rocksdb/table/block_based/block_based_table_reader.cc

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// 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.
#include "table/block_based/block_based_table_reader.h"
#include <algorithm>
#include <array>
#include <limits>
#include <string>
#include <utility>
#include <vector>
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
#include "cache/cache_entry_roles.h"
Stats for redundant insertions into block cache (#6681) Summary: Since read threads do not coordinate on loading data into block cache, two threads between Lookup and Insert can end up loading and inserting the same data. This is particularly concerning with cache_index_and_filter_blocks since those are hot and more likely to be race targets if ejected from (or not pre-populated in) the cache. Particularly with moves toward disaggregated / network storage, the cost of redundant retrieval might be high, and we should at least have some hard statistics from which we can estimate impact. Example with full filter thrashing "cliff": $ ./db_bench --benchmarks=fillrandom --num=15000000 --cache_index_and_filter_blocks -bloom_bits=10 ... $ ./db_bench --db=/tmp/rocksdbtest-172704/dbbench --use_existing_db --benchmarks=readrandom,stats --num=200000 --cache_index_and_filter_blocks --cache_size=$((130 * 1024 * 1024)) --bloom_bits=10 --threads=16 -statistics 2>&1 | egrep '^rocksdb.block.cache.(.*add|.*redundant)' | grep -v compress | sort rocksdb.block.cache.add COUNT : 14181 rocksdb.block.cache.add.failures COUNT : 0 rocksdb.block.cache.add.redundant COUNT : 476 rocksdb.block.cache.data.add COUNT : 12749 rocksdb.block.cache.data.add.redundant COUNT : 18 rocksdb.block.cache.filter.add COUNT : 1003 rocksdb.block.cache.filter.add.redundant COUNT : 217 rocksdb.block.cache.index.add COUNT : 429 rocksdb.block.cache.index.add.redundant COUNT : 241 $ ./db_bench --db=/tmp/rocksdbtest-172704/dbbench --use_existing_db --benchmarks=readrandom,stats --num=200000 --cache_index_and_filter_blocks --cache_size=$((120 * 1024 * 1024)) --bloom_bits=10 --threads=16 -statistics 2>&1 | egrep '^rocksdb.block.cache.(.*add|.*redundant)' | grep -v compress | sort rocksdb.block.cache.add COUNT : 1182223 rocksdb.block.cache.add.failures COUNT : 0 rocksdb.block.cache.add.redundant COUNT : 302728 rocksdb.block.cache.data.add COUNT : 31425 rocksdb.block.cache.data.add.redundant COUNT : 12 rocksdb.block.cache.filter.add COUNT : 795455 rocksdb.block.cache.filter.add.redundant COUNT : 130238 rocksdb.block.cache.index.add COUNT : 355343 rocksdb.block.cache.index.add.redundant COUNT : 172478 Pull Request resolved: https://github.com/facebook/rocksdb/pull/6681 Test Plan: Some manual testing (above) and unit test covering key metrics is included Reviewed By: ltamasi Differential Revision: D21134113 Pulled By: pdillinger fbshipit-source-id: c11497b5f00f4ffdfe919823904e52d0a1a91d87
2020-04-27 20:18:18 +00:00
#include "cache/sharded_cache.h"
#include "db/dbformat.h"
Introduce FullMergeV2 (eliminate memcpy from merge operators) Summary: This diff update the code to pin the merge operator operands while the merge operation is done, so that we can eliminate the memcpy cost, to do that we need a new public API for FullMerge that replace the std::deque<std::string> with std::vector<Slice> This diff is stacked on top of D56493 and D56511 In this diff we - Update FullMergeV2 arguments to be encapsulated in MergeOperationInput and MergeOperationOutput which will make it easier to add new arguments in the future - Replace std::deque<std::string> with std::vector<Slice> to pass operands - Replace MergeContext std::deque with std::vector (based on a simple benchmark I ran https://gist.github.com/IslamAbdelRahman/78fc86c9ab9f52b1df791e58943fb187) - Allow FullMergeV2 output to be an existing operand ``` [Everything in Memtable | 10K operands | 10 KB each | 1 operand per key] DEBUG_LEVEL=0 make db_bench -j64 && ./db_bench --benchmarks="mergerandom,readseq,readseq,readseq,readseq,readseq" --merge_operator="max" --merge_keys=10000 --num=10000 --disable_auto_compactions --value_size=10240 --write_buffer_size=1000000000 [FullMergeV2] readseq : 0.607 micros/op 1648235 ops/sec; 16121.2 MB/s readseq : 0.478 micros/op 2091546 ops/sec; 20457.2 MB/s readseq : 0.252 micros/op 3972081 ops/sec; 38850.5 MB/s readseq : 0.237 micros/op 4218328 ops/sec; 41259.0 MB/s readseq : 0.247 micros/op 4043927 ops/sec; 39553.2 MB/s [master] readseq : 3.935 micros/op 254140 ops/sec; 2485.7 MB/s readseq : 3.722 micros/op 268657 ops/sec; 2627.7 MB/s readseq : 3.149 micros/op 317605 ops/sec; 3106.5 MB/s readseq : 3.125 micros/op 320024 ops/sec; 3130.1 MB/s readseq : 4.075 micros/op 245374 ops/sec; 2400.0 MB/s ``` ``` [Everything in Memtable | 10K operands | 10 KB each | 10 operand per key] DEBUG_LEVEL=0 make db_bench -j64 && ./db_bench --benchmarks="mergerandom,readseq,readseq,readseq,readseq,readseq" --merge_operator="max" --merge_keys=1000 --num=10000 --disable_auto_compactions --value_size=10240 --write_buffer_size=1000000000 [FullMergeV2] readseq : 3.472 micros/op 288018 ops/sec; 2817.1 MB/s readseq : 2.304 micros/op 434027 ops/sec; 4245.2 MB/s readseq : 1.163 micros/op 859845 ops/sec; 8410.0 MB/s readseq : 1.192 micros/op 838926 ops/sec; 8205.4 MB/s readseq : 1.250 micros/op 800000 ops/sec; 7824.7 MB/s [master] readseq : 24.025 micros/op 41623 ops/sec; 407.1 MB/s readseq : 18.489 micros/op 54086 ops/sec; 529.0 MB/s readseq : 18.693 micros/op 53495 ops/sec; 523.2 MB/s readseq : 23.621 micros/op 42335 ops/sec; 414.1 MB/s readseq : 18.775 micros/op 53262 ops/sec; 521.0 MB/s ``` ``` [Everything in Block cache | 10K operands | 10 KB each | 1 operand per key] [FullMergeV2] $ DEBUG_LEVEL=0 make db_bench -j64 && ./db_bench --benchmarks="readseq,readseq,readseq,readseq,readseq" --merge_operator="max" --num=100000 --db="/dev/shm/merge-random-10K-10KB" --cache_size=1000000000 --use_existing_db --disable_auto_compactions readseq : 14.741 micros/op 67837 ops/sec; 663.5 MB/s readseq : 1.029 micros/op 971446 ops/sec; 9501.6 MB/s readseq : 0.974 micros/op 1026229 ops/sec; 10037.4 MB/s readseq : 0.965 micros/op 1036080 ops/sec; 10133.8 MB/s readseq : 0.943 micros/op 1060657 ops/sec; 10374.2 MB/s [master] readseq : 16.735 micros/op 59755 ops/sec; 584.5 MB/s readseq : 3.029 micros/op 330151 ops/sec; 3229.2 MB/s readseq : 3.136 micros/op 318883 ops/sec; 3119.0 MB/s readseq : 3.065 micros/op 326245 ops/sec; 3191.0 MB/s readseq : 3.014 micros/op 331813 ops/sec; 3245.4 MB/s ``` ``` [Everything in Block cache | 10K operands | 10 KB each | 10 operand per key] DEBUG_LEVEL=0 make db_bench -j64 && ./db_bench --benchmarks="readseq,readseq,readseq,readseq,readseq" --merge_operator="max" --num=100000 --db="/dev/shm/merge-random-10-operands-10K-10KB" --cache_size=1000000000 --use_existing_db --disable_auto_compactions [FullMergeV2] readseq : 24.325 micros/op 41109 ops/sec; 402.1 MB/s readseq : 1.470 micros/op 680272 ops/sec; 6653.7 MB/s readseq : 1.231 micros/op 812347 ops/sec; 7945.5 MB/s readseq : 1.091 micros/op 916590 ops/sec; 8965.1 MB/s readseq : 1.109 micros/op 901713 ops/sec; 8819.6 MB/s [master] readseq : 27.257 micros/op 36687 ops/sec; 358.8 MB/s readseq : 4.443 micros/op 225073 ops/sec; 2201.4 MB/s readseq : 5.830 micros/op 171526 ops/sec; 1677.7 MB/s readseq : 4.173 micros/op 239635 ops/sec; 2343.8 MB/s readseq : 4.150 micros/op 240963 ops/sec; 2356.8 MB/s ``` Test Plan: COMPILE_WITH_ASAN=1 make check -j64 Reviewers: yhchiang, andrewkr, sdong Reviewed By: sdong Subscribers: lovro, andrewkr, dhruba Differential Revision: https://reviews.facebook.net/D57075
2016-07-20 16:49:03 +00:00
#include "db/pinned_iterators_manager.h"
#include "file/file_prefetch_buffer.h"
#include "file/file_util.h"
#include "file/random_access_file_reader.h"
#include "monitoring/perf_context_imp.h"
#include "options/options_helper.h"
#include "port/lang.h"
#include "rocksdb/cache.h"
#include "rocksdb/comparator.h"
#include "rocksdb/env.h"
Introduce a new storage specific Env API (#5761) Summary: The current Env API encompasses both storage/file operations, as well as OS related operations. Most of the APIs return a Status, which does not have enough metadata about an error, such as whether its retry-able or not, scope (i.e fault domain) of the error etc., that may be required in order to properly handle a storage error. The file APIs also do not provide enough control over the IO SLA, such as timeout, prioritization, hinting about placement and redundancy etc. This PR separates out the file/storage APIs from Env into a new FileSystem class. The APIs are updated to return an IOStatus with metadata about the error, as well as to take an IOOptions structure as input in order to allow more control over the IO. The user can set both ```options.env``` and ```options.file_system``` to specify that RocksDB should use the former for OS related operations and the latter for storage operations. Internally, a ```CompositeEnvWrapper``` has been introduced that inherits from ```Env``` and redirects individual methods to either an ```Env``` implementation or the ```FileSystem``` as appropriate. When options are sanitized during ```DB::Open```, ```options.env``` is replaced with a newly allocated ```CompositeEnvWrapper``` instance if both env and file_system have been specified. This way, the rest of the RocksDB code can continue to function as before. This PR also ports PosixEnv to the new API by splitting it into two - PosixEnv and PosixFileSystem. PosixEnv is defined as a sub-class of CompositeEnvWrapper, and threading/time functions are overridden with Posix specific implementations in order to avoid an extra level of indirection. The ```CompositeEnvWrapper``` translates ```IOStatus``` return code to ```Status```, and sets the severity to ```kSoftError``` if the io_status is retryable. The error handling code in RocksDB can then recover the DB automatically. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5761 Differential Revision: D18868376 Pulled By: anand1976 fbshipit-source-id: 39efe18a162ea746fabac6360ff529baba48486f
2019-12-13 22:47:08 +00:00
#include "rocksdb/file_system.h"
#include "rocksdb/filter_policy.h"
#include "rocksdb/iterator.h"
#include "rocksdb/options.h"
#include "rocksdb/statistics.h"
#include "rocksdb/system_clock.h"
#include "rocksdb/table.h"
#include "rocksdb/table_properties.h"
#include "rocksdb/trace_record.h"
#include "table/block_based/binary_search_index_reader.h"
#include "table/block_based/block.h"
#include "table/block_based/block_based_filter_block.h"
#include "table/block_based/block_based_table_factory.h"
#include "table/block_based/block_based_table_iterator.h"
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
#include "table/block_based/block_like_traits.h"
#include "table/block_based/block_prefix_index.h"
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
#include "table/block_based/block_type.h"
#include "table/block_based/filter_block.h"
#include "table/block_based/full_filter_block.h"
#include "table/block_based/hash_index_reader.h"
#include "table/block_based/partitioned_filter_block.h"
#include "table/block_based/partitioned_index_reader.h"
#include "table/block_fetcher.h"
#include "table/format.h"
#include "table/get_context.h"
#include "table/internal_iterator.h"
#include "table/meta_blocks.h"
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
#include "table/multiget_context.h"
#include "table/persistent_cache_helper.h"
#include "table/persistent_cache_options.h"
#include "table/sst_file_writer_collectors.h"
#include "table/two_level_iterator.h"
#include "test_util/sync_point.h"
#include "util/coding.h"
#include "util/crc32c.h"
#include "util/stop_watch.h"
#include "util/string_util.h"
namespace ROCKSDB_NAMESPACE {
extern const uint64_t kBlockBasedTableMagicNumber;
extern const std::string kHashIndexPrefixesBlock;
extern const std::string kHashIndexPrefixesMetadataBlock;
BlockBasedTable::~BlockBasedTable() {
delete rep_;
}
std::atomic<uint64_t> BlockBasedTable::next_cache_key_id_(0);
namespace {
// Read the block identified by "handle" from "file".
// The only relevant option is options.verify_checksums for now.
// On failure return non-OK.
// On success fill *result and return OK - caller owns *result
// @param uncompression_dict Data for presetting the compression library's
// dictionary.
template <typename TBlocklike>
Status ReadBlockFromFile(
RandomAccessFileReader* file, FilePrefetchBuffer* prefetch_buffer,
const Footer& footer, const ReadOptions& options, const BlockHandle& handle,
std::unique_ptr<TBlocklike>* result, const ImmutableOptions& ioptions,
bool do_uncompress, bool maybe_compressed, BlockType block_type,
const UncompressionDict& uncompression_dict,
const PersistentCacheOptions& cache_options, size_t read_amp_bytes_per_bit,
MemoryAllocator* memory_allocator, bool for_compaction, bool using_zstd,
const FilterPolicy* filter_policy) {
assert(result);
BlockContents contents;
BlockFetcher block_fetcher(
file, prefetch_buffer, footer, options, handle, &contents, ioptions,
do_uncompress, maybe_compressed, block_type, uncompression_dict,
cache_options, memory_allocator, nullptr, for_compaction);
Status s = block_fetcher.ReadBlockContents();
if (s.ok()) {
result->reset(BlocklikeTraits<TBlocklike>::Create(
std::move(contents), read_amp_bytes_per_bit, ioptions.stats, using_zstd,
filter_policy));
}
return s;
}
// Release the cached entry and decrement its ref count.
// Do not force erase
void ReleaseCachedEntry(void* arg, void* h) {
Cache* cache = reinterpret_cast<Cache*>(arg);
Cache::Handle* handle = reinterpret_cast<Cache::Handle*>(h);
cache->Release(handle, false /* force_erase */);
}
// For hash based index, return true if prefix_extractor and
// prefix_extractor_block mismatch, false otherwise. This flag will be used
// as total_order_seek via NewIndexIterator
bool PrefixExtractorChanged(const TableProperties* table_properties,
const SliceTransform* prefix_extractor) {
// BlockBasedTableOptions::kHashSearch requires prefix_extractor to be set.
// Turn off hash index in prefix_extractor is not set; if prefix_extractor
// is set but prefix_extractor_block is not set, also disable hash index
if (prefix_extractor == nullptr || table_properties == nullptr ||
table_properties->prefix_extractor_name.empty()) {
return true;
}
// prefix_extractor and prefix_extractor_block are both non-empty
if (table_properties->prefix_extractor_name.compare(
prefix_extractor->Name()) != 0) {
return true;
} else {
return false;
}
}
CacheAllocationPtr CopyBufferToHeap(MemoryAllocator* allocator, Slice& buf) {
CacheAllocationPtr heap_buf;
heap_buf = AllocateBlock(buf.size(), allocator);
memcpy(heap_buf.get(), buf.data(), buf.size());
return heap_buf;
}
} // namespace
void BlockBasedTable::UpdateCacheHitMetrics(BlockType block_type,
GetContext* get_context,
size_t usage) const {
Statistics* const statistics = rep_->ioptions.stats;
PERF_COUNTER_ADD(block_cache_hit_count, 1);
PERF_COUNTER_BY_LEVEL_ADD(block_cache_hit_count, 1,
static_cast<uint32_t>(rep_->level));
if (get_context) {
++get_context->get_context_stats_.num_cache_hit;
get_context->get_context_stats_.num_cache_bytes_read += usage;
} else {
RecordTick(statistics, BLOCK_CACHE_HIT);
RecordTick(statistics, BLOCK_CACHE_BYTES_READ, usage);
}
switch (block_type) {
case BlockType::kFilter:
PERF_COUNTER_ADD(block_cache_filter_hit_count, 1);
if (get_context) {
++get_context->get_context_stats_.num_cache_filter_hit;
} else {
RecordTick(statistics, BLOCK_CACHE_FILTER_HIT);
}
break;
case BlockType::kCompressionDictionary:
// TODO: introduce perf counter for compression dictionary hit count
if (get_context) {
++get_context->get_context_stats_.num_cache_compression_dict_hit;
} else {
RecordTick(statistics, BLOCK_CACHE_COMPRESSION_DICT_HIT);
}
break;
case BlockType::kIndex:
PERF_COUNTER_ADD(block_cache_index_hit_count, 1);
if (get_context) {
++get_context->get_context_stats_.num_cache_index_hit;
} else {
RecordTick(statistics, BLOCK_CACHE_INDEX_HIT);
}
break;
default:
// TODO: introduce dedicated tickers/statistics/counters
// for range tombstones
if (get_context) {
++get_context->get_context_stats_.num_cache_data_hit;
} else {
RecordTick(statistics, BLOCK_CACHE_DATA_HIT);
}
break;
}
}
void BlockBasedTable::UpdateCacheMissMetrics(BlockType block_type,
GetContext* get_context) const {
Statistics* const statistics = rep_->ioptions.stats;
// TODO: introduce aggregate (not per-level) block cache miss count
PERF_COUNTER_BY_LEVEL_ADD(block_cache_miss_count, 1,
static_cast<uint32_t>(rep_->level));
if (get_context) {
++get_context->get_context_stats_.num_cache_miss;
} else {
RecordTick(statistics, BLOCK_CACHE_MISS);
}
// TODO: introduce perf counters for misses per block type
switch (block_type) {
case BlockType::kFilter:
if (get_context) {
++get_context->get_context_stats_.num_cache_filter_miss;
} else {
RecordTick(statistics, BLOCK_CACHE_FILTER_MISS);
}
break;
case BlockType::kCompressionDictionary:
if (get_context) {
++get_context->get_context_stats_.num_cache_compression_dict_miss;
} else {
RecordTick(statistics, BLOCK_CACHE_COMPRESSION_DICT_MISS);
}
break;
case BlockType::kIndex:
if (get_context) {
++get_context->get_context_stats_.num_cache_index_miss;
} else {
RecordTick(statistics, BLOCK_CACHE_INDEX_MISS);
}
break;
default:
// TODO: introduce dedicated tickers/statistics/counters
// for range tombstones
if (get_context) {
++get_context->get_context_stats_.num_cache_data_miss;
} else {
RecordTick(statistics, BLOCK_CACHE_DATA_MISS);
}
break;
}
}
void BlockBasedTable::UpdateCacheInsertionMetrics(
BlockType block_type, GetContext* get_context, size_t usage, bool redundant,
Statistics* const statistics) {
// TODO: introduce perf counters for block cache insertions
if (get_context) {
++get_context->get_context_stats_.num_cache_add;
Stats for redundant insertions into block cache (#6681) Summary: Since read threads do not coordinate on loading data into block cache, two threads between Lookup and Insert can end up loading and inserting the same data. This is particularly concerning with cache_index_and_filter_blocks since those are hot and more likely to be race targets if ejected from (or not pre-populated in) the cache. Particularly with moves toward disaggregated / network storage, the cost of redundant retrieval might be high, and we should at least have some hard statistics from which we can estimate impact. Example with full filter thrashing "cliff": $ ./db_bench --benchmarks=fillrandom --num=15000000 --cache_index_and_filter_blocks -bloom_bits=10 ... $ ./db_bench --db=/tmp/rocksdbtest-172704/dbbench --use_existing_db --benchmarks=readrandom,stats --num=200000 --cache_index_and_filter_blocks --cache_size=$((130 * 1024 * 1024)) --bloom_bits=10 --threads=16 -statistics 2>&1 | egrep '^rocksdb.block.cache.(.*add|.*redundant)' | grep -v compress | sort rocksdb.block.cache.add COUNT : 14181 rocksdb.block.cache.add.failures COUNT : 0 rocksdb.block.cache.add.redundant COUNT : 476 rocksdb.block.cache.data.add COUNT : 12749 rocksdb.block.cache.data.add.redundant COUNT : 18 rocksdb.block.cache.filter.add COUNT : 1003 rocksdb.block.cache.filter.add.redundant COUNT : 217 rocksdb.block.cache.index.add COUNT : 429 rocksdb.block.cache.index.add.redundant COUNT : 241 $ ./db_bench --db=/tmp/rocksdbtest-172704/dbbench --use_existing_db --benchmarks=readrandom,stats --num=200000 --cache_index_and_filter_blocks --cache_size=$((120 * 1024 * 1024)) --bloom_bits=10 --threads=16 -statistics 2>&1 | egrep '^rocksdb.block.cache.(.*add|.*redundant)' | grep -v compress | sort rocksdb.block.cache.add COUNT : 1182223 rocksdb.block.cache.add.failures COUNT : 0 rocksdb.block.cache.add.redundant COUNT : 302728 rocksdb.block.cache.data.add COUNT : 31425 rocksdb.block.cache.data.add.redundant COUNT : 12 rocksdb.block.cache.filter.add COUNT : 795455 rocksdb.block.cache.filter.add.redundant COUNT : 130238 rocksdb.block.cache.index.add COUNT : 355343 rocksdb.block.cache.index.add.redundant COUNT : 172478 Pull Request resolved: https://github.com/facebook/rocksdb/pull/6681 Test Plan: Some manual testing (above) and unit test covering key metrics is included Reviewed By: ltamasi Differential Revision: D21134113 Pulled By: pdillinger fbshipit-source-id: c11497b5f00f4ffdfe919823904e52d0a1a91d87
2020-04-27 20:18:18 +00:00
if (redundant) {
++get_context->get_context_stats_.num_cache_add_redundant;
}
get_context->get_context_stats_.num_cache_bytes_write += usage;
} else {
RecordTick(statistics, BLOCK_CACHE_ADD);
Stats for redundant insertions into block cache (#6681) Summary: Since read threads do not coordinate on loading data into block cache, two threads between Lookup and Insert can end up loading and inserting the same data. This is particularly concerning with cache_index_and_filter_blocks since those are hot and more likely to be race targets if ejected from (or not pre-populated in) the cache. Particularly with moves toward disaggregated / network storage, the cost of redundant retrieval might be high, and we should at least have some hard statistics from which we can estimate impact. Example with full filter thrashing "cliff": $ ./db_bench --benchmarks=fillrandom --num=15000000 --cache_index_and_filter_blocks -bloom_bits=10 ... $ ./db_bench --db=/tmp/rocksdbtest-172704/dbbench --use_existing_db --benchmarks=readrandom,stats --num=200000 --cache_index_and_filter_blocks --cache_size=$((130 * 1024 * 1024)) --bloom_bits=10 --threads=16 -statistics 2>&1 | egrep '^rocksdb.block.cache.(.*add|.*redundant)' | grep -v compress | sort rocksdb.block.cache.add COUNT : 14181 rocksdb.block.cache.add.failures COUNT : 0 rocksdb.block.cache.add.redundant COUNT : 476 rocksdb.block.cache.data.add COUNT : 12749 rocksdb.block.cache.data.add.redundant COUNT : 18 rocksdb.block.cache.filter.add COUNT : 1003 rocksdb.block.cache.filter.add.redundant COUNT : 217 rocksdb.block.cache.index.add COUNT : 429 rocksdb.block.cache.index.add.redundant COUNT : 241 $ ./db_bench --db=/tmp/rocksdbtest-172704/dbbench --use_existing_db --benchmarks=readrandom,stats --num=200000 --cache_index_and_filter_blocks --cache_size=$((120 * 1024 * 1024)) --bloom_bits=10 --threads=16 -statistics 2>&1 | egrep '^rocksdb.block.cache.(.*add|.*redundant)' | grep -v compress | sort rocksdb.block.cache.add COUNT : 1182223 rocksdb.block.cache.add.failures COUNT : 0 rocksdb.block.cache.add.redundant COUNT : 302728 rocksdb.block.cache.data.add COUNT : 31425 rocksdb.block.cache.data.add.redundant COUNT : 12 rocksdb.block.cache.filter.add COUNT : 795455 rocksdb.block.cache.filter.add.redundant COUNT : 130238 rocksdb.block.cache.index.add COUNT : 355343 rocksdb.block.cache.index.add.redundant COUNT : 172478 Pull Request resolved: https://github.com/facebook/rocksdb/pull/6681 Test Plan: Some manual testing (above) and unit test covering key metrics is included Reviewed By: ltamasi Differential Revision: D21134113 Pulled By: pdillinger fbshipit-source-id: c11497b5f00f4ffdfe919823904e52d0a1a91d87
2020-04-27 20:18:18 +00:00
if (redundant) {
RecordTick(statistics, BLOCK_CACHE_ADD_REDUNDANT);
}
RecordTick(statistics, BLOCK_CACHE_BYTES_WRITE, usage);
}
switch (block_type) {
case BlockType::kFilter:
if (get_context) {
++get_context->get_context_stats_.num_cache_filter_add;
Stats for redundant insertions into block cache (#6681) Summary: Since read threads do not coordinate on loading data into block cache, two threads between Lookup and Insert can end up loading and inserting the same data. This is particularly concerning with cache_index_and_filter_blocks since those are hot and more likely to be race targets if ejected from (or not pre-populated in) the cache. Particularly with moves toward disaggregated / network storage, the cost of redundant retrieval might be high, and we should at least have some hard statistics from which we can estimate impact. Example with full filter thrashing "cliff": $ ./db_bench --benchmarks=fillrandom --num=15000000 --cache_index_and_filter_blocks -bloom_bits=10 ... $ ./db_bench --db=/tmp/rocksdbtest-172704/dbbench --use_existing_db --benchmarks=readrandom,stats --num=200000 --cache_index_and_filter_blocks --cache_size=$((130 * 1024 * 1024)) --bloom_bits=10 --threads=16 -statistics 2>&1 | egrep '^rocksdb.block.cache.(.*add|.*redundant)' | grep -v compress | sort rocksdb.block.cache.add COUNT : 14181 rocksdb.block.cache.add.failures COUNT : 0 rocksdb.block.cache.add.redundant COUNT : 476 rocksdb.block.cache.data.add COUNT : 12749 rocksdb.block.cache.data.add.redundant COUNT : 18 rocksdb.block.cache.filter.add COUNT : 1003 rocksdb.block.cache.filter.add.redundant COUNT : 217 rocksdb.block.cache.index.add COUNT : 429 rocksdb.block.cache.index.add.redundant COUNT : 241 $ ./db_bench --db=/tmp/rocksdbtest-172704/dbbench --use_existing_db --benchmarks=readrandom,stats --num=200000 --cache_index_and_filter_blocks --cache_size=$((120 * 1024 * 1024)) --bloom_bits=10 --threads=16 -statistics 2>&1 | egrep '^rocksdb.block.cache.(.*add|.*redundant)' | grep -v compress | sort rocksdb.block.cache.add COUNT : 1182223 rocksdb.block.cache.add.failures COUNT : 0 rocksdb.block.cache.add.redundant COUNT : 302728 rocksdb.block.cache.data.add COUNT : 31425 rocksdb.block.cache.data.add.redundant COUNT : 12 rocksdb.block.cache.filter.add COUNT : 795455 rocksdb.block.cache.filter.add.redundant COUNT : 130238 rocksdb.block.cache.index.add COUNT : 355343 rocksdb.block.cache.index.add.redundant COUNT : 172478 Pull Request resolved: https://github.com/facebook/rocksdb/pull/6681 Test Plan: Some manual testing (above) and unit test covering key metrics is included Reviewed By: ltamasi Differential Revision: D21134113 Pulled By: pdillinger fbshipit-source-id: c11497b5f00f4ffdfe919823904e52d0a1a91d87
2020-04-27 20:18:18 +00:00
if (redundant) {
++get_context->get_context_stats_.num_cache_filter_add_redundant;
}
get_context->get_context_stats_.num_cache_filter_bytes_insert += usage;
} else {
RecordTick(statistics, BLOCK_CACHE_FILTER_ADD);
