fragmentation: 0.00448054% Bits/key stored: 10.1852 Average FP rate %: 0.963387 And from db_bench (block cache) with jemalloc: $ ./db_bench -db=/dev/shm/dbbench.no_optimize -benchmarks=fillrandom -format_version=5 -value_size=90 -bloom_bits=10 -num=2000000 -threads=8 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=false $ ./db_bench -db=/dev/shm/dbbench -benchmarks=fillrandom -format_version=5 -value_size=90 -bloom_bits=10 -num=2000000 -threads=8 -optimize_filters_for_memory -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=false $ (for FILE in /dev/shm/dbbench.no_optimize/*.sst; do ./sst_dump --file=$FILE --show_properties | grep 'filter block' ; done) | awk '{ t += $4; } END { print t; }' 17063835 $ (for FILE in /dev/shm/dbbench/*.sst; do ./sst_dump --file=$FILE --show_properties | grep 'filter block' ; done) | awk '{ t += $4; } END { print t; }' 17430747 $ #^ 2.1% additional filter storage $ ./db_bench -db=/dev/shm/dbbench.no_optimize -use_existing_db -benchmarks=readrandom,stats -statistics -bloom_bits=10 -num=2000000 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=false -duration=10 -cache_index_and_filter_blocks -cache_size=1000000000 rocksdb.block.cache.index.add COUNT : 33 rocksdb.block.cache.index.bytes.insert COUNT : 8440400 rocksdb.block.cache.filter.add COUNT : 33 rocksdb.block.cache.filter.bytes.insert COUNT : 21087528 rocksdb.bloom.filter.useful COUNT : 4963889 rocksdb.bloom.filter.full.positive COUNT : 1214081 rocksdb.bloom.filter.full.true.positive COUNT : 1161999 $ #^ 1.04 % observed FP rate $ ./db_bench -db=/dev/shm/dbbench -use_existing_db -benchmarks=readrandom,stats -statistics -bloom_bits=10 -num=2000000 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=false -optimize_filters_for_memory -duration=10 -cache_index_and_filter_blocks -cache_size=1000000000 rocksdb.block.cache.index.add COUNT : 33 rocksdb.block.cache.index.bytes.insert COUNT : 8448592 rocksdb.block.cache.filter.add COUNT : 33 rocksdb.block.cache.filter.bytes.insert COUNT : 18220328 rocksdb.bloom.filter.useful COUNT : 5360933 rocksdb.bloom.filter.full.positive COUNT : 1321315 rocksdb.bloom.filter.full.true.positive COUNT : 1262999 $ #^ 1.08 % observed FP rate, 13.6% less memory usage for filters (Due to specific key density, this example tends to generate filters that are "worse than average" for internal fragmentation. "Better than average" cases can show little or no improvement.) Pull Request resolved: https://github.com/facebook/rocksdb/pull/6427 Test Plan: unit test added, 'make check' with gcc, clang and valgrind Reviewed By: siying Differential Revision: D22124374 Pulled By: pdillinger fbshipit-source-id: f3e3aa152f9043ddf4fae25799e76341d0d8714e
2020-06-22 20:30:57 +00:00
block_based_options.optimize_filters_for_memory =
FLAGS_optimize_filters_for_memory;
block_based_options.index_shortening = index_shortening;
if (cache_ == nullptr) {
block_based_options.no_block_cache = true;
}
block_based_options.cache_index_and_filter_blocks =
FLAGS_cache_index_and_filter_blocks;
block_based_options.pin_l0_filter_and_index_blocks_in_cache =
FLAGS_pin_l0_filter_and_index_blocks_in_cache;
block_based_options.pin_top_level_index_and_filter =
FLAGS_pin_top_level_index_and_filter;
if (FLAGS_cache_high_pri_pool_ratio > 1e-6) { // > 0.0 + eps
block_based_options.cache_index_and_filter_blocks_with_high_priority =
true;
}
if (FLAGS_cache_high_pri_pool_ratio + FLAGS_cache_low_pri_pool_ratio >
1.0) {
fprintf(stderr,
"Sum of high_pri_pool_ratio and low_pri_pool_ratio "
"cannot exceed 1.0.\n");
}
block_based_options.block_cache = cache_;
Rewrite memory-charging feature's option API (#9926) Summary: **Context:** Previous PR https://github.com/facebook/rocksdb/pull/9748, https://github.com/facebook/rocksdb/pull/9073, https://github.com/facebook/rocksdb/pull/8428 added separate flag for each charged memory area. Such API design is not scalable as we charge more and more memory areas. Also, we foresee an opportunity to consolidate this feature with other cache usage related features such as `cache_index_and_filter_blocks` using `CacheEntryRole`. Therefore we decided to consolidate all these flags with `CacheUsageOptions cache_usage_options` and this PR serves as the first step by consolidating memory-charging related flags. **Summary:** - Replaced old API reference with new ones, including making `kCompressionDictionaryBuildingBuffer` opt-out and added a unit test for that - Added missing db bench/stress test for some memory charging features - Renamed related test suite to indicate they are under the same theme of memory charging - Refactored a commonly used mocked cache component in memory charging related tests to reduce code duplication - Replaced the phrases "memory tracking" / "cache reservation" (other than CacheReservationManager-related ones) with "memory charging" for standard description of this feature. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9926 Test Plan: - New unit test for opt-out `kCompressionDictionaryBuildingBuffer` `TEST_F(ChargeCompressionDictionaryBuildingBufferTest, Basic)` - New unit test for option validation/sanitization `TEST_F(CacheUsageOptionsOverridesTest, SanitizeAndValidateOptions)` - CI - db bench (in case querying new options introduces regression) **+0.5% micros/op**: `TEST_TMPDIR=/dev/shm/testdb ./db_bench -benchmarks=fillseq -db=$TEST_TMPDIR -charge_compression_dictionary_building_buffer=1(remove this for comparison) -compression_max_dict_bytes=10000 -disable_auto_compactions=1 -write_buffer_size=100000 -num=4000000 | egrep 'fillseq'` #-run | (pre-PR) avg micros/op | std micros/op | (post-PR) micros/op | std micros/op | change (%) -- | -- | -- | -- | -- | -- 10 | 3.9711 | 0.264408 | 3.9914 | 0.254563 | 0.5111933721 20 | 3.83905 | 0.0664488 | 3.8251 | 0.0695456 | **-0.3633711465** 40 | 3.86625 | 0.136669 | 3.8867 | 0.143765 | **0.5289363078** - db_stress: `python3 tools/db_crashtest.py blackbox -charge_compression_dictionary_building_buffer=1 -charge_filter_construction=1 -charge_table_reader=1 -cache_size=1` killed as normal Reviewed By: ajkr Differential Revision: D36054712 Pulled By: hx235 fbshipit-source-id: d406e90f5e0c5ea4dbcb585a484ad9302d4302af
2022-05-17 22:01:51 +00:00
block_based_options.cache_usage_options.options_overrides.insert(
{CacheEntryRole::kCompressionDictionaryBuildingBuffer,
{/*.charged = */ FLAGS_charge_compression_dictionary_building_buffer
? CacheEntryRoleOptions::Decision::kEnabled
: CacheEntryRoleOptions::Decision::kDisabled}});
block_based_options.cache_usage_options.options_overrides.insert(
{CacheEntryRole::kFilterConstruction,
{/*.charged = */ FLAGS_charge_filter_construction
? CacheEntryRoleOptions::Decision::kEnabled
: CacheEntryRoleOptions::Decision::kDisabled}});
block_based_options.cache_usage_options.options_overrides.insert(
{CacheEntryRole::kBlockBasedTableReader,
{/*.charged = */ FLAGS_charge_table_reader
? CacheEntryRoleOptions::Decision::kEnabled
: CacheEntryRoleOptions::Decision::kDisabled}});
Account memory of FileMetaData in global memory limit (#9924) Summary: **Context/Summary:** As revealed by heap profiling, allocation of `FileMetaData` for [newly created file added to a Version](https://github.com/facebook/rocksdb/pull/9924/files#diff-a6aa385940793f95a2c5b39cc670bd440c4547fa54fd44622f756382d5e47e43R774) can consume significant heap memory. This PR is to account that toward our global memory limit based on block cache capacity. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9924 Test Plan: - Previous `make check` verified there are only 2 places where the memory of the allocated `FileMetaData` can be released - New unit test `TEST_P(ChargeFileMetadataTestWithParam, Basic)` - db bench (CPU cost of `charge_file_metadata` in write and compact) - **write micros/op: -0.24%** : `TEST_TMPDIR=/dev/shm/testdb ./db_bench -benchmarks=fillseq -db=$TEST_TMPDIR -charge_file_metadata=1 (remove this option for pre-PR) -disable_auto_compactions=1 -write_buffer_size=100000 -num=4000000 | egrep 'fillseq'` - **compact micros/op -0.87%** : `TEST_TMPDIR=/dev/shm/testdb ./db_bench -benchmarks=fillseq -db=$TEST_TMPDIR -charge_file_metadata=1 -disable_auto_compactions=1 -write_buffer_size=100000 -num=4000000 -numdistinct=1000 && ./db_bench -benchmarks=compact -db=$TEST_TMPDIR -use_existing_db=1 -charge_file_metadata=1 -disable_auto_compactions=1 | egrep 'compact'` table 1 - write #-run | (pre-PR) avg micros/op | std micros/op | (post-PR) micros/op | std micros/op | change (%) -- | -- | -- | -- | -- | -- 10 | 3.9711 | 0.264408 | 3.9914 | 0.254563 | 0.5111933721 20 | 3.83905 | 0.0664488 | 3.8251 | 0.0695456 | -0.3633711465 40 | 3.86625 | 0.136669 | 3.8867 | 0.143765 | 0.5289363078 80 | 3.87828 | 0.119007 | 3.86791 | 0.115674 | **-0.2673865734** 160 | 3.87677 | 0.162231 | 3.86739 | 0.16663 | **-0.2419539978** table 2 - compact #-run | (pre-PR) avg micros/op | std micros/op | (post-PR) micros/op | std micros/op | change (%) -- | -- | -- | -- | -- | -- 10 | 2,399,650.00 | 96,375.80 | 2,359,537.00 | 53,243.60 | -1.67 20 | 2,410,480.00 | 89,988.00 | 2,433,580.00 | 91,121.20 | 0.96 40 | 2.41E+06 | 121811 | 2.39E+06 | 131525 | **-0.96** 80 | 2.40E+06 | 134503 | 2.39E+06 | 108799 | **-0.78** - stress test: `python3 tools/db_crashtest.py blackbox --charge_file_metadata=1 --cache_size=1` killed as normal Reviewed By: ajkr Differential Revision: D36055583 Pulled By: hx235 fbshipit-source-id: b60eab94707103cb1322cf815f05810ef0232625
2022-06-14 20:06:40 +00:00
block_based_options.cache_usage_options.options_overrides.insert(
{CacheEntryRole::kFileMetadata,
{/*.charged = */ FLAGS_charge_file_metadata
? CacheEntryRoleOptions::Decision::kEnabled
: CacheEntryRoleOptions::Decision::kDisabled}});
block_based_options.cache_usage_options.options_overrides.insert(
{CacheEntryRole::kBlobCache,
{/*.charged = */ FLAGS_charge_blob_cache
? CacheEntryRoleOptions::Decision::kEnabled
: CacheEntryRoleOptions::Decision::kDisabled}});
block_based_options.block_size = FLAGS_block_size;
block_based_options.block_restart_interval = FLAGS_block_restart_interval;
block_based_options.index_block_restart_interval =
FLAGS_index_block_restart_interval;
block_based_options.format_version =
static_cast<uint32_t>(FLAGS_format_version);
block_based_options.read_amp_bytes_per_bit = FLAGS_read_amp_bytes_per_bit;
block_based_options.enable_index_compression =
FLAGS_enable_index_compression;
block_based_options.block_align = FLAGS_block_align;
Ignore `total_order_seek` in DB::Get (#9427) Summary: Apparently setting total_order_seek=true for DB::Get was intended to allow accurate read semantics if the current prefix extractor doesn't match what was used to generate SST files on disk. But since prefix_extractor was made a mutable option in 5.14.0, we have been able to detect this case and provide the correct semantics regardless of the total_order_seek option. Since that time, the option has only made Get() slower in a reasonably common case: prefix_extractor unchanged and whole_key_filtering=false. So this change primarily removes unnecessary effect of total_order_seek on Get. Also cleans up some related comments. Also adds a -total_order_seek option to db_bench and canonicalizes handling of ReadOptions in db_bench so that command line options have the expected association with library features. (There is potential for change in regression test behavior, but the old behavior is likely indefensible, or some other inconsistency would need to be fixed.) TODO in follow-up work: there should be no reason for Get() to depend on current prefix extractor at all. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9427 Test Plan: Unit tests updated. Performance (using db_bench update) Create DB with `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -benchmarks=fillrandom -num=10000000 -disable_wal=1 -write_buffer_size=10000000 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12 -whole_key_filtering=0` Test with and without `-total_order_seek` on `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -use_existing_db -readonly -benchmarks=readrandom -num=10000000 -duration=40 -disable_wal=1 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12` Before this change, total_order_seek=false: 25188 ops/sec Before this change, total_order_seek=true: 1222 ops/sec (~20x slower) After this change, total_order_seek=false: 24570 ops/sec After this change, total_order_seek=true: 25012 ops/sec (indistinguishable) Reviewed By: siying Differential Revision: D33753458 Pulled By: pdillinger fbshipit-source-id: bf892f34907a5e407d9c40bd4d42f0adbcbe0014
2022-02-01 03:45:17 +00:00
block_based_options.whole_key_filtering = FLAGS_whole_key_filtering;
block_based_options.max_auto_readahead_size =
FLAGS_max_auto_readahead_size;
block_based_options.initial_auto_readahead_size =
FLAGS_initial_auto_readahead_size;
block_based_options.num_file_reads_for_auto_readahead =
FLAGS_num_file_reads_for_auto_readahead;
BlockBasedTableOptions::PrepopulateBlockCache prepopulate_block_cache =
block_based_options.prepopulate_block_cache;
switch (FLAGS_prepopulate_block_cache) {
case 0:
prepopulate_block_cache =
BlockBasedTableOptions::PrepopulateBlockCache::kDisable;
break;
case 1:
prepopulate_block_cache =
BlockBasedTableOptions::PrepopulateBlockCache::kFlushOnly;
break;
default:
fprintf(stderr, "Unknown prepopulate block cache mode\n");
}
block_based_options.prepopulate_block_cache = prepopulate_block_cache;
if (FLAGS_use_data_block_hash_index) {
block_based_options.data_block_index_type =
ROCKSDB_NAMESPACE::BlockBasedTableOptions::kDataBlockBinaryAndHash;
} else {
block_based_options.data_block_index_type =
ROCKSDB_NAMESPACE::BlockBasedTableOptions::kDataBlockBinarySearch;
}
block_based_options.data_block_hash_table_util_ratio =
FLAGS_data_block_hash_table_util_ratio;
if (FLAGS_read_cache_path != "") {
Status rc_status;
// Read cache need to be provided with a the Logger, we will put all
// reac cache logs in the read cache path in a file named rc_LOG
rc_status = FLAGS_env->CreateDirIfMissing(FLAGS_read_cache_path);
std::shared_ptr<Logger> read_cache_logger;
if (rc_status.ok()) {
rc_status = FLAGS_env->NewLogger(FLAGS_read_cache_path + "/rc_LOG",
&read_cache_logger);
}
if (rc_status.ok()) {
PersistentCacheConfig rc_cfg(FLAGS_env, FLAGS_read_cache_path,
FLAGS_read_cache_size,
read_cache_logger);
rc_cfg.enable_direct_reads = FLAGS_read_cache_direct_read;
rc_cfg.enable_direct_writes = FLAGS_read_cache_direct_write;
rc_cfg.writer_qdepth = 4;
rc_cfg.writer_dispatch_size = 4 * 1024;
auto pcache = std::make_shared<BlockCacheTier>(rc_cfg);
block_based_options.persistent_cache = pcache;
rc_status = pcache->Open();
}
if (!rc_status.ok()) {
fprintf(stderr, "Error initializing read cache, %s\n",
rc_status.ToString().c_str());
exit(1);
}
}
if (FLAGS_use_blob_cache) {
if (FLAGS_use_shared_block_and_blob_cache) {
options.blob_cache = cache_;
} else {
if (FLAGS_blob_cache_size > 0) {
LRUCacheOptions co;
co.capacity = FLAGS_blob_cache_size;
co.num_shard_bits = FLAGS_blob_cache_numshardbits;
co.memory_allocator = GetCacheAllocator();
options.blob_cache = NewLRUCache(co);
} else {
fprintf(
stderr,
"Unable to create a standalone blob cache if blob_cache_size "
"<= 0.\n");
exit(1);
}
}
switch (FLAGS_prepopulate_blob_cache) {
case 0:
options.prepopulate_blob_cache = PrepopulateBlobCache::kDisable;
break;
case 1:
options.prepopulate_blob_cache = PrepopulateBlobCache::kFlushOnly;
break;
default:
fprintf(stderr, "Unknown prepopulate blob cache mode\n");
exit(1);
}
fprintf(stdout,
"Integrated BlobDB: blob cache enabled"
", block and blob caches shared: %d",
FLAGS_use_shared_block_and_blob_cache);
if (!FLAGS_use_shared_block_and_blob_cache) {
fprintf(stdout,
", blob cache size %" PRIu64
", blob cache num shard bits: %d",
FLAGS_blob_cache_size, FLAGS_blob_cache_numshardbits);
}
fprintf(stdout, ", blob cache prepopulated: %d\n",
FLAGS_prepopulate_blob_cache);
} else {
fprintf(stdout, "Integrated BlobDB: blob cache disabled\n");
}
options.table_factory.reset(
NewBlockBasedTableFactory(block_based_options));
}
if (FLAGS_max_bytes_for_level_multiplier_additional_v.size() > 0) {
if (FLAGS_max_bytes_for_level_multiplier_additional_v.size() !=
static_cast<unsigned int>(FLAGS_num_levels)) {
fprintf(stderr, "Insufficient number of fanouts specified %d\n",
static_cast<int>(
FLAGS_max_bytes_for_level_multiplier_additional_v.size()));
exit(1);
}
options.max_bytes_for_level_multiplier_additional =
FLAGS_max_bytes_for_level_multiplier_additional_v;
}
options.level0_stop_writes_trigger = FLAGS_level0_stop_writes_trigger;
Improve statistics Summary: This adds more statistics to be reported by GetProperty("leveldb.stats"). The new stats include time spent waiting on stalls in MakeRoomForWrite. This also includes the total amplification rate where that is: (#bytes of sequential IO during compaction) / (#bytes from Put) This also includes a lot more data for the per-level compaction report. * Rn(MB) - MB read from level N during compaction between levels N and N+1 * Rnp1(MB) - MB read from level N+1 during compaction between levels N and N+1 * Wnew(MB) - new data written to the level during compaction * Amplify - ( Write(MB) + Rnp1(MB) ) / Rn(MB) * Rn - files read from level N during compaction between levels N and N+1 * Rnp1 - files read from level N+1 during compaction between levels N and N+1 * Wnp1 - files written to level N+1 during compaction between levels N and N+1 * NewW - new files written to level N+1 during compaction * Count - number of compactions done for this level This is the new output from DB::GetProperty("leveldb.stats"). The old output stopped at Write(MB) Compactions Level Files Size(MB) Time(sec) Read(MB) Write(MB) Rn(MB) Rnp1(MB) Wnew(MB) Amplify Read(MB/s) Write(MB/s) Rn Rnp1 Wnp1 NewW Count ------------------------------------------------------------------------------------------------------------------------------------- 0 3 6 33 0 576 0 0 576 -1.0 0.0 1.3 0 0 0 0 290 1 127 242 351 5316 5314 570 4747 567 17.0 12.1 12.1 287 2399 2685 286 32 2 161 328 54 822 824 326 496 328 4.0 1.9 1.9 160 251 411 160 161 Amplification: 22.3 rate, 0.56 GB in, 12.55 GB out Uptime(secs): 439.8 Stalls(secs): 206.938 level0_slowdown, 0.000 level0_numfiles, 24.129 memtable_compaction Task ID: # Blame Rev: Test Plan: run db_bench Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - (cherry picked from commit ecdeead38f86cc02e754d0032600742c4f02fec8) Reviewers: dhruba Differential Revision: https://reviews.facebook.net/D6153
2012-10-23 17:34:09 +00:00
options.level0_file_num_compaction_trigger =
FLAGS_level0_file_num_compaction_trigger;
options.level0_slowdown_writes_trigger =
FLAGS_level0_slowdown_writes_trigger;
options.compression = FLAGS_compression_type_e;
if (FLAGS_simulate_hybrid_fs_file != "") {
options.last_level_temperature = Temperature::kWarm;
}
options.preclude_last_level_data_seconds =
FLAGS_preclude_last_level_data_seconds;
options.preserve_internal_time_seconds =
FLAGS_preserve_internal_time_seconds;
options.sample_for_compression = FLAGS_sample_for_compression;
options.WAL_ttl_seconds = FLAGS_wal_ttl_seconds;
options.WAL_size_limit_MB = FLAGS_wal_size_limit_MB;
options.max_total_wal_size = FLAGS_max_total_wal_size;
Allow having different compression algorithms on different levels. Summary: The leveldb API is enhanced to support different compression algorithms at different levels. This adds the option min_level_to_compress to db_bench that specifies the minimum level for which compression should be done when compression is enabled. This can be used to disable compression for levels 0 and 1 which are likely to suffer from stalls because of the CPU load for memtable flushes and (L0,L1) compaction. Level 0 is special as it gets frequent memtable flushes. Level 1 is special as it frequently gets all:all file compactions between it and level 0. But all other levels could be the same. For any level N where N > 1, the rate of sequential IO for that level should be the same. The last level is the exception because it might not be full and because files from it are not read to compact with the next larger level. The same amount of time will be spent doing compaction at any level N excluding N=0, 1 or the last level. By this standard all of those levels should use the same compression. The difference is that the loss (using more disk space) from a faster compression algorithm is less significant for N=2 than for N=3. So we might be willing to trade disk space for faster write rates with no compression for L0 and L1, snappy for L2, zlib for L3. Using a faster compression algorithm for the mid levels also allows us to reclaim some cpu without trading off much loss in disk space overhead. Also note that little is to be gained by compressing levels 0 and 1. For a 4-level tree they account for 10% of the data. For a 5-level tree they account for 1% of the data. With compression enabled: * memtable flush rate is ~18MB/second * (L0,L1) compaction rate is ~30MB/second With compression enabled but min_level_to_compress=2 * memtable flush rate is ~320MB/second * (L0,L1) compaction rate is ~560MB/second This practicaly takes the same code from https://reviews.facebook.net/D6225 but makes the leveldb api more general purpose with a few additional lines of code. Test Plan: make check Differential Revision: https://reviews.facebook.net/D6261
2012-10-28 06:13:17 +00:00
if (FLAGS_min_level_to_compress >= 0) {
assert(FLAGS_min_level_to_compress <= FLAGS_num_levels);
options.compression_per_level.resize(FLAGS_num_levels);
for (int i = 0; i < FLAGS_min_level_to_compress; i++) {
Allow having different compression algorithms on different levels. Summary: The leveldb API is enhanced to support different compression algorithms at different levels. This adds the option min_level_to_compress to db_bench that specifies the minimum level for which compression should be done when compression is enabled. This can be used to disable compression for levels 0 and 1 which are likely to suffer from stalls because of the CPU load for memtable flushes and (L0,L1) compaction. Level 0 is special as it gets frequent memtable flushes. Level 1 is special as it frequently gets all:all file compactions between it and level 0. But all other levels could be the same. For any level N where N > 1, the rate of sequential IO for that level should be the same. The last level is the exception because it might not be full and because files from it are not read to compact with the next larger level. The same amount of time will be spent doing compaction at any level N excluding N=0, 1 or the last level. By this standard all of those levels should use the same compression. The difference is that the loss (using more disk space) from a faster compression algorithm is less significant for N=2 than for N=3. So we might be willing to trade disk space for faster write rates with no compression for L0 and L1, snappy for L2, zlib for L3. Using a faster compression algorithm for the mid levels also allows us to reclaim some cpu without trading off much loss in disk space overhead. Also note that little is to be gained by compressing levels 0 and 1. For a 4-level tree they account for 10% of the data. For a 5-level tree they account for 1% of the data. With compression enabled: * memtable flush rate is ~18MB/second * (L0,L1) compaction rate is ~30MB/second With compression enabled but min_level_to_compress=2 * memtable flush rate is ~320MB/second * (L0,L1) compaction rate is ~560MB/second This practicaly takes the same code from https://reviews.facebook.net/D6225 but makes the leveldb api more general purpose with a few additional lines of code. Test Plan: make check Differential Revision: https://reviews.facebook.net/D6261
2012-10-28 06:13:17 +00:00
options.compression_per_level[i] = kNoCompression;
}
for (int i = FLAGS_min_level_to_compress; i < FLAGS_num_levels; i++) {
options.compression_per_level[i] = FLAGS_compression_type_e;
Allow having different compression algorithms on different levels. Summary: The leveldb API is enhanced to support different compression algorithms at different levels. This adds the option min_level_to_compress to db_bench that specifies the minimum level for which compression should be done when compression is enabled. This can be used to disable compression for levels 0 and 1 which are likely to suffer from stalls because of the CPU load for memtable flushes and (L0,L1) compaction. Level 0 is special as it gets frequent memtable flushes. Level 1 is special as it frequently gets all:all file compactions between it and level 0. But all other levels could be the same. For any level N where N > 1, the rate of sequential IO for that level should be the same. The last level is the exception because it might not be full and because files from it are not read to compact with the next larger level. The same amount of time will be spent doing compaction at any level N excluding N=0, 1 or the last level. By this standard all of those levels should use the same compression. The difference is that the loss (using more disk space) from a faster compression algorithm is less significant for N=2 than for N=3. So we might be willing to trade disk space for faster write rates with no compression for L0 and L1, snappy for L2, zlib for L3. Using a faster compression algorithm for the mid levels also allows us to reclaim some cpu without trading off much loss in disk space overhead. Also note that little is to be gained by compressing levels 0 and 1. For a 4-level tree they account for 10% of the data. For a 5-level tree they account for 1% of the data. With compression enabled: * memtable flush rate is ~18MB/second * (L0,L1) compaction rate is ~30MB/second With compression enabled but min_level_to_compress=2 * memtable flush rate is ~320MB/second * (L0,L1) compaction rate is ~560MB/second This practicaly takes the same code from https://reviews.facebook.net/D6225 but makes the leveldb api more general purpose with a few additional lines of code. Test Plan: make check Differential Revision: https://reviews.facebook.net/D6261
2012-10-28 06:13:17 +00:00
}
}
options.soft_pending_compaction_bytes_limit =
FLAGS_soft_pending_compaction_bytes_limit;
options.hard_pending_compaction_bytes_limit =
FLAGS_hard_pending_compaction_bytes_limit;
options.delayed_write_rate = FLAGS_delayed_write_rate;
support for concurrent adds to memtable Summary: This diff adds support for concurrent adds to the skiplist memtable implementations. Memory allocation is made thread-safe by the addition of a spinlock, with small per-core buffers to avoid contention. Concurrent memtable writes are made via an additional method and don't impose a performance overhead on the non-concurrent case, so parallelism can be selected on a per-batch basis. Write thread synchronization is an increasing bottleneck for higher levels of concurrency, so this diff adds --enable_write_thread_adaptive_yield (default off). This feature causes threads joining a write batch group to spin for a short time (default 100 usec) using sched_yield, rather than going to sleep on a mutex. If the timing of the yield calls indicates that another thread has actually run during the yield then spinning is avoided. This option improves performance for concurrent situations even without parallel adds, although it has the potential to increase CPU usage (and the heuristic adaptation is not yet mature). Parallel writes are not currently compatible with inplace updates, update callbacks, or delete filtering. Enable it with --allow_concurrent_memtable_write (and --enable_write_thread_adaptive_yield). Parallel memtable writes are performance neutral when there is no actual parallelism, and in my experiments (SSD server-class Linux and varying contention and key sizes for fillrandom) they are always a performance win when there is more than one thread. Statistics are updated earlier in the write path, dropping the number of DB mutex acquisitions from 2 to 1 for almost all cases. This diff was motivated and inspired by Yahoo's cLSM work. It is more conservative than cLSM: RocksDB's write batch group leader role is preserved (along with all of the existing flush and write throttling logic) and concurrent writers are blocked until all memtable insertions have completed and the sequence number has been advanced, to preserve linearizability. My test config is "db_bench -benchmarks=fillrandom -threads=$T -batch_size=1 -memtablerep=skip_list -value_size=100 --num=1000000/$T -level0_slowdown_writes_trigger=9999 -level0_stop_writes_trigger=9999 -disable_auto_compactions --max_write_buffer_number=8 -max_background_flushes=8 --disable_wal --write_buffer_size=160000000 --block_size=16384 --allow_concurrent_memtable_write" on a two-socket Xeon E5-2660 @ 2.2Ghz with lots of memory and an SSD hard drive. With 1 thread I get ~440Kops/sec. Peak performance for 1 socket (numactl -N1) is slightly more than 1Mops/sec, at 16 threads. Peak performance across both sockets happens at 30 threads, and is ~900Kops/sec, although with fewer threads there is less performance loss when the system has background work. Test Plan: 1. concurrent stress tests for InlineSkipList and DynamicBloom 2. make clean; make check 3. make clean; DISABLE_JEMALLOC=1 make valgrind_check; valgrind db_bench 4. make clean; COMPILE_WITH_TSAN=1 make all check; db_bench 5. make clean; COMPILE_WITH_ASAN=1 make all check; db_bench 6. make clean; OPT=-DROCKSDB_LITE make check 7. verify no perf regressions when disabled Reviewers: igor, sdong Reviewed By: sdong Subscribers: MarkCallaghan, IslamAbdelRahman, anthony, yhchiang, rven, sdong, guyg8, kradhakrishnan, dhruba Differential Revision: https://reviews.facebook.net/D50589
2015-08-14 23:59:07 +00:00
options.allow_concurrent_memtable_write =
FLAGS_allow_concurrent_memtable_write;
Memtable sampling for mempurge heuristic. (#8628) Summary: Changes the API of the MemPurge process: the `bool experimental_allow_mempurge` and `experimental_mempurge_policy` flags have been replaced by a `double experimental_mempurge_threshold` option. This change of API reflects another major change introduced in this PR: the MemPurgeDecider() function now works by sampling the memtables being flushed to estimate the overall amount of useful payload (payload minus the garbage), and then compare this useful payload estimate with the `double experimental_mempurge_threshold` value. Therefore, when the value of this flag is `0.0` (default value), mempurge is simply deactivated. On the other hand, a value of `DBL_MAX` would be equivalent to always going through a mempurge regardless of the garbage ratio estimate. At the moment, a `double experimental_mempurge_threshold` value else than 0.0 or `DBL_MAX` is opnly supported`with the `SkipList` memtable representation. Regarding the sampling, this PR includes the introduction of a `MemTable::UniqueRandomSample` function that collects (approximately) random entries from the memtable by using the new `SkipList::Iterator::RandomSeek()` under the hood, or by iterating through each memtable entry, depending on the target sample size and the total number of entries. The unit tests have been readapted to support this new API. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8628 Reviewed By: pdillinger Differential Revision: D30149315 Pulled By: bjlemaire fbshipit-source-id: 1feef5390c95db6f4480ab4434716533d3947f27
2021-08-11 01:07:48 +00:00
options.experimental_mempurge_threshold =
FLAGS_experimental_mempurge_threshold;
options.inplace_update_support = FLAGS_inplace_update_support;
options.inplace_update_num_locks = FLAGS_inplace_update_num_locks;
support for concurrent adds to memtable Summary: This diff adds support for concurrent adds to the skiplist memtable implementations. Memory allocation is made thread-safe by the addition of a spinlock, with small per-core buffers to avoid contention. Concurrent memtable writes are made via an additional method and don't impose a performance overhead on the non-concurrent case, so parallelism can be selected on a per-batch basis. Write thread synchronization is an increasing bottleneck for higher levels of concurrency, so this diff adds --enable_write_thread_adaptive_yield (default off). This feature causes threads joining a write batch group to spin for a short time (default 100 usec) using sched_yield, rather than going to sleep on a mutex. If the timing of the yield calls indicates that another thread has actually run during the yield then spinning is avoided. This option improves performance for concurrent situations even without parallel adds, although it has the potential to increase CPU usage (and the heuristic adaptation is not yet mature). Parallel writes are not currently compatible with inplace updates, update callbacks, or delete filtering. Enable it with --allow_concurrent_memtable_write (and --enable_write_thread_adaptive_yield). Parallel memtable writes are performance neutral when there is no actual parallelism, and in my experiments (SSD server-class Linux and varying contention and key sizes for fillrandom) they are always a performance win when there is more than one thread. Statistics are updated earlier in the write path, dropping the number of DB mutex acquisitions from 2 to 1 for almost all cases. This diff was motivated and inspired by Yahoo's cLSM work. It is more conservative than cLSM: RocksDB's write batch group leader role is preserved (along with all of the existing flush and write throttling logic) and concurrent writers are blocked until all memtable insertions have completed and the sequence number has been advanced, to preserve linearizability. My test config is "db_bench -benchmarks=fillrandom -threads=$T -batch_size=1 -memtablerep=skip_list -value_size=100 --num=1000000/$T -level0_slowdown_writes_trigger=9999 -level0_stop_writes_trigger=9999 -disable_auto_compactions --max_write_buffer_number=8 -max_background_flushes=8 --disable_wal --write_buffer_size=160000000 --block_size=16384 --allow_concurrent_memtable_write" on a two-socket Xeon E5-2660 @ 2.2Ghz with lots of memory and an SSD hard drive. With 1 thread I get ~440Kops/sec. Peak performance for 1 socket (numactl -N1) is slightly more than 1Mops/sec, at 16 threads. Peak performance across both sockets happens at 30 threads, and is ~900Kops/sec, although with fewer threads there is less performance loss when the system has background work. Test Plan: 1. concurrent stress tests for InlineSkipList and DynamicBloom 2. make clean; make check 3. make clean; DISABLE_JEMALLOC=1 make valgrind_check; valgrind db_bench 4. make clean; COMPILE_WITH_TSAN=1 make all check; db_bench 5. make clean; COMPILE_WITH_ASAN=1 make all check; db_bench 6. make clean; OPT=-DROCKSDB_LITE make check 7. verify no perf regressions when disabled Reviewers: igor, sdong Reviewed By: sdong Subscribers: MarkCallaghan, IslamAbdelRahman, anthony, yhchiang, rven, sdong, guyg8, kradhakrishnan, dhruba Differential Revision: https://reviews.facebook.net/D50589
2015-08-14 23:59:07 +00:00
options.enable_write_thread_adaptive_yield =
FLAGS_enable_write_thread_adaptive_yield;
options.enable_pipelined_write = FLAGS_enable_pipelined_write;
Unordered Writes (#5218) Summary: Performing unordered writes in rocksdb when unordered_write option is set to true. When enabled the writes to memtable are done without joining any write thread. This offers much higher write throughput since the upcoming writes would not have to wait for the slowest memtable write to finish. The tradeoff is that the writes visible to a snapshot might change over time. If the application cannot tolerate that, it should implement its own mechanisms to work around that. Using TransactionDB with WRITE_PREPARED write policy is one way to achieve that. Doing so increases the max throughput by 2.2x without however compromising the snapshot guarantees. The patch is prepared based on an original by siying Existing unit tests are extended to include unordered_write option. Benchmark Results: ``` TEST_TMPDIR=/dev/shm/ ./db_bench_unordered --benchmarks=fillrandom --threads=32 --num=10000000 -max_write_buffer_number=16 --max_background_jobs=64 --batch_size=8 --writes=3000000 -level0_file_num_compaction_trigger=99999 --level0_slowdown_writes_trigger=99999 --level0_stop_writes_trigger=99999 -enable_pipelined_write=false -disable_auto_compactions --unordered_write=1 ``` With WAL - Vanilla RocksDB: 78.6 MB/s - WRITER_PREPARED with unordered_write: 177.8 MB/s (2.2x) - unordered_write: 368.9 MB/s (4.7x with relaxed snapshot guarantees) Without WAL - Vanilla RocksDB: 111.3 MB/s - WRITER_PREPARED with unordered_write: 259.3 MB/s MB/s (2.3x) - unordered_write: 645.6 MB/s (5.8x with relaxed snapshot guarantees) - WRITER_PREPARED with unordered_write disable concurrency control: 185.3 MB/s MB/s (2.35x) Limitations: - The feature is not yet extended to `max_successive_merges` > 0. The feature is also incompatible with `enable_pipelined_write` = true as well as with `allow_concurrent_memtable_write` = false. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5218 Differential Revision: D15219029 Pulled By: maysamyabandeh fbshipit-source-id: 38f2abc4af8780148c6128acdba2b3227bc81759
2019-05-14 00:43:47 +00:00
options.unordered_write = FLAGS_unordered_write;
support for concurrent adds to memtable Summary: This diff adds support for concurrent adds to the skiplist memtable implementations. Memory allocation is made thread-safe by the addition of a spinlock, with small per-core buffers to avoid contention. Concurrent memtable writes are made via an additional method and don't impose a performance overhead on the non-concurrent case, so parallelism can be selected on a per-batch basis. Write thread synchronization is an increasing bottleneck for higher levels of concurrency, so this diff adds --enable_write_thread_adaptive_yield (default off). This feature causes threads joining a write batch group to spin for a short time (default 100 usec) using sched_yield, rather than going to sleep on a mutex. If the timing of the yield calls indicates that another thread has actually run during the yield then spinning is avoided. This option improves performance for concurrent situations even without parallel adds, although it has the potential to increase CPU usage (and the heuristic adaptation is not yet mature). Parallel writes are not currently compatible with inplace updates, update callbacks, or delete filtering. Enable it with --allow_concurrent_memtable_write (and --enable_write_thread_adaptive_yield). Parallel memtable writes are performance neutral when there is no actual parallelism, and in my experiments (SSD server-class Linux and varying contention and key sizes for fillrandom) they are always a performance win when there is more than one thread. Statistics are updated earlier in the write path, dropping the number of DB mutex acquisitions from 2 to 1 for almost all cases. This diff was motivated and inspired by Yahoo's cLSM work. It is more conservative than cLSM: RocksDB's write batch group leader role is preserved (along with all of the existing flush and write throttling logic) and concurrent writers are blocked until all memtable insertions have completed and the sequence number has been advanced, to preserve linearizability. My test config is "db_bench -benchmarks=fillrandom -threads=$T -batch_size=1 -memtablerep=skip_list -value_size=100 --num=1000000/$T -level0_slowdown_writes_trigger=9999 -level0_stop_writes_trigger=9999 -disable_auto_compactions --max_write_buffer_number=8 -max_background_flushes=8 --disable_wal --write_buffer_size=160000000 --block_size=16384 --allow_concurrent_memtable_write" on a two-socket Xeon E5-2660 @ 2.2Ghz with lots of memory and an SSD hard drive. With 1 thread I get ~440Kops/sec. Peak performance for 1 socket (numactl -N1) is slightly more than 1Mops/sec, at 16 threads. Peak performance across both sockets happens at 30 threads, and is ~900Kops/sec, although with fewer threads there is less performance loss when the system has background work. Test Plan: 1. concurrent stress tests for InlineSkipList and DynamicBloom 2. make clean; make check 3. make clean; DISABLE_JEMALLOC=1 make valgrind_check; valgrind db_bench 4. make clean; COMPILE_WITH_TSAN=1 make all check; db_bench 5. make clean; COMPILE_WITH_ASAN=1 make all check; db_bench 6. make clean; OPT=-DROCKSDB_LITE make check 7. verify no perf regressions when disabled Reviewers: igor, sdong Reviewed By: sdong Subscribers: MarkCallaghan, IslamAbdelRahman, anthony, yhchiang, rven, sdong, guyg8, kradhakrishnan, dhruba Differential Revision: https://reviews.facebook.net/D50589
2015-08-14 23:59:07 +00:00
options.write_thread_max_yield_usec = FLAGS_write_thread_max_yield_usec;
options.write_thread_slow_yield_usec = FLAGS_write_thread_slow_yield_usec;
options.table_cache_numshardbits = FLAGS_table_cache_numshardbits;
options.max_compaction_bytes = FLAGS_max_compaction_bytes;
options.disable_auto_compactions = FLAGS_disable_auto_compactions;
options.optimize_filters_for_hits = FLAGS_optimize_filters_for_hits;
options.paranoid_checks = FLAGS_paranoid_checks;
options.force_consistency_checks = FLAGS_force_consistency_checks;
options.periodic_compaction_seconds = FLAGS_periodic_compaction_seconds;
Try to start TTL earlier with kMinOverlappingRatio is used (#8749) Summary: Right now, when options.ttl is set, compactions are triggered around the time when TTL is reached. This might cause extra compactions which are often bursty. This commit tries to mitigate it by picking those files earlier in normal compaction picking process. This is only implemented using kMinOverlappingRatio with Leveled compaction as it is the default value and it is more complicated to change other styles. When a file is aged more than ttl/2, RocksDB starts to boost the compaction priority of files in normal compaction picking process, and hope by the time TTL is reached, very few extra compaction is needed. In order for this to work, another change is made: during a compaction, if an output level file is older than ttl/2, cut output files based on original boundary (if it is not in the last level). This is to make sure that after an old file is moved to the next level, and new data is merged from the upper level, the new data falling into this range isn't reset with old timestamp. Without this change, in many cases, most files from one level will keep having old timestamp, even if they have newer data and we stuck in it. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8749 Test Plan: Add a unit test to test the boosting logic. Will add a unit test to test it end-to-end. Reviewed By: jay-zhuang Differential Revision: D30735261 fbshipit-source-id: 503c2d89250b22911eb99e72b379be154de3428e
2021-11-01 21:32:12 +00:00
options.ttl = FLAGS_ttl_seconds;
// fill storage options
options.advise_random_on_open = FLAGS_advise_random_on_open;
options.use_adaptive_mutex = FLAGS_use_adaptive_mutex;
options.bytes_per_sync = FLAGS_bytes_per_sync;
options.wal_bytes_per_sync = FLAGS_wal_bytes_per_sync;
// merge operator options
if (!FLAGS_merge_operator.empty()) {
s = MergeOperator::CreateFromString(config_options, FLAGS_merge_operator,
&options.merge_operator);
if (!s.ok()) {
fprintf(stderr, "invalid merge operator[%s]: %s\n",
FLAGS_merge_operator.c_str(), s.ToString().c_str());
exit(1);
}
}
options.max_successive_merges = FLAGS_max_successive_merges;
options.strict_max_successive_merges = FLAGS_strict_max_successive_merges;
options.report_bg_io_stats = FLAGS_report_bg_io_stats;
// set universal style compaction configurations, if applicable
if (FLAGS_universal_size_ratio != 0) {
options.compaction_options_universal.size_ratio =
FLAGS_universal_size_ratio;
}
if (FLAGS_universal_min_merge_width != 0) {
options.compaction_options_universal.min_merge_width =
FLAGS_universal_min_merge_width;
}
if (FLAGS_universal_max_merge_width != 0) {
options.compaction_options_universal.max_merge_width =
FLAGS_universal_max_merge_width;
}
if (FLAGS_universal_max_size_amplification_percent != 0) {
options.compaction_options_universal.max_size_amplification_percent =
FLAGS_universal_max_size_amplification_percent;
}
if (FLAGS_universal_compression_size_percent != -1) {
options.compaction_options_universal.compression_size_percent =
FLAGS_universal_compression_size_percent;
}
options.compaction_options_universal.allow_trivial_move =
FLAGS_universal_allow_trivial_move;
Incremental Space Amp Compactions in Universal Style (#8655) Summary: This commit introduces incremental compaction in univeral style for space amplification. This follows the first improvement mentioned in https://rocksdb.org/blog/2021/04/12/universal-improvements.html . The implemention simply picks up files about size of max_compaction_bytes to compact and execute if the penalty is not too big. More optimizations can be done in the future, e.g. prioritizing between this compaction and other types. But for now, the feature is supposed to be functional and can often reduce frequency of full compactions, although it can introduce penalty. In order to add cut files more efficiently so that more files from upper levels can be included, SST file cutting threshold (for current file + overlapping parent level files) is set to 1.5X of target file size. A 2MB target file size will generate files like this: https://gist.github.com/siying/29d2676fba417404f3c95e6c013c7de8 Number of files indeed increases but it is not out of control. Two set of write benchmarks are run: 1. For ingestion rate limited scenario, we can see full compaction is mostly eliminated: https://gist.github.com/siying/959bc1186066906831cf4c808d6e0a19 . The write amp increased from 7.7 to 9.4, as expected. After applying file cutting, the number is improved to 8.9. In another benchmark, the write amp is even better with the incremental approach: https://gist.github.com/siying/d1c16c286d7c59c4d7bba718ca198163 2. For ingestion rate unlimited scenario, incremental compaction turns out to be too expensive most of the time and is not executed, as expected. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8655 Test Plan: Add unit tests to the functionality. Reviewed By: ajkr Differential Revision: D31787034 fbshipit-source-id: ce813e63b15a61d5a56e97bf8902a1b28e011beb
2021-10-20 17:03:03 +00:00
options.compaction_options_universal.incremental =
FLAGS_universal_incremental;
if (FLAGS_thread_status_per_interval > 0) {
options.enable_thread_tracking = true;
}
if (FLAGS_user_timestamp_size > 0) {
if (FLAGS_user_timestamp_size != 8) {
fprintf(stderr, "Only 64 bits timestamps are supported.\n");
exit(1);
}
options.comparator = test::BytewiseComparatorWithU64TsWrapper();
}
options.allow_data_in_errors = FLAGS_allow_data_in_errors;
Stop tracking syncing live WAL for performance (#10330) Summary: With https://github.com/facebook/rocksdb/issues/10087, applications calling `SyncWAL()` or writing with `WriteOptions::sync=true` can suffer from performance regression. This PR reverts to original behavior of tracking the syncing of closed WALs. After we revert back to old behavior, recovery, whether kPointInTime or kAbsoluteConsistency, may fail to detect corruption in synced WALs if the corruption is in the live WAL. Pull Request resolved: https://github.com/facebook/rocksdb/pull/10330 Test Plan: make check Before https://github.com/facebook/rocksdb/issues/10087 ```bash fillsync : 750.269 micros/op 1332 ops/sec 75.027 seconds 100000 operations; 0.1 MB/s (100 ops) fillsync : 776.492 micros/op 1287 ops/sec 77.649 seconds 100000 operations; 0.1 MB/s (100 ops) fillsync [AVG 2 runs] : 1310 (± 44) ops/sec; 0.1 (± 0.0) MB/sec fillsync : 805.625 micros/op 1241 ops/sec 80.563 seconds 100000 operations; 0.1 MB/s (100 ops) fillsync [AVG 3 runs] : 1287 (± 51) ops/sec; 0.1 (± 0.0) MB/sec fillsync [AVG 3 runs] : 1287 (± 51) ops/sec; 0.1 (± 0.0) MB/sec fillsync [MEDIAN 3 runs] : 1287 ops/sec; 0.1 MB/sec ``` Before this PR and after https://github.com/facebook/rocksdb/issues/10087 ```bash fillsync : 1479.601 micros/op 675 ops/sec 147.960 seconds 100000 operations; 0.1 MB/s (100 ops) fillsync : 1626.080 micros/op 614 ops/sec 162.608 seconds 100000 operations; 0.1 MB/s (100 ops) fillsync [AVG 2 runs] : 645 (± 59) ops/sec; 0.1 (± 0.0) MB/sec fillsync : 1588.402 micros/op 629 ops/sec 158.840 seconds 100000 operations; 0.1 MB/s (100 ops) fillsync [AVG 3 runs] : 640 (± 35) ops/sec; 0.1 (± 0.0) MB/sec fillsync [AVG 3 runs] : 640 (± 35) ops/sec; 0.1 (± 0.0) MB/sec fillsync [MEDIAN 3 runs] : 629 ops/sec; 0.1 MB/sec ``` After this PR ```bash fillsync : 749.621 micros/op 1334 ops/sec 74.962 seconds 100000 operations; 0.1 MB/s (100 ops) fillsync : 865.577 micros/op 1155 ops/sec 86.558 seconds 100000 operations; 0.1 MB/s (100 ops) fillsync [AVG 2 runs] : 1244 (± 175) ops/sec; 0.1 (± 0.0) MB/sec fillsync : 845.837 micros/op 1182 ops/sec 84.584 seconds 100000 operations; 0.1 MB/s (100 ops) fillsync [AVG 3 runs] : 1223 (± 109) ops/sec; 0.1 (± 0.0) MB/sec fillsync [AVG 3 runs] : 1223 (± 109) ops/sec; 0.1 (± 0.0) MB/sec fillsync [MEDIAN 3 runs] : 1182 ops/sec; 0.1 MB/sec ``` Reviewed By: ajkr Differential Revision: D37725212 Pulled By: riversand963 fbshipit-source-id: 8fa7d13b3c7662be5d56351c42caf3266af937ae
2022-07-13 00:16:57 +00:00
options.track_and_verify_wals_in_manifest =
FLAGS_track_and_verify_wals_in_manifest;
// Integrated BlobDB
options.enable_blob_files = FLAGS_enable_blob_files;
options.min_blob_size = FLAGS_min_blob_size;
options.blob_file_size = FLAGS_blob_file_size;
options.blob_compression_type =
StringToCompressionType(FLAGS_blob_compression_type.c_str());
options.enable_blob_garbage_collection =
FLAGS_enable_blob_garbage_collection;
options.blob_garbage_collection_age_cutoff =
FLAGS_blob_garbage_collection_age_cutoff;
Make it possible to force the garbage collection of the oldest blob files (#8994) Summary: The current BlobDB garbage collection logic works by relocating the valid blobs from the oldest blob files as they are encountered during compaction, and cleaning up blob files once they contain nothing but garbage. However, with sufficiently skewed workloads, it is theoretically possible to end up in a situation when few or no compactions get scheduled for the SST files that contain references to the oldest blob files, which can lead to increased space amp due to the lack of GC. In order to efficiently handle such workloads, the patch adds a new BlobDB configuration option called `blob_garbage_collection_force_threshold`, which signals to BlobDB to schedule targeted compactions for the SST files that keep alive the oldest batch of blob files if the overall ratio of garbage in the given blob files meets the threshold *and* all the given blob files are eligible for GC based on `blob_garbage_collection_age_cutoff`. (For example, if the new option is set to 0.9, targeted compactions will get scheduled if the sum of garbage bytes meets or exceeds 90% of the sum of total bytes in the oldest blob files, assuming all affected blob files are below the age-based cutoff.) The net result of these targeted compactions is that the valid blobs in the oldest blob files are relocated and the oldest blob files themselves cleaned up (since *all* SST files that rely on them get compacted away). These targeted compactions are similar to periodic compactions in the sense that they force certain SST files that otherwise would not get picked up to undergo compaction and also in the sense that instead of merging files from multiple levels, they target a single file. (Note: such compactions might still include neighboring files from the same level due to the need of having a "clean cut" boundary but they never include any files from any other level.) This functionality is currently only supported with the leveled compaction style and is inactive by default (since the default value is set to 1.0, i.e. 100%). Pull Request resolved: https://github.com/facebook/rocksdb/pull/8994 Test Plan: Ran `make check` and tested using `db_bench` and the stress/crash tests. Reviewed By: riversand963 Differential Revision: D31489850 Pulled By: ltamasi fbshipit-source-id: 44057d511726a0e2a03c5d9313d7511b3f0c4eab
2021-10-12 01:00:44 +00:00
options.blob_garbage_collection_force_threshold =
FLAGS_blob_garbage_collection_force_threshold;
options.blob_compaction_readahead_size =
FLAGS_blob_compaction_readahead_size;
Make it possible to enable blob files starting from a certain LSM tree level (#10077) Summary: Currently, if blob files are enabled (i.e. `enable_blob_files` is true), large values are extracted both during flush/recovery (when SST files are written into level 0 of the LSM tree) and during compaction into any LSM tree level. For certain use cases that have a mix of short-lived and long-lived values, it might make sense to support extracting large values only during compactions whose output level is greater than or equal to a specified LSM tree level (e.g. compactions into L1/L2/... or above). This could reduce the space amplification caused by large values that are turned into garbage shortly after being written at the price of some write amplification incurred by long-lived values whose extraction to blob files is delayed. In order to achieve this, we would like to do the following: - Add a new configuration option `blob_file_starting_level` (default: 0) to `AdvancedColumnFamilyOptions` (and `MutableCFOptions` and extend the related logic) - Instantiate `BlobFileBuilder` in `BuildTable` (used during flush and recovery, where the LSM tree level is L0) and `CompactionJob` iff `enable_blob_files` is set and the LSM tree level is `>= blob_file_starting_level` - Add unit tests for the new functionality, and add the new option to our stress tests (`db_stress` and `db_crashtest.py` ) - Add the new option to our benchmarking tool `db_bench` and the BlobDB benchmark script `run_blob_bench.sh` - Add the new option to the `ldb` tool (see https://github.com/facebook/rocksdb/wiki/Administration-and-Data-Access-Tool) - Ideally extend the C and Java bindings with the new option - Update the BlobDB wiki to document the new option. Pull Request resolved: https://github.com/facebook/rocksdb/pull/10077 Reviewed By: ltamasi Differential Revision: D36884156 Pulled By: gangliao fbshipit-source-id: 942bab025f04633edca8564ed64791cb5e31627d
2022-06-03 03:04:33 +00:00
options.blob_file_starting_level = FLAGS_blob_file_starting_level;
if (FLAGS_readonly && FLAGS_transaction_db) {
fprintf(stderr, "Cannot use readonly flag with transaction_db\n");
exit(1);
}
if (FLAGS_use_secondary_db &&
(FLAGS_transaction_db || FLAGS_optimistic_transaction_db)) {
fprintf(stderr, "Cannot use use_secondary_db flag with transaction_db\n");
exit(1);
}
Add memtable per key-value checksum (#10281) Summary: Append per key-value checksum to internal key. These checksums are verified on read paths including Get, Iterator and during Flush. Get and Iterator will return `Corruption` status if there is a checksum verification failure. Flush will make DB become read-only upon memtable entry checksum verification failure. Pull Request resolved: https://github.com/facebook/rocksdb/pull/10281 Test Plan: - Added new unit test cases: `make check` - Benchmark on memtable insert ``` TEST_TMPDIR=/dev/shm/memtable_write ./db_bench -benchmarks=fillseq -disable_wal=true -max_write_buffer_number=100 -num=10000000 -min_write_buffer_number_to_merge=100 # avg over 10 runs Baseline: 1166936 ops/sec memtable 2 bytes kv checksum : 1.11674e+06 ops/sec (-4%) memtable 2 bytes kv checksum + write batch 8 bytes kv checksum: 1.08579e+06 ops/sec (-6.95%) write batch 8 bytes kv checksum: 1.17979e+06 ops/sec (+1.1%) ``` - Benchmark on only memtable read: ops/sec dropped 31% for `readseq` due to time spend on verifying checksum. ops/sec for `readrandom` dropped ~6.8%. ``` # Readseq sudo TEST_TMPDIR=/dev/shm/memtable_read ./db_bench -benchmarks=fillseq,readseq"[-X20]" -disable_wal=true -max_write_buffer_number=100 -num=10000000 -min_write_buffer_number_to_merge=100 readseq [AVG 20 runs] : 7432840 (± 212005) ops/sec; 822.3 (± 23.5) MB/sec readseq [MEDIAN 20 runs] : 7573878 ops/sec; 837.9 MB/sec With -memtable_protection_bytes_per_key=2: readseq [AVG 20 runs] : 5134607 (± 119596) ops/sec; 568.0 (± 13.2) MB/sec readseq [MEDIAN 20 runs] : 5232946 ops/sec; 578.9 MB/sec # Readrandom sudo TEST_TMPDIR=/dev/shm/memtable_read ./db_bench -benchmarks=fillrandom,readrandom"[-X10]" -disable_wal=true -max_write_buffer_number=100 -num=1000000 -min_write_buffer_number_to_merge=100 readrandom [AVG 10 runs] : 140236 (± 3938) ops/sec; 9.8 (± 0.3) MB/sec readrandom [MEDIAN 10 runs] : 140545 ops/sec; 9.8 MB/sec With -memtable_protection_bytes_per_key=2: readrandom [AVG 10 runs] : 130632 (± 2738) ops/sec; 9.1 (± 0.2) MB/sec readrandom [MEDIAN 10 runs] : 130341 ops/sec; 9.1 MB/sec ``` - Stress test: `python3 -u tools/db_crashtest.py whitebox --duration=1800` Reviewed By: ajkr Differential Revision: D37607896 Pulled By: cbi42 fbshipit-source-id: fdaefb475629d2471780d4a5f5bf81b44ee56113
2022-08-12 20:51:32 +00:00
options.memtable_protection_bytes_per_key =
FLAGS_memtable_protection_bytes_per_key;
Block per key-value checksum (#11287) Summary: add option `block_protection_bytes_per_key` and implementation for block per key-value checksum. The main changes are 1. checksum construction and verification in block.cc/h 2. pass the option `block_protection_bytes_per_key` around (mainly for methods defined in table_cache.h) 3. unit tests/crash test updates Tests: * Added unit tests * Crash test: `python3 tools/db_crashtest.py blackbox --simple --block_protection_bytes_per_key=1 --write_buffer_size=1048576` Follow up (maybe as a separate PR): make sure corruption status returned from BlockIters are correctly handled. Performance: Turning on block per KV protection has a non-trivial negative impact on read performance and costs additional memory. For memory, each block includes additional 24 bytes for checksum-related states beside checksum itself. For CPU, I set up a DB of size ~1.2GB with 5M keys (32 bytes key and 200 bytes value) which compacts to ~5 SST files (target file size 256 MB) in L6 without compression. I tested readrandom performance with various block cache size (to mimic various cache hit rates): ``` SETUP make OPTIMIZE_LEVEL="-O3" USE_LTO=1 DEBUG_LEVEL=0 -j32 db_bench ./db_bench -benchmarks=fillseq,compact0,waitforcompaction,compact,waitforcompaction -write_buffer_size=33554432 -level_compaction_dynamic_level_bytes=true -max_background_jobs=8 -target_file_size_base=268435456 --num=5000000 --key_size=32 --value_size=200 --compression_type=none BENCHMARK ./db_bench --use_existing_db -benchmarks=readtocache,readrandom[-X10] --num=5000000 --key_size=32 --disable_auto_compactions --reads=1000000 --block_protection_bytes_per_key=[0|1] --cache_size=$CACHESIZE The readrandom ops/sec looks like the following: Block cache size: 2GB 1.2GB * 0.9 1.2GB * 0.8 1.2GB * 0.5 8MB Main 240805 223604 198176 161653 139040 PR prot_bytes=0 238691 226693 200127 161082 141153 PR prot_bytes=1 214983 193199 178532 137013 108211 prot_bytes=1 vs -10% -15% -10.8% -15% -23% prot_bytes=0 ``` The benchmark has a lot of variance, but there was a 5% to 25% regression in this benchmark with different cache hit rates. Pull Request resolved: https://github.com/facebook/rocksdb/pull/11287 Reviewed By: ajkr Differential Revision: D43970708 Pulled By: cbi42 fbshipit-source-id: ef98d898b71779846fa74212b9ec9e08b7183940
2023-04-25 19:08:23 +00:00
options.block_protection_bytes_per_key =
FLAGS_block_protection_bytes_per_key;
}
void InitializeOptionsGeneral(Options* opts) {
// Be careful about what is set here to avoid accidentally overwriting
// settings already configured by OPTIONS file. Only configure settings that
// are needed for the benchmark to run, settings for shared objects that
// were not configured already, settings that require dynamically invoking
// APIs, and settings for the benchmark itself.