Stats for redundant insertions into block cache (#6681) Summary: Since read threads do not coordinate on loading data into block cache, two threads between Lookup and Insert can end up loading and inserting the same data. This is particularly concerning with cache_index_and_filter_blocks since those are hot and more likely to be race targets if ejected from (or not pre-populated in) the cache. Particularly with moves toward disaggregated / network storage, the cost of redundant retrieval might be high, and we should at least have some hard statistics from which we can estimate impact. Example with full filter thrashing "cliff": $ ./db_bench --benchmarks=fillrandom --num=15000000 --cache_index_and_filter_blocks -bloom_bits=10 ... $ ./db_bench --db=/tmp/rocksdbtest-172704/dbbench --use_existing_db --benchmarks=readrandom,stats --num=200000 --cache_index_and_filter_blocks --cache_size=$((130 * 1024 * 1024)) --bloom_bits=10 --threads=16 -statistics 2>&1 | egrep '^rocksdb.block.cache.(.*add|.*redundant)' | grep -v compress | sort rocksdb.block.cache.add COUNT : 14181 rocksdb.block.cache.add.failures COUNT : 0 rocksdb.block.cache.add.redundant COUNT : 476 rocksdb.block.cache.data.add COUNT : 12749 rocksdb.block.cache.data.add.redundant COUNT : 18 rocksdb.block.cache.filter.add COUNT : 1003 rocksdb.block.cache.filter.add.redundant COUNT : 217 rocksdb.block.cache.index.add COUNT : 429 rocksdb.block.cache.index.add.redundant COUNT : 241 $ ./db_bench --db=/tmp/rocksdbtest-172704/dbbench --use_existing_db --benchmarks=readrandom,stats --num=200000 --cache_index_and_filter_blocks --cache_size=$((120 * 1024 * 1024)) --bloom_bits=10 --threads=16 -statistics 2>&1 | egrep '^rocksdb.block.cache.(.*add|.*redundant)' | grep -v compress | sort rocksdb.block.cache.add COUNT : 1182223 rocksdb.block.cache.add.failures COUNT : 0 rocksdb.block.cache.add.redundant COUNT : 302728 rocksdb.block.cache.data.add COUNT : 31425 rocksdb.block.cache.data.add.redundant COUNT : 12 rocksdb.block.cache.filter.add COUNT : 795455 rocksdb.block.cache.filter.add.redundant COUNT : 130238 rocksdb.block.cache.index.add COUNT : 355343 rocksdb.block.cache.index.add.redundant COUNT : 172478 Pull Request resolved: https://github.com/facebook/rocksdb/pull/6681 Test Plan: Some manual testing (above) and unit test covering key metrics is included Reviewed By: ltamasi Differential Revision: D21134113 Pulled By: pdillinger fbshipit-source-id: c11497b5f00f4ffdfe919823904e52d0a1a91d87
2020-04-27 20:18:18 +00:00
if (redundant) {
RecordTick(statistics, BLOCK_CACHE_FILTER_ADD_REDUNDANT);
}
RecordTick(statistics, BLOCK_CACHE_FILTER_BYTES_INSERT, usage);
}
break;
case BlockType::kCompressionDictionary:
if (get_context) {
++get_context->get_context_stats_.num_cache_compression_dict_add;
Stats for redundant insertions into block cache (#6681) Summary: Since read threads do not coordinate on loading data into block cache, two threads between Lookup and Insert can end up loading and inserting the same data. This is particularly concerning with cache_index_and_filter_blocks since those are hot and more likely to be race targets if ejected from (or not pre-populated in) the cache. Particularly with moves toward disaggregated / network storage, the cost of redundant retrieval might be high, and we should at least have some hard statistics from which we can estimate impact. Example with full filter thrashing "cliff": $ ./db_bench --benchmarks=fillrandom --num=15000000 --cache_index_and_filter_blocks -bloom_bits=10 ... $ ./db_bench --db=/tmp/rocksdbtest-172704/dbbench --use_existing_db --benchmarks=readrandom,stats --num=200000 --cache_index_and_filter_blocks --cache_size=$((130 * 1024 * 1024)) --bloom_bits=10 --threads=16 -statistics 2>&1 | egrep '^rocksdb.block.cache.(.*add|.*redundant)' | grep -v compress | sort rocksdb.block.cache.add COUNT : 14181 rocksdb.block.cache.add.failures COUNT : 0 rocksdb.block.cache.add.redundant COUNT : 476 rocksdb.block.cache.data.add COUNT : 12749 rocksdb.block.cache.data.add.redundant COUNT : 18 rocksdb.block.cache.filter.add COUNT : 1003 rocksdb.block.cache.filter.add.redundant COUNT : 217 rocksdb.block.cache.index.add COUNT : 429 rocksdb.block.cache.index.add.redundant COUNT : 241 $ ./db_bench --db=/tmp/rocksdbtest-172704/dbbench --use_existing_db --benchmarks=readrandom,stats --num=200000 --cache_index_and_filter_blocks --cache_size=$((120 * 1024 * 1024)) --bloom_bits=10 --threads=16 -statistics 2>&1 | egrep '^rocksdb.block.cache.(.*add|.*redundant)' | grep -v compress | sort rocksdb.block.cache.add COUNT : 1182223 rocksdb.block.cache.add.failures COUNT : 0 rocksdb.block.cache.add.redundant COUNT : 302728 rocksdb.block.cache.data.add COUNT : 31425 rocksdb.block.cache.data.add.redundant COUNT : 12 rocksdb.block.cache.filter.add COUNT : 795455 rocksdb.block.cache.filter.add.redundant COUNT : 130238 rocksdb.block.cache.index.add COUNT : 355343 rocksdb.block.cache.index.add.redundant COUNT : 172478 Pull Request resolved: https://github.com/facebook/rocksdb/pull/6681 Test Plan: Some manual testing (above) and unit test covering key metrics is included Reviewed By: ltamasi Differential Revision: D21134113 Pulled By: pdillinger fbshipit-source-id: c11497b5f00f4ffdfe919823904e52d0a1a91d87
2020-04-27 20:18:18 +00:00
if (redundant) {
++get_context->get_context_stats_
.num_cache_compression_dict_add_redundant;
}
get_context->get_context_stats_
.num_cache_compression_dict_bytes_insert += usage;
} else {
RecordTick(statistics, BLOCK_CACHE_COMPRESSION_DICT_ADD);
Stats for redundant insertions into block cache (#6681) Summary: Since read threads do not coordinate on loading data into block cache, two threads between Lookup and Insert can end up loading and inserting the same data. This is particularly concerning with cache_index_and_filter_blocks since those are hot and more likely to be race targets if ejected from (or not pre-populated in) the cache. Particularly with moves toward disaggregated / network storage, the cost of redundant retrieval might be high, and we should at least have some hard statistics from which we can estimate impact. Example with full filter thrashing "cliff": $ ./db_bench --benchmarks=fillrandom --num=15000000 --cache_index_and_filter_blocks -bloom_bits=10 ... $ ./db_bench --db=/tmp/rocksdbtest-172704/dbbench --use_existing_db --benchmarks=readrandom,stats --num=200000 --cache_index_and_filter_blocks --cache_size=$((130 * 1024 * 1024)) --bloom_bits=10 --threads=16 -statistics 2>&1 | egrep '^rocksdb.block.cache.(.*add|.*redundant)' | grep -v compress | sort rocksdb.block.cache.add COUNT : 14181 rocksdb.block.cache.add.failures COUNT : 0 rocksdb.block.cache.add.redundant COUNT : 476 rocksdb.block.cache.data.add COUNT : 12749 rocksdb.block.cache.data.add.redundant COUNT : 18 rocksdb.block.cache.filter.add COUNT : 1003 rocksdb.block.cache.filter.add.redundant COUNT : 217 rocksdb.block.cache.index.add COUNT : 429 rocksdb.block.cache.index.add.redundant COUNT : 241 $ ./db_bench --db=/tmp/rocksdbtest-172704/dbbench --use_existing_db --benchmarks=readrandom,stats --num=200000 --cache_index_and_filter_blocks --cache_size=$((120 * 1024 * 1024)) --bloom_bits=10 --threads=16 -statistics 2>&1 | egrep '^rocksdb.block.cache.(.*add|.*redundant)' | grep -v compress | sort rocksdb.block.cache.add COUNT : 1182223 rocksdb.block.cache.add.failures COUNT : 0 rocksdb.block.cache.add.redundant COUNT : 302728 rocksdb.block.cache.data.add COUNT : 31425 rocksdb.block.cache.data.add.redundant COUNT : 12 rocksdb.block.cache.filter.add COUNT : 795455 rocksdb.block.cache.filter.add.redundant COUNT : 130238 rocksdb.block.cache.index.add COUNT : 355343 rocksdb.block.cache.index.add.redundant COUNT : 172478 Pull Request resolved: https://github.com/facebook/rocksdb/pull/6681 Test Plan: Some manual testing (above) and unit test covering key metrics is included Reviewed By: ltamasi Differential Revision: D21134113 Pulled By: pdillinger fbshipit-source-id: c11497b5f00f4ffdfe919823904e52d0a1a91d87
2020-04-27 20:18:18 +00:00
if (redundant) {
RecordTick(statistics, BLOCK_CACHE_COMPRESSION_DICT_ADD_REDUNDANT);
}
RecordTick(statistics, BLOCK_CACHE_COMPRESSION_DICT_BYTES_INSERT,
usage);
}
break;
case BlockType::kIndex:
if (get_context) {
++get_context->get_context_stats_.num_cache_index_add;
Stats for redundant insertions into block cache (#6681) Summary: Since read threads do not coordinate on loading data into block cache, two threads between Lookup and Insert can end up loading and inserting the same data. This is particularly concerning with cache_index_and_filter_blocks since those are hot and more likely to be race targets if ejected from (or not pre-populated in) the cache. Particularly with moves toward disaggregated / network storage, the cost of redundant retrieval might be high, and we should at least have some hard statistics from which we can estimate impact. Example with full filter thrashing "cliff": $ ./db_bench --benchmarks=fillrandom --num=15000000 --cache_index_and_filter_blocks -bloom_bits=10 ... $ ./db_bench --db=/tmp/rocksdbtest-172704/dbbench --use_existing_db --benchmarks=readrandom,stats --num=200000 --cache_index_and_filter_blocks --cache_size=$((130 * 1024 * 1024)) --bloom_bits=10 --threads=16 -statistics 2>&1 | egrep '^rocksdb.block.cache.(.*add|.*redundant)' | grep -v compress | sort rocksdb.block.cache.add COUNT : 14181 rocksdb.block.cache.add.failures COUNT : 0 rocksdb.block.cache.add.redundant COUNT : 476 rocksdb.block.cache.data.add COUNT : 12749 rocksdb.block.cache.data.add.redundant COUNT : 18 rocksdb.block.cache.filter.add COUNT : 1003 rocksdb.block.cache.filter.add.redundant COUNT : 217 rocksdb.block.cache.index.add COUNT : 429 rocksdb.block.cache.index.add.redundant COUNT : 241 $ ./db_bench --db=/tmp/rocksdbtest-172704/dbbench --use_existing_db --benchmarks=readrandom,stats --num=200000 --cache_index_and_filter_blocks --cache_size=$((120 * 1024 * 1024)) --bloom_bits=10 --threads=16 -statistics 2>&1 | egrep '^rocksdb.block.cache.(.*add|.*redundant)' | grep -v compress | sort rocksdb.block.cache.add COUNT : 1182223 rocksdb.block.cache.add.failures COUNT : 0 rocksdb.block.cache.add.redundant COUNT : 302728 rocksdb.block.cache.data.add COUNT : 31425 rocksdb.block.cache.data.add.redundant COUNT : 12 rocksdb.block.cache.filter.add COUNT : 795455 rocksdb.block.cache.filter.add.redundant COUNT : 130238 rocksdb.block.cache.index.add COUNT : 355343 rocksdb.block.cache.index.add.redundant COUNT : 172478 Pull Request resolved: https://github.com/facebook/rocksdb/pull/6681 Test Plan: Some manual testing (above) and unit test covering key metrics is included Reviewed By: ltamasi Differential Revision: D21134113 Pulled By: pdillinger fbshipit-source-id: c11497b5f00f4ffdfe919823904e52d0a1a91d87
2020-04-27 20:18:18 +00:00
if (redundant) {
++get_context->get_context_stats_.num_cache_index_add_redundant;
}
get_context->get_context_stats_.num_cache_index_bytes_insert += usage;
} else {
RecordTick(statistics, BLOCK_CACHE_INDEX_ADD);
Stats for redundant insertions into block cache (#6681) Summary: Since read threads do not coordinate on loading data into block cache, two threads between Lookup and Insert can end up loading and inserting the same data. This is particularly concerning with cache_index_and_filter_blocks since those are hot and more likely to be race targets if ejected from (or not pre-populated in) the cache. Particularly with moves toward disaggregated / network storage, the cost of redundant retrieval might be high, and we should at least have some hard statistics from which we can estimate impact. Example with full filter thrashing "cliff": $ ./db_bench --benchmarks=fillrandom --num=15000000 --cache_index_and_filter_blocks -bloom_bits=10 ... $ ./db_bench --db=/tmp/rocksdbtest-172704/dbbench --use_existing_db --benchmarks=readrandom,stats --num=200000 --cache_index_and_filter_blocks --cache_size=$((130 * 1024 * 1024)) --bloom_bits=10 --threads=16 -statistics 2>&1 | egrep '^rocksdb.block.cache.(.*add|.*redundant)' | grep -v compress | sort rocksdb.block.cache.add COUNT : 14181 rocksdb.block.cache.add.failures COUNT : 0 rocksdb.block.cache.add.redundant COUNT : 476 rocksdb.block.cache.data.add COUNT : 12749 rocksdb.block.cache.data.add.redundant COUNT : 18 rocksdb.block.cache.filter.add COUNT : 1003 rocksdb.block.cache.filter.add.redundant COUNT : 217 rocksdb.block.cache.index.add COUNT : 429 rocksdb.block.cache.index.add.redundant COUNT : 241 $ ./db_bench --db=/tmp/rocksdbtest-172704/dbbench --use_existing_db --benchmarks=readrandom,stats --num=200000 --cache_index_and_filter_blocks --cache_size=$((120 * 1024 * 1024)) --bloom_bits=10 --threads=16 -statistics 2>&1 | egrep '^rocksdb.block.cache.(.*add|.*redundant)' | grep -v compress | sort rocksdb.block.cache.add COUNT : 1182223 rocksdb.block.cache.add.failures COUNT : 0 rocksdb.block.cache.add.redundant COUNT : 302728 rocksdb.block.cache.data.add COUNT : 31425 rocksdb.block.cache.data.add.redundant COUNT : 12 rocksdb.block.cache.filter.add COUNT : 795455 rocksdb.block.cache.filter.add.redundant COUNT : 130238 rocksdb.block.cache.index.add COUNT : 355343 rocksdb.block.cache.index.add.redundant COUNT : 172478 Pull Request resolved: https://github.com/facebook/rocksdb/pull/6681 Test Plan: Some manual testing (above) and unit test covering key metrics is included Reviewed By: ltamasi Differential Revision: D21134113 Pulled By: pdillinger fbshipit-source-id: c11497b5f00f4ffdfe919823904e52d0a1a91d87
2020-04-27 20:18:18 +00:00
if (redundant) {
RecordTick(statistics, BLOCK_CACHE_INDEX_ADD_REDUNDANT);
}
RecordTick(statistics, BLOCK_CACHE_INDEX_BYTES_INSERT, usage);
}
break;
default:
// TODO: introduce dedicated tickers/statistics/counters
// for range tombstones
if (get_context) {
++get_context->get_context_stats_.num_cache_data_add;
Stats for redundant insertions into block cache (#6681) Summary: Since read threads do not coordinate on loading data into block cache, two threads between Lookup and Insert can end up loading and inserting the same data. This is particularly concerning with cache_index_and_filter_blocks since those are hot and more likely to be race targets if ejected from (or not pre-populated in) the cache. Particularly with moves toward disaggregated / network storage, the cost of redundant retrieval might be high, and we should at least have some hard statistics from which we can estimate impact. Example with full filter thrashing "cliff": $ ./db_bench --benchmarks=fillrandom --num=15000000 --cache_index_and_filter_blocks -bloom_bits=10 ... $ ./db_bench --db=/tmp/rocksdbtest-172704/dbbench --use_existing_db --benchmarks=readrandom,stats --num=200000 --cache_index_and_filter_blocks --cache_size=$((130 * 1024 * 1024)) --bloom_bits=10 --threads=16 -statistics 2>&1 | egrep '^rocksdb.block.cache.(.*add|.*redundant)' | grep -v compress | sort rocksdb.block.cache.add COUNT : 14181 rocksdb.block.cache.add.failures COUNT : 0 rocksdb.block.cache.add.redundant COUNT : 476 rocksdb.block.cache.data.add COUNT : 12749 rocksdb.block.cache.data.add.redundant COUNT : 18 rocksdb.block.cache.filter.add COUNT : 1003 rocksdb.block.cache.filter.add.redundant COUNT : 217 rocksdb.block.cache.index.add COUNT : 429 rocksdb.block.cache.index.add.redundant COUNT : 241 $ ./db_bench --db=/tmp/rocksdbtest-172704/dbbench --use_existing_db --benchmarks=readrandom,stats --num=200000 --cache_index_and_filter_blocks --cache_size=$((120 * 1024 * 1024)) --bloom_bits=10 --threads=16 -statistics 2>&1 | egrep '^rocksdb.block.cache.(.*add|.*redundant)' | grep -v compress | sort rocksdb.block.cache.add COUNT : 1182223 rocksdb.block.cache.add.failures COUNT : 0 rocksdb.block.cache.add.redundant COUNT : 302728 rocksdb.block.cache.data.add COUNT : 31425 rocksdb.block.cache.data.add.redundant COUNT : 12 rocksdb.block.cache.filter.add COUNT : 795455 rocksdb.block.cache.filter.add.redundant COUNT : 130238 rocksdb.block.cache.index.add COUNT : 355343 rocksdb.block.cache.index.add.redundant COUNT : 172478 Pull Request resolved: https://github.com/facebook/rocksdb/pull/6681 Test Plan: Some manual testing (above) and unit test covering key metrics is included Reviewed By: ltamasi Differential Revision: D21134113 Pulled By: pdillinger fbshipit-source-id: c11497b5f00f4ffdfe919823904e52d0a1a91d87
2020-04-27 20:18:18 +00:00
if (redundant) {
++get_context->get_context_stats_.num_cache_data_add_redundant;
}
get_context->get_context_stats_.num_cache_data_bytes_insert += usage;
} else {
RecordTick(statistics, BLOCK_CACHE_DATA_ADD);
Stats for redundant insertions into block cache (#6681) Summary: Since read threads do not coordinate on loading data into block cache, two threads between Lookup and Insert can end up loading and inserting the same data. This is particularly concerning with cache_index_and_filter_blocks since those are hot and more likely to be race targets if ejected from (or not pre-populated in) the cache. Particularly with moves toward disaggregated / network storage, the cost of redundant retrieval might be high, and we should at least have some hard statistics from which we can estimate impact. Example with full filter thrashing "cliff": $ ./db_bench --benchmarks=fillrandom --num=15000000 --cache_index_and_filter_blocks -bloom_bits=10 ... $ ./db_bench --db=/tmp/rocksdbtest-172704/dbbench --use_existing_db --benchmarks=readrandom,stats --num=200000 --cache_index_and_filter_blocks --cache_size=$((130 * 1024 * 1024)) --bloom_bits=10 --threads=16 -statistics 2>&1 | egrep '^rocksdb.block.cache.(.*add|.*redundant)' | grep -v compress | sort rocksdb.block.cache.add COUNT : 14181 rocksdb.block.cache.add.failures COUNT : 0 rocksdb.block.cache.add.redundant COUNT : 476 rocksdb.block.cache.data.add COUNT : 12749 rocksdb.block.cache.data.add.redundant COUNT : 18 rocksdb.block.cache.filter.add COUNT : 1003 rocksdb.block.cache.filter.add.redundant COUNT : 217 rocksdb.block.cache.index.add COUNT : 429 rocksdb.block.cache.index.add.redundant COUNT : 241 $ ./db_bench --db=/tmp/rocksdbtest-172704/dbbench --use_existing_db --benchmarks=readrandom,stats --num=200000 --cache_index_and_filter_blocks --cache_size=$((120 * 1024 * 1024)) --bloom_bits=10 --threads=16 -statistics 2>&1 | egrep '^rocksdb.block.cache.(.*add|.*redundant)' | grep -v compress | sort rocksdb.block.cache.add COUNT : 1182223 rocksdb.block.cache.add.failures COUNT : 0 rocksdb.block.cache.add.redundant COUNT : 302728 rocksdb.block.cache.data.add COUNT : 31425 rocksdb.block.cache.data.add.redundant COUNT : 12 rocksdb.block.cache.filter.add COUNT : 795455 rocksdb.block.cache.filter.add.redundant COUNT : 130238 rocksdb.block.cache.index.add COUNT : 355343 rocksdb.block.cache.index.add.redundant COUNT : 172478 Pull Request resolved: https://github.com/facebook/rocksdb/pull/6681 Test Plan: Some manual testing (above) and unit test covering key metrics is included Reviewed By: ltamasi Differential Revision: D21134113 Pulled By: pdillinger fbshipit-source-id: c11497b5f00f4ffdfe919823904e52d0a1a91d87
2020-04-27 20:18:18 +00:00
if (redundant) {
RecordTick(statistics, BLOCK_CACHE_DATA_ADD_REDUNDANT);
}
RecordTick(statistics, BLOCK_CACHE_DATA_BYTES_INSERT, usage);
}
break;
}
}
Cache::Handle* BlockBasedTable::GetEntryFromCache(
Parallelize secondary cache lookup in MultiGet (#8405) Summary: Implement the ```WaitAll()``` interface in ```LRUCache``` to allow callers to issue multiple lookups in parallel and wait for all of them to complete. Modify ```MultiGet``` to use this to parallelize the secondary cache lookups in order to reduce the overall latency. A call to ```cache->Lookup()``` returns a handle that has an incomplete value (nullptr), and the caller can call ```cache->IsReady()``` to check whether the lookup is complete, and pass a vector of handles to ```WaitAll``` to wait for completion. If any of the lookups fail, ```MultiGet``` will read the block from the SST file. Another change in this PR is to rename ```SecondaryCacheHandle``` to ```SecondaryCacheResultHandle``` as it more accurately describes the return result of the secondary cache lookup, which is more like a future. Tests: 1. Add unit tests in lru_cache_test 2. Benchmark results with no secondary cache configured Master - ``` readrandom : 41.175 micros/op 388562 ops/sec; 106.7 MB/s (7277999 of 7277999 found) readrandom : 41.217 micros/op 388160 ops/sec; 106.6 MB/s (7274999 of 7274999 found) multireadrandom : 10.309 micros/op 1552082 ops/sec; (28908992 of 28908992 found) multireadrandom : 10.321 micros/op 1550218 ops/sec; (29081984 of 29081984 found) ``` This PR - ``` readrandom : 41.158 micros/op 388723 ops/sec; 106.8 MB/s (7290999 of 7290999 found) readrandom : 41.185 micros/op 388463 ops/sec; 106.7 MB/s (7287999 of 7287999 found) multireadrandom : 10.277 micros/op 1556801 ops/sec; (29346944 of 29346944 found) multireadrandom : 10.253 micros/op 1560539 ops/sec; (29274944 of 29274944 found) ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/8405 Reviewed By: zhichao-cao Differential Revision: D29190509 Pulled By: anand1976 fbshipit-source-id: 6f8eff6246712af8a297cfe22ea0d1c3b2a01bb0
2021-06-18 16:35:03 +00:00
Cache* block_cache, const Slice& key, BlockType block_type, const bool wait,
Use new Insert and Lookup APIs in table reader to support secondary cache (#8315) Summary: Secondary cache is implemented to achieve the secondary cache tier for block cache. New Insert and Lookup APIs are introduced in https://github.com/facebook/rocksdb/issues/8271 . To support and use the secondary cache in block based table reader, this PR introduces the corresponding callback functions that will be used in secondary cache, and update the Insert and Lookup APIs accordingly. benchmarking: ./db_bench --benchmarks="fillrandom" -num=1000000 -key_size=32 -value_size=256 -use_direct_io_for_flush_and_compaction=true -db=/tmp/rocks_t/db -partition_index_and_filters=true ./db_bench -db=/tmp/rocks_t/db -use_existing_db=true -benchmarks=readrandom -num=1000000 -key_size=32 -value_size=256 -use_direct_reads=true -cache_size=1073741824 -cache_numshardbits=5 -cache_index_and_filter_blocks=true -read_random_exp_range=17 -statistics -partition_index_and_filters=true -stats_dump_period_sec=30 -reads=50000000 master benchmarking results: readrandom : 3.923 micros/op 254881 ops/sec; 33.4 MB/s (23849796 of 50000000 found) rocksdb.db.get.micros P50 : 2.820992 P95 : 5.636716 P99 : 16.450553 P100 : 8396.000000 COUNT : 50000000 SUM : 179947064 Current PR benchmarking results readrandom : 4.083 micros/op 244925 ops/sec; 32.1 MB/s (23849796 of 50000000 found) rocksdb.db.get.micros P50 : 2.967687 P95 : 5.754916 P99 : 15.665912 P100 : 8213.000000 COUNT : 50000000 SUM : 187250053 About 3.8% throughput reduction. P50: 5.2% increasing, P95, 2.09% increasing, P99 4.77% improvement Pull Request resolved: https://github.com/facebook/rocksdb/pull/8315 Test Plan: added the testing case Reviewed By: anand1976 Differential Revision: D28599774 Pulled By: zhichao-cao fbshipit-source-id: 098c4df0d7327d3a546df7604b2f1602f13044ed
2021-05-22 01:28:28 +00:00
GetContext* get_context, const Cache::CacheItemHelper* cache_helper,
const Cache::CreateCallback& create_cb, Cache::Priority priority) const {
auto cache_handle =
Parallelize secondary cache lookup in MultiGet (#8405) Summary: Implement the ```WaitAll()``` interface in ```LRUCache``` to allow callers to issue multiple lookups in parallel and wait for all of them to complete. Modify ```MultiGet``` to use this to parallelize the secondary cache lookups in order to reduce the overall latency. A call to ```cache->Lookup()``` returns a handle that has an incomplete value (nullptr), and the caller can call ```cache->IsReady()``` to check whether the lookup is complete, and pass a vector of handles to ```WaitAll``` to wait for completion. If any of the lookups fail, ```MultiGet``` will read the block from the SST file. Another change in this PR is to rename ```SecondaryCacheHandle``` to ```SecondaryCacheResultHandle``` as it more accurately describes the return result of the secondary cache lookup, which is more like a future. Tests: 1. Add unit tests in lru_cache_test 2. Benchmark results with no secondary cache configured Master - ``` readrandom : 41.175 micros/op 388562 ops/sec; 106.7 MB/s (7277999 of 7277999 found) readrandom : 41.217 micros/op 388160 ops/sec; 106.6 MB/s (7274999 of 7274999 found) multireadrandom : 10.309 micros/op 1552082 ops/sec; (28908992 of 28908992 found) multireadrandom : 10.321 micros/op 1550218 ops/sec; (29081984 of 29081984 found) ``` This PR - ``` readrandom : 41.158 micros/op 388723 ops/sec; 106.8 MB/s (7290999 of 7290999 found) readrandom : 41.185 micros/op 388463 ops/sec; 106.7 MB/s (7287999 of 7287999 found) multireadrandom : 10.277 micros/op 1556801 ops/sec; (29346944 of 29346944 found) multireadrandom : 10.253 micros/op 1560539 ops/sec; (29274944 of 29274944 found) ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/8405 Reviewed By: zhichao-cao Differential Revision: D29190509 Pulled By: anand1976 fbshipit-source-id: 6f8eff6246712af8a297cfe22ea0d1c3b2a01bb0
2021-06-18 16:35:03 +00:00
block_cache->Lookup(key, cache_helper, create_cb, priority, wait,
Use new Insert and Lookup APIs in table reader to support secondary cache (#8315) Summary: Secondary cache is implemented to achieve the secondary cache tier for block cache. New Insert and Lookup APIs are introduced in https://github.com/facebook/rocksdb/issues/8271 . To support and use the secondary cache in block based table reader, this PR introduces the corresponding callback functions that will be used in secondary cache, and update the Insert and Lookup APIs accordingly. benchmarking: ./db_bench --benchmarks="fillrandom" -num=1000000 -key_size=32 -value_size=256 -use_direct_io_for_flush_and_compaction=true -db=/tmp/rocks_t/db -partition_index_and_filters=true ./db_bench -db=/tmp/rocks_t/db -use_existing_db=true -benchmarks=readrandom -num=1000000 -key_size=32 -value_size=256 -use_direct_reads=true -cache_size=1073741824 -cache_numshardbits=5 -cache_index_and_filter_blocks=true -read_random_exp_range=17 -statistics -partition_index_and_filters=true -stats_dump_period_sec=30 -reads=50000000 master benchmarking results: readrandom : 3.923 micros/op 254881 ops/sec; 33.4 MB/s (23849796 of 50000000 found) rocksdb.db.get.micros P50 : 2.820992 P95 : 5.636716 P99 : 16.450553 P100 : 8396.000000 COUNT : 50000000 SUM : 179947064 Current PR benchmarking results readrandom : 4.083 micros/op 244925 ops/sec; 32.1 MB/s (23849796 of 50000000 found) rocksdb.db.get.micros P50 : 2.967687 P95 : 5.754916 P99 : 15.665912 P100 : 8213.000000 COUNT : 50000000 SUM : 187250053 About 3.8% throughput reduction. P50: 5.2% increasing, P95, 2.09% increasing, P99 4.77% improvement Pull Request resolved: https://github.com/facebook/rocksdb/pull/8315 Test Plan: added the testing case Reviewed By: anand1976 Differential Revision: D28599774 Pulled By: zhichao-cao fbshipit-source-id: 098c4df0d7327d3a546df7604b2f1602f13044ed
2021-05-22 01:28:28 +00:00
rep_->ioptions.statistics.get());
if (cache_handle != nullptr) {
UpdateCacheHitMetrics(block_type, get_context,
block_cache->GetUsage(cache_handle));
} else {
UpdateCacheMissMetrics(block_type, get_context);
}
return cache_handle;
}
// Helper function to setup the cache key's prefix for the Table.