Options& options = *opts;
// Always set these since they are harmless when not needed and prevent
// a guaranteed failure when they are needed.
options.create_missing_column_families = true;
options.create_if_missing = true;
if (options.statistics == nullptr) {
options.statistics = dbstats;
}
auto table_options =
options.table_factory->GetOptions<BlockBasedTableOptions>();
if (table_options != nullptr) {
if (FLAGS_cache_size > 0) {
// This violates this function's rules on when to set options. But we
// have to do it because the case of unconfigured block cache in OPTIONS
// file is indistinguishable (it is sanitized to 32MB by this point, not
// nullptr), and our regression tests assume this will be the shared
// block cache, even with OPTIONS file provided.
table_options->block_cache = cache_;
}
if (table_options->filter_policy == nullptr) {
if (FLAGS_bloom_bits < 0) {
table_options->filter_policy = BlockBasedTableOptions().filter_policy;
} else if (FLAGS_bloom_bits == 0) {
table_options->filter_policy.reset();
} else {
table_options->filter_policy.reset(
FLAGS_use_ribbon_filter ? NewRibbonFilterPolicy(FLAGS_bloom_bits)
: NewBloomFilterPolicy(FLAGS_bloom_bits));
Hide deprecated, inefficient block-based filter from public API (#9535) Summary: This change removes the ability to configure the deprecated, inefficient block-based filter in the public API. Options that would have enabled it now use "full" (and optionally partitioned) filters. Existing block-based filters can still be read and used, and a "back door" way to build them still exists, for testing and in case of trouble. About the only way this removal would cause an issue for users is if temporary memory for filter construction greatly increases. In HISTORY.md we suggest a few possible mitigations: partitioned filters, smaller SST files, or setting reserve_table_builder_memory=true. Or users who have customized a FilterPolicy using the CreateFilter/KeyMayMatch mechanism removed in https://github.com/facebook/rocksdb/issues/9501 will have to upgrade their code. (It's long past time for people to move to the new builder/reader customization interface.) This change also introduces some internal-use-only configuration strings for testing specific filter implementations while bypassing some compatibility / intelligence logic. This is intended to hint at a path toward making FilterPolicy Customizable, but it also gives us a "back door" way to configure block-based filter. Aside: updated db_bench so that -readonly implies -use_existing_db Pull Request resolved: https://github.com/facebook/rocksdb/pull/9535 Test Plan: Unit tests updated. Specifically, * BlockBasedTableTest.BlockReadCountTest is tweaked to validate the back door configuration interface and ignoring of `use_block_based_builder`. * BlockBasedTableTest.TracingGetTest is migrated from testing block-based filter access pattern to full filter access patter, by re-ordering some things. * Options test (pretty self-explanatory) Performance test - create with `./db_bench -db=/dev/shm/rocksdb1 -bloom_bits=10 -cache_index_and_filter_blocks=1 -benchmarks=fillrandom -num=10000000 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0` with and without `-use_block_based_filter`, which creates a DB with 21 SST files in L0. Read with `./db_bench -db=/dev/shm/rocksdb1 -readonly -bloom_bits=10 -cache_index_and_filter_blocks=1 -benchmarks=readrandom -num=10000000 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -duration=30` Without -use_block_based_filter: readrandom 464 ops/sec, 689280 KB DB With -use_block_based_filter: readrandom 169 ops/sec, 690996 KB DB No consistent difference with fillrandom Reviewed By: jay-zhuang Differential Revision: D34153871 Pulled By: pdillinger fbshipit-source-id: 31f4a933c542f8f09aca47fa64aec67832a69738
2022-02-12 15:04:09 +00:00
}
}
}
if (options.row_cache == nullptr) {
if (FLAGS_row_cache_size) {
if (FLAGS_cache_numshardbits >= 1) {
options.row_cache =
NewLRUCache(FLAGS_row_cache_size, FLAGS_cache_numshardbits);
} else {
options.row_cache = NewLRUCache(FLAGS_row_cache_size);
}
}
}
if (options.env == Env::Default()) {
options.env = FLAGS_env;
}
if (FLAGS_enable_io_prio) {
options.env->LowerThreadPoolIOPriority(Env::LOW);
options.env->LowerThreadPoolIOPriority(Env::HIGH);
}
if (FLAGS_enable_cpu_prio) {
options.env->LowerThreadPoolCPUPriority(Env::LOW);
options.env->LowerThreadPoolCPUPriority(Env::HIGH);
}
if (FLAGS_sine_write_rate) {
FLAGS_benchmark_write_rate_limit = static_cast<uint64_t>(SineRate(0));
}
if (options.rate_limiter == nullptr) {
if (FLAGS_rate_limiter_bytes_per_sec > 0) {
options.rate_limiter.reset(NewGenericRateLimiter(
FLAGS_rate_limiter_bytes_per_sec,
FLAGS_rate_limiter_refill_period_us, 10 /* fairness */,
Add rate-limiting support to batched MultiGet() (#10159) Summary: **Context/Summary:** https://github.com/facebook/rocksdb/pull/9424 added rate-limiting support for user reads, which does not include batched `MultiGet()`s that call `RandomAccessFileReader::MultiRead()`. The reason is that it's harder (compared with RandomAccessFileReader::Read()) to implement the ideal rate-limiting where we first call `RateLimiter::RequestToken()` for allowed bytes to multi-read and then consume those bytes by satisfying as many requests in `MultiRead()` as possible. For example, it can be tricky to decide whether we want partially fulfilled requests within one `MultiRead()` or not. However, due to a recent urgent user request, we decide to pursue an elementary (but a conditionally ineffective) solution where we accumulate enough rate limiter requests toward the total bytes needed by one `MultiRead()` before doing that `MultiRead()`. This is not ideal when the total bytes are huge as we will actually consume a huge bandwidth from rate-limiter causing a burst on disk. This is not what we ultimately want with rate limiter. Therefore a follow-up work is noted through TODO comments. Pull Request resolved: https://github.com/facebook/rocksdb/pull/10159 Test Plan: - Modified existing unit test `DBRateLimiterOnReadTest/DBRateLimiterOnReadTest.NewMultiGet` - Traced the underlying system calls `io_uring_enter` and verified they are 10 seconds apart from each other correctly under the setting of `strace -ftt -e trace=io_uring_enter ./db_bench -benchmarks=multireadrandom -db=/dev/shm/testdb2 -readonly -num=50 -threads=1 -multiread_batched=1 -batch_size=100 -duration=10 -rate_limiter_bytes_per_sec=200 -rate_limiter_refill_period_us=1000000 -rate_limit_bg_reads=1 -disable_auto_compactions=1 -rate_limit_user_ops=1` where each `MultiRead()` read about 2000 bytes (inspected by debugger) and the rate limiter grants 200 bytes per seconds. - Stress test: - Verified `./db_stress (-test_cf_consistency=1/test_batches_snapshots=1) -use_multiget=1 -cache_size=1048576 -rate_limiter_bytes_per_sec=10241024 -rate_limit_bg_reads=1 -rate_limit_user_ops=1` work Reviewed By: ajkr, anand1976 Differential Revision: D37135172 Pulled By: hx235 fbshipit-source-id: 73b8e8f14761e5d4b77235dfe5d41f4eea968bcd
2022-06-17 23:40:47 +00:00
// TODO: replace this with a more general FLAG for deciding
// RateLimiter::Mode as now we also rate-limit foreground reads e.g,
// Get()/MultiGet()
FLAGS_rate_limit_bg_reads ? RateLimiter::Mode::kReadsOnly
: RateLimiter::Mode::kWritesOnly,
Decouple `RateLimiter` burst size and refill period (#12379) Summary: When the rate limiter does not have any waiting requests, the first request to arrive may consume all of the available bandwidth, despite potentially having lower priority than requests that arrive later in the same refill interval. Then, those higher priority requests must wait for a refill. So even in scenarios in which we have an overall bandwidth surplus, the highest priority requests can be sporadically delayed up to a whole refill period. Alone, this isn't necessarily problematic as the refill period is configurable via `refill_period_us` and can be tuned down as needed until the max sporadic delay is tolerable. However, tuning down `refill_period_us` had a side effect of reducing burst size. Some users require a certain burst size to issue optimal I/O sizes to the underlying storage system. To satisfy those users, this PR decouples the refill period from the burst size. That way, the max sporadic delay can be limited without impacting I/O sizes issued to the underlying storage system. Pull Request resolved: https://github.com/facebook/rocksdb/pull/12379 Test Plan: The goal is to show we can now limit the max sporadic delay without impacting compaction's I/O size. The benchmark runs compaction with a large I/O size, while user reads simultaneously run at a low rate that does not consume all of the available bandwidth. The max sporadic delay is measured using the P100 of rocksdb.file.read.get.micros. I just used strace to verify the compaction reads follow `rate_limiter_single_burst_bytes` Setup: `./db_bench -benchmarks=fillrandom,flush -write_buffer_size=67108864 -disable_auto_compactions=true -value_size=256 -num=1048576` Benchmark: `./db_bench -benchmarks=readrandom -use_existing_db=true -num=1048576 -duration=10 -benchmark_read_rate_limit=4096 -rate_limiter_bytes_per_sec=67108864 -rate_limiter_refill_period_us=$refill_micros -rate_limiter_single_burst_bytes=16777216 -rate_limit_bg_reads=true -rate_limit_user_ops=true -statistics=true -cache_size=0 -stats_level=5 -compaction_readahead_size=16777216 -use_direct_reads=true` Results: refill_micros | rocksdb.file.read.get.micros (P100) -- | -- 10000 | 10802 100000 | 100240 1000000 | 922061 For verifying compaction read sizes: `strace -fye pread64 ./db_bench -benchmarks=compact -use_existing_db=true -rate_limiter_bytes_per_sec=67108864 -rate_limiter_refill_period_us=$refill_micros -rate_limiter_single_burst_bytes=16777216 -rate_limit_bg_reads=true -compaction_readahead_size=16777216 -use_direct_reads=true` Reviewed By: hx235 Differential Revision: D54165675 Pulled By: ajkr fbshipit-source-id: c5968486316cbfb7ff8e5b7d75d3589883dd1105
2024-02-27 00:55:13 +00:00
FLAGS_rate_limiter_auto_tuned,
FLAGS_rate_limiter_single_burst_bytes));
}
}
Auto recovery from out of space errors (#4164) Summary: This commit implements automatic recovery from a Status::NoSpace() error during background operations such as write callback, flush and compaction. The broad design is as follows - 1. Compaction errors are treated as soft errors and don't put the database in read-only mode. A compaction is delayed until enough free disk space is available to accomodate the compaction outputs, which is estimated based on the input size. This means that users can continue to write, and we rely on the WriteController to delay or stop writes if the compaction debt becomes too high due to persistent low disk space condition 2. Errors during write callback and flush are treated as hard errors, i.e the database is put in read-only mode and goes back to read-write only fater certain recovery actions are taken. 3. Both types of recovery rely on the SstFileManagerImpl to poll for sufficient disk space. We assume that there is a 1-1 mapping between an SFM and the underlying OS storage container. For cases where multiple DBs are hosted on a single storage container, the user is expected to allocate a single SFM instance and use the same one for all the DBs. If no SFM is specified by the user, DBImpl::Open() will allocate one, but this will be one per DB and each DB will recover independently. The recovery implemented by SFM is as follows - a) On the first occurance of an out of space error during compaction, subsequent compactions will be delayed until the disk free space check indicates enough available space. The required space is computed as the sum of input sizes. b) The free space check requirement will be removed once the amount of free space is greater than the size reserved by in progress compactions when the first error occured c) If the out of space error is a hard error, a background thread in SFM will poll for sufficient headroom before triggering the recovery of the database and putting it in write-only mode. The headroom is calculated as the sum of the write_buffer_size of all the DB instances associated with the SFM 4. EventListener callbacks will be called at the start and completion of automatic recovery. Users can disable the auto recov ery in the start callback, and later initiate it manually by calling DB::Resume() Todo: 1. More extensive testing 2. Add disk full condition to db_stress (follow-on PR) Pull Request resolved: https://github.com/facebook/rocksdb/pull/4164 Differential Revision: D9846378 Pulled By: anand1976 fbshipit-source-id: 80ea875dbd7f00205e19c82215ff6e37da10da4a
2018-09-15 20:36:19 +00:00
options.listeners.emplace_back(listener_);
if (options.file_checksum_gen_factory == nullptr) {
if (FLAGS_file_checksum) {
options.file_checksum_gen_factory.reset(
new FileChecksumGenCrc32cFactory());
}
Add rate limiter priority to ReadOptions (#9424) Summary: Users can set the priority for file reads associated with their operation by setting `ReadOptions::rate_limiter_priority` to something other than `Env::IO_TOTAL`. Rate limiting `VerifyChecksum()` and `VerifyFileChecksums()` is the motivation for this PR, so it also includes benchmarks and minor bug fixes to get that working. `RandomAccessFileReader::Read()` already had support for rate limiting compaction reads. I changed that rate limiting to be non-specific to compaction, but rather performed according to the passed in `Env::IOPriority`. Now the compaction read rate limiting is supported by setting `rate_limiter_priority = Env::IO_LOW` on its `ReadOptions`. There is no default value for the new `Env::IOPriority` parameter to `RandomAccessFileReader::Read()`. That means this PR goes through all callers (in some cases multiple layers up the call stack) to find a `ReadOptions` to provide the priority. There are TODOs for cases I believe it would be good to let user control the priority some day (e.g., file footer reads), and no TODO in cases I believe it doesn't matter (e.g., trace file reads). The API doc only lists the missing cases where a file read associated with a provided `ReadOptions` cannot be rate limited. For cases like file ingestion checksum calculation, there is no API to provide `ReadOptions` or `Env::IOPriority`, so I didn't count that as missing. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9424 Test Plan: - new unit tests - new benchmarks on ~50MB database with 1MB/s read rate limit and 100ms refill interval; verified with strace reads are chunked (at 0.1MB per chunk) and spaced roughly 100ms apart. - setup command: `./db_bench -benchmarks=fillrandom,compact -db=/tmp/testdb -target_file_size_base=1048576 -disable_auto_compactions=true -file_checksum=true` - benchmarks command: `strace -ttfe pread64 ./db_bench -benchmarks=verifychecksum,verifyfilechecksums -use_existing_db=true -db=/tmp/testdb -rate_limiter_bytes_per_sec=1048576 -rate_limit_bg_reads=1 -rate_limit_user_ops=true -file_checksum=true` - crash test using IO_USER priority on non-validation reads with https://github.com/facebook/rocksdb/issues/9567 reverted: `python3 tools/db_crashtest.py blackbox --max_key=1000000 --write_buffer_size=524288 --target_file_size_base=524288 --level_compaction_dynamic_level_bytes=true --duration=3600 --rate_limit_bg_reads=true --rate_limit_user_ops=true --rate_limiter_bytes_per_sec=10485760 --interval=10` Reviewed By: hx235 Differential Revision: D33747386 Pulled By: ajkr fbshipit-source-id: a2d985e97912fba8c54763798e04f006ccc56e0c
2022-02-17 07:17:03 +00:00
}
if (FLAGS_num_multi_db <= 1) {
OpenDb(options, FLAGS_db, &db_);
} else {
multi_dbs_.clear();
multi_dbs_.resize(FLAGS_num_multi_db);
auto wal_dir = options.wal_dir;
2014-04-29 19:33:57 +00:00
for (int i = 0; i < FLAGS_num_multi_db; i++) {
if (!wal_dir.empty()) {
options.wal_dir = GetPathForMultiple(wal_dir, i);
}
OpenDb(options, GetPathForMultiple(FLAGS_db, i), &multi_dbs_[i]);
}
options.wal_dir = wal_dir;
}
// KeepFilter is a noop filter, this can be used to test compaction filter
if (options.compaction_filter == nullptr) {
if (FLAGS_use_keep_filter) {
options.compaction_filter = new KeepFilter();
fprintf(stdout, "A noop compaction filter is used\n");
}
}
if (FLAGS_use_existing_keys) {
// Only work on single database
assert(db_.db != nullptr);
Ignore `total_order_seek` in DB::Get (#9427) Summary: Apparently setting total_order_seek=true for DB::Get was intended to allow accurate read semantics if the current prefix extractor doesn't match what was used to generate SST files on disk. But since prefix_extractor was made a mutable option in 5.14.0, we have been able to detect this case and provide the correct semantics regardless of the total_order_seek option. Since that time, the option has only made Get() slower in a reasonably common case: prefix_extractor unchanged and whole_key_filtering=false. So this change primarily removes unnecessary effect of total_order_seek on Get. Also cleans up some related comments. Also adds a -total_order_seek option to db_bench and canonicalizes handling of ReadOptions in db_bench so that command line options have the expected association with library features. (There is potential for change in regression test behavior, but the old behavior is likely indefensible, or some other inconsistency would need to be fixed.) TODO in follow-up work: there should be no reason for Get() to depend on current prefix extractor at all. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9427 Test Plan: Unit tests updated. Performance (using db_bench update) Create DB with `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -benchmarks=fillrandom -num=10000000 -disable_wal=1 -write_buffer_size=10000000 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12 -whole_key_filtering=0` Test with and without `-total_order_seek` on `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -use_existing_db -readonly -benchmarks=readrandom -num=10000000 -duration=40 -disable_wal=1 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12` Before this change, total_order_seek=false: 25188 ops/sec Before this change, total_order_seek=true: 1222 ops/sec (~20x slower) After this change, total_order_seek=false: 24570 ops/sec After this change, total_order_seek=true: 25012 ops/sec (indistinguishable) Reviewed By: siying Differential Revision: D33753458 Pulled By: pdillinger fbshipit-source-id: bf892f34907a5e407d9c40bd4d42f0adbcbe0014
2022-02-01 03:45:17 +00:00
ReadOptions read_opts; // before read_options_ initialized
read_opts.total_order_seek = true;
Iterator* iter = db_.db->NewIterator(read_opts);
for (iter->SeekToFirst(); iter->Valid(); iter->Next()) {
keys_.emplace_back(iter->key().ToString());
}
delete iter;
FLAGS_num = keys_.size();
}
}
void Open(Options* opts) {
if (!InitializeOptionsFromFile(opts)) {
InitializeOptionsFromFlags(opts);
}
InitializeOptionsGeneral(opts);
}
void OpenDb(Options options, const std::string& db_name,
DBWithColumnFamilies* db) {
Add -report_open_timing to db_bench (#8464) Summary: Hello and thanks for RocksDB, This PR adds support for ```-report_open_timing true``` to ```db_bench```. It can be useful when tuning RocksDB on filesystem/env with high latencies for file level operations (create/delete/rename...) seen during ```((Optimistic)Transaction)DB::Open```. Some examples: ``` > db_bench -benchmarks updaterandom -num 1 -db /dev/shm/db_bench > db_bench -benchmarks updaterandom -num 0 -db /dev/shm/db_bench -use_existing_db true -report_open_timing true -readonly true 2>&1 | grep OpenDb OpenDb: 3.90133 milliseconds > db_bench -benchmarks updaterandom -num 0 -db /dev/shm/db_bench -use_existing_db true -report_open_timing true -use_secondary_db true 2>&1 | grep OpenDb OpenDb: 3.33414 milliseconds > db_bench -benchmarks updaterandom -num 0 -db /dev/shm/db_bench -use_existing_db true -report_open_timing true 2>&1 | grep -A1 OpenDb OpenDb: 6.05423 milliseconds > db_bench -benchmarks updaterandom -num 1 > db_bench -benchmarks updaterandom -num 0 -use_existing_db true -report_open_timing true -readonly true 2>&1 | grep OpenDb OpenDb: 4.06859 milliseconds > db_bench -benchmarks updaterandom -num 0 -use_existing_db true -report_open_timing true -use_secondary_db true 2>&1 | grep OpenDb OpenDb: 2.85794 milliseconds > db_bench -benchmarks updaterandom -num 0 -use_existing_db true -report_open_timing true 2>&1 | grep OpenDb OpenDb: 6.46376 milliseconds > db_bench -benchmarks updaterandom -num 1 -db /clustered_fs/db_bench > db_bench -benchmarks updaterandom -num 0 -db /clustered_fs/db_bench -use_existing_db true -report_open_timing true -readonly true 2>&1 | grep OpenDb OpenDb: 3.79805 milliseconds > db_bench -benchmarks updaterandom -num 0 -db /clustered_fs/db_bench -use_existing_db true -report_open_timing true -use_secondary_db true 2>&1 | grep OpenDb OpenDb: 3.00174 milliseconds > db_bench -benchmarks updaterandom -num 0 -db /clustered_fs/db_bench -use_existing_db true -report_open_timing true 2>&1 | grep OpenDb OpenDb: 24.8732 milliseconds ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/8464 Reviewed By: hx235 Differential Revision: D29398096 Pulled By: zhichao-cao fbshipit-source-id: 8f05dc3284f084612a3f30234e39e1c37548f50c
2021-07-02 01:41:20 +00:00
uint64_t open_start = FLAGS_report_open_timing ? FLAGS_env->NowNanos() : 0;
Status s;
// Open with column families if necessary.
if (FLAGS_num_column_families > 1) {
size_t num_hot = FLAGS_num_column_families;
if (FLAGS_num_hot_column_families > 0 &&
FLAGS_num_hot_column_families < FLAGS_num_column_families) {
num_hot = FLAGS_num_hot_column_families;
} else {
FLAGS_num_hot_column_families = FLAGS_num_column_families;
}
std::vector<ColumnFamilyDescriptor> column_families;
for (size_t i = 0; i < num_hot; i++) {
column_families.emplace_back(ColumnFamilyName(i),
ColumnFamilyOptions(options));
}
std::vector<int> cfh_idx_to_prob;
if (!FLAGS_column_family_distribution.empty()) {
std::stringstream cf_prob_stream(FLAGS_column_family_distribution);
std::string cf_prob;
int sum = 0;
while (std::getline(cf_prob_stream, cf_prob, ',')) {
cfh_idx_to_prob.push_back(std::stoi(cf_prob));
sum += cfh_idx_to_prob.back();
}
if (sum != 100) {
fprintf(stderr, "column_family_distribution items must sum to 100\n");
exit(1);
}
if (cfh_idx_to_prob.size() != num_hot) {
fprintf(stderr,
"got %" ROCKSDB_PRIszt
" column_family_distribution items; expected "
"%" ROCKSDB_PRIszt "\n",
cfh_idx_to_prob.size(), num_hot);
exit(1);
}
}
if (FLAGS_readonly) {
s = DB::OpenForReadOnly(options, db_name, column_families, &db->cfh,
&db->db);
} else if (FLAGS_optimistic_transaction_db) {
s = OptimisticTransactionDB::Open(options, db_name, column_families,
&db->cfh, &db->opt_txn_db);
if (s.ok()) {
db->db = db->opt_txn_db->GetBaseDB();
}
} else if (FLAGS_transaction_db) {
TransactionDB* ptr;
TransactionDBOptions txn_db_options;
if (options.unordered_write) {
options.two_write_queues = true;
txn_db_options.skip_concurrency_control = true;
txn_db_options.write_policy = WRITE_PREPARED;
}
s = TransactionDB::Open(options, txn_db_options, db_name,
column_families, &db->cfh, &ptr);
if (s.ok()) {
db->db = ptr;
}
} else {
s = DB::Open(options, db_name, column_families, &db->cfh, &db->db);
}
db->cfh.resize(FLAGS_num_column_families);
db->num_created = num_hot;
db->num_hot = num_hot;
db->cfh_idx_to_prob = std::move(cfh_idx_to_prob);
} else if (FLAGS_readonly) {
s = DB::OpenForReadOnly(options, db_name, &db->db);
} else if (FLAGS_optimistic_transaction_db) {
s = OptimisticTransactionDB::Open(options, db_name, &db->opt_txn_db);
if (s.ok()) {
db->db = db->opt_txn_db->GetBaseDB();
}
} else if (FLAGS_transaction_db) {
TransactionDB* ptr = nullptr;
TransactionDBOptions txn_db_options;
if (options.unordered_write) {
options.two_write_queues = true;
txn_db_options.skip_concurrency_control = true;
txn_db_options.write_policy = WRITE_PREPARED;
}
s = CreateLoggerFromOptions(db_name, options, &options.info_log);
if (s.ok()) {
s = TransactionDB::Open(options, txn_db_options, db_name, &ptr);
}
if (s.ok()) {
db->db = ptr;
}
} else if (FLAGS_use_blob_db) {
// Stacked BlobDB
blob_db::BlobDBOptions blob_db_options;
blob_db_options.enable_garbage_collection = FLAGS_blob_db_enable_gc;
blob_db_options.garbage_collection_cutoff = FLAGS_blob_db_gc_cutoff;
blob_db_options.is_fifo = FLAGS_blob_db_is_fifo;
blob_db_options.max_db_size = FLAGS_blob_db_max_db_size;
blob_db_options.ttl_range_secs = FLAGS_blob_db_ttl_range_secs;
blob_db_options.min_blob_size = FLAGS_blob_db_min_blob_size;
blob_db_options.bytes_per_sync = FLAGS_blob_db_bytes_per_sync;
blob_db_options.blob_file_size = FLAGS_blob_db_file_size;
blob_db_options.compression = FLAGS_blob_db_compression_type_e;
blob_db::BlobDB* ptr = nullptr;
s = blob_db::BlobDB::Open(options, blob_db_options, db_name, &ptr);
if (s.ok()) {
db->db = ptr;
}
} else if (FLAGS_use_secondary_db) {
if (FLAGS_secondary_path.empty()) {
std::string default_secondary_path;
FLAGS_env->GetTestDirectory(&default_secondary_path);
default_secondary_path += "/dbbench_secondary";
FLAGS_secondary_path = default_secondary_path;
}
s = DB::OpenAsSecondary(options, db_name, FLAGS_secondary_path, &db->db);
if (s.ok() && FLAGS_secondary_update_interval > 0) {
secondary_update_thread_.reset(new port::Thread(
[this](int interval, DBWithColumnFamilies* _db) {
while (0 == secondary_update_stopped_.load(
std::memory_order_relaxed)) {
Status secondary_update_status =
_db->db->TryCatchUpWithPrimary();
if (!secondary_update_status.ok()) {
fprintf(stderr, "Failed to catch up with primary: %s\n",
secondary_update_status.ToString().c_str());
break;
}
++secondary_db_updates_;
FLAGS_env->SleepForMicroseconds(interval * 1000000);
}
},
FLAGS_secondary_update_interval, db));
}
Basic RocksDB follower implementation (#12540) Summary: A basic implementation of RocksDB follower mode, which opens a remote database (referred to as leader) on a distributed file system by tailing its MANIFEST. It leverages the secondary instance mode, but is different in some key ways - 1. It has its own directory with links to the leader's database 2. Periodically refreshes itself 3. (Future) Snapshot support 4. (Future) Garbage collection of obsolete links 5. (Long term) Memtable replication There are two main classes implementing this functionality - `DBImplFollower` and `OnDemandFileSystem`. The former is derived from `DBImplSecondary`. Similar to `DBImplSecondary`, it implements recovery and catch up through MANIFEST tailing using the `ReactiveVersionSet`, but does not consider logs. In a future PR, we will implement memtable replication, which will eliminate the need to catch up using logs. In addition, the recovery and catch-up tries to avoid directory listing as repeated metadata operations are expensive. The second main piece is the `OnDemandFileSystem`, which plugs in as an `Env` for the follower instance and creates the illusion of the follower directory as a clone of the leader directory. It creates links to SSTs on first reference. When the follower tails the MANIFEST and attempts to create a new `Version`, it calls `VerifyFileMetadata` to verify the size of the file, and optionally the unique ID of the file. During this process, links are created which prevent the underlying files from getting deallocated even if the leader deletes the files. TODOs: Deletion of obsolete links, snapshots, robust checking against misconfigurations, better observability etc. Pull Request resolved: https://github.com/facebook/rocksdb/pull/12540 Reviewed By: jowlyzhang Differential Revision: D56315718 Pulled By: anand1976 fbshipit-source-id: d19e1aca43a6af4000cb8622a718031b69ebd97b
2024-04-20 02:13:31 +00:00
} else if (FLAGS_open_as_follower) {
std::unique_ptr<DB> dbptr;
s = DB::OpenAsFollower(options, db_name, FLAGS_leader_path, &dbptr);
if (s.ok()) {
db->db = dbptr.release();
}
} else {
s = DB::Open(options, db_name, &db->db);
}
Add -report_open_timing to db_bench (#8464) Summary: Hello and thanks for RocksDB, This PR adds support for ```-report_open_timing true``` to ```db_bench```. It can be useful when tuning RocksDB on filesystem/env with high latencies for file level operations (create/delete/rename...) seen during ```((Optimistic)Transaction)DB::Open```. Some examples: ``` > db_bench -benchmarks updaterandom -num 1 -db /dev/shm/db_bench > db_bench -benchmarks updaterandom -num 0 -db /dev/shm/db_bench -use_existing_db true -report_open_timing true -readonly true 2>&1 | grep OpenDb OpenDb: 3.90133 milliseconds > db_bench -benchmarks updaterandom -num 0 -db /dev/shm/db_bench -use_existing_db true -report_open_timing true -use_secondary_db true 2>&1 | grep OpenDb OpenDb: 3.33414 milliseconds > db_bench -benchmarks updaterandom -num 0 -db /dev/shm/db_bench -use_existing_db true -report_open_timing true 2>&1 | grep -A1 OpenDb OpenDb: 6.05423 milliseconds > db_bench -benchmarks updaterandom -num 1 > db_bench -benchmarks updaterandom -num 0 -use_existing_db true -report_open_timing true -readonly true 2>&1 | grep OpenDb OpenDb: 4.06859 milliseconds > db_bench -benchmarks updaterandom -num 0 -use_existing_db true -report_open_timing true -use_secondary_db true 2>&1 | grep OpenDb OpenDb: 2.85794 milliseconds > db_bench -benchmarks updaterandom -num 0 -use_existing_db true -report_open_timing true 2>&1 | grep OpenDb OpenDb: 6.46376 milliseconds > db_bench -benchmarks updaterandom -num 1 -db /clustered_fs/db_bench > db_bench -benchmarks updaterandom -num 0 -db /clustered_fs/db_bench -use_existing_db true -report_open_timing true -readonly true 2>&1 | grep OpenDb OpenDb: 3.79805 milliseconds > db_bench -benchmarks updaterandom -num 0 -db /clustered_fs/db_bench -use_existing_db true -report_open_timing true -use_secondary_db true 2>&1 | grep OpenDb OpenDb: 3.00174 milliseconds > db_bench -benchmarks updaterandom -num 0 -db /clustered_fs/db_bench -use_existing_db true -report_open_timing true 2>&1 | grep OpenDb OpenDb: 24.8732 milliseconds ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/8464 Reviewed By: hx235 Differential Revision: D29398096 Pulled By: zhichao-cao fbshipit-source-id: 8f05dc3284f084612a3f30234e39e1c37548f50c
2021-07-02 01:41:20 +00:00
if (FLAGS_report_open_timing) {
std::cout << "OpenDb: "
<< (FLAGS_env->NowNanos() - open_start) / 1000000.0
<< " milliseconds\n";
}
if (!s.ok()) {
fprintf(stderr, "open error: %s\n", s.ToString().c_str());
exit(1);
}
}
enum WriteMode { RANDOM, SEQUENTIAL, UNIQUE_RANDOM };
void WriteSeqDeterministic(ThreadState* thread) {
DoDeterministicCompact(thread, open_options_.compaction_style, SEQUENTIAL);
}
void WriteUniqueRandomDeterministic(ThreadState* thread) {
DoDeterministicCompact(thread, open_options_.compaction_style,
UNIQUE_RANDOM);
}
void WriteSeq(ThreadState* thread) { DoWrite(thread, SEQUENTIAL); }
void WriteRandom(ThreadState* thread) { DoWrite(thread, RANDOM); }
void WriteUniqueRandom(ThreadState* thread) {
DoWrite(thread, UNIQUE_RANDOM);
}
class KeyGenerator {
public:
KeyGenerator(Random64* rand, WriteMode mode, uint64_t num,
uint64_t /*num_per_set*/ = 64 * 1024)
: rand_(rand), mode_(mode), num_(num), next_(0) {
if (mode_ == UNIQUE_RANDOM) {
// NOTE: if memory consumption of this approach becomes a concern,
// we can either break it into pieces and only random shuffle a section
// each time. Alternatively, use a bit map implementation
// (https://reviews.facebook.net/differential/diff/54627/)
values_.resize(num_);
for (uint64_t i = 0; i < num_; ++i) {
values_[i] = i;
}
RandomShuffle(values_.begin(), values_.end(),
static_cast<uint32_t>(*seed_base));
}
}
uint64_t Next() {
switch (mode_) {
case SEQUENTIAL:
return next_++;
case RANDOM:
return rand_->Next() % num_;
case UNIQUE_RANDOM:
assert(next_ < num_);
return values_[next_++];
}
assert(false);
return std::numeric_limits<uint64_t>::max();
}
Create fillanddeleteuniquerandom benchmark (db_bench), with new option flags. (#8593) Summary: Introduction of a new `fillanddeleteuniquerandom` benchmark (`db_bench`) with 5 new option flags to simulate a benchmark where the following sequence is repeated multiple times: "A set of keys S1 is inserted ('`disposable entries`'), then after some delay another set of keys S2 is inserted ('`persistent entries`') and the first set of keys S1 is deleted. S2 artificially represents the insertion of hypothetical results from some undefined computation done on the first set of keys S1. The next sequence can start as soon as the last disposable entry in the set S1 of this sequence is inserted, if the `delay` is non negligible." New flags: - `disposable_entries_delete_delay`: minimum delay in microseconds between insertion of the last `disposable` entry, and the start of the insertion of the first `persistent` entry. - `disposable_entries_batch_size`: number of `disposable` entries inserted at the beginning of each sequence. - `disposable_entries_value_size`: size of the random `value` string for the `disposable` entries. - `persistent_entries_batch_size`: number of `persistent` entries inserted at the end of each sequence, right before the deletion of the `disposable` entries starts. - `persistent_entries_value_size`: size of the random value string for the `persistent` entries. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8593 Reviewed By: pdillinger Differential Revision: D29974436 Pulled By: bjlemaire fbshipit-source-id: f578033e5b45e8268ba6fa6f38f4770c2e6e801d
2021-07-29 21:57:03 +00:00
// Only available for UNIQUE_RANDOM mode.
uint64_t Fetch(uint64_t index) {
assert(mode_ == UNIQUE_RANDOM);
assert(index < values_.size());
return values_[index];
}
private:
Random64* rand_;
WriteMode mode_;
const uint64_t num_;
uint64_t next_;
std::vector<uint64_t> values_;
};
DB* SelectDB(ThreadState* thread) { return SelectDBWithCfh(thread)->db; }
DBWithColumnFamilies* SelectDBWithCfh(ThreadState* thread) {
return SelectDBWithCfh(thread->rand.Next());
}
DBWithColumnFamilies* SelectDBWithCfh(uint64_t rand_int) {
if (db_.db != nullptr) {
return &db_;
} else {
return &multi_dbs_[rand_int % multi_dbs_.size()];
}
}
double SineRate(double x) {
return FLAGS_sine_a * sin((FLAGS_sine_b * x) + FLAGS_sine_c) + FLAGS_sine_d;
}
void DoWrite(ThreadState* thread, WriteMode write_mode) {
const int test_duration = write_mode == RANDOM ? FLAGS_duration : 0;
const int64_t num_ops = writes_ == 0 ? num_ : writes_;
size_t num_key_gens = 1;
if (db_.db == nullptr) {
num_key_gens = multi_dbs_.size();
}
std::vector<std::unique_ptr<KeyGenerator>> key_gens(num_key_gens);
int64_t max_ops = num_ops * num_key_gens;
int64_t ops_per_stage = max_ops;
if (FLAGS_num_column_families > 1 && FLAGS_num_hot_column_families > 0) {
ops_per_stage = (max_ops - 1) / (FLAGS_num_column_families /
FLAGS_num_hot_column_families) +
1;
}
Duration duration(test_duration, max_ops, ops_per_stage);
const uint64_t num_per_key_gen = num_ + max_num_range_tombstones_;
for (size_t i = 0; i < num_key_gens; i++) {
key_gens[i].reset(new KeyGenerator(&(thread->rand), write_mode,
num_per_key_gen, ops_per_stage));
}
if (num_ != FLAGS_num) {
char msg[100];
snprintf(msg, sizeof(msg), "(%" PRIu64 " ops)", num_);
thread->stats.AddMessage(msg);
}
RandomGenerator gen;
WriteBatch batch(/*reserved_bytes=*/0, /*max_bytes=*/0,
FLAGS_write_batch_protection_bytes_per_key,
user_timestamp_size_);
Status s;
int64_t bytes = 0;
std::unique_ptr<const char[]> key_guard;
Slice key = AllocateKey(&key_guard);
std::unique_ptr<const char[]> begin_key_guard;
Slice begin_key = AllocateKey(&begin_key_guard);
std::unique_ptr<const char[]> end_key_guard;
Slice end_key = AllocateKey(&end_key_guard);
Add overwrite_probability for filluniquerandom benchmark in db_bench (#8569) Summary: Add flags `overwrite_probability` and `overwrite_window_size` flag to `db_bench`. Add the possibility of performing a `filluniquerandom` benchmark with an overwrite probability. For each write operation, there is a probability _p_ that the write is an overwrite (_p_=`overwrite_probability`). When an overwrite is decided, the key is randomly chosen from the last _N_ keys previously inserted into the DB (with _N_=`overwrite_window_size`). When a pure write is decided, the key inserted into the DB is unique and therefore will not be an overwrite. The `overwrite_window_size` is used so that the user can decide if the overwrite are mostly targeting recently inserted keys (when `overwrite_window_size` is small compared to the total number of writes), or can also target keys inserted "a long time ago" (when `overwrite_window_size` is comparable to total number of writes). Note that total number of writes = # of unique insertions + # of overwrites. No unit test specifically added. Local testing show the following **throughputs** for `filluniquerandom` with 1M total writes: - bypass the code inserts (no `overwrite_probability` flag specified): ~14.0MB/s - `overwrite_probability=0.99`, `overwrite_window_size=10`: ~17.0MB/s - `overwrite_probability=0.10`, `overwrite_window_size=10`: ~14.0MB/s - `overwrite_probability=0.99`, `overwrite_window_size=1M`: ~14.5MB/s - `overwrite_probability=0.10`, `overwrite_window_size=1M`: ~14.0MB/s Pull Request resolved: https://github.com/facebook/rocksdb/pull/8569 Reviewed By: pdillinger Differential Revision: D29818631 Pulled By: bjlemaire fbshipit-source-id: d472b4ea4e457a4da7c4ee4f14b40cccd6a4587a
2021-07-21 18:32:36 +00:00
double p = 0.0;
Create fillanddeleteuniquerandom benchmark (db_bench), with new option flags. (#8593) Summary: Introduction of a new `fillanddeleteuniquerandom` benchmark (`db_bench`) with 5 new option flags to simulate a benchmark where the following sequence is repeated multiple times: "A set of keys S1 is inserted ('`disposable entries`'), then after some delay another set of keys S2 is inserted ('`persistent entries`') and the first set of keys S1 is deleted. S2 artificially represents the insertion of hypothetical results from some undefined computation done on the first set of keys S1. The next sequence can start as soon as the last disposable entry in the set S1 of this sequence is inserted, if the `delay` is non negligible." New flags: - `disposable_entries_delete_delay`: minimum delay in microseconds between insertion of the last `disposable` entry, and the start of the insertion of the first `persistent` entry. - `disposable_entries_batch_size`: number of `disposable` entries inserted at the beginning of each sequence. - `disposable_entries_value_size`: size of the random `value` string for the `disposable` entries. - `persistent_entries_batch_size`: number of `persistent` entries inserted at the end of each sequence, right before the deletion of the `disposable` entries starts. - `persistent_entries_value_size`: size of the random value string for the `persistent` entries. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8593 Reviewed By: pdillinger Differential Revision: D29974436 Pulled By: bjlemaire fbshipit-source-id: f578033e5b45e8268ba6fa6f38f4770c2e6e801d
2021-07-29 21:57:03 +00:00
uint64_t num_overwrites = 0, num_unique_keys = 0, num_selective_deletes = 0;
Add overwrite_probability for filluniquerandom benchmark in db_bench (#8569) Summary: Add flags `overwrite_probability` and `overwrite_window_size` flag to `db_bench`. Add the possibility of performing a `filluniquerandom` benchmark with an overwrite probability. For each write operation, there is a probability _p_ that the write is an overwrite (_p_=`overwrite_probability`). When an overwrite is decided, the key is randomly chosen from the last _N_ keys previously inserted into the DB (with _N_=`overwrite_window_size`). When a pure write is decided, the key inserted into the DB is unique and therefore will not be an overwrite. The `overwrite_window_size` is used so that the user can decide if the overwrite are mostly targeting recently inserted keys (when `overwrite_window_size` is small compared to the total number of writes), or can also target keys inserted "a long time ago" (when `overwrite_window_size` is comparable to total number of writes). Note that total number of writes = # of unique insertions + # of overwrites. No unit test specifically added. Local testing show the following **throughputs** for `filluniquerandom` with 1M total writes: - bypass the code inserts (no `overwrite_probability` flag specified): ~14.0MB/s - `overwrite_probability=0.99`, `overwrite_window_size=10`: ~17.0MB/s - `overwrite_probability=0.10`, `overwrite_window_size=10`: ~14.0MB/s - `overwrite_probability=0.99`, `overwrite_window_size=1M`: ~14.5MB/s - `overwrite_probability=0.10`, `overwrite_window_size=1M`: ~14.0MB/s Pull Request resolved: https://github.com/facebook/rocksdb/pull/8569 Reviewed By: pdillinger Differential Revision: D29818631 Pulled By: bjlemaire fbshipit-source-id: d472b4ea4e457a4da7c4ee4f14b40cccd6a4587a
2021-07-21 18:32:36 +00:00
// If user set overwrite_probability flag,
// check if value is in [0.0,1.0].
if (FLAGS_overwrite_probability > 0.0) {
p = FLAGS_overwrite_probability > 1.0 ? 1.0 : FLAGS_overwrite_probability;
// If overwrite set by user, and UNIQUE_RANDOM mode on,
// the overwrite_window_size must be > 0.
if (write_mode == UNIQUE_RANDOM && FLAGS_overwrite_window_size == 0) {
fprintf(stderr,
"Overwrite_window_size must be strictly greater than 0.\n");
ErrorExit();
}
}
// Default_random_engine provides slightly
// improved throughput over mt19937.
std::default_random_engine overwrite_gen{
static_cast<unsigned int>(*seed_base)};
Add overwrite_probability for filluniquerandom benchmark in db_bench (#8569) Summary: Add flags `overwrite_probability` and `overwrite_window_size` flag to `db_bench`. Add the possibility of performing a `filluniquerandom` benchmark with an overwrite probability. For each write operation, there is a probability _p_ that the write is an overwrite (_p_=`overwrite_probability`). When an overwrite is decided, the key is randomly chosen from the last _N_ keys previously inserted into the DB (with _N_=`overwrite_window_size`). When a pure write is decided, the key inserted into the DB is unique and therefore will not be an overwrite. The `overwrite_window_size` is used so that the user can decide if the overwrite are mostly targeting recently inserted keys (when `overwrite_window_size` is small compared to the total number of writes), or can also target keys inserted "a long time ago" (when `overwrite_window_size` is comparable to total number of writes). Note that total number of writes = # of unique insertions + # of overwrites. No unit test specifically added. Local testing show the following **throughputs** for `filluniquerandom` with 1M total writes: - bypass the code inserts (no `overwrite_probability` flag specified): ~14.0MB/s - `overwrite_probability=0.99`, `overwrite_window_size=10`: ~17.0MB/s - `overwrite_probability=0.10`, `overwrite_window_size=10`: ~14.0MB/s - `overwrite_probability=0.99`, `overwrite_window_size=1M`: ~14.5MB/s - `overwrite_probability=0.10`, `overwrite_window_size=1M`: ~14.0MB/s Pull Request resolved: https://github.com/facebook/rocksdb/pull/8569 Reviewed By: pdillinger Differential Revision: D29818631 Pulled By: bjlemaire fbshipit-source-id: d472b4ea4e457a4da7c4ee4f14b40cccd6a4587a
2021-07-21 18:32:36 +00:00
std::bernoulli_distribution overwrite_decider(p);
// Inserted key window is filled with the last N
// keys previously inserted into the DB (with
// N=FLAGS_overwrite_window_size).
// We use a deque struct because:
// - random access is O(1)
// - insertion/removal at beginning/end is also O(1).
std::deque<int64_t> inserted_key_window;
Random64 reservoir_id_gen(*seed_base);
Add overwrite_probability for filluniquerandom benchmark in db_bench (#8569) Summary: Add flags `overwrite_probability` and `overwrite_window_size` flag to `db_bench`. Add the possibility of performing a `filluniquerandom` benchmark with an overwrite probability. For each write operation, there is a probability _p_ that the write is an overwrite (_p_=`overwrite_probability`). When an overwrite is decided, the key is randomly chosen from the last _N_ keys previously inserted into the DB (with _N_=`overwrite_window_size`). When a pure write is decided, the key inserted into the DB is unique and therefore will not be an overwrite. The `overwrite_window_size` is used so that the user can decide if the overwrite are mostly targeting recently inserted keys (when `overwrite_window_size` is small compared to the total number of writes), or can also target keys inserted "a long time ago" (when `overwrite_window_size` is comparable to total number of writes). Note that total number of writes = # of unique insertions + # of overwrites. No unit test specifically added. Local testing show the following **throughputs** for `filluniquerandom` with 1M total writes: - bypass the code inserts (no `overwrite_probability` flag specified): ~14.0MB/s - `overwrite_probability=0.99`, `overwrite_window_size=10`: ~17.0MB/s - `overwrite_probability=0.10`, `overwrite_window_size=10`: ~14.0MB/s - `overwrite_probability=0.99`, `overwrite_window_size=1M`: ~14.5MB/s - `overwrite_probability=0.10`, `overwrite_window_size=1M`: ~14.0MB/s Pull Request resolved: https://github.com/facebook/rocksdb/pull/8569 Reviewed By: pdillinger Differential Revision: D29818631 Pulled By: bjlemaire fbshipit-source-id: d472b4ea4e457a4da7c4ee4f14b40cccd6a4587a
2021-07-21 18:32:36 +00:00
Create fillanddeleteuniquerandom benchmark (db_bench), with new option flags. (#8593) Summary: Introduction of a new `fillanddeleteuniquerandom` benchmark (`db_bench`) with 5 new option flags to simulate a benchmark where the following sequence is repeated multiple times: "A set of keys S1 is inserted ('`disposable entries`'), then after some delay another set of keys S2 is inserted ('`persistent entries`') and the first set of keys S1 is deleted. S2 artificially represents the insertion of hypothetical results from some undefined computation done on the first set of keys S1. The next sequence can start as soon as the last disposable entry in the set S1 of this sequence is inserted, if the `delay` is non negligible." New flags: - `disposable_entries_delete_delay`: minimum delay in microseconds between insertion of the last `disposable` entry, and the start of the insertion of the first `persistent` entry. - `disposable_entries_batch_size`: number of `disposable` entries inserted at the beginning of each sequence. - `disposable_entries_value_size`: size of the random `value` string for the `disposable` entries. - `persistent_entries_batch_size`: number of `persistent` entries inserted at the end of each sequence, right before the deletion of the `disposable` entries starts. - `persistent_entries_value_size`: size of the random value string for the `persistent` entries. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8593 Reviewed By: pdillinger Differential Revision: D29974436 Pulled By: bjlemaire fbshipit-source-id: f578033e5b45e8268ba6fa6f38f4770c2e6e801d
2021-07-29 21:57:03 +00:00
// --- Variables used in disposable/persistent keys simulation:
// The following variables are used when
// disposable_entries_batch_size is >0. We simualte a workload
// where the following sequence is repeated multiple times:
// "A set of keys S1 is inserted ('disposable entries'), then after
// some delay another set of keys S2 is inserted ('persistent entries')
// and the first set of keys S1 is deleted. S2 artificially represents
// the insertion of hypothetical results from some undefined computation
// done on the first set of keys S1. The next sequence can start as soon
// as the last disposable entry in the set S1 of this sequence is
// inserted, if the delay is non negligible"
bool skip_for_loop = false, is_disposable_entry = true;
std::vector<uint64_t> disposable_entries_index(num_key_gens, 0);
std::vector<uint64_t> persistent_ent_and_del_index(num_key_gens, 0);
const uint64_t kNumDispAndPersEntries =
FLAGS_disposable_entries_batch_size +
FLAGS_persistent_entries_batch_size;
if (kNumDispAndPersEntries > 0) {
if ((write_mode != UNIQUE_RANDOM) || (writes_per_range_tombstone_ > 0) ||
(p > 0.0)) {
fprintf(
stderr,
"Disposable/persistent deletes are not compatible with overwrites "
"and DeleteRanges; and are only supported in filluniquerandom.\n");
ErrorExit();
}
if (FLAGS_disposable_entries_value_size < 0 ||
FLAGS_persistent_entries_value_size < 0) {
fprintf(
stderr,
"disposable_entries_value_size and persistent_entries_value_size"
"have to be positive.\n");
ErrorExit();
}
}
Random rnd_disposable_entry(static_cast<uint32_t>(*seed_base));
Create fillanddeleteuniquerandom benchmark (db_bench), with new option flags. (#8593) Summary: Introduction of a new `fillanddeleteuniquerandom` benchmark (`db_bench`) with 5 new option flags to simulate a benchmark where the following sequence is repeated multiple times: "A set of keys S1 is inserted ('`disposable entries`'), then after some delay another set of keys S2 is inserted ('`persistent entries`') and the first set of keys S1 is deleted. S2 artificially represents the insertion of hypothetical results from some undefined computation done on the first set of keys S1. The next sequence can start as soon as the last disposable entry in the set S1 of this sequence is inserted, if the `delay` is non negligible." New flags: - `disposable_entries_delete_delay`: minimum delay in microseconds between insertion of the last `disposable` entry, and the start of the insertion of the first `persistent` entry. - `disposable_entries_batch_size`: number of `disposable` entries inserted at the beginning of each sequence. - `disposable_entries_value_size`: size of the random `value` string for the `disposable` entries. - `persistent_entries_batch_size`: number of `persistent` entries inserted at the end of each sequence, right before the deletion of the `disposable` entries starts. - `persistent_entries_value_size`: size of the random value string for the `persistent` entries. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8593 Reviewed By: pdillinger Differential Revision: D29974436 Pulled By: bjlemaire fbshipit-source-id: f578033e5b45e8268ba6fa6f38f4770c2e6e801d
2021-07-29 21:57:03 +00:00
std::string random_value;
// Queue that stores scheduled timestamp of disposable entries deletes,
// along with starting index of disposable entry keys to delete.
std::vector<std::queue<std::pair<uint64_t, uint64_t>>> disposable_entries_q(
num_key_gens);
// --- End of variables used in disposable/persistent keys simulation.
std::vector<std::unique_ptr<const char[]>> expanded_key_guards;
std::vector<Slice> expanded_keys;
if (FLAGS_expand_range_tombstones) {
expanded_key_guards.resize(range_tombstone_width_);
for (auto& expanded_key_guard : expanded_key_guards) {
expanded_keys.emplace_back(AllocateKey(&expanded_key_guard));
}
}
std::unique_ptr<char[]> ts_guard;
if (user_timestamp_size_ > 0) {
ts_guard.reset(new char[user_timestamp_size_]);
}
int64_t stage = 0;
int64_t num_written = 0;
For db_bench --benchmarks=fillseq with --num_multi_db load databases … (#9713) Summary: …in order This fixes https://github.com/facebook/rocksdb/issues/9650 For db_bench --benchmarks=fillseq --num_multi_db=X it loads databases in sequence rather than randomly choosing a database per Put. The benefits are: 1) avoids long delays between flushing memtables 2) avoids flushing memtables for all of them at the same point in time 3) puts same number of keys per database so that query tests will find keys as expected Pull Request resolved: https://github.com/facebook/rocksdb/pull/9713 Test Plan: Using db_bench.1 without the change and db_bench.2 with the change: for i in 1 2; do rm -rf /data/m/rx/* ; time ./db_bench.$i --db=/data/m/rx --benchmarks=fillseq --num_multi_db=4 --num=10000000; du -hs /data/m/rx ; done --- without the change fillseq : 3.188 micros/op 313682 ops/sec; 34.7 MB/s real 2m7.787s user 1m52.776s sys 0m46.549s 2.7G /data/m/rx --- with the change fillseq : 3.149 micros/op 317563 ops/sec; 35.1 MB/s real 2m6.196s user 1m51.482s sys 0m46.003s 2.7G /data/m/rx Also, temporarily added a printf to confirm that the code switches to the next database at the right time ZZ switch to db 1 at 10000000 ZZ switch to db 2 at 20000000 ZZ switch to db 3 at 30000000 for i in 1 2; do rm -rf /data/m/rx/* ; time ./db_bench.$i --db=/data/m/rx --benchmarks=fillseq,readrandom --num_multi_db=4 --num=100000; du -hs /data/m/rx ; done --- without the change, smaller database, note that not all keys are found by readrandom because databases have < and > --num keys fillseq : 3.176 micros/op 314805 ops/sec; 34.8 MB/s readrandom : 1.913 micros/op 522616 ops/sec; 57.7 MB/s (99873 of 100000 found) --- with the change, smaller database, note that all keys are found by readrandom fillseq : 3.110 micros/op 321566 ops/sec; 35.6 MB/s readrandom : 1.714 micros/op 583257 ops/sec; 64.5 MB/s (100000 of 100000 found) Reviewed By: jay-zhuang Differential Revision: D35030168 Pulled By: mdcallag fbshipit-source-id: 2a18c4ec571d954cf5a57b00a11802a3608823ee
2022-03-22 17:36:24 +00:00
int64_t next_seq_db_at = num_ops;
size_t id = 0;
Fragment memtable range tombstone in the write path (#10380) Summary: - Right now each read fragments the memtable range tombstones https://github.com/facebook/rocksdb/issues/4808. This PR explores the idea of fragmenting memtable range tombstones in the write path and reads can just read this cached fragmented tombstone without any fragmenting cost. This PR only does the caching for immutable memtable, and does so right before a memtable is added to an immutable memtable list. The fragmentation is done without holding mutex to minimize its performance impact. - db_bench is updated to print out the number of range deletions executed if there is any. Pull Request resolved: https://github.com/facebook/rocksdb/pull/10380 Test Plan: - CI, added asserts in various places to check whether a fragmented range tombstone list should have been constructed. - Benchmark: as this PR only optimizes immutable memtable path, the number of writes in the benchmark is chosen such an immutable memtable is created and range tombstones are in that memtable. ``` single thread: ./db_bench --benchmarks=fillrandom,readrandom --writes_per_range_tombstone=1 --max_write_buffer_number=100 --min_write_buffer_number_to_merge=100 --writes=500000 --reads=100000 --max_num_range_tombstones=100 multi_thread ./db_bench --benchmarks=fillrandom,readrandom --writes_per_range_tombstone=1 --max_write_buffer_number=100 --min_write_buffer_number_to_merge=100 --writes=15000 --reads=20000 --threads=32 --max_num_range_tombstones=100 ``` Commit 99cdf16464a057ca44de2f747541dedf651bae9e is included in benchmark result. It was an earlier attempt where tombstones are fragmented for each write operation. Reader threads share it using a shared_ptr which would slow down multi-thread read performance as seen in benchmark results. Results are averaged over 5 runs. Single thread result: | Max # tombstones | main fillrandom micros/op | 99cdf16464a057ca44de2f747541dedf651bae9e | Post PR | main readrandom micros/op | 99cdf16464a057ca44de2f747541dedf651bae9e | Post PR | | ------------- | ------------- |------------- |------------- |------------- |------------- |------------- | | 0 |6.68 |6.57 |6.72 |4.72 |4.79 |4.54 | | 1 |6.67 |6.58 |6.62 |5.41 |4.74 |4.72 | | 10 |6.59 |6.5 |6.56 |7.83 |4.69 |4.59 | | 100 |6.62 |6.75 |6.58 |29.57 |5.04 |5.09 | | 1000 |6.54 |6.82 |6.61 |320.33 |5.22 |5.21 | 32-thread result: note that "Max # tombstones" is per thread. | Max # tombstones | main fillrandom micros/op | 99cdf16464a057ca44de2f747541dedf651bae9e | Post PR | main readrandom micros/op | 99cdf16464a057ca44de2f747541dedf651bae9e | Post PR | | ------------- | ------------- |------------- |------------- |------------- |------------- |------------- | | 0 |234.52 |260.25 |239.42 |5.06 |5.38 |5.09 | | 1 |236.46 |262.0 |231.1 |19.57 |22.14 |5.45 | | 10 |236.95 |263.84 |251.49 |151.73 |21.61 |5.73 | | 100 |268.16 |296.8 |280.13 |2308.52 |22.27 |6.57 | Reviewed By: ajkr Differential Revision: D37916564 Pulled By: cbi42 fbshipit-source-id: 05d6d2e16df26c374c57ddcca13a5bfe9d5b731e
2022-08-05 19:02:33 +00:00
int64_t num_range_deletions = 0;
For db_bench --benchmarks=fillseq with --num_multi_db load databases … (#9713) Summary: …in order This fixes https://github.com/facebook/rocksdb/issues/9650 For db_bench --benchmarks=fillseq --num_multi_db=X it loads databases in sequence rather than randomly choosing a database per Put. The benefits are: 1) avoids long delays between flushing memtables 2) avoids flushing memtables for all of them at the same point in time 3) puts same number of keys per database so that query tests will find keys as expected Pull Request resolved: https://github.com/facebook/rocksdb/pull/9713 Test Plan: Using db_bench.1 without the change and db_bench.2 with the change: for i in 1 2; do rm -rf /data/m/rx/* ; time ./db_bench.$i --db=/data/m/rx --benchmarks=fillseq --num_multi_db=4 --num=10000000; du -hs /data/m/rx ; done --- without the change fillseq : 3.188 micros/op 313682 ops/sec; 34.7 MB/s real 2m7.787s user 1m52.776s sys 0m46.549s 2.7G /data/m/rx --- with the change fillseq : 3.149 micros/op 317563 ops/sec; 35.1 MB/s real 2m6.196s user 1m51.482s sys 0m46.003s 2.7G /data/m/rx Also, temporarily added a printf to confirm that the code switches to the next database at the right time ZZ switch to db 1 at 10000000 ZZ switch to db 2 at 20000000 ZZ switch to db 3 at 30000000 for i in 1 2; do rm -rf /data/m/rx/* ; time ./db_bench.$i --db=/data/m/rx --benchmarks=fillseq,readrandom --num_multi_db=4 --num=100000; du -hs /data/m/rx ; done --- without the change, smaller database, note that not all keys are found by readrandom because databases have < and > --num keys fillseq : 3.176 micros/op 314805 ops/sec; 34.8 MB/s readrandom : 1.913 micros/op 522616 ops/sec; 57.7 MB/s (99873 of 100000 found) --- with the change, smaller database, note that all keys are found by readrandom fillseq : 3.110 micros/op 321566 ops/sec; 35.6 MB/s readrandom : 1.714 micros/op 583257 ops/sec; 64.5 MB/s (100000 of 100000 found) Reviewed By: jay-zhuang Differential Revision: D35030168 Pulled By: mdcallag fbshipit-source-id: 2a18c4ec571d954cf5a57b00a11802a3608823ee
2022-03-22 17:36:24 +00:00
while ((num_per_key_gen != 0) && !duration.Done(entries_per_batch_)) {
if (duration.GetStage() != stage) {
stage = duration.GetStage();
if (db_.db != nullptr) {
db_.CreateNewCf(open_options_, stage);
} else {
for (auto& db : multi_dbs_) {
db.CreateNewCf(open_options_, stage);
}
}
}
For db_bench --benchmarks=fillseq with --num_multi_db load databases … (#9713) Summary: …in order This fixes https://github.com/facebook/rocksdb/issues/9650 For db_bench --benchmarks=fillseq --num_multi_db=X it loads databases in sequence rather than randomly choosing a database per Put. The benefits are: 1) avoids long delays between flushing memtables 2) avoids flushing memtables for all of them at the same point in time 3) puts same number of keys per database so that query tests will find keys as expected Pull Request resolved: https://github.com/facebook/rocksdb/pull/9713 Test Plan: Using db_bench.1 without the change and db_bench.2 with the change: for i in 1 2; do rm -rf /data/m/rx/* ; time ./db_bench.$i --db=/data/m/rx --benchmarks=fillseq --num_multi_db=4 --num=10000000; du -hs /data/m/rx ; done --- without the change fillseq : 3.188 micros/op 313682 ops/sec; 34.7 MB/s real 2m7.787s user 1m52.776s sys 0m46.549s 2.7G /data/m/rx --- with the change fillseq : 3.149 micros/op 317563 ops/sec; 35.1 MB/s real 2m6.196s user 1m51.482s sys 0m46.003s 2.7G /data/m/rx Also, temporarily added a printf to confirm that the code switches to the next database at the right time ZZ switch to db 1 at 10000000 ZZ switch to db 2 at 20000000 ZZ switch to db 3 at 30000000 for i in 1 2; do rm -rf /data/m/rx/* ; time ./db_bench.$i --db=/data/m/rx --benchmarks=fillseq,readrandom --num_multi_db=4 --num=100000; du -hs /data/m/rx ; done --- without the change, smaller database, note that not all keys are found by readrandom because databases have < and > --num keys fillseq : 3.176 micros/op 314805 ops/sec; 34.8 MB/s readrandom : 1.913 micros/op 522616 ops/sec; 57.7 MB/s (99873 of 100000 found) --- with the change, smaller database, note that all keys are found by readrandom fillseq : 3.110 micros/op 321566 ops/sec; 35.6 MB/s readrandom : 1.714 micros/op 583257 ops/sec; 64.5 MB/s (100000 of 100000 found) Reviewed By: jay-zhuang Differential Revision: D35030168 Pulled By: mdcallag fbshipit-source-id: 2a18c4ec571d954cf5a57b00a11802a3608823ee
2022-03-22 17:36:24 +00:00
if (write_mode != SEQUENTIAL) {
id = thread->rand.Next() % num_key_gens;
} else {
// When doing a sequential load with multiple databases, load them in
// order rather than all at the same time to avoid:
// 1) long delays between flushing memtables
// 2) flushing memtables for all of them at the same point in time
// 3) not putting the same number of keys in each database
if (num_written >= next_seq_db_at) {
next_seq_db_at += num_ops;
id++;
if (id >= num_key_gens) {
fprintf(stderr, "Logic error. Filled all databases\n");
ErrorExit();
}
}
}
DBWithColumnFamilies* db_with_cfh = SelectDBWithCfh(id);
For db_bench --benchmarks=fillseq with --num_multi_db load databases … (#9713) Summary: …in order This fixes https://github.com/facebook/rocksdb/issues/9650 For db_bench --benchmarks=fillseq --num_multi_db=X it loads databases in sequence rather than randomly choosing a database per Put. The benefits are: 1) avoids long delays between flushing memtables 2) avoids flushing memtables for all of them at the same point in time 3) puts same number of keys per database so that query tests will find keys as expected Pull Request resolved: https://github.com/facebook/rocksdb/pull/9713 Test Plan: Using db_bench.1 without the change and db_bench.2 with the change: for i in 1 2; do rm -rf /data/m/rx/* ; time ./db_bench.$i --db=/data/m/rx --benchmarks=fillseq --num_multi_db=4 --num=10000000; du -hs /data/m/rx ; done --- without the change fillseq : 3.188 micros/op 313682 ops/sec; 34.7 MB/s real 2m7.787s user 1m52.776s sys 0m46.549s 2.7G /data/m/rx --- with the change fillseq : 3.149 micros/op 317563 ops/sec; 35.1 MB/s real 2m6.196s user 1m51.482s sys 0m46.003s 2.7G /data/m/rx Also, temporarily added a printf to confirm that the code switches to the next database at the right time ZZ switch to db 1 at 10000000 ZZ switch to db 2 at 20000000 ZZ switch to db 3 at 30000000 for i in 1 2; do rm -rf /data/m/rx/* ; time ./db_bench.$i --db=/data/m/rx --benchmarks=fillseq,readrandom --num_multi_db=4 --num=100000; du -hs /data/m/rx ; done --- without the change, smaller database, note that not all keys are found by readrandom because databases have < and > --num keys fillseq : 3.176 micros/op 314805 ops/sec; 34.8 MB/s readrandom : 1.913 micros/op 522616 ops/sec; 57.7 MB/s (99873 of 100000 found) --- with the change, smaller database, note that all keys are found by readrandom fillseq : 3.110 micros/op 321566 ops/sec; 35.6 MB/s readrandom : 1.714 micros/op 583257 ops/sec; 64.5 MB/s (100000 of 100000 found) Reviewed By: jay-zhuang Differential Revision: D35030168 Pulled By: mdcallag fbshipit-source-id: 2a18c4ec571d954cf5a57b00a11802a3608823ee
2022-03-22 17:36:24 +00:00
batch.Clear();
int64_t batch_bytes = 0;
for (int64_t j = 0; j < entries_per_batch_; j++) {
Add overwrite_probability for filluniquerandom benchmark in db_bench (#8569) Summary: Add flags `overwrite_probability` and `overwrite_window_size` flag to `db_bench`. Add the possibility of performing a `filluniquerandom` benchmark with an overwrite probability. For each write operation, there is a probability _p_ that the write is an overwrite (_p_=`overwrite_probability`). When an overwrite is decided, the key is randomly chosen from the last _N_ keys previously inserted into the DB (with _N_=`overwrite_window_size`). When a pure write is decided, the key inserted into the DB is unique and therefore will not be an overwrite. The `overwrite_window_size` is used so that the user can decide if the overwrite are mostly targeting recently inserted keys (when `overwrite_window_size` is small compared to the total number of writes), or can also target keys inserted "a long time ago" (when `overwrite_window_size` is comparable to total number of writes). Note that total number of writes = # of unique insertions + # of overwrites. No unit test specifically added. Local testing show the following **throughputs** for `filluniquerandom` with 1M total writes: - bypass the code inserts (no `overwrite_probability` flag specified): ~14.0MB/s - `overwrite_probability=0.99`, `overwrite_window_size=10`: ~17.0MB/s - `overwrite_probability=0.10`, `overwrite_window_size=10`: ~14.0MB/s - `overwrite_probability=0.99`, `overwrite_window_size=1M`: ~14.5MB/s - `overwrite_probability=0.10`, `overwrite_window_size=1M`: ~14.0MB/s Pull Request resolved: https://github.com/facebook/rocksdb/pull/8569 Reviewed By: pdillinger Differential Revision: D29818631 Pulled By: bjlemaire fbshipit-source-id: d472b4ea4e457a4da7c4ee4f14b40cccd6a4587a
2021-07-21 18:32:36 +00:00
int64_t rand_num = 0;
if ((write_mode == UNIQUE_RANDOM) && (p > 0.0)) {
if ((inserted_key_window.size() > 0) &&
overwrite_decider(overwrite_gen)) {
num_overwrites++;
rand_num = inserted_key_window[reservoir_id_gen.Next() %
inserted_key_window.size()];
} else {
num_unique_keys++;
rand_num = key_gens[id]->Next();
if (inserted_key_window.size() < FLAGS_overwrite_window_size) {
inserted_key_window.push_back(rand_num);
} else {
inserted_key_window.pop_front();
inserted_key_window.push_back(rand_num);
}
}
Create fillanddeleteuniquerandom benchmark (db_bench), with new option flags. (#8593) Summary: Introduction of a new `fillanddeleteuniquerandom` benchmark (`db_bench`) with 5 new option flags to simulate a benchmark where the following sequence is repeated multiple times: "A set of keys S1 is inserted ('`disposable entries`'), then after some delay another set of keys S2 is inserted ('`persistent entries`') and the first set of keys S1 is deleted. S2 artificially represents the insertion of hypothetical results from some undefined computation done on the first set of keys S1. The next sequence can start as soon as the last disposable entry in the set S1 of this sequence is inserted, if the `delay` is non negligible." New flags: - `disposable_entries_delete_delay`: minimum delay in microseconds between insertion of the last `disposable` entry, and the start of the insertion of the first `persistent` entry. - `disposable_entries_batch_size`: number of `disposable` entries inserted at the beginning of each sequence. - `disposable_entries_value_size`: size of the random `value` string for the `disposable` entries. - `persistent_entries_batch_size`: number of `persistent` entries inserted at the end of each sequence, right before the deletion of the `disposable` entries starts. - `persistent_entries_value_size`: size of the random value string for the `persistent` entries. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8593 Reviewed By: pdillinger Differential Revision: D29974436 Pulled By: bjlemaire fbshipit-source-id: f578033e5b45e8268ba6fa6f38f4770c2e6e801d
2021-07-29 21:57:03 +00:00
} else if (kNumDispAndPersEntries > 0) {
// Check if queue is non-empty and if we need to insert
// 'persistent' KV entries (KV entries that are never deleted)
// and delete disposable entries previously inserted.
if (!disposable_entries_q[id].empty() &&
(disposable_entries_q[id].front().first <
FLAGS_env->NowMicros())) {
// If we need to perform a "merge op" pattern,
// we first write all the persistent KV entries not targeted
// by deletes, and then we write the disposable entries deletes.
if (persistent_ent_and_del_index[id] <
FLAGS_persistent_entries_batch_size) {
// Generate key to insert.
rand_num =
key_gens[id]->Fetch(disposable_entries_q[id].front().second +
FLAGS_disposable_entries_batch_size +
persistent_ent_and_del_index[id]);
persistent_ent_and_del_index[id]++;
is_disposable_entry = false;
skip_for_loop = false;
} else if (persistent_ent_and_del_index[id] <
kNumDispAndPersEntries) {
// Find key of the entry to delete.
rand_num =
key_gens[id]->Fetch(disposable_entries_q[id].front().second +
(persistent_ent_and_del_index[id] -
FLAGS_persistent_entries_batch_size));
persistent_ent_and_del_index[id]++;
GenerateKeyFromInt(rand_num, FLAGS_num, &key);
// For the delete operation, everything happens here and we
// skip the rest of the for-loop, which is designed for
// inserts.
if (FLAGS_num_column_families <= 1) {
batch.Delete(key);
} else {
// We use same rand_num as seed for key and column family so
// that we can deterministically find the cfh corresponding to a
// particular key while reading the key.
batch.Delete(db_with_cfh->GetCfh(rand_num), key);
}
// A delete only includes Key+Timestamp (no value).
batch_bytes += key_size_ + user_timestamp_size_;
bytes += key_size_ + user_timestamp_size_;
num_selective_deletes++;
// Skip rest of the for-loop (j=0, j<entries_per_batch_,j++).
skip_for_loop = true;
} else {
assert(false); // should never reach this point.