void BlockBasedTable::SetupCacheKeyPrefix(Rep* rep,
const std::string& db_session_id,
uint64_t file_num) {
assert(kMaxCacheKeyPrefixSize >= 10);
rep->cache_key_prefix_size = 0;
rep->compressed_cache_key_prefix_size = 0;
if (rep->table_options.block_cache != nullptr) {
GenerateCachePrefix<Cache, FSRandomAccessFile>(
rep->table_options.block_cache.get(), rep->file->file(),
&rep->cache_key_prefix[0], &rep->cache_key_prefix_size, db_session_id,
file_num);
}
if (rep->table_options.block_cache_compressed != nullptr) {
GenerateCachePrefix<Cache, FSRandomAccessFile>(
rep->table_options.block_cache_compressed.get(), rep->file->file(),
&rep->compressed_cache_key_prefix[0],
&rep->compressed_cache_key_prefix_size, db_session_id, file_num);
}
if (rep->table_options.persistent_cache != nullptr) {
char persistent_cache_key_prefix[kMaxCacheKeyPrefixSize];
size_t persistent_cache_key_prefix_size = 0;
GenerateCachePrefix<PersistentCache, FSRandomAccessFile>(
rep->table_options.persistent_cache.get(), rep->file->file(),
&persistent_cache_key_prefix[0], &persistent_cache_key_prefix_size,
db_session_id, file_num);
rep->persistent_cache_options =
PersistentCacheOptions(rep->table_options.persistent_cache,
std::string(persistent_cache_key_prefix,
persistent_cache_key_prefix_size),
rep->ioptions.stats);
}
}
namespace {
// Return True if table_properties has `user_prop_name` has a `true` value
// or it doesn't contain this property (for backward compatible).
bool IsFeatureSupported(const TableProperties& table_properties,
const std::string& user_prop_name, Logger* info_log) {
auto& props = table_properties.user_collected_properties;
auto pos = props.find(user_prop_name);
// Older version doesn't have this value set. Skip this check.
if (pos != props.end()) {
if (pos->second == kPropFalse) {
return false;
} else if (pos->second != kPropTrue) {
ROCKS_LOG_WARN(info_log, "Property %s has invalidate value %s",
user_prop_name.c_str(), pos->second.c_str());
}
}
return true;
}
// Caller has to ensure seqno is not nullptr.
Status GetGlobalSequenceNumber(const TableProperties& table_properties,
SequenceNumber largest_seqno,
SequenceNumber* seqno) {
const auto& props = table_properties.user_collected_properties;
const auto version_pos = props.find(ExternalSstFilePropertyNames::kVersion);
const auto seqno_pos = props.find(ExternalSstFilePropertyNames::kGlobalSeqno);
*seqno = kDisableGlobalSequenceNumber;
if (version_pos == props.end()) {
if (seqno_pos != props.end()) {
std::array<char, 200> msg_buf;
// This is not an external sst file, global_seqno is not supported.
snprintf(
msg_buf.data(), msg_buf.max_size(),
"A non-external sst file have global seqno property with value %s",
seqno_pos->second.c_str());
return Status::Corruption(msg_buf.data());
}
return Status::OK();
}
uint32_t version = DecodeFixed32(version_pos->second.c_str());
if (version < 2) {
if (seqno_pos != props.end() || version != 1) {
std::array<char, 200> msg_buf;
// This is a v1 external sst file, global_seqno is not supported.
snprintf(msg_buf.data(), msg_buf.max_size(),
"An external sst file with version %u have global seqno "
"property with value %s",
version, seqno_pos->second.c_str());
return Status::Corruption(msg_buf.data());
}
return Status::OK();
}
// Since we have a plan to deprecate global_seqno, we do not return failure
// if seqno_pos == props.end(). We rely on version_pos to detect whether the
// SST is external.
SequenceNumber global_seqno(0);
if (seqno_pos != props.end()) {
global_seqno = DecodeFixed64(seqno_pos->second.c_str());
}
// SstTableReader open table reader with kMaxSequenceNumber as largest_seqno
// to denote it is unknown.
if (largest_seqno < kMaxSequenceNumber) {
if (global_seqno == 0) {
global_seqno = largest_seqno;
}
if (global_seqno != largest_seqno) {
std::array<char, 200> msg_buf;
snprintf(
msg_buf.data(), msg_buf.max_size(),
"An external sst file with version %u have global seqno property "
"with value %s, while largest seqno in the file is %llu",
version, seqno_pos->second.c_str(),
static_cast<unsigned long long>(largest_seqno));
return Status::Corruption(msg_buf.data());
}
}
*seqno = global_seqno;
if (global_seqno > kMaxSequenceNumber) {
std::array<char, 200> msg_buf;
snprintf(msg_buf.data(), msg_buf.max_size(),
"An external sst file with version %u have global seqno property "
"with value %llu, which is greater than kMaxSequenceNumber",
version, static_cast<unsigned long long>(global_seqno));
return Status::Corruption(msg_buf.data());
}
return Status::OK();
}
} // namespace
Slice BlockBasedTable::GetCacheKey(const char* cache_key_prefix,
size_t cache_key_prefix_size,
const BlockHandle& handle, char* cache_key) {
assert(cache_key != nullptr);
assert(cache_key_prefix_size != 0);
assert(cache_key_prefix_size <= kMaxCacheKeyPrefixSize);
memcpy(cache_key, cache_key_prefix, cache_key_prefix_size);
char* end =
EncodeVarint64(cache_key + cache_key_prefix_size, handle.offset());
return Slice(cache_key, static_cast<size_t>(end - cache_key));
}
Status BlockBasedTable::Open(
const ReadOptions& read_options, const ImmutableOptions& ioptions,
const EnvOptions& env_options, const BlockBasedTableOptions& table_options,
const InternalKeyComparator& internal_comparator,
std::unique_ptr<RandomAccessFileReader>&& file, uint64_t file_size,
std::unique_ptr<TableReader>* table_reader,
const SliceTransform* prefix_extractor,
const bool prefetch_index_and_filter_in_cache, const bool skip_filters,
const int level, const bool immortal_table,
const SequenceNumber largest_seqno, const bool force_direct_prefetch,
TailPrefetchStats* tail_prefetch_stats,
BlockCacheTracer* const block_cache_tracer,
size_t max_file_size_for_l0_meta_pin, const std::string& cur_db_session_id,
uint64_t cur_file_num) {
table_reader->reset();
Status s;
Footer footer;
std::unique_ptr<FilePrefetchBuffer> prefetch_buffer;
// Only retain read_options.deadline and read_options.io_timeout.
// In future, we may retain more
// options. Specifically, w ignore verify_checksums and default to
// checksum verification anyway when creating the index and filter
// readers.
ReadOptions ro;
ro.deadline = read_options.deadline;
ro.io_timeout = read_options.io_timeout;
// prefetch both index and filters, down to all partitions
const bool prefetch_all = prefetch_index_and_filter_in_cache || level == 0;
const bool preload_all = !table_options.cache_index_and_filter_blocks;
if (!ioptions.allow_mmap_reads) {
s = PrefetchTail(ro, file.get(), file_size, force_direct_prefetch,
tail_prefetch_stats, prefetch_all, preload_all,
&prefetch_buffer);
// Return error in prefetch path to users.
if (!s.ok()) {
return s;
}
} else {
// Should not prefetch for mmap mode.
prefetch_buffer.reset(new FilePrefetchBuffer(
nullptr, 0, 0, false /* enable */, true /* track_min_offset */));
}
// Read in the following order:
// 1. Footer
// 2. [metaindex block]
// 3. [meta block: properties]
// 4. [meta block: range deletion tombstone]
// 5. [meta block: compression dictionary]
// 6. [meta block: index]
// 7. [meta block: filter]
IOOptions opts;
s = file->PrepareIOOptions(ro, opts);
if (s.ok()) {
s = ReadFooterFromFile(opts, file.get(), prefetch_buffer.get(), file_size,
&footer, kBlockBasedTableMagicNumber);
}
if (!s.ok()) {
return s;
}
if (!BlockBasedTableSupportedVersion(footer.version())) {
return Status::Corruption(
"Unknown Footer version. Maybe this file was created with newer "
"version of RocksDB?");
}
// We've successfully read the footer. We are ready to serve requests.
// Better not mutate rep_ after the creation. eg. internal_prefix_transform
// raw pointer will be used to create HashIndexReader, whose reset may
// access a dangling pointer.
BlockCacheLookupContext lookup_context{TableReaderCaller::kPrefetch};
Rep* rep = new BlockBasedTable::Rep(ioptions, env_options, table_options,
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
internal_comparator, skip_filters,
file_size, level, immortal_table);
rep->file = std::move(file);
rep->footer = footer;
rep->hash_index_allow_collision = table_options.hash_index_allow_collision;
// We need to wrap data with internal_prefix_transform to make sure it can
// handle prefix correctly.
if (prefix_extractor != nullptr) {
rep->internal_prefix_transform.reset(
new InternalKeySliceTransform(prefix_extractor));
}
// For fully portable/stable cache keys, we need to read the properties
// block before setting up cache keys. TODO: consider setting up a bootstrap
// cache key for PersistentCache to use for metaindex and properties blocks.
rep->persistent_cache_options = PersistentCacheOptions();
// Meta-blocks are not dictionary compressed. Explicitly set the dictionary
// handle to null, otherwise it may be seen as uninitialized during the below
// meta-block reads.
rep->compression_dict_handle = BlockHandle::NullBlockHandle();
// Read metaindex
std::unique_ptr<BlockBasedTable> new_table(
new BlockBasedTable(rep, block_cache_tracer));
std::unique_ptr<Block> metaindex;
std::unique_ptr<InternalIterator> metaindex_iter;
s = new_table->ReadMetaIndexBlock(ro, prefetch_buffer.get(), &metaindex,
&metaindex_iter);
if (!s.ok()) {
return s;
}
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
// Populates table_properties and some fields that depend on it,
// such as index_type.
s = new_table->ReadPropertiesBlock(ro, prefetch_buffer.get(),
metaindex_iter.get(), largest_seqno);
if (!s.ok()) {
return s;
}
// With properties loaded, we can set up portable/stable cache keys if
// necessary info is available
Embed original file number in SST table properties (#8686) Summary: I very recently realized that with https://github.com/facebook/rocksdb/issues/8669 we cannot later add file numbers to external SST files (so that more can share db session ids for better uniqueness properties), because of forward compatibility. We would have a version of RocksDB that assumes session IDs are unique on external SST files and therefore can't really break that invariant in future files. This change adds a table property for "orig_file_number" which is populated by normal SST files and also external SST files generated by SstFileWriter. SstFileWriter now keeps a db_session_id for life of the object and increments its own file numbers for embedding in table properties. (They are arguably "fake" file numbers because these numbers and not embedded in the file name.) While updating block_based_table_builder, I removed several unnecessary fields from Rep, because following the pattern would have created another unnecessary field. This change also updates block_based_table_reader to use this new property when available, which means that for newer SST files, we can determine the stable/original <db_session_id,file_number> unique identifier using just the file contents, not the file name. (It's a bit complicated; detailed comments in block_based_table_reader.) Also added DB host id to properties listing by sst_dump, which could be useful in debugging. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8686 Test Plan: majorly overhauled StableCacheKeys test for this change Reviewed By: zhichao-cao Differential Revision: D30457742 Pulled By: pdillinger fbshipit-source-id: 2e5ae7dddeb94fb9d8eac8a928486aed8b8cd445
2021-08-21 03:39:52 +00:00
std::string db_session_id;
uint64_t file_num;
if (rep->table_properties && !rep->table_properties->db_session_id.empty() &&
rep->table_properties->orig_file_number > 0) {
// We must have both properties to get a stable unique id because
// CreateColumnFamilyWithImport or IngestExternalFiles can change the
// file numbers on a file.
db_session_id = rep->table_properties->db_session_id;
file_num = rep->table_properties->orig_file_number;
} else {
// We have to use transient (but unique) cache keys based on current
// identifiers.
db_session_id = cur_db_session_id;
file_num = cur_file_num;
}
SetupCacheKeyPrefix(rep, db_session_id, file_num);
s = new_table->ReadRangeDelBlock(ro, prefetch_buffer.get(),
metaindex_iter.get(), internal_comparator,
&lookup_context);
if (!s.ok()) {
return s;
}
s = new_table->PrefetchIndexAndFilterBlocks(
ro, prefetch_buffer.get(), metaindex_iter.get(), new_table.get(),
prefetch_all, table_options, level, file_size,
max_file_size_for_l0_meta_pin, &lookup_context);
if (s.ok()) {
// Update tail prefetch stats
assert(prefetch_buffer.get() != nullptr);
if (tail_prefetch_stats != nullptr) {
assert(prefetch_buffer->min_offset_read() < file_size);
tail_prefetch_stats->RecordEffectiveSize(
static_cast<size_t>(file_size) - prefetch_buffer->min_offset_read());
}
*table_reader = std::move(new_table);
}
return s;
}
Status BlockBasedTable::PrefetchTail(
const ReadOptions& ro, RandomAccessFileReader* file, uint64_t file_size,
bool force_direct_prefetch, TailPrefetchStats* tail_prefetch_stats,
const bool prefetch_all, const bool preload_all,
std::unique_ptr<FilePrefetchBuffer>* prefetch_buffer) {
size_t tail_prefetch_size = 0;
if (tail_prefetch_stats != nullptr) {
// Multiple threads may get a 0 (no history) when running in parallel,
// but it will get cleared after the first of them finishes.
tail_prefetch_size = tail_prefetch_stats->GetSuggestedPrefetchSize();
}
if (tail_prefetch_size == 0) {
// Before read footer, readahead backwards to prefetch data. Do more
// readahead if we're going to read index/filter.
// TODO: This may incorrectly select small readahead in case partitioned
// index/filter is enabled and top-level partition pinning is enabled.
// That's because we need to issue readahead before we read the properties,
// at which point we don't yet know the index type.
tail_prefetch_size = prefetch_all || preload_all ? 512 * 1024 : 4 * 1024;
}
size_t prefetch_off;
size_t prefetch_len;
if (file_size < tail_prefetch_size) {
prefetch_off = 0;
prefetch_len = static_cast<size_t>(file_size);
} else {
prefetch_off = static_cast<size_t>(file_size - tail_prefetch_size);
prefetch_len = tail_prefetch_size;
}
TEST_SYNC_POINT_CALLBACK("BlockBasedTable::Open::TailPrefetchLen",
&tail_prefetch_size);
// Try file system prefetch
if (!file->use_direct_io() && !force_direct_prefetch) {
if (!file->Prefetch(prefetch_off, prefetch_len).IsNotSupported()) {
prefetch_buffer->reset(
new FilePrefetchBuffer(nullptr, 0, 0, false, true));
return Status::OK();
}
}
// Use `FilePrefetchBuffer`
prefetch_buffer->reset(new FilePrefetchBuffer(nullptr, 0, 0, true, true));
IOOptions opts;
Status s = file->PrepareIOOptions(ro, opts);
if (s.ok()) {
s = (*prefetch_buffer)->Prefetch(opts, file, prefetch_off, prefetch_len);
}
return s;
}
Status BlockBasedTable::TryReadPropertiesWithGlobalSeqno(
const ReadOptions& ro, FilePrefetchBuffer* prefetch_buffer,
const Slice& handle_value, TableProperties** table_properties) {
assert(table_properties != nullptr);
// If this is an external SST file ingested with write_global_seqno set to
// true, then we expect the checksum mismatch because checksum was written
// by SstFileWriter, but its global seqno in the properties block may have
// been changed during ingestion. In this case, we read the properties
// block, copy it to a memory buffer, change the global seqno to its
// original value, i.e. 0, and verify the checksum again.
BlockHandle props_block_handle;
CacheAllocationPtr tmp_buf;
Status s = ReadProperties(ro, handle_value, rep_->file.get(), prefetch_buffer,
rep_->footer, rep_->ioptions, table_properties,
false /* verify_checksum */, &props_block_handle,
&tmp_buf, false /* compression_type_missing */,
nullptr /* memory_allocator */);
if (s.ok() && tmp_buf) {
const auto seqno_pos_iter =
(*table_properties)
->properties_offsets.find(
ExternalSstFilePropertyNames::kGlobalSeqno);
size_t block_size = static_cast<size_t>(props_block_handle.size());
if (seqno_pos_iter != (*table_properties)->properties_offsets.end()) {
uint64_t global_seqno_offset = seqno_pos_iter->second;
EncodeFixed64(
tmp_buf.get() + global_seqno_offset - props_block_handle.offset(), 0);
}
s = ROCKSDB_NAMESPACE::VerifyBlockChecksum(
rep_->footer.checksum(), tmp_buf.get(), block_size,
rep_->file->file_name(), props_block_handle.offset());
}
return s;
}
Status BlockBasedTable::ReadPropertiesBlock(
const ReadOptions& ro, FilePrefetchBuffer* prefetch_buffer,
InternalIterator* meta_iter, const SequenceNumber largest_seqno) {
bool found_properties_block = true;
Status s;
s = SeekToPropertiesBlock(meta_iter, &found_properties_block);
if (!s.ok()) {
ROCKS_LOG_WARN(rep_->ioptions.logger,
"Error when seeking to properties block from file: %s",
s.ToString().c_str());
} else if (found_properties_block) {
s = meta_iter->status();
TableProperties* table_properties = nullptr;
if (s.ok()) {
s = ReadProperties(
ro, meta_iter->value(), rep_->file.get(), prefetch_buffer,
rep_->footer, rep_->ioptions, &table_properties,
true /* verify_checksum */, nullptr /* ret_block_handle */,
nullptr /* ret_block_contents */,
false /* compression_type_missing */, nullptr /* memory_allocator */);
}
IGNORE_STATUS_IF_ERROR(s);
if (s.IsCorruption()) {
s = TryReadPropertiesWithGlobalSeqno(
ro, prefetch_buffer, meta_iter->value(), &table_properties);
IGNORE_STATUS_IF_ERROR(s);
}
std::unique_ptr<TableProperties> props_guard;
if (table_properties != nullptr) {
props_guard.reset(table_properties);
}
if (!s.ok()) {
ROCKS_LOG_WARN(rep_->ioptions.logger,
"Encountered error while reading data from properties "
"block %s",
s.ToString().c_str());
} else {
assert(table_properties != nullptr);
rep_->table_properties.reset(props_guard.release());
rep_->blocks_maybe_compressed =
rep_->table_properties->compression_name !=
CompressionTypeToString(kNoCompression);
rep_->blocks_definitely_zstd_compressed =
(rep_->table_properties->compression_name ==
CompressionTypeToString(kZSTD) ||
rep_->table_properties->compression_name ==
CompressionTypeToString(kZSTDNotFinalCompression));
}
} else {
ROCKS_LOG_ERROR(rep_->ioptions.logger,
"Cannot find Properties block from file.");
}
#ifndef ROCKSDB_LITE
if (rep_->table_properties) {
ParseSliceTransform(rep_->table_properties->prefix_extractor_name,
&(rep_->table_prefix_extractor));
}
#endif // ROCKSDB_LITE
// Read the table properties, if provided.
if (rep_->table_properties) {
rep_->whole_key_filtering &=
IsFeatureSupported(*(rep_->table_properties),
BlockBasedTablePropertyNames::kWholeKeyFiltering,
rep_->ioptions.logger);
rep_->prefix_filtering &= IsFeatureSupported(
*(rep_->table_properties),
BlockBasedTablePropertyNames::kPrefixFiltering, rep_->ioptions.logger);
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
rep_->index_key_includes_seq =
rep_->table_properties->index_key_is_user_key == 0;
rep_->index_value_is_full =
rep_->table_properties->index_value_is_delta_encoded == 0;
// Update index_type with the true type.