}
// If disposable_entries_q needs to be updated (ie: when a selective
// insert+delete was successfully completed, pop the job out of the
// queue).
if (!disposable_entries_q[id].empty() &&
(disposable_entries_q[id].front().first <
FLAGS_env->NowMicros()) &&
persistent_ent_and_del_index[id] == kNumDispAndPersEntries) {
disposable_entries_q[id].pop();
persistent_ent_and_del_index[id] = 0;
}
// If we are deleting disposable entries, skip the rest of the
// for-loop since there is no key-value inserts at this moment in
// time.
if (skip_for_loop) {
continue;
}
}
// If no job is in the queue, then we keep inserting disposable KV
// entries that will be deleted later by a series of deletes.
else {
rand_num = key_gens[id]->Fetch(disposable_entries_index[id]);
disposable_entries_index[id]++;
is_disposable_entry = true;
if ((disposable_entries_index[id] %
FLAGS_disposable_entries_batch_size) == 0) {
// Skip the persistent KV entries inserts for now
disposable_entries_index[id] +=
FLAGS_persistent_entries_batch_size;
}
}
Add overwrite_probability for filluniquerandom benchmark in db_bench (#8569) Summary: Add flags `overwrite_probability` and `overwrite_window_size` flag to `db_bench`. Add the possibility of performing a `filluniquerandom` benchmark with an overwrite probability. For each write operation, there is a probability _p_ that the write is an overwrite (_p_=`overwrite_probability`). When an overwrite is decided, the key is randomly chosen from the last _N_ keys previously inserted into the DB (with _N_=`overwrite_window_size`). When a pure write is decided, the key inserted into the DB is unique and therefore will not be an overwrite. The `overwrite_window_size` is used so that the user can decide if the overwrite are mostly targeting recently inserted keys (when `overwrite_window_size` is small compared to the total number of writes), or can also target keys inserted "a long time ago" (when `overwrite_window_size` is comparable to total number of writes). Note that total number of writes = # of unique insertions + # of overwrites. No unit test specifically added. Local testing show the following **throughputs** for `filluniquerandom` with 1M total writes: - bypass the code inserts (no `overwrite_probability` flag specified): ~14.0MB/s - `overwrite_probability=0.99`, `overwrite_window_size=10`: ~17.0MB/s - `overwrite_probability=0.10`, `overwrite_window_size=10`: ~14.0MB/s - `overwrite_probability=0.99`, `overwrite_window_size=1M`: ~14.5MB/s - `overwrite_probability=0.10`, `overwrite_window_size=1M`: ~14.0MB/s Pull Request resolved: https://github.com/facebook/rocksdb/pull/8569 Reviewed By: pdillinger Differential Revision: D29818631 Pulled By: bjlemaire fbshipit-source-id: d472b4ea4e457a4da7c4ee4f14b40cccd6a4587a
2021-07-21 18:32:36 +00:00
} else {
rand_num = key_gens[id]->Next();
}
GenerateKeyFromInt(rand_num, FLAGS_num, &key);
Create fillanddeleteuniquerandom benchmark (db_bench), with new option flags. (#8593) Summary: Introduction of a new `fillanddeleteuniquerandom` benchmark (`db_bench`) with 5 new option flags to simulate a benchmark where the following sequence is repeated multiple times: "A set of keys S1 is inserted ('`disposable entries`'), then after some delay another set of keys S2 is inserted ('`persistent entries`') and the first set of keys S1 is deleted. S2 artificially represents the insertion of hypothetical results from some undefined computation done on the first set of keys S1. The next sequence can start as soon as the last disposable entry in the set S1 of this sequence is inserted, if the `delay` is non negligible." New flags: - `disposable_entries_delete_delay`: minimum delay in microseconds between insertion of the last `disposable` entry, and the start of the insertion of the first `persistent` entry. - `disposable_entries_batch_size`: number of `disposable` entries inserted at the beginning of each sequence. - `disposable_entries_value_size`: size of the random `value` string for the `disposable` entries. - `persistent_entries_batch_size`: number of `persistent` entries inserted at the end of each sequence, right before the deletion of the `disposable` entries starts. - `persistent_entries_value_size`: size of the random value string for the `persistent` entries. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8593 Reviewed By: pdillinger Differential Revision: D29974436 Pulled By: bjlemaire fbshipit-source-id: f578033e5b45e8268ba6fa6f38f4770c2e6e801d
2021-07-29 21:57:03 +00:00
Slice val;
if (kNumDispAndPersEntries > 0) {
random_value = rnd_disposable_entry.RandomString(
is_disposable_entry ? FLAGS_disposable_entries_value_size
: FLAGS_persistent_entries_value_size);
val = Slice(random_value);
num_unique_keys++;
} else {
val = gen.Generate();
}
if (use_blob_db_) {
// Stacked BlobDB
blob_db::BlobDB* blobdb =
static_cast<blob_db::BlobDB*>(db_with_cfh->db);
if (FLAGS_blob_db_max_ttl_range > 0) {
int ttl = rand() % FLAGS_blob_db_max_ttl_range;
s = blobdb->PutWithTTL(write_options_, key, val, ttl);
} else {
s = blobdb->Put(write_options_, key, val);
}
} else if (FLAGS_num_column_families <= 1) {
batch.Put(key, val);
} else {
// We use same rand_num as seed for key and column family so that we
// can deterministically find the cfh corresponding to a particular
// key while reading the key.
batch.Put(db_with_cfh->GetCfh(rand_num), key, val);
}
batch_bytes += val.size() + key_size_ + user_timestamp_size_;
bytes += val.size() + key_size_ + user_timestamp_size_;
++num_written;
Create fillanddeleteuniquerandom benchmark (db_bench), with new option flags. (#8593) Summary: Introduction of a new `fillanddeleteuniquerandom` benchmark (`db_bench`) with 5 new option flags to simulate a benchmark where the following sequence is repeated multiple times: "A set of keys S1 is inserted ('`disposable entries`'), then after some delay another set of keys S2 is inserted ('`persistent entries`') and the first set of keys S1 is deleted. S2 artificially represents the insertion of hypothetical results from some undefined computation done on the first set of keys S1. The next sequence can start as soon as the last disposable entry in the set S1 of this sequence is inserted, if the `delay` is non negligible." New flags: - `disposable_entries_delete_delay`: minimum delay in microseconds between insertion of the last `disposable` entry, and the start of the insertion of the first `persistent` entry. - `disposable_entries_batch_size`: number of `disposable` entries inserted at the beginning of each sequence. - `disposable_entries_value_size`: size of the random `value` string for the `disposable` entries. - `persistent_entries_batch_size`: number of `persistent` entries inserted at the end of each sequence, right before the deletion of the `disposable` entries starts. - `persistent_entries_value_size`: size of the random value string for the `persistent` entries. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8593 Reviewed By: pdillinger Differential Revision: D29974436 Pulled By: bjlemaire fbshipit-source-id: f578033e5b45e8268ba6fa6f38f4770c2e6e801d
2021-07-29 21:57:03 +00:00
// If all disposable entries have been inserted, then we need to
// add in the job queue a call for 'persistent entry insertions +
// disposable entry deletions'.
if (kNumDispAndPersEntries > 0 && is_disposable_entry &&
((disposable_entries_index[id] % kNumDispAndPersEntries) == 0)) {
// Queue contains [timestamp, starting_idx],
// timestamp = current_time + delay (minimum aboslute time when to
// start inserting the selective deletes) starting_idx = index in the
// keygen of the rand_num to generate the key of the first KV entry to
// delete (= key of the first selective delete).
disposable_entries_q[id].push(std::make_pair(
FLAGS_env->NowMicros() +
FLAGS_disposable_entries_delete_delay /* timestamp */,
disposable_entries_index[id] - kNumDispAndPersEntries
/*starting idx*/));
}
if (writes_per_range_tombstone_ > 0 &&
num_written > writes_before_delete_range_ &&
(num_written - writes_before_delete_range_) /
writes_per_range_tombstone_ <=
max_num_range_tombstones_ &&
(num_written - writes_before_delete_range_) %
writes_per_range_tombstone_ ==
0) {
Fragment memtable range tombstone in the write path (#10380) Summary: - Right now each read fragments the memtable range tombstones https://github.com/facebook/rocksdb/issues/4808. This PR explores the idea of fragmenting memtable range tombstones in the write path and reads can just read this cached fragmented tombstone without any fragmenting cost. This PR only does the caching for immutable memtable, and does so right before a memtable is added to an immutable memtable list. The fragmentation is done without holding mutex to minimize its performance impact. - db_bench is updated to print out the number of range deletions executed if there is any. Pull Request resolved: https://github.com/facebook/rocksdb/pull/10380 Test Plan: - CI, added asserts in various places to check whether a fragmented range tombstone list should have been constructed. - Benchmark: as this PR only optimizes immutable memtable path, the number of writes in the benchmark is chosen such an immutable memtable is created and range tombstones are in that memtable. ``` single thread: ./db_bench --benchmarks=fillrandom,readrandom --writes_per_range_tombstone=1 --max_write_buffer_number=100 --min_write_buffer_number_to_merge=100 --writes=500000 --reads=100000 --max_num_range_tombstones=100 multi_thread ./db_bench --benchmarks=fillrandom,readrandom --writes_per_range_tombstone=1 --max_write_buffer_number=100 --min_write_buffer_number_to_merge=100 --writes=15000 --reads=20000 --threads=32 --max_num_range_tombstones=100 ``` Commit 99cdf16464a057ca44de2f747541dedf651bae9e is included in benchmark result. It was an earlier attempt where tombstones are fragmented for each write operation. Reader threads share it using a shared_ptr which would slow down multi-thread read performance as seen in benchmark results. Results are averaged over 5 runs. Single thread result: | Max # tombstones | main fillrandom micros/op | 99cdf16464a057ca44de2f747541dedf651bae9e | Post PR | main readrandom micros/op | 99cdf16464a057ca44de2f747541dedf651bae9e | Post PR | | ------------- | ------------- |------------- |------------- |------------- |------------- |------------- | | 0 |6.68 |6.57 |6.72 |4.72 |4.79 |4.54 | | 1 |6.67 |6.58 |6.62 |5.41 |4.74 |4.72 | | 10 |6.59 |6.5 |6.56 |7.83 |4.69 |4.59 | | 100 |6.62 |6.75 |6.58 |29.57 |5.04 |5.09 | | 1000 |6.54 |6.82 |6.61 |320.33 |5.22 |5.21 | 32-thread result: note that "Max # tombstones" is per thread. | Max # tombstones | main fillrandom micros/op | 99cdf16464a057ca44de2f747541dedf651bae9e | Post PR | main readrandom micros/op | 99cdf16464a057ca44de2f747541dedf651bae9e | Post PR | | ------------- | ------------- |------------- |------------- |------------- |------------- |------------- | | 0 |234.52 |260.25 |239.42 |5.06 |5.38 |5.09 | | 1 |236.46 |262.0 |231.1 |19.57 |22.14 |5.45 | | 10 |236.95 |263.84 |251.49 |151.73 |21.61 |5.73 | | 100 |268.16 |296.8 |280.13 |2308.52 |22.27 |6.57 | Reviewed By: ajkr Differential Revision: D37916564 Pulled By: cbi42 fbshipit-source-id: 05d6d2e16df26c374c57ddcca13a5bfe9d5b731e
2022-08-05 19:02:33 +00:00
num_range_deletions++;
int64_t begin_num = key_gens[id]->Next();
if (FLAGS_expand_range_tombstones) {
for (int64_t offset = 0; offset < range_tombstone_width_;
++offset) {
GenerateKeyFromInt(begin_num + offset, FLAGS_num,
&expanded_keys[offset]);
if (use_blob_db_) {
// Stacked BlobDB
s = db_with_cfh->db->Delete(write_options_,
expanded_keys[offset]);
} else if (FLAGS_num_column_families <= 1) {
batch.Delete(expanded_keys[offset]);
} else {
batch.Delete(db_with_cfh->GetCfh(rand_num),
expanded_keys[offset]);
}
}
} else {
GenerateKeyFromInt(begin_num, FLAGS_num, &begin_key);
GenerateKeyFromInt(begin_num + range_tombstone_width_, FLAGS_num,
&end_key);
if (use_blob_db_) {
// Stacked BlobDB
s = db_with_cfh->db->DeleteRange(
write_options_, db_with_cfh->db->DefaultColumnFamily(),
begin_key, end_key);
} else if (FLAGS_num_column_families <= 1) {
batch.DeleteRange(begin_key, end_key);
} else {
batch.DeleteRange(db_with_cfh->GetCfh(rand_num), begin_key,
end_key);
}
}
}
}
if (thread->shared->write_rate_limiter.get() != nullptr) {
thread->shared->write_rate_limiter->Request(
batch_bytes, Env::IO_HIGH, nullptr /* stats */,
RateLimiter::OpType::kWrite);
// Set time at which last op finished to Now() to hide latency and
// sleep from rate limiter. Also, do the check once per batch, not
// once per write.
thread->stats.ResetLastOpTime();
}
if (user_timestamp_size_ > 0) {
Slice user_ts = mock_app_clock_->Allocate(ts_guard.get());
Revise APIs related to user-defined timestamp (#8946) Summary: ajkr reminded me that we have a rule of not including per-kv related data in `WriteOptions`. Namely, `WriteOptions` should not include information about "what-to-write", but should just include information about "how-to-write". According to this rule, `WriteOptions::timestamp` (experimental) is clearly a violation. Therefore, this PR removes `WriteOptions::timestamp` for compliance. After the removal, we need to pass timestamp info via another set of APIs. This PR proposes a set of overloaded functions `Put(write_opts, key, value, ts)`, `Delete(write_opts, key, ts)`, and `SingleDelete(write_opts, key, ts)`. Planned to add `Write(write_opts, batch, ts)`, but its complexity made me reconsider doing it in another PR (maybe). For better checking and returning error early, we also add a new set of APIs to `WriteBatch` that take extra `timestamp` information when writing to `WriteBatch`es. These set of APIs in `WriteBatchWithIndex` are currently not supported, and are on our TODO list. Removed `WriteBatch::AssignTimestamps()` and renamed `WriteBatch::AssignTimestamp()` to `WriteBatch::UpdateTimestamps()` since this method require that all keys have space for timestamps allocated already and multiple timestamps can be updated. The constructor of `WriteBatch` now takes a fourth argument `default_cf_ts_sz` which is the timestamp size of the default column family. This will be used to allocate space when calling APIs that do not specify a column family handle. Also, updated `DB::Get()`, `DB::MultiGet()`, `DB::NewIterator()`, `DB::NewIterators()` methods, replacing some assertions about timestamp to returning Status code. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8946 Test Plan: make check ./db_bench -benchmarks=fillseq,fillrandom,readrandom,readseq,deleterandom -user_timestamp_size=8 ./db_stress --user_timestamp_size=8 -nooverwritepercent=0 -test_secondary=0 -secondary_catch_up_one_in=0 -continuous_verification_interval=0 Make sure there is no perf regression by running the following ``` ./db_bench_opt -db=/dev/shm/rocksdb -use_existing_db=0 -level0_stop_writes_trigger=256 -level0_slowdown_writes_trigger=256 -level0_file_num_compaction_trigger=256 -disable_wal=1 -duration=10 -benchmarks=fillrandom ``` Before this PR ``` DB path: [/dev/shm/rocksdb] fillrandom : 1.831 micros/op 546235 ops/sec; 60.4 MB/s ``` After this PR ``` DB path: [/dev/shm/rocksdb] fillrandom : 1.820 micros/op 549404 ops/sec; 60.8 MB/s ``` Reviewed By: ltamasi Differential Revision: D33721359 Pulled By: riversand963 fbshipit-source-id: c131561534272c120ffb80711d42748d21badf09
2022-02-02 06:17:46 +00:00
s = batch.UpdateTimestamps(
user_ts, [this](uint32_t) { return user_timestamp_size_; });
if (!s.ok()) {
fprintf(stderr, "assign timestamp to write batch: %s\n",
s.ToString().c_str());
ErrorExit();
}
}
if (!use_blob_db_) {
// Not stacked BlobDB
s = db_with_cfh->db->Write(write_options_, &batch);
}
thread->stats.FinishedOps(db_with_cfh, db_with_cfh->db,
entries_per_batch_, kWrite);
if (FLAGS_sine_write_rate) {
uint64_t now = FLAGS_env->NowMicros();
uint64_t usecs_since_last;
if (now > thread->stats.GetSineInterval()) {
usecs_since_last = now - thread->stats.GetSineInterval();
} else {
usecs_since_last = 0;
}
if (usecs_since_last >
(FLAGS_sine_write_rate_interval_milliseconds * uint64_t{1000})) {
double usecs_since_start =
static_cast<double>(now - thread->stats.GetStart());
thread->stats.ResetSineInterval();
uint64_t write_rate =
static_cast<uint64_t>(SineRate(usecs_since_start / 1000000.0));
thread->shared->write_rate_limiter.reset(
NewGenericRateLimiter(write_rate));
}
}
Auto recovery from out of space errors (#4164) Summary: This commit implements automatic recovery from a Status::NoSpace() error during background operations such as write callback, flush and compaction. The broad design is as follows - 1. Compaction errors are treated as soft errors and don't put the database in read-only mode. A compaction is delayed until enough free disk space is available to accomodate the compaction outputs, which is estimated based on the input size. This means that users can continue to write, and we rely on the WriteController to delay or stop writes if the compaction debt becomes too high due to persistent low disk space condition 2. Errors during write callback and flush are treated as hard errors, i.e the database is put in read-only mode and goes back to read-write only fater certain recovery actions are taken. 3. Both types of recovery rely on the SstFileManagerImpl to poll for sufficient disk space. We assume that there is a 1-1 mapping between an SFM and the underlying OS storage container. For cases where multiple DBs are hosted on a single storage container, the user is expected to allocate a single SFM instance and use the same one for all the DBs. If no SFM is specified by the user, DBImpl::Open() will allocate one, but this will be one per DB and each DB will recover independently. The recovery implemented by SFM is as follows - a) On the first occurance of an out of space error during compaction, subsequent compactions will be delayed until the disk free space check indicates enough available space. The required space is computed as the sum of input sizes. b) The free space check requirement will be removed once the amount of free space is greater than the size reserved by in progress compactions when the first error occured c) If the out of space error is a hard error, a background thread in SFM will poll for sufficient headroom before triggering the recovery of the database and putting it in write-only mode. The headroom is calculated as the sum of the write_buffer_size of all the DB instances associated with the SFM 4. EventListener callbacks will be called at the start and completion of automatic recovery. Users can disable the auto recov ery in the start callback, and later initiate it manually by calling DB::Resume() Todo: 1. More extensive testing 2. Add disk full condition to db_stress (follow-on PR) Pull Request resolved: https://github.com/facebook/rocksdb/pull/4164 Differential Revision: D9846378 Pulled By: anand1976 fbshipit-source-id: 80ea875dbd7f00205e19c82215ff6e37da10da4a
2018-09-15 20:36:19 +00:00
if (!s.ok()) {
s = listener_->WaitForRecovery(600000000) ? Status::OK() : s;
}
if (!s.ok()) {
fprintf(stderr, "put error: %s\n", s.ToString().c_str());
ErrorExit();
}
}
Add overwrite_probability for filluniquerandom benchmark in db_bench (#8569) Summary: Add flags `overwrite_probability` and `overwrite_window_size` flag to `db_bench`. Add the possibility of performing a `filluniquerandom` benchmark with an overwrite probability. For each write operation, there is a probability _p_ that the write is an overwrite (_p_=`overwrite_probability`). When an overwrite is decided, the key is randomly chosen from the last _N_ keys previously inserted into the DB (with _N_=`overwrite_window_size`). When a pure write is decided, the key inserted into the DB is unique and therefore will not be an overwrite. The `overwrite_window_size` is used so that the user can decide if the overwrite are mostly targeting recently inserted keys (when `overwrite_window_size` is small compared to the total number of writes), or can also target keys inserted "a long time ago" (when `overwrite_window_size` is comparable to total number of writes). Note that total number of writes = # of unique insertions + # of overwrites. No unit test specifically added. Local testing show the following **throughputs** for `filluniquerandom` with 1M total writes: - bypass the code inserts (no `overwrite_probability` flag specified): ~14.0MB/s - `overwrite_probability=0.99`, `overwrite_window_size=10`: ~17.0MB/s - `overwrite_probability=0.10`, `overwrite_window_size=10`: ~14.0MB/s - `overwrite_probability=0.99`, `overwrite_window_size=1M`: ~14.5MB/s - `overwrite_probability=0.10`, `overwrite_window_size=1M`: ~14.0MB/s Pull Request resolved: https://github.com/facebook/rocksdb/pull/8569 Reviewed By: pdillinger Differential Revision: D29818631 Pulled By: bjlemaire fbshipit-source-id: d472b4ea4e457a4da7c4ee4f14b40cccd6a4587a
2021-07-21 18:32:36 +00:00
if ((write_mode == UNIQUE_RANDOM) && (p > 0.0)) {
fprintf(stdout,
Create fillanddeleteuniquerandom benchmark (db_bench), with new option flags. (#8593) Summary: Introduction of a new `fillanddeleteuniquerandom` benchmark (`db_bench`) with 5 new option flags to simulate a benchmark where the following sequence is repeated multiple times: "A set of keys S1 is inserted ('`disposable entries`'), then after some delay another set of keys S2 is inserted ('`persistent entries`') and the first set of keys S1 is deleted. S2 artificially represents the insertion of hypothetical results from some undefined computation done on the first set of keys S1. The next sequence can start as soon as the last disposable entry in the set S1 of this sequence is inserted, if the `delay` is non negligible." New flags: - `disposable_entries_delete_delay`: minimum delay in microseconds between insertion of the last `disposable` entry, and the start of the insertion of the first `persistent` entry. - `disposable_entries_batch_size`: number of `disposable` entries inserted at the beginning of each sequence. - `disposable_entries_value_size`: size of the random `value` string for the `disposable` entries. - `persistent_entries_batch_size`: number of `persistent` entries inserted at the end of each sequence, right before the deletion of the `disposable` entries starts. - `persistent_entries_value_size`: size of the random value string for the `persistent` entries. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8593 Reviewed By: pdillinger Differential Revision: D29974436 Pulled By: bjlemaire fbshipit-source-id: f578033e5b45e8268ba6fa6f38f4770c2e6e801d
2021-07-29 21:57:03 +00:00
"Number of unique keys inserted: %" PRIu64
Add overwrite_probability for filluniquerandom benchmark in db_bench (#8569) Summary: Add flags `overwrite_probability` and `overwrite_window_size` flag to `db_bench`. Add the possibility of performing a `filluniquerandom` benchmark with an overwrite probability. For each write operation, there is a probability _p_ that the write is an overwrite (_p_=`overwrite_probability`). When an overwrite is decided, the key is randomly chosen from the last _N_ keys previously inserted into the DB (with _N_=`overwrite_window_size`). When a pure write is decided, the key inserted into the DB is unique and therefore will not be an overwrite. The `overwrite_window_size` is used so that the user can decide if the overwrite are mostly targeting recently inserted keys (when `overwrite_window_size` is small compared to the total number of writes), or can also target keys inserted "a long time ago" (when `overwrite_window_size` is comparable to total number of writes). Note that total number of writes = # of unique insertions + # of overwrites. No unit test specifically added. Local testing show the following **throughputs** for `filluniquerandom` with 1M total writes: - bypass the code inserts (no `overwrite_probability` flag specified): ~14.0MB/s - `overwrite_probability=0.99`, `overwrite_window_size=10`: ~17.0MB/s - `overwrite_probability=0.10`, `overwrite_window_size=10`: ~14.0MB/s - `overwrite_probability=0.99`, `overwrite_window_size=1M`: ~14.5MB/s - `overwrite_probability=0.10`, `overwrite_window_size=1M`: ~14.0MB/s Pull Request resolved: https://github.com/facebook/rocksdb/pull/8569 Reviewed By: pdillinger Differential Revision: D29818631 Pulled By: bjlemaire fbshipit-source-id: d472b4ea4e457a4da7c4ee4f14b40cccd6a4587a
2021-07-21 18:32:36 +00:00
".\nNumber of overwrites: %" PRIu64 "\n",
num_unique_keys, num_overwrites);
Create fillanddeleteuniquerandom benchmark (db_bench), with new option flags. (#8593) Summary: Introduction of a new `fillanddeleteuniquerandom` benchmark (`db_bench`) with 5 new option flags to simulate a benchmark where the following sequence is repeated multiple times: "A set of keys S1 is inserted ('`disposable entries`'), then after some delay another set of keys S2 is inserted ('`persistent entries`') and the first set of keys S1 is deleted. S2 artificially represents the insertion of hypothetical results from some undefined computation done on the first set of keys S1. The next sequence can start as soon as the last disposable entry in the set S1 of this sequence is inserted, if the `delay` is non negligible." New flags: - `disposable_entries_delete_delay`: minimum delay in microseconds between insertion of the last `disposable` entry, and the start of the insertion of the first `persistent` entry. - `disposable_entries_batch_size`: number of `disposable` entries inserted at the beginning of each sequence. - `disposable_entries_value_size`: size of the random `value` string for the `disposable` entries. - `persistent_entries_batch_size`: number of `persistent` entries inserted at the end of each sequence, right before the deletion of the `disposable` entries starts. - `persistent_entries_value_size`: size of the random value string for the `persistent` entries. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8593 Reviewed By: pdillinger Differential Revision: D29974436 Pulled By: bjlemaire fbshipit-source-id: f578033e5b45e8268ba6fa6f38f4770c2e6e801d
2021-07-29 21:57:03 +00:00
} else if (kNumDispAndPersEntries > 0) {
fprintf(stdout,
"Number of unique keys inserted (disposable+persistent): %" PRIu64
".\nNumber of 'disposable entry delete': %" PRIu64 "\n",
num_written, num_selective_deletes);
Add overwrite_probability for filluniquerandom benchmark in db_bench (#8569) Summary: Add flags `overwrite_probability` and `overwrite_window_size` flag to `db_bench`. Add the possibility of performing a `filluniquerandom` benchmark with an overwrite probability. For each write operation, there is a probability _p_ that the write is an overwrite (_p_=`overwrite_probability`). When an overwrite is decided, the key is randomly chosen from the last _N_ keys previously inserted into the DB (with _N_=`overwrite_window_size`). When a pure write is decided, the key inserted into the DB is unique and therefore will not be an overwrite. The `overwrite_window_size` is used so that the user can decide if the overwrite are mostly targeting recently inserted keys (when `overwrite_window_size` is small compared to the total number of writes), or can also target keys inserted "a long time ago" (when `overwrite_window_size` is comparable to total number of writes). Note that total number of writes = # of unique insertions + # of overwrites. No unit test specifically added. Local testing show the following **throughputs** for `filluniquerandom` with 1M total writes: - bypass the code inserts (no `overwrite_probability` flag specified): ~14.0MB/s - `overwrite_probability=0.99`, `overwrite_window_size=10`: ~17.0MB/s - `overwrite_probability=0.10`, `overwrite_window_size=10`: ~14.0MB/s - `overwrite_probability=0.99`, `overwrite_window_size=1M`: ~14.5MB/s - `overwrite_probability=0.10`, `overwrite_window_size=1M`: ~14.0MB/s Pull Request resolved: https://github.com/facebook/rocksdb/pull/8569 Reviewed By: pdillinger Differential Revision: D29818631 Pulled By: bjlemaire fbshipit-source-id: d472b4ea4e457a4da7c4ee4f14b40cccd6a4587a
2021-07-21 18:32:36 +00:00
}
Fragment memtable range tombstone in the write path (#10380) Summary: - Right now each read fragments the memtable range tombstones https://github.com/facebook/rocksdb/issues/4808. This PR explores the idea of fragmenting memtable range tombstones in the write path and reads can just read this cached fragmented tombstone without any fragmenting cost. This PR only does the caching for immutable memtable, and does so right before a memtable is added to an immutable memtable list. The fragmentation is done without holding mutex to minimize its performance impact. - db_bench is updated to print out the number of range deletions executed if there is any. Pull Request resolved: https://github.com/facebook/rocksdb/pull/10380 Test Plan: - CI, added asserts in various places to check whether a fragmented range tombstone list should have been constructed. - Benchmark: as this PR only optimizes immutable memtable path, the number of writes in the benchmark is chosen such an immutable memtable is created and range tombstones are in that memtable. ``` single thread: ./db_bench --benchmarks=fillrandom,readrandom --writes_per_range_tombstone=1 --max_write_buffer_number=100 --min_write_buffer_number_to_merge=100 --writes=500000 --reads=100000 --max_num_range_tombstones=100 multi_thread ./db_bench --benchmarks=fillrandom,readrandom --writes_per_range_tombstone=1 --max_write_buffer_number=100 --min_write_buffer_number_to_merge=100 --writes=15000 --reads=20000 --threads=32 --max_num_range_tombstones=100 ``` Commit 99cdf16464a057ca44de2f747541dedf651bae9e is included in benchmark result. It was an earlier attempt where tombstones are fragmented for each write operation. Reader threads share it using a shared_ptr which would slow down multi-thread read performance as seen in benchmark results. Results are averaged over 5 runs. Single thread result: | Max # tombstones | main fillrandom micros/op | 99cdf16464a057ca44de2f747541dedf651bae9e | Post PR | main readrandom micros/op | 99cdf16464a057ca44de2f747541dedf651bae9e | Post PR | | ------------- | ------------- |------------- |------------- |------------- |------------- |------------- | | 0 |6.68 |6.57 |6.72 |4.72 |4.79 |4.54 | | 1 |6.67 |6.58 |6.62 |5.41 |4.74 |4.72 | | 10 |6.59 |6.5 |6.56 |7.83 |4.69 |4.59 | | 100 |6.62 |6.75 |6.58 |29.57 |5.04 |5.09 | | 1000 |6.54 |6.82 |6.61 |320.33 |5.22 |5.21 | 32-thread result: note that "Max # tombstones" is per thread. | Max # tombstones | main fillrandom micros/op | 99cdf16464a057ca44de2f747541dedf651bae9e | Post PR | main readrandom micros/op | 99cdf16464a057ca44de2f747541dedf651bae9e | Post PR | | ------------- | ------------- |------------- |------------- |------------- |------------- |------------- | | 0 |234.52 |260.25 |239.42 |5.06 |5.38 |5.09 | | 1 |236.46 |262.0 |231.1 |19.57 |22.14 |5.45 | | 10 |236.95 |263.84 |251.49 |151.73 |21.61 |5.73 | | 100 |268.16 |296.8 |280.13 |2308.52 |22.27 |6.57 | Reviewed By: ajkr Differential Revision: D37916564 Pulled By: cbi42 fbshipit-source-id: 05d6d2e16df26c374c57ddcca13a5bfe9d5b731e
2022-08-05 19:02:33 +00:00
if (num_range_deletions > 0) {
std::cout << "Number of range deletions: " << num_range_deletions
<< std::endl;
}
thread->stats.AddBytes(bytes);
}
Status DoDeterministicCompact(ThreadState* thread,
CompactionStyle compaction_style,
WriteMode write_mode) {
ColumnFamilyMetaData meta;
std::vector<DB*> db_list;
if (db_.db != nullptr) {
db_list.push_back(db_.db);
} else {
for (auto& db : multi_dbs_) {
db_list.push_back(db.db);
}
}
std::vector<Options> options_list;
for (auto db : db_list) {
options_list.push_back(db->GetOptions());
if (compaction_style != kCompactionStyleFIFO) {
db->SetOptions({{"disable_auto_compactions", "1"},
{"level0_slowdown_writes_trigger", "400000000"},
{"level0_stop_writes_trigger", "400000000"}});
} else {
db->SetOptions({{"disable_auto_compactions", "1"}});
}
}
assert(!db_list.empty());
auto num_db = db_list.size();
size_t num_levels = static_cast<size_t>(open_options_.num_levels);
size_t output_level = open_options_.num_levels - 1;
std::vector<std::vector<std::vector<SstFileMetaData>>> sorted_runs(num_db);
std::vector<size_t> num_files_at_level0(num_db, 0);
if (compaction_style == kCompactionStyleLevel) {
if (num_levels == 0) {
return Status::InvalidArgument("num_levels should be larger than 1");
}
bool should_stop = false;
while (!should_stop) {
if (sorted_runs[0].empty()) {
DoWrite(thread, write_mode);
} else {
DoWrite(thread, UNIQUE_RANDOM);
}
for (size_t i = 0; i < num_db; i++) {
auto db = db_list[i];
db->Flush(FlushOptions());
db->GetColumnFamilyMetaData(&meta);
if (num_files_at_level0[i] == meta.levels[0].files.size() ||
writes_ == 0) {
should_stop = true;
continue;
}
sorted_runs[i].emplace_back(
meta.levels[0].files.begin(),
meta.levels[0].files.end() - num_files_at_level0[i]);
num_files_at_level0[i] = meta.levels[0].files.size();
if (sorted_runs[i].back().size() == 1) {
should_stop = true;
continue;
}
if (sorted_runs[i].size() == output_level) {
auto& L1 = sorted_runs[i].back();
L1.erase(L1.begin(), L1.begin() + L1.size() / 3);
should_stop = true;
continue;
}
}
writes_ /=
static_cast<int64_t>(open_options_.max_bytes_for_level_multiplier);
}
for (size_t i = 0; i < num_db; i++) {
if (sorted_runs[i].size() < num_levels - 1) {
fprintf(stderr, "n is too small to fill %" ROCKSDB_PRIszt " levels\n",
num_levels);
exit(1);
}
}
for (size_t i = 0; i < num_db; i++) {
auto db = db_list[i];
auto compactionOptions = CompactionOptions();
compactionOptions.compression = FLAGS_compression_type_e;
auto options = db->GetOptions();
MutableCFOptions mutable_cf_options(options);
for (size_t j = 0; j < sorted_runs[i].size(); j++) {
compactionOptions.output_file_size_limit = MaxFileSizeForLevel(
mutable_cf_options, static_cast<int>(output_level),
compaction_style);
std::cout << sorted_runs[i][j].size() << std::endl;
db->CompactFiles(
compactionOptions,
{sorted_runs[i][j].back().name, sorted_runs[i][j].front().name},
static_cast<int>(output_level - j) /*level*/);
}
}
} else if (compaction_style == kCompactionStyleUniversal) {
auto ratio = open_options_.compaction_options_universal.size_ratio;
bool should_stop = false;
while (!should_stop) {
if (sorted_runs[0].empty()) {
DoWrite(thread, write_mode);
} else {
DoWrite(thread, UNIQUE_RANDOM);
}
for (size_t i = 0; i < num_db; i++) {
auto db = db_list[i];
db->Flush(FlushOptions());
db->GetColumnFamilyMetaData(&meta);
if (num_files_at_level0[i] == meta.levels[0].files.size() ||
writes_ == 0) {
should_stop = true;
continue;
}
sorted_runs[i].emplace_back(
meta.levels[0].files.begin(),
meta.levels[0].files.end() - num_files_at_level0[i]);
num_files_at_level0[i] = meta.levels[0].files.size();
if (sorted_runs[i].back().size() == 1) {
should_stop = true;
continue;
}
num_files_at_level0[i] = meta.levels[0].files.size();
}
writes_ = static_cast<int64_t>(writes_ * static_cast<double>(100) /
(ratio + 200));
}
for (size_t i = 0; i < num_db; i++) {
if (sorted_runs[i].size() < num_levels) {
fprintf(stderr, "n is too small to fill %" ROCKSDB_PRIszt " levels\n",
num_levels);
exit(1);
}
}
for (size_t i = 0; i < num_db; i++) {
auto db = db_list[i];
auto compactionOptions = CompactionOptions();
compactionOptions.compression = FLAGS_compression_type_e;
auto options = db->GetOptions();
MutableCFOptions mutable_cf_options(options);
for (size_t j = 0; j < sorted_runs[i].size(); j++) {
compactionOptions.output_file_size_limit = MaxFileSizeForLevel(
mutable_cf_options, static_cast<int>(output_level),
compaction_style);
db->CompactFiles(
compactionOptions,
{sorted_runs[i][j].back().name, sorted_runs[i][j].front().name},
(output_level > j ? static_cast<int>(output_level - j)
: 0) /*level*/);
}
}
} else if (compaction_style == kCompactionStyleFIFO) {
if (num_levels != 1) {
return Status::InvalidArgument(
"num_levels should be 1 for FIFO compaction");
}
if (FLAGS_num_multi_db != 0) {
return Status::InvalidArgument("Doesn't support multiDB");
}
auto db = db_list[0];
std::vector<std::string> file_names;
while (true) {
if (sorted_runs[0].empty()) {
DoWrite(thread, write_mode);
} else {
DoWrite(thread, UNIQUE_RANDOM);
}
db->Flush(FlushOptions());
db->GetColumnFamilyMetaData(&meta);
auto total_size = meta.levels[0].size;
if (total_size >=
db->GetOptions().compaction_options_fifo.max_table_files_size) {
for (const auto& file_meta : meta.levels[0].files) {
file_names.emplace_back(file_meta.name);
}
break;
}
}
// TODO(shuzhang1989): Investigate why CompactFiles not working
// auto compactionOptions = CompactionOptions();
// db->CompactFiles(compactionOptions, file_names, 0);
auto compactionOptions = CompactRangeOptions();
compactionOptions.max_subcompactions =
static_cast<uint32_t>(FLAGS_subcompactions);
db->CompactRange(compactionOptions, nullptr, nullptr);
} else {
fprintf(stdout,
"%-12s : skipped (-compaction_stype=kCompactionStyleNone)\n",
"filldeterministic");
return Status::InvalidArgument("None compaction is not supported");
}
// Verify seqno and key range
// Note: the seqno get changed at the max level by implementation
// optimization, so skip the check of the max level.
#ifndef NDEBUG
for (size_t k = 0; k < num_db; k++) {
auto db = db_list[k];
db->GetColumnFamilyMetaData(&meta);
// verify the number of sorted runs
if (compaction_style == kCompactionStyleLevel) {
assert(num_levels - 1 == sorted_runs[k].size());
} else if (compaction_style == kCompactionStyleUniversal) {
assert(meta.levels[0].files.size() + num_levels - 1 ==
sorted_runs[k].size());
} else if (compaction_style == kCompactionStyleFIFO) {
// TODO(gzh): FIFO compaction
db->GetColumnFamilyMetaData(&meta);
auto total_size = meta.levels[0].size;
assert(total_size <=
db->GetOptions().compaction_options_fifo.max_table_files_size);
break;
}
// verify smallest/largest seqno and key range of each sorted run
auto max_level = num_levels - 1;
int level;
for (size_t i = 0; i < sorted_runs[k].size(); i++) {
level = static_cast<int>(max_level - i);
SequenceNumber sorted_run_smallest_seqno = kMaxSequenceNumber;
SequenceNumber sorted_run_largest_seqno = 0;
std::string sorted_run_smallest_key, sorted_run_largest_key;
bool first_key = true;
for (const auto& fileMeta : sorted_runs[k][i]) {
sorted_run_smallest_seqno =
std::min(sorted_run_smallest_seqno, fileMeta.smallest_seqno);
sorted_run_largest_seqno =
std::max(sorted_run_largest_seqno, fileMeta.largest_seqno);
if (first_key ||
db->DefaultColumnFamily()->GetComparator()->Compare(
fileMeta.smallestkey, sorted_run_smallest_key) < 0) {
sorted_run_smallest_key = fileMeta.smallestkey;
}
if (first_key ||
db->DefaultColumnFamily()->GetComparator()->Compare(
fileMeta.largestkey, sorted_run_largest_key) > 0) {
sorted_run_largest_key = fileMeta.largestkey;
}
first_key = false;
}
if (compaction_style == kCompactionStyleLevel ||
(compaction_style == kCompactionStyleUniversal && level > 0)) {
SequenceNumber level_smallest_seqno = kMaxSequenceNumber;
SequenceNumber level_largest_seqno = 0;
for (const auto& fileMeta : meta.levels[level].files) {
level_smallest_seqno =
std::min(level_smallest_seqno, fileMeta.smallest_seqno);
level_largest_seqno =
std::max(level_largest_seqno, fileMeta.largest_seqno);
}
assert(sorted_run_smallest_key ==
meta.levels[level].files.front().smallestkey);
assert(sorted_run_largest_key ==
meta.levels[level].files.back().largestkey);
if (level != static_cast<int>(max_level)) {
// compaction at max_level would change sequence number
assert(sorted_run_smallest_seqno == level_smallest_seqno);
assert(sorted_run_largest_seqno == level_largest_seqno);
}
} else if (compaction_style == kCompactionStyleUniversal) {
// level <= 0 means sorted runs on level 0
auto level0_file =
meta.levels[0].files[sorted_runs[k].size() - 1 - i];
assert(sorted_run_smallest_key == level0_file.smallestkey);
assert(sorted_run_largest_key == level0_file.largestkey);
if (level != static_cast<int>(max_level)) {
assert(sorted_run_smallest_seqno == level0_file.smallest_seqno);
assert(sorted_run_largest_seqno == level0_file.largest_seqno);
}
}
}
}
#endif
// print the size of each sorted_run
for (size_t k = 0; k < num_db; k++) {
auto db = db_list[k];
fprintf(stdout,
"---------------------- DB %" ROCKSDB_PRIszt
" LSM ---------------------\n",
k);
db->GetColumnFamilyMetaData(&meta);
for (auto& levelMeta : meta.levels) {
if (levelMeta.files.empty()) {
continue;
}
if (levelMeta.level == 0) {
for (auto& fileMeta : levelMeta.files) {
fprintf(stdout, "Level[%d]: %s(size: %" PRIi64 " bytes)\n",
levelMeta.level, fileMeta.name.c_str(), fileMeta.size);
}
} else {
fprintf(stdout, "Level[%d]: %s - %s(total size: %" PRIi64 " bytes)\n",
levelMeta.level, levelMeta.files.front().name.c_str(),
levelMeta.files.back().name.c_str(), levelMeta.size);
}
}
}
for (size_t i = 0; i < num_db; i++) {
db_list[i]->SetOptions(
{{"disable_auto_compactions",
std::to_string(options_list[i].disable_auto_compactions)},
{"level0_slowdown_writes_trigger",
std::to_string(options_list[i].level0_slowdown_writes_trigger)},
{"level0_stop_writes_trigger",
std::to_string(options_list[i].level0_stop_writes_trigger)}});
}
return Status::OK();
}
void ReadSequential(ThreadState* thread) {
if (db_.db != nullptr) {
ReadSequential(thread, db_.db);
} else {
for (const auto& db_with_cfh : multi_dbs_) {
ReadSequential(thread, db_with_cfh.db);
}
}
}
void ReadSequential(ThreadState* thread, DB* db) {
Ignore `total_order_seek` in DB::Get (#9427) Summary: Apparently setting total_order_seek=true for DB::Get was intended to allow accurate read semantics if the current prefix extractor doesn't match what was used to generate SST files on disk. But since prefix_extractor was made a mutable option in 5.14.0, we have been able to detect this case and provide the correct semantics regardless of the total_order_seek option. Since that time, the option has only made Get() slower in a reasonably common case: prefix_extractor unchanged and whole_key_filtering=false. So this change primarily removes unnecessary effect of total_order_seek on Get. Also cleans up some related comments. Also adds a -total_order_seek option to db_bench and canonicalizes handling of ReadOptions in db_bench so that command line options have the expected association with library features. (There is potential for change in regression test behavior, but the old behavior is likely indefensible, or some other inconsistency would need to be fixed.) TODO in follow-up work: there should be no reason for Get() to depend on current prefix extractor at all. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9427 Test Plan: Unit tests updated. Performance (using db_bench update) Create DB with `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -benchmarks=fillrandom -num=10000000 -disable_wal=1 -write_buffer_size=10000000 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12 -whole_key_filtering=0` Test with and without `-total_order_seek` on `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -use_existing_db -readonly -benchmarks=readrandom -num=10000000 -duration=40 -disable_wal=1 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12` Before this change, total_order_seek=false: 25188 ops/sec Before this change, total_order_seek=true: 1222 ops/sec (~20x slower) After this change, total_order_seek=false: 24570 ops/sec After this change, total_order_seek=true: 25012 ops/sec (indistinguishable) Reviewed By: siying Differential Revision: D33753458 Pulled By: pdillinger fbshipit-source-id: bf892f34907a5e407d9c40bd4d42f0adbcbe0014
2022-02-01 03:45:17 +00:00
ReadOptions options = read_options_;
std::unique_ptr<char[]> ts_guard;
Slice ts;
if (user_timestamp_size_ > 0) {
ts_guard.reset(new char[user_timestamp_size_]);
ts = mock_app_clock_->GetTimestampForRead(thread->rand, ts_guard.get());
options.timestamp = &ts;
}
options.adaptive_readahead = FLAGS_adaptive_readahead;
options.async_io = FLAGS_async_io;
options.auto_readahead_size = FLAGS_auto_readahead_size;
Iterator* iter = db->NewIterator(options);
int64_t i = 0;
int64_t bytes = 0;
for (iter->SeekToFirst(); i < reads_ && iter->Valid(); iter->Next()) {
bytes += iter->key().size() + iter->value().size();
thread->stats.FinishedOps(nullptr, db, 1, kRead);
++i;
if (thread->shared->read_rate_limiter.get() != nullptr &&
i % 1024 == 1023) {
thread->shared->read_rate_limiter->Request(1024, Env::IO_HIGH,
nullptr /* stats */,
RateLimiter::OpType::kRead);
}
}
delete iter;
thread->stats.AddBytes(bytes);
}
void ReadToRowCache(ThreadState* thread) {
int64_t read = 0;
int64_t found = 0;
int64_t bytes = 0;
int64_t key_rand = 0;
std::unique_ptr<const char[]> key_guard;
Slice key = AllocateKey(&key_guard);
PinnableSlice pinnable_val;
while (key_rand < FLAGS_num) {
DBWithColumnFamilies* db_with_cfh = SelectDBWithCfh(thread);
// We use same key_rand as seed for key and column family so that we can
// deterministically find the cfh corresponding to a particular key, as it
// is done in DoWrite method.
GenerateKeyFromInt(key_rand, FLAGS_num, &key);
key_rand++;
read++;
Status s;
if (FLAGS_num_column_families > 1) {
Ignore `total_order_seek` in DB::Get (#9427) Summary: Apparently setting total_order_seek=true for DB::Get was intended to allow accurate read semantics if the current prefix extractor doesn't match what was used to generate SST files on disk. But since prefix_extractor was made a mutable option in 5.14.0, we have been able to detect this case and provide the correct semantics regardless of the total_order_seek option. Since that time, the option has only made Get() slower in a reasonably common case: prefix_extractor unchanged and whole_key_filtering=false. So this change primarily removes unnecessary effect of total_order_seek on Get. Also cleans up some related comments. Also adds a -total_order_seek option to db_bench and canonicalizes handling of ReadOptions in db_bench so that command line options have the expected association with library features. (There is potential for change in regression test behavior, but the old behavior is likely indefensible, or some other inconsistency would need to be fixed.) TODO in follow-up work: there should be no reason for Get() to depend on current prefix extractor at all. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9427 Test Plan: Unit tests updated. Performance (using db_bench update) Create DB with `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -benchmarks=fillrandom -num=10000000 -disable_wal=1 -write_buffer_size=10000000 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12 -whole_key_filtering=0` Test with and without `-total_order_seek` on `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -use_existing_db -readonly -benchmarks=readrandom -num=10000000 -duration=40 -disable_wal=1 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12` Before this change, total_order_seek=false: 25188 ops/sec Before this change, total_order_seek=true: 1222 ops/sec (~20x slower) After this change, total_order_seek=false: 24570 ops/sec After this change, total_order_seek=true: 25012 ops/sec (indistinguishable) Reviewed By: siying Differential Revision: D33753458 Pulled By: pdillinger fbshipit-source-id: bf892f34907a5e407d9c40bd4d42f0adbcbe0014
2022-02-01 03:45:17 +00:00
s = db_with_cfh->db->Get(read_options_, db_with_cfh->GetCfh(key_rand),
key, &pinnable_val);
} else {
pinnable_val.Reset();
Ignore `total_order_seek` in DB::Get (#9427) Summary: Apparently setting total_order_seek=true for DB::Get was intended to allow accurate read semantics if the current prefix extractor doesn't match what was used to generate SST files on disk. But since prefix_extractor was made a mutable option in 5.14.0, we have been able to detect this case and provide the correct semantics regardless of the total_order_seek option. Since that time, the option has only made Get() slower in a reasonably common case: prefix_extractor unchanged and whole_key_filtering=false. So this change primarily removes unnecessary effect of total_order_seek on Get. Also cleans up some related comments. Also adds a -total_order_seek option to db_bench and canonicalizes handling of ReadOptions in db_bench so that command line options have the expected association with library features. (There is potential for change in regression test behavior, but the old behavior is likely indefensible, or some other inconsistency would need to be fixed.) TODO in follow-up work: there should be no reason for Get() to depend on current prefix extractor at all. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9427 Test Plan: Unit tests updated. Performance (using db_bench update) Create DB with `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -benchmarks=fillrandom -num=10000000 -disable_wal=1 -write_buffer_size=10000000 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12 -whole_key_filtering=0` Test with and without `-total_order_seek` on `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -use_existing_db -readonly -benchmarks=readrandom -num=10000000 -duration=40 -disable_wal=1 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12` Before this change, total_order_seek=false: 25188 ops/sec Before this change, total_order_seek=true: 1222 ops/sec (~20x slower) After this change, total_order_seek=false: 24570 ops/sec After this change, total_order_seek=true: 25012 ops/sec (indistinguishable) Reviewed By: siying Differential Revision: D33753458 Pulled By: pdillinger fbshipit-source-id: bf892f34907a5e407d9c40bd4d42f0adbcbe0014
2022-02-01 03:45:17 +00:00
s = db_with_cfh->db->Get(read_options_,
db_with_cfh->db->DefaultColumnFamily(), key,
&pinnable_val);
}
if (s.ok()) {
found++;
bytes += key.size() + pinnable_val.size();
} else if (!s.IsNotFound()) {
fprintf(stderr, "Get returned an error: %s\n", s.ToString().c_str());
abort();
}
if (thread->shared->read_rate_limiter.get() != nullptr &&
read % 256 == 255) {
thread->shared->read_rate_limiter->Request(
256, Env::IO_HIGH, nullptr /* stats */, RateLimiter::OpType::kRead);
}
thread->stats.FinishedOps(db_with_cfh, db_with_cfh->db, 1, kRead);
}
char msg[100];
snprintf(msg, sizeof(msg), "(%" PRIu64 " of %" PRIu64 " found)\n", found,
read);
thread->stats.AddBytes(bytes);
thread->stats.AddMessage(msg);
}
void ReadReverse(ThreadState* thread) {
if (db_.db != nullptr) {
ReadReverse(thread, db_.db);
} else {
for (const auto& db_with_cfh : multi_dbs_) {
ReadReverse(thread, db_with_cfh.db);
}
}
}
void ReadReverse(ThreadState* thread, DB* db) {
Ignore `total_order_seek` in DB::Get (#9427) Summary: Apparently setting total_order_seek=true for DB::Get was intended to allow accurate read semantics if the current prefix extractor doesn't match what was used to generate SST files on disk. But since prefix_extractor was made a mutable option in 5.14.0, we have been able to detect this case and provide the correct semantics regardless of the total_order_seek option. Since that time, the option has only made Get() slower in a reasonably common case: prefix_extractor unchanged and whole_key_filtering=false. So this change primarily removes unnecessary effect of total_order_seek on Get. Also cleans up some related comments. Also adds a -total_order_seek option to db_bench and canonicalizes handling of ReadOptions in db_bench so that command line options have the expected association with library features. (There is potential for change in regression test behavior, but the old behavior is likely indefensible, or some other inconsistency would need to be fixed.) TODO in follow-up work: there should be no reason for Get() to depend on current prefix extractor at all. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9427 Test Plan: Unit tests updated. Performance (using db_bench update) Create DB with `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -benchmarks=fillrandom -num=10000000 -disable_wal=1 -write_buffer_size=10000000 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12 -whole_key_filtering=0` Test with and without `-total_order_seek` on `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -use_existing_db -readonly -benchmarks=readrandom -num=10000000 -duration=40 -disable_wal=1 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12` Before this change, total_order_seek=false: 25188 ops/sec Before this change, total_order_seek=true: 1222 ops/sec (~20x slower) After this change, total_order_seek=false: 24570 ops/sec After this change, total_order_seek=true: 25012 ops/sec (indistinguishable) Reviewed By: siying Differential Revision: D33753458 Pulled By: pdillinger fbshipit-source-id: bf892f34907a5e407d9c40bd4d42f0adbcbe0014
2022-02-01 03:45:17 +00:00
Iterator* iter = db->NewIterator(read_options_);
int64_t i = 0;
int64_t bytes = 0;
for (iter->SeekToLast(); i < reads_ && iter->Valid(); iter->Prev()) {
bytes += iter->key().size() + iter->value().size();
thread->stats.FinishedOps(nullptr, db, 1, kRead);
++i;
if (thread->shared->read_rate_limiter.get() != nullptr &&
i % 1024 == 1023) {
thread->shared->read_rate_limiter->Request(1024, Env::IO_HIGH,
nullptr /* stats */,
RateLimiter::OpType::kRead);
}
}
delete iter;
thread->stats.AddBytes(bytes);
}
void ReadRandomFast(ThreadState* thread) {
int64_t read = 0;
int64_t found = 0;
int64_t nonexist = 0;
Ignore `total_order_seek` in DB::Get (#9427) Summary: Apparently setting total_order_seek=true for DB::Get was intended to allow accurate read semantics if the current prefix extractor doesn't match what was used to generate SST files on disk. But since prefix_extractor was made a mutable option in 5.14.0, we have been able to detect this case and provide the correct semantics regardless of the total_order_seek option. Since that time, the option has only made Get() slower in a reasonably common case: prefix_extractor unchanged and whole_key_filtering=false. So this change primarily removes unnecessary effect of total_order_seek on Get. Also cleans up some related comments. Also adds a -total_order_seek option to db_bench and canonicalizes handling of ReadOptions in db_bench so that command line options have the expected association with library features. (There is potential for change in regression test behavior, but the old behavior is likely indefensible, or some other inconsistency would need to be fixed.) TODO in follow-up work: there should be no reason for Get() to depend on current prefix extractor at all. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9427 Test Plan: Unit tests updated. Performance (using db_bench update) Create DB with `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -benchmarks=fillrandom -num=10000000 -disable_wal=1 -write_buffer_size=10000000 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12 -whole_key_filtering=0` Test with and without `-total_order_seek` on `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -use_existing_db -readonly -benchmarks=readrandom -num=10000000 -duration=40 -disable_wal=1 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12` Before this change, total_order_seek=false: 25188 ops/sec Before this change, total_order_seek=true: 1222 ops/sec (~20x slower) After this change, total_order_seek=false: 24570 ops/sec After this change, total_order_seek=true: 25012 ops/sec (indistinguishable) Reviewed By: siying Differential Revision: D33753458 Pulled By: pdillinger fbshipit-source-id: bf892f34907a5e407d9c40bd4d42f0adbcbe0014
2022-02-01 03:45:17 +00:00
ReadOptions options = read_options_;
std::unique_ptr<const char[]> key_guard;
Slice key = AllocateKey(&key_guard);
std::string value;
Slice ts;
std::unique_ptr<char[]> ts_guard;
if (user_timestamp_size_ > 0) {
ts_guard.reset(new char[user_timestamp_size_]);
}
DB* db = SelectDBWithCfh(thread)->db;
int64_t pot = 1;
while (pot < FLAGS_num) {
pot <<= 1;
}
Duration duration(FLAGS_duration, reads_);
do {
for (int i = 0; i < 100; ++i) {
int64_t key_rand = thread->rand.Next() & (pot - 1);
GenerateKeyFromInt(key_rand, FLAGS_num, &key);
++read;
std::string ts_ret;
std::string* ts_ptr = nullptr;
if (user_timestamp_size_ > 0) {
ts = mock_app_clock_->GetTimestampForRead(thread->rand,
ts_guard.get());
options.timestamp = &ts;
ts_ptr = &ts_ret;
}
auto status = db->Get(options, key, &value, ts_ptr);
if (status.ok()) {
++found;
} else if (!status.IsNotFound()) {
2015-01-22 02:23:12 +00:00
fprintf(stderr, "Get returned an error: %s\n",
status.ToString().c_str());
abort();
}
if (key_rand >= FLAGS_num) {
++nonexist;
}
}
if (thread->shared->read_rate_limiter.get() != nullptr) {
thread->shared->read_rate_limiter->Request(
100, Env::IO_HIGH, nullptr /* stats */, RateLimiter::OpType::kRead);
}
thread->stats.FinishedOps(nullptr, db, 100, kRead);
} while (!duration.Done(100));
char msg[100];
snprintf(msg, sizeof(msg),
"(%" PRIu64 " of %" PRIu64
" found, "
"issued %" PRIu64 " non-exist keys)\n",
found, read, nonexist);
thread->stats.AddMessage(msg);
}
int64_t GetRandomKey(Random64* rand) {
uint64_t rand_int = rand->Next();
int64_t key_rand;
if (read_random_exp_range_ == 0) {
key_rand = rand_int % FLAGS_num;
} else {
const uint64_t kBigInt = static_cast<uint64_t>(1U) << 62;
long double order = -static_cast<long double>(rand_int % kBigInt) /
static_cast<long double>(kBigInt) *
read_random_exp_range_;
long double exp_ran = std::exp(order);
uint64_t rand_num =
static_cast<int64_t>(exp_ran * static_cast<long double>(FLAGS_num));
// Map to a different number to avoid locality.
const uint64_t kBigPrime = 0x5bd1e995;
// Overflow is like %(2^64). Will have little impact of results.
key_rand = static_cast<int64_t>((rand_num * kBigPrime) % FLAGS_num);
}
return key_rand;
}
void ReadRandom(ThreadState* thread) {
int64_t read = 0;
int64_t found = 0;
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
2015-03-30 18:28:25 +00:00
int64_t bytes = 0;
Introduce a new MultiGet batching implementation (#5011) Summary: This PR introduces a new MultiGet() API, with the underlying implementation grouping keys based on SST file and batching lookups in a file. The reason for the new API is twofold - the definition allows callers to allocate storage for status and values on stack instead of std::vector, as well as return values as PinnableSlices in order to avoid copying, and it keeps the original MultiGet() implementation intact while we experiment with batching. Batching is useful when there is some spatial locality to the keys being queries, as well as larger batch sizes. The main benefits are due to - 1. Fewer function calls, especially to BlockBasedTableReader::MultiGet() and FullFilterBlockReader::KeysMayMatch() 2. Bloom filter cachelines can be prefetched, hiding the cache miss latency The next step is to optimize the binary searches in the level_storage_info, index blocks and data blocks, since we could reduce the number of key comparisons if the keys are relatively close to each other. The batching optimizations also need to be extended to other formats, such as PlainTable and filter formats. This also needs to be added to db_stress. Benchmark results from db_bench for various batch size/locality of reference combinations are given below. Locality was simulated by offsetting the keys in a batch by a stride length. Each SST file is about 8.6MB uncompressed and key/value size is 16/100 uncompressed. To focus on the cpu benefit of batching, the runs were single threaded and bound to the same cpu to eliminate interference from other system events. The results show a 10-25% improvement in micros/op from smaller to larger batch sizes (4 - 32). Batch Sizes 1 | 2 | 4 | 8 | 16 | 32 Random pattern (Stride length 0) 4.158 | 4.109 | 4.026 | 4.05 | 4.1 | 4.074 - Get 4.438 | 4.302 | 4.165 | 4.122 | 4.096 | 4.075 - MultiGet (no batching) 4.461 | 4.256 | 4.277 | 4.11 | 4.182 | 4.14 - MultiGet (w/ batching) Good locality (Stride length 16) 4.048 | 3.659 | 3.248 | 2.99 | 2.84 | 2.753 4.429 | 3.728 | 3.406 | 3.053 | 2.911 | 2.781 4.452 | 3.45 | 2.833 | 2.451 | 2.233 | 2.135 Good locality (Stride length 256) 4.066 | 3.786 | 3.581 | 3.447 | 3.415 | 3.232 4.406 | 4.005 | 3.644 | 3.49 | 3.381 | 3.268 4.393 | 3.649 | 3.186 | 2.882 | 2.676 | 2.62 Medium locality (Stride length 4096) 4.012 | 3.922 | 3.768 | 3.61 | 3.582 | 3.555 4.364 | 4.057 | 3.791 | 3.65 | 3.57 | 3.465 4.479 | 3.758 | 3.316 | 3.077 | 2.959 | 2.891 dbbench command used (on a DB with 4 levels, 12 million keys)- TEST_TMPDIR=/dev/shm numactl -C 10 ./db_bench.tmp -use_existing_db=true -benchmarks="readseq,multireadrandom" -write_buffer_size=4194304 -target_file_size_base=4194304 -max_bytes_for_level_base=16777216 -num=12000000 -reads=12000000 -duration=90 -threads=1 -compression_type=none -cache_size=4194304000 -batch_size=32 -disable_auto_compactions=true -bloom_bits=10 -cache_index_and_filter_blocks=true -pin_l0_filter_and_index_blocks_in_cache=true -multiread_batched=true -multiread_stride=4 Pull Request resolved: https://github.com/facebook/rocksdb/pull/5011 Differential Revision: D14348703 Pulled By: anand1976 fbshipit-source-id: 774406dab3776d979c809522a67bedac6c17f84b
2019-04-11 21:24:09 +00:00
int num_keys = 0;
int64_t key_rand = 0;
Ignore `total_order_seek` in DB::Get (#9427) Summary: Apparently setting total_order_seek=true for DB::Get was intended to allow accurate read semantics if the current prefix extractor doesn't match what was used to generate SST files on disk. But since prefix_extractor was made a mutable option in 5.14.0, we have been able to detect this case and provide the correct semantics regardless of the total_order_seek option. Since that time, the option has only made Get() slower in a reasonably common case: prefix_extractor unchanged and whole_key_filtering=false. So this change primarily removes unnecessary effect of total_order_seek on Get. Also cleans up some related comments. Also adds a -total_order_seek option to db_bench and canonicalizes handling of ReadOptions in db_bench so that command line options have the expected association with library features. (There is potential for change in regression test behavior, but the old behavior is likely indefensible, or some other inconsistency would need to be fixed.) TODO in follow-up work: there should be no reason for Get() to depend on current prefix extractor at all. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9427 Test Plan: Unit tests updated. Performance (using db_bench update) Create DB with `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -benchmarks=fillrandom -num=10000000 -disable_wal=1 -write_buffer_size=10000000 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12 -whole_key_filtering=0` Test with and without `-total_order_seek` on `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -use_existing_db -readonly -benchmarks=readrandom -num=10000000 -duration=40 -disable_wal=1 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12` Before this change, total_order_seek=false: 25188 ops/sec Before this change, total_order_seek=true: 1222 ops/sec (~20x slower) After this change, total_order_seek=false: 24570 ops/sec After this change, total_order_seek=true: 25012 ops/sec (indistinguishable) Reviewed By: siying Differential Revision: D33753458 Pulled By: pdillinger fbshipit-source-id: bf892f34907a5e407d9c40bd4d42f0adbcbe0014
2022-02-01 03:45:17 +00:00
ReadOptions options = read_options_;
std::unique_ptr<const char[]> key_guard;
Slice key = AllocateKey(&key_guard);
PinnableSlice pinnable_val;
std::vector<PinnableSlice> pinnable_vals;
if (read_operands_) {
// Start off with a small-ish value that'll be increased later if
// `GetMergeOperands()` tells us it is not large enough.
pinnable_vals.resize(8);
}
std::unique_ptr<char[]> ts_guard;
Slice ts;
if (user_timestamp_size_ > 0) {
ts_guard.reset(new char[user_timestamp_size_]);
}
Duration duration(FLAGS_duration, reads_);
while (!duration.Done(1)) {
DBWithColumnFamilies* db_with_cfh = SelectDBWithCfh(thread);
// We use same key_rand as seed for key and column family so that we can
// deterministically find the cfh corresponding to a particular key, as it
// is done in DoWrite method.
Introduce a new MultiGet batching implementation (#5011) Summary: This PR introduces a new MultiGet() API, with the underlying implementation grouping keys based on SST file and batching lookups in a file. The reason for the new API is twofold - the definition allows callers to allocate storage for status and values on stack instead of std::vector, as well as return values as PinnableSlices in order to avoid copying, and it keeps the original MultiGet() implementation intact while we experiment with batching. Batching is useful when there is some spatial locality to the keys being queries, as well as larger batch sizes. The main benefits are due to - 1. Fewer function calls, especially to BlockBasedTableReader::MultiGet() and FullFilterBlockReader::KeysMayMatch() 2. Bloom filter cachelines can be prefetched, hiding the cache miss latency The next step is to optimize the binary searches in the level_storage_info, index blocks and data blocks, since we could reduce the number of key comparisons if the keys are relatively close to each other. The batching optimizations also need to be extended to other formats, such as PlainTable and filter formats. This also needs to be added to db_stress. Benchmark results from db_bench for various batch size/locality of reference combinations are given below. Locality was simulated by offsetting the keys in a batch by a stride length. Each SST file is about 8.6MB uncompressed and key/value size is 16/100 uncompressed. To focus on the cpu benefit of batching, the runs were single threaded and bound to the same cpu to eliminate interference from other system events. The results show a 10-25% improvement in micros/op from smaller to larger batch sizes (4 - 32). Batch Sizes 1 | 2 | 4 | 8 | 16 | 32 Random pattern (Stride length 0) 4.158 | 4.109 | 4.026 | 4.05 | 4.1 | 4.074 - Get 4.438 | 4.302 | 4.165 | 4.122 | 4.096 | 4.075 - MultiGet (no batching) 4.461 | 4.256 | 4.277 | 4.11 | 4.182 | 4.14 - MultiGet (w/ batching) Good locality (Stride length 16) 4.048 | 3.659 | 3.248 | 2.99 | 2.84 | 2.753 4.429 | 3.728 | 3.406 | 3.053 | 2.911 | 2.781 4.452 | 3.45 | 2.833 | 2.451 | 2.233 | 2.135 Good locality (Stride length 256) 4.066 | 3.786 | 3.581 | 3.447 | 3.415 | 3.232 4.406 | 4.005 | 3.644 | 3.49 | 3.381 | 3.268 4.393 | 3.649 | 3.186 | 2.882 | 2.676 | 2.62 Medium locality (Stride length 4096) 4.012 | 3.922 | 3.768 | 3.61 | 3.582 | 3.555 4.364 | 4.057 | 3.791 | 3.65 | 3.57 | 3.465 4.479 | 3.758 | 3.316 | 3.077 | 2.959 | 2.891 dbbench command used (on a DB with 4 levels, 12 million keys)- TEST_TMPDIR=/dev/shm numactl -C 10 ./db_bench.tmp -use_existing_db=true -benchmarks="readseq,multireadrandom" -write_buffer_size=4194304 -target_file_size_base=4194304 -max_bytes_for_level_base=16777216 -num=12000000 -reads=12000000 -duration=90 -threads=1 -compression_type=none -cache_size=4194304000 -batch_size=32 -disable_auto_compactions=true -bloom_bits=10 -cache_index_and_filter_blocks=true -pin_l0_filter_and_index_blocks_in_cache=true -multiread_batched=true -multiread_stride=4 Pull Request resolved: https://github.com/facebook/rocksdb/pull/5011 Differential Revision: D14348703 Pulled By: anand1976 fbshipit-source-id: 774406dab3776d979c809522a67bedac6c17f84b
2019-04-11 21:24:09 +00:00
if (entries_per_batch_ > 1 && FLAGS_multiread_stride) {
if (++num_keys == entries_per_batch_) {
num_keys = 0;
key_rand = GetRandomKey(&thread->rand);
if ((key_rand + (entries_per_batch_ - 1) * FLAGS_multiread_stride) >=
FLAGS_num) {
key_rand = FLAGS_num - entries_per_batch_ * FLAGS_multiread_stride;
}
} else {
key_rand += FLAGS_multiread_stride;
}
} else {
key_rand = GetRandomKey(&thread->rand);
}
GenerateKeyFromInt(key_rand, FLAGS_num, &key);
read++;
std::string ts_ret;
std::string* ts_ptr = nullptr;
if (user_timestamp_size_ > 0) {
ts = mock_app_clock_->GetTimestampForRead(thread->rand, ts_guard.get());
options.timestamp = &ts;
ts_ptr = &ts_ret;
}
Status s;
pinnable_val.Reset();
for (size_t i = 0; i < pinnable_vals.size(); ++i) {
pinnable_vals[i].Reset();
}
ColumnFamilyHandle* cfh;
if (FLAGS_num_column_families > 1) {
cfh = db_with_cfh->GetCfh(key_rand);
} else {
cfh = db_with_cfh->db->DefaultColumnFamily();
}
if (read_operands_) {
GetMergeOperandsOptions get_merge_operands_options;
get_merge_operands_options.expected_max_number_of_operands =
static_cast<int>(pinnable_vals.size());
int number_of_operands;
s = db_with_cfh->db->GetMergeOperands(
options, cfh, key, pinnable_vals.data(),
&get_merge_operands_options, &number_of_operands);
if (s.IsIncomplete()) {
// Should only happen a few times when we encounter a key that had
// more merge operands than any key seen so far. Production use case
// would typically retry in such event to get all the operands so do
// that here.
pinnable_vals.resize(number_of_operands);
get_merge_operands_options.expected_max_number_of_operands =
static_cast<int>(pinnable_vals.size());
s = db_with_cfh->db->GetMergeOperands(
options, cfh, key, pinnable_vals.data(),
&get_merge_operands_options, &number_of_operands);
}
} else {
s = db_with_cfh->db->Get(options, cfh, key, &pinnable_val, ts_ptr);
}
if (s.ok()) {
found++;
bytes += key.size() + pinnable_val.size() + user_timestamp_size_;
for (size_t i = 0; i < pinnable_vals.size(); ++i) {
bytes += pinnable_vals[i].size();
Avoid allocations/copies for large `GetMergeOperands()` results (#10458) Summary: This PR avoids allocations and copies for the result of `GetMergeOperands()` when the average operand size is at least 256 bytes and the total operands size is at least 32KB. The `GetMergeOperands()` already included `PinnableSlice` but was calling `PinSelf()` (i.e., allocating and copying) for each operand. When this optimization takes effect, we instead call `PinSlice()` to skip that allocation and copy. Resources are pinned in order for the `PinnableSlice` to point to valid memory even after `GetMergeOperands()` returns. The pinned resources include a referenced `SuperVersion`, a `MergingContext`, and a `PinnedIteratorsManager`. They are bundled into a `GetMergeOperandsState`. We use `SharedCleanablePtr` to share that bundle among all `PinnableSlice`s populated by `GetMergeOperands()`. That way, the last `PinnableSlice` to be `Reset()` will cleanup the bundle, including unreferencing the `SuperVersion`. Pull Request resolved: https://github.com/facebook/rocksdb/pull/10458 Test Plan: - new DB level test - measured benefit/regression in a number of memtable scenarios Setup command: ``` $ ./db_bench -benchmarks=mergerandom -merge_operator=StringAppendOperator -num=$num -writes=16384 -key_size=16 -value_size=$value_sz -compression_type=none -write_buffer_size=1048576000 ``` Benchmark command: ``` ./db_bench -threads=$threads -use_existing_db=true -avoid_flush_during_recovery=true -write_buffer_size=1048576000 -benchmarks=readrandomoperands -merge_operator=StringAppendOperator -num=$num -duration=10 ``` Worst regression is when a key has many tiny operands: - Parameters: num=1 (implying 16384 operands per key), value_sz=8, threads=1 - `GetMergeOperands()` latency increases 682 micros -> 800 micros (+17%) The regression disappears into the noise (<1% difference) if we remove the `Reset()` loop and the size counting loop. The former is arguably needed regardless of this PR as the convention in `Get()` and `MultiGet()` is to `Reset()` the input `PinnableSlice`s at the start. The latter could be optimized to count the size as we accumulate operands rather than after the fact. Best improvement is when a key has large operands and high concurrency: - Parameters: num=4 (implying 4096 operands per key), value_sz=2KB, threads=32 - `GetMergeOperands()` latency decreases 11492 micros -> 437 micros (-96%). Reviewed By: cbi42 Differential Revision: D38336578 Pulled By: ajkr fbshipit-source-id: 48146d127e04cb7f2d4d2939a2b9dff3aba18258
2022-08-04 07:42:13 +00:00
pinnable_vals[i].Reset();
}
} else if (!s.IsNotFound()) {
2015-01-22 02:23:12 +00:00
fprintf(stderr, "Get returned an error: %s\n", s.ToString().c_str());
abort();
}
if (thread->shared->read_rate_limiter.get() != nullptr &&
read % 256 == 255) {
thread->shared->read_rate_limiter->Request(
256, Env::IO_HIGH, nullptr /* stats */, RateLimiter::OpType::kRead);
}
thread->stats.FinishedOps(db_with_cfh, db_with_cfh->db, 1, kRead);
}
char msg[100];
snprintf(msg, sizeof(msg), "(%" PRIu64 " of %" PRIu64 " found)\n", found,
read);
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
2015-03-30 18:28:25 +00:00
thread->stats.AddBytes(bytes);
thread->stats.AddMessage(msg);
}
// Calls MultiGet over a list of keys from a random distribution.