// If table properties don't contain index type, we assume that the table
// is in very old format and has kBinarySearch index type.
auto& props = rep_->table_properties->user_collected_properties;
auto pos = props.find(BlockBasedTablePropertyNames::kIndexType);
if (pos != props.end()) {
rep_->index_type = static_cast<BlockBasedTableOptions::IndexType>(
DecodeFixed32(pos->second.c_str()));
}
rep_->index_has_first_key =
rep_->index_type == BlockBasedTableOptions::kBinarySearchWithFirstKey;
s = GetGlobalSequenceNumber(*(rep_->table_properties), largest_seqno,
&(rep_->global_seqno));
if (!s.ok()) {
ROCKS_LOG_ERROR(rep_->ioptions.logger, "%s", s.ToString().c_str());
}
}
return s;
}
Status BlockBasedTable::ReadRangeDelBlock(
const ReadOptions& read_options, FilePrefetchBuffer* prefetch_buffer,
InternalIterator* meta_iter,
Create a BlockCacheLookupContext to enable fine-grained block cache tracing. (#5421) Summary: BlockCacheLookupContext only contains the caller for now. We will trace block accesses at five places: 1. BlockBasedTable::GetFilter. 2. BlockBasedTable::GetUncompressedDict. 3. BlockBasedTable::MaybeReadAndLoadToCache. (To trace access on data, index, and range deletion block.) 4. BlockBasedTable::Get. (To trace the referenced key and whether the referenced key exists in a fetched data block.) 5. BlockBasedTable::MultiGet. (To trace the referenced key and whether the referenced key exists in a fetched data block.) We create the context at: 1. BlockBasedTable::Get. (kUserGet) 2. BlockBasedTable::MultiGet. (kUserMGet) 3. BlockBasedTable::NewIterator. (either kUserIterator, kCompaction, or external SST ingestion calls this function.) 4. BlockBasedTable::Open. (kPrefetch) 5. Index/Filter::CacheDependencies. (kPrefetch) 6. BlockBasedTable::ApproximateOffsetOf. (kCompaction or kUserApproximateSize). I loaded 1 million key-value pairs into the database and ran the readrandom benchmark with a single thread. I gave the block cache 10 GB to make sure all reads hit the block cache after warmup. The throughput is comparable. Throughput of this PR: 231334 ops/s. Throughput of the master branch: 238428 ops/s. Experiment setup: RocksDB: version 6.2 Date: Mon Jun 10 10:42:51 2019 CPU: 24 * Intel Core Processor (Skylake) CPUCache: 16384 KB Keys: 20 bytes each Values: 100 bytes each (100 bytes after compression) Entries: 1000000 Prefix: 20 bytes Keys per prefix: 0 RawSize: 114.4 MB (estimated) FileSize: 114.4 MB (estimated) Write rate: 0 bytes/second Read rate: 0 ops/second Compression: NoCompression Compression sampling rate: 0 Memtablerep: skip_list Perf Level: 1 Load command: ./db_bench --benchmarks="fillseq" --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --statistics --cache_index_and_filter_blocks --cache_size=10737418240 --disable_auto_compactions=1 --disable_wal=1 --compression_type=none --min_level_to_compress=-1 --compression_ratio=1 --num=1000000 Run command: ./db_bench --benchmarks="readrandom,stats" --use_existing_db --threads=1 --duration=120 --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --statistics --cache_index_and_filter_blocks --cache_size=10737418240 --disable_auto_compactions=1 --disable_wal=1 --compression_type=none --min_level_to_compress=-1 --compression_ratio=1 --num=1000000 --duration=120 TODOs: 1. Create a caller for external SST file ingestion and differentiate the callers for iterator. 2. Integrate tracer to trace block cache accesses. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5421 Differential Revision: D15704258 Pulled By: HaoyuHuang fbshipit-source-id: 4aa8a55f8cb1576ffb367bfa3186a91d8f06d93a
2019-06-10 22:30:05 +00:00
const InternalKeyComparator& internal_comparator,
BlockCacheLookupContext* lookup_context) {
Status s;
bool found_range_del_block;
BlockHandle range_del_handle;
s = SeekToRangeDelBlock(meta_iter, &found_range_del_block, &range_del_handle);
if (!s.ok()) {
ROCKS_LOG_WARN(
rep_->ioptions.logger,
"Error when seeking to range delete tombstones block from file: %s",
s.ToString().c_str());
} else if (found_range_del_block && !range_del_handle.IsNull()) {
std::unique_ptr<InternalIterator> iter(NewDataBlockIterator<DataBlockIter>(
Create a BlockCacheLookupContext to enable fine-grained block cache tracing. (#5421) Summary: BlockCacheLookupContext only contains the caller for now. We will trace block accesses at five places: 1. BlockBasedTable::GetFilter. 2. BlockBasedTable::GetUncompressedDict. 3. BlockBasedTable::MaybeReadAndLoadToCache. (To trace access on data, index, and range deletion block.) 4. BlockBasedTable::Get. (To trace the referenced key and whether the referenced key exists in a fetched data block.) 5. BlockBasedTable::MultiGet. (To trace the referenced key and whether the referenced key exists in a fetched data block.) We create the context at: 1. BlockBasedTable::Get. (kUserGet) 2. BlockBasedTable::MultiGet. (kUserMGet) 3. BlockBasedTable::NewIterator. (either kUserIterator, kCompaction, or external SST ingestion calls this function.) 4. BlockBasedTable::Open. (kPrefetch) 5. Index/Filter::CacheDependencies. (kPrefetch) 6. BlockBasedTable::ApproximateOffsetOf. (kCompaction or kUserApproximateSize). I loaded 1 million key-value pairs into the database and ran the readrandom benchmark with a single thread. I gave the block cache 10 GB to make sure all reads hit the block cache after warmup. The throughput is comparable. Throughput of this PR: 231334 ops/s. Throughput of the master branch: 238428 ops/s. Experiment setup: RocksDB: version 6.2 Date: Mon Jun 10 10:42:51 2019 CPU: 24 * Intel Core Processor (Skylake) CPUCache: 16384 KB Keys: 20 bytes each Values: 100 bytes each (100 bytes after compression) Entries: 1000000 Prefix: 20 bytes Keys per prefix: 0 RawSize: 114.4 MB (estimated) FileSize: 114.4 MB (estimated) Write rate: 0 bytes/second Read rate: 0 ops/second Compression: NoCompression Compression sampling rate: 0 Memtablerep: skip_list Perf Level: 1 Load command: ./db_bench --benchmarks="fillseq" --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --statistics --cache_index_and_filter_blocks --cache_size=10737418240 --disable_auto_compactions=1 --disable_wal=1 --compression_type=none --min_level_to_compress=-1 --compression_ratio=1 --num=1000000 Run command: ./db_bench --benchmarks="readrandom,stats" --use_existing_db --threads=1 --duration=120 --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --statistics --cache_index_and_filter_blocks --cache_size=10737418240 --disable_auto_compactions=1 --disable_wal=1 --compression_type=none --min_level_to_compress=-1 --compression_ratio=1 --num=1000000 --duration=120 TODOs: 1. Create a caller for external SST file ingestion and differentiate the callers for iterator. 2. Integrate tracer to trace block cache accesses. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5421 Differential Revision: D15704258 Pulled By: HaoyuHuang fbshipit-source-id: 4aa8a55f8cb1576ffb367bfa3186a91d8f06d93a
2019-06-10 22:30:05 +00:00
read_options, range_del_handle,
/*input_iter=*/nullptr, BlockType::kRangeDeletion,
/*get_context=*/nullptr, lookup_context, Status(), prefetch_buffer));
assert(iter != nullptr);
s = iter->status();
Cache fragmented range tombstones in BlockBasedTableReader (#4493) Summary: This allows tombstone fragmenting to only be performed when the table is opened, and cached for subsequent accesses. On the same DB used in #4449, running `readrandom` results in the following: ``` readrandom : 0.983 micros/op 1017076 ops/sec; 78.3 MB/s (63103 of 100000 found) ``` Now that Get performance in the presence of range tombstones is reasonable, I also compared the performance between a DB with range tombstones, "expanded" range tombstones (several point tombstones that cover the same keys the equivalent range tombstone would cover, a common workaround for DeleteRange), and no range tombstones. The created DBs had 5 million keys each, and DeleteRange was called at regular intervals (depending on the total number of range tombstones being written) after 4.5 million Puts. The table below summarizes the results of a `readwhilewriting` benchmark (in order to provide somewhat more realistic results): ``` Tombstones? | avg micros/op | stddev micros/op | avg ops/s | stddev ops/s ----------------- | ------------- | ---------------- | ------------ | ------------ None | 0.6186 | 0.04637 | 1,625,252.90 | 124,679.41 500 Expanded | 0.6019 | 0.03628 | 1,666,670.40 | 101,142.65 500 Unexpanded | 0.6435 | 0.03994 | 1,559,979.40 | 104,090.52 1k Expanded | 0.6034 | 0.04349 | 1,665,128.10 | 125,144.57 1k Unexpanded | 0.6261 | 0.03093 | 1,600,457.50 | 79,024.94 5k Expanded | 0.6163 | 0.05926 | 1,636,668.80 | 154,888.85 5k Unexpanded | 0.6402 | 0.04002 | 1,567,804.70 | 100,965.55 10k Expanded | 0.6036 | 0.05105 | 1,667,237.70 | 142,830.36 10k Unexpanded | 0.6128 | 0.02598 | 1,634,633.40 | 72,161.82 25k Expanded | 0.6198 | 0.04542 | 1,620,980.50 | 116,662.93 25k Unexpanded | 0.5478 | 0.0362 | 1,833,059.10 | 121,233.81 50k Expanded | 0.5104 | 0.04347 | 1,973,107.90 | 184,073.49 50k Unexpanded | 0.4528 | 0.03387 | 2,219,034.50 | 170,984.32 ``` After a large enough quantity of range tombstones are written, range tombstone Gets can become faster than reading from an equivalent DB with several point tombstones. Pull Request resolved: https://github.com/facebook/rocksdb/pull/4493 Differential Revision: D10842844 Pulled By: abhimadan fbshipit-source-id: a7d44534f8120e6aabb65779d26c6b9df954c509
2018-10-26 02:25:00 +00:00
if (!s.ok()) {
ROCKS_LOG_WARN(
rep_->ioptions.logger,
Cache fragmented range tombstones in BlockBasedTableReader (#4493) Summary: This allows tombstone fragmenting to only be performed when the table is opened, and cached for subsequent accesses. On the same DB used in #4449, running `readrandom` results in the following: ``` readrandom : 0.983 micros/op 1017076 ops/sec; 78.3 MB/s (63103 of 100000 found) ``` Now that Get performance in the presence of range tombstones is reasonable, I also compared the performance between a DB with range tombstones, "expanded" range tombstones (several point tombstones that cover the same keys the equivalent range tombstone would cover, a common workaround for DeleteRange), and no range tombstones. The created DBs had 5 million keys each, and DeleteRange was called at regular intervals (depending on the total number of range tombstones being written) after 4.5 million Puts. The table below summarizes the results of a `readwhilewriting` benchmark (in order to provide somewhat more realistic results): ``` Tombstones? | avg micros/op | stddev micros/op | avg ops/s | stddev ops/s ----------------- | ------------- | ---------------- | ------------ | ------------ None | 0.6186 | 0.04637 | 1,625,252.90 | 124,679.41 500 Expanded | 0.6019 | 0.03628 | 1,666,670.40 | 101,142.65 500 Unexpanded | 0.6435 | 0.03994 | 1,559,979.40 | 104,090.52 1k Expanded | 0.6034 | 0.04349 | 1,665,128.10 | 125,144.57 1k Unexpanded | 0.6261 | 0.03093 | 1,600,457.50 | 79,024.94 5k Expanded | 0.6163 | 0.05926 | 1,636,668.80 | 154,888.85 5k Unexpanded | 0.6402 | 0.04002 | 1,567,804.70 | 100,965.55 10k Expanded | 0.6036 | 0.05105 | 1,667,237.70 | 142,830.36 10k Unexpanded | 0.6128 | 0.02598 | 1,634,633.40 | 72,161.82 25k Expanded | 0.6198 | 0.04542 | 1,620,980.50 | 116,662.93 25k Unexpanded | 0.5478 | 0.0362 | 1,833,059.10 | 121,233.81 50k Expanded | 0.5104 | 0.04347 | 1,973,107.90 | 184,073.49 50k Unexpanded | 0.4528 | 0.03387 | 2,219,034.50 | 170,984.32 ``` After a large enough quantity of range tombstones are written, range tombstone Gets can become faster than reading from an equivalent DB with several point tombstones. Pull Request resolved: https://github.com/facebook/rocksdb/pull/4493 Differential Revision: D10842844 Pulled By: abhimadan fbshipit-source-id: a7d44534f8120e6aabb65779d26c6b9df954c509
2018-10-26 02:25:00 +00:00
"Encountered error while reading data from range del block %s",
s.ToString().c_str());
IGNORE_STATUS_IF_ERROR(s);
} else {
rep_->fragmented_range_dels =
std::make_shared<FragmentedRangeTombstoneList>(std::move(iter),
internal_comparator);
}
}
return s;
}
Status BlockBasedTable::PrefetchIndexAndFilterBlocks(
const ReadOptions& ro, FilePrefetchBuffer* prefetch_buffer,
InternalIterator* meta_iter, BlockBasedTable* new_table, bool prefetch_all,
Create a BlockCacheLookupContext to enable fine-grained block cache tracing. (#5421) Summary: BlockCacheLookupContext only contains the caller for now. We will trace block accesses at five places: 1. BlockBasedTable::GetFilter. 2. BlockBasedTable::GetUncompressedDict. 3. BlockBasedTable::MaybeReadAndLoadToCache. (To trace access on data, index, and range deletion block.) 4. BlockBasedTable::Get. (To trace the referenced key and whether the referenced key exists in a fetched data block.) 5. BlockBasedTable::MultiGet. (To trace the referenced key and whether the referenced key exists in a fetched data block.) We create the context at: 1. BlockBasedTable::Get. (kUserGet) 2. BlockBasedTable::MultiGet. (kUserMGet) 3. BlockBasedTable::NewIterator. (either kUserIterator, kCompaction, or external SST ingestion calls this function.) 4. BlockBasedTable::Open. (kPrefetch) 5. Index/Filter::CacheDependencies. (kPrefetch) 6. BlockBasedTable::ApproximateOffsetOf. (kCompaction or kUserApproximateSize). I loaded 1 million key-value pairs into the database and ran the readrandom benchmark with a single thread. I gave the block cache 10 GB to make sure all reads hit the block cache after warmup. The throughput is comparable. Throughput of this PR: 231334 ops/s. Throughput of the master branch: 238428 ops/s. Experiment setup: RocksDB: version 6.2 Date: Mon Jun 10 10:42:51 2019 CPU: 24 * Intel Core Processor (Skylake) CPUCache: 16384 KB Keys: 20 bytes each Values: 100 bytes each (100 bytes after compression) Entries: 1000000 Prefix: 20 bytes Keys per prefix: 0 RawSize: 114.4 MB (estimated) FileSize: 114.4 MB (estimated) Write rate: 0 bytes/second Read rate: 0 ops/second Compression: NoCompression Compression sampling rate: 0 Memtablerep: skip_list Perf Level: 1 Load command: ./db_bench --benchmarks="fillseq" --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --statistics --cache_index_and_filter_blocks --cache_size=10737418240 --disable_auto_compactions=1 --disable_wal=1 --compression_type=none --min_level_to_compress=-1 --compression_ratio=1 --num=1000000 Run command: ./db_bench --benchmarks="readrandom,stats" --use_existing_db --threads=1 --duration=120 --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --statistics --cache_index_and_filter_blocks --cache_size=10737418240 --disable_auto_compactions=1 --disable_wal=1 --compression_type=none --min_level_to_compress=-1 --compression_ratio=1 --num=1000000 --duration=120 TODOs: 1. Create a caller for external SST file ingestion and differentiate the callers for iterator. 2. Integrate tracer to trace block cache accesses. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5421 Differential Revision: D15704258 Pulled By: HaoyuHuang fbshipit-source-id: 4aa8a55f8cb1576ffb367bfa3186a91d8f06d93a
2019-06-10 22:30:05 +00:00
const BlockBasedTableOptions& table_options, const int level,
size_t file_size, size_t max_file_size_for_l0_meta_pin,
Create a BlockCacheLookupContext to enable fine-grained block cache tracing. (#5421) Summary: BlockCacheLookupContext only contains the caller for now. We will trace block accesses at five places: 1. BlockBasedTable::GetFilter. 2. BlockBasedTable::GetUncompressedDict. 3. BlockBasedTable::MaybeReadAndLoadToCache. (To trace access on data, index, and range deletion block.) 4. BlockBasedTable::Get. (To trace the referenced key and whether the referenced key exists in a fetched data block.) 5. BlockBasedTable::MultiGet. (To trace the referenced key and whether the referenced key exists in a fetched data block.) We create the context at: 1. BlockBasedTable::Get. (kUserGet) 2. BlockBasedTable::MultiGet. (kUserMGet) 3. BlockBasedTable::NewIterator. (either kUserIterator, kCompaction, or external SST ingestion calls this function.) 4. BlockBasedTable::Open. (kPrefetch) 5. Index/Filter::CacheDependencies. (kPrefetch) 6. BlockBasedTable::ApproximateOffsetOf. (kCompaction or kUserApproximateSize). I loaded 1 million key-value pairs into the database and ran the readrandom benchmark with a single thread. I gave the block cache 10 GB to make sure all reads hit the block cache after warmup. The throughput is comparable. Throughput of this PR: 231334 ops/s. Throughput of the master branch: 238428 ops/s. Experiment setup: RocksDB: version 6.2 Date: Mon Jun 10 10:42:51 2019 CPU: 24 * Intel Core Processor (Skylake) CPUCache: 16384 KB Keys: 20 bytes each Values: 100 bytes each (100 bytes after compression) Entries: 1000000 Prefix: 20 bytes Keys per prefix: 0 RawSize: 114.4 MB (estimated) FileSize: 114.4 MB (estimated) Write rate: 0 bytes/second Read rate: 0 ops/second Compression: NoCompression Compression sampling rate: 0 Memtablerep: skip_list Perf Level: 1 Load command: ./db_bench --benchmarks="fillseq" --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --statistics --cache_index_and_filter_blocks --cache_size=10737418240 --disable_auto_compactions=1 --disable_wal=1 --compression_type=none --min_level_to_compress=-1 --compression_ratio=1 --num=1000000 Run command: ./db_bench --benchmarks="readrandom,stats" --use_existing_db --threads=1 --duration=120 --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --statistics --cache_index_and_filter_blocks --cache_size=10737418240 --disable_auto_compactions=1 --disable_wal=1 --compression_type=none --min_level_to_compress=-1 --compression_ratio=1 --num=1000000 --duration=120 TODOs: 1. Create a caller for external SST file ingestion and differentiate the callers for iterator. 2. Integrate tracer to trace block cache accesses. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5421 Differential Revision: D15704258 Pulled By: HaoyuHuang fbshipit-source-id: 4aa8a55f8cb1576ffb367bfa3186a91d8f06d93a
2019-06-10 22:30:05 +00:00
BlockCacheLookupContext* lookup_context) {
Status s;
// Find filter handle and filter type
if (rep_->filter_policy) {
for (auto filter_type :
{Rep::FilterType::kFullFilter, Rep::FilterType::kPartitionedFilter,
Rep::FilterType::kBlockFilter}) {
std::string prefix;
switch (filter_type) {
case Rep::FilterType::kFullFilter:
prefix = kFullFilterBlockPrefix;
break;
case Rep::FilterType::kPartitionedFilter:
prefix = kPartitionedFilterBlockPrefix;
break;
case Rep::FilterType::kBlockFilter:
prefix = kFilterBlockPrefix;
break;
default:
assert(0);
}
std::string filter_block_key = prefix;
filter_block_key.append(rep_->filter_policy->Name());
if (FindMetaBlock(meta_iter, filter_block_key, &rep_->filter_handle)
.ok()) {
rep_->filter_type = filter_type;
break;
}
}
}
// Partition filters cannot be enabled without partition indexes
assert(rep_->filter_type != Rep::FilterType::kPartitionedFilter ||
rep_->index_type == BlockBasedTableOptions::kTwoLevelIndexSearch);
// Find compression dictionary handle
bool found_compression_dict = false;
s = SeekToCompressionDictBlock(meta_iter, &found_compression_dict,
&rep_->compression_dict_handle);
if (!s.ok()) {
return s;
}
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
BlockBasedTableOptions::IndexType index_type = rep_->index_type;
const bool use_cache = table_options.cache_index_and_filter_blocks;
const bool maybe_flushed =
level == 0 && file_size <= max_file_size_for_l0_meta_pin;
std::function<bool(PinningTier, PinningTier)> is_pinned =
[maybe_flushed, &is_pinned](PinningTier pinning_tier,
PinningTier fallback_pinning_tier) {
// Fallback to fallback would lead to infinite recursion. Disallow it.
assert(fallback_pinning_tier != PinningTier::kFallback);
switch (pinning_tier) {
case PinningTier::kFallback:
return is_pinned(fallback_pinning_tier,
PinningTier::kNone /* fallback_pinning_tier */);
case PinningTier::kNone:
return false;
case PinningTier::kFlushedAndSimilar:
return maybe_flushed;
case PinningTier::kAll:
return true;
};
// In GCC, this is needed to suppress `control reaches end of non-void
// function [-Werror=return-type]`.
assert(false);
return false;
};
const bool pin_top_level_index = is_pinned(
table_options.metadata_cache_options.top_level_index_pinning,
table_options.pin_top_level_index_and_filter ? PinningTier::kAll
: PinningTier::kNone);
const bool pin_partition =
is_pinned(table_options.metadata_cache_options.partition_pinning,
table_options.pin_l0_filter_and_index_blocks_in_cache
? PinningTier::kFlushedAndSimilar
: PinningTier::kNone);
const bool pin_unpartitioned =
is_pinned(table_options.metadata_cache_options.unpartitioned_pinning,
table_options.pin_l0_filter_and_index_blocks_in_cache
? PinningTier::kFlushedAndSimilar
: PinningTier::kNone);
// pin the first level of index
const bool pin_index =
index_type == BlockBasedTableOptions::kTwoLevelIndexSearch
? pin_top_level_index
: pin_unpartitioned;
// prefetch the first level of index
const bool prefetch_index = prefetch_all || pin_index;
std::unique_ptr<IndexReader> index_reader;
s = new_table->CreateIndexReader(ro, prefetch_buffer, meta_iter, use_cache,
prefetch_index, pin_index, lookup_context,
&index_reader);
if (!s.ok()) {
return s;
}
rep_->index_reader = std::move(index_reader);
// The partitions of partitioned index are always stored in cache. They
// are hence follow the configuration for pin and prefetch regardless of
// the value of cache_index_and_filter_blocks
if (prefetch_all || pin_partition) {
s = rep_->index_reader->CacheDependencies(ro, pin_partition);
}
if (!s.ok()) {
return s;
}
// pin the first level of filter
const bool pin_filter =
rep_->filter_type == Rep::FilterType::kPartitionedFilter
? pin_top_level_index
: pin_unpartitioned;
// prefetch the first level of filter
const bool prefetch_filter = prefetch_all || pin_filter;
if (rep_->filter_policy) {
auto filter = new_table->CreateFilterBlockReader(
ro, prefetch_buffer, use_cache, prefetch_filter, pin_filter,
lookup_context);
if (filter) {
// Refer to the comment above about paritioned indexes always being cached
if (prefetch_all || pin_partition) {
s = filter->CacheDependencies(ro, pin_partition);
if (!s.ok()) {
return s;
}
}
rep_->filter = std::move(filter);
}
}
if (!rep_->compression_dict_handle.IsNull()) {
std::unique_ptr<UncompressionDictReader> uncompression_dict_reader;
s = UncompressionDictReader::Create(
this, ro, prefetch_buffer, use_cache, prefetch_all || pin_unpartitioned,
pin_unpartitioned, lookup_context, &uncompression_dict_reader);
if (!s.ok()) {
return s;
}
rep_->uncompression_dict_reader = std::move(uncompression_dict_reader);
}
assert(s.ok());
return s;
}
void BlockBasedTable::SetupForCompaction() {
switch (rep_->ioptions.access_hint_on_compaction_start) {
case Options::NONE:
break;
case Options::NORMAL:
Introduce a new storage specific Env API (#5761) Summary: The current Env API encompasses both storage/file operations, as well as OS related operations. Most of the APIs return a Status, which does not have enough metadata about an error, such as whether its retry-able or not, scope (i.e fault domain) of the error etc., that may be required in order to properly handle a storage error. The file APIs also do not provide enough control over the IO SLA, such as timeout, prioritization, hinting about placement and redundancy etc. This PR separates out the file/storage APIs from Env into a new FileSystem class. The APIs are updated to return an IOStatus with metadata about the error, as well as to take an IOOptions structure as input in order to allow more control over the IO. The user can set both ```options.env``` and ```options.file_system``` to specify that RocksDB should use the former for OS related operations and the latter for storage operations. Internally, a ```CompositeEnvWrapper``` has been introduced that inherits from ```Env``` and redirects individual methods to either an ```Env``` implementation or the ```FileSystem``` as appropriate. When options are sanitized during ```DB::Open```, ```options.env``` is replaced with a newly allocated ```CompositeEnvWrapper``` instance if both env and file_system have been specified. This way, the rest of the RocksDB code can continue to function as before. This PR also ports PosixEnv to the new API by splitting it into two - PosixEnv and PosixFileSystem. PosixEnv is defined as a sub-class of CompositeEnvWrapper, and threading/time functions are overridden with Posix specific implementations in order to avoid an extra level of indirection. The ```CompositeEnvWrapper``` translates ```IOStatus``` return code to ```Status```, and sets the severity to ```kSoftError``` if the io_status is retryable. The error handling code in RocksDB can then recover the DB automatically. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5761 Differential Revision: D18868376 Pulled By: anand1976 fbshipit-source-id: 39efe18a162ea746fabac6360ff529baba48486f
2019-12-13 22:47:08 +00:00
rep_->file->file()->Hint(FSRandomAccessFile::kNormal);
break;
case Options::SEQUENTIAL:
Introduce a new storage specific Env API (#5761) Summary: The current Env API encompasses both storage/file operations, as well as OS related operations. Most of the APIs return a Status, which does not have enough metadata about an error, such as whether its retry-able or not, scope (i.e fault domain) of the error etc., that may be required in order to properly handle a storage error. The file APIs also do not provide enough control over the IO SLA, such as timeout, prioritization, hinting about placement and redundancy etc. This PR separates out the file/storage APIs from Env into a new FileSystem class. The APIs are updated to return an IOStatus with metadata about the error, as well as to take an IOOptions structure as input in order to allow more control over the IO. The user can set both ```options.env``` and ```options.file_system``` to specify that RocksDB should use the former for OS related operations and the latter for storage operations. Internally, a ```CompositeEnvWrapper``` has been introduced that inherits from ```Env``` and redirects individual methods to either an ```Env``` implementation or the ```FileSystem``` as appropriate. When options are sanitized during ```DB::Open```, ```options.env``` is replaced with a newly allocated ```CompositeEnvWrapper``` instance if both env and file_system have been specified. This way, the rest of the RocksDB code can continue to function as before. This PR also ports PosixEnv to the new API by splitting it into two - PosixEnv and PosixFileSystem. PosixEnv is defined as a sub-class of CompositeEnvWrapper, and threading/time functions are overridden with Posix specific implementations in order to avoid an extra level of indirection. The ```CompositeEnvWrapper``` translates ```IOStatus``` return code to ```Status```, and sets the severity to ```kSoftError``` if the io_status is retryable. The error handling code in RocksDB can then recover the DB automatically. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5761 Differential Revision: D18868376 Pulled By: anand1976 fbshipit-source-id: 39efe18a162ea746fabac6360ff529baba48486f
2019-12-13 22:47:08 +00:00
rep_->file->file()->Hint(FSRandomAccessFile::kSequential);
break;
case Options::WILLNEED:
Introduce a new storage specific Env API (#5761) Summary: The current Env API encompasses both storage/file operations, as well as OS related operations. Most of the APIs return a Status, which does not have enough metadata about an error, such as whether its retry-able or not, scope (i.e fault domain) of the error etc., that may be required in order to properly handle a storage error. The file APIs also do not provide enough control over the IO SLA, such as timeout, prioritization, hinting about placement and redundancy etc. This PR separates out the file/storage APIs from Env into a new FileSystem class. The APIs are updated to return an IOStatus with metadata about the error, as well as to take an IOOptions structure as input in order to allow more control over the IO. The user can set both ```options.env``` and ```options.file_system``` to specify that RocksDB should use the former for OS related operations and the latter for storage operations. Internally, a ```CompositeEnvWrapper``` has been introduced that inherits from ```Env``` and redirects individual methods to either an ```Env``` implementation or the ```FileSystem``` as appropriate. When options are sanitized during ```DB::Open```, ```options.env``` is replaced with a newly allocated ```CompositeEnvWrapper``` instance if both env and file_system have been specified. This way, the rest of the RocksDB code can continue to function as before. This PR also ports PosixEnv to the new API by splitting it into two - PosixEnv and PosixFileSystem. PosixEnv is defined as a sub-class of CompositeEnvWrapper, and threading/time functions are overridden with Posix specific implementations in order to avoid an extra level of indirection. The ```CompositeEnvWrapper``` translates ```IOStatus``` return code to ```Status```, and sets the severity to ```kSoftError``` if the io_status is retryable. The error handling code in RocksDB can then recover the DB automatically. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5761 Differential Revision: D18868376 Pulled By: anand1976 fbshipit-source-id: 39efe18a162ea746fabac6360ff529baba48486f
2019-12-13 22:47:08 +00:00
rep_->file->file()->Hint(FSRandomAccessFile::kWillNeed);
break;
default:
assert(false);
}
}
std::shared_ptr<const TableProperties> BlockBasedTable::GetTableProperties()
const {
return rep_->table_properties;
}
size_t BlockBasedTable::ApproximateMemoryUsage() const {
size_t usage = 0;
if (rep_->filter) {
usage += rep_->filter->ApproximateMemoryUsage();
}
if (rep_->index_reader) {
usage += rep_->index_reader->ApproximateMemoryUsage();
}
if (rep_->uncompression_dict_reader) {
usage += rep_->uncompression_dict_reader->ApproximateMemoryUsage();
}
return usage;
}
// Load the meta-index-block from the file. On success, return the loaded
// metaindex
// block and its iterator.
Status BlockBasedTable::ReadMetaIndexBlock(
const ReadOptions& ro, FilePrefetchBuffer* prefetch_buffer,
std::unique_ptr<Block>* metaindex_block,
std::unique_ptr<InternalIterator>* iter) {
// TODO(sanjay): Skip this if footer.metaindex_handle() size indicates
// it is an empty block.
std::unique_ptr<Block> metaindex;
Status s = ReadBlockFromFile(
rep_->file.get(), prefetch_buffer, rep_->footer, ro,
rep_->footer.metaindex_handle(), &metaindex, rep_->ioptions,
true /* decompress */, true /*maybe_compressed*/, BlockType::kMetaIndex,
UncompressionDict::GetEmptyDict(), rep_->persistent_cache_options,
0 /* read_amp_bytes_per_bit */, GetMemoryAllocator(rep_->table_options),
false /* for_compaction */, rep_->blocks_definitely_zstd_compressed,
nullptr /* filter_policy */);
if (!s.ok()) {
ROCKS_LOG_ERROR(rep_->ioptions.logger,
"Encountered error while reading data from properties"
" block %s",
s.ToString().c_str());
return s;
}
*metaindex_block = std::move(metaindex);
// meta block uses bytewise comparator.
iter->reset(metaindex_block->get()->NewDataIterator(
Separate internal and user key comparators in `BlockIter` (#6944) Summary: Replace `BlockIter::comparator_` and `IndexBlockIter::user_comparator_wrapper_` with a concrete `UserComparatorWrapper` and `InternalKeyComparator`. The motivation for this change was the inconvenience of not knowing the concrete type of `BlockIter::comparator_`, which prevented calling specialized internal key comparison functions to optimize comparison of keys with global seqno applied. Pull Request resolved: https://github.com/facebook/rocksdb/pull/6944 Test Plan: benchmark setup -- single file DBs, in-memory, no compression. "normal_db" created by regular flush; "ingestion_db" created by ingesting a file. Both DBs have same contents. ``` $ TEST_TMPDIR=/dev/shm/normal_db/ ./db_bench -benchmarks=fillrandom,compact -write_buffer_size=10485760000 -disable_auto_compactions=true -compression_type=none -num=1000000 $ ./ldb write_extern_sst ./tmp.sst --db=/dev/shm/ingestion_db/dbbench/ --compression_type=no --hex --create_if_missing < <(./sst_dump --command=scan --output_hex --file=/dev/shm/normal_db/dbbench/000007.sst | awk 'began {print "0x" substr($1, 2, length($1) - 2), "==>", "0x" $5} ; /^Sst file format: block-based/ {began=1}') $ ./ldb ingest_extern_sst ./tmp.sst --db=/dev/shm/ingestion_db/dbbench/ ``` benchmark run command: ``` $ TEST_TMPDIR=/dev/shm/$DB/ ./db_bench -benchmarks=seekrandom -seek_nexts=$SEEK_NEXT -use_existing_db=true -cache_index_and_filter_blocks=false -num=1000000 -cache_size=0 -threads=1 -reads=200000000 -mmap_read=1 -verify_checksum=false ``` results: perf improved marginally for ingestion_db and did not change significantly for normal_db: SEEK_NEXT | DB | code | ops/sec | % change -- | -- | -- | -- | -- 0 | normal_db | master | 350880 |   0 | normal_db | PR6944 | 351040 | 0.0 0 | ingestion_db | master | 343255 |   0 | ingestion_db | PR6944 | 349424 | 1.8 10 | normal_db | master | 218711 |   10 | normal_db | PR6944 | 217892 | -0.4 10 | ingestion_db | master | 220334 |   10 | ingestion_db | PR6944 | 226437 | 2.8 Reviewed By: pdillinger Differential Revision: D21924676 Pulled By: ajkr fbshipit-source-id: ea4288a2eefa8112eb6c651a671c1de18c12e538
2020-07-08 00:25:08 +00:00
BytewiseComparator(), kDisableGlobalSequenceNumber));
return Status::OK();
}
template <typename TBlocklike>
Status BlockBasedTable::GetDataBlockFromCache(
const Slice& block_cache_key, const Slice& compressed_block_cache_key,
Cache* block_cache, Cache* block_cache_compressed,
const ReadOptions& read_options, CachableEntry<TBlocklike>* block,
const UncompressionDict& uncompression_dict, BlockType block_type,
Parallelize secondary cache lookup in MultiGet (#8405) Summary: Implement the ```WaitAll()``` interface in ```LRUCache``` to allow callers to issue multiple lookups in parallel and wait for all of them to complete. Modify ```MultiGet``` to use this to parallelize the secondary cache lookups in order to reduce the overall latency. A call to ```cache->Lookup()``` returns a handle that has an incomplete value (nullptr), and the caller can call ```cache->IsReady()``` to check whether the lookup is complete, and pass a vector of handles to ```WaitAll``` to wait for completion. If any of the lookups fail, ```MultiGet``` will read the block from the SST file. Another change in this PR is to rename ```SecondaryCacheHandle``` to ```SecondaryCacheResultHandle``` as it more accurately describes the return result of the secondary cache lookup, which is more like a future. Tests: 1. Add unit tests in lru_cache_test 2. Benchmark results with no secondary cache configured Master - ``` readrandom : 41.175 micros/op 388562 ops/sec; 106.7 MB/s (7277999 of 7277999 found) readrandom : 41.217 micros/op 388160 ops/sec; 106.6 MB/s (7274999 of 7274999 found) multireadrandom : 10.309 micros/op 1552082 ops/sec; (28908992 of 28908992 found) multireadrandom : 10.321 micros/op 1550218 ops/sec; (29081984 of 29081984 found) ``` This PR - ``` readrandom : 41.158 micros/op 388723 ops/sec; 106.8 MB/s (7290999 of 7290999 found) readrandom : 41.185 micros/op 388463 ops/sec; 106.7 MB/s (7287999 of 7287999 found) multireadrandom : 10.277 micros/op 1556801 ops/sec; (29346944 of 29346944 found) multireadrandom : 10.253 micros/op 1560539 ops/sec; (29274944 of 29274944 found) ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/8405 Reviewed By: zhichao-cao Differential Revision: D29190509 Pulled By: anand1976 fbshipit-source-id: 6f8eff6246712af8a297cfe22ea0d1c3b2a01bb0
2021-06-18 16:35:03 +00:00
const bool wait, GetContext* get_context) const {
const size_t read_amp_bytes_per_bit =
block_type == BlockType::kData
? rep_->table_options.read_amp_bytes_per_bit
: 0;
assert(block);
assert(block->IsEmpty());
Use new Insert and Lookup APIs in table reader to support secondary cache (#8315) Summary: Secondary cache is implemented to achieve the secondary cache tier for block cache. New Insert and Lookup APIs are introduced in https://github.com/facebook/rocksdb/issues/8271 . To support and use the secondary cache in block based table reader, this PR introduces the corresponding callback functions that will be used in secondary cache, and update the Insert and Lookup APIs accordingly. benchmarking: ./db_bench --benchmarks="fillrandom" -num=1000000 -key_size=32 -value_size=256 -use_direct_io_for_flush_and_compaction=true -db=/tmp/rocks_t/db -partition_index_and_filters=true ./db_bench -db=/tmp/rocks_t/db -use_existing_db=true -benchmarks=readrandom -num=1000000 -key_size=32 -value_size=256 -use_direct_reads=true -cache_size=1073741824 -cache_numshardbits=5 -cache_index_and_filter_blocks=true -read_random_exp_range=17 -statistics -partition_index_and_filters=true -stats_dump_period_sec=30 -reads=50000000 master benchmarking results: readrandom : 3.923 micros/op 254881 ops/sec; 33.4 MB/s (23849796 of 50000000 found) rocksdb.db.get.micros P50 : 2.820992 P95 : 5.636716 P99 : 16.450553 P100 : 8396.000000 COUNT : 50000000 SUM : 179947064 Current PR benchmarking results readrandom : 4.083 micros/op 244925 ops/sec; 32.1 MB/s (23849796 of 50000000 found) rocksdb.db.get.micros P50 : 2.967687 P95 : 5.754916 P99 : 15.665912 P100 : 8213.000000 COUNT : 50000000 SUM : 187250053 About 3.8% throughput reduction. P50: 5.2% increasing, P95, 2.09% increasing, P99 4.77% improvement Pull Request resolved: https://github.com/facebook/rocksdb/pull/8315 Test Plan: added the testing case Reviewed By: anand1976 Differential Revision: D28599774 Pulled By: zhichao-cao fbshipit-source-id: 098c4df0d7327d3a546df7604b2f1602f13044ed
2021-05-22 01:28:28 +00:00
const Cache::Priority priority =
rep_->table_options.cache_index_and_filter_blocks_with_high_priority &&
(block_type == BlockType::kFilter ||
block_type == BlockType::kCompressionDictionary ||
block_type == BlockType::kIndex)
? Cache::Priority::HIGH
: Cache::Priority::LOW;
Status s;
BlockContents* compressed_block = nullptr;
Cache::Handle* block_cache_compressed_handle = nullptr;
Use new Insert and Lookup APIs in table reader to support secondary cache (#8315) Summary: Secondary cache is implemented to achieve the secondary cache tier for block cache. New Insert and Lookup APIs are introduced in https://github.com/facebook/rocksdb/issues/8271 . To support and use the secondary cache in block based table reader, this PR introduces the corresponding callback functions that will be used in secondary cache, and update the Insert and Lookup APIs accordingly. benchmarking: ./db_bench --benchmarks="fillrandom" -num=1000000 -key_size=32 -value_size=256 -use_direct_io_for_flush_and_compaction=true -db=/tmp/rocks_t/db -partition_index_and_filters=true ./db_bench -db=/tmp/rocks_t/db -use_existing_db=true -benchmarks=readrandom -num=1000000 -key_size=32 -value_size=256 -use_direct_reads=true -cache_size=1073741824 -cache_numshardbits=5 -cache_index_and_filter_blocks=true -read_random_exp_range=17 -statistics -partition_index_and_filters=true -stats_dump_period_sec=30 -reads=50000000 master benchmarking results: readrandom : 3.923 micros/op 254881 ops/sec; 33.4 MB/s (23849796 of 50000000 found) rocksdb.db.get.micros P50 : 2.820992 P95 : 5.636716 P99 : 16.450553 P100 : 8396.000000 COUNT : 50000000 SUM : 179947064 Current PR benchmarking results readrandom : 4.083 micros/op 244925 ops/sec; 32.1 MB/s (23849796 of 50000000 found) rocksdb.db.get.micros P50 : 2.967687 P95 : 5.754916 P99 : 15.665912 P100 : 8213.000000 COUNT : 50000000 SUM : 187250053 About 3.8% throughput reduction. P50: 5.2% increasing, P95, 2.09% increasing, P99 4.77% improvement Pull Request resolved: https://github.com/facebook/rocksdb/pull/8315 Test Plan: added the testing case Reviewed By: anand1976 Differential Revision: D28599774 Pulled By: zhichao-cao fbshipit-source-id: 098c4df0d7327d3a546df7604b2f1602f13044ed
2021-05-22 01:28:28 +00:00
Statistics* statistics = rep_->ioptions.statistics.get();
bool using_zstd = rep_->blocks_definitely_zstd_compressed;
const FilterPolicy* filter_policy = rep_->filter_policy;
Cache::CreateCallback create_cb = GetCreateCallback<TBlocklike>(
read_amp_bytes_per_bit, statistics, using_zstd, filter_policy);
// Lookup uncompressed cache first
if (block_cache != nullptr) {
Use new Insert and Lookup APIs in table reader to support secondary cache (#8315) Summary: Secondary cache is implemented to achieve the secondary cache tier for block cache. New Insert and Lookup APIs are introduced in https://github.com/facebook/rocksdb/issues/8271 . To support and use the secondary cache in block based table reader, this PR introduces the corresponding callback functions that will be used in secondary cache, and update the Insert and Lookup APIs accordingly. benchmarking: ./db_bench --benchmarks="fillrandom" -num=1000000 -key_size=32 -value_size=256 -use_direct_io_for_flush_and_compaction=true -db=/tmp/rocks_t/db -partition_index_and_filters=true ./db_bench -db=/tmp/rocks_t/db -use_existing_db=true -benchmarks=readrandom -num=1000000 -key_size=32 -value_size=256 -use_direct_reads=true -cache_size=1073741824 -cache_numshardbits=5 -cache_index_and_filter_blocks=true -read_random_exp_range=17 -statistics -partition_index_and_filters=true -stats_dump_period_sec=30 -reads=50000000 master benchmarking results: readrandom : 3.923 micros/op 254881 ops/sec; 33.4 MB/s (23849796 of 50000000 found) rocksdb.db.get.micros P50 : 2.820992 P95 : 5.636716 P99 : 16.450553 P100 : 8396.000000 COUNT : 50000000 SUM : 179947064 Current PR benchmarking results readrandom : 4.083 micros/op 244925 ops/sec; 32.1 MB/s (23849796 of 50000000 found) rocksdb.db.get.micros P50 : 2.967687 P95 : 5.754916 P99 : 15.665912 P100 : 8213.000000 COUNT : 50000000 SUM : 187250053 About 3.8% throughput reduction. P50: 5.2% increasing, P95, 2.09% increasing, P99 4.77% improvement Pull Request resolved: https://github.com/facebook/rocksdb/pull/8315 Test Plan: added the testing case Reviewed By: anand1976 Differential Revision: D28599774 Pulled By: zhichao-cao fbshipit-source-id: 098c4df0d7327d3a546df7604b2f1602f13044ed
2021-05-22 01:28:28 +00:00
auto cache_handle = GetEntryFromCache(
Parallelize secondary cache lookup in MultiGet (#8405) Summary: Implement the ```WaitAll()``` interface in ```LRUCache``` to allow callers to issue multiple lookups in parallel and wait for all of them to complete. Modify ```MultiGet``` to use this to parallelize the secondary cache lookups in order to reduce the overall latency. A call to ```cache->Lookup()``` returns a handle that has an incomplete value (nullptr), and the caller can call ```cache->IsReady()``` to check whether the lookup is complete, and pass a vector of handles to ```WaitAll``` to wait for completion. If any of the lookups fail, ```MultiGet``` will read the block from the SST file. Another change in this PR is to rename ```SecondaryCacheHandle``` to ```SecondaryCacheResultHandle``` as it more accurately describes the return result of the secondary cache lookup, which is more like a future. Tests: 1. Add unit tests in lru_cache_test 2. Benchmark results with no secondary cache configured Master - ``` readrandom : 41.175 micros/op 388562 ops/sec; 106.7 MB/s (7277999 of 7277999 found) readrandom : 41.217 micros/op 388160 ops/sec; 106.6 MB/s (7274999 of 7274999 found) multireadrandom : 10.309 micros/op 1552082 ops/sec; (28908992 of 28908992 found) multireadrandom : 10.321 micros/op 1550218 ops/sec; (29081984 of 29081984 found) ``` This PR - ``` readrandom : 41.158 micros/op 388723 ops/sec; 106.8 MB/s (7290999 of 7290999 found) readrandom : 41.185 micros/op 388463 ops/sec; 106.7 MB/s (7287999 of 7287999 found) multireadrandom : 10.277 micros/op 1556801 ops/sec; (29346944 of 29346944 found) multireadrandom : 10.253 micros/op 1560539 ops/sec; (29274944 of 29274944 found) ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/8405 Reviewed By: zhichao-cao Differential Revision: D29190509 Pulled By: anand1976 fbshipit-source-id: 6f8eff6246712af8a297cfe22ea0d1c3b2a01bb0
2021-06-18 16:35:03 +00:00
block_cache, block_cache_key, block_type, wait, get_context,
Use new Insert and Lookup APIs in table reader to support secondary cache (#8315) Summary: Secondary cache is implemented to achieve the secondary cache tier for block cache. New Insert and Lookup APIs are introduced in https://github.com/facebook/rocksdb/issues/8271 . To support and use the secondary cache in block based table reader, this PR introduces the corresponding callback functions that will be used in secondary cache, and update the Insert and Lookup APIs accordingly. benchmarking: ./db_bench --benchmarks="fillrandom" -num=1000000 -key_size=32 -value_size=256 -use_direct_io_for_flush_and_compaction=true -db=/tmp/rocks_t/db -partition_index_and_filters=true ./db_bench -db=/tmp/rocks_t/db -use_existing_db=true -benchmarks=readrandom -num=1000000 -key_size=32 -value_size=256 -use_direct_reads=true -cache_size=1073741824 -cache_numshardbits=5 -cache_index_and_filter_blocks=true -read_random_exp_range=17 -statistics -partition_index_and_filters=true -stats_dump_period_sec=30 -reads=50000000 master benchmarking results: readrandom : 3.923 micros/op 254881 ops/sec; 33.4 MB/s (23849796 of 50000000 found) rocksdb.db.get.micros P50 : 2.820992 P95 : 5.636716 P99 : 16.450553 P100 : 8396.000000 COUNT : 50000000 SUM : 179947064 Current PR benchmarking results readrandom : 4.083 micros/op 244925 ops/sec; 32.1 MB/s (23849796 of 50000000 found) rocksdb.db.get.micros P50 : 2.967687 P95 : 5.754916 P99 : 15.665912 P100 : 8213.000000 COUNT : 50000000 SUM : 187250053 About 3.8% throughput reduction. P50: 5.2% increasing, P95, 2.09% increasing, P99 4.77% improvement Pull Request resolved: https://github.com/facebook/rocksdb/pull/8315 Test Plan: added the testing case Reviewed By: anand1976 Differential Revision: D28599774 Pulled By: zhichao-cao fbshipit-source-id: 098c4df0d7327d3a546df7604b2f1602f13044ed
2021-05-22 01:28:28 +00:00
BlocklikeTraits<TBlocklike>::GetCacheItemHelper(block_type), create_cb,
priority);
if (cache_handle != nullptr) {
block->SetCachedValue(
reinterpret_cast<TBlocklike*>(block_cache->Value(cache_handle)),
block_cache, cache_handle);
return s;
}
}
// If not found, search from the compressed block cache.
assert(block->IsEmpty());
if (block_cache_compressed == nullptr) {
return s;
}
assert(!compressed_block_cache_key.empty());
Use new Insert and Lookup APIs in table reader to support secondary cache (#8315) Summary: Secondary cache is implemented to achieve the secondary cache tier for block cache. New Insert and Lookup APIs are introduced in https://github.com/facebook/rocksdb/issues/8271 . To support and use the secondary cache in block based table reader, this PR introduces the corresponding callback functions that will be used in secondary cache, and update the Insert and Lookup APIs accordingly. benchmarking: ./db_bench --benchmarks="fillrandom" -num=1000000 -key_size=32 -value_size=256 -use_direct_io_for_flush_and_compaction=true -db=/tmp/rocks_t/db -partition_index_and_filters=true ./db_bench -db=/tmp/rocks_t/db -use_existing_db=true -benchmarks=readrandom -num=1000000 -key_size=32 -value_size=256 -use_direct_reads=true -cache_size=1073741824 -cache_numshardbits=5 -cache_index_and_filter_blocks=true -read_random_exp_range=17 -statistics -partition_index_and_filters=true -stats_dump_period_sec=30 -reads=50000000 master benchmarking results: readrandom : 3.923 micros/op 254881 ops/sec; 33.4 MB/s (23849796 of 50000000 found) rocksdb.db.get.micros P50 : 2.820992 P95 : 5.636716 P99 : 16.450553 P100 : 8396.000000 COUNT : 50000000 SUM : 179947064 Current PR benchmarking results readrandom : 4.083 micros/op 244925 ops/sec; 32.1 MB/s (23849796 of 50000000 found) rocksdb.db.get.micros P50 : 2.967687 P95 : 5.754916 P99 : 15.665912 P100 : 8213.000000 COUNT : 50000000 SUM : 187250053 About 3.8% throughput reduction. P50: 5.2% increasing, P95, 2.09% increasing, P99 4.77% improvement Pull Request resolved: https://github.com/facebook/rocksdb/pull/8315 Test Plan: added the testing case Reviewed By: anand1976 Differential Revision: D28599774 Pulled By: zhichao-cao fbshipit-source-id: 098c4df0d7327d3a546df7604b2f1602f13044ed
2021-05-22 01:28:28 +00:00
BlockContents contents;
Cache::CreateCallback create_cb_special = GetCreateCallback<BlockContents>(
read_amp_bytes_per_bit, statistics, using_zstd, filter_policy);
Use new Insert and Lookup APIs in table reader to support secondary cache (#8315) Summary: Secondary cache is implemented to achieve the secondary cache tier for block cache. New Insert and Lookup APIs are introduced in https://github.com/facebook/rocksdb/issues/8271 . To support and use the secondary cache in block based table reader, this PR introduces the corresponding callback functions that will be used in secondary cache, and update the Insert and Lookup APIs accordingly. benchmarking: ./db_bench --benchmarks="fillrandom" -num=1000000 -key_size=32 -value_size=256 -use_direct_io_for_flush_and_compaction=true -db=/tmp/rocks_t/db -partition_index_and_filters=true ./db_bench -db=/tmp/rocks_t/db -use_existing_db=true -benchmarks=readrandom -num=1000000 -key_size=32 -value_size=256 -use_direct_reads=true -cache_size=1073741824 -cache_numshardbits=5 -cache_index_and_filter_blocks=true -read_random_exp_range=17 -statistics -partition_index_and_filters=true -stats_dump_period_sec=30 -reads=50000000 master benchmarking results: readrandom : 3.923 micros/op 254881 ops/sec; 33.4 MB/s (23849796 of 50000000 found) rocksdb.db.get.micros P50 : 2.820992 P95 : 5.636716 P99 : 16.450553 P100 : 8396.000000 COUNT : 50000000 SUM : 179947064 Current PR benchmarking results readrandom : 4.083 micros/op 244925 ops/sec; 32.1 MB/s (23849796 of 50000000 found) rocksdb.db.get.micros P50 : 2.967687 P95 : 5.754916 P99 : 15.665912 P100 : 8213.000000 COUNT : 50000000 SUM : 187250053 About 3.8% throughput reduction. P50: 5.2% increasing, P95, 2.09% increasing, P99 4.77% improvement Pull Request resolved: https://github.com/facebook/rocksdb/pull/8315 Test Plan: added the testing case Reviewed By: anand1976 Differential Revision: D28599774 Pulled By: zhichao-cao fbshipit-source-id: 098c4df0d7327d3a546df7604b2f1602f13044ed
2021-05-22 01:28:28 +00:00
block_cache_compressed_handle = block_cache_compressed->Lookup(
compressed_block_cache_key,
BlocklikeTraits<BlockContents>::GetCacheItemHelper(block_type),
create_cb_special, priority, true);
// if we found in the compressed cache, then uncompress and insert into
// uncompressed cache
if (block_cache_compressed_handle == nullptr) {
RecordTick(statistics, BLOCK_CACHE_COMPRESSED_MISS);
return s;
}
// found compressed block
RecordTick(statistics, BLOCK_CACHE_COMPRESSED_HIT);
compressed_block = reinterpret_cast<BlockContents*>(
block_cache_compressed->Value(block_cache_compressed_handle));
CompressionType compression_type = compressed_block->get_compression_type();
assert(compression_type != kNoCompression);
// Retrieve the uncompressed contents into a new buffer
UncompressionContext context(compression_type);
UncompressionInfo info(context, uncompression_dict, compression_type);
s = UncompressBlockContents(
info, compressed_block->data.data(), compressed_block->data.size(),
&contents, rep_->table_options.format_version, rep_->ioptions,
GetMemoryAllocator(rep_->table_options));
Use new Insert and Lookup APIs in table reader to support secondary cache (#8315) Summary: Secondary cache is implemented to achieve the secondary cache tier for block cache. New Insert and Lookup APIs are introduced in https://github.com/facebook/rocksdb/issues/8271 . To support and use the secondary cache in block based table reader, this PR introduces the corresponding callback functions that will be used in secondary cache, and update the Insert and Lookup APIs accordingly. benchmarking: ./db_bench --benchmarks="fillrandom" -num=1000000 -key_size=32 -value_size=256 -use_direct_io_for_flush_and_compaction=true -db=/tmp/rocks_t/db -partition_index_and_filters=true ./db_bench -db=/tmp/rocks_t/db -use_existing_db=true -benchmarks=readrandom -num=1000000 -key_size=32 -value_size=256 -use_direct_reads=true -cache_size=1073741824 -cache_numshardbits=5 -cache_index_and_filter_blocks=true -read_random_exp_range=17 -statistics -partition_index_and_filters=true -stats_dump_period_sec=30 -reads=50000000 master benchmarking results: readrandom : 3.923 micros/op 254881 ops/sec; 33.4 MB/s (23849796 of 50000000 found) rocksdb.db.get.micros P50 : 2.820992 P95 : 5.636716 P99 : 16.450553 P100 : 8396.000000 COUNT : 50000000 SUM : 179947064 Current PR benchmarking results readrandom : 4.083 micros/op 244925 ops/sec; 32.1 MB/s (23849796 of 50000000 found) rocksdb.db.get.micros P50 : 2.967687 P95 : 5.754916 P99 : 15.665912 P100 : 8213.000000 COUNT : 50000000 SUM : 187250053 About 3.8% throughput reduction. P50: 5.2% increasing, P95, 2.09% increasing, P99 4.77% improvement Pull Request resolved: https://github.com/facebook/rocksdb/pull/8315 Test Plan: added the testing case Reviewed By: anand1976 Differential Revision: D28599774 Pulled By: zhichao-cao fbshipit-source-id: 098c4df0d7327d3a546df7604b2f1602f13044ed
2021-05-22 01:28:28 +00:00
// Insert uncompressed block into block cache, the priority is based on the
// data block type.