// Returns the total number of keys found.
void MultiReadRandom(ThreadState* thread) {
int64_t read = 0;
int64_t bytes = 0;
int64_t num_multireads = 0;
int64_t found = 0;
Ignore `total_order_seek` in DB::Get (#9427) Summary: Apparently setting total_order_seek=true for DB::Get was intended to allow accurate read semantics if the current prefix extractor doesn't match what was used to generate SST files on disk. But since prefix_extractor was made a mutable option in 5.14.0, we have been able to detect this case and provide the correct semantics regardless of the total_order_seek option. Since that time, the option has only made Get() slower in a reasonably common case: prefix_extractor unchanged and whole_key_filtering=false. So this change primarily removes unnecessary effect of total_order_seek on Get. Also cleans up some related comments. Also adds a -total_order_seek option to db_bench and canonicalizes handling of ReadOptions in db_bench so that command line options have the expected association with library features. (There is potential for change in regression test behavior, but the old behavior is likely indefensible, or some other inconsistency would need to be fixed.) TODO in follow-up work: there should be no reason for Get() to depend on current prefix extractor at all. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9427 Test Plan: Unit tests updated. Performance (using db_bench update) Create DB with `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -benchmarks=fillrandom -num=10000000 -disable_wal=1 -write_buffer_size=10000000 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12 -whole_key_filtering=0` Test with and without `-total_order_seek` on `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -use_existing_db -readonly -benchmarks=readrandom -num=10000000 -duration=40 -disable_wal=1 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12` Before this change, total_order_seek=false: 25188 ops/sec Before this change, total_order_seek=true: 1222 ops/sec (~20x slower) After this change, total_order_seek=false: 24570 ops/sec After this change, total_order_seek=true: 25012 ops/sec (indistinguishable) Reviewed By: siying Differential Revision: D33753458 Pulled By: pdillinger fbshipit-source-id: bf892f34907a5e407d9c40bd4d42f0adbcbe0014
2022-02-01 03:45:17 +00:00
ReadOptions options = read_options_;
std::vector<Slice> keys;
std::vector<std::unique_ptr<const char[]>> key_guards;
std::vector<std::string> values(entries_per_batch_);
Introduce a new MultiGet batching implementation (#5011) Summary: This PR introduces a new MultiGet() API, with the underlying implementation grouping keys based on SST file and batching lookups in a file. The reason for the new API is twofold - the definition allows callers to allocate storage for status and values on stack instead of std::vector, as well as return values as PinnableSlices in order to avoid copying, and it keeps the original MultiGet() implementation intact while we experiment with batching. Batching is useful when there is some spatial locality to the keys being queries, as well as larger batch sizes. The main benefits are due to - 1. Fewer function calls, especially to BlockBasedTableReader::MultiGet() and FullFilterBlockReader::KeysMayMatch() 2. Bloom filter cachelines can be prefetched, hiding the cache miss latency The next step is to optimize the binary searches in the level_storage_info, index blocks and data blocks, since we could reduce the number of key comparisons if the keys are relatively close to each other. The batching optimizations also need to be extended to other formats, such as PlainTable and filter formats. This also needs to be added to db_stress. Benchmark results from db_bench for various batch size/locality of reference combinations are given below. Locality was simulated by offsetting the keys in a batch by a stride length. Each SST file is about 8.6MB uncompressed and key/value size is 16/100 uncompressed. To focus on the cpu benefit of batching, the runs were single threaded and bound to the same cpu to eliminate interference from other system events. The results show a 10-25% improvement in micros/op from smaller to larger batch sizes (4 - 32). Batch Sizes 1 | 2 | 4 | 8 | 16 | 32 Random pattern (Stride length 0) 4.158 | 4.109 | 4.026 | 4.05 | 4.1 | 4.074 - Get 4.438 | 4.302 | 4.165 | 4.122 | 4.096 | 4.075 - MultiGet (no batching) 4.461 | 4.256 | 4.277 | 4.11 | 4.182 | 4.14 - MultiGet (w/ batching) Good locality (Stride length 16) 4.048 | 3.659 | 3.248 | 2.99 | 2.84 | 2.753 4.429 | 3.728 | 3.406 | 3.053 | 2.911 | 2.781 4.452 | 3.45 | 2.833 | 2.451 | 2.233 | 2.135 Good locality (Stride length 256) 4.066 | 3.786 | 3.581 | 3.447 | 3.415 | 3.232 4.406 | 4.005 | 3.644 | 3.49 | 3.381 | 3.268 4.393 | 3.649 | 3.186 | 2.882 | 2.676 | 2.62 Medium locality (Stride length 4096) 4.012 | 3.922 | 3.768 | 3.61 | 3.582 | 3.555 4.364 | 4.057 | 3.791 | 3.65 | 3.57 | 3.465 4.479 | 3.758 | 3.316 | 3.077 | 2.959 | 2.891 dbbench command used (on a DB with 4 levels, 12 million keys)- TEST_TMPDIR=/dev/shm numactl -C 10 ./db_bench.tmp -use_existing_db=true -benchmarks="readseq,multireadrandom" -write_buffer_size=4194304 -target_file_size_base=4194304 -max_bytes_for_level_base=16777216 -num=12000000 -reads=12000000 -duration=90 -threads=1 -compression_type=none -cache_size=4194304000 -batch_size=32 -disable_auto_compactions=true -bloom_bits=10 -cache_index_and_filter_blocks=true -pin_l0_filter_and_index_blocks_in_cache=true -multiread_batched=true -multiread_stride=4 Pull Request resolved: https://github.com/facebook/rocksdb/pull/5011 Differential Revision: D14348703 Pulled By: anand1976 fbshipit-source-id: 774406dab3776d979c809522a67bedac6c17f84b
2019-04-11 21:24:09 +00:00
PinnableSlice* pin_values = new PinnableSlice[entries_per_batch_];
std::unique_ptr<PinnableSlice[]> pin_values_guard(pin_values);
Introduce a new MultiGet batching implementation (#5011) Summary: This PR introduces a new MultiGet() API, with the underlying implementation grouping keys based on SST file and batching lookups in a file. The reason for the new API is twofold - the definition allows callers to allocate storage for status and values on stack instead of std::vector, as well as return values as PinnableSlices in order to avoid copying, and it keeps the original MultiGet() implementation intact while we experiment with batching. Batching is useful when there is some spatial locality to the keys being queries, as well as larger batch sizes. The main benefits are due to - 1. Fewer function calls, especially to BlockBasedTableReader::MultiGet() and FullFilterBlockReader::KeysMayMatch() 2. Bloom filter cachelines can be prefetched, hiding the cache miss latency The next step is to optimize the binary searches in the level_storage_info, index blocks and data blocks, since we could reduce the number of key comparisons if the keys are relatively close to each other. The batching optimizations also need to be extended to other formats, such as PlainTable and filter formats. This also needs to be added to db_stress. Benchmark results from db_bench for various batch size/locality of reference combinations are given below. Locality was simulated by offsetting the keys in a batch by a stride length. Each SST file is about 8.6MB uncompressed and key/value size is 16/100 uncompressed. To focus on the cpu benefit of batching, the runs were single threaded and bound to the same cpu to eliminate interference from other system events. The results show a 10-25% improvement in micros/op from smaller to larger batch sizes (4 - 32). Batch Sizes 1 | 2 | 4 | 8 | 16 | 32 Random pattern (Stride length 0) 4.158 | 4.109 | 4.026 | 4.05 | 4.1 | 4.074 - Get 4.438 | 4.302 | 4.165 | 4.122 | 4.096 | 4.075 - MultiGet (no batching) 4.461 | 4.256 | 4.277 | 4.11 | 4.182 | 4.14 - MultiGet (w/ batching) Good locality (Stride length 16) 4.048 | 3.659 | 3.248 | 2.99 | 2.84 | 2.753 4.429 | 3.728 | 3.406 | 3.053 | 2.911 | 2.781 4.452 | 3.45 | 2.833 | 2.451 | 2.233 | 2.135 Good locality (Stride length 256) 4.066 | 3.786 | 3.581 | 3.447 | 3.415 | 3.232 4.406 | 4.005 | 3.644 | 3.49 | 3.381 | 3.268 4.393 | 3.649 | 3.186 | 2.882 | 2.676 | 2.62 Medium locality (Stride length 4096) 4.012 | 3.922 | 3.768 | 3.61 | 3.582 | 3.555 4.364 | 4.057 | 3.791 | 3.65 | 3.57 | 3.465 4.479 | 3.758 | 3.316 | 3.077 | 2.959 | 2.891 dbbench command used (on a DB with 4 levels, 12 million keys)- TEST_TMPDIR=/dev/shm numactl -C 10 ./db_bench.tmp -use_existing_db=true -benchmarks="readseq,multireadrandom" -write_buffer_size=4194304 -target_file_size_base=4194304 -max_bytes_for_level_base=16777216 -num=12000000 -reads=12000000 -duration=90 -threads=1 -compression_type=none -cache_size=4194304000 -batch_size=32 -disable_auto_compactions=true -bloom_bits=10 -cache_index_and_filter_blocks=true -pin_l0_filter_and_index_blocks_in_cache=true -multiread_batched=true -multiread_stride=4 Pull Request resolved: https://github.com/facebook/rocksdb/pull/5011 Differential Revision: D14348703 Pulled By: anand1976 fbshipit-source-id: 774406dab3776d979c809522a67bedac6c17f84b
2019-04-11 21:24:09 +00:00
std::vector<Status> stat_list(entries_per_batch_);
2014-04-29 19:33:57 +00:00
while (static_cast<int64_t>(keys.size()) < entries_per_batch_) {
key_guards.push_back(std::unique_ptr<const char[]>());
keys.push_back(AllocateKey(&key_guards.back()));
}
std::unique_ptr<char[]> ts_guard;
if (user_timestamp_size_ > 0) {
ts_guard.reset(new char[user_timestamp_size_]);
}
Duration duration(FLAGS_duration, reads_);
while (!duration.Done(entries_per_batch_)) {
DB* db = SelectDB(thread);
Introduce a new MultiGet batching implementation (#5011) Summary: This PR introduces a new MultiGet() API, with the underlying implementation grouping keys based on SST file and batching lookups in a file. The reason for the new API is twofold - the definition allows callers to allocate storage for status and values on stack instead of std::vector, as well as return values as PinnableSlices in order to avoid copying, and it keeps the original MultiGet() implementation intact while we experiment with batching. Batching is useful when there is some spatial locality to the keys being queries, as well as larger batch sizes. The main benefits are due to - 1. Fewer function calls, especially to BlockBasedTableReader::MultiGet() and FullFilterBlockReader::KeysMayMatch() 2. Bloom filter cachelines can be prefetched, hiding the cache miss latency The next step is to optimize the binary searches in the level_storage_info, index blocks and data blocks, since we could reduce the number of key comparisons if the keys are relatively close to each other. The batching optimizations also need to be extended to other formats, such as PlainTable and filter formats. This also needs to be added to db_stress. Benchmark results from db_bench for various batch size/locality of reference combinations are given below. Locality was simulated by offsetting the keys in a batch by a stride length. Each SST file is about 8.6MB uncompressed and key/value size is 16/100 uncompressed. To focus on the cpu benefit of batching, the runs were single threaded and bound to the same cpu to eliminate interference from other system events. The results show a 10-25% improvement in micros/op from smaller to larger batch sizes (4 - 32). Batch Sizes 1 | 2 | 4 | 8 | 16 | 32 Random pattern (Stride length 0) 4.158 | 4.109 | 4.026 | 4.05 | 4.1 | 4.074 - Get 4.438 | 4.302 | 4.165 | 4.122 | 4.096 | 4.075 - MultiGet (no batching) 4.461 | 4.256 | 4.277 | 4.11 | 4.182 | 4.14 - MultiGet (w/ batching) Good locality (Stride length 16) 4.048 | 3.659 | 3.248 | 2.99 | 2.84 | 2.753 4.429 | 3.728 | 3.406 | 3.053 | 2.911 | 2.781 4.452 | 3.45 | 2.833 | 2.451 | 2.233 | 2.135 Good locality (Stride length 256) 4.066 | 3.786 | 3.581 | 3.447 | 3.415 | 3.232 4.406 | 4.005 | 3.644 | 3.49 | 3.381 | 3.268 4.393 | 3.649 | 3.186 | 2.882 | 2.676 | 2.62 Medium locality (Stride length 4096) 4.012 | 3.922 | 3.768 | 3.61 | 3.582 | 3.555 4.364 | 4.057 | 3.791 | 3.65 | 3.57 | 3.465 4.479 | 3.758 | 3.316 | 3.077 | 2.959 | 2.891 dbbench command used (on a DB with 4 levels, 12 million keys)- TEST_TMPDIR=/dev/shm numactl -C 10 ./db_bench.tmp -use_existing_db=true -benchmarks="readseq,multireadrandom" -write_buffer_size=4194304 -target_file_size_base=4194304 -max_bytes_for_level_base=16777216 -num=12000000 -reads=12000000 -duration=90 -threads=1 -compression_type=none -cache_size=4194304000 -batch_size=32 -disable_auto_compactions=true -bloom_bits=10 -cache_index_and_filter_blocks=true -pin_l0_filter_and_index_blocks_in_cache=true -multiread_batched=true -multiread_stride=4 Pull Request resolved: https://github.com/facebook/rocksdb/pull/5011 Differential Revision: D14348703 Pulled By: anand1976 fbshipit-source-id: 774406dab3776d979c809522a67bedac6c17f84b
2019-04-11 21:24:09 +00:00
if (FLAGS_multiread_stride) {
int64_t key = GetRandomKey(&thread->rand);
if ((key + (entries_per_batch_ - 1) * FLAGS_multiread_stride) >=
static_cast<int64_t>(FLAGS_num)) {
Introduce a new MultiGet batching implementation (#5011) Summary: This PR introduces a new MultiGet() API, with the underlying implementation grouping keys based on SST file and batching lookups in a file. The reason for the new API is twofold - the definition allows callers to allocate storage for status and values on stack instead of std::vector, as well as return values as PinnableSlices in order to avoid copying, and it keeps the original MultiGet() implementation intact while we experiment with batching. Batching is useful when there is some spatial locality to the keys being queries, as well as larger batch sizes. The main benefits are due to - 1. Fewer function calls, especially to BlockBasedTableReader::MultiGet() and FullFilterBlockReader::KeysMayMatch() 2. Bloom filter cachelines can be prefetched, hiding the cache miss latency The next step is to optimize the binary searches in the level_storage_info, index blocks and data blocks, since we could reduce the number of key comparisons if the keys are relatively close to each other. The batching optimizations also need to be extended to other formats, such as PlainTable and filter formats. This also needs to be added to db_stress. Benchmark results from db_bench for various batch size/locality of reference combinations are given below. Locality was simulated by offsetting the keys in a batch by a stride length. Each SST file is about 8.6MB uncompressed and key/value size is 16/100 uncompressed. To focus on the cpu benefit of batching, the runs were single threaded and bound to the same cpu to eliminate interference from other system events. The results show a 10-25% improvement in micros/op from smaller to larger batch sizes (4 - 32). Batch Sizes 1 | 2 | 4 | 8 | 16 | 32 Random pattern (Stride length 0) 4.158 | 4.109 | 4.026 | 4.05 | 4.1 | 4.074 - Get 4.438 | 4.302 | 4.165 | 4.122 | 4.096 | 4.075 - MultiGet (no batching) 4.461 | 4.256 | 4.277 | 4.11 | 4.182 | 4.14 - MultiGet (w/ batching) Good locality (Stride length 16) 4.048 | 3.659 | 3.248 | 2.99 | 2.84 | 2.753 4.429 | 3.728 | 3.406 | 3.053 | 2.911 | 2.781 4.452 | 3.45 | 2.833 | 2.451 | 2.233 | 2.135 Good locality (Stride length 256) 4.066 | 3.786 | 3.581 | 3.447 | 3.415 | 3.232 4.406 | 4.005 | 3.644 | 3.49 | 3.381 | 3.268 4.393 | 3.649 | 3.186 | 2.882 | 2.676 | 2.62 Medium locality (Stride length 4096) 4.012 | 3.922 | 3.768 | 3.61 | 3.582 | 3.555 4.364 | 4.057 | 3.791 | 3.65 | 3.57 | 3.465 4.479 | 3.758 | 3.316 | 3.077 | 2.959 | 2.891 dbbench command used (on a DB with 4 levels, 12 million keys)- TEST_TMPDIR=/dev/shm numactl -C 10 ./db_bench.tmp -use_existing_db=true -benchmarks="readseq,multireadrandom" -write_buffer_size=4194304 -target_file_size_base=4194304 -max_bytes_for_level_base=16777216 -num=12000000 -reads=12000000 -duration=90 -threads=1 -compression_type=none -cache_size=4194304000 -batch_size=32 -disable_auto_compactions=true -bloom_bits=10 -cache_index_and_filter_blocks=true -pin_l0_filter_and_index_blocks_in_cache=true -multiread_batched=true -multiread_stride=4 Pull Request resolved: https://github.com/facebook/rocksdb/pull/5011 Differential Revision: D14348703 Pulled By: anand1976 fbshipit-source-id: 774406dab3776d979c809522a67bedac6c17f84b
2019-04-11 21:24:09 +00:00
key = FLAGS_num - entries_per_batch_ * FLAGS_multiread_stride;
}
for (int64_t i = 0; i < entries_per_batch_; ++i) {
GenerateKeyFromInt(key, FLAGS_num, &keys[i]);
key += FLAGS_multiread_stride;
}
} else {
for (int64_t i = 0; i < entries_per_batch_; ++i) {
GenerateKeyFromInt(GetRandomKey(&thread->rand), FLAGS_num, &keys[i]);
}
}
Slice ts;
if (user_timestamp_size_ > 0) {
ts = mock_app_clock_->GetTimestampForRead(thread->rand, ts_guard.get());
options.timestamp = &ts;
}
Introduce a new MultiGet batching implementation (#5011) Summary: This PR introduces a new MultiGet() API, with the underlying implementation grouping keys based on SST file and batching lookups in a file. The reason for the new API is twofold - the definition allows callers to allocate storage for status and values on stack instead of std::vector, as well as return values as PinnableSlices in order to avoid copying, and it keeps the original MultiGet() implementation intact while we experiment with batching. Batching is useful when there is some spatial locality to the keys being queries, as well as larger batch sizes. The main benefits are due to - 1. Fewer function calls, especially to BlockBasedTableReader::MultiGet() and FullFilterBlockReader::KeysMayMatch() 2. Bloom filter cachelines can be prefetched, hiding the cache miss latency The next step is to optimize the binary searches in the level_storage_info, index blocks and data blocks, since we could reduce the number of key comparisons if the keys are relatively close to each other. The batching optimizations also need to be extended to other formats, such as PlainTable and filter formats. This also needs to be added to db_stress. Benchmark results from db_bench for various batch size/locality of reference combinations are given below. Locality was simulated by offsetting the keys in a batch by a stride length. Each SST file is about 8.6MB uncompressed and key/value size is 16/100 uncompressed. To focus on the cpu benefit of batching, the runs were single threaded and bound to the same cpu to eliminate interference from other system events. The results show a 10-25% improvement in micros/op from smaller to larger batch sizes (4 - 32). Batch Sizes 1 | 2 | 4 | 8 | 16 | 32 Random pattern (Stride length 0) 4.158 | 4.109 | 4.026 | 4.05 | 4.1 | 4.074 - Get 4.438 | 4.302 | 4.165 | 4.122 | 4.096 | 4.075 - MultiGet (no batching) 4.461 | 4.256 | 4.277 | 4.11 | 4.182 | 4.14 - MultiGet (w/ batching) Good locality (Stride length 16) 4.048 | 3.659 | 3.248 | 2.99 | 2.84 | 2.753 4.429 | 3.728 | 3.406 | 3.053 | 2.911 | 2.781 4.452 | 3.45 | 2.833 | 2.451 | 2.233 | 2.135 Good locality (Stride length 256) 4.066 | 3.786 | 3.581 | 3.447 | 3.415 | 3.232 4.406 | 4.005 | 3.644 | 3.49 | 3.381 | 3.268 4.393 | 3.649 | 3.186 | 2.882 | 2.676 | 2.62 Medium locality (Stride length 4096) 4.012 | 3.922 | 3.768 | 3.61 | 3.582 | 3.555 4.364 | 4.057 | 3.791 | 3.65 | 3.57 | 3.465 4.479 | 3.758 | 3.316 | 3.077 | 2.959 | 2.891 dbbench command used (on a DB with 4 levels, 12 million keys)- TEST_TMPDIR=/dev/shm numactl -C 10 ./db_bench.tmp -use_existing_db=true -benchmarks="readseq,multireadrandom" -write_buffer_size=4194304 -target_file_size_base=4194304 -max_bytes_for_level_base=16777216 -num=12000000 -reads=12000000 -duration=90 -threads=1 -compression_type=none -cache_size=4194304000 -batch_size=32 -disable_auto_compactions=true -bloom_bits=10 -cache_index_and_filter_blocks=true -pin_l0_filter_and_index_blocks_in_cache=true -multiread_batched=true -multiread_stride=4 Pull Request resolved: https://github.com/facebook/rocksdb/pull/5011 Differential Revision: D14348703 Pulled By: anand1976 fbshipit-source-id: 774406dab3776d979c809522a67bedac6c17f84b
2019-04-11 21:24:09 +00:00
if (!FLAGS_multiread_batched) {
std::vector<Status> statuses = db->MultiGet(options, keys, &values);
assert(static_cast<int64_t>(statuses.size()) == entries_per_batch_);
read += entries_per_batch_;
num_multireads++;
for (int64_t i = 0; i < entries_per_batch_; ++i) {
if (statuses[i].ok()) {
bytes += keys[i].size() + values[i].size() + user_timestamp_size_;
Introduce a new MultiGet batching implementation (#5011) Summary: This PR introduces a new MultiGet() API, with the underlying implementation grouping keys based on SST file and batching lookups in a file. The reason for the new API is twofold - the definition allows callers to allocate storage for status and values on stack instead of std::vector, as well as return values as PinnableSlices in order to avoid copying, and it keeps the original MultiGet() implementation intact while we experiment with batching. Batching is useful when there is some spatial locality to the keys being queries, as well as larger batch sizes. The main benefits are due to - 1. Fewer function calls, especially to BlockBasedTableReader::MultiGet() and FullFilterBlockReader::KeysMayMatch() 2. Bloom filter cachelines can be prefetched, hiding the cache miss latency The next step is to optimize the binary searches in the level_storage_info, index blocks and data blocks, since we could reduce the number of key comparisons if the keys are relatively close to each other. The batching optimizations also need to be extended to other formats, such as PlainTable and filter formats. This also needs to be added to db_stress. Benchmark results from db_bench for various batch size/locality of reference combinations are given below. Locality was simulated by offsetting the keys in a batch by a stride length. Each SST file is about 8.6MB uncompressed and key/value size is 16/100 uncompressed. To focus on the cpu benefit of batching, the runs were single threaded and bound to the same cpu to eliminate interference from other system events. The results show a 10-25% improvement in micros/op from smaller to larger batch sizes (4 - 32). Batch Sizes 1 | 2 | 4 | 8 | 16 | 32 Random pattern (Stride length 0) 4.158 | 4.109 | 4.026 | 4.05 | 4.1 | 4.074 - Get 4.438 | 4.302 | 4.165 | 4.122 | 4.096 | 4.075 - MultiGet (no batching) 4.461 | 4.256 | 4.277 | 4.11 | 4.182 | 4.14 - MultiGet (w/ batching) Good locality (Stride length 16) 4.048 | 3.659 | 3.248 | 2.99 | 2.84 | 2.753 4.429 | 3.728 | 3.406 | 3.053 | 2.911 | 2.781 4.452 | 3.45 | 2.833 | 2.451 | 2.233 | 2.135 Good locality (Stride length 256) 4.066 | 3.786 | 3.581 | 3.447 | 3.415 | 3.232 4.406 | 4.005 | 3.644 | 3.49 | 3.381 | 3.268 4.393 | 3.649 | 3.186 | 2.882 | 2.676 | 2.62 Medium locality (Stride length 4096) 4.012 | 3.922 | 3.768 | 3.61 | 3.582 | 3.555 4.364 | 4.057 | 3.791 | 3.65 | 3.57 | 3.465 4.479 | 3.758 | 3.316 | 3.077 | 2.959 | 2.891 dbbench command used (on a DB with 4 levels, 12 million keys)- TEST_TMPDIR=/dev/shm numactl -C 10 ./db_bench.tmp -use_existing_db=true -benchmarks="readseq,multireadrandom" -write_buffer_size=4194304 -target_file_size_base=4194304 -max_bytes_for_level_base=16777216 -num=12000000 -reads=12000000 -duration=90 -threads=1 -compression_type=none -cache_size=4194304000 -batch_size=32 -disable_auto_compactions=true -bloom_bits=10 -cache_index_and_filter_blocks=true -pin_l0_filter_and_index_blocks_in_cache=true -multiread_batched=true -multiread_stride=4 Pull Request resolved: https://github.com/facebook/rocksdb/pull/5011 Differential Revision: D14348703 Pulled By: anand1976 fbshipit-source-id: 774406dab3776d979c809522a67bedac6c17f84b
2019-04-11 21:24:09 +00:00
++found;
} else if (!statuses[i].IsNotFound()) {
fprintf(stderr, "MultiGet returned an error: %s\n",
statuses[i].ToString().c_str());
abort();
}
}
} else {
db->MultiGet(options, db->DefaultColumnFamily(), keys.size(),
keys.data(), pin_values, stat_list.data());
read += entries_per_batch_;
num_multireads++;
for (int64_t i = 0; i < entries_per_batch_; ++i) {
if (stat_list[i].ok()) {
bytes +=
keys[i].size() + pin_values[i].size() + user_timestamp_size_;
Introduce a new MultiGet batching implementation (#5011) Summary: This PR introduces a new MultiGet() API, with the underlying implementation grouping keys based on SST file and batching lookups in a file. The reason for the new API is twofold - the definition allows callers to allocate storage for status and values on stack instead of std::vector, as well as return values as PinnableSlices in order to avoid copying, and it keeps the original MultiGet() implementation intact while we experiment with batching. Batching is useful when there is some spatial locality to the keys being queries, as well as larger batch sizes. The main benefits are due to - 1. Fewer function calls, especially to BlockBasedTableReader::MultiGet() and FullFilterBlockReader::KeysMayMatch() 2. Bloom filter cachelines can be prefetched, hiding the cache miss latency The next step is to optimize the binary searches in the level_storage_info, index blocks and data blocks, since we could reduce the number of key comparisons if the keys are relatively close to each other. The batching optimizations also need to be extended to other formats, such as PlainTable and filter formats. This also needs to be added to db_stress. Benchmark results from db_bench for various batch size/locality of reference combinations are given below. Locality was simulated by offsetting the keys in a batch by a stride length. Each SST file is about 8.6MB uncompressed and key/value size is 16/100 uncompressed. To focus on the cpu benefit of batching, the runs were single threaded and bound to the same cpu to eliminate interference from other system events. The results show a 10-25% improvement in micros/op from smaller to larger batch sizes (4 - 32). Batch Sizes 1 | 2 | 4 | 8 | 16 | 32 Random pattern (Stride length 0) 4.158 | 4.109 | 4.026 | 4.05 | 4.1 | 4.074 - Get 4.438 | 4.302 | 4.165 | 4.122 | 4.096 | 4.075 - MultiGet (no batching) 4.461 | 4.256 | 4.277 | 4.11 | 4.182 | 4.14 - MultiGet (w/ batching) Good locality (Stride length 16) 4.048 | 3.659 | 3.248 | 2.99 | 2.84 | 2.753 4.429 | 3.728 | 3.406 | 3.053 | 2.911 | 2.781 4.452 | 3.45 | 2.833 | 2.451 | 2.233 | 2.135 Good locality (Stride length 256) 4.066 | 3.786 | 3.581 | 3.447 | 3.415 | 3.232 4.406 | 4.005 | 3.644 | 3.49 | 3.381 | 3.268 4.393 | 3.649 | 3.186 | 2.882 | 2.676 | 2.62 Medium locality (Stride length 4096) 4.012 | 3.922 | 3.768 | 3.61 | 3.582 | 3.555 4.364 | 4.057 | 3.791 | 3.65 | 3.57 | 3.465 4.479 | 3.758 | 3.316 | 3.077 | 2.959 | 2.891 dbbench command used (on a DB with 4 levels, 12 million keys)- TEST_TMPDIR=/dev/shm numactl -C 10 ./db_bench.tmp -use_existing_db=true -benchmarks="readseq,multireadrandom" -write_buffer_size=4194304 -target_file_size_base=4194304 -max_bytes_for_level_base=16777216 -num=12000000 -reads=12000000 -duration=90 -threads=1 -compression_type=none -cache_size=4194304000 -batch_size=32 -disable_auto_compactions=true -bloom_bits=10 -cache_index_and_filter_blocks=true -pin_l0_filter_and_index_blocks_in_cache=true -multiread_batched=true -multiread_stride=4 Pull Request resolved: https://github.com/facebook/rocksdb/pull/5011 Differential Revision: D14348703 Pulled By: anand1976 fbshipit-source-id: 774406dab3776d979c809522a67bedac6c17f84b
2019-04-11 21:24:09 +00:00
++found;
} else if (!stat_list[i].IsNotFound()) {
fprintf(stderr, "MultiGet returned an error: %s\n",
stat_list[i].ToString().c_str());
abort();
}
stat_list[i] = Status::OK();
pin_values[i].Reset();
}
}
if (thread->shared->read_rate_limiter.get() != nullptr &&
num_multireads % 256 == 255) {
thread->shared->read_rate_limiter->Request(
256 * entries_per_batch_, Env::IO_HIGH, nullptr /* stats */,
RateLimiter::OpType::kRead);
}
thread->stats.FinishedOps(nullptr, db, entries_per_batch_, kRead);
}
char msg[100];
snprintf(msg, sizeof(msg), "(%" PRIu64 " of %" PRIu64 " found)", found,
read);
thread->stats.AddBytes(bytes);
thread->stats.AddMessage(msg);
}
For ApproximateSizes, pro-rate table metadata size over data blocks (#6784) Summary: The implementation of GetApproximateSizes was inconsistent in its treatment of the size of non-data blocks of SST files, sometimes including and sometimes now. This was at its worst with large portion of table file used by filters and querying a small range that crossed a table boundary: the size estimate would include large filter size. It's conceivable that someone might want only to know the size in terms of data blocks, but I believe that's unlikely enough to ignore for now. Similarly, there's no evidence the internal function AppoximateOffsetOf is used for anything other than a one-sided ApproximateSize, so I intend to refactor to remove redundancy in a follow-up commit. So to fix this, GetApproximateSizes (and implementation details ApproximateSize and ApproximateOffsetOf) now consistently include in their returned sizes a portion of table file metadata (incl filters and indexes) based on the size portion of the data blocks in range. In other words, if a key range covers data blocks that are X% by size of all the table's data blocks, returned approximate size is X% of the total file size. It would technically be more accurate to attribute metadata based on number of keys, but that's not computationally efficient with data available and rarely a meaningful difference. Also includes miscellaneous comment improvements / clarifications. Also included is a new approximatesizerandom benchmark for db_bench. No significant performance difference seen with this change, whether ~700 ops/sec with cache_index_and_filter_blocks and small cache or ~150k ops/sec without cache_index_and_filter_blocks. Pull Request resolved: https://github.com/facebook/rocksdb/pull/6784 Test Plan: Test added to DBTest.ApproximateSizesFilesWithErrorMargin. Old code running new test... [ RUN ] DBTest.ApproximateSizesFilesWithErrorMargin db/db_test.cc:1562: Failure Expected: (size) <= (11 * 100), actual: 9478 vs 1100 Other tests updated to reflect consistent accounting of metadata. Reviewed By: siying Differential Revision: D21334706 Pulled By: pdillinger fbshipit-source-id: 6f86870e45213334fedbe9c73b4ebb1d8d611185
2020-06-02 19:27:59 +00:00
// Calls ApproximateSize over random key ranges.
void ApproximateSizeRandom(ThreadState* thread) {
int64_t size_sum = 0;
int64_t num_sizes = 0;
const size_t batch_size = entries_per_batch_;
std::vector<Range> ranges;
std::vector<Slice> lkeys;
std::vector<std::unique_ptr<const char[]>> lkey_guards;
std::vector<Slice> rkeys;
std::vector<std::unique_ptr<const char[]>> rkey_guards;
std::vector<uint64_t> sizes;
while (ranges.size() < batch_size) {
// Ugly without C++17 return from emplace_back
lkey_guards.emplace_back();
rkey_guards.emplace_back();
lkeys.emplace_back(AllocateKey(&lkey_guards.back()));
rkeys.emplace_back(AllocateKey(&rkey_guards.back()));
ranges.emplace_back(lkeys.back(), rkeys.back());
sizes.push_back(0);
}
Duration duration(FLAGS_duration, reads_);
while (!duration.Done(1)) {
DB* db = SelectDB(thread);
for (size_t i = 0; i < batch_size; ++i) {
int64_t lkey = GetRandomKey(&thread->rand);
int64_t rkey = GetRandomKey(&thread->rand);
if (lkey > rkey) {
std::swap(lkey, rkey);
}
GenerateKeyFromInt(lkey, FLAGS_num, &lkeys[i]);
GenerateKeyFromInt(rkey, FLAGS_num, &rkeys[i]);
}
db->GetApproximateSizes(
ranges.data(), static_cast<int>(entries_per_batch_), sizes.data());
For ApproximateSizes, pro-rate table metadata size over data blocks (#6784) Summary: The implementation of GetApproximateSizes was inconsistent in its treatment of the size of non-data blocks of SST files, sometimes including and sometimes now. This was at its worst with large portion of table file used by filters and querying a small range that crossed a table boundary: the size estimate would include large filter size. It's conceivable that someone might want only to know the size in terms of data blocks, but I believe that's unlikely enough to ignore for now. Similarly, there's no evidence the internal function AppoximateOffsetOf is used for anything other than a one-sided ApproximateSize, so I intend to refactor to remove redundancy in a follow-up commit. So to fix this, GetApproximateSizes (and implementation details ApproximateSize and ApproximateOffsetOf) now consistently include in their returned sizes a portion of table file metadata (incl filters and indexes) based on the size portion of the data blocks in range. In other words, if a key range covers data blocks that are X% by size of all the table's data blocks, returned approximate size is X% of the total file size. It would technically be more accurate to attribute metadata based on number of keys, but that's not computationally efficient with data available and rarely a meaningful difference. Also includes miscellaneous comment improvements / clarifications. Also included is a new approximatesizerandom benchmark for db_bench. No significant performance difference seen with this change, whether ~700 ops/sec with cache_index_and_filter_blocks and small cache or ~150k ops/sec without cache_index_and_filter_blocks. Pull Request resolved: https://github.com/facebook/rocksdb/pull/6784 Test Plan: Test added to DBTest.ApproximateSizesFilesWithErrorMargin. Old code running new test... [ RUN ] DBTest.ApproximateSizesFilesWithErrorMargin db/db_test.cc:1562: Failure Expected: (size) <= (11 * 100), actual: 9478 vs 1100 Other tests updated to reflect consistent accounting of metadata. Reviewed By: siying Differential Revision: D21334706 Pulled By: pdillinger fbshipit-source-id: 6f86870e45213334fedbe9c73b4ebb1d8d611185
2020-06-02 19:27:59 +00:00
num_sizes += entries_per_batch_;
for (int64_t size : sizes) {
size_sum += size;
}
thread->stats.FinishedOps(nullptr, db, entries_per_batch_, kOthers);
}
char msg[100];
snprintf(msg, sizeof(msg), "(Avg approx size=%g)",
static_cast<double>(size_sum) / static_cast<double>(num_sizes));
thread->stats.AddMessage(msg);
}
Workload generator (Mixgraph) based on prefix hotness (#5953) Summary: In the previous PR https://github.com/facebook/rocksdb/issues/4788, user can use db_bench mix_graph option to generate the workload that is from the social graph. The key is generated based on the key access hotness. In this PR, user can further model the key-range hotness and fit those to two-term-exponential distribution. First, user cuts the whole key space into small key ranges (e.g., key-ranges are the same size and the key-range number is the number of SST files). Then, user calculates the average access count per key of each key-range as the key-range hotness. Next, user fits the key-range hotness to two-term-exponential distribution (f(x) = f(x) = a*exp(b*x) + c*exp(d*x)) and generate the value of a, b, c, and d. They are the parameters in db_bench: prefix_dist_a, prefix_dist_b, prefix_dist_c, and prefix_dist_d. Finally, user can run db_bench by specify the parameters. For example: `./db_bench --benchmarks="mixgraph" -use_direct_io_for_flush_and_compaction=true -use_direct_reads=true -cache_size=268435456 -key_dist_a=0.002312 -key_dist_b=0.3467 -keyrange_dist_a=14.18 -keyrange_dist_b=-2.917 -keyrange_dist_c=0.0164 -keyrange_dist_d=-0.08082 -keyrange_num=30 -value_k=0.2615 -value_sigma=25.45 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.85 -mix_put_ratio=0.14 -mix_seek_ratio=0.01 -sine_mix_rate_interval_milliseconds=5000 -sine_a=350 -sine_b=0.0105 -sine_d=50000 --perf_level=2 -reads=1000000 -num=5000000 -key_size=48` Pull Request resolved: https://github.com/facebook/rocksdb/pull/5953 Test Plan: run db_bench with different parameters and checked the results. Differential Revision: D18053527 Pulled By: zhichao-cao fbshipit-source-id: 171f8b3142bd76462f1967c58345ad7e4f84bab7
2019-11-06 20:50:33 +00:00
// The inverse function of Pareto distribution
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 18:40:44 +00:00
int64_t ParetoCdfInversion(double u, double theta, double k, double sigma) {
double ret;
if (k == 0.0) {
ret = theta - sigma * std::log(u);
} else {
ret = theta + sigma * (std::pow(u, -1 * k) - 1) / k;
}
return static_cast<int64_t>(ceil(ret));
}
Workload generator (Mixgraph) based on prefix hotness (#5953) Summary: In the previous PR https://github.com/facebook/rocksdb/issues/4788, user can use db_bench mix_graph option to generate the workload that is from the social graph. The key is generated based on the key access hotness. In this PR, user can further model the key-range hotness and fit those to two-term-exponential distribution. First, user cuts the whole key space into small key ranges (e.g., key-ranges are the same size and the key-range number is the number of SST files). Then, user calculates the average access count per key of each key-range as the key-range hotness. Next, user fits the key-range hotness to two-term-exponential distribution (f(x) = f(x) = a*exp(b*x) + c*exp(d*x)) and generate the value of a, b, c, and d. They are the parameters in db_bench: prefix_dist_a, prefix_dist_b, prefix_dist_c, and prefix_dist_d. Finally, user can run db_bench by specify the parameters. For example: `./db_bench --benchmarks="mixgraph" -use_direct_io_for_flush_and_compaction=true -use_direct_reads=true -cache_size=268435456 -key_dist_a=0.002312 -key_dist_b=0.3467 -keyrange_dist_a=14.18 -keyrange_dist_b=-2.917 -keyrange_dist_c=0.0164 -keyrange_dist_d=-0.08082 -keyrange_num=30 -value_k=0.2615 -value_sigma=25.45 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.85 -mix_put_ratio=0.14 -mix_seek_ratio=0.01 -sine_mix_rate_interval_milliseconds=5000 -sine_a=350 -sine_b=0.0105 -sine_d=50000 --perf_level=2 -reads=1000000 -num=5000000 -key_size=48` Pull Request resolved: https://github.com/facebook/rocksdb/pull/5953 Test Plan: run db_bench with different parameters and checked the results. Differential Revision: D18053527 Pulled By: zhichao-cao fbshipit-source-id: 171f8b3142bd76462f1967c58345ad7e4f84bab7
2019-11-06 20:50:33 +00:00
// The inverse function of power distribution (y=ax^b)
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 18:40:44 +00:00
int64_t PowerCdfInversion(double u, double a, double b) {
double ret;
ret = std::pow((u / a), (1 / b));
return static_cast<int64_t>(ceil(ret));
}
// Add the noice to the QPS
double AddNoise(double origin, double noise_ratio) {
if (noise_ratio < 0.0 || noise_ratio > 1.0) {
return origin;
}
int band_int = static_cast<int>(FLAGS_sine_a);
double delta = (rand() % band_int - band_int / 2) * noise_ratio;
if (origin + delta < 0) {
return origin;
} else {
return (origin + delta);
}
}
Workload generator (Mixgraph) based on prefix hotness (#5953) Summary: In the previous PR https://github.com/facebook/rocksdb/issues/4788, user can use db_bench mix_graph option to generate the workload that is from the social graph. The key is generated based on the key access hotness. In this PR, user can further model the key-range hotness and fit those to two-term-exponential distribution. First, user cuts the whole key space into small key ranges (e.g., key-ranges are the same size and the key-range number is the number of SST files). Then, user calculates the average access count per key of each key-range as the key-range hotness. Next, user fits the key-range hotness to two-term-exponential distribution (f(x) = f(x) = a*exp(b*x) + c*exp(d*x)) and generate the value of a, b, c, and d. They are the parameters in db_bench: prefix_dist_a, prefix_dist_b, prefix_dist_c, and prefix_dist_d. Finally, user can run db_bench by specify the parameters. For example: `./db_bench --benchmarks="mixgraph" -use_direct_io_for_flush_and_compaction=true -use_direct_reads=true -cache_size=268435456 -key_dist_a=0.002312 -key_dist_b=0.3467 -keyrange_dist_a=14.18 -keyrange_dist_b=-2.917 -keyrange_dist_c=0.0164 -keyrange_dist_d=-0.08082 -keyrange_num=30 -value_k=0.2615 -value_sigma=25.45 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.85 -mix_put_ratio=0.14 -mix_seek_ratio=0.01 -sine_mix_rate_interval_milliseconds=5000 -sine_a=350 -sine_b=0.0105 -sine_d=50000 --perf_level=2 -reads=1000000 -num=5000000 -key_size=48` Pull Request resolved: https://github.com/facebook/rocksdb/pull/5953 Test Plan: run db_bench with different parameters and checked the results. Differential Revision: D18053527 Pulled By: zhichao-cao fbshipit-source-id: 171f8b3142bd76462f1967c58345ad7e4f84bab7
2019-11-06 20:50:33 +00:00
// Decide the ratio of different query types
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 18:40:44 +00:00
// 0 Get, 1 Put, 2 Seek, 3 SeekForPrev, 4 Delete, 5 SingleDelete, 6 merge
class QueryDecider {
public:
std::vector<int> type_;
std::vector<double> ratio_;
int range_;
QueryDecider() = default;
~QueryDecider() = default;
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 18:40:44 +00:00
Status Initiate(std::vector<double> ratio_input) {
int range_max = 1000;
double sum = 0.0;
for (auto& ratio : ratio_input) {
sum += ratio;
}
range_ = 0;
for (auto& ratio : ratio_input) {
range_ += static_cast<int>(ceil(range_max * (ratio / sum)));
type_.push_back(range_);
ratio_.push_back(ratio / sum);
}
return Status::OK();
}
int GetType(int64_t rand_num) {
if (rand_num < 0) {
rand_num = rand_num * (-1);
}
assert(range_ != 0);
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 18:40:44 +00:00
int pos = static_cast<int>(rand_num % range_);
for (int i = 0; i < static_cast<int>(type_.size()); i++) {
if (pos < type_[i]) {
return i;
}
}
return 0;
}
};
Workload generator (Mixgraph) based on prefix hotness (#5953) Summary: In the previous PR https://github.com/facebook/rocksdb/issues/4788, user can use db_bench mix_graph option to generate the workload that is from the social graph. The key is generated based on the key access hotness. In this PR, user can further model the key-range hotness and fit those to two-term-exponential distribution. First, user cuts the whole key space into small key ranges (e.g., key-ranges are the same size and the key-range number is the number of SST files). Then, user calculates the average access count per key of each key-range as the key-range hotness. Next, user fits the key-range hotness to two-term-exponential distribution (f(x) = f(x) = a*exp(b*x) + c*exp(d*x)) and generate the value of a, b, c, and d. They are the parameters in db_bench: prefix_dist_a, prefix_dist_b, prefix_dist_c, and prefix_dist_d. Finally, user can run db_bench by specify the parameters. For example: `./db_bench --benchmarks="mixgraph" -use_direct_io_for_flush_and_compaction=true -use_direct_reads=true -cache_size=268435456 -key_dist_a=0.002312 -key_dist_b=0.3467 -keyrange_dist_a=14.18 -keyrange_dist_b=-2.917 -keyrange_dist_c=0.0164 -keyrange_dist_d=-0.08082 -keyrange_num=30 -value_k=0.2615 -value_sigma=25.45 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.85 -mix_put_ratio=0.14 -mix_seek_ratio=0.01 -sine_mix_rate_interval_milliseconds=5000 -sine_a=350 -sine_b=0.0105 -sine_d=50000 --perf_level=2 -reads=1000000 -num=5000000 -key_size=48` Pull Request resolved: https://github.com/facebook/rocksdb/pull/5953 Test Plan: run db_bench with different parameters and checked the results. Differential Revision: D18053527 Pulled By: zhichao-cao fbshipit-source-id: 171f8b3142bd76462f1967c58345ad7e4f84bab7
2019-11-06 20:50:33 +00:00
// KeyrangeUnit is the struct of a keyrange. It is used in a keyrange vector
// to transfer a random value to one keyrange based on the hotness.
struct KeyrangeUnit {
int64_t keyrange_start;
int64_t keyrange_access;
int64_t keyrange_keys;
};
// From our observations, the prefix hotness (key-range hotness) follows
// the two-term-exponential distribution: f(x) = a*exp(b*x) + c*exp(d*x).
// However, we cannot directly use the inverse function to decide a
// key-range from a random distribution. To achieve it, we create a list of
// KeyrangeUnit, each KeyrangeUnit occupies a range of integers whose size is
// decided based on the hotness of the key-range. When a random value is
// generated based on uniform distribution, we map it to the KeyrangeUnit Vec
// and one KeyrangeUnit is selected. The probability of a KeyrangeUnit being
// selected is the same as the hotness of this KeyrangeUnit. After that, the
// key can be randomly allocated to the key-range of this KeyrangeUnit, or we
// can based on the power distribution (y=ax^b) to generate the offset of
// the key in the selected key-range. In this way, we generate the keyID
// based on the hotness of the prefix and also the key hotness distribution.
class GenerateTwoTermExpKeys {
public:
// Avoid uninitialized warning-as-error in some compilers
int64_t keyrange_rand_max_ = 0;
int64_t keyrange_size_ = 0;
int64_t keyrange_num_ = 0;
Workload generator (Mixgraph) based on prefix hotness (#5953) Summary: In the previous PR https://github.com/facebook/rocksdb/issues/4788, user can use db_bench mix_graph option to generate the workload that is from the social graph. The key is generated based on the key access hotness. In this PR, user can further model the key-range hotness and fit those to two-term-exponential distribution. First, user cuts the whole key space into small key ranges (e.g., key-ranges are the same size and the key-range number is the number of SST files). Then, user calculates the average access count per key of each key-range as the key-range hotness. Next, user fits the key-range hotness to two-term-exponential distribution (f(x) = f(x) = a*exp(b*x) + c*exp(d*x)) and generate the value of a, b, c, and d. They are the parameters in db_bench: prefix_dist_a, prefix_dist_b, prefix_dist_c, and prefix_dist_d. Finally, user can run db_bench by specify the parameters. For example: `./db_bench --benchmarks="mixgraph" -use_direct_io_for_flush_and_compaction=true -use_direct_reads=true -cache_size=268435456 -key_dist_a=0.002312 -key_dist_b=0.3467 -keyrange_dist_a=14.18 -keyrange_dist_b=-2.917 -keyrange_dist_c=0.0164 -keyrange_dist_d=-0.08082 -keyrange_num=30 -value_k=0.2615 -value_sigma=25.45 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.85 -mix_put_ratio=0.14 -mix_seek_ratio=0.01 -sine_mix_rate_interval_milliseconds=5000 -sine_a=350 -sine_b=0.0105 -sine_d=50000 --perf_level=2 -reads=1000000 -num=5000000 -key_size=48` Pull Request resolved: https://github.com/facebook/rocksdb/pull/5953 Test Plan: run db_bench with different parameters and checked the results. Differential Revision: D18053527 Pulled By: zhichao-cao fbshipit-source-id: 171f8b3142bd76462f1967c58345ad7e4f84bab7
2019-11-06 20:50:33 +00:00
std::vector<KeyrangeUnit> keyrange_set_;
// Initiate the KeyrangeUnit vector and calculate the size of each
// KeyrangeUnit.
Status InitiateExpDistribution(int64_t total_keys, double prefix_a,
double prefix_b, double prefix_c,
double prefix_d) {
int64_t amplify = 0;
int64_t keyrange_start = 0;
if (FLAGS_keyrange_num <= 0) {
keyrange_num_ = 1;
} else {
keyrange_num_ = FLAGS_keyrange_num;
}
keyrange_size_ = total_keys / keyrange_num_;
// Calculate the key-range shares size based on the input parameters
for (int64_t pfx = keyrange_num_; pfx >= 1; pfx--) {
// Step 1. Calculate the probability that this key range will be
// accessed in a query. It is based on the two-term expoential
// distribution
double keyrange_p = prefix_a * std::exp(prefix_b * pfx) +
prefix_c * std::exp(prefix_d * pfx);
if (keyrange_p < std::pow(10.0, -16.0)) {
keyrange_p = 0.0;
}
// Step 2. Calculate the amplify
// In order to allocate a query to a key-range based on the random
// number generated for this query, we need to extend the probability
// of each key range from [0,1] to [0, amplify]. Amplify is calculated
// by 1/(smallest key-range probability). In this way, we ensure that
// all key-ranges are assigned with an Integer that >=0
if (amplify == 0 && keyrange_p > 0) {
amplify = static_cast<int64_t>(std::floor(1 / keyrange_p)) + 1;
}
// Step 3. For each key-range, we calculate its position in the
// [0, amplify] range, including the start, the size (keyrange_access)
KeyrangeUnit p_unit;
p_unit.keyrange_start = keyrange_start;
if (0.0 >= keyrange_p) {
p_unit.keyrange_access = 0;
} else {
p_unit.keyrange_access =
static_cast<int64_t>(std::floor(amplify * keyrange_p));
}
p_unit.keyrange_keys = keyrange_size_;
keyrange_set_.push_back(p_unit);
keyrange_start += p_unit.keyrange_access;
}
keyrange_rand_max_ = keyrange_start;
// Step 4. Shuffle the key-ranges randomly
// Since the access probability is calculated from small to large,
// If we do not re-allocate them, hot key-ranges are always at the end
// and cold key-ranges are at the begin of the key space. Therefore, the
// key-ranges are shuffled and the rand seed is only decide by the
// key-range hotness distribution. With the same distribution parameters
// the shuffle results are the same.
Random64 rand_loca(keyrange_rand_max_);
for (int64_t i = 0; i < FLAGS_keyrange_num; i++) {
int64_t pos = rand_loca.Next() % FLAGS_keyrange_num;
assert(i >= 0 && i < static_cast<int64_t>(keyrange_set_.size()) &&
pos >= 0 && pos < static_cast<int64_t>(keyrange_set_.size()));
std::swap(keyrange_set_[i], keyrange_set_[pos]);
}
// Step 5. Recalculate the prefix start postion after shuffling
int64_t offset = 0;
for (auto& p_unit : keyrange_set_) {
p_unit.keyrange_start = offset;
offset += p_unit.keyrange_access;
}
return Status::OK();
}
// Generate the Key ID according to the input ini_rand and key distribution
int64_t DistGetKeyID(int64_t ini_rand, double key_dist_a,
double key_dist_b) {
int64_t keyrange_rand = ini_rand % keyrange_rand_max_;
// Calculate and select one key-range that contains the new key
int64_t start = 0, end = static_cast<int64_t>(keyrange_set_.size());
while (start + 1 < end) {
int64_t mid = start + (end - start) / 2;
assert(mid >= 0 && mid < static_cast<int64_t>(keyrange_set_.size()));
if (keyrange_rand < keyrange_set_[mid].keyrange_start) {
end = mid;
} else {
start = mid;
}
}
int64_t keyrange_id = start;
// Select one key in the key-range and compose the keyID
int64_t key_offset = 0, key_seed;
if (key_dist_a == 0.0 || key_dist_b == 0.0) {
Workload generator (Mixgraph) based on prefix hotness (#5953) Summary: In the previous PR https://github.com/facebook/rocksdb/issues/4788, user can use db_bench mix_graph option to generate the workload that is from the social graph. The key is generated based on the key access hotness. In this PR, user can further model the key-range hotness and fit those to two-term-exponential distribution. First, user cuts the whole key space into small key ranges (e.g., key-ranges are the same size and the key-range number is the number of SST files). Then, user calculates the average access count per key of each key-range as the key-range hotness. Next, user fits the key-range hotness to two-term-exponential distribution (f(x) = f(x) = a*exp(b*x) + c*exp(d*x)) and generate the value of a, b, c, and d. They are the parameters in db_bench: prefix_dist_a, prefix_dist_b, prefix_dist_c, and prefix_dist_d. Finally, user can run db_bench by specify the parameters. For example: `./db_bench --benchmarks="mixgraph" -use_direct_io_for_flush_and_compaction=true -use_direct_reads=true -cache_size=268435456 -key_dist_a=0.002312 -key_dist_b=0.3467 -keyrange_dist_a=14.18 -keyrange_dist_b=-2.917 -keyrange_dist_c=0.0164 -keyrange_dist_d=-0.08082 -keyrange_num=30 -value_k=0.2615 -value_sigma=25.45 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.85 -mix_put_ratio=0.14 -mix_seek_ratio=0.01 -sine_mix_rate_interval_milliseconds=5000 -sine_a=350 -sine_b=0.0105 -sine_d=50000 --perf_level=2 -reads=1000000 -num=5000000 -key_size=48` Pull Request resolved: https://github.com/facebook/rocksdb/pull/5953 Test Plan: run db_bench with different parameters and checked the results. Differential Revision: D18053527 Pulled By: zhichao-cao fbshipit-source-id: 171f8b3142bd76462f1967c58345ad7e4f84bab7
2019-11-06 20:50:33 +00:00
key_offset = ini_rand % keyrange_size_;
} else {
double u =
static_cast<double>(ini_rand % keyrange_size_) / keyrange_size_;
Workload generator (Mixgraph) based on prefix hotness (#5953) Summary: In the previous PR https://github.com/facebook/rocksdb/issues/4788, user can use db_bench mix_graph option to generate the workload that is from the social graph. The key is generated based on the key access hotness. In this PR, user can further model the key-range hotness and fit those to two-term-exponential distribution. First, user cuts the whole key space into small key ranges (e.g., key-ranges are the same size and the key-range number is the number of SST files). Then, user calculates the average access count per key of each key-range as the key-range hotness. Next, user fits the key-range hotness to two-term-exponential distribution (f(x) = f(x) = a*exp(b*x) + c*exp(d*x)) and generate the value of a, b, c, and d. They are the parameters in db_bench: prefix_dist_a, prefix_dist_b, prefix_dist_c, and prefix_dist_d. Finally, user can run db_bench by specify the parameters. For example: `./db_bench --benchmarks="mixgraph" -use_direct_io_for_flush_and_compaction=true -use_direct_reads=true -cache_size=268435456 -key_dist_a=0.002312 -key_dist_b=0.3467 -keyrange_dist_a=14.18 -keyrange_dist_b=-2.917 -keyrange_dist_c=0.0164 -keyrange_dist_d=-0.08082 -keyrange_num=30 -value_k=0.2615 -value_sigma=25.45 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.85 -mix_put_ratio=0.14 -mix_seek_ratio=0.01 -sine_mix_rate_interval_milliseconds=5000 -sine_a=350 -sine_b=0.0105 -sine_d=50000 --perf_level=2 -reads=1000000 -num=5000000 -key_size=48` Pull Request resolved: https://github.com/facebook/rocksdb/pull/5953 Test Plan: run db_bench with different parameters and checked the results. Differential Revision: D18053527 Pulled By: zhichao-cao fbshipit-source-id: 171f8b3142bd76462f1967c58345ad7e4f84bab7
2019-11-06 20:50:33 +00:00
key_seed = static_cast<int64_t>(
ceil(std::pow((u / key_dist_a), (1 / key_dist_b))));
Workload generator (Mixgraph) based on prefix hotness (#5953) Summary: In the previous PR https://github.com/facebook/rocksdb/issues/4788, user can use db_bench mix_graph option to generate the workload that is from the social graph. The key is generated based on the key access hotness. In this PR, user can further model the key-range hotness and fit those to two-term-exponential distribution. First, user cuts the whole key space into small key ranges (e.g., key-ranges are the same size and the key-range number is the number of SST files). Then, user calculates the average access count per key of each key-range as the key-range hotness. Next, user fits the key-range hotness to two-term-exponential distribution (f(x) = f(x) = a*exp(b*x) + c*exp(d*x)) and generate the value of a, b, c, and d. They are the parameters in db_bench: prefix_dist_a, prefix_dist_b, prefix_dist_c, and prefix_dist_d. Finally, user can run db_bench by specify the parameters. For example: `./db_bench --benchmarks="mixgraph" -use_direct_io_for_flush_and_compaction=true -use_direct_reads=true -cache_size=268435456 -key_dist_a=0.002312 -key_dist_b=0.3467 -keyrange_dist_a=14.18 -keyrange_dist_b=-2.917 -keyrange_dist_c=0.0164 -keyrange_dist_d=-0.08082 -keyrange_num=30 -value_k=0.2615 -value_sigma=25.45 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.85 -mix_put_ratio=0.14 -mix_seek_ratio=0.01 -sine_mix_rate_interval_milliseconds=5000 -sine_a=350 -sine_b=0.0105 -sine_d=50000 --perf_level=2 -reads=1000000 -num=5000000 -key_size=48` Pull Request resolved: https://github.com/facebook/rocksdb/pull/5953 Test Plan: run db_bench with different parameters and checked the results. Differential Revision: D18053527 Pulled By: zhichao-cao fbshipit-source-id: 171f8b3142bd76462f1967c58345ad7e4f84bab7
2019-11-06 20:50:33 +00:00
Random64 rand_key(key_seed);
key_offset = rand_key.Next() % keyrange_size_;
Workload generator (Mixgraph) based on prefix hotness (#5953) Summary: In the previous PR https://github.com/facebook/rocksdb/issues/4788, user can use db_bench mix_graph option to generate the workload that is from the social graph. The key is generated based on the key access hotness. In this PR, user can further model the key-range hotness and fit those to two-term-exponential distribution. First, user cuts the whole key space into small key ranges (e.g., key-ranges are the same size and the key-range number is the number of SST files). Then, user calculates the average access count per key of each key-range as the key-range hotness. Next, user fits the key-range hotness to two-term-exponential distribution (f(x) = f(x) = a*exp(b*x) + c*exp(d*x)) and generate the value of a, b, c, and d. They are the parameters in db_bench: prefix_dist_a, prefix_dist_b, prefix_dist_c, and prefix_dist_d. Finally, user can run db_bench by specify the parameters. For example: `./db_bench --benchmarks="mixgraph" -use_direct_io_for_flush_and_compaction=true -use_direct_reads=true -cache_size=268435456 -key_dist_a=0.002312 -key_dist_b=0.3467 -keyrange_dist_a=14.18 -keyrange_dist_b=-2.917 -keyrange_dist_c=0.0164 -keyrange_dist_d=-0.08082 -keyrange_num=30 -value_k=0.2615 -value_sigma=25.45 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.85 -mix_put_ratio=0.14 -mix_seek_ratio=0.01 -sine_mix_rate_interval_milliseconds=5000 -sine_a=350 -sine_b=0.0105 -sine_d=50000 --perf_level=2 -reads=1000000 -num=5000000 -key_size=48` Pull Request resolved: https://github.com/facebook/rocksdb/pull/5953 Test Plan: run db_bench with different parameters and checked the results. Differential Revision: D18053527 Pulled By: zhichao-cao fbshipit-source-id: 171f8b3142bd76462f1967c58345ad7e4f84bab7
2019-11-06 20:50:33 +00:00
}
return keyrange_size_ * keyrange_id + key_offset;
}
};
// The social graph workload mixed with Get, Put, Iterator queries.
Workload generator (Mixgraph) based on prefix hotness (#5953) Summary: In the previous PR https://github.com/facebook/rocksdb/issues/4788, user can use db_bench mix_graph option to generate the workload that is from the social graph. The key is generated based on the key access hotness. In this PR, user can further model the key-range hotness and fit those to two-term-exponential distribution. First, user cuts the whole key space into small key ranges (e.g., key-ranges are the same size and the key-range number is the number of SST files). Then, user calculates the average access count per key of each key-range as the key-range hotness. Next, user fits the key-range hotness to two-term-exponential distribution (f(x) = f(x) = a*exp(b*x) + c*exp(d*x)) and generate the value of a, b, c, and d. They are the parameters in db_bench: prefix_dist_a, prefix_dist_b, prefix_dist_c, and prefix_dist_d. Finally, user can run db_bench by specify the parameters. For example: `./db_bench --benchmarks="mixgraph" -use_direct_io_for_flush_and_compaction=true -use_direct_reads=true -cache_size=268435456 -key_dist_a=0.002312 -key_dist_b=0.3467 -keyrange_dist_a=14.18 -keyrange_dist_b=-2.917 -keyrange_dist_c=0.0164 -keyrange_dist_d=-0.08082 -keyrange_num=30 -value_k=0.2615 -value_sigma=25.45 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.85 -mix_put_ratio=0.14 -mix_seek_ratio=0.01 -sine_mix_rate_interval_milliseconds=5000 -sine_a=350 -sine_b=0.0105 -sine_d=50000 --perf_level=2 -reads=1000000 -num=5000000 -key_size=48` Pull Request resolved: https://github.com/facebook/rocksdb/pull/5953 Test Plan: run db_bench with different parameters and checked the results. Differential Revision: D18053527 Pulled By: zhichao-cao fbshipit-source-id: 171f8b3142bd76462f1967c58345ad7e4f84bab7
2019-11-06 20:50:33 +00:00
// The value size and iterator length follow Pareto distribution.
// The overall key access follow power distribution. If user models the
// workload based on different key-ranges (or different prefixes), user
// can use two-term-exponential distribution to fit the workload. User
// needs to decide the ratio between Get, Put, Iterator queries before
Workload generator (Mixgraph) based on prefix hotness (#5953) Summary: In the previous PR https://github.com/facebook/rocksdb/issues/4788, user can use db_bench mix_graph option to generate the workload that is from the social graph. The key is generated based on the key access hotness. In this PR, user can further model the key-range hotness and fit those to two-term-exponential distribution. First, user cuts the whole key space into small key ranges (e.g., key-ranges are the same size and the key-range number is the number of SST files). Then, user calculates the average access count per key of each key-range as the key-range hotness. Next, user fits the key-range hotness to two-term-exponential distribution (f(x) = f(x) = a*exp(b*x) + c*exp(d*x)) and generate the value of a, b, c, and d. They are the parameters in db_bench: prefix_dist_a, prefix_dist_b, prefix_dist_c, and prefix_dist_d. Finally, user can run db_bench by specify the parameters. For example: `./db_bench --benchmarks="mixgraph" -use_direct_io_for_flush_and_compaction=true -use_direct_reads=true -cache_size=268435456 -key_dist_a=0.002312 -key_dist_b=0.3467 -keyrange_dist_a=14.18 -keyrange_dist_b=-2.917 -keyrange_dist_c=0.0164 -keyrange_dist_d=-0.08082 -keyrange_num=30 -value_k=0.2615 -value_sigma=25.45 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.85 -mix_put_ratio=0.14 -mix_seek_ratio=0.01 -sine_mix_rate_interval_milliseconds=5000 -sine_a=350 -sine_b=0.0105 -sine_d=50000 --perf_level=2 -reads=1000000 -num=5000000 -key_size=48` Pull Request resolved: https://github.com/facebook/rocksdb/pull/5953 Test Plan: run db_bench with different parameters and checked the results. Differential Revision: D18053527 Pulled By: zhichao-cao fbshipit-source-id: 171f8b3142bd76462f1967c58345ad7e4f84bab7
2019-11-06 20:50:33 +00:00
// starting the benchmark.