if (s.ok()) {
std::unique_ptr<TBlocklike> block_holder(
BlocklikeTraits<TBlocklike>::Create(
std::move(contents), read_amp_bytes_per_bit, statistics,
Store the filter bits reader alongside the filter block contents (#5936) Summary: Amongst other things, PR https://github.com/facebook/rocksdb/issues/5504 refactored the filter block readers so that only the filter block contents are stored in the block cache (as opposed to the earlier design where the cache stored the filter block reader itself, leading to potentially dangling pointers and concurrency bugs). However, this change introduced a performance hit since with the new code, the metadata fields are re-parsed upon every access. This patch reunites the block contents with the filter bits reader to eliminate this overhead; since this is still a self-contained pure data object, it is safe to store it in the cache. (Note: this is similar to how the zstd digest is handled.) Pull Request resolved: https://github.com/facebook/rocksdb/pull/5936 Test Plan: make asan_check filter_bench results for the old code: ``` $ ./filter_bench -quick WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 26.7153 Number of filters: 16669 Total memory (MB): 200.009 Bits/key actual: 10.0647 ---------------------------- Inside queries... Dry run (46b) ns/op: 33.4258 Single filter ns/op: 42.5974 Random filter ns/op: 217.861 ---------------------------- Outside queries... Dry run (25d) ns/op: 32.4217 Single filter ns/op: 50.9855 Random filter ns/op: 219.167 Average FP rate %: 1.13993 ---------------------------- Done. (For more info, run with -legend or -help.) $ ./filter_bench -quick -use_full_block_reader WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 26.5172 Number of filters: 16669 Total memory (MB): 200.009 Bits/key actual: 10.0647 ---------------------------- Inside queries... Dry run (46b) ns/op: 32.3556 Single filter ns/op: 83.2239 Random filter ns/op: 370.676 ---------------------------- Outside queries... Dry run (25d) ns/op: 32.2265 Single filter ns/op: 93.5651 Random filter ns/op: 408.393 Average FP rate %: 1.13993 ---------------------------- Done. (For more info, run with -legend or -help.) ``` With the new code: ``` $ ./filter_bench -quick WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 25.4285 Number of filters: 16669 Total memory (MB): 200.009 Bits/key actual: 10.0647 ---------------------------- Inside queries... Dry run (46b) ns/op: 31.0594 Single filter ns/op: 43.8974 Random filter ns/op: 226.075 ---------------------------- Outside queries... Dry run (25d) ns/op: 31.0295 Single filter ns/op: 50.3824 Random filter ns/op: 226.805 Average FP rate %: 1.13993 ---------------------------- Done. (For more info, run with -legend or -help.) $ ./filter_bench -quick -use_full_block_reader WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 26.5308 Number of filters: 16669 Total memory (MB): 200.009 Bits/key actual: 10.0647 ---------------------------- Inside queries... Dry run (46b) ns/op: 33.2968 Single filter ns/op: 58.6163 Random filter ns/op: 291.434 ---------------------------- Outside queries... Dry run (25d) ns/op: 32.1839 Single filter ns/op: 66.9039 Random filter ns/op: 292.828 Average FP rate %: 1.13993 ---------------------------- Done. (For more info, run with -legend or -help.) ``` Differential Revision: D17991712 Pulled By: ltamasi fbshipit-source-id: 7ea205550217bfaaa1d5158ebd658e5832e60f29
2019-10-19 02:30:47 +00:00
rep_->blocks_definitely_zstd_compressed,
rep_->table_options.filter_policy.get())); // uncompressed block
if (block_cache != nullptr && block_holder->own_bytes() &&
read_options.fill_cache) {
size_t charge = block_holder->ApproximateMemoryUsage();
Cache::Handle* cache_handle = nullptr;
Use new Insert and Lookup APIs in table reader to support secondary cache (#8315) Summary: Secondary cache is implemented to achieve the secondary cache tier for block cache. New Insert and Lookup APIs are introduced in https://github.com/facebook/rocksdb/issues/8271 . To support and use the secondary cache in block based table reader, this PR introduces the corresponding callback functions that will be used in secondary cache, and update the Insert and Lookup APIs accordingly. benchmarking: ./db_bench --benchmarks="fillrandom" -num=1000000 -key_size=32 -value_size=256 -use_direct_io_for_flush_and_compaction=true -db=/tmp/rocks_t/db -partition_index_and_filters=true ./db_bench -db=/tmp/rocks_t/db -use_existing_db=true -benchmarks=readrandom -num=1000000 -key_size=32 -value_size=256 -use_direct_reads=true -cache_size=1073741824 -cache_numshardbits=5 -cache_index_and_filter_blocks=true -read_random_exp_range=17 -statistics -partition_index_and_filters=true -stats_dump_period_sec=30 -reads=50000000 master benchmarking results: readrandom : 3.923 micros/op 254881 ops/sec; 33.4 MB/s (23849796 of 50000000 found) rocksdb.db.get.micros P50 : 2.820992 P95 : 5.636716 P99 : 16.450553 P100 : 8396.000000 COUNT : 50000000 SUM : 179947064 Current PR benchmarking results readrandom : 4.083 micros/op 244925 ops/sec; 32.1 MB/s (23849796 of 50000000 found) rocksdb.db.get.micros P50 : 2.967687 P95 : 5.754916 P99 : 15.665912 P100 : 8213.000000 COUNT : 50000000 SUM : 187250053 About 3.8% throughput reduction. P50: 5.2% increasing, P95, 2.09% increasing, P99 4.77% improvement Pull Request resolved: https://github.com/facebook/rocksdb/pull/8315 Test Plan: added the testing case Reviewed By: anand1976 Differential Revision: D28599774 Pulled By: zhichao-cao fbshipit-source-id: 098c4df0d7327d3a546df7604b2f1602f13044ed
2021-05-22 01:28:28 +00:00
s = block_cache->Insert(
block_cache_key, block_holder.get(),
BlocklikeTraits<TBlocklike>::GetCacheItemHelper(block_type), charge,
&cache_handle, priority);
if (s.ok()) {
assert(cache_handle != nullptr);
block->SetCachedValue(block_holder.release(), block_cache,
cache_handle);
Stats for redundant insertions into block cache (#6681) Summary: Since read threads do not coordinate on loading data into block cache, two threads between Lookup and Insert can end up loading and inserting the same data. This is particularly concerning with cache_index_and_filter_blocks since those are hot and more likely to be race targets if ejected from (or not pre-populated in) the cache. Particularly with moves toward disaggregated / network storage, the cost of redundant retrieval might be high, and we should at least have some hard statistics from which we can estimate impact. Example with full filter thrashing "cliff": $ ./db_bench --benchmarks=fillrandom --num=15000000 --cache_index_and_filter_blocks -bloom_bits=10 ... $ ./db_bench --db=/tmp/rocksdbtest-172704/dbbench --use_existing_db --benchmarks=readrandom,stats --num=200000 --cache_index_and_filter_blocks --cache_size=$((130 * 1024 * 1024)) --bloom_bits=10 --threads=16 -statistics 2>&1 | egrep '^rocksdb.block.cache.(.*add|.*redundant)' | grep -v compress | sort rocksdb.block.cache.add COUNT : 14181 rocksdb.block.cache.add.failures COUNT : 0 rocksdb.block.cache.add.redundant COUNT : 476 rocksdb.block.cache.data.add COUNT : 12749 rocksdb.block.cache.data.add.redundant COUNT : 18 rocksdb.block.cache.filter.add COUNT : 1003 rocksdb.block.cache.filter.add.redundant COUNT : 217 rocksdb.block.cache.index.add COUNT : 429 rocksdb.block.cache.index.add.redundant COUNT : 241 $ ./db_bench --db=/tmp/rocksdbtest-172704/dbbench --use_existing_db --benchmarks=readrandom,stats --num=200000 --cache_index_and_filter_blocks --cache_size=$((120 * 1024 * 1024)) --bloom_bits=10 --threads=16 -statistics 2>&1 | egrep '^rocksdb.block.cache.(.*add|.*redundant)' | grep -v compress | sort rocksdb.block.cache.add COUNT : 1182223 rocksdb.block.cache.add.failures COUNT : 0 rocksdb.block.cache.add.redundant COUNT : 302728 rocksdb.block.cache.data.add COUNT : 31425 rocksdb.block.cache.data.add.redundant COUNT : 12 rocksdb.block.cache.filter.add COUNT : 795455 rocksdb.block.cache.filter.add.redundant COUNT : 130238 rocksdb.block.cache.index.add COUNT : 355343 rocksdb.block.cache.index.add.redundant COUNT : 172478 Pull Request resolved: https://github.com/facebook/rocksdb/pull/6681 Test Plan: Some manual testing (above) and unit test covering key metrics is included Reviewed By: ltamasi Differential Revision: D21134113 Pulled By: pdillinger fbshipit-source-id: c11497b5f00f4ffdfe919823904e52d0a1a91d87
2020-04-27 20:18:18 +00:00
UpdateCacheInsertionMetrics(block_type, get_context, charge,
s.IsOkOverwritten(), rep_->ioptions.stats);
} else {
RecordTick(statistics, BLOCK_CACHE_ADD_FAILURES);
}
} else {
block->SetOwnedValue(block_holder.release());
}
}
// Release hold on compressed cache entry
block_cache_compressed->Release(block_cache_compressed_handle);
return s;
}
template <typename TBlocklike>
Status BlockBasedTable::PutDataBlockToCache(
const Slice& block_cache_key, const Slice& compressed_block_cache_key,
Cache* block_cache, Cache* block_cache_compressed,
CachableEntry<TBlocklike>* cached_block, BlockContents* raw_block_contents,
CompressionType raw_block_comp_type,
const UncompressionDict& uncompression_dict,
MemoryAllocator* memory_allocator, BlockType block_type,
GetContext* get_context) const {
const ImmutableOptions& ioptions = rep_->ioptions;
const uint32_t format_version = rep_->table_options.format_version;
const size_t read_amp_bytes_per_bit =
block_type == BlockType::kData
? rep_->table_options.read_amp_bytes_per_bit
: 0;
const Cache::Priority priority =
rep_->table_options.cache_index_and_filter_blocks_with_high_priority &&
(block_type == BlockType::kFilter ||
block_type == BlockType::kCompressionDictionary ||
block_type == BlockType::kIndex)
? Cache::Priority::HIGH
: Cache::Priority::LOW;
assert(cached_block);
assert(cached_block->IsEmpty());
Status s;
Statistics* statistics = ioptions.stats;
std::unique_ptr<TBlocklike> block_holder;
if (raw_block_comp_type != kNoCompression) {
// Retrieve the uncompressed contents into a new buffer
BlockContents uncompressed_block_contents;
UncompressionContext context(raw_block_comp_type);
UncompressionInfo info(context, uncompression_dict, raw_block_comp_type);
s = UncompressBlockContents(info, raw_block_contents->data.data(),
raw_block_contents->data.size(),
&uncompressed_block_contents, format_version,
ioptions, memory_allocator);
if (!s.ok()) {
return s;
}
block_holder.reset(BlocklikeTraits<TBlocklike>::Create(
std::move(uncompressed_block_contents), read_amp_bytes_per_bit,
Store the filter bits reader alongside the filter block contents (#5936) Summary: Amongst other things, PR https://github.com/facebook/rocksdb/issues/5504 refactored the filter block readers so that only the filter block contents are stored in the block cache (as opposed to the earlier design where the cache stored the filter block reader itself, leading to potentially dangling pointers and concurrency bugs). However, this change introduced a performance hit since with the new code, the metadata fields are re-parsed upon every access. This patch reunites the block contents with the filter bits reader to eliminate this overhead; since this is still a self-contained pure data object, it is safe to store it in the cache. (Note: this is similar to how the zstd digest is handled.) Pull Request resolved: https://github.com/facebook/rocksdb/pull/5936 Test Plan: make asan_check filter_bench results for the old code: ``` $ ./filter_bench -quick WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 26.7153 Number of filters: 16669 Total memory (MB): 200.009 Bits/key actual: 10.0647 ---------------------------- Inside queries... Dry run (46b) ns/op: 33.4258 Single filter ns/op: 42.5974 Random filter ns/op: 217.861 ---------------------------- Outside queries... Dry run (25d) ns/op: 32.4217 Single filter ns/op: 50.9855 Random filter ns/op: 219.167 Average FP rate %: 1.13993 ---------------------------- Done. (For more info, run with -legend or -help.) $ ./filter_bench -quick -use_full_block_reader WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 26.5172 Number of filters: 16669 Total memory (MB): 200.009 Bits/key actual: 10.0647 ---------------------------- Inside queries... Dry run (46b) ns/op: 32.3556 Single filter ns/op: 83.2239 Random filter ns/op: 370.676 ---------------------------- Outside queries... Dry run (25d) ns/op: 32.2265 Single filter ns/op: 93.5651 Random filter ns/op: 408.393 Average FP rate %: 1.13993 ---------------------------- Done. (For more info, run with -legend or -help.) ``` With the new code: ``` $ ./filter_bench -quick WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 25.4285 Number of filters: 16669 Total memory (MB): 200.009 Bits/key actual: 10.0647 ---------------------------- Inside queries... Dry run (46b) ns/op: 31.0594 Single filter ns/op: 43.8974 Random filter ns/op: 226.075 ---------------------------- Outside queries... Dry run (25d) ns/op: 31.0295 Single filter ns/op: 50.3824 Random filter ns/op: 226.805 Average FP rate %: 1.13993 ---------------------------- Done. (For more info, run with -legend or -help.) $ ./filter_bench -quick -use_full_block_reader WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 26.5308 Number of filters: 16669 Total memory (MB): 200.009 Bits/key actual: 10.0647 ---------------------------- Inside queries... Dry run (46b) ns/op: 33.2968 Single filter ns/op: 58.6163 Random filter ns/op: 291.434 ---------------------------- Outside queries... Dry run (25d) ns/op: 32.1839 Single filter ns/op: 66.9039 Random filter ns/op: 292.828 Average FP rate %: 1.13993 ---------------------------- Done. (For more info, run with -legend or -help.) ``` Differential Revision: D17991712 Pulled By: ltamasi fbshipit-source-id: 7ea205550217bfaaa1d5158ebd658e5832e60f29
2019-10-19 02:30:47 +00:00
statistics, rep_->blocks_definitely_zstd_compressed,
rep_->table_options.filter_policy.get()));
} else {
block_holder.reset(BlocklikeTraits<TBlocklike>::Create(
std::move(*raw_block_contents), read_amp_bytes_per_bit, statistics,
rep_->blocks_definitely_zstd_compressed,
Store the filter bits reader alongside the filter block contents (#5936) Summary: Amongst other things, PR https://github.com/facebook/rocksdb/issues/5504 refactored the filter block readers so that only the filter block contents are stored in the block cache (as opposed to the earlier design where the cache stored the filter block reader itself, leading to potentially dangling pointers and concurrency bugs). However, this change introduced a performance hit since with the new code, the metadata fields are re-parsed upon every access. This patch reunites the block contents with the filter bits reader to eliminate this overhead; since this is still a self-contained pure data object, it is safe to store it in the cache. (Note: this is similar to how the zstd digest is handled.) Pull Request resolved: https://github.com/facebook/rocksdb/pull/5936 Test Plan: make asan_check filter_bench results for the old code: ``` $ ./filter_bench -quick WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 26.7153 Number of filters: 16669 Total memory (MB): 200.009 Bits/key actual: 10.0647 ---------------------------- Inside queries... Dry run (46b) ns/op: 33.4258 Single filter ns/op: 42.5974 Random filter ns/op: 217.861 ---------------------------- Outside queries... Dry run (25d) ns/op: 32.4217 Single filter ns/op: 50.9855 Random filter ns/op: 219.167 Average FP rate %: 1.13993 ---------------------------- Done. (For more info, run with -legend or -help.) $ ./filter_bench -quick -use_full_block_reader WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 26.5172 Number of filters: 16669 Total memory (MB): 200.009 Bits/key actual: 10.0647 ---------------------------- Inside queries... Dry run (46b) ns/op: 32.3556 Single filter ns/op: 83.2239 Random filter ns/op: 370.676 ---------------------------- Outside queries... Dry run (25d) ns/op: 32.2265 Single filter ns/op: 93.5651 Random filter ns/op: 408.393 Average FP rate %: 1.13993 ---------------------------- Done. (For more info, run with -legend or -help.) ``` With the new code: ``` $ ./filter_bench -quick WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 25.4285 Number of filters: 16669 Total memory (MB): 200.009 Bits/key actual: 10.0647 ---------------------------- Inside queries... Dry run (46b) ns/op: 31.0594 Single filter ns/op: 43.8974 Random filter ns/op: 226.075 ---------------------------- Outside queries... Dry run (25d) ns/op: 31.0295 Single filter ns/op: 50.3824 Random filter ns/op: 226.805 Average FP rate %: 1.13993 ---------------------------- Done. (For more info, run with -legend or -help.) $ ./filter_bench -quick -use_full_block_reader WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 26.5308 Number of filters: 16669 Total memory (MB): 200.009 Bits/key actual: 10.0647 ---------------------------- Inside queries... Dry run (46b) ns/op: 33.2968 Single filter ns/op: 58.6163 Random filter ns/op: 291.434 ---------------------------- Outside queries... Dry run (25d) ns/op: 32.1839 Single filter ns/op: 66.9039 Random filter ns/op: 292.828 Average FP rate %: 1.13993 ---------------------------- Done. (For more info, run with -legend or -help.) ``` Differential Revision: D17991712 Pulled By: ltamasi fbshipit-source-id: 7ea205550217bfaaa1d5158ebd658e5832e60f29
2019-10-19 02:30:47 +00:00
rep_->table_options.filter_policy.get()));
}
// Insert compressed block into compressed block cache.
// Release the hold on the compressed cache entry immediately.
if (block_cache_compressed != nullptr &&
raw_block_comp_type != kNoCompression && raw_block_contents != nullptr &&
raw_block_contents->own_bytes()) {
#ifndef NDEBUG
assert(raw_block_contents->is_raw_block);
#endif // NDEBUG
// We cannot directly put raw_block_contents because this could point to
// an object in the stack.
BlockContents* block_cont_for_comp_cache =
new BlockContents(std::move(*raw_block_contents));
s = block_cache_compressed->Insert(
compressed_block_cache_key, block_cont_for_comp_cache,
Use new Insert and Lookup APIs in table reader to support secondary cache (#8315) Summary: Secondary cache is implemented to achieve the secondary cache tier for block cache. New Insert and Lookup APIs are introduced in https://github.com/facebook/rocksdb/issues/8271 . To support and use the secondary cache in block based table reader, this PR introduces the corresponding callback functions that will be used in secondary cache, and update the Insert and Lookup APIs accordingly. benchmarking: ./db_bench --benchmarks="fillrandom" -num=1000000 -key_size=32 -value_size=256 -use_direct_io_for_flush_and_compaction=true -db=/tmp/rocks_t/db -partition_index_and_filters=true ./db_bench -db=/tmp/rocks_t/db -use_existing_db=true -benchmarks=readrandom -num=1000000 -key_size=32 -value_size=256 -use_direct_reads=true -cache_size=1073741824 -cache_numshardbits=5 -cache_index_and_filter_blocks=true -read_random_exp_range=17 -statistics -partition_index_and_filters=true -stats_dump_period_sec=30 -reads=50000000 master benchmarking results: readrandom : 3.923 micros/op 254881 ops/sec; 33.4 MB/s (23849796 of 50000000 found) rocksdb.db.get.micros P50 : 2.820992 P95 : 5.636716 P99 : 16.450553 P100 : 8396.000000 COUNT : 50000000 SUM : 179947064 Current PR benchmarking results readrandom : 4.083 micros/op 244925 ops/sec; 32.1 MB/s (23849796 of 50000000 found) rocksdb.db.get.micros P50 : 2.967687 P95 : 5.754916 P99 : 15.665912 P100 : 8213.000000 COUNT : 50000000 SUM : 187250053 About 3.8% throughput reduction. P50: 5.2% increasing, P95, 2.09% increasing, P99 4.77% improvement Pull Request resolved: https://github.com/facebook/rocksdb/pull/8315 Test Plan: added the testing case Reviewed By: anand1976 Differential Revision: D28599774 Pulled By: zhichao-cao fbshipit-source-id: 098c4df0d7327d3a546df7604b2f1602f13044ed
2021-05-22 01:28:28 +00:00
BlocklikeTraits<BlockContents>::GetCacheItemHelper(block_type),
block_cont_for_comp_cache->ApproximateMemoryUsage());
if (s.ok()) {
// Avoid the following code to delete this cached block.
RecordTick(statistics, BLOCK_CACHE_COMPRESSED_ADD);
} else {
RecordTick(statistics, BLOCK_CACHE_COMPRESSED_ADD_FAILURES);
delete block_cont_for_comp_cache;
}
}
// insert into uncompressed block cache
if (block_cache != nullptr && block_holder->own_bytes()) {
size_t charge = block_holder->ApproximateMemoryUsage();
Cache::Handle* cache_handle = nullptr;
Use new Insert and Lookup APIs in table reader to support secondary cache (#8315) Summary: Secondary cache is implemented to achieve the secondary cache tier for block cache. New Insert and Lookup APIs are introduced in https://github.com/facebook/rocksdb/issues/8271 . To support and use the secondary cache in block based table reader, this PR introduces the corresponding callback functions that will be used in secondary cache, and update the Insert and Lookup APIs accordingly. benchmarking: ./db_bench --benchmarks="fillrandom" -num=1000000 -key_size=32 -value_size=256 -use_direct_io_for_flush_and_compaction=true -db=/tmp/rocks_t/db -partition_index_and_filters=true ./db_bench -db=/tmp/rocks_t/db -use_existing_db=true -benchmarks=readrandom -num=1000000 -key_size=32 -value_size=256 -use_direct_reads=true -cache_size=1073741824 -cache_numshardbits=5 -cache_index_and_filter_blocks=true -read_random_exp_range=17 -statistics -partition_index_and_filters=true -stats_dump_period_sec=30 -reads=50000000 master benchmarking results: readrandom : 3.923 micros/op 254881 ops/sec; 33.4 MB/s (23849796 of 50000000 found) rocksdb.db.get.micros P50 : 2.820992 P95 : 5.636716 P99 : 16.450553 P100 : 8396.000000 COUNT : 50000000 SUM : 179947064 Current PR benchmarking results readrandom : 4.083 micros/op 244925 ops/sec; 32.1 MB/s (23849796 of 50000000 found) rocksdb.db.get.micros P50 : 2.967687 P95 : 5.754916 P99 : 15.665912 P100 : 8213.000000 COUNT : 50000000 SUM : 187250053 About 3.8% throughput reduction. P50: 5.2% increasing, P95, 2.09% increasing, P99 4.77% improvement Pull Request resolved: https://github.com/facebook/rocksdb/pull/8315 Test Plan: added the testing case Reviewed By: anand1976 Differential Revision: D28599774 Pulled By: zhichao-cao fbshipit-source-id: 098c4df0d7327d3a546df7604b2f1602f13044ed
2021-05-22 01:28:28 +00:00
s = block_cache->Insert(
block_cache_key, block_holder.get(),
BlocklikeTraits<TBlocklike>::GetCacheItemHelper(block_type), charge,
&cache_handle, priority);
if (s.ok()) {
assert(cache_handle != nullptr);
cached_block->SetCachedValue(block_holder.release(), block_cache,
cache_handle);
Stats for redundant insertions into block cache (#6681) Summary: Since read threads do not coordinate on loading data into block cache, two threads between Lookup and Insert can end up loading and inserting the same data. This is particularly concerning with cache_index_and_filter_blocks since those are hot and more likely to be race targets if ejected from (or not pre-populated in) the cache. Particularly with moves toward disaggregated / network storage, the cost of redundant retrieval might be high, and we should at least have some hard statistics from which we can estimate impact. Example with full filter thrashing "cliff": $ ./db_bench --benchmarks=fillrandom --num=15000000 --cache_index_and_filter_blocks -bloom_bits=10 ... $ ./db_bench --db=/tmp/rocksdbtest-172704/dbbench --use_existing_db --benchmarks=readrandom,stats --num=200000 --cache_index_and_filter_blocks --cache_size=$((130 * 1024 * 1024)) --bloom_bits=10 --threads=16 -statistics 2>&1 | egrep '^rocksdb.block.cache.(.*add|.*redundant)' | grep -v compress | sort rocksdb.block.cache.add COUNT : 14181 rocksdb.block.cache.add.failures COUNT : 0 rocksdb.block.cache.add.redundant COUNT : 476 rocksdb.block.cache.data.add COUNT : 12749 rocksdb.block.cache.data.add.redundant COUNT : 18 rocksdb.block.cache.filter.add COUNT : 1003 rocksdb.block.cache.filter.add.redundant COUNT : 217 rocksdb.block.cache.index.add COUNT : 429 rocksdb.block.cache.index.add.redundant COUNT : 241 $ ./db_bench --db=/tmp/rocksdbtest-172704/dbbench --use_existing_db --benchmarks=readrandom,stats --num=200000 --cache_index_and_filter_blocks --cache_size=$((120 * 1024 * 1024)) --bloom_bits=10 --threads=16 -statistics 2>&1 | egrep '^rocksdb.block.cache.(.*add|.*redundant)' | grep -v compress | sort rocksdb.block.cache.add COUNT : 1182223 rocksdb.block.cache.add.failures COUNT : 0 rocksdb.block.cache.add.redundant COUNT : 302728 rocksdb.block.cache.data.add COUNT : 31425 rocksdb.block.cache.data.add.redundant COUNT : 12 rocksdb.block.cache.filter.add COUNT : 795455 rocksdb.block.cache.filter.add.redundant COUNT : 130238 rocksdb.block.cache.index.add COUNT : 355343 rocksdb.block.cache.index.add.redundant COUNT : 172478 Pull Request resolved: https://github.com/facebook/rocksdb/pull/6681 Test Plan: Some manual testing (above) and unit test covering key metrics is included Reviewed By: ltamasi Differential Revision: D21134113 Pulled By: pdillinger fbshipit-source-id: c11497b5f00f4ffdfe919823904e52d0a1a91d87
2020-04-27 20:18:18 +00:00
UpdateCacheInsertionMetrics(block_type, get_context, charge,
s.IsOkOverwritten(), rep_->ioptions.stats);
} else {
RecordTick(statistics, BLOCK_CACHE_ADD_FAILURES);
}
} else {
cached_block->SetOwnedValue(block_holder.release());
}
return s;
}
std::unique_ptr<FilterBlockReader> BlockBasedTable::CreateFilterBlockReader(
const ReadOptions& ro, FilePrefetchBuffer* prefetch_buffer, bool use_cache,
bool prefetch, bool pin, BlockCacheLookupContext* lookup_context) {
auto& rep = rep_;
auto filter_type = rep->filter_type;
if (filter_type == Rep::FilterType::kNoFilter) {
return std::unique_ptr<FilterBlockReader>();
}
assert(rep->filter_policy);
switch (filter_type) {
case Rep::FilterType::kPartitionedFilter:
return PartitionedFilterBlockReader::Create(
this, ro, prefetch_buffer, use_cache, prefetch, pin, lookup_context);
case Rep::FilterType::kBlockFilter:
return BlockBasedFilterBlockReader::Create(
this, ro, prefetch_buffer, use_cache, prefetch, pin, lookup_context);
case Rep::FilterType::kFullFilter:
return FullFilterBlockReader::Create(this, ro, prefetch_buffer, use_cache,
prefetch, pin, lookup_context);
default:
// filter_type is either kNoFilter (exited the function at the first if),
// or it must be covered in this switch block
assert(false);
return std::unique_ptr<FilterBlockReader>();
}
}
// disable_prefix_seek should be set to true when prefix_extractor found in SST
// differs from the one in mutable_cf_options and index type is HashBasedIndex
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
InternalIteratorBase<IndexValue>* BlockBasedTable::NewIndexIterator(
const ReadOptions& read_options, bool disable_prefix_seek,
Create a BlockCacheLookupContext to enable fine-grained block cache tracing. (#5421) Summary: BlockCacheLookupContext only contains the caller for now. We will trace block accesses at five places: 1. BlockBasedTable::GetFilter. 2. BlockBasedTable::GetUncompressedDict. 3. BlockBasedTable::MaybeReadAndLoadToCache. (To trace access on data, index, and range deletion block.) 4. BlockBasedTable::Get. (To trace the referenced key and whether the referenced key exists in a fetched data block.) 5. BlockBasedTable::MultiGet. (To trace the referenced key and whether the referenced key exists in a fetched data block.) We create the context at: 1. BlockBasedTable::Get. (kUserGet) 2. BlockBasedTable::MultiGet. (kUserMGet) 3. BlockBasedTable::NewIterator. (either kUserIterator, kCompaction, or external SST ingestion calls this function.) 4. BlockBasedTable::Open. (kPrefetch) 5. Index/Filter::CacheDependencies. (kPrefetch) 6. BlockBasedTable::ApproximateOffsetOf. (kCompaction or kUserApproximateSize). I loaded 1 million key-value pairs into the database and ran the readrandom benchmark with a single thread. I gave the block cache 10 GB to make sure all reads hit the block cache after warmup. The throughput is comparable. Throughput of this PR: 231334 ops/s. Throughput of the master branch: 238428 ops/s. Experiment setup: RocksDB: version 6.2 Date: Mon Jun 10 10:42:51 2019 CPU: 24 * Intel Core Processor (Skylake) CPUCache: 16384 KB Keys: 20 bytes each Values: 100 bytes each (100 bytes after compression) Entries: 1000000 Prefix: 20 bytes Keys per prefix: 0 RawSize: 114.4 MB (estimated) FileSize: 114.4 MB (estimated) Write rate: 0 bytes/second Read rate: 0 ops/second Compression: NoCompression Compression sampling rate: 0 Memtablerep: skip_list Perf Level: 1 Load command: ./db_bench --benchmarks="fillseq" --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --statistics --cache_index_and_filter_blocks --cache_size=10737418240 --disable_auto_compactions=1 --disable_wal=1 --compression_type=none --min_level_to_compress=-1 --compression_ratio=1 --num=1000000 Run command: ./db_bench --benchmarks="readrandom,stats" --use_existing_db --threads=1 --duration=120 --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --statistics --cache_index_and_filter_blocks --cache_size=10737418240 --disable_auto_compactions=1 --disable_wal=1 --compression_type=none --min_level_to_compress=-1 --compression_ratio=1 --num=1000000 --duration=120 TODOs: 1. Create a caller for external SST file ingestion and differentiate the callers for iterator. 2. Integrate tracer to trace block cache accesses. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5421 Differential Revision: D15704258 Pulled By: HaoyuHuang fbshipit-source-id: 4aa8a55f8cb1576ffb367bfa3186a91d8f06d93a
2019-06-10 22:30:05 +00:00
IndexBlockIter* input_iter, GetContext* get_context,
BlockCacheLookupContext* lookup_context) const {
assert(rep_ != nullptr);
assert(rep_->index_reader != nullptr);
// We don't return pinned data from index blocks, so no need
// to set `block_contents_pinned`.
return rep_->index_reader->NewIterator(read_options, disable_prefix_seek,
Create a BlockCacheLookupContext to enable fine-grained block cache tracing. (#5421) Summary: BlockCacheLookupContext only contains the caller for now. We will trace block accesses at five places: 1. BlockBasedTable::GetFilter. 2. BlockBasedTable::GetUncompressedDict. 3. BlockBasedTable::MaybeReadAndLoadToCache. (To trace access on data, index, and range deletion block.) 4. BlockBasedTable::Get. (To trace the referenced key and whether the referenced key exists in a fetched data block.) 5. BlockBasedTable::MultiGet. (To trace the referenced key and whether the referenced key exists in a fetched data block.) We create the context at: 1. BlockBasedTable::Get. (kUserGet) 2. BlockBasedTable::MultiGet. (kUserMGet) 3. BlockBasedTable::NewIterator. (either kUserIterator, kCompaction, or external SST ingestion calls this function.) 4. BlockBasedTable::Open. (kPrefetch) 5. Index/Filter::CacheDependencies. (kPrefetch) 6. BlockBasedTable::ApproximateOffsetOf. (kCompaction or kUserApproximateSize). I loaded 1 million key-value pairs into the database and ran the readrandom benchmark with a single thread. I gave the block cache 10 GB to make sure all reads hit the block cache after warmup. The throughput is comparable. Throughput of this PR: 231334 ops/s. Throughput of the master branch: 238428 ops/s. Experiment setup: RocksDB: version 6.2 Date: Mon Jun 10 10:42:51 2019 CPU: 24 * Intel Core Processor (Skylake) CPUCache: 16384 KB Keys: 20 bytes each Values: 100 bytes each (100 bytes after compression) Entries: 1000000 Prefix: 20 bytes Keys per prefix: 0 RawSize: 114.4 MB (estimated) FileSize: 114.4 MB (estimated) Write rate: 0 bytes/second Read rate: 0 ops/second Compression: NoCompression Compression sampling rate: 0 Memtablerep: skip_list Perf Level: 1 Load command: ./db_bench --benchmarks="fillseq" --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --statistics --cache_index_and_filter_blocks --cache_size=10737418240 --disable_auto_compactions=1 --disable_wal=1 --compression_type=none --min_level_to_compress=-1 --compression_ratio=1 --num=1000000 Run command: ./db_bench --benchmarks="readrandom,stats" --use_existing_db --threads=1 --duration=120 --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --statistics --cache_index_and_filter_blocks --cache_size=10737418240 --disable_auto_compactions=1 --disable_wal=1 --compression_type=none --min_level_to_compress=-1 --compression_ratio=1 --num=1000000 --duration=120 TODOs: 1. Create a caller for external SST file ingestion and differentiate the callers for iterator. 2. Integrate tracer to trace block cache accesses. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5421 Differential Revision: D15704258 Pulled By: HaoyuHuang fbshipit-source-id: 4aa8a55f8cb1576ffb367bfa3186a91d8f06d93a
2019-06-10 22:30:05 +00:00
input_iter, get_context,
lookup_context);
}
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
template <>
DataBlockIter* BlockBasedTable::InitBlockIterator<DataBlockIter>(
const Rep* rep, Block* block, BlockType block_type,
DataBlockIter* input_iter, bool block_contents_pinned) {
Separate internal and user key comparators in `BlockIter` (#6944) Summary: Replace `BlockIter::comparator_` and `IndexBlockIter::user_comparator_wrapper_` with a concrete `UserComparatorWrapper` and `InternalKeyComparator`. The motivation for this change was the inconvenience of not knowing the concrete type of `BlockIter::comparator_`, which prevented calling specialized internal key comparison functions to optimize comparison of keys with global seqno applied. Pull Request resolved: https://github.com/facebook/rocksdb/pull/6944 Test Plan: benchmark setup -- single file DBs, in-memory, no compression. "normal_db" created by regular flush; "ingestion_db" created by ingesting a file. Both DBs have same contents. ``` $ TEST_TMPDIR=/dev/shm/normal_db/ ./db_bench -benchmarks=fillrandom,compact -write_buffer_size=10485760000 -disable_auto_compactions=true -compression_type=none -num=1000000 $ ./ldb write_extern_sst ./tmp.sst --db=/dev/shm/ingestion_db/dbbench/ --compression_type=no --hex --create_if_missing < <(./sst_dump --command=scan --output_hex --file=/dev/shm/normal_db/dbbench/000007.sst | awk 'began {print "0x" substr($1, 2, length($1) - 2), "==>", "0x" $5} ; /^Sst file format: block-based/ {began=1}') $ ./ldb ingest_extern_sst ./tmp.sst --db=/dev/shm/ingestion_db/dbbench/ ``` benchmark run command: ``` $ TEST_TMPDIR=/dev/shm/$DB/ ./db_bench -benchmarks=seekrandom -seek_nexts=$SEEK_NEXT -use_existing_db=true -cache_index_and_filter_blocks=false -num=1000000 -cache_size=0 -threads=1 -reads=200000000 -mmap_read=1 -verify_checksum=false ``` results: perf improved marginally for ingestion_db and did not change significantly for normal_db: SEEK_NEXT | DB | code | ops/sec | % change -- | -- | -- | -- | -- 0 | normal_db | master | 350880 |   0 | normal_db | PR6944 | 351040 | 0.0 0 | ingestion_db | master | 343255 |   0 | ingestion_db | PR6944 | 349424 | 1.8 10 | normal_db | master | 218711 |   10 | normal_db | PR6944 | 217892 | -0.4 10 | ingestion_db | master | 220334 |   10 | ingestion_db | PR6944 | 226437 | 2.8 Reviewed By: pdillinger Differential Revision: D21924676 Pulled By: ajkr fbshipit-source-id: ea4288a2eefa8112eb6c651a671c1de18c12e538
2020-07-08 00:25:08 +00:00
return block->NewDataIterator(rep->internal_comparator.user_comparator(),
rep->get_global_seqno(block_type), input_iter,
rep->ioptions.stats, block_contents_pinned);
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
}
template <>
IndexBlockIter* BlockBasedTable::InitBlockIterator<IndexBlockIter>(
const Rep* rep, Block* block, BlockType block_type,
IndexBlockIter* input_iter, bool block_contents_pinned) {
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
return block->NewIndexIterator(
Separate internal and user key comparators in `BlockIter` (#6944) Summary: Replace `BlockIter::comparator_` and `IndexBlockIter::user_comparator_wrapper_` with a concrete `UserComparatorWrapper` and `InternalKeyComparator`. The motivation for this change was the inconvenience of not knowing the concrete type of `BlockIter::comparator_`, which prevented calling specialized internal key comparison functions to optimize comparison of keys with global seqno applied. Pull Request resolved: https://github.com/facebook/rocksdb/pull/6944 Test Plan: benchmark setup -- single file DBs, in-memory, no compression. "normal_db" created by regular flush; "ingestion_db" created by ingesting a file. Both DBs have same contents. ``` $ TEST_TMPDIR=/dev/shm/normal_db/ ./db_bench -benchmarks=fillrandom,compact -write_buffer_size=10485760000 -disable_auto_compactions=true -compression_type=none -num=1000000 $ ./ldb write_extern_sst ./tmp.sst --db=/dev/shm/ingestion_db/dbbench/ --compression_type=no --hex --create_if_missing < <(./sst_dump --command=scan --output_hex --file=/dev/shm/normal_db/dbbench/000007.sst | awk 'began {print "0x" substr($1, 2, length($1) - 2), "==>", "0x" $5} ; /^Sst file format: block-based/ {began=1}') $ ./ldb ingest_extern_sst ./tmp.sst --db=/dev/shm/ingestion_db/dbbench/ ``` benchmark run command: ``` $ TEST_TMPDIR=/dev/shm/$DB/ ./db_bench -benchmarks=seekrandom -seek_nexts=$SEEK_NEXT -use_existing_db=true -cache_index_and_filter_blocks=false -num=1000000 -cache_size=0 -threads=1 -reads=200000000 -mmap_read=1 -verify_checksum=false ``` results: perf improved marginally for ingestion_db and did not change significantly for normal_db: SEEK_NEXT | DB | code | ops/sec | % change -- | -- | -- | -- | -- 0 | normal_db | master | 350880 |   0 | normal_db | PR6944 | 351040 | 0.0 0 | ingestion_db | master | 343255 |   0 | ingestion_db | PR6944 | 349424 | 1.8 10 | normal_db | master | 218711 |   10 | normal_db | PR6944 | 217892 | -0.4 10 | ingestion_db | master | 220334 |   10 | ingestion_db | PR6944 | 226437 | 2.8 Reviewed By: pdillinger Differential Revision: D21924676 Pulled By: ajkr fbshipit-source-id: ea4288a2eefa8112eb6c651a671c1de18c12e538
2020-07-08 00:25:08 +00:00
rep->internal_comparator.user_comparator(),
rep->get_global_seqno(block_type), input_iter, rep->ioptions.stats,
/* total_order_seek */ true, rep->index_has_first_key,
rep->index_key_includes_seq, rep->index_value_is_full,
block_contents_pinned);
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
}
// If contents is nullptr, this function looks up the block caches for the
// data block referenced by handle, and read the block from disk if necessary.
// If contents is non-null, it skips the cache lookup and disk read, since
// the caller has already read it. In both cases, if ro.fill_cache is true,
// it inserts the block into the block cache.
template <typename TBlocklike>
Status BlockBasedTable::MaybeReadBlockAndLoadToCache(
FilePrefetchBuffer* prefetch_buffer, const ReadOptions& ro,
const BlockHandle& handle, const UncompressionDict& uncompression_dict,
Parallelize secondary cache lookup in MultiGet (#8405) Summary: Implement the ```WaitAll()``` interface in ```LRUCache``` to allow callers to issue multiple lookups in parallel and wait for all of them to complete. Modify ```MultiGet``` to use this to parallelize the secondary cache lookups in order to reduce the overall latency. A call to ```cache->Lookup()``` returns a handle that has an incomplete value (nullptr), and the caller can call ```cache->IsReady()``` to check whether the lookup is complete, and pass a vector of handles to ```WaitAll``` to wait for completion. If any of the lookups fail, ```MultiGet``` will read the block from the SST file. Another change in this PR is to rename ```SecondaryCacheHandle``` to ```SecondaryCacheResultHandle``` as it more accurately describes the return result of the secondary cache lookup, which is more like a future. Tests: 1. Add unit tests in lru_cache_test 2. Benchmark results with no secondary cache configured Master - ``` readrandom : 41.175 micros/op 388562 ops/sec; 106.7 MB/s (7277999 of 7277999 found) readrandom : 41.217 micros/op 388160 ops/sec; 106.6 MB/s (7274999 of 7274999 found) multireadrandom : 10.309 micros/op 1552082 ops/sec; (28908992 of 28908992 found) multireadrandom : 10.321 micros/op 1550218 ops/sec; (29081984 of 29081984 found) ``` This PR - ``` readrandom : 41.158 micros/op 388723 ops/sec; 106.8 MB/s (7290999 of 7290999 found) readrandom : 41.185 micros/op 388463 ops/sec; 106.7 MB/s (7287999 of 7287999 found) multireadrandom : 10.277 micros/op 1556801 ops/sec; (29346944 of 29346944 found) multireadrandom : 10.253 micros/op 1560539 ops/sec; (29274944 of 29274944 found) ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/8405 Reviewed By: zhichao-cao Differential Revision: D29190509 Pulled By: anand1976 fbshipit-source-id: 6f8eff6246712af8a297cfe22ea0d1c3b2a01bb0
2021-06-18 16:35:03 +00:00
const bool wait, CachableEntry<TBlocklike>* block_entry,
BlockType block_type, GetContext* get_context,
BlockCacheLookupContext* lookup_context, BlockContents* contents) const {
assert(block_entry != nullptr);
const bool no_io = (ro.read_tier == kBlockCacheTier);
Cache* block_cache = rep_->table_options.block_cache.get();
Cache* block_cache_compressed =
rep_->table_options.block_cache_compressed.get();
// First, try to get the block from the cache
//
// If either block cache is enabled, we'll try to read from it.
Status s;
char cache_key[kMaxCacheKeyPrefixSize + kMaxVarint64Length];
char compressed_cache_key[kMaxCacheKeyPrefixSize + kMaxVarint64Length];
Slice key /* key to the block cache */;
Slice ckey /* key to the compressed block cache */;
bool is_cache_hit = false;
if (block_cache != nullptr || block_cache_compressed != nullptr) {
// create key for block cache
if (block_cache != nullptr) {
key = GetCacheKey(rep_->cache_key_prefix, rep_->cache_key_prefix_size,
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
handle, cache_key);
}
if (block_cache_compressed != nullptr) {
ckey = GetCacheKey(rep_->compressed_cache_key_prefix,
rep_->compressed_cache_key_prefix_size, handle,
compressed_cache_key);
}
if (!contents) {
s = GetDataBlockFromCache(key, ckey, block_cache, block_cache_compressed,
ro, block_entry, uncompression_dict, block_type,
Parallelize secondary cache lookup in MultiGet (#8405) Summary: Implement the ```WaitAll()``` interface in ```LRUCache``` to allow callers to issue multiple lookups in parallel and wait for all of them to complete. Modify ```MultiGet``` to use this to parallelize the secondary cache lookups in order to reduce the overall latency. A call to ```cache->Lookup()``` returns a handle that has an incomplete value (nullptr), and the caller can call ```cache->IsReady()``` to check whether the lookup is complete, and pass a vector of handles to ```WaitAll``` to wait for completion. If any of the lookups fail, ```MultiGet``` will read the block from the SST file. Another change in this PR is to rename ```SecondaryCacheHandle``` to ```SecondaryCacheResultHandle``` as it more accurately describes the return result of the secondary cache lookup, which is more like a future. Tests: 1. Add unit tests in lru_cache_test 2. Benchmark results with no secondary cache configured Master - ``` readrandom : 41.175 micros/op 388562 ops/sec; 106.7 MB/s (7277999 of 7277999 found) readrandom : 41.217 micros/op 388160 ops/sec; 106.6 MB/s (7274999 of 7274999 found) multireadrandom : 10.309 micros/op 1552082 ops/sec; (28908992 of 28908992 found) multireadrandom : 10.321 micros/op 1550218 ops/sec; (29081984 of 29081984 found) ``` This PR - ``` readrandom : 41.158 micros/op 388723 ops/sec; 106.8 MB/s (7290999 of 7290999 found) readrandom : 41.185 micros/op 388463 ops/sec; 106.7 MB/s (7287999 of 7287999 found) multireadrandom : 10.277 micros/op 1556801 ops/sec; (29346944 of 29346944 found) multireadrandom : 10.253 micros/op 1560539 ops/sec; (29274944 of 29274944 found) ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/8405 Reviewed By: zhichao-cao Differential Revision: D29190509 Pulled By: anand1976 fbshipit-source-id: 6f8eff6246712af8a297cfe22ea0d1c3b2a01bb0
2021-06-18 16:35:03 +00:00
wait, get_context);
// Value could still be null at this point, so check the cache handle
// and update the read pattern for prefetching
if (block_entry->GetValue() || block_entry->GetCacheHandle()) {
// TODO(haoyu): Differentiate cache hit on uncompressed block cache and
// compressed block cache.
is_cache_hit = true;
if (prefetch_buffer) {
// Update the block details so that PrefetchBuffer can use the read
// pattern to determine if reads are sequential or not for
// prefetching. It should also take in account blocks read from cache.
prefetch_buffer->UpdateReadPattern(handle.offset(),
block_size(handle));
}
}
}
// Can't find the block from the cache. If I/O is allowed, read from the
// file.
Parallelize secondary cache lookup in MultiGet (#8405) Summary: Implement the ```WaitAll()``` interface in ```LRUCache``` to allow callers to issue multiple lookups in parallel and wait for all of them to complete. Modify ```MultiGet``` to use this to parallelize the secondary cache lookups in order to reduce the overall latency. A call to ```cache->Lookup()``` returns a handle that has an incomplete value (nullptr), and the caller can call ```cache->IsReady()``` to check whether the lookup is complete, and pass a vector of handles to ```WaitAll``` to wait for completion. If any of the lookups fail, ```MultiGet``` will read the block from the SST file. Another change in this PR is to rename ```SecondaryCacheHandle``` to ```SecondaryCacheResultHandle``` as it more accurately describes the return result of the secondary cache lookup, which is more like a future. Tests: 1. Add unit tests in lru_cache_test 2. Benchmark results with no secondary cache configured Master - ``` readrandom : 41.175 micros/op 388562 ops/sec; 106.7 MB/s (7277999 of 7277999 found) readrandom : 41.217 micros/op 388160 ops/sec; 106.6 MB/s (7274999 of 7274999 found) multireadrandom : 10.309 micros/op 1552082 ops/sec; (28908992 of 28908992 found) multireadrandom : 10.321 micros/op 1550218 ops/sec; (29081984 of 29081984 found) ``` This PR - ``` readrandom : 41.158 micros/op 388723 ops/sec; 106.8 MB/s (7290999 of 7290999 found) readrandom : 41.185 micros/op 388463 ops/sec; 106.7 MB/s (7287999 of 7287999 found) multireadrandom : 10.277 micros/op 1556801 ops/sec; (29346944 of 29346944 found) multireadrandom : 10.253 micros/op 1560539 ops/sec; (29274944 of 29274944 found) ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/8405 Reviewed By: zhichao-cao Differential Revision: D29190509 Pulled By: anand1976 fbshipit-source-id: 6f8eff6246712af8a297cfe22ea0d1c3b2a01bb0
2021-06-18 16:35:03 +00:00
if (block_entry->GetValue() == nullptr &&
block_entry->GetCacheHandle() == nullptr && !no_io && ro.fill_cache) {
Statistics* statistics = rep_->ioptions.stats;
const bool maybe_compressed =
block_type != BlockType::kFilter &&
block_type != BlockType::kCompressionDictionary &&
rep_->blocks_maybe_compressed;
const bool do_uncompress = maybe_compressed && !block_cache_compressed;
CompressionType raw_block_comp_type;
BlockContents raw_block_contents;
if (!contents) {
StopWatch sw(rep_->ioptions.clock, statistics, READ_BLOCK_GET_MICROS);
BlockFetcher block_fetcher(
rep_->file.get(), prefetch_buffer, rep_->footer, ro, handle,
&raw_block_contents, rep_->ioptions, do_uncompress,
maybe_compressed, block_type, uncompression_dict,
rep_->persistent_cache_options,
GetMemoryAllocator(rep_->table_options),
GetMemoryAllocatorForCompressedBlock(rep_->table_options));
s = block_fetcher.ReadBlockContents();
raw_block_comp_type = block_fetcher.get_compression_type();
contents = &raw_block_contents;
if (get_context) {
switch (block_type) {
case BlockType::kIndex:
++get_context->get_context_stats_.num_index_read;
break;
case BlockType::kFilter:
++get_context->get_context_stats_.num_filter_read;
break;
case BlockType::kData:
++get_context->get_context_stats_.num_data_read;
break;
default:
break;
}
}
} else {
raw_block_comp_type = contents->get_compression_type();
}
if (s.ok()) {
// If filling cache is allowed and a cache is configured, try to put the
// block to the cache.
s = PutDataBlockToCache(
key, ckey, block_cache, block_cache_compressed, block_entry,
contents, raw_block_comp_type, uncompression_dict,
GetMemoryAllocator(rep_->table_options), block_type, get_context);
}
}
}
// Fill lookup_context.
if (block_cache_tracer_ && block_cache_tracer_->is_tracing_enabled() &&
lookup_context) {
size_t usage = 0;
uint64_t nkeys = 0;
if (block_entry->GetValue()) {
// Approximate the number of keys in the block using restarts.
nkeys =
rep_->table_options.block_restart_interval *
BlocklikeTraits<TBlocklike>::GetNumRestarts(*block_entry->GetValue());
usage = block_entry->GetValue()->ApproximateMemoryUsage();
}
TraceType trace_block_type = TraceType::kTraceMax;
switch (block_type) {
case BlockType::kData:
trace_block_type = TraceType::kBlockTraceDataBlock;
break;
case BlockType::kFilter:
trace_block_type = TraceType::kBlockTraceFilterBlock;
break;
case BlockType::kCompressionDictionary:
trace_block_type = TraceType::kBlockTraceUncompressionDictBlock;
break;
case BlockType::kRangeDeletion:
trace_block_type = TraceType::kBlockTraceRangeDeletionBlock;
break;
case BlockType::kIndex:
trace_block_type = TraceType::kBlockTraceIndexBlock;
break;
default:
// This cannot happen.
assert(false);
break;
}
bool no_insert = no_io || !ro.fill_cache;
if (BlockCacheTraceHelper::IsGetOrMultiGetOnDataBlock(
trace_block_type, lookup_context->caller)) {
// Defer logging the access to Get() and MultiGet() to trace additional
// information, e.g., referenced_key_exist_in_block.