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 18:40:44 +00:00
void MixGraph(ThreadState* thread) {
int64_t gets = 0;
int64_t puts = 0;
2022-03-22 00:30:51 +00:00
int64_t get_found = 0;
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 18:40:44 +00:00
int64_t seek = 0;
int64_t seek_found = 0;
int64_t bytes = 0;
2022-03-22 00:30:51 +00:00
double total_scan_length = 0;
double total_val_size = 0;
const int64_t default_value_max = 1 * 1024 * 1024;
int64_t value_max = default_value_max;
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 18:40:44 +00:00
int64_t scan_len_max = FLAGS_mix_max_scan_len;
double write_rate = 1000000.0;
double read_rate = 1000000.0;
Workload generator (Mixgraph) based on prefix hotness (#5953) Summary: In the previous PR https://github.com/facebook/rocksdb/issues/4788, user can use db_bench mix_graph option to generate the workload that is from the social graph. The key is generated based on the key access hotness. In this PR, user can further model the key-range hotness and fit those to two-term-exponential distribution. First, user cuts the whole key space into small key ranges (e.g., key-ranges are the same size and the key-range number is the number of SST files). Then, user calculates the average access count per key of each key-range as the key-range hotness. Next, user fits the key-range hotness to two-term-exponential distribution (f(x) = f(x) = a*exp(b*x) + c*exp(d*x)) and generate the value of a, b, c, and d. They are the parameters in db_bench: prefix_dist_a, prefix_dist_b, prefix_dist_c, and prefix_dist_d. Finally, user can run db_bench by specify the parameters. For example: `./db_bench --benchmarks="mixgraph" -use_direct_io_for_flush_and_compaction=true -use_direct_reads=true -cache_size=268435456 -key_dist_a=0.002312 -key_dist_b=0.3467 -keyrange_dist_a=14.18 -keyrange_dist_b=-2.917 -keyrange_dist_c=0.0164 -keyrange_dist_d=-0.08082 -keyrange_num=30 -value_k=0.2615 -value_sigma=25.45 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.85 -mix_put_ratio=0.14 -mix_seek_ratio=0.01 -sine_mix_rate_interval_milliseconds=5000 -sine_a=350 -sine_b=0.0105 -sine_d=50000 --perf_level=2 -reads=1000000 -num=5000000 -key_size=48` Pull Request resolved: https://github.com/facebook/rocksdb/pull/5953 Test Plan: run db_bench with different parameters and checked the results. Differential Revision: D18053527 Pulled By: zhichao-cao fbshipit-source-id: 171f8b3142bd76462f1967c58345ad7e4f84bab7
2019-11-06 20:50:33 +00:00
bool use_prefix_modeling = false;
bool use_random_modeling = false;
Workload generator (Mixgraph) based on prefix hotness (#5953) Summary: In the previous PR https://github.com/facebook/rocksdb/issues/4788, user can use db_bench mix_graph option to generate the workload that is from the social graph. The key is generated based on the key access hotness. In this PR, user can further model the key-range hotness and fit those to two-term-exponential distribution. First, user cuts the whole key space into small key ranges (e.g., key-ranges are the same size and the key-range number is the number of SST files). Then, user calculates the average access count per key of each key-range as the key-range hotness. Next, user fits the key-range hotness to two-term-exponential distribution (f(x) = f(x) = a*exp(b*x) + c*exp(d*x)) and generate the value of a, b, c, and d. They are the parameters in db_bench: prefix_dist_a, prefix_dist_b, prefix_dist_c, and prefix_dist_d. Finally, user can run db_bench by specify the parameters. For example: `./db_bench --benchmarks="mixgraph" -use_direct_io_for_flush_and_compaction=true -use_direct_reads=true -cache_size=268435456 -key_dist_a=0.002312 -key_dist_b=0.3467 -keyrange_dist_a=14.18 -keyrange_dist_b=-2.917 -keyrange_dist_c=0.0164 -keyrange_dist_d=-0.08082 -keyrange_num=30 -value_k=0.2615 -value_sigma=25.45 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.85 -mix_put_ratio=0.14 -mix_seek_ratio=0.01 -sine_mix_rate_interval_milliseconds=5000 -sine_a=350 -sine_b=0.0105 -sine_d=50000 --perf_level=2 -reads=1000000 -num=5000000 -key_size=48` Pull Request resolved: https://github.com/facebook/rocksdb/pull/5953 Test Plan: run db_bench with different parameters and checked the results. Differential Revision: D18053527 Pulled By: zhichao-cao fbshipit-source-id: 171f8b3142bd76462f1967c58345ad7e4f84bab7
2019-11-06 20:50:33 +00:00
GenerateTwoTermExpKeys gen_exp;
std::vector<double> ratio{FLAGS_mix_get_ratio, FLAGS_mix_put_ratio,
FLAGS_mix_seek_ratio};
char value_buffer[default_value_max];
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 18:40:44 +00:00
QueryDecider query;
RandomGenerator gen;
Status s;
if (value_max > FLAGS_mix_max_value_size) {
value_max = FLAGS_mix_max_value_size;
}
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 18:40:44 +00:00
std::unique_ptr<const char[]> key_guard;
Slice key = AllocateKey(&key_guard);
PinnableSlice pinnable_val;
query.Initiate(ratio);
// the limit of qps initiation
if (FLAGS_sine_mix_rate) {
thread->shared->read_rate_limiter.reset(
NewGenericRateLimiter(static_cast<int64_t>(read_rate)));
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 18:40:44 +00:00
thread->shared->write_rate_limiter.reset(
NewGenericRateLimiter(static_cast<int64_t>(write_rate)));
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 18:40:44 +00:00
}
Workload generator (Mixgraph) based on prefix hotness (#5953) Summary: In the previous PR https://github.com/facebook/rocksdb/issues/4788, user can use db_bench mix_graph option to generate the workload that is from the social graph. The key is generated based on the key access hotness. In this PR, user can further model the key-range hotness and fit those to two-term-exponential distribution. First, user cuts the whole key space into small key ranges (e.g., key-ranges are the same size and the key-range number is the number of SST files). Then, user calculates the average access count per key of each key-range as the key-range hotness. Next, user fits the key-range hotness to two-term-exponential distribution (f(x) = f(x) = a*exp(b*x) + c*exp(d*x)) and generate the value of a, b, c, and d. They are the parameters in db_bench: prefix_dist_a, prefix_dist_b, prefix_dist_c, and prefix_dist_d. Finally, user can run db_bench by specify the parameters. For example: `./db_bench --benchmarks="mixgraph" -use_direct_io_for_flush_and_compaction=true -use_direct_reads=true -cache_size=268435456 -key_dist_a=0.002312 -key_dist_b=0.3467 -keyrange_dist_a=14.18 -keyrange_dist_b=-2.917 -keyrange_dist_c=0.0164 -keyrange_dist_d=-0.08082 -keyrange_num=30 -value_k=0.2615 -value_sigma=25.45 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.85 -mix_put_ratio=0.14 -mix_seek_ratio=0.01 -sine_mix_rate_interval_milliseconds=5000 -sine_a=350 -sine_b=0.0105 -sine_d=50000 --perf_level=2 -reads=1000000 -num=5000000 -key_size=48` Pull Request resolved: https://github.com/facebook/rocksdb/pull/5953 Test Plan: run db_bench with different parameters and checked the results. Differential Revision: D18053527 Pulled By: zhichao-cao fbshipit-source-id: 171f8b3142bd76462f1967c58345ad7e4f84bab7
2019-11-06 20:50:33 +00:00
// Decide if user wants to use prefix based key generation
if (FLAGS_keyrange_dist_a != 0.0 || FLAGS_keyrange_dist_b != 0.0 ||
FLAGS_keyrange_dist_c != 0.0 || FLAGS_keyrange_dist_d != 0.0) {
use_prefix_modeling = true;
gen_exp.InitiateExpDistribution(
FLAGS_num, FLAGS_keyrange_dist_a, FLAGS_keyrange_dist_b,
FLAGS_keyrange_dist_c, FLAGS_keyrange_dist_d);
}
if (FLAGS_key_dist_a == 0 || FLAGS_key_dist_b == 0) {
use_random_modeling = true;
}
Workload generator (Mixgraph) based on prefix hotness (#5953) Summary: In the previous PR https://github.com/facebook/rocksdb/issues/4788, user can use db_bench mix_graph option to generate the workload that is from the social graph. The key is generated based on the key access hotness. In this PR, user can further model the key-range hotness and fit those to two-term-exponential distribution. First, user cuts the whole key space into small key ranges (e.g., key-ranges are the same size and the key-range number is the number of SST files). Then, user calculates the average access count per key of each key-range as the key-range hotness. Next, user fits the key-range hotness to two-term-exponential distribution (f(x) = f(x) = a*exp(b*x) + c*exp(d*x)) and generate the value of a, b, c, and d. They are the parameters in db_bench: prefix_dist_a, prefix_dist_b, prefix_dist_c, and prefix_dist_d. Finally, user can run db_bench by specify the parameters. For example: `./db_bench --benchmarks="mixgraph" -use_direct_io_for_flush_and_compaction=true -use_direct_reads=true -cache_size=268435456 -key_dist_a=0.002312 -key_dist_b=0.3467 -keyrange_dist_a=14.18 -keyrange_dist_b=-2.917 -keyrange_dist_c=0.0164 -keyrange_dist_d=-0.08082 -keyrange_num=30 -value_k=0.2615 -value_sigma=25.45 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.85 -mix_put_ratio=0.14 -mix_seek_ratio=0.01 -sine_mix_rate_interval_milliseconds=5000 -sine_a=350 -sine_b=0.0105 -sine_d=50000 --perf_level=2 -reads=1000000 -num=5000000 -key_size=48` Pull Request resolved: https://github.com/facebook/rocksdb/pull/5953 Test Plan: run db_bench with different parameters and checked the results. Differential Revision: D18053527 Pulled By: zhichao-cao fbshipit-source-id: 171f8b3142bd76462f1967c58345ad7e4f84bab7
2019-11-06 20:50:33 +00:00
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 18:40:44 +00:00
Duration duration(FLAGS_duration, reads_);
while (!duration.Done(1)) {
DBWithColumnFamilies* db_with_cfh = SelectDBWithCfh(thread);
Workload generator (Mixgraph) based on prefix hotness (#5953) Summary: In the previous PR https://github.com/facebook/rocksdb/issues/4788, user can use db_bench mix_graph option to generate the workload that is from the social graph. The key is generated based on the key access hotness. In this PR, user can further model the key-range hotness and fit those to two-term-exponential distribution. First, user cuts the whole key space into small key ranges (e.g., key-ranges are the same size and the key-range number is the number of SST files). Then, user calculates the average access count per key of each key-range as the key-range hotness. Next, user fits the key-range hotness to two-term-exponential distribution (f(x) = f(x) = a*exp(b*x) + c*exp(d*x)) and generate the value of a, b, c, and d. They are the parameters in db_bench: prefix_dist_a, prefix_dist_b, prefix_dist_c, and prefix_dist_d. Finally, user can run db_bench by specify the parameters. For example: `./db_bench --benchmarks="mixgraph" -use_direct_io_for_flush_and_compaction=true -use_direct_reads=true -cache_size=268435456 -key_dist_a=0.002312 -key_dist_b=0.3467 -keyrange_dist_a=14.18 -keyrange_dist_b=-2.917 -keyrange_dist_c=0.0164 -keyrange_dist_d=-0.08082 -keyrange_num=30 -value_k=0.2615 -value_sigma=25.45 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.85 -mix_put_ratio=0.14 -mix_seek_ratio=0.01 -sine_mix_rate_interval_milliseconds=5000 -sine_a=350 -sine_b=0.0105 -sine_d=50000 --perf_level=2 -reads=1000000 -num=5000000 -key_size=48` Pull Request resolved: https://github.com/facebook/rocksdb/pull/5953 Test Plan: run db_bench with different parameters and checked the results. Differential Revision: D18053527 Pulled By: zhichao-cao fbshipit-source-id: 171f8b3142bd76462f1967c58345ad7e4f84bab7
2019-11-06 20:50:33 +00:00
int64_t ini_rand, rand_v, key_rand, key_seed;
ini_rand = GetRandomKey(&thread->rand);
rand_v = ini_rand % FLAGS_num;
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 18:40:44 +00:00
double u = static_cast<double>(rand_v) / FLAGS_num;
Workload generator (Mixgraph) based on prefix hotness (#5953) Summary: In the previous PR https://github.com/facebook/rocksdb/issues/4788, user can use db_bench mix_graph option to generate the workload that is from the social graph. The key is generated based on the key access hotness. In this PR, user can further model the key-range hotness and fit those to two-term-exponential distribution. First, user cuts the whole key space into small key ranges (e.g., key-ranges are the same size and the key-range number is the number of SST files). Then, user calculates the average access count per key of each key-range as the key-range hotness. Next, user fits the key-range hotness to two-term-exponential distribution (f(x) = f(x) = a*exp(b*x) + c*exp(d*x)) and generate the value of a, b, c, and d. They are the parameters in db_bench: prefix_dist_a, prefix_dist_b, prefix_dist_c, and prefix_dist_d. Finally, user can run db_bench by specify the parameters. For example: `./db_bench --benchmarks="mixgraph" -use_direct_io_for_flush_and_compaction=true -use_direct_reads=true -cache_size=268435456 -key_dist_a=0.002312 -key_dist_b=0.3467 -keyrange_dist_a=14.18 -keyrange_dist_b=-2.917 -keyrange_dist_c=0.0164 -keyrange_dist_d=-0.08082 -keyrange_num=30 -value_k=0.2615 -value_sigma=25.45 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.85 -mix_put_ratio=0.14 -mix_seek_ratio=0.01 -sine_mix_rate_interval_milliseconds=5000 -sine_a=350 -sine_b=0.0105 -sine_d=50000 --perf_level=2 -reads=1000000 -num=5000000 -key_size=48` Pull Request resolved: https://github.com/facebook/rocksdb/pull/5953 Test Plan: run db_bench with different parameters and checked the results. Differential Revision: D18053527 Pulled By: zhichao-cao fbshipit-source-id: 171f8b3142bd76462f1967c58345ad7e4f84bab7
2019-11-06 20:50:33 +00:00
// Generate the keyID based on the key hotness and prefix hotness
if (use_random_modeling) {
key_rand = ini_rand;
} else if (use_prefix_modeling) {
Workload generator (Mixgraph) based on prefix hotness (#5953) Summary: In the previous PR https://github.com/facebook/rocksdb/issues/4788, user can use db_bench mix_graph option to generate the workload that is from the social graph. The key is generated based on the key access hotness. In this PR, user can further model the key-range hotness and fit those to two-term-exponential distribution. First, user cuts the whole key space into small key ranges (e.g., key-ranges are the same size and the key-range number is the number of SST files). Then, user calculates the average access count per key of each key-range as the key-range hotness. Next, user fits the key-range hotness to two-term-exponential distribution (f(x) = f(x) = a*exp(b*x) + c*exp(d*x)) and generate the value of a, b, c, and d. They are the parameters in db_bench: prefix_dist_a, prefix_dist_b, prefix_dist_c, and prefix_dist_d. Finally, user can run db_bench by specify the parameters. For example: `./db_bench --benchmarks="mixgraph" -use_direct_io_for_flush_and_compaction=true -use_direct_reads=true -cache_size=268435456 -key_dist_a=0.002312 -key_dist_b=0.3467 -keyrange_dist_a=14.18 -keyrange_dist_b=-2.917 -keyrange_dist_c=0.0164 -keyrange_dist_d=-0.08082 -keyrange_num=30 -value_k=0.2615 -value_sigma=25.45 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.85 -mix_put_ratio=0.14 -mix_seek_ratio=0.01 -sine_mix_rate_interval_milliseconds=5000 -sine_a=350 -sine_b=0.0105 -sine_d=50000 --perf_level=2 -reads=1000000 -num=5000000 -key_size=48` Pull Request resolved: https://github.com/facebook/rocksdb/pull/5953 Test Plan: run db_bench with different parameters and checked the results. Differential Revision: D18053527 Pulled By: zhichao-cao fbshipit-source-id: 171f8b3142bd76462f1967c58345ad7e4f84bab7
2019-11-06 20:50:33 +00:00
key_rand =
gen_exp.DistGetKeyID(ini_rand, FLAGS_key_dist_a, FLAGS_key_dist_b);
} else {
key_seed = PowerCdfInversion(u, FLAGS_key_dist_a, FLAGS_key_dist_b);
Random64 rand(key_seed);
key_rand = static_cast<int64_t>(rand.Next()) % FLAGS_num;
}
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 18:40:44 +00:00
GenerateKeyFromInt(key_rand, FLAGS_num, &key);
int query_type = query.GetType(rand_v);
// change the qps
uint64_t now = FLAGS_env->NowMicros();
uint64_t usecs_since_last;
if (now > thread->stats.GetSineInterval()) {
usecs_since_last = now - thread->stats.GetSineInterval();
} else {
usecs_since_last = 0;
}
if (FLAGS_sine_mix_rate &&
usecs_since_last >
(FLAGS_sine_mix_rate_interval_milliseconds * uint64_t{1000})) {
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 18:40:44 +00:00
double usecs_since_start =
static_cast<double>(now - thread->stats.GetStart());
thread->stats.ResetSineInterval();
double mix_rate_with_noise = AddNoise(
SineRate(usecs_since_start / 1000000.0), FLAGS_sine_mix_rate_noise);
read_rate = mix_rate_with_noise * (query.ratio_[0] + query.ratio_[2]);
write_rate = mix_rate_with_noise * query.ratio_[1];
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 18:40:44 +00:00
if (read_rate > 0) {
thread->shared->read_rate_limiter->SetBytesPerSecond(
static_cast<int64_t>(read_rate));
}
if (write_rate > 0) {
thread->shared->write_rate_limiter->SetBytesPerSecond(
static_cast<int64_t>(write_rate));
}
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 18:40:44 +00:00
}
// Start the query
if (query_type == 0) {
// the Get query
gets++;
if (FLAGS_num_column_families > 1) {
Ignore `total_order_seek` in DB::Get (#9427) Summary: Apparently setting total_order_seek=true for DB::Get was intended to allow accurate read semantics if the current prefix extractor doesn't match what was used to generate SST files on disk. But since prefix_extractor was made a mutable option in 5.14.0, we have been able to detect this case and provide the correct semantics regardless of the total_order_seek option. Since that time, the option has only made Get() slower in a reasonably common case: prefix_extractor unchanged and whole_key_filtering=false. So this change primarily removes unnecessary effect of total_order_seek on Get. Also cleans up some related comments. Also adds a -total_order_seek option to db_bench and canonicalizes handling of ReadOptions in db_bench so that command line options have the expected association with library features. (There is potential for change in regression test behavior, but the old behavior is likely indefensible, or some other inconsistency would need to be fixed.) TODO in follow-up work: there should be no reason for Get() to depend on current prefix extractor at all. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9427 Test Plan: Unit tests updated. Performance (using db_bench update) Create DB with `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -benchmarks=fillrandom -num=10000000 -disable_wal=1 -write_buffer_size=10000000 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12 -whole_key_filtering=0` Test with and without `-total_order_seek` on `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -use_existing_db -readonly -benchmarks=readrandom -num=10000000 -duration=40 -disable_wal=1 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12` Before this change, total_order_seek=false: 25188 ops/sec Before this change, total_order_seek=true: 1222 ops/sec (~20x slower) After this change, total_order_seek=false: 24570 ops/sec After this change, total_order_seek=true: 25012 ops/sec (indistinguishable) Reviewed By: siying Differential Revision: D33753458 Pulled By: pdillinger fbshipit-source-id: bf892f34907a5e407d9c40bd4d42f0adbcbe0014
2022-02-01 03:45:17 +00:00
s = db_with_cfh->db->Get(read_options_, db_with_cfh->GetCfh(key_rand),
key, &pinnable_val);
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 18:40:44 +00:00
} else {
pinnable_val.Reset();
Ignore `total_order_seek` in DB::Get (#9427) Summary: Apparently setting total_order_seek=true for DB::Get was intended to allow accurate read semantics if the current prefix extractor doesn't match what was used to generate SST files on disk. But since prefix_extractor was made a mutable option in 5.14.0, we have been able to detect this case and provide the correct semantics regardless of the total_order_seek option. Since that time, the option has only made Get() slower in a reasonably common case: prefix_extractor unchanged and whole_key_filtering=false. So this change primarily removes unnecessary effect of total_order_seek on Get. Also cleans up some related comments. Also adds a -total_order_seek option to db_bench and canonicalizes handling of ReadOptions in db_bench so that command line options have the expected association with library features. (There is potential for change in regression test behavior, but the old behavior is likely indefensible, or some other inconsistency would need to be fixed.) TODO in follow-up work: there should be no reason for Get() to depend on current prefix extractor at all. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9427 Test Plan: Unit tests updated. Performance (using db_bench update) Create DB with `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -benchmarks=fillrandom -num=10000000 -disable_wal=1 -write_buffer_size=10000000 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12 -whole_key_filtering=0` Test with and without `-total_order_seek` on `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -use_existing_db -readonly -benchmarks=readrandom -num=10000000 -duration=40 -disable_wal=1 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12` Before this change, total_order_seek=false: 25188 ops/sec Before this change, total_order_seek=true: 1222 ops/sec (~20x slower) After this change, total_order_seek=false: 24570 ops/sec After this change, total_order_seek=true: 25012 ops/sec (indistinguishable) Reviewed By: siying Differential Revision: D33753458 Pulled By: pdillinger fbshipit-source-id: bf892f34907a5e407d9c40bd4d42f0adbcbe0014
2022-02-01 03:45:17 +00:00
s = db_with_cfh->db->Get(read_options_,
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 18:40:44 +00:00
db_with_cfh->db->DefaultColumnFamily(), key,
&pinnable_val);
}
if (s.ok()) {
2022-03-22 00:30:51 +00:00
get_found++;
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 18:40:44 +00:00
bytes += key.size() + pinnable_val.size();
} else if (!s.IsNotFound()) {
fprintf(stderr, "Get returned an error: %s\n", s.ToString().c_str());
abort();
}
2022-03-22 00:30:51 +00:00
if (thread->shared->read_rate_limiter && (gets + seek) % 100 == 0) {
thread->shared->read_rate_limiter->Request(100, Env::IO_HIGH,
nullptr /*stats*/);
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 18:40:44 +00:00
}
thread->stats.FinishedOps(db_with_cfh, db_with_cfh->db, 1, kRead);
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 18:40:44 +00:00
} else if (query_type == 1) {
// the Put query
puts++;
int64_t val_size = ParetoCdfInversion(u, FLAGS_value_theta,
FLAGS_value_k, FLAGS_value_sigma);
2022-03-22 00:30:51 +00:00
if (val_size < 10) {
val_size = 10;
} else if (val_size > value_max) {
val_size = val_size % value_max;
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 18:40:44 +00:00
}
2022-03-22 00:30:51 +00:00
total_val_size += val_size;
s = db_with_cfh->db->Put(
write_options_, key,
gen.Generate(static_cast<unsigned int>(val_size)));
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 18:40:44 +00:00
if (!s.ok()) {
fprintf(stderr, "put error: %s\n", s.ToString().c_str());
ErrorExit();
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 18:40:44 +00:00
}
if (thread->shared->write_rate_limiter && puts % 100 == 0) {
thread->shared->write_rate_limiter->Request(100, Env::IO_HIGH,
nullptr /*stats*/);
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 18:40:44 +00:00
}
thread->stats.FinishedOps(db_with_cfh, db_with_cfh->db, 1, kWrite);
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 18:40:44 +00:00
} else if (query_type == 2) {
// Seek query
if (db_with_cfh->db != nullptr) {
Iterator* single_iter = nullptr;
Ignore `total_order_seek` in DB::Get (#9427) Summary: Apparently setting total_order_seek=true for DB::Get was intended to allow accurate read semantics if the current prefix extractor doesn't match what was used to generate SST files on disk. But since prefix_extractor was made a mutable option in 5.14.0, we have been able to detect this case and provide the correct semantics regardless of the total_order_seek option. Since that time, the option has only made Get() slower in a reasonably common case: prefix_extractor unchanged and whole_key_filtering=false. So this change primarily removes unnecessary effect of total_order_seek on Get. Also cleans up some related comments. Also adds a -total_order_seek option to db_bench and canonicalizes handling of ReadOptions in db_bench so that command line options have the expected association with library features. (There is potential for change in regression test behavior, but the old behavior is likely indefensible, or some other inconsistency would need to be fixed.) TODO in follow-up work: there should be no reason for Get() to depend on current prefix extractor at all. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9427 Test Plan: Unit tests updated. Performance (using db_bench update) Create DB with `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -benchmarks=fillrandom -num=10000000 -disable_wal=1 -write_buffer_size=10000000 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12 -whole_key_filtering=0` Test with and without `-total_order_seek` on `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -use_existing_db -readonly -benchmarks=readrandom -num=10000000 -duration=40 -disable_wal=1 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12` Before this change, total_order_seek=false: 25188 ops/sec Before this change, total_order_seek=true: 1222 ops/sec (~20x slower) After this change, total_order_seek=false: 24570 ops/sec After this change, total_order_seek=true: 25012 ops/sec (indistinguishable) Reviewed By: siying Differential Revision: D33753458 Pulled By: pdillinger fbshipit-source-id: bf892f34907a5e407d9c40bd4d42f0adbcbe0014
2022-02-01 03:45:17 +00:00
single_iter = db_with_cfh->db->NewIterator(read_options_);
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 18:40:44 +00:00
if (single_iter != nullptr) {
single_iter->Seek(key);
seek++;
if (single_iter->Valid() && single_iter->key().compare(key) == 0) {
seek_found++;
}
int64_t scan_length =
ParetoCdfInversion(u, FLAGS_iter_theta, FLAGS_iter_k,
FLAGS_iter_sigma) %
scan_len_max;
for (int64_t j = 0; j < scan_length && single_iter->Valid(); j++) {
Slice value = single_iter->value();
memcpy(value_buffer, value.data(),
std::min(value.size(), sizeof(value_buffer)));
bytes += single_iter->key().size() + single_iter->value().size();
single_iter->Next();
assert(single_iter->status().ok());
2022-03-22 00:30:51 +00:00
total_scan_length++;
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 18:40:44 +00:00
}
}
delete single_iter;
}
thread->stats.FinishedOps(db_with_cfh, db_with_cfh->db, 1, kSeek);
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 18:40:44 +00:00
}
}
char msg[256];
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 18:40:44 +00:00
snprintf(msg, sizeof(msg),
2022-03-22 00:30:51 +00:00
"( Gets:%" PRIu64 " Puts:%" PRIu64 " Seek:%" PRIu64
", reads %" PRIu64 " in %" PRIu64
" found, "
"avg size: %.1f value, %.1f scan)\n",
gets, puts, seek, get_found + seek_found, gets + seek,
total_val_size / puts, total_scan_length / seek);
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 18:40:44 +00:00
thread->stats.AddBytes(bytes);
thread->stats.AddMessage(msg);
}
void IteratorCreation(ThreadState* thread) {
Duration duration(FLAGS_duration, reads_);
Ignore `total_order_seek` in DB::Get (#9427) Summary: Apparently setting total_order_seek=true for DB::Get was intended to allow accurate read semantics if the current prefix extractor doesn't match what was used to generate SST files on disk. But since prefix_extractor was made a mutable option in 5.14.0, we have been able to detect this case and provide the correct semantics regardless of the total_order_seek option. Since that time, the option has only made Get() slower in a reasonably common case: prefix_extractor unchanged and whole_key_filtering=false. So this change primarily removes unnecessary effect of total_order_seek on Get. Also cleans up some related comments. Also adds a -total_order_seek option to db_bench and canonicalizes handling of ReadOptions in db_bench so that command line options have the expected association with library features. (There is potential for change in regression test behavior, but the old behavior is likely indefensible, or some other inconsistency would need to be fixed.) TODO in follow-up work: there should be no reason for Get() to depend on current prefix extractor at all. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9427 Test Plan: Unit tests updated. Performance (using db_bench update) Create DB with `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -benchmarks=fillrandom -num=10000000 -disable_wal=1 -write_buffer_size=10000000 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12 -whole_key_filtering=0` Test with and without `-total_order_seek` on `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -use_existing_db -readonly -benchmarks=readrandom -num=10000000 -duration=40 -disable_wal=1 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12` Before this change, total_order_seek=false: 25188 ops/sec Before this change, total_order_seek=true: 1222 ops/sec (~20x slower) After this change, total_order_seek=false: 24570 ops/sec After this change, total_order_seek=true: 25012 ops/sec (indistinguishable) Reviewed By: siying Differential Revision: D33753458 Pulled By: pdillinger fbshipit-source-id: bf892f34907a5e407d9c40bd4d42f0adbcbe0014
2022-02-01 03:45:17 +00:00
ReadOptions options = read_options_;
std::unique_ptr<char[]> ts_guard;
if (user_timestamp_size_ > 0) {
ts_guard.reset(new char[user_timestamp_size_]);
}
while (!duration.Done(1)) {
DB* db = SelectDB(thread);
Slice ts;
if (user_timestamp_size_ > 0) {
ts = mock_app_clock_->GetTimestampForRead(thread->rand, ts_guard.get());
options.timestamp = &ts;
}
Iterator* iter = db->NewIterator(options);
delete iter;
thread->stats.FinishedOps(nullptr, db, 1, kOthers);
}
}
void IteratorCreationWhileWriting(ThreadState* thread) {
if (thread->tid > 0) {
IteratorCreation(thread);
} else {
BGWriter(thread, kWrite);
}
}
void SeekRandom(ThreadState* thread) {
int64_t read = 0;
int64_t found = 0;
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
2015-03-30 18:28:25 +00:00
int64_t bytes = 0;
Ignore `total_order_seek` in DB::Get (#9427) Summary: Apparently setting total_order_seek=true for DB::Get was intended to allow accurate read semantics if the current prefix extractor doesn't match what was used to generate SST files on disk. But since prefix_extractor was made a mutable option in 5.14.0, we have been able to detect this case and provide the correct semantics regardless of the total_order_seek option. Since that time, the option has only made Get() slower in a reasonably common case: prefix_extractor unchanged and whole_key_filtering=false. So this change primarily removes unnecessary effect of total_order_seek on Get. Also cleans up some related comments. Also adds a -total_order_seek option to db_bench and canonicalizes handling of ReadOptions in db_bench so that command line options have the expected association with library features. (There is potential for change in regression test behavior, but the old behavior is likely indefensible, or some other inconsistency would need to be fixed.) TODO in follow-up work: there should be no reason for Get() to depend on current prefix extractor at all. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9427 Test Plan: Unit tests updated. Performance (using db_bench update) Create DB with `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -benchmarks=fillrandom -num=10000000 -disable_wal=1 -write_buffer_size=10000000 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12 -whole_key_filtering=0` Test with and without `-total_order_seek` on `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -use_existing_db -readonly -benchmarks=readrandom -num=10000000 -duration=40 -disable_wal=1 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12` Before this change, total_order_seek=false: 25188 ops/sec Before this change, total_order_seek=true: 1222 ops/sec (~20x slower) After this change, total_order_seek=false: 24570 ops/sec After this change, total_order_seek=true: 25012 ops/sec (indistinguishable) Reviewed By: siying Differential Revision: D33753458 Pulled By: pdillinger fbshipit-source-id: bf892f34907a5e407d9c40bd4d42f0adbcbe0014
2022-02-01 03:45:17 +00:00
ReadOptions options = read_options_;
std::unique_ptr<char[]> ts_guard;
Slice ts;
if (user_timestamp_size_ > 0) {
ts_guard.reset(new char[user_timestamp_size_]);
ts = mock_app_clock_->GetTimestampForRead(thread->rand, ts_guard.get());
options.timestamp = &ts;
}
std::vector<Iterator*> tailing_iters;
if (FLAGS_use_tailing_iterator) {
if (db_.db != nullptr) {
tailing_iters.push_back(db_.db->NewIterator(options));
} else {
for (const auto& db_with_cfh : multi_dbs_) {
tailing_iters.push_back(db_with_cfh.db->NewIterator(options));
}
}
}
Fix auto_prefix_mode performance with partitioned filters (#10012) Summary: Essentially refactored the RangeMayExist implementation in FullFilterBlockReader to FilterBlockReaderCommon so that it applies to partitioned filters as well. (The function is not called for the block-based filter case.) RangeMayExist is essentially a series of checks around a possible PrefixMayExist, and I'm confident those checks should be the same for partitioned as for full filters. (I think it's likely that bugs remain in those checks, but this change is overall a simplifying one.) Added auto_prefix_mode support to db_bench Other small fixes as well Fixes https://github.com/facebook/rocksdb/issues/10003 Pull Request resolved: https://github.com/facebook/rocksdb/pull/10012 Test Plan: Expanded unit test that uses statistics to check for filter optimization, fails without the production code changes here Performance: populate two DBs with ``` TEST_TMPDIR=/dev/shm/rocksdb_nonpartitioned ./db_bench -benchmarks=fillrandom -num=10000000 -disable_wal=1 -write_buffer_size=30000000 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=8 TEST_TMPDIR=/dev/shm/rocksdb_partitioned ./db_bench -benchmarks=fillrandom -num=10000000 -disable_wal=1 -write_buffer_size=30000000 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=8 -partition_index_and_filters ``` Observe no measurable change in non-partitioned performance ``` TEST_TMPDIR=/dev/shm/rocksdb_nonpartitioned ./db_bench -benchmarks=seekrandom[-X1000] -num=10000000 -readonly -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=8 -auto_prefix_mode -cache_index_and_filter_blocks=1 -cache_size=1000000000 -duration 20 ``` Before: seekrandom [AVG 15 runs] : 11798 (± 331) ops/sec After: seekrandom [AVG 15 runs] : 11724 (± 315) ops/sec Observe big improvement with partitioned (also supported by bloom use statistics) ``` TEST_TMPDIR=/dev/shm/rocksdb_partitioned ./db_bench -benchmarks=seekrandom[-X1000] -num=10000000 -readonly -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=8 -partition_index_and_filters -auto_prefix_mode -cache_index_and_filter_blocks=1 -cache_size=1000000000 -duration 20 ``` Before: seekrandom [AVG 12 runs] : 2942 (± 57) ops/sec After: seekrandom [AVG 12 runs] : 7489 (± 184) ops/sec Reviewed By: siying Differential Revision: D36469796 Pulled By: pdillinger fbshipit-source-id: bcf1e2a68d347b32adb2b27384f945434e7a266d
2022-05-19 20:09:03 +00:00
options.auto_prefix_mode = FLAGS_auto_prefix_mode;
std::unique_ptr<const char[]> key_guard;
Slice key = AllocateKey(&key_guard);
std::unique_ptr<const char[]> upper_bound_key_guard;
Slice upper_bound = AllocateKey(&upper_bound_key_guard);
std::unique_ptr<const char[]> lower_bound_key_guard;
Slice lower_bound = AllocateKey(&lower_bound_key_guard);
Duration duration(FLAGS_duration, reads_);
char value_buffer[256];
while (!duration.Done(1)) {
int64_t seek_pos = thread->rand.Next() % FLAGS_num;
GenerateKeyFromIntForSeek(static_cast<uint64_t>(seek_pos), FLAGS_num,
&key);
if (FLAGS_max_scan_distance != 0) {
if (FLAGS_reverse_iterator) {
GenerateKeyFromInt(
static_cast<uint64_t>(std::max(
static_cast<int64_t>(0), seek_pos - FLAGS_max_scan_distance)),
FLAGS_num, &lower_bound);
options.iterate_lower_bound = &lower_bound;
} else {
auto min_num =
std::min(FLAGS_num, seek_pos + FLAGS_max_scan_distance);
GenerateKeyFromInt(static_cast<uint64_t>(min_num), FLAGS_num,
&upper_bound);
options.iterate_upper_bound = &upper_bound;
}
Fix auto_prefix_mode performance with partitioned filters (#10012) Summary: Essentially refactored the RangeMayExist implementation in FullFilterBlockReader to FilterBlockReaderCommon so that it applies to partitioned filters as well. (The function is not called for the block-based filter case.) RangeMayExist is essentially a series of checks around a possible PrefixMayExist, and I'm confident those checks should be the same for partitioned as for full filters. (I think it's likely that bugs remain in those checks, but this change is overall a simplifying one.) Added auto_prefix_mode support to db_bench Other small fixes as well Fixes https://github.com/facebook/rocksdb/issues/10003 Pull Request resolved: https://github.com/facebook/rocksdb/pull/10012 Test Plan: Expanded unit test that uses statistics to check for filter optimization, fails without the production code changes here Performance: populate two DBs with ``` TEST_TMPDIR=/dev/shm/rocksdb_nonpartitioned ./db_bench -benchmarks=fillrandom -num=10000000 -disable_wal=1 -write_buffer_size=30000000 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=8 TEST_TMPDIR=/dev/shm/rocksdb_partitioned ./db_bench -benchmarks=fillrandom -num=10000000 -disable_wal=1 -write_buffer_size=30000000 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=8 -partition_index_and_filters ``` Observe no measurable change in non-partitioned performance ``` TEST_TMPDIR=/dev/shm/rocksdb_nonpartitioned ./db_bench -benchmarks=seekrandom[-X1000] -num=10000000 -readonly -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=8 -auto_prefix_mode -cache_index_and_filter_blocks=1 -cache_size=1000000000 -duration 20 ``` Before: seekrandom [AVG 15 runs] : 11798 (± 331) ops/sec After: seekrandom [AVG 15 runs] : 11724 (± 315) ops/sec Observe big improvement with partitioned (also supported by bloom use statistics) ``` TEST_TMPDIR=/dev/shm/rocksdb_partitioned ./db_bench -benchmarks=seekrandom[-X1000] -num=10000000 -readonly -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=8 -partition_index_and_filters -auto_prefix_mode -cache_index_and_filter_blocks=1 -cache_size=1000000000 -duration 20 ``` Before: seekrandom [AVG 12 runs] : 2942 (± 57) ops/sec After: seekrandom [AVG 12 runs] : 7489 (± 184) ops/sec Reviewed By: siying Differential Revision: D36469796 Pulled By: pdillinger fbshipit-source-id: bcf1e2a68d347b32adb2b27384f945434e7a266d
2022-05-19 20:09:03 +00:00
} else if (FLAGS_auto_prefix_mode && prefix_extractor_ &&
!FLAGS_reverse_iterator) {
// Set upper bound to next prefix
auto mutable_upper_bound = const_cast<char*>(upper_bound.data());
std::memcpy(mutable_upper_bound, key.data(), prefix_size_);
mutable_upper_bound[prefix_size_ - 1]++;
upper_bound = Slice(upper_bound.data(), prefix_size_);
options.iterate_upper_bound = &upper_bound;
}
// Pick a Iterator to use
uint64_t db_idx_to_use =
(db_.db == nullptr)
? (uint64_t{thread->rand.Next()} % multi_dbs_.size())
: 0;
std::unique_ptr<Iterator> single_iter;
Iterator* iter_to_use;
if (FLAGS_use_tailing_iterator) {
iter_to_use = tailing_iters[db_idx_to_use];
} else {
if (db_.db != nullptr) {
single_iter.reset(db_.db->NewIterator(options));
} else {
single_iter.reset(multi_dbs_[db_idx_to_use].db->NewIterator(options));
}
iter_to_use = single_iter.get();
}
iter_to_use->Seek(key);
read++;
if (iter_to_use->Valid() && iter_to_use->key().compare(key) == 0) {
found++;
}
for (int j = 0; j < FLAGS_seek_nexts && iter_to_use->Valid(); ++j) {
// Copy out iterator's value to make sure we read them.
Slice value = iter_to_use->value();
memcpy(value_buffer, value.data(),
std::min(value.size(), sizeof(value_buffer)));
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
2015-03-30 18:28:25 +00:00
bytes += iter_to_use->key().size() + iter_to_use->value().size();
if (!FLAGS_reverse_iterator) {
iter_to_use->Next();
} else {
iter_to_use->Prev();
}
assert(iter_to_use->status().ok());
}
if (thread->shared->read_rate_limiter.get() != nullptr &&
read % 256 == 255) {
thread->shared->read_rate_limiter->Request(
256, Env::IO_HIGH, nullptr /* stats */, RateLimiter::OpType::kRead);
}
thread->stats.FinishedOps(&db_, db_.db, 1, kSeek);
}
for (auto iter : tailing_iters) {
delete iter;
}
char msg[100];
snprintf(msg, sizeof(msg), "(%" PRIu64 " of %" PRIu64 " found)\n", found,
read);
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
2015-03-30 18:28:25 +00:00
thread->stats.AddBytes(bytes);
thread->stats.AddMessage(msg);
}
void SeekRandomWhileWriting(ThreadState* thread) {
if (thread->tid > 0) {
SeekRandom(thread);
} else {
BGWriter(thread, kWrite);
}
}
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
2015-03-30 18:28:25 +00:00
void SeekRandomWhileMerging(ThreadState* thread) {
if (thread->tid > 0) {
SeekRandom(thread);
} else {
BGWriter(thread, kMerge);
}
}
void DoDelete(ThreadState* thread, bool seq) {
WriteBatch batch(/*reserved_bytes=*/0, /*max_bytes=*/0,
FLAGS_write_batch_protection_bytes_per_key,
user_timestamp_size_);
Duration duration(seq ? 0 : FLAGS_duration, deletes_);
int64_t i = 0;
std::unique_ptr<const char[]> key_guard;
Slice key = AllocateKey(&key_guard);
std::unique_ptr<char[]> ts_guard;
Slice ts;
if (user_timestamp_size_ > 0) {
ts_guard.reset(new char[user_timestamp_size_]);
}
while (!duration.Done(entries_per_batch_)) {
DB* db = SelectDB(thread);
batch.Clear();
for (int64_t j = 0; j < entries_per_batch_; ++j) {
const int64_t k = seq ? i + j : (thread->rand.Next() % FLAGS_num);
GenerateKeyFromInt(k, FLAGS_num, &key);
batch.Delete(key);
}
Status s;
if (user_timestamp_size_ > 0) {
ts = mock_app_clock_->Allocate(ts_guard.get());
Revise APIs related to user-defined timestamp (#8946) Summary: ajkr reminded me that we have a rule of not including per-kv related data in `WriteOptions`. Namely, `WriteOptions` should not include information about "what-to-write", but should just include information about "how-to-write". According to this rule, `WriteOptions::timestamp` (experimental) is clearly a violation. Therefore, this PR removes `WriteOptions::timestamp` for compliance. After the removal, we need to pass timestamp info via another set of APIs. This PR proposes a set of overloaded functions `Put(write_opts, key, value, ts)`, `Delete(write_opts, key, ts)`, and `SingleDelete(write_opts, key, ts)`. Planned to add `Write(write_opts, batch, ts)`, but its complexity made me reconsider doing it in another PR (maybe). For better checking and returning error early, we also add a new set of APIs to `WriteBatch` that take extra `timestamp` information when writing to `WriteBatch`es. These set of APIs in `WriteBatchWithIndex` are currently not supported, and are on our TODO list. Removed `WriteBatch::AssignTimestamps()` and renamed `WriteBatch::AssignTimestamp()` to `WriteBatch::UpdateTimestamps()` since this method require that all keys have space for timestamps allocated already and multiple timestamps can be updated. The constructor of `WriteBatch` now takes a fourth argument `default_cf_ts_sz` which is the timestamp size of the default column family. This will be used to allocate space when calling APIs that do not specify a column family handle. Also, updated `DB::Get()`, `DB::MultiGet()`, `DB::NewIterator()`, `DB::NewIterators()` methods, replacing some assertions about timestamp to returning Status code. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8946 Test Plan: make check ./db_bench -benchmarks=fillseq,fillrandom,readrandom,readseq,deleterandom -user_timestamp_size=8 ./db_stress --user_timestamp_size=8 -nooverwritepercent=0 -test_secondary=0 -secondary_catch_up_one_in=0 -continuous_verification_interval=0 Make sure there is no perf regression by running the following ``` ./db_bench_opt -db=/dev/shm/rocksdb -use_existing_db=0 -level0_stop_writes_trigger=256 -level0_slowdown_writes_trigger=256 -level0_file_num_compaction_trigger=256 -disable_wal=1 -duration=10 -benchmarks=fillrandom ``` Before this PR ``` DB path: [/dev/shm/rocksdb] fillrandom : 1.831 micros/op 546235 ops/sec; 60.4 MB/s ``` After this PR ``` DB path: [/dev/shm/rocksdb] fillrandom : 1.820 micros/op 549404 ops/sec; 60.8 MB/s ``` Reviewed By: ltamasi Differential Revision: D33721359 Pulled By: riversand963 fbshipit-source-id: c131561534272c120ffb80711d42748d21badf09
2022-02-02 06:17:46 +00:00
s = batch.UpdateTimestamps(
ts, [this](uint32_t) { return user_timestamp_size_; });
if (!s.ok()) {
fprintf(stderr, "assign timestamp: %s\n", s.ToString().c_str());
ErrorExit();
}
}
s = db->Write(write_options_, &batch);
thread->stats.FinishedOps(nullptr, db, entries_per_batch_, kDelete);
if (!s.ok()) {
fprintf(stderr, "del error: %s\n", s.ToString().c_str());
exit(1);
}
i += entries_per_batch_;
}
}
void DeleteSeq(ThreadState* thread) { DoDelete(thread, true); }
void DeleteRandom(ThreadState* thread) { DoDelete(thread, false); }
void ReadWhileWriting(ThreadState* thread) {
if (thread->tid > 0) {
ReadRandom(thread);
} else {
BGWriter(thread, kWrite);
}
}
void MultiReadWhileWriting(ThreadState* thread) {
if (thread->tid > 0) {
MultiReadRandom(thread);
} else {
BGWriter(thread, kWrite);
}
}
void ReadWhileMerging(ThreadState* thread) {
if (thread->tid > 0) {
ReadRandom(thread);
} else {
BGWriter(thread, kMerge);
}
}
void BGWriter(ThreadState* thread, enum OperationType write_merge) {
// Special thread that keeps writing until other threads are done.
RandomGenerator gen;
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
2015-03-30 18:28:25 +00:00
int64_t bytes = 0;
std::unique_ptr<RateLimiter> write_rate_limiter;
if (FLAGS_benchmark_write_rate_limit > 0) {
write_rate_limiter.reset(
NewGenericRateLimiter(FLAGS_benchmark_write_rate_limit));
}
// Don't merge stats from this thread with the readers.
thread->stats.SetExcludeFromMerge();
std::unique_ptr<const char[]> key_guard;
Slice key = AllocateKey(&key_guard);
std::unique_ptr<char[]> ts_guard;
std::unique_ptr<const char[]> begin_key_guard;
Slice begin_key = AllocateKey(&begin_key_guard);
std::unique_ptr<const char[]> end_key_guard;
Slice end_key = AllocateKey(&end_key_guard);
uint64_t num_range_deletions = 0;
std::vector<std::unique_ptr<const char[]>> expanded_key_guards;
std::vector<Slice> expanded_keys;
if (FLAGS_expand_range_tombstones) {
expanded_key_guards.resize(range_tombstone_width_);
for (auto& expanded_key_guard : expanded_key_guards) {
expanded_keys.emplace_back(AllocateKey(&expanded_key_guard));
}
}
if (user_timestamp_size_ > 0) {
ts_guard.reset(new char[user_timestamp_size_]);
}
uint32_t written = 0;
bool hint_printed = false;
while (true) {
DB* db = SelectDB(thread);
{
MutexLock l(&thread->shared->mu);
if (FLAGS_finish_after_writes && written == writes_) {
fprintf(stderr, "Exiting the writer after %u writes...\n", written);
break;
}
if (thread->shared->num_done + 1 >= thread->shared->num_initialized) {
// Other threads have finished
if (FLAGS_finish_after_writes) {
// Wait for the writes to be finished
if (!hint_printed) {
fprintf(stderr, "Reads are finished. Have %d more writes to do\n",
static_cast<int>(writes_) - written);
hint_printed = true;
}
} else {
// Finish the write immediately
break;
}
}
}
GenerateKeyFromInt(thread->rand.Next() % FLAGS_num, FLAGS_num, &key);
Status s;
Slice val = gen.Generate();
Slice ts;
if (user_timestamp_size_ > 0) {
ts = mock_app_clock_->Allocate(ts_guard.get());
}
if (write_merge == kWrite) {
Revise APIs related to user-defined timestamp (#8946) Summary: ajkr reminded me that we have a rule of not including per-kv related data in `WriteOptions`. Namely, `WriteOptions` should not include information about "what-to-write", but should just include information about "how-to-write". According to this rule, `WriteOptions::timestamp` (experimental) is clearly a violation. Therefore, this PR removes `WriteOptions::timestamp` for compliance. After the removal, we need to pass timestamp info via another set of APIs. This PR proposes a set of overloaded functions `Put(write_opts, key, value, ts)`, `Delete(write_opts, key, ts)`, and `SingleDelete(write_opts, key, ts)`. Planned to add `Write(write_opts, batch, ts)`, but its complexity made me reconsider doing it in another PR (maybe). For better checking and returning error early, we also add a new set of APIs to `WriteBatch` that take extra `timestamp` information when writing to `WriteBatch`es. These set of APIs in `WriteBatchWithIndex` are currently not supported, and are on our TODO list. Removed `WriteBatch::AssignTimestamps()` and renamed `WriteBatch::AssignTimestamp()` to `WriteBatch::UpdateTimestamps()` since this method require that all keys have space for timestamps allocated already and multiple timestamps can be updated. The constructor of `WriteBatch` now takes a fourth argument `default_cf_ts_sz` which is the timestamp size of the default column family. This will be used to allocate space when calling APIs that do not specify a column family handle. Also, updated `DB::Get()`, `DB::MultiGet()`, `DB::NewIterator()`, `DB::NewIterators()` methods, replacing some assertions about timestamp to returning Status code. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8946 Test Plan: make check ./db_bench -benchmarks=fillseq,fillrandom,readrandom,readseq,deleterandom -user_timestamp_size=8 ./db_stress --user_timestamp_size=8 -nooverwritepercent=0 -test_secondary=0 -secondary_catch_up_one_in=0 -continuous_verification_interval=0 Make sure there is no perf regression by running the following ``` ./db_bench_opt -db=/dev/shm/rocksdb -use_existing_db=0 -level0_stop_writes_trigger=256 -level0_slowdown_writes_trigger=256 -level0_file_num_compaction_trigger=256 -disable_wal=1 -duration=10 -benchmarks=fillrandom ``` Before this PR ``` DB path: [/dev/shm/rocksdb] fillrandom : 1.831 micros/op 546235 ops/sec; 60.4 MB/s ``` After this PR ``` DB path: [/dev/shm/rocksdb] fillrandom : 1.820 micros/op 549404 ops/sec; 60.8 MB/s ``` Reviewed By: ltamasi Differential Revision: D33721359 Pulled By: riversand963 fbshipit-source-id: c131561534272c120ffb80711d42748d21badf09
2022-02-02 06:17:46 +00:00
if (user_timestamp_size_ == 0) {
s = db->Put(write_options_, key, val);
} else {
s = db->Put(write_options_, key, ts, val);
}
} else {
s = db->Merge(write_options_, key, val);
}
// Restore write_options_
written++;
if (!s.ok()) {
fprintf(stderr, "put or merge error: %s\n", s.ToString().c_str());
exit(1);
}
bytes += key.size() + val.size() + user_timestamp_size_;
thread->stats.FinishedOps(&db_, db_.db, 1, kWrite);
if (FLAGS_benchmark_write_rate_limit > 0) {
write_rate_limiter->Request(key.size() + val.size(), Env::IO_HIGH,
nullptr /* stats */,
RateLimiter::OpType::kWrite);
}
if (writes_per_range_tombstone_ > 0 &&
written > writes_before_delete_range_ &&
(written - writes_before_delete_range_) /
writes_per_range_tombstone_ <=
max_num_range_tombstones_ &&
(written - writes_before_delete_range_) %
writes_per_range_tombstone_ ==
0) {
num_range_deletions++;
int64_t begin_num = thread->rand.Next() % FLAGS_num;
if (FLAGS_expand_range_tombstones) {
for (int64_t offset = 0; offset < range_tombstone_width_; ++offset) {
GenerateKeyFromInt(begin_num + offset, FLAGS_num,
&expanded_keys[offset]);
if (!db->Delete(write_options_, expanded_keys[offset]).ok()) {
fprintf(stderr, "delete error: %s\n", s.ToString().c_str());
exit(1);
}
}
} else {
GenerateKeyFromInt(begin_num, FLAGS_num, &begin_key);
GenerateKeyFromInt(begin_num + range_tombstone_width_, FLAGS_num,
&end_key);
if (!db->DeleteRange(write_options_, db->DefaultColumnFamily(),
begin_key, end_key)
.ok()) {
fprintf(stderr, "deleterange error: %s\n", s.ToString().c_str());
exit(1);
}
}
thread->stats.FinishedOps(&db_, db_.db, 1, kWrite);
// TODO: DeleteRange is not included in calculcation of bytes/rate
// limiter request
}
}
if (num_range_deletions > 0) {
std::cout << "Number of range deletions: " << num_range_deletions
<< std::endl;
}
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
2015-03-30 18:28:25 +00:00
thread->stats.AddBytes(bytes);
}
void ReadWhileScanning(ThreadState* thread) {
if (thread->tid > 0) {
ReadRandom(thread);
} else {
BGScan(thread);
}
}
void BGScan(ThreadState* thread) {
if (FLAGS_num_multi_db > 0) {
fprintf(stderr, "Not supporting multiple DBs.\n");
abort();
}
assert(db_.db != nullptr);
Ignore `total_order_seek` in DB::Get (#9427) Summary: Apparently setting total_order_seek=true for DB::Get was intended to allow accurate read semantics if the current prefix extractor doesn't match what was used to generate SST files on disk. But since prefix_extractor was made a mutable option in 5.14.0, we have been able to detect this case and provide the correct semantics regardless of the total_order_seek option. Since that time, the option has only made Get() slower in a reasonably common case: prefix_extractor unchanged and whole_key_filtering=false. So this change primarily removes unnecessary effect of total_order_seek on Get. Also cleans up some related comments. Also adds a -total_order_seek option to db_bench and canonicalizes handling of ReadOptions in db_bench so that command line options have the expected association with library features. (There is potential for change in regression test behavior, but the old behavior is likely indefensible, or some other inconsistency would need to be fixed.) TODO in follow-up work: there should be no reason for Get() to depend on current prefix extractor at all. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9427 Test Plan: Unit tests updated. Performance (using db_bench update) Create DB with `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -benchmarks=fillrandom -num=10000000 -disable_wal=1 -write_buffer_size=10000000 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12 -whole_key_filtering=0` Test with and without `-total_order_seek` on `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -use_existing_db -readonly -benchmarks=readrandom -num=10000000 -duration=40 -disable_wal=1 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12` Before this change, total_order_seek=false: 25188 ops/sec Before this change, total_order_seek=true: 1222 ops/sec (~20x slower) After this change, total_order_seek=false: 24570 ops/sec After this change, total_order_seek=true: 25012 ops/sec (indistinguishable) Reviewed By: siying Differential Revision: D33753458 Pulled By: pdillinger fbshipit-source-id: bf892f34907a5e407d9c40bd4d42f0adbcbe0014
2022-02-01 03:45:17 +00:00
ReadOptions read_options = read_options_;
std::unique_ptr<char[]> ts_guard;
Slice ts;
if (user_timestamp_size_ > 0) {
ts_guard.reset(new char[user_timestamp_size_]);
ts = mock_app_clock_->GetTimestampForRead(thread->rand, ts_guard.get());
read_options.timestamp = &ts;
}
Iterator* iter = db_.db->NewIterator(read_options);
fprintf(stderr, "num reads to do %" PRIu64 "\n", reads_);
Duration duration(FLAGS_duration, reads_);
uint64_t num_seek_to_first = 0;
uint64_t num_next = 0;
while (!duration.Done(1)) {
if (!iter->Valid()) {
iter->SeekToFirst();
num_seek_to_first++;
} else if (!iter->status().ok()) {
fprintf(stderr, "Iterator error: %s\n",
iter->status().ToString().c_str());
abort();
} else {
iter->Next();
num_next++;
}
thread->stats.FinishedOps(&db_, db_.db, 1, kSeek);
}
(void)num_seek_to_first;
(void)num_next;
delete iter;
}
// Given a key K and value V, this puts (K+"0", V), (K+"1", V), (K+"2", V)
// in DB atomically i.e in a single batch. Also refer GetMany.
Status PutMany(DB* db, const WriteOptions& writeoptions, const Slice& key,
const Slice& value) {
std::string suffixes[3] = {"2", "1", "0"};
std::string keys[3];
WriteBatch batch(/*reserved_bytes=*/0, /*max_bytes=*/0,
FLAGS_write_batch_protection_bytes_per_key,
user_timestamp_size_);
Status s;
for (int i = 0; i < 3; i++) {
keys[i] = key.ToString() + suffixes[i];
batch.Put(keys[i], value);
}
std::unique_ptr<char[]> ts_guard;
if (user_timestamp_size_ > 0) {
ts_guard.reset(new char[user_timestamp_size_]);
Slice ts = mock_app_clock_->Allocate(ts_guard.get());
Revise APIs related to user-defined timestamp (#8946) Summary: ajkr reminded me that we have a rule of not including per-kv related data in `WriteOptions`. Namely, `WriteOptions` should not include information about "what-to-write", but should just include information about "how-to-write". According to this rule, `WriteOptions::timestamp` (experimental) is clearly a violation. Therefore, this PR removes `WriteOptions::timestamp` for compliance. After the removal, we need to pass timestamp info via another set of APIs. This PR proposes a set of overloaded functions `Put(write_opts, key, value, ts)`, `Delete(write_opts, key, ts)`, and `SingleDelete(write_opts, key, ts)`. Planned to add `Write(write_opts, batch, ts)`, but its complexity made me reconsider doing it in another PR (maybe). For better checking and returning error early, we also add a new set of APIs to `WriteBatch` that take extra `timestamp` information when writing to `WriteBatch`es. These set of APIs in `WriteBatchWithIndex` are currently not supported, and are on our TODO list. Removed `WriteBatch::AssignTimestamps()` and renamed `WriteBatch::AssignTimestamp()` to `WriteBatch::UpdateTimestamps()` since this method require that all keys have space for timestamps allocated already and multiple timestamps can be updated. The constructor of `WriteBatch` now takes a fourth argument `default_cf_ts_sz` which is the timestamp size of the default column family. This will be used to allocate space when calling APIs that do not specify a column family handle. Also, updated `DB::Get()`, `DB::MultiGet()`, `DB::NewIterator()`, `DB::NewIterators()` methods, replacing some assertions about timestamp to returning Status code. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8946 Test Plan: make check ./db_bench -benchmarks=fillseq,fillrandom,readrandom,readseq,deleterandom -user_timestamp_size=8 ./db_stress --user_timestamp_size=8 -nooverwritepercent=0 -test_secondary=0 -secondary_catch_up_one_in=0 -continuous_verification_interval=0 Make sure there is no perf regression by running the following ``` ./db_bench_opt -db=/dev/shm/rocksdb -use_existing_db=0 -level0_stop_writes_trigger=256 -level0_slowdown_writes_trigger=256 -level0_file_num_compaction_trigger=256 -disable_wal=1 -duration=10 -benchmarks=fillrandom ``` Before this PR ``` DB path: [/dev/shm/rocksdb] fillrandom : 1.831 micros/op 546235 ops/sec; 60.4 MB/s ``` After this PR ``` DB path: [/dev/shm/rocksdb] fillrandom : 1.820 micros/op 549404 ops/sec; 60.8 MB/s ``` Reviewed By: ltamasi Differential Revision: D33721359 Pulled By: riversand963 fbshipit-source-id: c131561534272c120ffb80711d42748d21badf09
2022-02-02 06:17:46 +00:00
s = batch.UpdateTimestamps(
ts, [this](uint32_t) { return user_timestamp_size_; });
if (!s.ok()) {
fprintf(stderr, "assign timestamp to batch: %s\n",
s.ToString().c_str());
ErrorExit();
}
}
s = db->Write(writeoptions, &batch);
return s;
}
// Given a key K, this deletes (K+"0", V), (K+"1", V), (K+"2", V)
// in DB atomically i.e in a single batch. Also refer GetMany.
Status DeleteMany(DB* db, const WriteOptions& writeoptions,
const Slice& key) {
std::string suffixes[3] = {"1", "2", "0"};
std::string keys[3];
WriteBatch batch(0, 0, FLAGS_write_batch_protection_bytes_per_key,
Revise APIs related to user-defined timestamp (#8946) Summary: ajkr reminded me that we have a rule of not including per-kv related data in `WriteOptions`. Namely, `WriteOptions` should not include information about "what-to-write", but should just include information about "how-to-write". According to this rule, `WriteOptions::timestamp` (experimental) is clearly a violation. Therefore, this PR removes `WriteOptions::timestamp` for compliance. After the removal, we need to pass timestamp info via another set of APIs. This PR proposes a set of overloaded functions `Put(write_opts, key, value, ts)`, `Delete(write_opts, key, ts)`, and `SingleDelete(write_opts, key, ts)`. Planned to add `Write(write_opts, batch, ts)`, but its complexity made me reconsider doing it in another PR (maybe). For better checking and returning error early, we also add a new set of APIs to `WriteBatch` that take extra `timestamp` information when writing to `WriteBatch`es. These set of APIs in `WriteBatchWithIndex` are currently not supported, and are on our TODO list. Removed `WriteBatch::AssignTimestamps()` and renamed `WriteBatch::AssignTimestamp()` to `WriteBatch::UpdateTimestamps()` since this method require that all keys have space for timestamps allocated already and multiple timestamps can be updated. The constructor of `WriteBatch` now takes a fourth argument `default_cf_ts_sz` which is the timestamp size of the default column family. This will be used to allocate space when calling APIs that do not specify a column family handle. Also, updated `DB::Get()`, `DB::MultiGet()`, `DB::NewIterator()`, `DB::NewIterators()` methods, replacing some assertions about timestamp to returning Status code. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8946 Test Plan: make check ./db_bench -benchmarks=fillseq,fillrandom,readrandom,readseq,deleterandom -user_timestamp_size=8 ./db_stress --user_timestamp_size=8 -nooverwritepercent=0 -test_secondary=0 -secondary_catch_up_one_in=0 -continuous_verification_interval=0 Make sure there is no perf regression by running the following ``` ./db_bench_opt -db=/dev/shm/rocksdb -use_existing_db=0 -level0_stop_writes_trigger=256 -level0_slowdown_writes_trigger=256 -level0_file_num_compaction_trigger=256 -disable_wal=1 -duration=10 -benchmarks=fillrandom ``` Before this PR ``` DB path: [/dev/shm/rocksdb] fillrandom : 1.831 micros/op 546235 ops/sec; 60.4 MB/s ``` After this PR ``` DB path: [/dev/shm/rocksdb] fillrandom : 1.820 micros/op 549404 ops/sec; 60.8 MB/s ``` Reviewed By: ltamasi Differential Revision: D33721359 Pulled By: riversand963 fbshipit-source-id: c131561534272c120ffb80711d42748d21badf09
2022-02-02 06:17:46 +00:00
user_timestamp_size_);
Status s;
for (int i = 0; i < 3; i++) {
keys[i] = key.ToString() + suffixes[i];
batch.Delete(keys[i]);
}
std::unique_ptr<char[]> ts_guard;
if (user_timestamp_size_ > 0) {
ts_guard.reset(new char[user_timestamp_size_]);
Slice ts = mock_app_clock_->Allocate(ts_guard.get());
Revise APIs related to user-defined timestamp (#8946) Summary: ajkr reminded me that we have a rule of not including per-kv related data in `WriteOptions`. Namely, `WriteOptions` should not include information about "what-to-write", but should just include information about "how-to-write". According to this rule, `WriteOptions::timestamp` (experimental) is clearly a violation. Therefore, this PR removes `WriteOptions::timestamp` for compliance. After the removal, we need to pass timestamp info via another set of APIs. This PR proposes a set of overloaded functions `Put(write_opts, key, value, ts)`, `Delete(write_opts, key, ts)`, and `SingleDelete(write_opts, key, ts)`. Planned to add `Write(write_opts, batch, ts)`, but its complexity made me reconsider doing it in another PR (maybe). For better checking and returning error early, we also add a new set of APIs to `WriteBatch` that take extra `timestamp` information when writing to `WriteBatch`es. These set of APIs in `WriteBatchWithIndex` are currently not supported, and are on our TODO list. Removed `WriteBatch::AssignTimestamps()` and renamed `WriteBatch::AssignTimestamp()` to `WriteBatch::UpdateTimestamps()` since this method require that all keys have space for timestamps allocated already and multiple timestamps can be updated. The constructor of `WriteBatch` now takes a fourth argument `default_cf_ts_sz` which is the timestamp size of the default column family. This will be used to allocate space when calling APIs that do not specify a column family handle. Also, updated `DB::Get()`, `DB::MultiGet()`, `DB::NewIterator()`, `DB::NewIterators()` methods, replacing some assertions about timestamp to returning Status code. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8946 Test Plan: make check ./db_bench -benchmarks=fillseq,fillrandom,readrandom,readseq,deleterandom -user_timestamp_size=8 ./db_stress --user_timestamp_size=8 -nooverwritepercent=0 -test_secondary=0 -secondary_catch_up_one_in=0 -continuous_verification_interval=0 Make sure there is no perf regression by running the following ``` ./db_bench_opt -db=/dev/shm/rocksdb -use_existing_db=0 -level0_stop_writes_trigger=256 -level0_slowdown_writes_trigger=256 -level0_file_num_compaction_trigger=256 -disable_wal=1 -duration=10 -benchmarks=fillrandom ``` Before this PR ``` DB path: [/dev/shm/rocksdb] fillrandom : 1.831 micros/op 546235 ops/sec; 60.4 MB/s ``` After this PR ``` DB path: [/dev/shm/rocksdb] fillrandom : 1.820 micros/op 549404 ops/sec; 60.8 MB/s ``` Reviewed By: ltamasi Differential Revision: D33721359 Pulled By: riversand963 fbshipit-source-id: c131561534272c120ffb80711d42748d21badf09
2022-02-02 06:17:46 +00:00
s = batch.UpdateTimestamps(
ts, [this](uint32_t) { return user_timestamp_size_; });
if (!s.ok()) {
fprintf(stderr, "assign timestamp to batch: %s\n",
s.ToString().c_str());
ErrorExit();
}
}
s = db->Write(writeoptions, &batch);
return s;
}
// Given a key K and value V, this gets values for K+"0", K+"1" and K+"2"
// in the same snapshot, and verifies that all the values are identical.
// ASSUMES that PutMany was used to put (K, V) into the DB.
Ignore `total_order_seek` in DB::Get (#9427) Summary: Apparently setting total_order_seek=true for DB::Get was intended to allow accurate read semantics if the current prefix extractor doesn't match what was used to generate SST files on disk. But since prefix_extractor was made a mutable option in 5.14.0, we have been able to detect this case and provide the correct semantics regardless of the total_order_seek option. Since that time, the option has only made Get() slower in a reasonably common case: prefix_extractor unchanged and whole_key_filtering=false. So this change primarily removes unnecessary effect of total_order_seek on Get. Also cleans up some related comments. Also adds a -total_order_seek option to db_bench and canonicalizes handling of ReadOptions in db_bench so that command line options have the expected association with library features. (There is potential for change in regression test behavior, but the old behavior is likely indefensible, or some other inconsistency would need to be fixed.) TODO in follow-up work: there should be no reason for Get() to depend on current prefix extractor at all. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9427 Test Plan: Unit tests updated. Performance (using db_bench update) Create DB with `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -benchmarks=fillrandom -num=10000000 -disable_wal=1 -write_buffer_size=10000000 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12 -whole_key_filtering=0` Test with and without `-total_order_seek` on `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -use_existing_db -readonly -benchmarks=readrandom -num=10000000 -duration=40 -disable_wal=1 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12` Before this change, total_order_seek=false: 25188 ops/sec Before this change, total_order_seek=true: 1222 ops/sec (~20x slower) After this change, total_order_seek=false: 24570 ops/sec After this change, total_order_seek=true: 25012 ops/sec (indistinguishable) Reviewed By: siying Differential Revision: D33753458 Pulled By: pdillinger fbshipit-source-id: bf892f34907a5e407d9c40bd4d42f0adbcbe0014
2022-02-01 03:45:17 +00:00
Status GetMany(DB* db, const Slice& key, std::string* value) {
std::string suffixes[3] = {"0", "1", "2"};
std::string keys[3];
Slice key_slices[3];
std::string values[3];
Ignore `total_order_seek` in DB::Get (#9427) Summary: Apparently setting total_order_seek=true for DB::Get was intended to allow accurate read semantics if the current prefix extractor doesn't match what was used to generate SST files on disk. But since prefix_extractor was made a mutable option in 5.14.0, we have been able to detect this case and provide the correct semantics regardless of the total_order_seek option. Since that time, the option has only made Get() slower in a reasonably common case: prefix_extractor unchanged and whole_key_filtering=false. So this change primarily removes unnecessary effect of total_order_seek on Get. Also cleans up some related comments. Also adds a -total_order_seek option to db_bench and canonicalizes handling of ReadOptions in db_bench so that command line options have the expected association with library features. (There is potential for change in regression test behavior, but the old behavior is likely indefensible, or some other inconsistency would need to be fixed.) TODO in follow-up work: there should be no reason for Get() to depend on current prefix extractor at all. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9427 Test Plan: Unit tests updated. Performance (using db_bench update) Create DB with `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -benchmarks=fillrandom -num=10000000 -disable_wal=1 -write_buffer_size=10000000 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12 -whole_key_filtering=0` Test with and without `-total_order_seek` on `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -use_existing_db -readonly -benchmarks=readrandom -num=10000000 -duration=40 -disable_wal=1 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12` Before this change, total_order_seek=false: 25188 ops/sec Before this change, total_order_seek=true: 1222 ops/sec (~20x slower) After this change, total_order_seek=false: 24570 ops/sec After this change, total_order_seek=true: 25012 ops/sec (indistinguishable) Reviewed By: siying Differential Revision: D33753458 Pulled By: pdillinger fbshipit-source-id: bf892f34907a5e407d9c40bd4d42f0adbcbe0014
2022-02-01 03:45:17 +00:00
ReadOptions readoptionscopy = read_options_;
std::unique_ptr<char[]> ts_guard;
Slice ts;
if (user_timestamp_size_ > 0) {
ts_guard.reset(new char[user_timestamp_size_]);
ts = mock_app_clock_->Allocate(ts_guard.get());
readoptionscopy.timestamp = &ts;
}
readoptionscopy.snapshot = db->GetSnapshot();
Status s;
for (int i = 0; i < 3; i++) {
keys[i] = key.ToString() + suffixes[i];
key_slices[i] = keys[i];
s = db->Get(readoptionscopy, key_slices[i], value);
if (!s.ok() && !s.IsNotFound()) {
fprintf(stderr, "get error: %s\n", s.ToString().c_str());
values[i] = "";
// we continue after error rather than exiting so that we can
// find more errors if any
} else if (s.IsNotFound()) {
values[i] = "";
} else {
values[i] = *value;
}
}
db->ReleaseSnapshot(readoptionscopy.snapshot);
if ((values[0] != values[1]) || (values[1] != values[2])) {
fprintf(stderr, "inconsistent values for key %s: %s, %s, %s\n",
key.ToString().c_str(), values[0].c_str(), values[1].c_str(),
values[2].c_str());
// we continue after error rather than exiting so that we can
// find more errors if any
}
return s;
}
// Differs from readrandomwriterandom in the following ways:
// (a) Uses GetMany/PutMany to read/write key values. Refer to those funcs.
// (b) Does deletes as well (per FLAGS_deletepercent)
// (c) In order to achieve high % of 'found' during lookups, and to do
// multiple writes (including puts and deletes) it uses upto
// FLAGS_numdistinct distinct keys instead of FLAGS_num distinct keys.
// (d) Does not have a MultiGet option.
void RandomWithVerify(ThreadState* thread) {
RandomGenerator gen;
std::string value;
int64_t found = 0;
int get_weight = 0;
int put_weight = 0;
int delete_weight = 0;
int64_t gets_done = 0;
int64_t puts_done = 0;
int64_t deletes_done = 0;
std::unique_ptr<const char[]> key_guard;
Slice key = AllocateKey(&key_guard);
// the number of iterations is the larger of read_ or write_
for (int64_t i = 0; i < readwrites_; i++) {
DB* db = SelectDB(thread);
if (get_weight == 0 && put_weight == 0 && delete_weight == 0) {
// one batch completed, reinitialize for next batch
get_weight = FLAGS_readwritepercent;
delete_weight = FLAGS_deletepercent;
put_weight = 100 - get_weight - delete_weight;
}
GenerateKeyFromInt(thread->rand.Next() % FLAGS_numdistinct,
FLAGS_numdistinct, &key);
if (get_weight > 0) {
// do all the gets first
Ignore `total_order_seek` in DB::Get (#9427) Summary: Apparently setting total_order_seek=true for DB::Get was intended to allow accurate read semantics if the current prefix extractor doesn't match what was used to generate SST files on disk. But since prefix_extractor was made a mutable option in 5.14.0, we have been able to detect this case and provide the correct semantics regardless of the total_order_seek option. Since that time, the option has only made Get() slower in a reasonably common case: prefix_extractor unchanged and whole_key_filtering=false. So this change primarily removes unnecessary effect of total_order_seek on Get. Also cleans up some related comments. Also adds a -total_order_seek option to db_bench and canonicalizes handling of ReadOptions in db_bench so that command line options have the expected association with library features. (There is potential for change in regression test behavior, but the old behavior is likely indefensible, or some other inconsistency would need to be fixed.) TODO in follow-up work: there should be no reason for Get() to depend on current prefix extractor at all. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9427 Test Plan: Unit tests updated. Performance (using db_bench update) Create DB with `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -benchmarks=fillrandom -num=10000000 -disable_wal=1 -write_buffer_size=10000000 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12 -whole_key_filtering=0` Test with and without `-total_order_seek` on `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -use_existing_db -readonly -benchmarks=readrandom -num=10000000 -duration=40 -disable_wal=1 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12` Before this change, total_order_seek=false: 25188 ops/sec Before this change, total_order_seek=true: 1222 ops/sec (~20x slower) After this change, total_order_seek=false: 24570 ops/sec After this change, total_order_seek=true: 25012 ops/sec (indistinguishable) Reviewed By: siying Differential Revision: D33753458 Pulled By: pdillinger fbshipit-source-id: bf892f34907a5e407d9c40bd4d42f0adbcbe0014
2022-02-01 03:45:17 +00:00
Status s = GetMany(db, key, &value);
if (!s.ok() && !s.IsNotFound()) {
fprintf(stderr, "getmany error: %s\n", s.ToString().c_str());
// we continue after error rather than exiting so that we can
// find more errors if any
} else if (!s.IsNotFound()) {
found++;
}
get_weight--;
gets_done++;
thread->stats.FinishedOps(&db_, db_.db, 1, kRead);
} else if (put_weight > 0) {
// then do all the corresponding number of puts
// for all the gets we have done earlier
Status s = PutMany(db, write_options_, key, gen.Generate());
if (!s.ok()) {
fprintf(stderr, "putmany error: %s\n", s.ToString().c_str());
exit(1);
}
put_weight--;
puts_done++;
thread->stats.FinishedOps(&db_, db_.db, 1, kWrite);
} else if (delete_weight > 0) {
Status s = DeleteMany(db, write_options_, key);
if (!s.ok()) {
fprintf(stderr, "deletemany error: %s\n", s.ToString().c_str());
exit(1);
}
delete_weight--;
deletes_done++;
thread->stats.FinishedOps(&db_, db_.db, 1, kDelete);
}
}
char msg[128];
Pull from https://reviews.facebook.net/D10917 Summary: Pull Mark's patch and slightly revise it. I revised another place in db_impl.cc with similar new formula. Test Plan: make all check. Also run "time ./db_bench --num=2500000000 --numdistinct=2200000000". It has run for 20+ hours and hasn't finished. Looks good so far: Installed stack trace handler for SIGILL SIGSEGV SIGBUS SIGABRT LevelDB: version 2.0 Date: Tue Aug 20 23:11:55 2013 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 2500000000 RawSize: 276565.6 MB (estimated) FileSize: 157356.3 MB (estimated) Write rate limit: 0 Compression: snappy WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ DB path: [/tmp/leveldbtest-3088/dbbench] fillseq : 7202.000 micros/op 138 ops/sec; DB path: [/tmp/leveldbtest-3088/dbbench] fillsync : 7148.000 micros/op 139 ops/sec; (2500000 ops) DB path: [/tmp/leveldbtest-3088/dbbench] fillrandom : 7105.000 micros/op 140 ops/sec; DB path: [/tmp/leveldbtest-3088/dbbench] overwrite : 6930.000 micros/op 144 ops/sec; DB path: [/tmp/leveldbtest-3088/dbbench] readrandom : 1.020 micros/op 980507 ops/sec; (0 of 2500000000 found) DB path: [/tmp/leveldbtest-3088/dbbench] readrandom : 1.021 micros/op 979620 ops/sec; (0 of 2500000000 found) DB path: [/tmp/leveldbtest-3088/dbbench] readseq : 113.000 micros/op 8849 ops/sec; DB path: [/tmp/leveldbtest-3088/dbbench] readreverse : 102.000 micros/op 9803 ops/sec; DB path: [/tmp/leveldbtest-3088/dbbench] Created bg thread 0x7f0ac17f7700 compact : 111701.000 micros/op 8 ops/sec; DB path: [/tmp/leveldbtest-3088/dbbench] readrandom : 1.020 micros/op 980376 ops/sec; (0 of 2500000000 found) DB path: [/tmp/leveldbtest-3088/dbbench] readseq : 120.000 micros/op 8333 ops/sec; DB path: [/tmp/leveldbtest-3088/dbbench] readreverse : 29.000 micros/op 34482 ops/sec; DB path: [/tmp/leveldbtest-3088/dbbench] ... finished 618100000 ops Reviewers: MarkCallaghan, haobo, dhruba, chip Reviewed By: dhruba Differential Revision: https://reviews.facebook.net/D12441
2013-08-23 05:37:13 +00:00
snprintf(msg, sizeof(msg),
"( get:%" PRIu64 " put:%" PRIu64 " del:%" PRIu64 " total:%" PRIu64
" found:%" PRIu64 ")",
gets_done, puts_done, deletes_done, readwrites_, found);
thread->stats.AddMessage(msg);
}
// This is different from ReadWhileWriting because it does not use
// an extra thread.
void ReadRandomWriteRandom(ThreadState* thread) {
Ignore `total_order_seek` in DB::Get (#9427) Summary: Apparently setting total_order_seek=true for DB::Get was intended to allow accurate read semantics if the current prefix extractor doesn't match what was used to generate SST files on disk. But since prefix_extractor was made a mutable option in 5.14.0, we have been able to detect this case and provide the correct semantics regardless of the total_order_seek option. Since that time, the option has only made Get() slower in a reasonably common case: prefix_extractor unchanged and whole_key_filtering=false. So this change primarily removes unnecessary effect of total_order_seek on Get. Also cleans up some related comments. Also adds a -total_order_seek option to db_bench and canonicalizes handling of ReadOptions in db_bench so that command line options have the expected association with library features. (There is potential for change in regression test behavior, but the old behavior is likely indefensible, or some other inconsistency would need to be fixed.) TODO in follow-up work: there should be no reason for Get() to depend on current prefix extractor at all. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9427 Test Plan: Unit tests updated. Performance (using db_bench update) Create DB with `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -benchmarks=fillrandom -num=10000000 -disable_wal=1 -write_buffer_size=10000000 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12 -whole_key_filtering=0` Test with and without `-total_order_seek` on `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -use_existing_db -readonly -benchmarks=readrandom -num=10000000 -duration=40 -disable_wal=1 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12` Before this change, total_order_seek=false: 25188 ops/sec Before this change, total_order_seek=true: 1222 ops/sec (~20x slower) After this change, total_order_seek=false: 24570 ops/sec After this change, total_order_seek=true: 25012 ops/sec (indistinguishable) Reviewed By: siying Differential Revision: D33753458 Pulled By: pdillinger fbshipit-source-id: bf892f34907a5e407d9c40bd4d42f0adbcbe0014
2022-02-01 03:45:17 +00:00
ReadOptions options = read_options_;
RandomGenerator gen;
std::string value;
int64_t found = 0;
int get_weight = 0;
int put_weight = 0;
int64_t reads_done = 0;
int64_t writes_done = 0;
Duration duration(FLAGS_duration, readwrites_);
std::unique_ptr<const char[]> key_guard;
Slice key = AllocateKey(&key_guard);
std::unique_ptr<char[]> ts_guard;
if (user_timestamp_size_ > 0) {
ts_guard.reset(new char[user_timestamp_size_]);
}
// the number of iterations is the larger of read_ or write_
while (!duration.Done(1)) {
DB* db = SelectDB(thread);
GenerateKeyFromInt(thread->rand.Next() % FLAGS_num, FLAGS_num, &key);
if (get_weight == 0 && put_weight == 0) {
// one batch completed, reinitialize for next batch
get_weight = FLAGS_readwritepercent;
put_weight = 100 - get_weight;
}
if (get_weight > 0) {
// do all the gets first
Slice ts;
if (user_timestamp_size_ > 0) {
ts = mock_app_clock_->GetTimestampForRead(thread->rand,
ts_guard.get());
options.timestamp = &ts;
}
Status s = db->Get(options, key, &value);
if (!s.ok() && !s.IsNotFound()) {
fprintf(stderr, "get error: %s\n", s.ToString().c_str());
// we continue after error rather than exiting so that we can
// find more errors if any
} else if (!s.IsNotFound()) {
found++;
}
get_weight--;
reads_done++;
thread->stats.FinishedOps(nullptr, db, 1, kRead);
} else if (put_weight > 0) {
// then do all the corresponding number of puts
// for all the gets we have done earlier
Revise APIs related to user-defined timestamp (#8946) Summary: ajkr reminded me that we have a rule of not including per-kv related data in `WriteOptions`. Namely, `WriteOptions` should not include information about "what-to-write", but should just include information about "how-to-write". According to this rule, `WriteOptions::timestamp` (experimental) is clearly a violation. Therefore, this PR removes `WriteOptions::timestamp` for compliance. After the removal, we need to pass timestamp info via another set of APIs. This PR proposes a set of overloaded functions `Put(write_opts, key, value, ts)`, `Delete(write_opts, key, ts)`, and `SingleDelete(write_opts, key, ts)`. Planned to add `Write(write_opts, batch, ts)`, but its complexity made me reconsider doing it in another PR (maybe). For better checking and returning error early, we also add a new set of APIs to `WriteBatch` that take extra `timestamp` information when writing to `WriteBatch`es. These set of APIs in `WriteBatchWithIndex` are currently not supported, and are on our TODO list. Removed `WriteBatch::AssignTimestamps()` and renamed `WriteBatch::AssignTimestamp()` to `WriteBatch::UpdateTimestamps()` since this method require that all keys have space for timestamps allocated already and multiple timestamps can be updated. The constructor of `WriteBatch` now takes a fourth argument `default_cf_ts_sz` which is the timestamp size of the default column family. This will be used to allocate space when calling APIs that do not specify a column family handle. Also, updated `DB::Get()`, `DB::MultiGet()`, `DB::NewIterator()`, `DB::NewIterators()` methods, replacing some assertions about timestamp to returning Status code. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8946 Test Plan: make check ./db_bench -benchmarks=fillseq,fillrandom,readrandom,readseq,deleterandom -user_timestamp_size=8 ./db_stress --user_timestamp_size=8 -nooverwritepercent=0 -test_secondary=0 -secondary_catch_up_one_in=0 -continuous_verification_interval=0 Make sure there is no perf regression by running the following ``` ./db_bench_opt -db=/dev/shm/rocksdb -use_existing_db=0 -level0_stop_writes_trigger=256 -level0_slowdown_writes_trigger=256 -level0_file_num_compaction_trigger=256 -disable_wal=1 -duration=10 -benchmarks=fillrandom ``` Before this PR ``` DB path: [/dev/shm/rocksdb] fillrandom : 1.831 micros/op 546235 ops/sec; 60.4 MB/s ``` After this PR ``` DB path: [/dev/shm/rocksdb] fillrandom : 1.820 micros/op 549404 ops/sec; 60.8 MB/s ``` Reviewed By: ltamasi Differential Revision: D33721359 Pulled By: riversand963 fbshipit-source-id: c131561534272c120ffb80711d42748d21badf09
2022-02-02 06:17:46 +00:00
Status s;
if (user_timestamp_size_ > 0) {
Revise APIs related to user-defined timestamp (#8946) Summary: ajkr reminded me that we have a rule of not including per-kv related data in `WriteOptions`. Namely, `WriteOptions` should not include information about "what-to-write", but should just include information about "how-to-write". According to this rule, `WriteOptions::timestamp` (experimental) is clearly a violation. Therefore, this PR removes `WriteOptions::timestamp` for compliance. After the removal, we need to pass timestamp info via another set of APIs. This PR proposes a set of overloaded functions `Put(write_opts, key, value, ts)`, `Delete(write_opts, key, ts)`, and `SingleDelete(write_opts, key, ts)`. Planned to add `Write(write_opts, batch, ts)`, but its complexity made me reconsider doing it in another PR (maybe). For better checking and returning error early, we also add a new set of APIs to `WriteBatch` that take extra `timestamp` information when writing to `WriteBatch`es. These set of APIs in `WriteBatchWithIndex` are currently not supported, and are on our TODO list. Removed `WriteBatch::AssignTimestamps()` and renamed `WriteBatch::AssignTimestamp()` to `WriteBatch::UpdateTimestamps()` since this method require that all keys have space for timestamps allocated already and multiple timestamps can be updated. The constructor of `WriteBatch` now takes a fourth argument `default_cf_ts_sz` which is the timestamp size of the default column family. This will be used to allocate space when calling APIs that do not specify a column family handle. Also, updated `DB::Get()`, `DB::MultiGet()`, `DB::NewIterator()`, `DB::NewIterators()` methods, replacing some assertions about timestamp to returning Status code. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8946 Test Plan: make check ./db_bench -benchmarks=fillseq,fillrandom,readrandom,readseq,deleterandom -user_timestamp_size=8 ./db_stress --user_timestamp_size=8 -nooverwritepercent=0 -test_secondary=0 -secondary_catch_up_one_in=0 -continuous_verification_interval=0 Make sure there is no perf regression by running the following ``` ./db_bench_opt -db=/dev/shm/rocksdb -use_existing_db=0 -level0_stop_writes_trigger=256 -level0_slowdown_writes_trigger=256 -level0_file_num_compaction_trigger=256 -disable_wal=1 -duration=10 -benchmarks=fillrandom ``` Before this PR ``` DB path: [/dev/shm/rocksdb] fillrandom : 1.831 micros/op 546235 ops/sec; 60.4 MB/s ``` After this PR ``` DB path: [/dev/shm/rocksdb] fillrandom : 1.820 micros/op 549404 ops/sec; 60.8 MB/s ``` Reviewed By: ltamasi Differential Revision: D33721359 Pulled By: riversand963 fbshipit-source-id: c131561534272c120ffb80711d42748d21badf09
2022-02-02 06:17:46 +00:00
Slice ts = mock_app_clock_->Allocate(ts_guard.get());
s = db->Put(write_options_, key, ts, gen.Generate());
} else {
s = db->Put(write_options_, key, gen.Generate());
}
if (!s.ok()) {
fprintf(stderr, "put error: %s\n", s.ToString().c_str());
ErrorExit();
}
put_weight--;
writes_done++;
thread->stats.FinishedOps(nullptr, db, 1, kWrite);
}
}
char msg[100];
snprintf(msg, sizeof(msg),
"( reads:%" PRIu64 " writes:%" PRIu64 " total:%" PRIu64
" found:%" PRIu64 ")",
reads_done, writes_done, readwrites_, found);
thread->stats.AddMessage(msg);
}
//
// Read-modify-write for random keys
void UpdateRandom(ThreadState* thread) {
Ignore `total_order_seek` in DB::Get (#9427) Summary: Apparently setting total_order_seek=true for DB::Get was intended to allow accurate read semantics if the current prefix extractor doesn't match what was used to generate SST files on disk. But since prefix_extractor was made a mutable option in 5.14.0, we have been able to detect this case and provide the correct semantics regardless of the total_order_seek option. Since that time, the option has only made Get() slower in a reasonably common case: prefix_extractor unchanged and whole_key_filtering=false. So this change primarily removes unnecessary effect of total_order_seek on Get. Also cleans up some related comments. Also adds a -total_order_seek option to db_bench and canonicalizes handling of ReadOptions in db_bench so that command line options have the expected association with library features. (There is potential for change in regression test behavior, but the old behavior is likely indefensible, or some other inconsistency would need to be fixed.) TODO in follow-up work: there should be no reason for Get() to depend on current prefix extractor at all. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9427 Test Plan: Unit tests updated. Performance (using db_bench update) Create DB with `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -benchmarks=fillrandom -num=10000000 -disable_wal=1 -write_buffer_size=10000000 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12 -whole_key_filtering=0` Test with and without `-total_order_seek` on `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -use_existing_db -readonly -benchmarks=readrandom -num=10000000 -duration=40 -disable_wal=1 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12` Before this change, total_order_seek=false: 25188 ops/sec Before this change, total_order_seek=true: 1222 ops/sec (~20x slower) After this change, total_order_seek=false: 24570 ops/sec After this change, total_order_seek=true: 25012 ops/sec (indistinguishable) Reviewed By: siying Differential Revision: D33753458 Pulled By: pdillinger fbshipit-source-id: bf892f34907a5e407d9c40bd4d42f0adbcbe0014
2022-02-01 03:45:17 +00:00
ReadOptions options = read_options_;
RandomGenerator gen;
std::string value;
int64_t found = 0;
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
2015-03-30 18:28:25 +00:00
int64_t bytes = 0;
Duration duration(FLAGS_duration, readwrites_);
std::unique_ptr<const char[]> key_guard;
Slice key = AllocateKey(&key_guard);
std::unique_ptr<char[]> ts_guard;
if (user_timestamp_size_ > 0) {
ts_guard.reset(new char[user_timestamp_size_]);
}
// the number of iterations is the larger of read_ or write_
while (!duration.Done(1)) {
DB* db = SelectDB(thread);
GenerateKeyFromInt(thread->rand.Next() % FLAGS_num, FLAGS_num, &key);
Slice ts;
if (user_timestamp_size_ > 0) {
// Read with newest timestamp because we are doing rmw.
ts = mock_app_clock_->Allocate(ts_guard.get());
options.timestamp = &ts;
}
auto status = db->Get(options, key, &value);
if (status.ok()) {
++found;
bytes += key.size() + value.size() + user_timestamp_size_;
} else if (!status.IsNotFound()) {
2015-01-22 02:23:12 +00:00
fprintf(stderr, "Get returned an error: %s\n",
status.ToString().c_str());
abort();
}
if (thread->shared->write_rate_limiter) {
thread->shared->write_rate_limiter->Request(
key.size() + value.size(), Env::IO_HIGH, nullptr /*stats*/,
RateLimiter::OpType::kWrite);
}
Slice val = gen.Generate();
Revise APIs related to user-defined timestamp (#8946) Summary: ajkr reminded me that we have a rule of not including per-kv related data in `WriteOptions`. Namely, `WriteOptions` should not include information about "what-to-write", but should just include information about "how-to-write". According to this rule, `WriteOptions::timestamp` (experimental) is clearly a violation. Therefore, this PR removes `WriteOptions::timestamp` for compliance. After the removal, we need to pass timestamp info via another set of APIs. This PR proposes a set of overloaded functions `Put(write_opts, key, value, ts)`, `Delete(write_opts, key, ts)`, and `SingleDelete(write_opts, key, ts)`. Planned to add `Write(write_opts, batch, ts)`, but its complexity made me reconsider doing it in another PR (maybe). For better checking and returning error early, we also add a new set of APIs to `WriteBatch` that take extra `timestamp` information when writing to `WriteBatch`es. These set of APIs in `WriteBatchWithIndex` are currently not supported, and are on our TODO list. Removed `WriteBatch::AssignTimestamps()` and renamed `WriteBatch::AssignTimestamp()` to `WriteBatch::UpdateTimestamps()` since this method require that all keys have space for timestamps allocated already and multiple timestamps can be updated. The constructor of `WriteBatch` now takes a fourth argument `default_cf_ts_sz` which is the timestamp size of the default column family. This will be used to allocate space when calling APIs that do not specify a column family handle. Also, updated `DB::Get()`, `DB::MultiGet()`, `DB::NewIterator()`, `DB::NewIterators()` methods, replacing some assertions about timestamp to returning Status code. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8946 Test Plan: make check ./db_bench -benchmarks=fillseq,fillrandom,readrandom,readseq,deleterandom -user_timestamp_size=8 ./db_stress --user_timestamp_size=8 -nooverwritepercent=0 -test_secondary=0 -secondary_catch_up_one_in=0 -continuous_verification_interval=0 Make sure there is no perf regression by running the following ``` ./db_bench_opt -db=/dev/shm/rocksdb -use_existing_db=0 -level0_stop_writes_trigger=256 -level0_slowdown_writes_trigger=256 -level0_file_num_compaction_trigger=256 -disable_wal=1 -duration=10 -benchmarks=fillrandom ``` Before this PR ``` DB path: [/dev/shm/rocksdb] fillrandom : 1.831 micros/op 546235 ops/sec; 60.4 MB/s ``` After this PR ``` DB path: [/dev/shm/rocksdb] fillrandom : 1.820 micros/op 549404 ops/sec; 60.8 MB/s ``` Reviewed By: ltamasi Differential Revision: D33721359 Pulled By: riversand963 fbshipit-source-id: c131561534272c120ffb80711d42748d21badf09
2022-02-02 06:17:46 +00:00
Status s;
if (user_timestamp_size_ > 0) {
ts = mock_app_clock_->Allocate(ts_guard.get());
Revise APIs related to user-defined timestamp (#8946) Summary: ajkr reminded me that we have a rule of not including per-kv related data in `WriteOptions`. Namely, `WriteOptions` should not include information about "what-to-write", but should just include information about "how-to-write". According to this rule, `WriteOptions::timestamp` (experimental) is clearly a violation. Therefore, this PR removes `WriteOptions::timestamp` for compliance. After the removal, we need to pass timestamp info via another set of APIs. This PR proposes a set of overloaded functions `Put(write_opts, key, value, ts)`, `Delete(write_opts, key, ts)`, and `SingleDelete(write_opts, key, ts)`. Planned to add `Write(write_opts, batch, ts)`, but its complexity made me reconsider doing it in another PR (maybe). For better checking and returning error early, we also add a new set of APIs to `WriteBatch` that take extra `timestamp` information when writing to `WriteBatch`es. These set of APIs in `WriteBatchWithIndex` are currently not supported, and are on our TODO list. Removed `WriteBatch::AssignTimestamps()` and renamed `WriteBatch::AssignTimestamp()` to `WriteBatch::UpdateTimestamps()` since this method require that all keys have space for timestamps allocated already and multiple timestamps can be updated. The constructor of `WriteBatch` now takes a fourth argument `default_cf_ts_sz` which is the timestamp size of the default column family. This will be used to allocate space when calling APIs that do not specify a column family handle. Also, updated `DB::Get()`, `DB::MultiGet()`, `DB::NewIterator()`, `DB::NewIterators()` methods, replacing some assertions about timestamp to returning Status code. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8946 Test Plan: make check ./db_bench -benchmarks=fillseq,fillrandom,readrandom,readseq,deleterandom -user_timestamp_size=8 ./db_stress --user_timestamp_size=8 -nooverwritepercent=0 -test_secondary=0 -secondary_catch_up_one_in=0 -continuous_verification_interval=0 Make sure there is no perf regression by running the following ``` ./db_bench_opt -db=/dev/shm/rocksdb -use_existing_db=0 -level0_stop_writes_trigger=256 -level0_slowdown_writes_trigger=256 -level0_file_num_compaction_trigger=256 -disable_wal=1 -duration=10 -benchmarks=fillrandom ``` Before this PR ``` DB path: [/dev/shm/rocksdb] fillrandom : 1.831 micros/op 546235 ops/sec; 60.4 MB/s ``` After this PR ``` DB path: [/dev/shm/rocksdb] fillrandom : 1.820 micros/op 549404 ops/sec; 60.8 MB/s ``` Reviewed By: ltamasi Differential Revision: D33721359 Pulled By: riversand963 fbshipit-source-id: c131561534272c120ffb80711d42748d21badf09
2022-02-02 06:17:46 +00:00
s = db->Put(write_options_, key, ts, val);
} else {
s = db->Put(write_options_, key, val);
}
if (!s.ok()) {
fprintf(stderr, "put error: %s\n", s.ToString().c_str());
exit(1);
}
bytes += key.size() + val.size() + user_timestamp_size_;
thread->stats.FinishedOps(nullptr, db, 1, kUpdate);
}
char msg[100];
snprintf(msg, sizeof(msg), "( updates:%" PRIu64 " found:%" PRIu64 ")",
readwrites_, found);
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
2015-03-30 18:28:25 +00:00
thread->stats.AddBytes(bytes);
thread->stats.AddMessage(msg);
}
// Read-XOR-write for random keys. Xors the existing value with a randomly
// generated value, and stores the result. Assuming A in the array of bytes
// representing the existing value, we generate an array B of the same size,
// then compute C = A^B as C[i]=A[i]^B[i], and store C
void XORUpdateRandom(ThreadState* thread) {
Ignore `total_order_seek` in DB::Get (#9427) Summary: Apparently setting total_order_seek=true for DB::Get was intended to allow accurate read semantics if the current prefix extractor doesn't match what was used to generate SST files on disk. But since prefix_extractor was made a mutable option in 5.14.0, we have been able to detect this case and provide the correct semantics regardless of the total_order_seek option. Since that time, the option has only made Get() slower in a reasonably common case: prefix_extractor unchanged and whole_key_filtering=false. So this change primarily removes unnecessary effect of total_order_seek on Get. Also cleans up some related comments. Also adds a -total_order_seek option to db_bench and canonicalizes handling of ReadOptions in db_bench so that command line options have the expected association with library features. (There is potential for change in regression test behavior, but the old behavior is likely indefensible, or some other inconsistency would need to be fixed.) TODO in follow-up work: there should be no reason for Get() to depend on current prefix extractor at all. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9427 Test Plan: Unit tests updated. Performance (using db_bench update) Create DB with `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -benchmarks=fillrandom -num=10000000 -disable_wal=1 -write_buffer_size=10000000 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12 -whole_key_filtering=0` Test with and without `-total_order_seek` on `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -use_existing_db -readonly -benchmarks=readrandom -num=10000000 -duration=40 -disable_wal=1 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12` Before this change, total_order_seek=false: 25188 ops/sec Before this change, total_order_seek=true: 1222 ops/sec (~20x slower) After this change, total_order_seek=false: 24570 ops/sec After this change, total_order_seek=true: 25012 ops/sec (indistinguishable) Reviewed By: siying Differential Revision: D33753458 Pulled By: pdillinger fbshipit-source-id: bf892f34907a5e407d9c40bd4d42f0adbcbe0014
2022-02-01 03:45:17 +00:00
ReadOptions options = read_options_;
RandomGenerator gen;
std::string existing_value;
int64_t found = 0;
Duration duration(FLAGS_duration, readwrites_);
BytesXOROperator xor_operator;
std::unique_ptr<const char[]> key_guard;
Slice key = AllocateKey(&key_guard);
std::unique_ptr<char[]> ts_guard;
if (user_timestamp_size_ > 0) {
ts_guard.reset(new char[user_timestamp_size_]);
}
// the number of iterations is the larger of read_ or write_
while (!duration.Done(1)) {
DB* db = SelectDB(thread);
GenerateKeyFromInt(thread->rand.Next() % FLAGS_num, FLAGS_num, &key);
Slice ts;
if (user_timestamp_size_ > 0) {
ts = mock_app_clock_->Allocate(ts_guard.get());
options.timestamp = &ts;
}
auto status = db->Get(options, key, &existing_value);
if (status.ok()) {
++found;
} else if (!status.IsNotFound()) {
fprintf(stderr, "Get returned an error: %s\n",
status.ToString().c_str());
exit(1);
}
Slice value =
gen.Generate(static_cast<unsigned int>(existing_value.size()));
std::string new_value;
if (status.ok()) {
Slice existing_value_slice = Slice(existing_value);
xor_operator.XOR(&existing_value_slice, value, &new_value);
} else {
xor_operator.XOR(nullptr, value, &new_value);
}
Revise APIs related to user-defined timestamp (#8946) Summary: ajkr reminded me that we have a rule of not including per-kv related data in `WriteOptions`. Namely, `WriteOptions` should not include information about "what-to-write", but should just include information about "how-to-write". According to this rule, `WriteOptions::timestamp` (experimental) is clearly a violation. Therefore, this PR removes `WriteOptions::timestamp` for compliance. After the removal, we need to pass timestamp info via another set of APIs. This PR proposes a set of overloaded functions `Put(write_opts, key, value, ts)`, `Delete(write_opts, key, ts)`, and `SingleDelete(write_opts, key, ts)`. Planned to add `Write(write_opts, batch, ts)`, but its complexity made me reconsider doing it in another PR (maybe). For better checking and returning error early, we also add a new set of APIs to `WriteBatch` that take extra `timestamp` information when writing to `WriteBatch`es. These set of APIs in `WriteBatchWithIndex` are currently not supported, and are on our TODO list. Removed `WriteBatch::AssignTimestamps()` and renamed `WriteBatch::AssignTimestamp()` to `WriteBatch::UpdateTimestamps()` since this method require that all keys have space for timestamps allocated already and multiple timestamps can be updated. The constructor of `WriteBatch` now takes a fourth argument `default_cf_ts_sz` which is the timestamp size of the default column family. This will be used to allocate space when calling APIs that do not specify a column family handle. Also, updated `DB::Get()`, `DB::MultiGet()`, `DB::NewIterator()`, `DB::NewIterators()` methods, replacing some assertions about timestamp to returning Status code. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8946 Test Plan: make check ./db_bench -benchmarks=fillseq,fillrandom,readrandom,readseq,deleterandom -user_timestamp_size=8 ./db_stress --user_timestamp_size=8 -nooverwritepercent=0 -test_secondary=0 -secondary_catch_up_one_in=0 -continuous_verification_interval=0 Make sure there is no perf regression by running the following ``` ./db_bench_opt -db=/dev/shm/rocksdb -use_existing_db=0 -level0_stop_writes_trigger=256 -level0_slowdown_writes_trigger=256 -level0_file_num_compaction_trigger=256 -disable_wal=1 -duration=10 -benchmarks=fillrandom ``` Before this PR ``` DB path: [/dev/shm/rocksdb] fillrandom : 1.831 micros/op 546235 ops/sec; 60.4 MB/s ``` After this PR ``` DB path: [/dev/shm/rocksdb] fillrandom : 1.820 micros/op 549404 ops/sec; 60.8 MB/s ``` Reviewed By: ltamasi Differential Revision: D33721359 Pulled By: riversand963 fbshipit-source-id: c131561534272c120ffb80711d42748d21badf09
2022-02-02 06:17:46 +00:00
Status s;
if (user_timestamp_size_ > 0) {
ts = mock_app_clock_->Allocate(ts_guard.get());
Revise APIs related to user-defined timestamp (#8946) Summary: ajkr reminded me that we have a rule of not including per-kv related data in `WriteOptions`. Namely, `WriteOptions` should not include information about "what-to-write", but should just include information about "how-to-write". According to this rule, `WriteOptions::timestamp` (experimental) is clearly a violation. Therefore, this PR removes `WriteOptions::timestamp` for compliance. After the removal, we need to pass timestamp info via another set of APIs. This PR proposes a set of overloaded functions `Put(write_opts, key, value, ts)`, `Delete(write_opts, key, ts)`, and `SingleDelete(write_opts, key, ts)`. Planned to add `Write(write_opts, batch, ts)`, but its complexity made me reconsider doing it in another PR (maybe). For better checking and returning error early, we also add a new set of APIs to `WriteBatch` that take extra `timestamp` information when writing to `WriteBatch`es. These set of APIs in `WriteBatchWithIndex` are currently not supported, and are on our TODO list. Removed `WriteBatch::AssignTimestamps()` and renamed `WriteBatch::AssignTimestamp()` to `WriteBatch::UpdateTimestamps()` since this method require that all keys have space for timestamps allocated already and multiple timestamps can be updated. The constructor of `WriteBatch` now takes a fourth argument `default_cf_ts_sz` which is the timestamp size of the default column family. This will be used to allocate space when calling APIs that do not specify a column family handle. Also, updated `DB::Get()`, `DB::MultiGet()`, `DB::NewIterator()`, `DB::NewIterators()` methods, replacing some assertions about timestamp to returning Status code. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8946 Test Plan: make check ./db_bench -benchmarks=fillseq,fillrandom,readrandom,readseq,deleterandom -user_timestamp_size=8 ./db_stress --user_timestamp_size=8 -nooverwritepercent=0 -test_secondary=0 -secondary_catch_up_one_in=0 -continuous_verification_interval=0 Make sure there is no perf regression by running the following ``` ./db_bench_opt -db=/dev/shm/rocksdb -use_existing_db=0 -level0_stop_writes_trigger=256 -level0_slowdown_writes_trigger=256 -level0_file_num_compaction_trigger=256 -disable_wal=1 -duration=10 -benchmarks=fillrandom ``` Before this PR ``` DB path: [/dev/shm/rocksdb] fillrandom : 1.831 micros/op 546235 ops/sec; 60.4 MB/s ``` After this PR ``` DB path: [/dev/shm/rocksdb] fillrandom : 1.820 micros/op 549404 ops/sec; 60.8 MB/s ``` Reviewed By: ltamasi Differential Revision: D33721359 Pulled By: riversand963 fbshipit-source-id: c131561534272c120ffb80711d42748d21badf09
2022-02-02 06:17:46 +00:00
s = db->Put(write_options_, key, ts, Slice(new_value));
} else {
s = db->Put(write_options_, key, Slice(new_value));
}
if (!s.ok()) {
fprintf(stderr, "put error: %s\n", s.ToString().c_str());
ErrorExit();
}
thread->stats.FinishedOps(nullptr, db, 1);
}
char msg[100];
snprintf(msg, sizeof(msg), "( updates:%" PRIu64 " found:%" PRIu64 ")",
readwrites_, found);
thread->stats.AddMessage(msg);
}
// Read-modify-write for random keys.
// Each operation causes the key grow by value_size (simulating an append).
// Generally used for benchmarking against merges of similar type
void AppendRandom(ThreadState* thread) {
Ignore `total_order_seek` in DB::Get (#9427) Summary: Apparently setting total_order_seek=true for DB::Get was intended to allow accurate read semantics if the current prefix extractor doesn't match what was used to generate SST files on disk. But since prefix_extractor was made a mutable option in 5.14.0, we have been able to detect this case and provide the correct semantics regardless of the total_order_seek option. Since that time, the option has only made Get() slower in a reasonably common case: prefix_extractor unchanged and whole_key_filtering=false. So this change primarily removes unnecessary effect of total_order_seek on Get. Also cleans up some related comments. Also adds a -total_order_seek option to db_bench and canonicalizes handling of ReadOptions in db_bench so that command line options have the expected association with library features. (There is potential for change in regression test behavior, but the old behavior is likely indefensible, or some other inconsistency would need to be fixed.) TODO in follow-up work: there should be no reason for Get() to depend on current prefix extractor at all. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9427 Test Plan: Unit tests updated. Performance (using db_bench update) Create DB with `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -benchmarks=fillrandom -num=10000000 -disable_wal=1 -write_buffer_size=10000000 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12 -whole_key_filtering=0` Test with and without `-total_order_seek` on `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -use_existing_db -readonly -benchmarks=readrandom -num=10000000 -duration=40 -disable_wal=1 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12` Before this change, total_order_seek=false: 25188 ops/sec Before this change, total_order_seek=true: 1222 ops/sec (~20x slower) After this change, total_order_seek=false: 24570 ops/sec After this change, total_order_seek=true: 25012 ops/sec (indistinguishable) Reviewed By: siying Differential Revision: D33753458 Pulled By: pdillinger fbshipit-source-id: bf892f34907a5e407d9c40bd4d42f0adbcbe0014
2022-02-01 03:45:17 +00:00
ReadOptions options = read_options_;
RandomGenerator gen;
std::string value;
int64_t found = 0;
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
2015-03-30 18:28:25 +00:00
int64_t bytes = 0;
std::unique_ptr<const char[]> key_guard;
Slice key = AllocateKey(&key_guard);
std::unique_ptr<char[]> ts_guard;
if (user_timestamp_size_ > 0) {
ts_guard.reset(new char[user_timestamp_size_]);
}
// The number of iterations is the larger of read_ or write_
Duration duration(FLAGS_duration, readwrites_);
while (!duration.Done(1)) {
DB* db = SelectDB(thread);
GenerateKeyFromInt(thread->rand.Next() % FLAGS_num, FLAGS_num, &key);
Slice ts;
if (user_timestamp_size_ > 0) {
ts = mock_app_clock_->Allocate(ts_guard.get());
options.timestamp = &ts;
}
auto status = db->Get(options, key, &value);
if (status.ok()) {
++found;
bytes += key.size() + value.size() + user_timestamp_size_;
} else if (!status.IsNotFound()) {
2015-01-22 02:23:12 +00:00
fprintf(stderr, "Get returned an error: %s\n",
status.ToString().c_str());
abort();
} else {
// If not existing, then just assume an empty string of data
value.clear();
}
// Update the value (by appending data)
Slice operand = gen.Generate();
if (value.size() > 0) {
// Use a delimiter to match the semantics for StringAppendOperator
value.append(1, ',');
}
value.append(operand.data(), operand.size());
Revise APIs related to user-defined timestamp (#8946) Summary: ajkr reminded me that we have a rule of not including per-kv related data in `WriteOptions`. Namely, `WriteOptions` should not include information about "what-to-write", but should just include information about "how-to-write". According to this rule, `WriteOptions::timestamp` (experimental) is clearly a violation. Therefore, this PR removes `WriteOptions::timestamp` for compliance. After the removal, we need to pass timestamp info via another set of APIs. This PR proposes a set of overloaded functions `Put(write_opts, key, value, ts)`, `Delete(write_opts, key, ts)`, and `SingleDelete(write_opts, key, ts)`. Planned to add `Write(write_opts, batch, ts)`, but its complexity made me reconsider doing it in another PR (maybe). For better checking and returning error early, we also add a new set of APIs to `WriteBatch` that take extra `timestamp` information when writing to `WriteBatch`es. These set of APIs in `WriteBatchWithIndex` are currently not supported, and are on our TODO list. Removed `WriteBatch::AssignTimestamps()` and renamed `WriteBatch::AssignTimestamp()` to `WriteBatch::UpdateTimestamps()` since this method require that all keys have space for timestamps allocated already and multiple timestamps can be updated. The constructor of `WriteBatch` now takes a fourth argument `default_cf_ts_sz` which is the timestamp size of the default column family. This will be used to allocate space when calling APIs that do not specify a column family handle. Also, updated `DB::Get()`, `DB::MultiGet()`, `DB::NewIterator()`, `DB::NewIterators()` methods, replacing some assertions about timestamp to returning Status code. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8946 Test Plan: make check ./db_bench -benchmarks=fillseq,fillrandom,readrandom,readseq,deleterandom -user_timestamp_size=8 ./db_stress --user_timestamp_size=8 -nooverwritepercent=0 -test_secondary=0 -secondary_catch_up_one_in=0 -continuous_verification_interval=0 Make sure there is no perf regression by running the following ``` ./db_bench_opt -db=/dev/shm/rocksdb -use_existing_db=0 -level0_stop_writes_trigger=256 -level0_slowdown_writes_trigger=256 -level0_file_num_compaction_trigger=256 -disable_wal=1 -duration=10 -benchmarks=fillrandom ``` Before this PR ``` DB path: [/dev/shm/rocksdb] fillrandom : 1.831 micros/op 546235 ops/sec; 60.4 MB/s ``` After this PR ``` DB path: [/dev/shm/rocksdb] fillrandom : 1.820 micros/op 549404 ops/sec; 60.8 MB/s ``` Reviewed By: ltamasi Differential Revision: D33721359 Pulled By: riversand963 fbshipit-source-id: c131561534272c120ffb80711d42748d21badf09
2022-02-02 06:17:46 +00:00
Status s;
if (user_timestamp_size_ > 0) {
ts = mock_app_clock_->Allocate(ts_guard.get());
Revise APIs related to user-defined timestamp (#8946) Summary: ajkr reminded me that we have a rule of not including per-kv related data in `WriteOptions`. Namely, `WriteOptions` should not include information about "what-to-write", but should just include information about "how-to-write". According to this rule, `WriteOptions::timestamp` (experimental) is clearly a violation. Therefore, this PR removes `WriteOptions::timestamp` for compliance. After the removal, we need to pass timestamp info via another set of APIs. This PR proposes a set of overloaded functions `Put(write_opts, key, value, ts)`, `Delete(write_opts, key, ts)`, and `SingleDelete(write_opts, key, ts)`. Planned to add `Write(write_opts, batch, ts)`, but its complexity made me reconsider doing it in another PR (maybe). For better checking and returning error early, we also add a new set of APIs to `WriteBatch` that take extra `timestamp` information when writing to `WriteBatch`es. These set of APIs in `WriteBatchWithIndex` are currently not supported, and are on our TODO list. Removed `WriteBatch::AssignTimestamps()` and renamed `WriteBatch::AssignTimestamp()` to `WriteBatch::UpdateTimestamps()` since this method require that all keys have space for timestamps allocated already and multiple timestamps can be updated. The constructor of `WriteBatch` now takes a fourth argument `default_cf_ts_sz` which is the timestamp size of the default column family. This will be used to allocate space when calling APIs that do not specify a column family handle. Also, updated `DB::Get()`, `DB::MultiGet()`, `DB::NewIterator()`, `DB::NewIterators()` methods, replacing some assertions about timestamp to returning Status code. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8946 Test Plan: make check ./db_bench -benchmarks=fillseq,fillrandom,readrandom,readseq,deleterandom -user_timestamp_size=8 ./db_stress --user_timestamp_size=8 -nooverwritepercent=0 -test_secondary=0 -secondary_catch_up_one_in=0 -continuous_verification_interval=0 Make sure there is no perf regression by running the following ``` ./db_bench_opt -db=/dev/shm/rocksdb -use_existing_db=0 -level0_stop_writes_trigger=256 -level0_slowdown_writes_trigger=256 -level0_file_num_compaction_trigger=256 -disable_wal=1 -duration=10 -benchmarks=fillrandom ``` Before this PR ``` DB path: [/dev/shm/rocksdb] fillrandom : 1.831 micros/op 546235 ops/sec; 60.4 MB/s ``` After this PR ``` DB path: [/dev/shm/rocksdb] fillrandom : 1.820 micros/op 549404 ops/sec; 60.8 MB/s ``` Reviewed By: ltamasi Differential Revision: D33721359 Pulled By: riversand963 fbshipit-source-id: c131561534272c120ffb80711d42748d21badf09
2022-02-02 06:17:46 +00:00
s = db->Put(write_options_, key, ts, value);
} else {
// Write back to the database
s = db->Put(write_options_, key, value);
}
if (!s.ok()) {
fprintf(stderr, "put error: %s\n", s.ToString().c_str());
ErrorExit();
}
bytes += key.size() + value.size() + user_timestamp_size_;
thread->stats.FinishedOps(nullptr, db, 1, kUpdate);
}
char msg[100];
snprintf(msg, sizeof(msg), "( updates:%" PRIu64 " found:%" PRIu64 ")",
readwrites_, found);
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
2015-03-30 18:28:25 +00:00
thread->stats.AddBytes(bytes);
thread->stats.AddMessage(msg);
}
// Read-modify-write for random keys (using MergeOperator)
// The merge operator to use should be defined by FLAGS_merge_operator
// Adjust FLAGS_value_size so that the keys are reasonable for this operator
// Assumes that the merge operator is non-null (i.e.: is well-defined)
//
// For example, use FLAGS_merge_operator="uint64add" and FLAGS_value_size=8
// to simulate random additions over 64-bit integers using merge.
//
// The number of merges on the same key can be controlled by adjusting
// FLAGS_merge_keys.
void MergeRandom(ThreadState* thread) {
RandomGenerator gen;
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
2015-03-30 18:28:25 +00:00
int64_t bytes = 0;
std::unique_ptr<const char[]> key_guard;
Slice key = AllocateKey(&key_guard);
// The number of iterations is the larger of read_ or write_
Duration duration(FLAGS_duration, readwrites_);
while (!duration.Done(1)) {
DBWithColumnFamilies* db_with_cfh = SelectDBWithCfh(thread);
int64_t key_rand = thread->rand.Next() % merge_keys_;
GenerateKeyFromInt(key_rand, merge_keys_, &key);
Status s;
Slice val = gen.Generate();
if (FLAGS_num_column_families > 1) {
s = db_with_cfh->db->Merge(write_options_,
db_with_cfh->GetCfh(key_rand), key, val);
} else {
s = db_with_cfh->db->Merge(
write_options_, db_with_cfh->db->DefaultColumnFamily(), key, val);
}
if (!s.ok()) {
fprintf(stderr, "merge error: %s\n", s.ToString().c_str());
exit(1);
}
bytes += key.size() + val.size();
thread->stats.FinishedOps(nullptr, db_with_cfh->db, 1, kMerge);
}
// Print some statistics
char msg[100];
snprintf(msg, sizeof(msg), "( updates:%" PRIu64 ")", readwrites_);
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
2015-03-30 18:28:25 +00:00
thread->stats.AddBytes(bytes);
thread->stats.AddMessage(msg);
}
// Read and merge random keys. The amount of reads and merges are controlled
// by adjusting FLAGS_num and FLAGS_mergereadpercent. The number of distinct
// keys (and thus also the number of reads and merges on the same key) can be
// adjusted with FLAGS_merge_keys.
//
// As with MergeRandom, the merge operator to use should be defined by
// FLAGS_merge_operator.
void ReadRandomMergeRandom(ThreadState* thread) {
RandomGenerator gen;
std::string value;
int64_t num_hits = 0;
int64_t num_gets = 0;
int64_t num_merges = 0;
size_t max_length = 0;
std::unique_ptr<const char[]> key_guard;
Slice key = AllocateKey(&key_guard);
// the number of iterations is the larger of read_ or write_
Duration duration(FLAGS_duration, readwrites_);
while (!duration.Done(1)) {
DB* db = SelectDB(thread);
GenerateKeyFromInt(thread->rand.Next() % merge_keys_, merge_keys_, &key);
bool do_merge = int(thread->rand.Next() % 100) < FLAGS_mergereadpercent;
if (do_merge) {
Status s = db->Merge(write_options_, key, gen.Generate());
if (!s.ok()) {
fprintf(stderr, "merge error: %s\n", s.ToString().c_str());
exit(1);
}
num_merges++;
thread->stats.FinishedOps(nullptr, db, 1, kMerge);
} else {
Ignore `total_order_seek` in DB::Get (#9427) Summary: Apparently setting total_order_seek=true for DB::Get was intended to allow accurate read semantics if the current prefix extractor doesn't match what was used to generate SST files on disk. But since prefix_extractor was made a mutable option in 5.14.0, we have been able to detect this case and provide the correct semantics regardless of the total_order_seek option. Since that time, the option has only made Get() slower in a reasonably common case: prefix_extractor unchanged and whole_key_filtering=false. So this change primarily removes unnecessary effect of total_order_seek on Get. Also cleans up some related comments. Also adds a -total_order_seek option to db_bench and canonicalizes handling of ReadOptions in db_bench so that command line options have the expected association with library features. (There is potential for change in regression test behavior, but the old behavior is likely indefensible, or some other inconsistency would need to be fixed.) TODO in follow-up work: there should be no reason for Get() to depend on current prefix extractor at all. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9427 Test Plan: Unit tests updated. Performance (using db_bench update) Create DB with `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -benchmarks=fillrandom -num=10000000 -disable_wal=1 -write_buffer_size=10000000 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12 -whole_key_filtering=0` Test with and without `-total_order_seek` on `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -use_existing_db -readonly -benchmarks=readrandom -num=10000000 -duration=40 -disable_wal=1 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12` Before this change, total_order_seek=false: 25188 ops/sec Before this change, total_order_seek=true: 1222 ops/sec (~20x slower) After this change, total_order_seek=false: 24570 ops/sec After this change, total_order_seek=true: 25012 ops/sec (indistinguishable) Reviewed By: siying Differential Revision: D33753458 Pulled By: pdillinger fbshipit-source-id: bf892f34907a5e407d9c40bd4d42f0adbcbe0014
2022-02-01 03:45:17 +00:00
Status s = db->Get(read_options_, key, &value);
if (value.length() > max_length) {
max_length = value.length();
}
if (!s.ok() && !s.IsNotFound()) {
fprintf(stderr, "get error: %s\n", s.ToString().c_str());
// we continue after error rather than exiting so that we can
// find more errors if any
} else if (!s.IsNotFound()) {
num_hits++;
}
num_gets++;
thread->stats.FinishedOps(nullptr, db, 1, kRead);
}
}
char msg[100];
snprintf(msg, sizeof(msg),
"(reads:%" PRIu64 " merges:%" PRIu64 " total:%" PRIu64
" hits:%" PRIu64 " maxlength:%" ROCKSDB_PRIszt ")",
num_gets, num_merges, readwrites_, num_hits, max_length);
thread->stats.AddMessage(msg);
}
SkipListRep::LookaheadIterator Summary: This diff introduces the `lookahead` argument to `SkipListFactory()`. This is an optimization for the tailing use case which includes many seeks. E.g. consider the following operations on a skip list iterator: Seek(x), Next(), Next(), Seek(x+2), Next(), Seek(x+3), Next(), Next(), ... If `lookahead` is positive, `SkipListRep` will return an iterator which also keeps track of the previously visited node. Seek() then first does a linear search starting from that node (up to `lookahead` steps). As in the tailing example above, this may require fewer than ~log(n) comparisons as with regular skip list search. Test Plan: Added a new benchmark (`fillseekseq`) which simulates the usage pattern. It first writes N records (with consecutive keys), then measures how much time it takes to read them by calling `Seek()` and `Next()`. $ time ./db_bench -num 10000000 -benchmarks fillseekseq -prefix_size 1 \ -key_size 8 -write_buffer_size $[1024*1024*1024] -value_size 50 \ -seekseq_next 2 -skip_list_lookahead=0 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.389 micros/op 2569047 ops/sec; real 0m21.806s user 0m12.106s sys 0m9.672s $ time ./db_bench [...] -skip_list_lookahead=2 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.153 micros/op 6540684 ops/sec; real 0m19.469s user 0m10.192s sys 0m9.252s Reviewers: ljin, sdong, igor Reviewed By: igor Subscribers: dhruba, leveldb, march, lovro Differential Revision: https://reviews.facebook.net/D23997
2014-09-23 22:52:28 +00:00
void WriteSeqSeekSeq(ThreadState* thread) {
writes_ = FLAGS_num;
DoWrite(thread, SEQUENTIAL);
// exclude writes from the ops/sec calculation
thread->stats.Start(thread->tid);
DB* db = SelectDB(thread);
Ignore `total_order_seek` in DB::Get (#9427) Summary: Apparently setting total_order_seek=true for DB::Get was intended to allow accurate read semantics if the current prefix extractor doesn't match what was used to generate SST files on disk. But since prefix_extractor was made a mutable option in 5.14.0, we have been able to detect this case and provide the correct semantics regardless of the total_order_seek option. Since that time, the option has only made Get() slower in a reasonably common case: prefix_extractor unchanged and whole_key_filtering=false. So this change primarily removes unnecessary effect of total_order_seek on Get. Also cleans up some related comments. Also adds a -total_order_seek option to db_bench and canonicalizes handling of ReadOptions in db_bench so that command line options have the expected association with library features. (There is potential for change in regression test behavior, but the old behavior is likely indefensible, or some other inconsistency would need to be fixed.) TODO in follow-up work: there should be no reason for Get() to depend on current prefix extractor at all. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9427 Test Plan: Unit tests updated. Performance (using db_bench update) Create DB with `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -benchmarks=fillrandom -num=10000000 -disable_wal=1 -write_buffer_size=10000000 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12 -whole_key_filtering=0` Test with and without `-total_order_seek` on `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -use_existing_db -readonly -benchmarks=readrandom -num=10000000 -duration=40 -disable_wal=1 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12` Before this change, total_order_seek=false: 25188 ops/sec Before this change, total_order_seek=true: 1222 ops/sec (~20x slower) After this change, total_order_seek=false: 24570 ops/sec After this change, total_order_seek=true: 25012 ops/sec (indistinguishable) Reviewed By: siying Differential Revision: D33753458 Pulled By: pdillinger fbshipit-source-id: bf892f34907a5e407d9c40bd4d42f0adbcbe0014
2022-02-01 03:45:17 +00:00
ReadOptions read_opts = read_options_;
std::unique_ptr<char[]> ts_guard;
Slice ts;
if (user_timestamp_size_ > 0) {
ts_guard.reset(new char[user_timestamp_size_]);
ts = mock_app_clock_->GetTimestampForRead(thread->rand, ts_guard.get());
read_opts.timestamp = &ts;
}
std::unique_ptr<Iterator> iter(db->NewIterator(read_opts));
SkipListRep::LookaheadIterator Summary: This diff introduces the `lookahead` argument to `SkipListFactory()`. This is an optimization for the tailing use case which includes many seeks. E.g. consider the following operations on a skip list iterator: Seek(x), Next(), Next(), Seek(x+2), Next(), Seek(x+3), Next(), Next(), ... If `lookahead` is positive, `SkipListRep` will return an iterator which also keeps track of the previously visited node. Seek() then first does a linear search starting from that node (up to `lookahead` steps). As in the tailing example above, this may require fewer than ~log(n) comparisons as with regular skip list search. Test Plan: Added a new benchmark (`fillseekseq`) which simulates the usage pattern. It first writes N records (with consecutive keys), then measures how much time it takes to read them by calling `Seek()` and `Next()`. $ time ./db_bench -num 10000000 -benchmarks fillseekseq -prefix_size 1 \ -key_size 8 -write_buffer_size $[1024*1024*1024] -value_size 50 \ -seekseq_next 2 -skip_list_lookahead=0 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.389 micros/op 2569047 ops/sec; real 0m21.806s user 0m12.106s sys 0m9.672s $ time ./db_bench [...] -skip_list_lookahead=2 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.153 micros/op 6540684 ops/sec; real 0m19.469s user 0m10.192s sys 0m9.252s Reviewers: ljin, sdong, igor Reviewed By: igor Subscribers: dhruba, leveldb, march, lovro Differential Revision: https://reviews.facebook.net/D23997
2014-09-23 22:52:28 +00:00
std::unique_ptr<const char[]> key_guard;
Slice key = AllocateKey(&key_guard);
SkipListRep::LookaheadIterator Summary: This diff introduces the `lookahead` argument to `SkipListFactory()`. This is an optimization for the tailing use case which includes many seeks. E.g. consider the following operations on a skip list iterator: Seek(x), Next(), Next(), Seek(x+2), Next(), Seek(x+3), Next(), Next(), ... If `lookahead` is positive, `SkipListRep` will return an iterator which also keeps track of the previously visited node. Seek() then first does a linear search starting from that node (up to `lookahead` steps). As in the tailing example above, this may require fewer than ~log(n) comparisons as with regular skip list search. Test Plan: Added a new benchmark (`fillseekseq`) which simulates the usage pattern. It first writes N records (with consecutive keys), then measures how much time it takes to read them by calling `Seek()` and `Next()`. $ time ./db_bench -num 10000000 -benchmarks fillseekseq -prefix_size 1 \ -key_size 8 -write_buffer_size $[1024*1024*1024] -value_size 50 \ -seekseq_next 2 -skip_list_lookahead=0 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.389 micros/op 2569047 ops/sec; real 0m21.806s user 0m12.106s sys 0m9.672s $ time ./db_bench [...] -skip_list_lookahead=2 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.153 micros/op 6540684 ops/sec; real 0m19.469s user 0m10.192s sys 0m9.252s Reviewers: ljin, sdong, igor Reviewed By: igor Subscribers: dhruba, leveldb, march, lovro Differential Revision: https://reviews.facebook.net/D23997
2014-09-23 22:52:28 +00:00
for (int64_t i = 0; i < FLAGS_num; ++i) {
GenerateKeyFromInt(i, FLAGS_num, &key);
iter->Seek(key);
assert(iter->Valid() && iter->key() == key);
thread->stats.FinishedOps(nullptr, db, 1, kSeek);
SkipListRep::LookaheadIterator Summary: This diff introduces the `lookahead` argument to `SkipListFactory()`. This is an optimization for the tailing use case which includes many seeks. E.g. consider the following operations on a skip list iterator: Seek(x), Next(), Next(), Seek(x+2), Next(), Seek(x+3), Next(), Next(), ... If `lookahead` is positive, `SkipListRep` will return an iterator which also keeps track of the previously visited node. Seek() then first does a linear search starting from that node (up to `lookahead` steps). As in the tailing example above, this may require fewer than ~log(n) comparisons as with regular skip list search. Test Plan: Added a new benchmark (`fillseekseq`) which simulates the usage pattern. It first writes N records (with consecutive keys), then measures how much time it takes to read them by calling `Seek()` and `Next()`. $ time ./db_bench -num 10000000 -benchmarks fillseekseq -prefix_size 1 \ -key_size 8 -write_buffer_size $[1024*1024*1024] -value_size 50 \ -seekseq_next 2 -skip_list_lookahead=0 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.389 micros/op 2569047 ops/sec; real 0m21.806s user 0m12.106s sys 0m9.672s $ time ./db_bench [...] -skip_list_lookahead=2 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.153 micros/op 6540684 ops/sec; real 0m19.469s user 0m10.192s sys 0m9.252s Reviewers: ljin, sdong, igor Reviewed By: igor Subscribers: dhruba, leveldb, march, lovro Differential Revision: https://reviews.facebook.net/D23997
2014-09-23 22:52:28 +00:00
for (int j = 0; j < FLAGS_seek_nexts && i + 1 < FLAGS_num; ++j) {
if (!FLAGS_reverse_iterator) {
iter->Next();
} else {
iter->Prev();
}
SkipListRep::LookaheadIterator Summary: This diff introduces the `lookahead` argument to `SkipListFactory()`. This is an optimization for the tailing use case which includes many seeks. E.g. consider the following operations on a skip list iterator: Seek(x), Next(), Next(), Seek(x+2), Next(), Seek(x+3), Next(), Next(), ... If `lookahead` is positive, `SkipListRep` will return an iterator which also keeps track of the previously visited node. Seek() then first does a linear search starting from that node (up to `lookahead` steps). As in the tailing example above, this may require fewer than ~log(n) comparisons as with regular skip list search. Test Plan: Added a new benchmark (`fillseekseq`) which simulates the usage pattern. It first writes N records (with consecutive keys), then measures how much time it takes to read them by calling `Seek()` and `Next()`. $ time ./db_bench -num 10000000 -benchmarks fillseekseq -prefix_size 1 \ -key_size 8 -write_buffer_size $[1024*1024*1024] -value_size 50 \ -seekseq_next 2 -skip_list_lookahead=0 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.389 micros/op 2569047 ops/sec; real 0m21.806s user 0m12.106s sys 0m9.672s $ time ./db_bench [...] -skip_list_lookahead=2 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.153 micros/op 6540684 ops/sec; real 0m19.469s user 0m10.192s sys 0m9.252s Reviewers: ljin, sdong, igor Reviewed By: igor Subscribers: dhruba, leveldb, march, lovro Differential Revision: https://reviews.facebook.net/D23997
2014-09-23 22:52:28 +00:00
GenerateKeyFromInt(++i, FLAGS_num, &key);
assert(iter->Valid() && iter->key() == key);
thread->stats.FinishedOps(nullptr, db, 1, kSeek);
SkipListRep::LookaheadIterator Summary: This diff introduces the `lookahead` argument to `SkipListFactory()`. This is an optimization for the tailing use case which includes many seeks. E.g. consider the following operations on a skip list iterator: Seek(x), Next(), Next(), Seek(x+2), Next(), Seek(x+3), Next(), Next(), ... If `lookahead` is positive, `SkipListRep` will return an iterator which also keeps track of the previously visited node. Seek() then first does a linear search starting from that node (up to `lookahead` steps). As in the tailing example above, this may require fewer than ~log(n) comparisons as with regular skip list search. Test Plan: Added a new benchmark (`fillseekseq`) which simulates the usage pattern. It first writes N records (with consecutive keys), then measures how much time it takes to read them by calling `Seek()` and `Next()`. $ time ./db_bench -num 10000000 -benchmarks fillseekseq -prefix_size 1 \ -key_size 8 -write_buffer_size $[1024*1024*1024] -value_size 50 \ -seekseq_next 2 -skip_list_lookahead=0 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.389 micros/op 2569047 ops/sec; real 0m21.806s user 0m12.106s sys 0m9.672s $ time ./db_bench [...] -skip_list_lookahead=2 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.153 micros/op 6540684 ops/sec; real 0m19.469s user 0m10.192s sys 0m9.252s Reviewers: ljin, sdong, igor Reviewed By: igor Subscribers: dhruba, leveldb, march, lovro Differential Revision: https://reviews.facebook.net/D23997
2014-09-23 22:52:28 +00:00
}
iter->Seek(key);
assert(iter->Valid() && iter->key() == key);
thread->stats.FinishedOps(nullptr, db, 1, kSeek);
SkipListRep::LookaheadIterator Summary: This diff introduces the `lookahead` argument to `SkipListFactory()`. This is an optimization for the tailing use case which includes many seeks. E.g. consider the following operations on a skip list iterator: Seek(x), Next(), Next(), Seek(x+2), Next(), Seek(x+3), Next(), Next(), ... If `lookahead` is positive, `SkipListRep` will return an iterator which also keeps track of the previously visited node. Seek() then first does a linear search starting from that node (up to `lookahead` steps). As in the tailing example above, this may require fewer than ~log(n) comparisons as with regular skip list search. Test Plan: Added a new benchmark (`fillseekseq`) which simulates the usage pattern. It first writes N records (with consecutive keys), then measures how much time it takes to read them by calling `Seek()` and `Next()`. $ time ./db_bench -num 10000000 -benchmarks fillseekseq -prefix_size 1 \ -key_size 8 -write_buffer_size $[1024*1024*1024] -value_size 50 \ -seekseq_next 2 -skip_list_lookahead=0 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.389 micros/op 2569047 ops/sec; real 0m21.806s user 0m12.106s sys 0m9.672s $ time ./db_bench [...] -skip_list_lookahead=2 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.153 micros/op 6540684 ops/sec; real 0m19.469s user 0m10.192s sys 0m9.252s Reviewers: ljin, sdong, igor Reviewed By: igor Subscribers: dhruba, leveldb, march, lovro Differential Revision: https://reviews.facebook.net/D23997
2014-09-23 22:52:28 +00:00
}
}
New API to get all merge operands for a Key (#5604) Summary: This is a new API added to db.h to allow for fetching all merge operands associated with a Key. The main motivation for this API is to support use cases where doing a full online merge is not necessary as it is performance sensitive. Example use-cases: 1. Update subset of columns and read subset of columns - Imagine a SQL Table, a row is encoded as a K/V pair (as it is done in MyRocks). If there are many columns and users only updated one of them, we can use merge operator to reduce write amplification. While users only read one or two columns in the read query, this feature can avoid a full merging of the whole row, and save some CPU. 2. Updating very few attributes in a value which is a JSON-like document - Updating one attribute can be done efficiently using merge operator, while reading back one attribute can be done more efficiently if we don't need to do a full merge. ---------------------------------------------------------------------------------------------------- API : Status GetMergeOperands( const ReadOptions& options, ColumnFamilyHandle* column_family, const Slice& key, PinnableSlice* merge_operands, GetMergeOperandsOptions* get_merge_operands_options, int* number_of_operands) Example usage : int size = 100; int number_of_operands = 0; std::vector<PinnableSlice> values(size); GetMergeOperandsOptions merge_operands_info; db_->GetMergeOperands(ReadOptions(), db_->DefaultColumnFamily(), "k1", values.data(), merge_operands_info, &number_of_operands); Description : Returns all the merge operands corresponding to the key. If the number of merge operands in DB is greater than merge_operands_options.expected_max_number_of_operands no merge operands are returned and status is Incomplete. Merge operands returned are in the order of insertion. merge_operands-> Points to an array of at-least merge_operands_options.expected_max_number_of_operands and the caller is responsible for allocating it. If the status returned is Incomplete then number_of_operands will contain the total number of merge operands found in DB for key. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5604 Test Plan: Added unit test and perf test in db_bench that can be run using the command: ./db_bench -benchmarks=getmergeoperands --merge_operator=sortlist Differential Revision: D16657366 Pulled By: vjnadimpalli fbshipit-source-id: 0faadd752351745224ee12d4ae9ef3cb529951bf
2019-08-06 21:22:34 +00:00
bool binary_search(std::vector<int>& data, int start, int end, int key) {
if (data.empty()) {
return false;
}
if (start > end) {
return false;
}
New API to get all merge operands for a Key (#5604) Summary: This is a new API added to db.h to allow for fetching all merge operands associated with a Key. The main motivation for this API is to support use cases where doing a full online merge is not necessary as it is performance sensitive. Example use-cases: 1. Update subset of columns and read subset of columns - Imagine a SQL Table, a row is encoded as a K/V pair (as it is done in MyRocks). If there are many columns and users only updated one of them, we can use merge operator to reduce write amplification. While users only read one or two columns in the read query, this feature can avoid a full merging of the whole row, and save some CPU. 2. Updating very few attributes in a value which is a JSON-like document - Updating one attribute can be done efficiently using merge operator, while reading back one attribute can be done more efficiently if we don't need to do a full merge. ---------------------------------------------------------------------------------------------------- API : Status GetMergeOperands( const ReadOptions& options, ColumnFamilyHandle* column_family, const Slice& key, PinnableSlice* merge_operands, GetMergeOperandsOptions* get_merge_operands_options, int* number_of_operands) Example usage : int size = 100; int number_of_operands = 0; std::vector<PinnableSlice> values(size); GetMergeOperandsOptions merge_operands_info; db_->GetMergeOperands(ReadOptions(), db_->DefaultColumnFamily(), "k1", values.data(), merge_operands_info, &number_of_operands); Description : Returns all the merge operands corresponding to the key. If the number of merge operands in DB is greater than merge_operands_options.expected_max_number_of_operands no merge operands are returned and status is Incomplete. Merge operands returned are in the order of insertion. merge_operands-> Points to an array of at-least merge_operands_options.expected_max_number_of_operands and the caller is responsible for allocating it. If the status returned is Incomplete then number_of_operands will contain the total number of merge operands found in DB for key. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5604 Test Plan: Added unit test and perf test in db_bench that can be run using the command: ./db_bench -benchmarks=getmergeoperands --merge_operator=sortlist Differential Revision: D16657366 Pulled By: vjnadimpalli fbshipit-source-id: 0faadd752351745224ee12d4ae9ef3cb529951bf
2019-08-06 21:22:34 +00:00
int mid = start + (end - start) / 2;
if (mid > static_cast<int>(data.size()) - 1) {
return false;
}
New API to get all merge operands for a Key (#5604) Summary: This is a new API added to db.h to allow for fetching all merge operands associated with a Key. The main motivation for this API is to support use cases where doing a full online merge is not necessary as it is performance sensitive. Example use-cases: 1. Update subset of columns and read subset of columns - Imagine a SQL Table, a row is encoded as a K/V pair (as it is done in MyRocks). If there are many columns and users only updated one of them, we can use merge operator to reduce write amplification. While users only read one or two columns in the read query, this feature can avoid a full merging of the whole row, and save some CPU. 2. Updating very few attributes in a value which is a JSON-like document - Updating one attribute can be done efficiently using merge operator, while reading back one attribute can be done more efficiently if we don't need to do a full merge. ---------------------------------------------------------------------------------------------------- API : Status GetMergeOperands( const ReadOptions& options, ColumnFamilyHandle* column_family, const Slice& key, PinnableSlice* merge_operands, GetMergeOperandsOptions* get_merge_operands_options, int* number_of_operands) Example usage : int size = 100; int number_of_operands = 0; std::vector<PinnableSlice> values(size); GetMergeOperandsOptions merge_operands_info; db_->GetMergeOperands(ReadOptions(), db_->DefaultColumnFamily(), "k1", values.data(), merge_operands_info, &number_of_operands); Description : Returns all the merge operands corresponding to the key. If the number of merge operands in DB is greater than merge_operands_options.expected_max_number_of_operands no merge operands are returned and status is Incomplete. Merge operands returned are in the order of insertion. merge_operands-> Points to an array of at-least merge_operands_options.expected_max_number_of_operands and the caller is responsible for allocating it. If the status returned is Incomplete then number_of_operands will contain the total number of merge operands found in DB for key. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5604 Test Plan: Added unit test and perf test in db_bench that can be run using the command: ./db_bench -benchmarks=getmergeoperands --merge_operator=sortlist Differential Revision: D16657366 Pulled By: vjnadimpalli fbshipit-source-id: 0faadd752351745224ee12d4ae9ef3cb529951bf
2019-08-06 21:22:34 +00:00
if (data[mid] == key) {
return true;
} else if (data[mid] > key) {
return binary_search(data, start, mid - 1, key);
} else {
return binary_search(data, mid + 1, end, key);
}
}
// Does a bunch of merge operations for a key(key1) where the merge operand
// is a sorted list. Next performance comparison is done between doing a Get
// for key1 followed by searching for another key(key2) in the large sorted
// list vs calling GetMergeOperands for key1 and then searching for the key2
// in all the sorted sub-lists. Later case is expected to be a lot faster.
void GetMergeOperands(ThreadState* thread) {
DB* db = SelectDB(thread);
const int kTotalValues = 100000;
const int kListSize = 100;
std::string key = "my_key";
std::string value;
for (int i = 1; i < kTotalValues; i++) {
if (i % kListSize == 0) {
// Remove trailing ','
value.pop_back();
db->Merge(WriteOptions(), key, value);
value.clear();
} else {
value.append(std::to_string(i)).append(",");
}
}
SortList s;
std::vector<int> data;
// This value can be experimented with and it will demonstrate the
// perf difference between doing a Get and searching for lookup_key in the
// resultant large sorted list vs doing GetMergeOperands and searching
// for lookup_key within this resultant sorted sub-lists.
int lookup_key = 1;
// Get API call
std::cout << "--- Get API call --- \n";
PinnableSlice p_slice;
uint64_t st = FLAGS_env->NowNanos();
db->Get(ReadOptions(), db->DefaultColumnFamily(), key, &p_slice);
s.MakeVector(data, p_slice);
bool found =
binary_search(data, 0, static_cast<int>(data.size() - 1), lookup_key);
std::cout << "Found key? " << std::to_string(found) << "\n";
uint64_t sp = FLAGS_env->NowNanos();
std::cout << "Get: " << (sp - st) / 1000000000.0 << " seconds\n";
std::string* dat_ = p_slice.GetSelf();
std::cout << "Sample data from Get API call: " << dat_->substr(0, 10)
<< "\n";
data.clear();
// GetMergeOperands API call
std::cout << "--- GetMergeOperands API --- \n";
std::vector<PinnableSlice> a_slice((kTotalValues / kListSize) + 1);
st = FLAGS_env->NowNanos();
int number_of_operands = 0;
GetMergeOperandsOptions get_merge_operands_options;
get_merge_operands_options.expected_max_number_of_operands =
(kTotalValues / 100) + 1;
db->GetMergeOperands(ReadOptions(), db->DefaultColumnFamily(), key,
a_slice.data(), &get_merge_operands_options,
&number_of_operands);
for (PinnableSlice& psl : a_slice) {
s.MakeVector(data, psl);
found =
binary_search(data, 0, static_cast<int>(data.size() - 1), lookup_key);
data.clear();
if (found) {
break;
}
New API to get all merge operands for a Key (#5604) Summary: This is a new API added to db.h to allow for fetching all merge operands associated with a Key. The main motivation for this API is to support use cases where doing a full online merge is not necessary as it is performance sensitive. Example use-cases: 1. Update subset of columns and read subset of columns - Imagine a SQL Table, a row is encoded as a K/V pair (as it is done in MyRocks). If there are many columns and users only updated one of them, we can use merge operator to reduce write amplification. While users only read one or two columns in the read query, this feature can avoid a full merging of the whole row, and save some CPU. 2. Updating very few attributes in a value which is a JSON-like document - Updating one attribute can be done efficiently using merge operator, while reading back one attribute can be done more efficiently if we don't need to do a full merge. ---------------------------------------------------------------------------------------------------- API : Status GetMergeOperands( const ReadOptions& options, ColumnFamilyHandle* column_family, const Slice& key, PinnableSlice* merge_operands, GetMergeOperandsOptions* get_merge_operands_options, int* number_of_operands) Example usage : int size = 100; int number_of_operands = 0; std::vector<PinnableSlice> values(size); GetMergeOperandsOptions merge_operands_info; db_->GetMergeOperands(ReadOptions(), db_->DefaultColumnFamily(), "k1", values.data(), merge_operands_info, &number_of_operands); Description : Returns all the merge operands corresponding to the key. If the number of merge operands in DB is greater than merge_operands_options.expected_max_number_of_operands no merge operands are returned and status is Incomplete. Merge operands returned are in the order of insertion. merge_operands-> Points to an array of at-least merge_operands_options.expected_max_number_of_operands and the caller is responsible for allocating it. If the status returned is Incomplete then number_of_operands will contain the total number of merge operands found in DB for key. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5604 Test Plan: Added unit test and perf test in db_bench that can be run using the command: ./db_bench -benchmarks=getmergeoperands --merge_operator=sortlist Differential Revision: D16657366 Pulled By: vjnadimpalli fbshipit-source-id: 0faadd752351745224ee12d4ae9ef3cb529951bf
2019-08-06 21:22:34 +00:00
}
std::cout << "Found key? " << std::to_string(found) << "\n";
sp = FLAGS_env->NowNanos();
std::cout << "Get Merge operands: " << (sp - st) / 1000000000.0
<< " seconds \n";
int to_print = 0;
std::cout << "Sample data from GetMergeOperands API call: ";
for (PinnableSlice& psl : a_slice) {
std::cout << "List: " << to_print << " : " << *psl.GetSelf() << "\n";
if (to_print++ > 2) {
break;
}
New API to get all merge operands for a Key (#5604) Summary: This is a new API added to db.h to allow for fetching all merge operands associated with a Key. The main motivation for this API is to support use cases where doing a full online merge is not necessary as it is performance sensitive. Example use-cases: 1. Update subset of columns and read subset of columns - Imagine a SQL Table, a row is encoded as a K/V pair (as it is done in MyRocks). If there are many columns and users only updated one of them, we can use merge operator to reduce write amplification. While users only read one or two columns in the read query, this feature can avoid a full merging of the whole row, and save some CPU. 2. Updating very few attributes in a value which is a JSON-like document - Updating one attribute can be done efficiently using merge operator, while reading back one attribute can be done more efficiently if we don't need to do a full merge. ---------------------------------------------------------------------------------------------------- API : Status GetMergeOperands( const ReadOptions& options, ColumnFamilyHandle* column_family, const Slice& key, PinnableSlice* merge_operands, GetMergeOperandsOptions* get_merge_operands_options, int* number_of_operands) Example usage : int size = 100; int number_of_operands = 0; std::vector<PinnableSlice> values(size); GetMergeOperandsOptions merge_operands_info; db_->GetMergeOperands(ReadOptions(), db_->DefaultColumnFamily(), "k1", values.data(), merge_operands_info, &number_of_operands); Description : Returns all the merge operands corresponding to the key. If the number of merge operands in DB is greater than merge_operands_options.expected_max_number_of_operands no merge operands are returned and status is Incomplete. Merge operands returned are in the order of insertion. merge_operands-> Points to an array of at-least merge_operands_options.expected_max_number_of_operands and the caller is responsible for allocating it. If the status returned is Incomplete then number_of_operands will contain the total number of merge operands found in DB for key. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5604 Test Plan: Added unit test and perf test in db_bench that can be run using the command: ./db_bench -benchmarks=getmergeoperands --merge_operator=sortlist Differential Revision: D16657366 Pulled By: vjnadimpalli fbshipit-source-id: 0faadd752351745224ee12d4ae9ef3cb529951bf
2019-08-06 21:22:34 +00:00
}
}
Add rate limiter priority to ReadOptions (#9424) Summary: Users can set the priority for file reads associated with their operation by setting `ReadOptions::rate_limiter_priority` to something other than `Env::IO_TOTAL`. Rate limiting `VerifyChecksum()` and `VerifyFileChecksums()` is the motivation for this PR, so it also includes benchmarks and minor bug fixes to get that working. `RandomAccessFileReader::Read()` already had support for rate limiting compaction reads. I changed that rate limiting to be non-specific to compaction, but rather performed according to the passed in `Env::IOPriority`. Now the compaction read rate limiting is supported by setting `rate_limiter_priority = Env::IO_LOW` on its `ReadOptions`. There is no default value for the new `Env::IOPriority` parameter to `RandomAccessFileReader::Read()`. That means this PR goes through all callers (in some cases multiple layers up the call stack) to find a `ReadOptions` to provide the priority. There are TODOs for cases I believe it would be good to let user control the priority some day (e.g., file footer reads), and no TODO in cases I believe it doesn't matter (e.g., trace file reads). The API doc only lists the missing cases where a file read associated with a provided `ReadOptions` cannot be rate limited. For cases like file ingestion checksum calculation, there is no API to provide `ReadOptions` or `Env::IOPriority`, so I didn't count that as missing. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9424 Test Plan: - new unit tests - new benchmarks on ~50MB database with 1MB/s read rate limit and 100ms refill interval; verified with strace reads are chunked (at 0.1MB per chunk) and spaced roughly 100ms apart. - setup command: `./db_bench -benchmarks=fillrandom,compact -db=/tmp/testdb -target_file_size_base=1048576 -disable_auto_compactions=true -file_checksum=true` - benchmarks command: `strace -ttfe pread64 ./db_bench -benchmarks=verifychecksum,verifyfilechecksums -use_existing_db=true -db=/tmp/testdb -rate_limiter_bytes_per_sec=1048576 -rate_limit_bg_reads=1 -rate_limit_user_ops=true -file_checksum=true` - crash test using IO_USER priority on non-validation reads with https://github.com/facebook/rocksdb/issues/9567 reverted: `python3 tools/db_crashtest.py blackbox --max_key=1000000 --write_buffer_size=524288 --target_file_size_base=524288 --level_compaction_dynamic_level_bytes=true --duration=3600 --rate_limit_bg_reads=true --rate_limit_user_ops=true --rate_limiter_bytes_per_sec=10485760 --interval=10` Reviewed By: hx235 Differential Revision: D33747386 Pulled By: ajkr fbshipit-source-id: a2d985e97912fba8c54763798e04f006ccc56e0c
2022-02-17 07:17:03 +00:00
void VerifyChecksum(ThreadState* thread) {
DB* db = SelectDB(thread);
ReadOptions ro;
ro.adaptive_readahead = FLAGS_adaptive_readahead;
ro.async_io = FLAGS_async_io;
Add rate limiter priority to ReadOptions (#9424) Summary: Users can set the priority for file reads associated with their operation by setting `ReadOptions::rate_limiter_priority` to something other than `Env::IO_TOTAL`. Rate limiting `VerifyChecksum()` and `VerifyFileChecksums()` is the motivation for this PR, so it also includes benchmarks and minor bug fixes to get that working. `RandomAccessFileReader::Read()` already had support for rate limiting compaction reads. I changed that rate limiting to be non-specific to compaction, but rather performed according to the passed in `Env::IOPriority`. Now the compaction read rate limiting is supported by setting `rate_limiter_priority = Env::IO_LOW` on its `ReadOptions`. There is no default value for the new `Env::IOPriority` parameter to `RandomAccessFileReader::Read()`. That means this PR goes through all callers (in some cases multiple layers up the call stack) to find a `ReadOptions` to provide the priority. There are TODOs for cases I believe it would be good to let user control the priority some day (e.g., file footer reads), and no TODO in cases I believe it doesn't matter (e.g., trace file reads). The API doc only lists the missing cases where a file read associated with a provided `ReadOptions` cannot be rate limited. For cases like file ingestion checksum calculation, there is no API to provide `ReadOptions` or `Env::IOPriority`, so I didn't count that as missing. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9424 Test Plan: - new unit tests - new benchmarks on ~50MB database with 1MB/s read rate limit and 100ms refill interval; verified with strace reads are chunked (at 0.1MB per chunk) and spaced roughly 100ms apart. - setup command: `./db_bench -benchmarks=fillrandom,compact -db=/tmp/testdb -target_file_size_base=1048576 -disable_auto_compactions=true -file_checksum=true` - benchmarks command: `strace -ttfe pread64 ./db_bench -benchmarks=verifychecksum,verifyfilechecksums -use_existing_db=true -db=/tmp/testdb -rate_limiter_bytes_per_sec=1048576 -rate_limit_bg_reads=1 -rate_limit_user_ops=true -file_checksum=true` - crash test using IO_USER priority on non-validation reads with https://github.com/facebook/rocksdb/issues/9567 reverted: `python3 tools/db_crashtest.py blackbox --max_key=1000000 --write_buffer_size=524288 --target_file_size_base=524288 --level_compaction_dynamic_level_bytes=true --duration=3600 --rate_limit_bg_reads=true --rate_limit_user_ops=true --rate_limiter_bytes_per_sec=10485760 --interval=10` Reviewed By: hx235 Differential Revision: D33747386 Pulled By: ajkr fbshipit-source-id: a2d985e97912fba8c54763798e04f006ccc56e0c
2022-02-17 07:17:03 +00:00
ro.rate_limiter_priority =
FLAGS_rate_limit_user_ops ? Env::IO_USER : Env::IO_TOTAL;
ro.readahead_size = FLAGS_readahead_size;
ro.auto_readahead_size = FLAGS_auto_readahead_size;
Add rate limiter priority to ReadOptions (#9424) Summary: Users can set the priority for file reads associated with their operation by setting `ReadOptions::rate_limiter_priority` to something other than `Env::IO_TOTAL`. Rate limiting `VerifyChecksum()` and `VerifyFileChecksums()` is the motivation for this PR, so it also includes benchmarks and minor bug fixes to get that working. `RandomAccessFileReader::Read()` already had support for rate limiting compaction reads. I changed that rate limiting to be non-specific to compaction, but rather performed according to the passed in `Env::IOPriority`. Now the compaction read rate limiting is supported by setting `rate_limiter_priority = Env::IO_LOW` on its `ReadOptions`. There is no default value for the new `Env::IOPriority` parameter to `RandomAccessFileReader::Read()`. That means this PR goes through all callers (in some cases multiple layers up the call stack) to find a `ReadOptions` to provide the priority. There are TODOs for cases I believe it would be good to let user control the priority some day (e.g., file footer reads), and no TODO in cases I believe it doesn't matter (e.g., trace file reads). The API doc only lists the missing cases where a file read associated with a provided `ReadOptions` cannot be rate limited. For cases like file ingestion checksum calculation, there is no API to provide `ReadOptions` or `Env::IOPriority`, so I didn't count that as missing. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9424 Test Plan: - new unit tests - new benchmarks on ~50MB database with 1MB/s read rate limit and 100ms refill interval; verified with strace reads are chunked (at 0.1MB per chunk) and spaced roughly 100ms apart. - setup command: `./db_bench -benchmarks=fillrandom,compact -db=/tmp/testdb -target_file_size_base=1048576 -disable_auto_compactions=true -file_checksum=true` - benchmarks command: `strace -ttfe pread64 ./db_bench -benchmarks=verifychecksum,verifyfilechecksums -use_existing_db=true -db=/tmp/testdb -rate_limiter_bytes_per_sec=1048576 -rate_limit_bg_reads=1 -rate_limit_user_ops=true -file_checksum=true` - crash test using IO_USER priority on non-validation reads with https://github.com/facebook/rocksdb/issues/9567 reverted: `python3 tools/db_crashtest.py blackbox --max_key=1000000 --write_buffer_size=524288 --target_file_size_base=524288 --level_compaction_dynamic_level_bytes=true --duration=3600 --rate_limit_bg_reads=true --rate_limit_user_ops=true --rate_limiter_bytes_per_sec=10485760 --interval=10` Reviewed By: hx235 Differential Revision: D33747386 Pulled By: ajkr fbshipit-source-id: a2d985e97912fba8c54763798e04f006ccc56e0c
2022-02-17 07:17:03 +00:00
Status s = db->VerifyChecksum(ro);
if (!s.ok()) {
fprintf(stderr, "VerifyChecksum() failed: %s\n", s.ToString().c_str());
exit(1);
}
}
void VerifyFileChecksums(ThreadState* thread) {
DB* db = SelectDB(thread);
ReadOptions ro;
ro.adaptive_readahead = FLAGS_adaptive_readahead;
ro.async_io = FLAGS_async_io;
Add rate limiter priority to ReadOptions (#9424) Summary: Users can set the priority for file reads associated with their operation by setting `ReadOptions::rate_limiter_priority` to something other than `Env::IO_TOTAL`. Rate limiting `VerifyChecksum()` and `VerifyFileChecksums()` is the motivation for this PR, so it also includes benchmarks and minor bug fixes to get that working. `RandomAccessFileReader::Read()` already had support for rate limiting compaction reads. I changed that rate limiting to be non-specific to compaction, but rather performed according to the passed in `Env::IOPriority`. Now the compaction read rate limiting is supported by setting `rate_limiter_priority = Env::IO_LOW` on its `ReadOptions`. There is no default value for the new `Env::IOPriority` parameter to `RandomAccessFileReader::Read()`. That means this PR goes through all callers (in some cases multiple layers up the call stack) to find a `ReadOptions` to provide the priority. There are TODOs for cases I believe it would be good to let user control the priority some day (e.g., file footer reads), and no TODO in cases I believe it doesn't matter (e.g., trace file reads). The API doc only lists the missing cases where a file read associated with a provided `ReadOptions` cannot be rate limited. For cases like file ingestion checksum calculation, there is no API to provide `ReadOptions` or `Env::IOPriority`, so I didn't count that as missing. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9424 Test Plan: - new unit tests - new benchmarks on ~50MB database with 1MB/s read rate limit and 100ms refill interval; verified with strace reads are chunked (at 0.1MB per chunk) and spaced roughly 100ms apart. - setup command: `./db_bench -benchmarks=fillrandom,compact -db=/tmp/testdb -target_file_size_base=1048576 -disable_auto_compactions=true -file_checksum=true` - benchmarks command: `strace -ttfe pread64 ./db_bench -benchmarks=verifychecksum,verifyfilechecksums -use_existing_db=true -db=/tmp/testdb -rate_limiter_bytes_per_sec=1048576 -rate_limit_bg_reads=1 -rate_limit_user_ops=true -file_checksum=true` - crash test using IO_USER priority on non-validation reads with https://github.com/facebook/rocksdb/issues/9567 reverted: `python3 tools/db_crashtest.py blackbox --max_key=1000000 --write_buffer_size=524288 --target_file_size_base=524288 --level_compaction_dynamic_level_bytes=true --duration=3600 --rate_limit_bg_reads=true --rate_limit_user_ops=true --rate_limiter_bytes_per_sec=10485760 --interval=10` Reviewed By: hx235 Differential Revision: D33747386 Pulled By: ajkr fbshipit-source-id: a2d985e97912fba8c54763798e04f006ccc56e0c
2022-02-17 07:17:03 +00:00
ro.rate_limiter_priority =
FLAGS_rate_limit_user_ops ? Env::IO_USER : Env::IO_TOTAL;
ro.readahead_size = FLAGS_readahead_size;
ro.auto_readahead_size = FLAGS_auto_readahead_size;
Add rate limiter priority to ReadOptions (#9424) Summary: Users can set the priority for file reads associated with their operation by setting `ReadOptions::rate_limiter_priority` to something other than `Env::IO_TOTAL`. Rate limiting `VerifyChecksum()` and `VerifyFileChecksums()` is the motivation for this PR, so it also includes benchmarks and minor bug fixes to get that working. `RandomAccessFileReader::Read()` already had support for rate limiting compaction reads. I changed that rate limiting to be non-specific to compaction, but rather performed according to the passed in `Env::IOPriority`. Now the compaction read rate limiting is supported by setting `rate_limiter_priority = Env::IO_LOW` on its `ReadOptions`. There is no default value for the new `Env::IOPriority` parameter to `RandomAccessFileReader::Read()`. That means this PR goes through all callers (in some cases multiple layers up the call stack) to find a `ReadOptions` to provide the priority. There are TODOs for cases I believe it would be good to let user control the priority some day (e.g., file footer reads), and no TODO in cases I believe it doesn't matter (e.g., trace file reads). The API doc only lists the missing cases where a file read associated with a provided `ReadOptions` cannot be rate limited. For cases like file ingestion checksum calculation, there is no API to provide `ReadOptions` or `Env::IOPriority`, so I didn't count that as missing. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9424 Test Plan: - new unit tests - new benchmarks on ~50MB database with 1MB/s read rate limit and 100ms refill interval; verified with strace reads are chunked (at 0.1MB per chunk) and spaced roughly 100ms apart. - setup command: `./db_bench -benchmarks=fillrandom,compact -db=/tmp/testdb -target_file_size_base=1048576 -disable_auto_compactions=true -file_checksum=true` - benchmarks command: `strace -ttfe pread64 ./db_bench -benchmarks=verifychecksum,verifyfilechecksums -use_existing_db=true -db=/tmp/testdb -rate_limiter_bytes_per_sec=1048576 -rate_limit_bg_reads=1 -rate_limit_user_ops=true -file_checksum=true` - crash test using IO_USER priority on non-validation reads with https://github.com/facebook/rocksdb/issues/9567 reverted: `python3 tools/db_crashtest.py blackbox --max_key=1000000 --write_buffer_size=524288 --target_file_size_base=524288 --level_compaction_dynamic_level_bytes=true --duration=3600 --rate_limit_bg_reads=true --rate_limit_user_ops=true --rate_limiter_bytes_per_sec=10485760 --interval=10` Reviewed By: hx235 Differential Revision: D33747386 Pulled By: ajkr fbshipit-source-id: a2d985e97912fba8c54763798e04f006ccc56e0c
2022-02-17 07:17:03 +00:00
Status s = db->VerifyFileChecksums(ro);
if (!s.ok()) {
fprintf(stderr, "VerifyFileChecksums() failed: %s\n",
s.ToString().c_str());
exit(1);
}
}
// This benchmark stress tests Transactions. For a given --duration (or
// total number of --writes, a Transaction will perform a read-modify-write
// to increment the value of a key in each of N(--transaction-sets) sets of
// keys (where each set has --num keys). If --threads is set, this will be
// done in parallel.
//
// To test transactions, use --transaction_db=true. Not setting this
// parameter
// will run the same benchmark without transactions.
//
// RandomTransactionVerify() will then validate the correctness of the results
// by checking if the sum of all keys in each set is the same.
void RandomTransaction(ThreadState* thread) {
Duration duration(FLAGS_duration, readwrites_);
2016-03-15 17:57:33 +00:00
uint16_t num_prefix_ranges = static_cast<uint16_t>(FLAGS_transaction_sets);
uint64_t transactions_done = 0;
if (num_prefix_ranges == 0 || num_prefix_ranges > 9999) {
fprintf(stderr, "invalid value for transaction_sets\n");
abort();
}
TransactionOptions txn_options;
txn_options.lock_timeout = FLAGS_transaction_lock_timeout;
txn_options.set_snapshot = FLAGS_transaction_set_snapshot;
RandomTransactionInserter inserter(&thread->rand, write_options_,
Ignore `total_order_seek` in DB::Get (#9427) Summary: Apparently setting total_order_seek=true for DB::Get was intended to allow accurate read semantics if the current prefix extractor doesn't match what was used to generate SST files on disk. But since prefix_extractor was made a mutable option in 5.14.0, we have been able to detect this case and provide the correct semantics regardless of the total_order_seek option. Since that time, the option has only made Get() slower in a reasonably common case: prefix_extractor unchanged and whole_key_filtering=false. So this change primarily removes unnecessary effect of total_order_seek on Get. Also cleans up some related comments. Also adds a -total_order_seek option to db_bench and canonicalizes handling of ReadOptions in db_bench so that command line options have the expected association with library features. (There is potential for change in regression test behavior, but the old behavior is likely indefensible, or some other inconsistency would need to be fixed.) TODO in follow-up work: there should be no reason for Get() to depend on current prefix extractor at all. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9427 Test Plan: Unit tests updated. Performance (using db_bench update) Create DB with `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -benchmarks=fillrandom -num=10000000 -disable_wal=1 -write_buffer_size=10000000 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12 -whole_key_filtering=0` Test with and without `-total_order_seek` on `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -use_existing_db -readonly -benchmarks=readrandom -num=10000000 -duration=40 -disable_wal=1 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12` Before this change, total_order_seek=false: 25188 ops/sec Before this change, total_order_seek=true: 1222 ops/sec (~20x slower) After this change, total_order_seek=false: 24570 ops/sec After this change, total_order_seek=true: 25012 ops/sec (indistinguishable) Reviewed By: siying Differential Revision: D33753458 Pulled By: pdillinger fbshipit-source-id: bf892f34907a5e407d9c40bd4d42f0adbcbe0014
2022-02-01 03:45:17 +00:00
read_options_, FLAGS_num,
num_prefix_ranges);
if (FLAGS_num_multi_db > 1) {
fprintf(stderr,
"Cannot run RandomTransaction benchmark with "
"FLAGS_multi_db > 1.");
abort();
}
while (!duration.Done(1)) {
bool success;
// RandomTransactionInserter will attempt to insert a key for each
// # of FLAGS_transaction_sets
if (FLAGS_optimistic_transaction_db) {
success = inserter.OptimisticTransactionDBInsert(db_.opt_txn_db);
} else if (FLAGS_transaction_db) {
Prefer static_cast in place of most reinterpret_cast (#12308) Summary: The following are risks associated with pointer-to-pointer reinterpret_cast: * Can produce the "wrong result" (crash or memory corruption). IIRC, in theory this can happen for any up-cast or down-cast for a non-standard-layout type, though in practice would only happen for multiple inheritance cases (where the base class pointer might be "inside" the derived object). We don't use multiple inheritance a lot, but we do. * Can mask useful compiler errors upon code change, including converting between unrelated pointer types that you are expecting to be related, and converting between pointer and scalar types unintentionally. I can only think of some obscure cases where static_cast could be troublesome when it compiles as a replacement: * Going through `void*` could plausibly cause unnecessary or broken pointer arithmetic. Suppose we have `struct Derived: public Base1, public Base2`. If we have `Derived*` -> `void*` -> `Base2*` -> `Derived*` through reinterpret casts, this could plausibly work (though technical UB) assuming the `Base2*` is not dereferenced. Changing to static cast could introduce breaking pointer arithmetic. * Unnecessary (but safe) pointer arithmetic could arise in a case like `Derived*` -> `Base2*` -> `Derived*` where before the Base2 pointer might not have been dereferenced. This could potentially affect performance. With some light scripting, I tried replacing pointer-to-pointer reinterpret_casts with static_cast and kept the cases that still compile. Most occurrences of reinterpret_cast have successfully been changed (except for java/ and third-party/). 294 changed, 257 remain. A couple of related interventions included here: * Previously Cache::Handle was not actually derived from in the implementations and just used as a `void*` stand-in with reinterpret_cast. Now there is a relationship to allow static_cast. In theory, this could introduce pointer arithmetic (as described above) but is unlikely without multiple inheritance AND non-empty Cache::Handle. * Remove some unnecessary casts to void* as this is allowed to be implicit (for better or worse). Most of the remaining reinterpret_casts are for converting to/from raw bytes of objects. We could consider better idioms for these patterns in follow-up work. I wish there were a way to implement a template variant of static_cast that would only compile if no pointer arithmetic is generated, but best I can tell, this is not possible. AFAIK the best you could do is a dynamic check that the void* conversion after the static cast is unchanged. Pull Request resolved: https://github.com/facebook/rocksdb/pull/12308 Test Plan: existing tests, CI Reviewed By: ltamasi Differential Revision: D53204947 Pulled By: pdillinger fbshipit-source-id: 9de23e618263b0d5b9820f4e15966876888a16e2
2024-02-07 18:44:11 +00:00
TransactionDB* txn_db = static_cast<TransactionDB*>(db_.db);
success = inserter.TransactionDBInsert(txn_db, txn_options);
} else {
success = inserter.DBInsert(db_.db);
}
if (!success) {
fprintf(stderr, "Unexpected error: %s\n",
inserter.GetLastStatus().ToString().c_str());
abort();
}
thread->stats.FinishedOps(nullptr, db_.db, 1, kOthers);
transactions_done++;
}
char msg[100];
if (FLAGS_optimistic_transaction_db || FLAGS_transaction_db) {
snprintf(msg, sizeof(msg),
"( transactions:%" PRIu64 " aborts:%" PRIu64 ")",
transactions_done, inserter.GetFailureCount());
} else {
snprintf(msg, sizeof(msg), "( batches:%" PRIu64 " )", transactions_done);
}
thread->stats.AddMessage(msg);
thread->stats.AddBytes(static_cast<int64_t>(inserter.GetBytesInserted()));
}
// Verifies consistency of data after RandomTransaction() has been run.
// Since each iteration of RandomTransaction() incremented a key in each set
// by the same value, the sum of the keys in each set should be the same.
void RandomTransactionVerify() {
if (!FLAGS_transaction_db && !FLAGS_optimistic_transaction_db) {
// transactions not used, nothing to verify.
return;
}
Status s = RandomTransactionInserter::Verify(
db_.db, static_cast<uint16_t>(FLAGS_transaction_sets));
if (s.ok()) {
fprintf(stdout, "RandomTransactionVerify Success.\n");
} else {
fprintf(stdout, "RandomTransactionVerify FAILED!!\n");
}
}
Support for SingleDelete() Summary: This patch fixes #7460559. It introduces SingleDelete as a new database operation. This operation can be used to delete keys that were never overwritten (no put following another put of the same key). If an overwritten key is single deleted the behavior is undefined. Single deletion of a non-existent key has no effect but multiple consecutive single deletions are not allowed (see limitations). In contrast to the conventional Delete() operation, the deletion entry is removed along with the value when the two are lined up in a compaction. Note: The semantics are similar to @igor's prototype that allowed to have this behavior on the granularity of a column family ( https://reviews.facebook.net/D42093 ). This new patch, however, is more aggressive when it comes to removing tombstones: It removes the SingleDelete together with the value whenever there is no snapshot between them while the older patch only did this when the sequence number of the deletion was older than the earliest snapshot. Most of the complex additions are in the Compaction Iterator, all other changes should be relatively straightforward. The patch also includes basic support for single deletions in db_stress and db_bench. Limitations: - Not compatible with cuckoo hash tables - Single deletions cannot be used in combination with merges and normal deletions on the same key (other keys are not affected by this) - Consecutive single deletions are currently not allowed (and older version of this patch supported this so it could be resurrected if needed) Test Plan: make all check Reviewers: yhchiang, sdong, rven, anthony, yoshinorim, igor Reviewed By: igor Subscribers: maykov, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D43179
2015-09-17 18:42:56 +00:00
// Writes and deletes random keys without overwriting keys.
//
// This benchmark is intended to partially replicate the behavior of MyRocks
// secondary indices: All data is stored in keys and updates happen by
// deleting the old version of the key and inserting the new version.
void RandomReplaceKeys(ThreadState* thread) {
std::unique_ptr<const char[]> key_guard;
Slice key = AllocateKey(&key_guard);
std::unique_ptr<char[]> ts_guard;
if (user_timestamp_size_ > 0) {
ts_guard.reset(new char[user_timestamp_size_]);
}
Support for SingleDelete() Summary: This patch fixes #7460559. It introduces SingleDelete as a new database operation. This operation can be used to delete keys that were never overwritten (no put following another put of the same key). If an overwritten key is single deleted the behavior is undefined. Single deletion of a non-existent key has no effect but multiple consecutive single deletions are not allowed (see limitations). In contrast to the conventional Delete() operation, the deletion entry is removed along with the value when the two are lined up in a compaction. Note: The semantics are similar to @igor's prototype that allowed to have this behavior on the granularity of a column family ( https://reviews.facebook.net/D42093 ). This new patch, however, is more aggressive when it comes to removing tombstones: It removes the SingleDelete together with the value whenever there is no snapshot between them while the older patch only did this when the sequence number of the deletion was older than the earliest snapshot. Most of the complex additions are in the Compaction Iterator, all other changes should be relatively straightforward. The patch also includes basic support for single deletions in db_stress and db_bench. Limitations: - Not compatible with cuckoo hash tables - Single deletions cannot be used in combination with merges and normal deletions on the same key (other keys are not affected by this) - Consecutive single deletions are currently not allowed (and older version of this patch supported this so it could be resurrected if needed) Test Plan: make all check Reviewers: yhchiang, sdong, rven, anthony, yoshinorim, igor Reviewed By: igor Subscribers: maykov, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D43179
2015-09-17 18:42:56 +00:00
std::vector<uint32_t> counters(FLAGS_numdistinct, 0);
size_t max_counter = 50;
RandomGenerator gen;
Status s;
DB* db = SelectDB(thread);
for (int64_t i = 0; i < FLAGS_numdistinct; i++) {
GenerateKeyFromInt(i * max_counter, FLAGS_num, &key);
if (user_timestamp_size_ > 0) {
Revise APIs related to user-defined timestamp (#8946) Summary: ajkr reminded me that we have a rule of not including per-kv related data in `WriteOptions`. Namely, `WriteOptions` should not include information about "what-to-write", but should just include information about "how-to-write". According to this rule, `WriteOptions::timestamp` (experimental) is clearly a violation. Therefore, this PR removes `WriteOptions::timestamp` for compliance. After the removal, we need to pass timestamp info via another set of APIs. This PR proposes a set of overloaded functions `Put(write_opts, key, value, ts)`, `Delete(write_opts, key, ts)`, and `SingleDelete(write_opts, key, ts)`. Planned to add `Write(write_opts, batch, ts)`, but its complexity made me reconsider doing it in another PR (maybe). For better checking and returning error early, we also add a new set of APIs to `WriteBatch` that take extra `timestamp` information when writing to `WriteBatch`es. These set of APIs in `WriteBatchWithIndex` are currently not supported, and are on our TODO list. Removed `WriteBatch::AssignTimestamps()` and renamed `WriteBatch::AssignTimestamp()` to `WriteBatch::UpdateTimestamps()` since this method require that all keys have space for timestamps allocated already and multiple timestamps can be updated. The constructor of `WriteBatch` now takes a fourth argument `default_cf_ts_sz` which is the timestamp size of the default column family. This will be used to allocate space when calling APIs that do not specify a column family handle. Also, updated `DB::Get()`, `DB::MultiGet()`, `DB::NewIterator()`, `DB::NewIterators()` methods, replacing some assertions about timestamp to returning Status code. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8946 Test Plan: make check ./db_bench -benchmarks=fillseq,fillrandom,readrandom,readseq,deleterandom -user_timestamp_size=8 ./db_stress --user_timestamp_size=8 -nooverwritepercent=0 -test_secondary=0 -secondary_catch_up_one_in=0 -continuous_verification_interval=0 Make sure there is no perf regression by running the following ``` ./db_bench_opt -db=/dev/shm/rocksdb -use_existing_db=0 -level0_stop_writes_trigger=256 -level0_slowdown_writes_trigger=256 -level0_file_num_compaction_trigger=256 -disable_wal=1 -duration=10 -benchmarks=fillrandom ``` Before this PR ``` DB path: [/dev/shm/rocksdb] fillrandom : 1.831 micros/op 546235 ops/sec; 60.4 MB/s ``` After this PR ``` DB path: [/dev/shm/rocksdb] fillrandom : 1.820 micros/op 549404 ops/sec; 60.8 MB/s ``` Reviewed By: ltamasi Differential Revision: D33721359 Pulled By: riversand963 fbshipit-source-id: c131561534272c120ffb80711d42748d21badf09
2022-02-02 06:17:46 +00:00
Slice ts = mock_app_clock_->Allocate(ts_guard.get());
s = db->Put(write_options_, key, ts, gen.Generate());
} else {
s = db->Put(write_options_, key, gen.Generate());
}
Support for SingleDelete() Summary: This patch fixes #7460559. It introduces SingleDelete as a new database operation. This operation can be used to delete keys that were never overwritten (no put following another put of the same key). If an overwritten key is single deleted the behavior is undefined. Single deletion of a non-existent key has no effect but multiple consecutive single deletions are not allowed (see limitations). In contrast to the conventional Delete() operation, the deletion entry is removed along with the value when the two are lined up in a compaction. Note: The semantics are similar to @igor's prototype that allowed to have this behavior on the granularity of a column family ( https://reviews.facebook.net/D42093 ). This new patch, however, is more aggressive when it comes to removing tombstones: It removes the SingleDelete together with the value whenever there is no snapshot between them while the older patch only did this when the sequence number of the deletion was older than the earliest snapshot. Most of the complex additions are in the Compaction Iterator, all other changes should be relatively straightforward. The patch also includes basic support for single deletions in db_stress and db_bench. Limitations: - Not compatible with cuckoo hash tables - Single deletions cannot be used in combination with merges and normal deletions on the same key (other keys are not affected by this) - Consecutive single deletions are currently not allowed (and older version of this patch supported this so it could be resurrected if needed) Test Plan: make all check Reviewers: yhchiang, sdong, rven, anthony, yoshinorim, igor Reviewed By: igor Subscribers: maykov, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D43179
2015-09-17 18:42:56 +00:00
if (!s.ok()) {
fprintf(stderr, "Operation failed: %s\n", s.ToString().c_str());
exit(1);
}
}
db->GetSnapshot();
std::default_random_engine generator;
std::normal_distribution<double> distribution(FLAGS_numdistinct / 2.0,
FLAGS_stddev);
Duration duration(FLAGS_duration, FLAGS_num);
while (!duration.Done(1)) {
int64_t rnd_id = static_cast<int64_t>(distribution(generator));
int64_t key_id = std::max(std::min(FLAGS_numdistinct - 1, rnd_id),
static_cast<int64_t>(0));
GenerateKeyFromInt(key_id * max_counter + counters[key_id], FLAGS_num,
&key);
if (user_timestamp_size_ > 0) {
Revise APIs related to user-defined timestamp (#8946) Summary: ajkr reminded me that we have a rule of not including per-kv related data in `WriteOptions`. Namely, `WriteOptions` should not include information about "what-to-write", but should just include information about "how-to-write". According to this rule, `WriteOptions::timestamp` (experimental) is clearly a violation. Therefore, this PR removes `WriteOptions::timestamp` for compliance. After the removal, we need to pass timestamp info via another set of APIs. This PR proposes a set of overloaded functions `Put(write_opts, key, value, ts)`, `Delete(write_opts, key, ts)`, and `SingleDelete(write_opts, key, ts)`. Planned to add `Write(write_opts, batch, ts)`, but its complexity made me reconsider doing it in another PR (maybe). For better checking and returning error early, we also add a new set of APIs to `WriteBatch` that take extra `timestamp` information when writing to `WriteBatch`es. These set of APIs in `WriteBatchWithIndex` are currently not supported, and are on our TODO list. Removed `WriteBatch::AssignTimestamps()` and renamed `WriteBatch::AssignTimestamp()` to `WriteBatch::UpdateTimestamps()` since this method require that all keys have space for timestamps allocated already and multiple timestamps can be updated. The constructor of `WriteBatch` now takes a fourth argument `default_cf_ts_sz` which is the timestamp size of the default column family. This will be used to allocate space when calling APIs that do not specify a column family handle. Also, updated `DB::Get()`, `DB::MultiGet()`, `DB::NewIterator()`, `DB::NewIterators()` methods, replacing some assertions about timestamp to returning Status code. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8946 Test Plan: make check ./db_bench -benchmarks=fillseq,fillrandom,readrandom,readseq,deleterandom -user_timestamp_size=8 ./db_stress --user_timestamp_size=8 -nooverwritepercent=0 -test_secondary=0 -secondary_catch_up_one_in=0 -continuous_verification_interval=0 Make sure there is no perf regression by running the following ``` ./db_bench_opt -db=/dev/shm/rocksdb -use_existing_db=0 -level0_stop_writes_trigger=256 -level0_slowdown_writes_trigger=256 -level0_file_num_compaction_trigger=256 -disable_wal=1 -duration=10 -benchmarks=fillrandom ``` Before this PR ``` DB path: [/dev/shm/rocksdb] fillrandom : 1.831 micros/op 546235 ops/sec; 60.4 MB/s ``` After this PR ``` DB path: [/dev/shm/rocksdb] fillrandom : 1.820 micros/op 549404 ops/sec; 60.8 MB/s ``` Reviewed By: ltamasi Differential Revision: D33721359 Pulled By: riversand963 fbshipit-source-id: c131561534272c120ffb80711d42748d21badf09
2022-02-02 06:17:46 +00:00
Slice ts = mock_app_clock_->Allocate(ts_guard.get());
s = FLAGS_use_single_deletes ? db->SingleDelete(write_options_, key, ts)
: db->Delete(write_options_, key, ts);
} else {
s = FLAGS_use_single_deletes ? db->SingleDelete(write_options_, key)
: db->Delete(write_options_, key);
}
Support for SingleDelete() Summary: This patch fixes #7460559. It introduces SingleDelete as a new database operation. This operation can be used to delete keys that were never overwritten (no put following another put of the same key). If an overwritten key is single deleted the behavior is undefined. Single deletion of a non-existent key has no effect but multiple consecutive single deletions are not allowed (see limitations). In contrast to the conventional Delete() operation, the deletion entry is removed along with the value when the two are lined up in a compaction. Note: The semantics are similar to @igor's prototype that allowed to have this behavior on the granularity of a column family ( https://reviews.facebook.net/D42093 ). This new patch, however, is more aggressive when it comes to removing tombstones: It removes the SingleDelete together with the value whenever there is no snapshot between them while the older patch only did this when the sequence number of the deletion was older than the earliest snapshot. Most of the complex additions are in the Compaction Iterator, all other changes should be relatively straightforward. The patch also includes basic support for single deletions in db_stress and db_bench. Limitations: - Not compatible with cuckoo hash tables - Single deletions cannot be used in combination with merges and normal deletions on the same key (other keys are not affected by this) - Consecutive single deletions are currently not allowed (and older version of this patch supported this so it could be resurrected if needed) Test Plan: make all check Reviewers: yhchiang, sdong, rven, anthony, yoshinorim, igor Reviewed By: igor Subscribers: maykov, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D43179
2015-09-17 18:42:56 +00:00
if (s.ok()) {
counters[key_id] = (counters[key_id] + 1) % max_counter;
GenerateKeyFromInt(key_id * max_counter + counters[key_id], FLAGS_num,
&key);
if (user_timestamp_size_ > 0) {
Revise APIs related to user-defined timestamp (#8946) Summary: ajkr reminded me that we have a rule of not including per-kv related data in `WriteOptions`. Namely, `WriteOptions` should not include information about "what-to-write", but should just include information about "how-to-write". According to this rule, `WriteOptions::timestamp` (experimental) is clearly a violation. Therefore, this PR removes `WriteOptions::timestamp` for compliance. After the removal, we need to pass timestamp info via another set of APIs. This PR proposes a set of overloaded functions `Put(write_opts, key, value, ts)`, `Delete(write_opts, key, ts)`, and `SingleDelete(write_opts, key, ts)`. Planned to add `Write(write_opts, batch, ts)`, but its complexity made me reconsider doing it in another PR (maybe). For better checking and returning error early, we also add a new set of APIs to `WriteBatch` that take extra `timestamp` information when writing to `WriteBatch`es. These set of APIs in `WriteBatchWithIndex` are currently not supported, and are on our TODO list. Removed `WriteBatch::AssignTimestamps()` and renamed `WriteBatch::AssignTimestamp()` to `WriteBatch::UpdateTimestamps()` since this method require that all keys have space for timestamps allocated already and multiple timestamps can be updated. The constructor of `WriteBatch` now takes a fourth argument `default_cf_ts_sz` which is the timestamp size of the default column family. This will be used to allocate space when calling APIs that do not specify a column family handle. Also, updated `DB::Get()`, `DB::MultiGet()`, `DB::NewIterator()`, `DB::NewIterators()` methods, replacing some assertions about timestamp to returning Status code. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8946 Test Plan: make check ./db_bench -benchmarks=fillseq,fillrandom,readrandom,readseq,deleterandom -user_timestamp_size=8 ./db_stress --user_timestamp_size=8 -nooverwritepercent=0 -test_secondary=0 -secondary_catch_up_one_in=0 -continuous_verification_interval=0 Make sure there is no perf regression by running the following ``` ./db_bench_opt -db=/dev/shm/rocksdb -use_existing_db=0 -level0_stop_writes_trigger=256 -level0_slowdown_writes_trigger=256 -level0_file_num_compaction_trigger=256 -disable_wal=1 -duration=10 -benchmarks=fillrandom ``` Before this PR ``` DB path: [/dev/shm/rocksdb] fillrandom : 1.831 micros/op 546235 ops/sec; 60.4 MB/s ``` After this PR ``` DB path: [/dev/shm/rocksdb] fillrandom : 1.820 micros/op 549404 ops/sec; 60.8 MB/s ``` Reviewed By: ltamasi Differential Revision: D33721359 Pulled By: riversand963 fbshipit-source-id: c131561534272c120ffb80711d42748d21badf09
2022-02-02 06:17:46 +00:00
Slice ts = mock_app_clock_->Allocate(ts_guard.get());
s = db->Put(write_options_, key, ts, Slice());
} else {
s = db->Put(write_options_, key, Slice());
}
Support for SingleDelete() Summary: This patch fixes #7460559. It introduces SingleDelete as a new database operation. This operation can be used to delete keys that were never overwritten (no put following another put of the same key). If an overwritten key is single deleted the behavior is undefined. Single deletion of a non-existent key has no effect but multiple consecutive single deletions are not allowed (see limitations). In contrast to the conventional Delete() operation, the deletion entry is removed along with the value when the two are lined up in a compaction. Note: The semantics are similar to @igor's prototype that allowed to have this behavior on the granularity of a column family ( https://reviews.facebook.net/D42093 ). This new patch, however, is more aggressive when it comes to removing tombstones: It removes the SingleDelete together with the value whenever there is no snapshot between them while the older patch only did this when the sequence number of the deletion was older than the earliest snapshot. Most of the complex additions are in the Compaction Iterator, all other changes should be relatively straightforward. The patch also includes basic support for single deletions in db_stress and db_bench. Limitations: - Not compatible with cuckoo hash tables - Single deletions cannot be used in combination with merges and normal deletions on the same key (other keys are not affected by this) - Consecutive single deletions are currently not allowed (and older version of this patch supported this so it could be resurrected if needed) Test Plan: make all check Reviewers: yhchiang, sdong, rven, anthony, yoshinorim, igor Reviewed By: igor Subscribers: maykov, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D43179
2015-09-17 18:42:56 +00:00
}
if (!s.ok()) {
fprintf(stderr, "Operation failed: %s\n", s.ToString().c_str());
exit(1);
}
thread->stats.FinishedOps(nullptr, db, 1, kOthers);
Support for SingleDelete() Summary: This patch fixes #7460559. It introduces SingleDelete as a new database operation. This operation can be used to delete keys that were never overwritten (no put following another put of the same key). If an overwritten key is single deleted the behavior is undefined. Single deletion of a non-existent key has no effect but multiple consecutive single deletions are not allowed (see limitations). In contrast to the conventional Delete() operation, the deletion entry is removed along with the value when the two are lined up in a compaction. Note: The semantics are similar to @igor's prototype that allowed to have this behavior on the granularity of a column family ( https://reviews.facebook.net/D42093 ). This new patch, however, is more aggressive when it comes to removing tombstones: It removes the SingleDelete together with the value whenever there is no snapshot between them while the older patch only did this when the sequence number of the deletion was older than the earliest snapshot. Most of the complex additions are in the Compaction Iterator, all other changes should be relatively straightforward. The patch also includes basic support for single deletions in db_stress and db_bench. Limitations: - Not compatible with cuckoo hash tables - Single deletions cannot be used in combination with merges and normal deletions on the same key (other keys are not affected by this) - Consecutive single deletions are currently not allowed (and older version of this patch supported this so it could be resurrected if needed) Test Plan: make all check Reviewers: yhchiang, sdong, rven, anthony, yoshinorim, igor Reviewed By: igor Subscribers: maykov, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D43179
2015-09-17 18:42:56 +00:00
}
char msg[200];
snprintf(msg, sizeof(msg),
"use single deletes: %d, "
"standard deviation: %lf\n",
FLAGS_use_single_deletes, FLAGS_stddev);
thread->stats.AddMessage(msg);
}
Write a benchmark to emulate time series data Summary: Add a benchmark to `db_bench`. In this benchmark, a write thread will populate time series data in the format of 'id | timestamp', and multiple read threads will randomly retrieve all data from one id at a time. Test Plan: Run the benchmark: `num=134217728;bpl=536870912;mb=67108864;overlap=10;mcz=2;del=300000000;levels=6;ctrig=4;delay=8;stop=12;wbn=3;mbc=20;wbs=134217728;dds=0;sync=0;t=32;vs=800;bs=4096;cs=17179869184;of=500000;wps=0;si=10000000; kir=100000; dir=/data/users/jhli/test/; ./db_bench --benchmarks=timeseries --disable_seek_compaction=1 --mmap_read=0 --statistics=1 --histogram=1 --num=$num --threads=$t --value_size=$vs --block_size=$bs --cache_size=$cs --bloom_bits=10 --cache_numshardbits=6 --open_files=$of --verify_checksum=1 --db=$dir --sync=$sync --disable_wal=0 --compression_type=none --stats_interval=$si --compression_ratio=1 --disable_data_sync=$dds --write_buffer_size=$wbs --target_file_size_base=$mb --max_write_buffer_number=$wbn --max_background_compactions=$mbc --level0_file_num_compaction_trigger=$ctrig --level0_slowdown_writes_trigger=$delay --level0_stop_writes_trigger=$stop --num_levels=$levels --delete_obsolete_files_period_micros=$del --min_level_to_compress=$mcz --max_grandparent_overlap_factor=$overlap --stats_per_interval=1 --max_bytes_for_level_base=$bpl --use_existing_db=0 --key_id_range=$kir` Reviewers: andrewkr, sdong Reviewed By: sdong Subscribers: lgalanis, andrewkr, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D60651
2016-08-01 19:28:51 +00:00
void TimeSeriesReadOrDelete(ThreadState* thread, bool do_deletion) {
int64_t read = 0;
int64_t found = 0;
int64_t bytes = 0;
Iterator* iter = nullptr;
// Only work on single database
assert(db_.db != nullptr);
Ignore `total_order_seek` in DB::Get (#9427) Summary: Apparently setting total_order_seek=true for DB::Get was intended to allow accurate read semantics if the current prefix extractor doesn't match what was used to generate SST files on disk. But since prefix_extractor was made a mutable option in 5.14.0, we have been able to detect this case and provide the correct semantics regardless of the total_order_seek option. Since that time, the option has only made Get() slower in a reasonably common case: prefix_extractor unchanged and whole_key_filtering=false. So this change primarily removes unnecessary effect of total_order_seek on Get. Also cleans up some related comments. Also adds a -total_order_seek option to db_bench and canonicalizes handling of ReadOptions in db_bench so that command line options have the expected association with library features. (There is potential for change in regression test behavior, but the old behavior is likely indefensible, or some other inconsistency would need to be fixed.) TODO in follow-up work: there should be no reason for Get() to depend on current prefix extractor at all. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9427 Test Plan: Unit tests updated. Performance (using db_bench update) Create DB with `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -benchmarks=fillrandom -num=10000000 -disable_wal=1 -write_buffer_size=10000000 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12 -whole_key_filtering=0` Test with and without `-total_order_seek` on `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -use_existing_db -readonly -benchmarks=readrandom -num=10000000 -duration=40 -disable_wal=1 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12` Before this change, total_order_seek=false: 25188 ops/sec Before this change, total_order_seek=true: 1222 ops/sec (~20x slower) After this change, total_order_seek=false: 24570 ops/sec After this change, total_order_seek=true: 25012 ops/sec (indistinguishable) Reviewed By: siying Differential Revision: D33753458 Pulled By: pdillinger fbshipit-source-id: bf892f34907a5e407d9c40bd4d42f0adbcbe0014
2022-02-01 03:45:17 +00:00
iter = db_.db->NewIterator(read_options_);
Write a benchmark to emulate time series data Summary: Add a benchmark to `db_bench`. In this benchmark, a write thread will populate time series data in the format of 'id | timestamp', and multiple read threads will randomly retrieve all data from one id at a time. Test Plan: Run the benchmark: `num=134217728;bpl=536870912;mb=67108864;overlap=10;mcz=2;del=300000000;levels=6;ctrig=4;delay=8;stop=12;wbn=3;mbc=20;wbs=134217728;dds=0;sync=0;t=32;vs=800;bs=4096;cs=17179869184;of=500000;wps=0;si=10000000; kir=100000; dir=/data/users/jhli/test/; ./db_bench --benchmarks=timeseries --disable_seek_compaction=1 --mmap_read=0 --statistics=1 --histogram=1 --num=$num --threads=$t --value_size=$vs --block_size=$bs --cache_size=$cs --bloom_bits=10 --cache_numshardbits=6 --open_files=$of --verify_checksum=1 --db=$dir --sync=$sync --disable_wal=0 --compression_type=none --stats_interval=$si --compression_ratio=1 --disable_data_sync=$dds --write_buffer_size=$wbs --target_file_size_base=$mb --max_write_buffer_number=$wbn --max_background_compactions=$mbc --level0_file_num_compaction_trigger=$ctrig --level0_slowdown_writes_trigger=$delay --level0_stop_writes_trigger=$stop --num_levels=$levels --delete_obsolete_files_period_micros=$del --min_level_to_compress=$mcz --max_grandparent_overlap_factor=$overlap --stats_per_interval=1 --max_bytes_for_level_base=$bpl --use_existing_db=0 --key_id_range=$kir` Reviewers: andrewkr, sdong Reviewed By: sdong Subscribers: lgalanis, andrewkr, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D60651
2016-08-01 19:28:51 +00:00
std::unique_ptr<const char[]> key_guard;
Slice key = AllocateKey(&key_guard);
char value_buffer[256];
while (true) {
{
MutexLock l(&thread->shared->mu);
if (thread->shared->num_done >= 1) {
// Write thread have finished
break;
}
}
if (!FLAGS_use_tailing_iterator) {
delete iter;
Ignore `total_order_seek` in DB::Get (#9427) Summary: Apparently setting total_order_seek=true for DB::Get was intended to allow accurate read semantics if the current prefix extractor doesn't match what was used to generate SST files on disk. But since prefix_extractor was made a mutable option in 5.14.0, we have been able to detect this case and provide the correct semantics regardless of the total_order_seek option. Since that time, the option has only made Get() slower in a reasonably common case: prefix_extractor unchanged and whole_key_filtering=false. So this change primarily removes unnecessary effect of total_order_seek on Get. Also cleans up some related comments. Also adds a -total_order_seek option to db_bench and canonicalizes handling of ReadOptions in db_bench so that command line options have the expected association with library features. (There is potential for change in regression test behavior, but the old behavior is likely indefensible, or some other inconsistency would need to be fixed.) TODO in follow-up work: there should be no reason for Get() to depend on current prefix extractor at all. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9427 Test Plan: Unit tests updated. Performance (using db_bench update) Create DB with `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -benchmarks=fillrandom -num=10000000 -disable_wal=1 -write_buffer_size=10000000 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12 -whole_key_filtering=0` Test with and without `-total_order_seek` on `TEST_TMPDIR=/dev/shm/rocksdb ./db_bench -use_existing_db -readonly -benchmarks=readrandom -num=10000000 -duration=40 -disable_wal=1 -bloom_bits=16 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -prefix_size=12` Before this change, total_order_seek=false: 25188 ops/sec Before this change, total_order_seek=true: 1222 ops/sec (~20x slower) After this change, total_order_seek=false: 24570 ops/sec After this change, total_order_seek=true: 25012 ops/sec (indistinguishable) Reviewed By: siying Differential Revision: D33753458 Pulled By: pdillinger fbshipit-source-id: bf892f34907a5e407d9c40bd4d42f0adbcbe0014
2022-02-01 03:45:17 +00:00
iter = db_.db->NewIterator(read_options_);
Write a benchmark to emulate time series data Summary: Add a benchmark to `db_bench`. In this benchmark, a write thread will populate time series data in the format of 'id | timestamp', and multiple read threads will randomly retrieve all data from one id at a time. Test Plan: Run the benchmark: `num=134217728;bpl=536870912;mb=67108864;overlap=10;mcz=2;del=300000000;levels=6;ctrig=4;delay=8;stop=12;wbn=3;mbc=20;wbs=134217728;dds=0;sync=0;t=32;vs=800;bs=4096;cs=17179869184;of=500000;wps=0;si=10000000; kir=100000; dir=/data/users/jhli/test/; ./db_bench --benchmarks=timeseries --disable_seek_compaction=1 --mmap_read=0 --statistics=1 --histogram=1 --num=$num --threads=$t --value_size=$vs --block_size=$bs --cache_size=$cs --bloom_bits=10 --cache_numshardbits=6 --open_files=$of --verify_checksum=1 --db=$dir --sync=$sync --disable_wal=0 --compression_type=none --stats_interval=$si --compression_ratio=1 --disable_data_sync=$dds --write_buffer_size=$wbs --target_file_size_base=$mb --max_write_buffer_number=$wbn --max_background_compactions=$mbc --level0_file_num_compaction_trigger=$ctrig --level0_slowdown_writes_trigger=$delay --level0_stop_writes_trigger=$stop --num_levels=$levels --delete_obsolete_files_period_micros=$del --min_level_to_compress=$mcz --max_grandparent_overlap_factor=$overlap --stats_per_interval=1 --max_bytes_for_level_base=$bpl --use_existing_db=0 --key_id_range=$kir` Reviewers: andrewkr, sdong Reviewed By: sdong Subscribers: lgalanis, andrewkr, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D60651
2016-08-01 19:28:51 +00:00
}
// Pick a Iterator to use
int64_t key_id = thread->rand.Next() % FLAGS_key_id_range;
GenerateKeyFromInt(key_id, FLAGS_num, &key);
// Reset last 8 bytes to 0
char* start = const_cast<char*>(key.data());
start += key.size() - 8;
memset(start, 0, 8);
++read;
bool key_found = false;
// Seek the prefix
for (iter->Seek(key); iter->Valid() && iter->key().starts_with(key);
iter->Next()) {
key_found = true;
// Copy out iterator's value to make sure we read them.
if (do_deletion) {
bytes += iter->key().size();
if (KeyExpired(timestamp_emulator_.get(), iter->key())) {
thread->stats.FinishedOps(&db_, db_.db, 1, kDelete);
db_.db->Delete(write_options_, iter->key());
} else {
break;
}
} else {
bytes += iter->key().size() + iter->value().size();
thread->stats.FinishedOps(&db_, db_.db, 1, kRead);
Slice value = iter->value();
memcpy(value_buffer, value.data(),
std::min(value.size(), sizeof(value_buffer)));
assert(iter->status().ok());
}
}
found += key_found;
if (thread->shared->read_rate_limiter.get() != nullptr) {
thread->shared->read_rate_limiter->Request(
1, Env::IO_HIGH, nullptr /* stats */, RateLimiter::OpType::kRead);
}
Write a benchmark to emulate time series data Summary: Add a benchmark to `db_bench`. In this benchmark, a write thread will populate time series data in the format of 'id | timestamp', and multiple read threads will randomly retrieve all data from one id at a time. Test Plan: Run the benchmark: `num=134217728;bpl=536870912;mb=67108864;overlap=10;mcz=2;del=300000000;levels=6;ctrig=4;delay=8;stop=12;wbn=3;mbc=20;wbs=134217728;dds=0;sync=0;t=32;vs=800;bs=4096;cs=17179869184;of=500000;wps=0;si=10000000; kir=100000; dir=/data/users/jhli/test/; ./db_bench --benchmarks=timeseries --disable_seek_compaction=1 --mmap_read=0 --statistics=1 --histogram=1 --num=$num --threads=$t --value_size=$vs --block_size=$bs --cache_size=$cs --bloom_bits=10 --cache_numshardbits=6 --open_files=$of --verify_checksum=1 --db=$dir --sync=$sync --disable_wal=0 --compression_type=none --stats_interval=$si --compression_ratio=1 --disable_data_sync=$dds --write_buffer_size=$wbs --target_file_size_base=$mb --max_write_buffer_number=$wbn --max_background_compactions=$mbc --level0_file_num_compaction_trigger=$ctrig --level0_slowdown_writes_trigger=$delay --level0_stop_writes_trigger=$stop --num_levels=$levels --delete_obsolete_files_period_micros=$del --min_level_to_compress=$mcz --max_grandparent_overlap_factor=$overlap --stats_per_interval=1 --max_bytes_for_level_base=$bpl --use_existing_db=0 --key_id_range=$kir` Reviewers: andrewkr, sdong Reviewed By: sdong Subscribers: lgalanis, andrewkr, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D60651
2016-08-01 19:28:51 +00:00
}
delete iter;
char msg[100];
snprintf(msg, sizeof(msg), "(%" PRIu64 " of %" PRIu64 " found)", found,
read);
thread->stats.AddBytes(bytes);
thread->stats.AddMessage(msg);
}
void TimeSeriesWrite(ThreadState* thread) {
// Special thread that keeps writing until other threads are done.
RandomGenerator gen;
int64_t bytes = 0;
// Don't merge stats from this thread with the readers.
thread->stats.SetExcludeFromMerge();
std::unique_ptr<RateLimiter> write_rate_limiter;
if (FLAGS_benchmark_write_rate_limit > 0) {
write_rate_limiter.reset(
NewGenericRateLimiter(FLAGS_benchmark_write_rate_limit));
}
std::unique_ptr<const char[]> key_guard;
Slice key = AllocateKey(&key_guard);
Duration duration(FLAGS_duration, writes_);
while (!duration.Done(1)) {
DB* db = SelectDB(thread);
uint64_t key_id = thread->rand.Next() % FLAGS_key_id_range;
// Write key id
GenerateKeyFromInt(key_id, FLAGS_num, &key);
// Write timestamp
char* start = const_cast<char*>(key.data());
char* pos = start + 8;
int bytes_to_fill =
std::min(key_size_ - static_cast<int>(pos - start), 8);
uint64_t timestamp_value = timestamp_emulator_->Get();
if (port::kLittleEndian) {
for (int i = 0; i < bytes_to_fill; ++i) {
pos[i] = (timestamp_value >> ((bytes_to_fill - i - 1) << 3)) & 0xFF;
}
} else {
memcpy(pos, static_cast<void*>(&timestamp_value), bytes_to_fill);
}
timestamp_emulator_->Inc();
Status s;
Slice val = gen.Generate();
s = db->Put(write_options_, key, val);
Write a benchmark to emulate time series data Summary: Add a benchmark to `db_bench`. In this benchmark, a write thread will populate time series data in the format of 'id | timestamp', and multiple read threads will randomly retrieve all data from one id at a time. Test Plan: Run the benchmark: `num=134217728;bpl=536870912;mb=67108864;overlap=10;mcz=2;del=300000000;levels=6;ctrig=4;delay=8;stop=12;wbn=3;mbc=20;wbs=134217728;dds=0;sync=0;t=32;vs=800;bs=4096;cs=17179869184;of=500000;wps=0;si=10000000; kir=100000; dir=/data/users/jhli/test/; ./db_bench --benchmarks=timeseries --disable_seek_compaction=1 --mmap_read=0 --statistics=1 --histogram=1 --num=$num --threads=$t --value_size=$vs --block_size=$bs --cache_size=$cs --bloom_bits=10 --cache_numshardbits=6 --open_files=$of --verify_checksum=1 --db=$dir --sync=$sync --disable_wal=0 --compression_type=none --stats_interval=$si --compression_ratio=1 --disable_data_sync=$dds --write_buffer_size=$wbs --target_file_size_base=$mb --max_write_buffer_number=$wbn --max_background_compactions=$mbc --level0_file_num_compaction_trigger=$ctrig --level0_slowdown_writes_trigger=$delay --level0_stop_writes_trigger=$stop --num_levels=$levels --delete_obsolete_files_period_micros=$del --min_level_to_compress=$mcz --max_grandparent_overlap_factor=$overlap --stats_per_interval=1 --max_bytes_for_level_base=$bpl --use_existing_db=0 --key_id_range=$kir` Reviewers: andrewkr, sdong Reviewed By: sdong Subscribers: lgalanis, andrewkr, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D60651
2016-08-01 19:28:51 +00:00
if (!s.ok()) {
fprintf(stderr, "put error: %s\n", s.ToString().c_str());
ErrorExit();
Write a benchmark to emulate time series data Summary: Add a benchmark to `db_bench`. In this benchmark, a write thread will populate time series data in the format of 'id | timestamp', and multiple read threads will randomly retrieve all data from one id at a time. Test Plan: Run the benchmark: `num=134217728;bpl=536870912;mb=67108864;overlap=10;mcz=2;del=300000000;levels=6;ctrig=4;delay=8;stop=12;wbn=3;mbc=20;wbs=134217728;dds=0;sync=0;t=32;vs=800;bs=4096;cs=17179869184;of=500000;wps=0;si=10000000; kir=100000; dir=/data/users/jhli/test/; ./db_bench --benchmarks=timeseries --disable_seek_compaction=1 --mmap_read=0 --statistics=1 --histogram=1 --num=$num --threads=$t --value_size=$vs --block_size=$bs --cache_size=$cs --bloom_bits=10 --cache_numshardbits=6 --open_files=$of --verify_checksum=1 --db=$dir --sync=$sync --disable_wal=0 --compression_type=none --stats_interval=$si --compression_ratio=1 --disable_data_sync=$dds --write_buffer_size=$wbs --target_file_size_base=$mb --max_write_buffer_number=$wbn --max_background_compactions=$mbc --level0_file_num_compaction_trigger=$ctrig --level0_slowdown_writes_trigger=$delay --level0_stop_writes_trigger=$stop --num_levels=$levels --delete_obsolete_files_period_micros=$del --min_level_to_compress=$mcz --max_grandparent_overlap_factor=$overlap --stats_per_interval=1 --max_bytes_for_level_base=$bpl --use_existing_db=0 --key_id_range=$kir` Reviewers: andrewkr, sdong Reviewed By: sdong Subscribers: lgalanis, andrewkr, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D60651
2016-08-01 19:28:51 +00:00
}
bytes = key.size() + val.size();
Write a benchmark to emulate time series data Summary: Add a benchmark to `db_bench`. In this benchmark, a write thread will populate time series data in the format of 'id | timestamp', and multiple read threads will randomly retrieve all data from one id at a time. Test Plan: Run the benchmark: `num=134217728;bpl=536870912;mb=67108864;overlap=10;mcz=2;del=300000000;levels=6;ctrig=4;delay=8;stop=12;wbn=3;mbc=20;wbs=134217728;dds=0;sync=0;t=32;vs=800;bs=4096;cs=17179869184;of=500000;wps=0;si=10000000; kir=100000; dir=/data/users/jhli/test/; ./db_bench --benchmarks=timeseries --disable_seek_compaction=1 --mmap_read=0 --statistics=1 --histogram=1 --num=$num --threads=$t --value_size=$vs --block_size=$bs --cache_size=$cs --bloom_bits=10 --cache_numshardbits=6 --open_files=$of --verify_checksum=1 --db=$dir --sync=$sync --disable_wal=0 --compression_type=none --stats_interval=$si --compression_ratio=1 --disable_data_sync=$dds --write_buffer_size=$wbs --target_file_size_base=$mb --max_write_buffer_number=$wbn --max_background_compactions=$mbc --level0_file_num_compaction_trigger=$ctrig --level0_slowdown_writes_trigger=$delay --level0_stop_writes_trigger=$stop --num_levels=$levels --delete_obsolete_files_period_micros=$del --min_level_to_compress=$mcz --max_grandparent_overlap_factor=$overlap --stats_per_interval=1 --max_bytes_for_level_base=$bpl --use_existing_db=0 --key_id_range=$kir` Reviewers: andrewkr, sdong Reviewed By: sdong Subscribers: lgalanis, andrewkr, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D60651
2016-08-01 19:28:51 +00:00
thread->stats.FinishedOps(&db_, db_.db, 1, kWrite);
thread->stats.AddBytes(bytes);
if (FLAGS_benchmark_write_rate_limit > 0) {
write_rate_limiter->Request(key.size() + val.size(), Env::IO_HIGH,
nullptr /* stats */,
RateLimiter::OpType::kWrite);
Write a benchmark to emulate time series data Summary: Add a benchmark to `db_bench`. In this benchmark, a write thread will populate time series data in the format of 'id | timestamp', and multiple read threads will randomly retrieve all data from one id at a time. Test Plan: Run the benchmark: `num=134217728;bpl=536870912;mb=67108864;overlap=10;mcz=2;del=300000000;levels=6;ctrig=4;delay=8;stop=12;wbn=3;mbc=20;wbs=134217728;dds=0;sync=0;t=32;vs=800;bs=4096;cs=17179869184;of=500000;wps=0;si=10000000; kir=100000; dir=/data/users/jhli/test/; ./db_bench --benchmarks=timeseries --disable_seek_compaction=1 --mmap_read=0 --statistics=1 --histogram=1 --num=$num --threads=$t --value_size=$vs --block_size=$bs --cache_size=$cs --bloom_bits=10 --cache_numshardbits=6 --open_files=$of --verify_checksum=1 --db=$dir --sync=$sync --disable_wal=0 --compression_type=none --stats_interval=$si --compression_ratio=1 --disable_data_sync=$dds --write_buffer_size=$wbs --target_file_size_base=$mb --max_write_buffer_number=$wbn --max_background_compactions=$mbc --level0_file_num_compaction_trigger=$ctrig --level0_slowdown_writes_trigger=$delay --level0_stop_writes_trigger=$stop --num_levels=$levels --delete_obsolete_files_period_micros=$del --min_level_to_compress=$mcz --max_grandparent_overlap_factor=$overlap --stats_per_interval=1 --max_bytes_for_level_base=$bpl --use_existing_db=0 --key_id_range=$kir` Reviewers: andrewkr, sdong Reviewed By: sdong Subscribers: lgalanis, andrewkr, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D60651
2016-08-01 19:28:51 +00:00
}
}
}
void TimeSeries(ThreadState* thread) {
if (thread->tid > 0) {
bool do_deletion = FLAGS_expire_style == "delete" &&
thread->tid <= FLAGS_num_deletion_threads;
TimeSeriesReadOrDelete(thread, do_deletion);
} else {
TimeSeriesWrite(thread);
thread->stats.Stop();
thread->stats.Report("timeseries write");
}
}
void Compact(ThreadState* thread) {
DB* db = SelectDB(thread);
CompactRangeOptions cro;
cro.bottommost_level_compaction =
BottommostLevelCompaction::kForceOptimized;
cro.max_subcompactions = static_cast<uint32_t>(FLAGS_subcompactions);
db->CompactRange(cro, nullptr, nullptr);
}
void CompactAll() {
CompactRangeOptions cro;
cro.max_subcompactions = static_cast<uint32_t>(FLAGS_subcompactions);
if (db_.db != nullptr) {
db_.db->CompactRange(cro, nullptr, nullptr);
}
for (const auto& db_with_cfh : multi_dbs_) {
db_with_cfh.db->CompactRange(cro, nullptr, nullptr);
}
}
void WaitForCompactionHelper(DBWithColumnFamilies& db) {
fprintf(stdout, "waitforcompaction(%s): started\n",
db.db->GetName().c_str());
Replace existing waitforcompaction with new WaitForCompact API in db_bench_tool (#11727) Summary: As the new API to wait for compaction is available (https://github.com/facebook/rocksdb/issues/11436), we can now replace the existing logic of waiting in db_bench_tool with the new API. Pull Request resolved: https://github.com/facebook/rocksdb/pull/11727 Test Plan: ``` ./db_bench --benchmarks="fillrandom,compactall,waitforcompaction,readrandom" ``` **Before change** ``` Set seed to 1692635571470041 because --seed was 0 Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags Integrated BlobDB: blob cache disabled RocksDB: version 8.6.0 Date: Mon Aug 21 09:33:40 2023 CPU: 80 * Intel(R) Xeon(R) Gold 6138 CPU @ 2.00GHz CPUCache: 28160 KB Keys: 16 bytes each (+ 0 bytes user-defined timestamp) Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Read rate: 0 ops/second Compression: Snappy Compression sampling rate: 0 Memtablerep: SkipListFactory Perf Level: 1 WARNING: Optimization is disabled: benchmarks unnecessarily slow WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags Integrated BlobDB: blob cache disabled DB path: [/tmp/rocksdbtest-226125/dbbench] fillrandom : 51.826 micros/op 19295 ops/sec 51.826 seconds 1000000 operations; 2.1 MB/s waitforcompaction(/tmp/rocksdbtest-226125/dbbench): started waitforcompaction(/tmp/rocksdbtest-226125/dbbench): finished waitforcompaction(/tmp/rocksdbtest-226125/dbbench): started waitforcompaction(/tmp/rocksdbtest-226125/dbbench): finished DB path: [/tmp/rocksdbtest-226125/dbbench] readrandom : 39.042 micros/op 25613 ops/sec 39.042 seconds 1000000 operations; 1.8 MB/s (632886 of 1000000 found) ``` **After change** ``` Set seed to 1692636574431745 because --seed was 0 Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags Integrated BlobDB: blob cache disabled RocksDB: version 8.6.0 Date: Mon Aug 21 09:49:34 2023 CPU: 80 * Intel(R) Xeon(R) Gold 6138 CPU @ 2.00GHz CPUCache: 28160 KB Keys: 16 bytes each (+ 0 bytes user-defined timestamp) Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Read rate: 0 ops/second Compression: Snappy Compression sampling rate: 0 Memtablerep: SkipListFactory Perf Level: 1 WARNING: Optimization is disabled: benchmarks unnecessarily slow WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags Integrated BlobDB: blob cache disabled DB path: [/tmp/rocksdbtest-226125/dbbench] fillrandom : 51.271 micros/op 19504 ops/sec 51.271 seconds 1000000 operations; 2.2 MB/s waitforcompaction(/tmp/rocksdbtest-226125/dbbench): started waitforcompaction(/tmp/rocksdbtest-226125/dbbench): finished with status (OK) DB path: [/tmp/rocksdbtest-226125/dbbench] readrandom : 39.264 micros/op 25468 ops/sec 39.264 seconds 1000000 operations; 1.8 MB/s (632921 of 1000000 found) ``` Reviewed By: ajkr Differential Revision: D48524667 Pulled By: jaykorean fbshipit-source-id: 1052a15b2ed79a35165ec4d9998d0454b2552ef4
2023-08-21 19:14:57 +00:00
Status s = db.db->WaitForCompact(WaitForCompactOptions());
Replace existing waitforcompaction with new WaitForCompact API in db_bench_tool (#11727) Summary: As the new API to wait for compaction is available (https://github.com/facebook/rocksdb/issues/11436), we can now replace the existing logic of waiting in db_bench_tool with the new API. Pull Request resolved: https://github.com/facebook/rocksdb/pull/11727 Test Plan: ``` ./db_bench --benchmarks="fillrandom,compactall,waitforcompaction,readrandom" ``` **Before change** ``` Set seed to 1692635571470041 because --seed was 0 Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags Integrated BlobDB: blob cache disabled RocksDB: version 8.6.0 Date: Mon Aug 21 09:33:40 2023 CPU: 80 * Intel(R) Xeon(R) Gold 6138 CPU @ 2.00GHz CPUCache: 28160 KB Keys: 16 bytes each (+ 0 bytes user-defined timestamp) Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Read rate: 0 ops/second Compression: Snappy Compression sampling rate: 0 Memtablerep: SkipListFactory Perf Level: 1 WARNING: Optimization is disabled: benchmarks unnecessarily slow WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags Integrated BlobDB: blob cache disabled DB path: [/tmp/rocksdbtest-226125/dbbench] fillrandom : 51.826 micros/op 19295 ops/sec 51.826 seconds 1000000 operations; 2.1 MB/s waitforcompaction(/tmp/rocksdbtest-226125/dbbench): started waitforcompaction(/tmp/rocksdbtest-226125/dbbench): finished waitforcompaction(/tmp/rocksdbtest-226125/dbbench): started waitforcompaction(/tmp/rocksdbtest-226125/dbbench): finished DB path: [/tmp/rocksdbtest-226125/dbbench] readrandom : 39.042 micros/op 25613 ops/sec 39.042 seconds 1000000 operations; 1.8 MB/s (632886 of 1000000 found) ``` **After change** ``` Set seed to 1692636574431745 because --seed was 0 Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags Integrated BlobDB: blob cache disabled RocksDB: version 8.6.0 Date: Mon Aug 21 09:49:34 2023 CPU: 80 * Intel(R) Xeon(R) Gold 6138 CPU @ 2.00GHz CPUCache: 28160 KB Keys: 16 bytes each (+ 0 bytes user-defined timestamp) Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Read rate: 0 ops/second Compression: Snappy Compression sampling rate: 0 Memtablerep: SkipListFactory Perf Level: 1 WARNING: Optimization is disabled: benchmarks unnecessarily slow WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags Integrated BlobDB: blob cache disabled DB path: [/tmp/rocksdbtest-226125/dbbench] fillrandom : 51.271 micros/op 19504 ops/sec 51.271 seconds 1000000 operations; 2.2 MB/s waitforcompaction(/tmp/rocksdbtest-226125/dbbench): started waitforcompaction(/tmp/rocksdbtest-226125/dbbench): finished with status (OK) DB path: [/tmp/rocksdbtest-226125/dbbench] readrandom : 39.264 micros/op 25468 ops/sec 39.264 seconds 1000000 operations; 1.8 MB/s (632921 of 1000000 found) ``` Reviewed By: ajkr Differential Revision: D48524667 Pulled By: jaykorean fbshipit-source-id: 1052a15b2ed79a35165ec4d9998d0454b2552ef4
2023-08-21 19:14:57 +00:00
fprintf(stdout, "waitforcompaction(%s): finished with status (%s)\n",
db.db->GetName().c_str(), s.ToString().c_str());
}
void WaitForCompaction() {
// Give background threads a chance to wake
FLAGS_env->SleepForMicroseconds(5 * 1000000);
if (db_.db != nullptr) {
WaitForCompactionHelper(db_);
} else {
for (auto& db_with_cfh : multi_dbs_) {
WaitForCompactionHelper(db_with_cfh);
}
}
}
bool CompactLevelHelper(DBWithColumnFamilies& db_with_cfh, int from_level) {
std::vector<LiveFileMetaData> files;
db_with_cfh.db->GetLiveFilesMetaData(&files);
assert(from_level == 0 || from_level == 1);
int real_from_level = from_level;
if (real_from_level > 0) {
// With dynamic leveled compaction the first level with data beyond L0
// might not be L1.
real_from_level = std::numeric_limits<int>::max();
for (auto& f : files) {
if (f.level > 0 && f.level < real_from_level) {
real_from_level = f.level;
}
}
if (real_from_level == std::numeric_limits<int>::max()) {
fprintf(stdout, "compact%d found 0 files to compact\n", from_level);
return true;
}
}
// The goal is to compact from from_level to the level that follows it,
// and with dynamic leveled compaction the next level might not be
// real_from_level+1
int next_level = std::numeric_limits<int>::max();
std::vector<std::string> files_to_compact;
for (auto& f : files) {
if (f.level == real_from_level) {
files_to_compact.push_back(f.name);
} else if (f.level > real_from_level && f.level < next_level) {
next_level = f.level;
}
}
if (files_to_compact.empty()) {
fprintf(stdout, "compact%d found 0 files to compact\n", from_level);
return true;
} else if (next_level == std::numeric_limits<int>::max()) {
// There is no data beyond real_from_level. So we are done.
fprintf(stdout, "compact%d found no data beyond L%d\n", from_level,
real_from_level);
return true;
}
fprintf(stdout, "compact%d found %d files to compact from L%d to L%d\n",
from_level, static_cast<int>(files_to_compact.size()),
real_from_level, next_level);
ROCKSDB_NAMESPACE::CompactionOptions options;
// Lets RocksDB use the configured compression for this level
options.compression = ROCKSDB_NAMESPACE::kDisableCompressionOption;
ROCKSDB_NAMESPACE::ColumnFamilyDescriptor cfDesc;
db_with_cfh.db->DefaultColumnFamily()->GetDescriptor(&cfDesc);
options.output_file_size_limit = cfDesc.options.target_file_size_base;
Status status =
db_with_cfh.db->CompactFiles(options, files_to_compact, next_level);
if (!status.ok()) {
// This can fail for valid reasons including the operation was aborted
// or a filename is invalid because background compaction removed it.
// Having read the current cases for which an error is raised I prefer
// not to figure out whether an exception should be thrown here.
fprintf(stderr, "compact%d CompactFiles failed: %s\n", from_level,
status.ToString().c_str());
return false;
}
return true;
}
void CompactLevel(int from_level) {
if (db_.db != nullptr) {
while (!CompactLevelHelper(db_, from_level)) {
WaitForCompaction();
}
}
for (auto& db_with_cfh : multi_dbs_) {
while (!CompactLevelHelper(db_with_cfh, from_level)) {
WaitForCompaction();
}
}
}
void Flush() {
FlushOptions flush_opt;
flush_opt.wait = true;
if (db_.db != nullptr) {
Make --benchmarks=flush flush the default column family (#9887) Summary: db_bench --benchmarks=flush wasn't flushing the default column family. This is for https://github.com/facebook/rocksdb/issues/9880 Pull Request resolved: https://github.com/facebook/rocksdb/pull/9887 Test Plan: Confirm that flush works (*.log is empty) when "flush" added to benchmark list Confirm that *.log is not empty otherwise. Repeat for all combinations for: uses column families, uses multiple databases ./db_bench --benchmarks=overwrite --num=10000 ls -lrt /tmp/rocksdbtest-2260/dbbench/*.log -rw-r--r-- 1 me users 1380286 Apr 21 10:47 /tmp/rocksdbtest-2260/dbbench/000004.log ./db_bench --benchmarks=overwrite,flush --num=10000 ls -lrt /tmp/rocksdbtest-2260/dbbench/*.log -rw-r--r-- 1 me users 0 Apr 21 10:48 /tmp/rocksdbtest-2260/dbbench/000008.log ./db_bench --benchmarks=overwrite --num=10000 --num_column_families=4 ls -lrt /tmp/rocksdbtest-2260/dbbench/*.log -rw-r--r-- 1 me users 1387823 Apr 21 10:49 /tmp/rocksdbtest-2260/dbbench/000004.log ./db_bench --benchmarks=overwrite,flush --num=10000 --num_column_families=4 ls -lrt /tmp/rocksdbtest-2260/dbbench/*.log -rw-r--r-- 1 me users 0 Apr 21 10:51 /tmp/rocksdbtest-2260/dbbench/000014.log ./db_bench --benchmarks=overwrite --num=10000 --num_multi_db=2 ls -lrt /tmp/rocksdbtest-2260/dbbench/[01]/*.log -rw-r--r-- 1 me users 1380838 Apr 21 10:55 /tmp/rocksdbtest-2260/dbbench/0/000004.log -rw-r--r-- 1 me users 1379734 Apr 21 10:55 /tmp/rocksdbtest-2260/dbbench/1/000004.log ./db_bench --benchmarks=overwrite,flush --num=10000 --num_multi_db=2 ls -lrt /tmp/rocksdbtest-2260/dbbench/[01]/*.log -rw-r--r-- 1 me users 0 Apr 21 10:57 /tmp/rocksdbtest-2260/dbbench/0/000013.log -rw-r--r-- 1 me users 0 Apr 21 10:57 /tmp/rocksdbtest-2260/dbbench/1/000013.log ./db_bench --benchmarks=overwrite --num=10000 --num_column_families=4 --num_multi_db=2 ls -lrt /tmp/rocksdbtest-2260/dbbench/[01]/*.log -rw-r--r-- 1 me users 1395108 Apr 21 10:52 /tmp/rocksdbtest-2260/dbbench/1/000004.log -rw-r--r-- 1 me users 1380411 Apr 21 10:52 /tmp/rocksdbtest-2260/dbbench/0/000004.log ./db_bench --benchmarks=overwrite,flush --num=10000 --num_column_families=4 --num_multi_db=2 ls -lrt /tmp/rocksdbtest-2260/dbbench/[01]/*.log -rw-r--r-- 1 me users 0 Apr 21 10:54 /tmp/rocksdbtest-2260/dbbench/0/000022.log -rw-r--r-- 1 me users 0 Apr 21 10:54 /tmp/rocksdbtest-2260/dbbench/1/000022.log Reviewed By: ajkr Differential Revision: D36026777 Pulled By: mdcallag fbshipit-source-id: d42d3d7efceea7b9a25bbbc0f04461d2b7301122
2022-05-03 16:37:49 +00:00
Status s;
if (FLAGS_num_column_families > 1) {
s = db_.db->Flush(flush_opt, db_.cfh);
} else {
s = db_.db->Flush(flush_opt, db_.db->DefaultColumnFamily());
}
if (!s.ok()) {
fprintf(stderr, "Flush failed: %s\n", s.ToString().c_str());
exit(1);
}
} else {
for (const auto& db_with_cfh : multi_dbs_) {
Make --benchmarks=flush flush the default column family (#9887) Summary: db_bench --benchmarks=flush wasn't flushing the default column family. This is for https://github.com/facebook/rocksdb/issues/9880 Pull Request resolved: https://github.com/facebook/rocksdb/pull/9887 Test Plan: Confirm that flush works (*.log is empty) when "flush" added to benchmark list Confirm that *.log is not empty otherwise. Repeat for all combinations for: uses column families, uses multiple databases ./db_bench --benchmarks=overwrite --num=10000 ls -lrt /tmp/rocksdbtest-2260/dbbench/*.log -rw-r--r-- 1 me users 1380286 Apr 21 10:47 /tmp/rocksdbtest-2260/dbbench/000004.log ./db_bench --benchmarks=overwrite,flush --num=10000 ls -lrt /tmp/rocksdbtest-2260/dbbench/*.log -rw-r--r-- 1 me users 0 Apr 21 10:48 /tmp/rocksdbtest-2260/dbbench/000008.log ./db_bench --benchmarks=overwrite --num=10000 --num_column_families=4 ls -lrt /tmp/rocksdbtest-2260/dbbench/*.log -rw-r--r-- 1 me users 1387823 Apr 21 10:49 /tmp/rocksdbtest-2260/dbbench/000004.log ./db_bench --benchmarks=overwrite,flush --num=10000 --num_column_families=4 ls -lrt /tmp/rocksdbtest-2260/dbbench/*.log -rw-r--r-- 1 me users 0 Apr 21 10:51 /tmp/rocksdbtest-2260/dbbench/000014.log ./db_bench --benchmarks=overwrite --num=10000 --num_multi_db=2 ls -lrt /tmp/rocksdbtest-2260/dbbench/[01]/*.log -rw-r--r-- 1 me users 1380838 Apr 21 10:55 /tmp/rocksdbtest-2260/dbbench/0/000004.log -rw-r--r-- 1 me users 1379734 Apr 21 10:55 /tmp/rocksdbtest-2260/dbbench/1/000004.log ./db_bench --benchmarks=overwrite,flush --num=10000 --num_multi_db=2 ls -lrt /tmp/rocksdbtest-2260/dbbench/[01]/*.log -rw-r--r-- 1 me users 0 Apr 21 10:57 /tmp/rocksdbtest-2260/dbbench/0/000013.log -rw-r--r-- 1 me users 0 Apr 21 10:57 /tmp/rocksdbtest-2260/dbbench/1/000013.log ./db_bench --benchmarks=overwrite --num=10000 --num_column_families=4 --num_multi_db=2 ls -lrt /tmp/rocksdbtest-2260/dbbench/[01]/*.log -rw-r--r-- 1 me users 1395108 Apr 21 10:52 /tmp/rocksdbtest-2260/dbbench/1/000004.log -rw-r--r-- 1 me users 1380411 Apr 21 10:52 /tmp/rocksdbtest-2260/dbbench/0/000004.log ./db_bench --benchmarks=overwrite,flush --num=10000 --num_column_families=4 --num_multi_db=2 ls -lrt /tmp/rocksdbtest-2260/dbbench/[01]/*.log -rw-r--r-- 1 me users 0 Apr 21 10:54 /tmp/rocksdbtest-2260/dbbench/0/000022.log -rw-r--r-- 1 me users 0 Apr 21 10:54 /tmp/rocksdbtest-2260/dbbench/1/000022.log Reviewed By: ajkr Differential Revision: D36026777 Pulled By: mdcallag fbshipit-source-id: d42d3d7efceea7b9a25bbbc0f04461d2b7301122
2022-05-03 16:37:49 +00:00
Status s;
if (FLAGS_num_column_families > 1) {
s = db_with_cfh.db->Flush(flush_opt, db_with_cfh.cfh);
} else {
s = db_with_cfh.db->Flush(flush_opt,
db_with_cfh.db->DefaultColumnFamily());
}
if (!s.ok()) {
fprintf(stderr, "Flush failed: %s\n", s.ToString().c_str());
exit(1);
}
}
}
fprintf(stdout, "flush memtable\n");
}
void ResetStats() {
if (db_.db != nullptr) {
db_.db->ResetStats();
}
for (const auto& db_with_cfh : multi_dbs_) {
db_with_cfh.db->ResetStats();
}
}
void PrintStatsHistory() {
if (db_.db != nullptr) {
PrintStatsHistoryImpl(db_.db, false);
}
for (const auto& db_with_cfh : multi_dbs_) {
PrintStatsHistoryImpl(db_with_cfh.db, true);
}
}
void PrintStatsHistoryImpl(DB* db, bool print_header) {
if (print_header) {
fprintf(stdout, "\n==== DB: %s ===\n", db->GetName().c_str());
}
std::unique_ptr<StatsHistoryIterator> shi;
Status s =
db->GetStatsHistory(0, std::numeric_limits<uint64_t>::max(), &shi);
if (!s.ok()) {
fprintf(stdout, "%s\n", s.ToString().c_str());
return;
}
assert(shi);
while (shi->Valid()) {
uint64_t stats_time = shi->GetStatsTime();
fprintf(stdout, "------ %s ------\n",
TimeToHumanString(static_cast<int>(stats_time)).c_str());
for (auto& entry : shi->GetStatsMap()) {
fprintf(stdout, " %" PRIu64 " %s %" PRIu64 "\n", stats_time,
entry.first.c_str(), entry.second);
}
shi->Next();
}
}
Automatic table sizing for HyperClockCache (AutoHCC) (#11738) Summary: This change add an experimental next-generation HyperClockCache (HCC) with automatic sizing of the underlying hash table. Both the existing version (stable) and the new version (experimental for now) of HCC are available depending on whether an estimated average entry charge is provided in HyperClockCacheOptions. Internally, we call the two implementations AutoHyperClockCache (new) and FixedHyperClockCache (existing). The performance characteristics and much of the underlying logic are similar enough that AutoHCC is likely to make FixedHCC obsolete, and so it's best considered an evolution of the same technology or solution rather than an alternative. More specifically, both implementations share essentially the same logic for managing the state of individual entries in the cache, including metadata for reference counting and counting clocks for eviction. This metadata, which I like to call the "low-level HCC protocol," includes a read-write lock on entries, but relaxed consistency requirements on the cache (e.g. allowing rare duplication) means high-level cache operations never wait for these low-level per-entry locks. FixedHCC is fully wait-free. AutoHCC is different in how entries are indexed into an efficient hash table. AutoHCC is "essentially wait-free" as there is no pattern of typical high-level operations on a large cache that can lead to one thread waiting on another to complete some work, though it can happen in some unusual/unlucky cases, or atypical uses such as erasing specific cache keys. Table growth and entry reclamation is more complex in AutoHCC compared to FixedHCC, so uses some localized locking to manage that. AutoHCC uses linear hashing to grow the table as needed, with low latency and to a precise size. AutoHCC depends on anonymous mmap support from the OS (currently verified working on Linux, MacOS, and Windows) to allow the array underlying a hash table to grow in place without wasting resident memory on space reserved but unused. AutoHCC uses a form of chaining while FixedHCC uses open addressing and double hashing. More specifics: * In developing this PR, a rare availability bug (minor) was noticed in the existing HCC implementation of Release()+erase_if_last_ref, which is now inherited into AutoHCC. Fixing this without a performance regression will not be simple, so is left for follow-up work. * Some existing unit tests required adjustment of operational parameters or conditions to work with the new behaviors of AutoHCC. A number of bugs were found and fixed in the validation process, including getting unit tests in good working order. * Added an option to cache_bench, `-degenerate_hash_bits` for correctness stress testing described below. For this, the tool uses the reverse-engineered hash function for HCC to generate keys in which the specified number of hash bits, in critical positions, have a fixed value. Essentially each degenerate hash bit will half the number of chain heads utilized and double the average chain length. Pull Request resolved: https://github.com/facebook/rocksdb/pull/11738 Test Plan: unit tests updated, and already added to db crash test. Also ## Correctness The code includes generous assertions to check for unexpected states, especially at destruction time, so should be able to detect critical concurrency bugs. Less serious "availability bugs" in which cache data is hidden or cleanly lost are more difficult to detect, but also less scary for data correctness (as long as performance is good and the design is sound). In average operation, the structure is extremely low stress and low contention (see next section) so stressing the corner case logic requires artificially stressing the operating conditions. First, we keep the structure small to increase the number of threads hitting the same chain or entry, and just one cache shard. Second, we artificially degrade the hashing so that chains are much longer than typical, using the new `-degenerate_hash_bits` option to cache_bench. Third, we re-create the structure from scratch frequently in order to exercise the Grow logic repeatedly and to get the benefit of the consistency checks in the structure's destructor in debug builds. For cache_bench this also means disabling the single-threaded "populate cache" step (normally used for steady state performance testing). And of course use many more threads than cores to have many preemptions. An effective test for working out bugs was this (using debug build of course): ``` while ./cache_bench -cache_type=auto_hyper_clock_cache -histograms=0 -cache_size=8000000 -threads=100 -populate_cache=0 -ops_per_thread=10000 -degenerate_hash_bits=6 -num_shard_bits=0; do :; done ``` Or even smaller cases. This setup has around 27 utilized chains, with around 35 entries each, and yield-waits more than 1 million times per second (very high contention; see next section). I have let this run for hours searching for any lingering issues. I've also run cache_bench under ASAN, UBSAN, and TSAN. ## Essentially wait free There is a counter for number of yield() calls when one thread is waiting on another. When we pre-populate the structure in a single thread, ``` ./cache_bench -cache_type=auto_hyper_clock_cache -histograms=0 -populate_cache=1 -ops_per_thread=200000 2>&1 | grep Yield ``` We see something on the order of 1 yield call per second across 16 threads, even when we load the system other other jobs (parallel compilation). With -populate_cache=0, there are more yield opportunities with parallel table growth. On an otherwise unloaded system, we still see very small (single digit) yield counts, with a chance of getting into the thousands, and getting into 10s of thousands per second during table growth phase if the system is loaded with other jobs. However, I am not worried about this if performance is still good (see next section). ## Overall performance Although cache_bench initially suggested performance very close to FixedHCC, there was a very noticeable performance hit under a db_bench setup like used in validating https://github.com/facebook/rocksdb/issues/10626. Much of the difference has been reduced by optimizing Lookup with a "naive" pass that will almost always find entries quickly, and only falling back to the careful Lookup algorithm when not found in the first pass. Setups (chosen to be sensitive to block cache performance), and compiled with USE_CLANG=1 JEMALLOC=1 PORTABLE=0 DEBUG_LEVEL=0: ``` TEST_TMPDIR=/dev/shm base/db_bench -benchmarks=fillrandom -num=30000000 -disable_wal=1 -bloom_bits=16 ``` ### No regression on FixedHCC Running before & after builds at the same time on a 48 core machine. ``` TEST_TMPDIR=/dev/shm /usr/bin/time ./db_bench -benchmarks=readrandom[-X10],block_cache_entry_stats,cache_report_problems -readonly -num=30000000 -bloom_bits=16 -cache_index_and_filter_blocks=1 -cache_size=610000000 -duration 20 -threads=24 -cache_type=fixed_hyper_clock_cache -seed=1234 ``` Before: readrandom [AVG 10 runs] : 847234 (± 8150) ops/sec; 59.2 (± 0.6) MB/sec 703MB max RSS After: readrandom [AVG 10 runs] : 851021 (± 7929) ops/sec; 59.5 (± 0.6) MB/sec 706MB max RSS Probably no material difference. ### Single-threaded performance Using `[-X2]` and `-threads=1` and `-duration=30`, running all three at the same time: lru_cache: 55100 ops/sec, then 55862 ops/sec (627MB max RSS) fixed_hyper_clock_cache: 60496 ops/sec, then 61231 ops/sec (626MB max RSS) auto_hyper_clock_cache: 47560 ops/sec, then 56081 ops/sec (626MB max RSS) So AutoHCC has more ramp-up cost in the first pass as the cache grows to the appropriate size. (In single-threaded operation, the parallelizability and per-op low latency of table growth is overall slower.) However, once up to size, its performance is comparable to LRUCache. FixedHCC's lean operations still win overall when a good estimate is available. If we look at HCC table stats, we can see that this configuration is not favorable to AutoHCC (and I have verified that other memory sizes do not yield substantially different results, until shards are under-sized for the full filters): FixedHCC: Slot occupancy stats: Overall 47% (124991/262144), Min/Max/Window = 28%/64%/500, MaxRun{Pos/Neg} = 17/22 AutoHCC: Slot occupancy stats: Overall 59% (125781/209682), Min/Max/Window = 43%/82%/500, MaxRun{Pos/Neg} = 76/16 Head occupancy stats: Overall 43% (92259/209682), Min/Max/Window = 24%/74%/500, MaxRun{Pos/Neg} = 19/26 Entries at home count: 53350 FixedHCC configuration is relatively good for speed, and not ideal for space utilization. As is typical, AutoHCC has tighter control on metadata usage (209682 x 64 bytes rather than 262144 x 64 bytes), and the higher load factor is slightly worse for speed. LRUCache also has more metadata usage, at 199680 x 96 bytes of tracked metadata (plus roughly another 10% of that untracked in the head pointers), and that metadata is subject to fragmentation. ### Parallel performance, high hit rate Now using `[-X10]` and `-threads=10`, all three at the same time lru_cache: [AVG 10 runs] : 263629 (± 1425) ops/sec; 18.4 (± 0.1) MB/sec 655MB max RSS, 97.1% cache hit rate fixed_hyper_clock_cache: [AVG 10 runs] : 479590 (± 8114) ops/sec; 33.5 (± 0.6) MB/sec 651MB max RSS, 97.1% cache hit rate auto_hyper_clock_cache: [AVG 10 runs] : 418687 (± 5915) ops/sec; 29.3 (± 0.4) MB/sec 657MB max RSS, 97.1% cache hit rate Even with just 10-way parallelism for each cache (though 30+/48 cores busy overall), LRUCache is already showing performance degradation, while AutoHCC is in the neighborhood of FixedHCC. And that brings us to the question of how AutoHCC holds up under extreme parallelism, so now independent runs with `-threads=100` (overloading 48 cores). lru_cache: 438613 ops/sec, 827MB max RSS fixed_hyper_clock_cache: 1651310 ops/sec, 812MB max RSS auto_hyper_clock_cache: 1505875 ops/sec, 821MB max RSS (Yield count: 1089 over 30s) Clearly, AutoHCC holds up extremely well under extreme parallelism, even closing some of the modest performance gap with FixedHCC. ### Parallel performance, low hit rate To get down to roughly 50% cache hit rate, we use `-cache_index_and_filter_blocks=0 -cache_size=1650000000` with `-threads=10`. Here the extra cost of running counting clock eviction, especially on the chains of AutoHCC, are evident, especially with the lower contention of cache_index_and_filter_blocks=0: lru_cache: 725231 ops/sec, 1770MB max RSS, 51.3% hit rate fixed_hyper_clock_cache: 638620 ops/sec, 1765MB max RSS, 50.2% hit rate auto_hyper_clock_cache: 541018 ops/sec, 1777MB max RSS, 50.8% hit rate Reviewed By: jowlyzhang Differential Revision: D48784755 Pulled By: pdillinger fbshipit-source-id: e79813dc087474ac427637dd282a14fa3011a6e4
2023-09-01 22:44:38 +00:00
void CacheReportProblems() {
auto debug_logger = std::make_shared<StderrLogger>(DEBUG_LEVEL);
cache_->ReportProblems(debug_logger);
}
void PrintStats(const char* key) {
if (db_.db != nullptr) {
PrintStats(db_.db, key, false);
}
for (const auto& db_with_cfh : multi_dbs_) {
PrintStats(db_with_cfh.db, key, true);
}
}
void PrintStats(DB* db, const char* key, bool print_header = false) {
if (print_header) {
fprintf(stdout, "\n==== DB: %s ===\n", db->GetName().c_str());
}
std::string stats;
if (!db->GetProperty(key, &stats)) {
stats = "(failed)";
}
fprintf(stdout, "\n%s\n", stats.c_str());
}
void PrintStats(const std::vector<std::string>& keys) {
if (db_.db != nullptr) {
PrintStats(db_.db, keys);
}
for (const auto& db_with_cfh : multi_dbs_) {
PrintStats(db_with_cfh.db, keys, true);
}
}
void PrintStats(DB* db, const std::vector<std::string>& keys,
bool print_header = false) {
if (print_header) {
fprintf(stdout, "\n==== DB: %s ===\n", db->GetName().c_str());
}
for (const auto& key : keys) {
std::string stats;
if (!db->GetProperty(key, &stats)) {
stats = "(failed)";
}
fprintf(stdout, "%s: %s\n", key.c_str(), stats.c_str());
}
}
void Replay(ThreadState* thread) {
if (db_.db != nullptr) {
Replay(thread, &db_);
}
}
void Replay(ThreadState* /*thread*/, DBWithColumnFamilies* db_with_cfh) {
Status s;
std::unique_ptr<TraceReader> trace_reader;
s = NewFileTraceReader(FLAGS_env, EnvOptions(), FLAGS_trace_file,
&trace_reader);
if (!s.ok()) {
fprintf(
stderr,
"Encountered an error creating a TraceReader from the trace file. "
"Error: %s\n",
s.ToString().c_str());
exit(1);
}
std::unique_ptr<Replayer> replayer;
s = db_with_cfh->db->NewDefaultReplayer(db_with_cfh->cfh,
std::move(trace_reader), &replayer);
if (!s.ok()) {
fprintf(stderr,
"Encountered an error creating a default Replayer. "
"Error: %s\n",
s.ToString().c_str());
exit(1);
}
s = replayer->Prepare();
if (!s.ok()) {
fprintf(stderr, "Prepare for replay failed. Error: %s\n",
s.ToString().c_str());
}
s = replayer->Replay(
ReplayOptions(static_cast<uint32_t>(FLAGS_trace_replay_threads),
FLAGS_trace_replay_fast_forward),
nullptr);
replayer.reset();
if (s.ok()) {
fprintf(stdout, "Replay completed from trace_file: %s\n",
FLAGS_trace_file.c_str());
} else {
fprintf(stderr, "Replay failed. Error: %s\n", s.ToString().c_str());
}
}
Support read rate-limiting in SequentialFileReader (#9973) Summary: Added rate limiter and read rate-limiting support to SequentialFileReader. I've updated call sites to SequentialFileReader::Read with appropriate IO priority (or left a TODO and specified IO_TOTAL for now). The PR is separated into four commits: the first one added the rate-limiting support, but with some fixes in the unit test since the number of request bytes from rate limiter in SequentialFileReader are not accurate (there is overcharge at EOF). The second commit fixed this by allowing SequentialFileReader to check file size and determine how many bytes are left in the file to read. The third commit added benchmark related code. The fourth commit moved the logic of using file size to avoid overcharging the rate limiter into backup engine (the main user of SequentialFileReader). Pull Request resolved: https://github.com/facebook/rocksdb/pull/9973 Test Plan: - `make check`, backup_engine_test covers usage of SequentialFileReader with rate limiter. - Run db_bench to check if rate limiting is throttling as expected: Verified that reads and writes are together throttled at 2MB/s, and at 0.2MB chunks that are 100ms apart. - Set up: `./db_bench --benchmarks=fillrandom -db=/dev/shm/test_rocksdb` - Benchmark: ``` strace -ttfe read,write ./db_bench --benchmarks=backup -db=/dev/shm/test_rocksdb --backup_rate_limit=2097152 --use_existing_db strace -ttfe read,write ./db_bench --benchmarks=restore -db=/dev/shm/test_rocksdb --restore_rate_limit=2097152 --use_existing_db ``` - db bench on backup and restore to ensure no performance regression. - backup (avg over 50 runs): pre-change: 1.90443e+06 micros/op; post-change: 1.8993e+06 micros/op (improve by 0.2%) - restore (avg over 50 runs): pre-change: 1.79105e+06 micros/op; post-change: 1.78192e+06 micros/op (improve by 0.5%) ``` # Set up ./db_bench --benchmarks=fillrandom -db=/tmp/test_rocksdb -num=10000000 # benchmark TEST_TMPDIR=/tmp/test_rocksdb NUM_RUN=50 for ((j=0;j<$NUM_RUN;j++)) do ./db_bench -db=$TEST_TMPDIR -num=10000000 -benchmarks=backup -use_existing_db | egrep 'backup' # Restore #./db_bench -db=$TEST_TMPDIR -num=10000000 -benchmarks=restore -use_existing_db done > rate_limit.txt && awk -v NUM_RUN=$NUM_RUN '{sum+=$3;sum_sqrt+=$3^2}END{print sum/NUM_RUN, sqrt(sum_sqrt/NUM_RUN-(sum/NUM_RUN)^2)}' rate_limit.txt >> rate_limit_2.txt ``` Reviewed By: hx235 Differential Revision: D36327418 Pulled By: cbi42 fbshipit-source-id: e75d4307cff815945482df5ba630c1e88d064691
2022-05-24 17:28:57 +00:00
void Backup(ThreadState* thread) {
DB* db = SelectDB(thread);
std::unique_ptr<BackupEngineOptions> engine_options(
new BackupEngineOptions(FLAGS_backup_dir));
Status s;
BackupEngine* backup_engine;
if (FLAGS_backup_rate_limit > 0) {
engine_options->backup_rate_limiter.reset(NewGenericRateLimiter(
FLAGS_backup_rate_limit, 100000 /* refill_period_us */,
10 /* fairness */, RateLimiter::Mode::kAllIo));
}
// Build new backup of the entire DB
engine_options->destroy_old_data = true;
s = BackupEngine::Open(FLAGS_env, *engine_options, &backup_engine);
assert(s.ok());
s = backup_engine->CreateNewBackup(db);
assert(s.ok());
std::vector<BackupInfo> backup_info;
backup_engine->GetBackupInfo(&backup_info);
// Verify that a new backup is created
assert(backup_info.size() == 1);
}
void Restore(ThreadState* /* thread */) {
std::unique_ptr<BackupEngineOptions> engine_options(
new BackupEngineOptions(FLAGS_backup_dir));
if (FLAGS_restore_rate_limit > 0) {
engine_options->restore_rate_limiter.reset(NewGenericRateLimiter(
FLAGS_restore_rate_limit, 100000 /* refill_period_us */,
10 /* fairness */, RateLimiter::Mode::kAllIo));
}
BackupEngineReadOnly* backup_engine;
Status s =
BackupEngineReadOnly::Open(FLAGS_env, *engine_options, &backup_engine);
assert(s.ok());
s = backup_engine->RestoreDBFromLatestBackup(FLAGS_restore_dir,
FLAGS_restore_dir);
assert(s.ok());
delete backup_engine;
}
};
Separeate main from bench functionality to allow cusomizations Summary: Isolate db_bench functionality from main so custom benchmark code can be written and managed Test Plan: Tested commands ./build_tools/regression_build_test.sh ./db_bench --db=/tmp/rocksdbtest-12321/dbbench --stats_interval_seconds=1 --num=1000 ./db_bench --db=/tmp/rocksdbtest-12321/dbbench --stats_interval_seconds=1 --num=1000 --reads=500 --writes=500 ./db_bench --db=/tmp/rocksdbtest-12321/dbbench --stats_interval_seconds=1 --num=1000 --merge_keys=100 --numdistinct=100 --num_column_families=3 --num_hot_column_families=1 ./db_bench --stats_interval_seconds=1 --num=1000 --bloom_locality=1 --seed=5 --threads=5 ./db_bench --duration=60 --value_size=50 --seek_nexts=10 --reverse_iterator=true --usee_uint64_comparator=true --batch-size=5 ./db_bench --duration=60 --value_size=50 --seek_nexts=10 --reverse_iterator=true --use_uint64_comparator=true --batch_size=5 ./db_bench --duration=60 --value_size=50 --seek_nexts=10 --reverse_iterator=true --usee_uint64_comparator=true --batch-size=5 Test Results - https://phabricator.fb.com/P56130387 Additional tests for: ./db_bench --duration=60 --value_size=50 --seek_nexts=10 --reverse_iterator=true --use_uint64_comparator=true --batch_size=5 --key_size=8 --merge_operator=put ./db_bench --stats_interval_seconds=1 --num=1000 --bloom_locality=1 --seed=5 --threads=5 --merge_operator=uint64add Results: https://phabricator.fb.com/P56130607 Reviewers: yhchiang, sdong Reviewed By: sdong Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D53991
2016-02-16 14:17:31 +00:00
int db_bench_tool(int argc, char** argv) {
ROCKSDB_NAMESPACE::port::InstallStackTraceHandler();
ConfigOptions config_options;
static bool initialized = false;
if (!initialized) {
SetUsageMessage(std::string("\nUSAGE:\n") + std::string(argv[0]) +
" [OPTIONS]...");
SetVersionString(GetRocksVersionAsString(true));
initialized = true;
}
ParseCommandLineFlags(&argc, &argv, true);
FLAGS_compaction_style_e =
(ROCKSDB_NAMESPACE::CompactionStyle)FLAGS_compaction_style;
if (FLAGS_statistics && !FLAGS_statistics_string.empty()) {
fprintf(stderr,
"Cannot provide both --statistics and --statistics_string.\n");
exit(1);
}
if (!FLAGS_statistics_string.empty()) {
Status s = Statistics::CreateFromString(config_options,
FLAGS_statistics_string, &dbstats);
if (dbstats == nullptr) {
fprintf(stderr,
"No Statistics registered matching string: %s status=%s\n",
FLAGS_statistics_string.c_str(), s.ToString().c_str());
exit(1);
}
}
if (FLAGS_statistics) {
dbstats = ROCKSDB_NAMESPACE::CreateDBStatistics();
}
if (dbstats) {
dbstats->set_stats_level(static_cast<StatsLevel>(FLAGS_stats_level));
}
FLAGS_compaction_pri_e =
(ROCKSDB_NAMESPACE::CompactionPri)FLAGS_compaction_pri;
std::vector<std::string> fanout = ROCKSDB_NAMESPACE::StringSplit(
FLAGS_max_bytes_for_level_multiplier_additional, ',');
for (size_t j = 0; j < fanout.size(); j++) {
FLAGS_max_bytes_for_level_multiplier_additional_v.push_back(
#ifndef CYGWIN
std::stoi(fanout[j]));
#else
stoi(fanout[j]));
#endif
}
FLAGS_compression_type_e =
StringToCompressionType(FLAGS_compression_type.c_str());
FLAGS_wal_compression_e =
StringToCompressionType(FLAGS_wal_compression.c_str());
Prevent double caching in the compressed secondary cache (#9747) Summary: ### **Summary:** When both LRU Cache and CompressedSecondaryCache are configured together, there possibly are some data blocks double cached. **Changes include:** 1. Update IS_PROMOTED to IS_IN_SECONDARY_CACHE to prevent confusions. 2. This PR updates SecondaryCacheResultHandle and use IsErasedFromSecondaryCache to determine whether the handle is erased in the secondary cache. Then, the caller can determine whether to SetIsInSecondaryCache(). 3. Rename LRUSecondaryCache to CompressedSecondaryCache. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9747 Test Plan: **Test Scripts:** 1. Populate a DB. The on disk footprint is 482 MB. The data is set to be 50% compressible, so the total decompressed size is expected to be 964 MB. ./db_bench --benchmarks=fillrandom --num=10000000 -db=/db_bench_1 2. overwrite it to a stable state: ./db_bench --benchmarks=overwrite,stats --num=10000000 -use_existing_db -duration=10 --benchmark_write_rate_limit=2000000 -db=/db_bench_1 4. Run read tests with diffeernt cache setting: T1: ./db_bench --benchmarks=seekrandom,stats --threads=16 --num=10000000 -use_existing_db -duration=120 --benchmark_write_rate_limit=52000000 -use_direct_reads --cache_size=520000000 --statistics -db=/db_bench_1 T2: ./db_bench --benchmarks=seekrandom,stats --threads=16 --num=10000000 -use_existing_db -duration=120 --benchmark_write_rate_limit=52000000 -use_direct_reads --cache_size=320000000 -compressed_secondary_cache_size=400000000 --statistics -use_compressed_secondary_cache -db=/db_bench_1 T3: ./db_bench --benchmarks=seekrandom,stats --threads=16 --num=10000000 -use_existing_db -duration=120 --benchmark_write_rate_limit=52000000 -use_direct_reads --cache_size=520000000 -compressed_secondary_cache_size=400000000 --statistics -use_compressed_secondary_cache -db=/db_bench_1 T4: ./db_bench --benchmarks=seekrandom,stats --threads=16 --num=10000000 -use_existing_db -duration=120 --benchmark_write_rate_limit=52000000 -use_direct_reads --cache_size=20000000 -compressed_secondary_cache_size=500000000 --statistics -use_compressed_secondary_cache -db=/db_bench_1 **Before this PR** | Cache Size | Compressed Secondary Cache Size | Cache Hit Rate | |------------|-------------------------------------|----------------| |520 MB | 0 MB | 85.5% | |320 MB | 400 MB | 96.2% | |520 MB | 400 MB | 98.3% | |20 MB | 500 MB | 98.8% | **Before this PR** | Cache Size | Compressed Secondary Cache Size | Cache Hit Rate | |------------|-------------------------------------|----------------| |520 MB | 0 MB | 85.5% | |320 MB | 400 MB | 99.9% | |520 MB | 400 MB | 99.9% | |20 MB | 500 MB | 99.2% | Reviewed By: anand1976 Differential Revision: D35117499 Pulled By: gitbw95 fbshipit-source-id: ea2657749fc13efebe91a8a1b56bc61d6a224a12
2022-04-11 20:28:33 +00:00
FLAGS_compressed_secondary_cache_compression_type_e = StringToCompressionType(
FLAGS_compressed_secondary_cache_compression_type.c_str());
Add a secondary cache implementation based on LRUCache 1 (#9518) Summary: **Summary:** RocksDB uses a block cache to reduce IO and make queries more efficient. The block cache is based on the LRU algorithm (LRUCache) and keeps objects containing uncompressed data, such as Block, ParsedFullFilterBlock etc. It allows the user to configure a second level cache (rocksdb::SecondaryCache) to extend the primary block cache by holding items evicted from it. Some of the major RocksDB users, like MyRocks, use direct IO and would like to use a primary block cache for uncompressed data and a secondary cache for compressed data. The latter allows us to mitigate the loss of the Linux page cache due to direct IO. This PR includes a concrete implementation of rocksdb::SecondaryCache that integrates with compression libraries such as LZ4 and implements an LRU cache to hold compressed blocks. Pull Request resolved: https://github.com/facebook/rocksdb/pull/9518 Test Plan: In this PR, the lru_secondary_cache_test.cc includes the following tests: 1. The unit tests for the secondary cache with either compression or no compression, such as basic tests, fails tests. 2. The integration tests with both primary cache and this secondary cache . **Follow Up:** 1. Statistics (e.g. compression ratio) will be added in another PR. 2. Once this implementation is ready, I will do some shadow testing and benchmarking with UDB to measure the impact. Reviewed By: anand1976 Differential Revision: D34430930 Pulled By: gitbw95 fbshipit-source-id: 218d78b672a2f914856d8a90ff32f2f5b5043ded
2022-02-24 00:06:27 +00:00
// Stacked BlobDB
FLAGS_blob_db_compression_type_e =
StringToCompressionType(FLAGS_blob_db_compression_type.c_str());
int env_opts = !FLAGS_env_uri.empty() + !FLAGS_fs_uri.empty();
if (env_opts > 1) {
fprintf(stderr, "Error: --env_uri and --fs_uri are mutually exclusive\n");
exit(1);
}
if (env_opts == 1) {
Status s = Env::CreateFromUri(config_options, FLAGS_env_uri, FLAGS_fs_uri,
&FLAGS_env, &env_guard);
if (!s.ok()) {
fprintf(stderr, "Failed creating env: %s\n", s.ToString().c_str());
exit(1);
}
} else if (FLAGS_simulate_hdd || FLAGS_simulate_hybrid_fs_file != "") {
//**TODO: Make the simulate fs something that can be loaded
// from the ObjectRegistry...
static std::shared_ptr<ROCKSDB_NAMESPACE::Env> composite_env =
NewCompositeEnv(std::make_shared<SimulatedHybridFileSystem>(
FileSystem::Default(), FLAGS_simulate_hybrid_fs_file,
/*throughput_multiplier=*/
int{FLAGS_simulate_hybrid_hdd_multipliers},
/*is_full_fs_warm=*/FLAGS_simulate_hdd));
FLAGS_env = composite_env.get();
}
Hide deprecated, inefficient block-based filter from public API (#9535) Summary: This change removes the ability to configure the deprecated, inefficient block-based filter in the public API. Options that would have enabled it now use "full" (and optionally partitioned) filters. Existing block-based filters can still be read and used, and a "back door" way to build them still exists, for testing and in case of trouble. About the only way this removal would cause an issue for users is if temporary memory for filter construction greatly increases. In HISTORY.md we suggest a few possible mitigations: partitioned filters, smaller SST files, or setting reserve_table_builder_memory=true. Or users who have customized a FilterPolicy using the CreateFilter/KeyMayMatch mechanism removed in https://github.com/facebook/rocksdb/issues/9501 will have to upgrade their code. (It's long past time for people to move to the new builder/reader customization interface.) This change also introduces some internal-use-only configuration strings for testing specific filter implementations while bypassing some compatibility / intelligence logic. This is intended to hint at a path toward making FilterPolicy Customizable, but it also gives us a "back door" way to configure block-based filter. Aside: updated db_bench so that -readonly implies -use_existing_db Pull Request resolved: https://github.com/facebook/rocksdb/pull/9535 Test Plan: Unit tests updated. Specifically, * BlockBasedTableTest.BlockReadCountTest is tweaked to validate the back door configuration interface and ignoring of `use_block_based_builder`. * BlockBasedTableTest.TracingGetTest is migrated from testing block-based filter access pattern to full filter access patter, by re-ordering some things. * Options test (pretty self-explanatory) Performance test - create with `./db_bench -db=/dev/shm/rocksdb1 -bloom_bits=10 -cache_index_and_filter_blocks=1 -benchmarks=fillrandom -num=10000000 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0` with and without `-use_block_based_filter`, which creates a DB with 21 SST files in L0. Read with `./db_bench -db=/dev/shm/rocksdb1 -readonly -bloom_bits=10 -cache_index_and_filter_blocks=1 -benchmarks=readrandom -num=10000000 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -duration=30` Without -use_block_based_filter: readrandom 464 ops/sec, 689280 KB DB With -use_block_based_filter: readrandom 169 ops/sec, 690996 KB DB No consistent difference with fillrandom Reviewed By: jay-zhuang Differential Revision: D34153871 Pulled By: pdillinger fbshipit-source-id: 31f4a933c542f8f09aca47fa64aec67832a69738
2022-02-12 15:04:09 +00:00
// Let -readonly imply -use_existing_db
FLAGS_use_existing_db |= FLAGS_readonly;
if (FLAGS_build_info) {
std::string build_info;
std::cout << GetRocksBuildInfoAsString(build_info, true) << std::endl;
// Similar to --version, nothing else will be done when this flag is set
exit(0);
}
db_bench should use a good seed when --seed is not set or set to 0 (#9740) Summary: This is for https://github.com/facebook/rocksdb/issues/9737 I have wasted more than a few hours running db_bench benchmarks where --seed was not set and getting better than expected results because cache hit rates are great because multiple invocations of db_bench used the same value for --seed or did not set it, and then all used 0. The result is that all see the same sequence of keys. Others have done the same. The problem is worse in that it is easy to miss and the result is a benchmark with results that are misleading. A good way to avoid this is to set it to the equivalent of gettimeofday() when either --seed is not set or it is set to 0 (the default). With this change the actual seed is printed when it was 0 at process start: Set seed to 1647992570365606 because --seed was 0 Pull Request resolved: https://github.com/facebook/rocksdb/pull/9740 Test Plan: Perf results: ./db_bench --benchmarks=fillseq,readrandom --num=1000000 --reads=4000000 readrandom : 6.469 micros/op 154583 ops/sec; 17.1 MB/s (4000000 of 4000000 found) ./db_bench --benchmarks=fillseq,readrandom --num=1000000 --reads=4000000 --seed=0 readrandom : 6.565 micros/op 152321 ops/sec; 16.9 MB/s (4000000 of 4000000 found) ./db_bench --benchmarks=fillseq,readrandom --num=1000000 --reads=4000000 --seed=1 readrandom : 6.461 micros/op 154777 ops/sec; 17.1 MB/s (4000000 of 4000000 found) ./db_bench --benchmarks=fillseq,readrandom --num=1000000 --reads=4000000 --seed=2 readrandom : 6.525 micros/op 153244 ops/sec; 17.0 MB/s (4000000 of 4000000 found) Reviewed By: jay-zhuang Differential Revision: D35145361 Pulled By: mdcallag fbshipit-source-id: 2b35b153ccec46b27d7c9405997523555fc51267
2022-03-25 17:12:27 +00:00
if (!FLAGS_seed) {
uint64_t now = FLAGS_env->GetSystemClock()->NowMicros();
seed_base = static_cast<int64_t>(now);
fprintf(stdout, "Set seed to %" PRIu64 " because --seed was 0\n",
*seed_base);
db_bench should use a good seed when --seed is not set or set to 0 (#9740) Summary: This is for https://github.com/facebook/rocksdb/issues/9737 I have wasted more than a few hours running db_bench benchmarks where --seed was not set and getting better than expected results because cache hit rates are great because multiple invocations of db_bench used the same value for --seed or did not set it, and then all used 0. The result is that all see the same sequence of keys. Others have done the same. The problem is worse in that it is easy to miss and the result is a benchmark with results that are misleading. A good way to avoid this is to set it to the equivalent of gettimeofday() when either --seed is not set or it is set to 0 (the default). With this change the actual seed is printed when it was 0 at process start: Set seed to 1647992570365606 because --seed was 0 Pull Request resolved: https://github.com/facebook/rocksdb/pull/9740 Test Plan: Perf results: ./db_bench --benchmarks=fillseq,readrandom --num=1000000 --reads=4000000 readrandom : 6.469 micros/op 154583 ops/sec; 17.1 MB/s (4000000 of 4000000 found) ./db_bench --benchmarks=fillseq,readrandom --num=1000000 --reads=4000000 --seed=0 readrandom : 6.565 micros/op 152321 ops/sec; 16.9 MB/s (4000000 of 4000000 found) ./db_bench --benchmarks=fillseq,readrandom --num=1000000 --reads=4000000 --seed=1 readrandom : 6.461 micros/op 154777 ops/sec; 17.1 MB/s (4000000 of 4000000 found) ./db_bench --benchmarks=fillseq,readrandom --num=1000000 --reads=4000000 --seed=2 readrandom : 6.525 micros/op 153244 ops/sec; 17.0 MB/s (4000000 of 4000000 found) Reviewed By: jay-zhuang Differential Revision: D35145361 Pulled By: mdcallag fbshipit-source-id: 2b35b153ccec46b27d7c9405997523555fc51267
2022-03-25 17:12:27 +00:00
} else {
seed_base = FLAGS_seed;
}
if (FLAGS_use_existing_keys && !FLAGS_use_existing_db) {
fprintf(stderr,
"`-use_existing_db` must be true for `-use_existing_keys` to be "
"settable\n");
exit(1);
}
FLAGS_value_size_distribution_type_e =
StringToDistributionType(FLAGS_value_size_distribution_type.c_str());
// Note options sanitization may increase thread pool sizes according to
// max_background_flushes/max_background_compactions/max_background_jobs
FLAGS_env->SetBackgroundThreads(FLAGS_num_high_pri_threads,
ROCKSDB_NAMESPACE::Env::Priority::HIGH);
Introduce bottom-pri thread pool for large universal compactions Summary: When we had a single thread pool for compactions, a thread could be busy for a long time (minutes) executing a compaction involving the bottom level. In multi-instance setups, the entire thread pool could be consumed by such bottom-level compactions. Then, top-level compactions (e.g., a few L0 files) would be blocked for a long time ("head-of-line blocking"). Such top-level compactions are critical to prevent compaction stalls as they can quickly reduce number of L0 files / sorted runs. This diff introduces a bottom-priority queue for universal compactions including the bottom level. This alleviates the head-of-line blocking situation for fast, top-level compactions. - Added `Env::Priority::BOTTOM` thread pool. This feature is only enabled if user explicitly configures it to have a positive number of threads. - Changed `ThreadPoolImpl`'s default thread limit from one to zero. This change is invisible to users as we call `IncBackgroundThreadsIfNeeded` on the low-pri/high-pri pools during `DB::Open` with values of at least one. It is necessary, though, for bottom-pri to start with zero threads so the feature is disabled by default. - Separated `ManualCompaction` into two parts in `PrepickedCompaction`. `PrepickedCompaction` is used for any compaction that's picked outside of its execution thread, either manual or automatic. - Forward universal compactions involving last level to the bottom pool (worker thread's entry point is `BGWorkBottomCompaction`). - Track `bg_bottom_compaction_scheduled_` so we can wait for bottom-level compactions to finish. We don't count them against the background jobs limits. So users of this feature will get an extra compaction for free. Closes https://github.com/facebook/rocksdb/pull/2580 Differential Revision: D5422916 Pulled By: ajkr fbshipit-source-id: a74bd11f1ea4933df3739b16808bb21fcd512333
2017-08-03 22:36:28 +00:00
FLAGS_env->SetBackgroundThreads(FLAGS_num_bottom_pri_threads,
ROCKSDB_NAMESPACE::Env::Priority::BOTTOM);
FLAGS_env->SetBackgroundThreads(FLAGS_num_low_pri_threads,
ROCKSDB_NAMESPACE::Env::Priority::LOW);
// Choose a location for the test database if none given with --db=<path>
if (FLAGS_db.empty()) {
std::string default_db_path;
FLAGS_env->GetTestDirectory(&default_db_path);
default_db_path += "/dbbench";
FLAGS_db = default_db_path;
}
Support read rate-limiting in SequentialFileReader (#9973) Summary: Added rate limiter and read rate-limiting support to SequentialFileReader. I've updated call sites to SequentialFileReader::Read with appropriate IO priority (or left a TODO and specified IO_TOTAL for now). The PR is separated into four commits: the first one added the rate-limiting support, but with some fixes in the unit test since the number of request bytes from rate limiter in SequentialFileReader are not accurate (there is overcharge at EOF). The second commit fixed this by allowing SequentialFileReader to check file size and determine how many bytes are left in the file to read. The third commit added benchmark related code. The fourth commit moved the logic of using file size to avoid overcharging the rate limiter into backup engine (the main user of SequentialFileReader). Pull Request resolved: https://github.com/facebook/rocksdb/pull/9973 Test Plan: - `make check`, backup_engine_test covers usage of SequentialFileReader with rate limiter. - Run db_bench to check if rate limiting is throttling as expected: Verified that reads and writes are together throttled at 2MB/s, and at 0.2MB chunks that are 100ms apart. - Set up: `./db_bench --benchmarks=fillrandom -db=/dev/shm/test_rocksdb` - Benchmark: ``` strace -ttfe read,write ./db_bench --benchmarks=backup -db=/dev/shm/test_rocksdb --backup_rate_limit=2097152 --use_existing_db strace -ttfe read,write ./db_bench --benchmarks=restore -db=/dev/shm/test_rocksdb --restore_rate_limit=2097152 --use_existing_db ``` - db bench on backup and restore to ensure no performance regression. - backup (avg over 50 runs): pre-change: 1.90443e+06 micros/op; post-change: 1.8993e+06 micros/op (improve by 0.2%) - restore (avg over 50 runs): pre-change: 1.79105e+06 micros/op; post-change: 1.78192e+06 micros/op (improve by 0.5%) ``` # Set up ./db_bench --benchmarks=fillrandom -db=/tmp/test_rocksdb -num=10000000 # benchmark TEST_TMPDIR=/tmp/test_rocksdb NUM_RUN=50 for ((j=0;j<$NUM_RUN;j++)) do ./db_bench -db=$TEST_TMPDIR -num=10000000 -benchmarks=backup -use_existing_db | egrep 'backup' # Restore #./db_bench -db=$TEST_TMPDIR -num=10000000 -benchmarks=restore -use_existing_db done > rate_limit.txt && awk -v NUM_RUN=$NUM_RUN '{sum+=$3;sum_sqrt+=$3^2}END{print sum/NUM_RUN, sqrt(sum_sqrt/NUM_RUN-(sum/NUM_RUN)^2)}' rate_limit.txt >> rate_limit_2.txt ``` Reviewed By: hx235 Differential Revision: D36327418 Pulled By: cbi42 fbshipit-source-id: e75d4307cff815945482df5ba630c1e88d064691
2022-05-24 17:28:57 +00:00
if (FLAGS_backup_dir.empty()) {
FLAGS_backup_dir = FLAGS_db + "/backup";
}
if (FLAGS_restore_dir.empty()) {
FLAGS_restore_dir = FLAGS_db + "/restore";
}
if (FLAGS_stats_interval_seconds > 0) {
// When both are set then FLAGS_stats_interval determines the frequency
// at which the timer is checked for FLAGS_stats_interval_seconds
FLAGS_stats_interval = 1000;
}
if (FLAGS_seek_missing_prefix && FLAGS_prefix_size <= 8) {
fprintf(stderr, "prefix_size > 8 required by --seek_missing_prefix\n");
exit(1);
}
ROCKSDB_NAMESPACE::Benchmark benchmark;
benchmark.Run();
if (FLAGS_print_malloc_stats) {
std::string stats_string;
ROCKSDB_NAMESPACE::DumpMallocStats(&stats_string);
fprintf(stdout, "Malloc stats:\n%s\n", stats_string.c_str());
}
return 0;
}
} // namespace ROCKSDB_NAMESPACE
#endif