// Make a copy of the block key here since it will be logged later.
lookup_context->FillLookupContext(
is_cache_hit, no_insert, trace_block_type,
/*block_size=*/usage, /*block_key=*/key.ToString(), nkeys);
} else {
// Avoid making copy of block_key and cf_name when constructing the access
// record.
BlockCacheTraceRecord access_record(
rep_->ioptions.clock->NowMicros(),
/*block_key=*/"", trace_block_type,
/*block_size=*/usage, rep_->cf_id_for_tracing(),
/*cf_name=*/"", rep_->level_for_tracing(),
rep_->sst_number_for_tracing(), lookup_context->caller, is_cache_hit,
no_insert, lookup_context->get_id,
lookup_context->get_from_user_specified_snapshot,
/*referenced_key=*/"");
// TODO: Should handle this error?
block_cache_tracer_
->WriteBlockAccess(access_record, key, rep_->cf_name_for_tracing(),
lookup_context->referenced_key)
.PermitUncheckedError();
}
}
assert(s.ok() || block_entry->GetValue() == nullptr);
return s;
}
// This function reads multiple data blocks from disk using Env::MultiRead()
// and optionally inserts them into the block cache. It uses the scratch
// buffer provided by the caller, which is contiguous. If scratch is a nullptr
// it allocates a separate buffer for each block. Typically, if the blocks
// need to be uncompressed and there is no compressed block cache, callers
// can allocate a temporary scratch buffer in order to minimize memory
// allocations.
// If options.fill_cache is true, it inserts the blocks into cache. If its
// false and scratch is non-null and the blocks are uncompressed, it copies
// the buffers to heap. In any case, the CachableEntry<Block> returned will
// own the data bytes.
Merge adjacent file block reads in RocksDB MultiGet() and Add uncompressed block to cache (#6089) Summary: In the current MultiGet, if the KV-pairs do not belong to the data blocks in the block cache, multiple blocks are read from a SST. It will trigger one block read for each block request and read them in parallel. In some cases, if some data blocks are adjacent in the SST, the reads for these blocks can be combined to a single large read, which can reduce the system calls and reduce the read latency if possible. Considering to fill the block cache, if multiple data blocks are in the same memory buffer, we need to copy them to the heap separately. Therefore, only in the case that 1) data block compression is enabled, and 2) compressed block cache is null, we can do combined read. Otherwise, extra memory copy is needed, which may cause extra overhead. In the current case, data blocks will be uncompressed to a new memory space. Also, in the case that 1) data block compression is enabled, and 2) compressed block cache is null, it is possible the data block is actually not compressed. In the current logic, these data blocks will not be added to the uncompressed_cache. So if memory buffer is shared and the data block is not compressed, the data block are copied to the head and fill the cache. Pull Request resolved: https://github.com/facebook/rocksdb/pull/6089 Test Plan: Added test case to ParallelIO.MultiGet. Pass make asan_check Differential Revision: D18734668 Pulled By: zhichao-cao fbshipit-source-id: 67c5615ed373e51e42635fd74b36f8f3a66d5da4
2019-12-16 23:55:33 +00:00
// If compression is enabled and also there is no compressed block cache,
// the adjacent blocks are read out in one IO (combined read)
// batch - A MultiGetRange with only those keys with unique data blocks not
// found in cache
// handles - A vector of block handles. Some of them me be NULL handles
// scratch - An optional contiguous buffer to read compressed blocks into
void BlockBasedTable::RetrieveMultipleBlocks(
const ReadOptions& options, const MultiGetRange* batch,
const autovector<BlockHandle, MultiGetContext::MAX_BATCH_SIZE>* handles,
autovector<Status, MultiGetContext::MAX_BATCH_SIZE>* statuses,
autovector<CachableEntry<Block>, MultiGetContext::MAX_BATCH_SIZE>* results,
char* scratch, const UncompressionDict& uncompression_dict) const {
RandomAccessFileReader* file = rep_->file.get();
const Footer& footer = rep_->footer;
const ImmutableOptions& ioptions = rep_->ioptions;
size_t read_amp_bytes_per_bit = rep_->table_options.read_amp_bytes_per_bit;
MemoryAllocator* memory_allocator = GetMemoryAllocator(rep_->table_options);
if (ioptions.allow_mmap_reads) {
size_t idx_in_batch = 0;
for (auto mget_iter = batch->begin(); mget_iter != batch->end();
++mget_iter, ++idx_in_batch) {
BlockCacheLookupContext lookup_data_block_context(
TableReaderCaller::kUserMultiGet);
const BlockHandle& handle = (*handles)[idx_in_batch];
if (handle.IsNull()) {
continue;
}
Fix regression affecting partitioned indexes/filters when cache_index_and_filter_blocks is false (#5705) Summary: PR https://github.com/facebook/rocksdb/issues/5298 (and subsequent related patches) unintentionally changed the semantics of cache_index_and_filter_blocks: historically, this option only affected the main index/filter block; with the changes, it affects index/filter partitions as well. This can cause performance issues when cache_index_and_filter_blocks is false since in this case, partitions are neither cached nor preloaded (i.e. they are loaded on demand upon each access). The patch reverts to the earlier behavior, that is, partitions are cached similarly to data blocks regardless of the value of the above option. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5705 Test Plan: make check ./db_bench -benchmarks=fillrandom --statistics --stats_interval_seconds=1 --duration=30 --num=500000000 --bloom_bits=20 --partition_index_and_filters=true --cache_index_and_filter_blocks=false ./db_bench -benchmarks=readrandom --use_existing_db --statistics --stats_interval_seconds=1 --duration=10 --num=500000000 --bloom_bits=20 --partition_index_and_filters=true --cache_index_and_filter_blocks=false --cache_size=8000000000 Relevant statistics from the readrandom benchmark with the old code: rocksdb.block.cache.index.miss COUNT : 0 rocksdb.block.cache.index.hit COUNT : 0 rocksdb.block.cache.index.add COUNT : 0 rocksdb.block.cache.index.bytes.insert COUNT : 0 rocksdb.block.cache.index.bytes.evict COUNT : 0 rocksdb.block.cache.filter.miss COUNT : 0 rocksdb.block.cache.filter.hit COUNT : 0 rocksdb.block.cache.filter.add COUNT : 0 rocksdb.block.cache.filter.bytes.insert COUNT : 0 rocksdb.block.cache.filter.bytes.evict COUNT : 0 With the new code: rocksdb.block.cache.index.miss COUNT : 2500 rocksdb.block.cache.index.hit COUNT : 42696 rocksdb.block.cache.index.add COUNT : 2500 rocksdb.block.cache.index.bytes.insert COUNT : 4050048 rocksdb.block.cache.index.bytes.evict COUNT : 0 rocksdb.block.cache.filter.miss COUNT : 2500 rocksdb.block.cache.filter.hit COUNT : 4550493 rocksdb.block.cache.filter.add COUNT : 2500 rocksdb.block.cache.filter.bytes.insert COUNT : 10331040 rocksdb.block.cache.filter.bytes.evict COUNT : 0 Differential Revision: D16817382 Pulled By: ltamasi fbshipit-source-id: 28a516b0da1f041a03313e0b70b28cf5cf205d00
2019-08-15 01:13:14 +00:00
(*statuses)[idx_in_batch] =
RetrieveBlock(nullptr, options, handle, uncompression_dict,
&(*results)[idx_in_batch], BlockType::kData,
mget_iter->get_context, &lookup_data_block_context,
Parallelize secondary cache lookup in MultiGet (#8405) Summary: Implement the ```WaitAll()``` interface in ```LRUCache``` to allow callers to issue multiple lookups in parallel and wait for all of them to complete. Modify ```MultiGet``` to use this to parallelize the secondary cache lookups in order to reduce the overall latency. A call to ```cache->Lookup()``` returns a handle that has an incomplete value (nullptr), and the caller can call ```cache->IsReady()``` to check whether the lookup is complete, and pass a vector of handles to ```WaitAll``` to wait for completion. If any of the lookups fail, ```MultiGet``` will read the block from the SST file. Another change in this PR is to rename ```SecondaryCacheHandle``` to ```SecondaryCacheResultHandle``` as it more accurately describes the return result of the secondary cache lookup, which is more like a future. Tests: 1. Add unit tests in lru_cache_test 2. Benchmark results with no secondary cache configured Master - ``` readrandom : 41.175 micros/op 388562 ops/sec; 106.7 MB/s (7277999 of 7277999 found) readrandom : 41.217 micros/op 388160 ops/sec; 106.6 MB/s (7274999 of 7274999 found) multireadrandom : 10.309 micros/op 1552082 ops/sec; (28908992 of 28908992 found) multireadrandom : 10.321 micros/op 1550218 ops/sec; (29081984 of 29081984 found) ``` This PR - ``` readrandom : 41.158 micros/op 388723 ops/sec; 106.8 MB/s (7290999 of 7290999 found) readrandom : 41.185 micros/op 388463 ops/sec; 106.7 MB/s (7287999 of 7287999 found) multireadrandom : 10.277 micros/op 1556801 ops/sec; (29346944 of 29346944 found) multireadrandom : 10.253 micros/op 1560539 ops/sec; (29274944 of 29274944 found) ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/8405 Reviewed By: zhichao-cao Differential Revision: D29190509 Pulled By: anand1976 fbshipit-source-id: 6f8eff6246712af8a297cfe22ea0d1c3b2a01bb0
2021-06-18 16:35:03 +00:00
/* for_compaction */ false, /* use_cache */ true,
/* wait_for_cache */ true);
}
return;
}
// In direct IO mode, blocks share the direct io buffer.
// Otherwise, blocks share the scratch buffer.
const bool use_shared_buffer = file->use_direct_io() || scratch != nullptr;
Introduce a new storage specific Env API (#5761) Summary: The current Env API encompasses both storage/file operations, as well as OS related operations. Most of the APIs return a Status, which does not have enough metadata about an error, such as whether its retry-able or not, scope (i.e fault domain) of the error etc., that may be required in order to properly handle a storage error. The file APIs also do not provide enough control over the IO SLA, such as timeout, prioritization, hinting about placement and redundancy etc. This PR separates out the file/storage APIs from Env into a new FileSystem class. The APIs are updated to return an IOStatus with metadata about the error, as well as to take an IOOptions structure as input in order to allow more control over the IO. The user can set both ```options.env``` and ```options.file_system``` to specify that RocksDB should use the former for OS related operations and the latter for storage operations. Internally, a ```CompositeEnvWrapper``` has been introduced that inherits from ```Env``` and redirects individual methods to either an ```Env``` implementation or the ```FileSystem``` as appropriate. When options are sanitized during ```DB::Open```, ```options.env``` is replaced with a newly allocated ```CompositeEnvWrapper``` instance if both env and file_system have been specified. This way, the rest of the RocksDB code can continue to function as before. This PR also ports PosixEnv to the new API by splitting it into two - PosixEnv and PosixFileSystem. PosixEnv is defined as a sub-class of CompositeEnvWrapper, and threading/time functions are overridden with Posix specific implementations in order to avoid an extra level of indirection. The ```CompositeEnvWrapper``` translates ```IOStatus``` return code to ```Status```, and sets the severity to ```kSoftError``` if the io_status is retryable. The error handling code in RocksDB can then recover the DB automatically. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5761 Differential Revision: D18868376 Pulled By: anand1976 fbshipit-source-id: 39efe18a162ea746fabac6360ff529baba48486f
2019-12-13 22:47:08 +00:00
autovector<FSReadRequest, MultiGetContext::MAX_BATCH_SIZE> read_reqs;
size_t buf_offset = 0;
size_t idx_in_batch = 0;
Merge adjacent file block reads in RocksDB MultiGet() and Add uncompressed block to cache (#6089) Summary: In the current MultiGet, if the KV-pairs do not belong to the data blocks in the block cache, multiple blocks are read from a SST. It will trigger one block read for each block request and read them in parallel. In some cases, if some data blocks are adjacent in the SST, the reads for these blocks can be combined to a single large read, which can reduce the system calls and reduce the read latency if possible. Considering to fill the block cache, if multiple data blocks are in the same memory buffer, we need to copy them to the heap separately. Therefore, only in the case that 1) data block compression is enabled, and 2) compressed block cache is null, we can do combined read. Otherwise, extra memory copy is needed, which may cause extra overhead. In the current case, data blocks will be uncompressed to a new memory space. Also, in the case that 1) data block compression is enabled, and 2) compressed block cache is null, it is possible the data block is actually not compressed. In the current logic, these data blocks will not be added to the uncompressed_cache. So if memory buffer is shared and the data block is not compressed, the data block are copied to the head and fill the cache. Pull Request resolved: https://github.com/facebook/rocksdb/pull/6089 Test Plan: Added test case to ParallelIO.MultiGet. Pass make asan_check Differential Revision: D18734668 Pulled By: zhichao-cao fbshipit-source-id: 67c5615ed373e51e42635fd74b36f8f3a66d5da4
2019-12-16 23:55:33 +00:00
uint64_t prev_offset = 0;
size_t prev_len = 0;
autovector<size_t, MultiGetContext::MAX_BATCH_SIZE> req_idx_for_block;
autovector<size_t, MultiGetContext::MAX_BATCH_SIZE> req_offset_for_block;
for (auto mget_iter = batch->begin(); mget_iter != batch->end();
++mget_iter, ++idx_in_batch) {
const BlockHandle& handle = (*handles)[idx_in_batch];
if (handle.IsNull()) {
continue;
}
Merge adjacent file block reads in RocksDB MultiGet() and Add uncompressed block to cache (#6089) Summary: In the current MultiGet, if the KV-pairs do not belong to the data blocks in the block cache, multiple blocks are read from a SST. It will trigger one block read for each block request and read them in parallel. In some cases, if some data blocks are adjacent in the SST, the reads for these blocks can be combined to a single large read, which can reduce the system calls and reduce the read latency if possible. Considering to fill the block cache, if multiple data blocks are in the same memory buffer, we need to copy them to the heap separately. Therefore, only in the case that 1) data block compression is enabled, and 2) compressed block cache is null, we can do combined read. Otherwise, extra memory copy is needed, which may cause extra overhead. In the current case, data blocks will be uncompressed to a new memory space. Also, in the case that 1) data block compression is enabled, and 2) compressed block cache is null, it is possible the data block is actually not compressed. In the current logic, these data blocks will not be added to the uncompressed_cache. So if memory buffer is shared and the data block is not compressed, the data block are copied to the head and fill the cache. Pull Request resolved: https://github.com/facebook/rocksdb/pull/6089 Test Plan: Added test case to ParallelIO.MultiGet. Pass make asan_check Differential Revision: D18734668 Pulled By: zhichao-cao fbshipit-source-id: 67c5615ed373e51e42635fd74b36f8f3a66d5da4
2019-12-16 23:55:33 +00:00
size_t prev_end = static_cast<size_t>(prev_offset) + prev_len;
// If current block is adjacent to the previous one, at the same time,
// compression is enabled and there is no compressed cache, we combine
// the two block read as one.
// We don't combine block reads here in direct IO mode, because when doing
// direct IO read, the block requests will be realigned and merged when
// necessary.
if (use_shared_buffer && !file->use_direct_io() &&
prev_end == handle.offset()) {
Merge adjacent file block reads in RocksDB MultiGet() and Add uncompressed block to cache (#6089) Summary: In the current MultiGet, if the KV-pairs do not belong to the data blocks in the block cache, multiple blocks are read from a SST. It will trigger one block read for each block request and read them in parallel. In some cases, if some data blocks are adjacent in the SST, the reads for these blocks can be combined to a single large read, which can reduce the system calls and reduce the read latency if possible. Considering to fill the block cache, if multiple data blocks are in the same memory buffer, we need to copy them to the heap separately. Therefore, only in the case that 1) data block compression is enabled, and 2) compressed block cache is null, we can do combined read. Otherwise, extra memory copy is needed, which may cause extra overhead. In the current case, data blocks will be uncompressed to a new memory space. Also, in the case that 1) data block compression is enabled, and 2) compressed block cache is null, it is possible the data block is actually not compressed. In the current logic, these data blocks will not be added to the uncompressed_cache. So if memory buffer is shared and the data block is not compressed, the data block are copied to the head and fill the cache. Pull Request resolved: https://github.com/facebook/rocksdb/pull/6089 Test Plan: Added test case to ParallelIO.MultiGet. Pass make asan_check Differential Revision: D18734668 Pulled By: zhichao-cao fbshipit-source-id: 67c5615ed373e51e42635fd74b36f8f3a66d5da4
2019-12-16 23:55:33 +00:00
req_offset_for_block.emplace_back(prev_len);
prev_len += block_size(handle);
} else {
// No compression or current block and previous one is not adjacent:
// Step 1, create a new request for previous blocks
if (prev_len != 0) {
FSReadRequest req;
req.offset = prev_offset;
req.len = prev_len;
if (file->use_direct_io()) {
req.scratch = nullptr;
} else if (use_shared_buffer) {
Merge adjacent file block reads in RocksDB MultiGet() and Add uncompressed block to cache (#6089) Summary: In the current MultiGet, if the KV-pairs do not belong to the data blocks in the block cache, multiple blocks are read from a SST. It will trigger one block read for each block request and read them in parallel. In some cases, if some data blocks are adjacent in the SST, the reads for these blocks can be combined to a single large read, which can reduce the system calls and reduce the read latency if possible. Considering to fill the block cache, if multiple data blocks are in the same memory buffer, we need to copy them to the heap separately. Therefore, only in the case that 1) data block compression is enabled, and 2) compressed block cache is null, we can do combined read. Otherwise, extra memory copy is needed, which may cause extra overhead. In the current case, data blocks will be uncompressed to a new memory space. Also, in the case that 1) data block compression is enabled, and 2) compressed block cache is null, it is possible the data block is actually not compressed. In the current logic, these data blocks will not be added to the uncompressed_cache. So if memory buffer is shared and the data block is not compressed, the data block are copied to the head and fill the cache. Pull Request resolved: https://github.com/facebook/rocksdb/pull/6089 Test Plan: Added test case to ParallelIO.MultiGet. Pass make asan_check Differential Revision: D18734668 Pulled By: zhichao-cao fbshipit-source-id: 67c5615ed373e51e42635fd74b36f8f3a66d5da4
2019-12-16 23:55:33 +00:00
req.scratch = scratch + buf_offset;
buf_offset += req.len;
} else {
req.scratch = new char[req.len];
Merge adjacent file block reads in RocksDB MultiGet() and Add uncompressed block to cache (#6089) Summary: In the current MultiGet, if the KV-pairs do not belong to the data blocks in the block cache, multiple blocks are read from a SST. It will trigger one block read for each block request and read them in parallel. In some cases, if some data blocks are adjacent in the SST, the reads for these blocks can be combined to a single large read, which can reduce the system calls and reduce the read latency if possible. Considering to fill the block cache, if multiple data blocks are in the same memory buffer, we need to copy them to the heap separately. Therefore, only in the case that 1) data block compression is enabled, and 2) compressed block cache is null, we can do combined read. Otherwise, extra memory copy is needed, which may cause extra overhead. In the current case, data blocks will be uncompressed to a new memory space. Also, in the case that 1) data block compression is enabled, and 2) compressed block cache is null, it is possible the data block is actually not compressed. In the current logic, these data blocks will not be added to the uncompressed_cache. So if memory buffer is shared and the data block is not compressed, the data block are copied to the head and fill the cache. Pull Request resolved: https://github.com/facebook/rocksdb/pull/6089 Test Plan: Added test case to ParallelIO.MultiGet. Pass make asan_check Differential Revision: D18734668 Pulled By: zhichao-cao fbshipit-source-id: 67c5615ed373e51e42635fd74b36f8f3a66d5da4
2019-12-16 23:55:33 +00:00
}
read_reqs.emplace_back(req);
}
// Step 2, remeber the previous block info
prev_offset = handle.offset();
prev_len = block_size(handle);
req_offset_for_block.emplace_back(0);
}
req_idx_for_block.emplace_back(read_reqs.size());
PERF_COUNTER_ADD(block_read_count, 1);
PERF_COUNTER_ADD(block_read_byte, block_size(handle));
Merge adjacent file block reads in RocksDB MultiGet() and Add uncompressed block to cache (#6089) Summary: In the current MultiGet, if the KV-pairs do not belong to the data blocks in the block cache, multiple blocks are read from a SST. It will trigger one block read for each block request and read them in parallel. In some cases, if some data blocks are adjacent in the SST, the reads for these blocks can be combined to a single large read, which can reduce the system calls and reduce the read latency if possible. Considering to fill the block cache, if multiple data blocks are in the same memory buffer, we need to copy them to the heap separately. Therefore, only in the case that 1) data block compression is enabled, and 2) compressed block cache is null, we can do combined read. Otherwise, extra memory copy is needed, which may cause extra overhead. In the current case, data blocks will be uncompressed to a new memory space. Also, in the case that 1) data block compression is enabled, and 2) compressed block cache is null, it is possible the data block is actually not compressed. In the current logic, these data blocks will not be added to the uncompressed_cache. So if memory buffer is shared and the data block is not compressed, the data block are copied to the head and fill the cache. Pull Request resolved: https://github.com/facebook/rocksdb/pull/6089 Test Plan: Added test case to ParallelIO.MultiGet. Pass make asan_check Differential Revision: D18734668 Pulled By: zhichao-cao fbshipit-source-id: 67c5615ed373e51e42635fd74b36f8f3a66d5da4
2019-12-16 23:55:33 +00:00
}
// Handle the last block and process the pending last request
if (prev_len != 0) {
Introduce a new storage specific Env API (#5761) Summary: The current Env API encompasses both storage/file operations, as well as OS related operations. Most of the APIs return a Status, which does not have enough metadata about an error, such as whether its retry-able or not, scope (i.e fault domain) of the error etc., that may be required in order to properly handle a storage error. The file APIs also do not provide enough control over the IO SLA, such as timeout, prioritization, hinting about placement and redundancy etc. This PR separates out the file/storage APIs from Env into a new FileSystem class. The APIs are updated to return an IOStatus with metadata about the error, as well as to take an IOOptions structure as input in order to allow more control over the IO. The user can set both ```options.env``` and ```options.file_system``` to specify that RocksDB should use the former for OS related operations and the latter for storage operations. Internally, a ```CompositeEnvWrapper``` has been introduced that inherits from ```Env``` and redirects individual methods to either an ```Env``` implementation or the ```FileSystem``` as appropriate. When options are sanitized during ```DB::Open```, ```options.env``` is replaced with a newly allocated ```CompositeEnvWrapper``` instance if both env and file_system have been specified. This way, the rest of the RocksDB code can continue to function as before. This PR also ports PosixEnv to the new API by splitting it into two - PosixEnv and PosixFileSystem. PosixEnv is defined as a sub-class of CompositeEnvWrapper, and threading/time functions are overridden with Posix specific implementations in order to avoid an extra level of indirection. The ```CompositeEnvWrapper``` translates ```IOStatus``` return code to ```Status```, and sets the severity to ```kSoftError``` if the io_status is retryable. The error handling code in RocksDB can then recover the DB automatically. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5761 Differential Revision: D18868376 Pulled By: anand1976 fbshipit-source-id: 39efe18a162ea746fabac6360ff529baba48486f
2019-12-13 22:47:08 +00:00
FSReadRequest req;
Merge adjacent file block reads in RocksDB MultiGet() and Add uncompressed block to cache (#6089) Summary: In the current MultiGet, if the KV-pairs do not belong to the data blocks in the block cache, multiple blocks are read from a SST. It will trigger one block read for each block request and read them in parallel. In some cases, if some data blocks are adjacent in the SST, the reads for these blocks can be combined to a single large read, which can reduce the system calls and reduce the read latency if possible. Considering to fill the block cache, if multiple data blocks are in the same memory buffer, we need to copy them to the heap separately. Therefore, only in the case that 1) data block compression is enabled, and 2) compressed block cache is null, we can do combined read. Otherwise, extra memory copy is needed, which may cause extra overhead. In the current case, data blocks will be uncompressed to a new memory space. Also, in the case that 1) data block compression is enabled, and 2) compressed block cache is null, it is possible the data block is actually not compressed. In the current logic, these data blocks will not be added to the uncompressed_cache. So if memory buffer is shared and the data block is not compressed, the data block are copied to the head and fill the cache. Pull Request resolved: https://github.com/facebook/rocksdb/pull/6089 Test Plan: Added test case to ParallelIO.MultiGet. Pass make asan_check Differential Revision: D18734668 Pulled By: zhichao-cao fbshipit-source-id: 67c5615ed373e51e42635fd74b36f8f3a66d5da4
2019-12-16 23:55:33 +00:00
req.offset = prev_offset;
req.len = prev_len;
if (file->use_direct_io()) {
req.scratch = nullptr;
} else if (use_shared_buffer) {
req.scratch = scratch + buf_offset;
} else {
req.scratch = new char[req.len];
}
read_reqs.emplace_back(req);
}
AlignedBuf direct_io_buf;
{
IOOptions opts;
IOStatus s = file->PrepareIOOptions(options, opts);
if (s.ok()) {
s = file->MultiRead(opts, &read_reqs[0], read_reqs.size(),
&direct_io_buf);
}
if (!s.ok()) {
// Discard all the results in this batch if there is any time out
// or overall MultiRead error
for (FSReadRequest& req : read_reqs) {
req.status = s;
}
}
}
idx_in_batch = 0;
Merge adjacent file block reads in RocksDB MultiGet() and Add uncompressed block to cache (#6089) Summary: In the current MultiGet, if the KV-pairs do not belong to the data blocks in the block cache, multiple blocks are read from a SST. It will trigger one block read for each block request and read them in parallel. In some cases, if some data blocks are adjacent in the SST, the reads for these blocks can be combined to a single large read, which can reduce the system calls and reduce the read latency if possible. Considering to fill the block cache, if multiple data blocks are in the same memory buffer, we need to copy them to the heap separately. Therefore, only in the case that 1) data block compression is enabled, and 2) compressed block cache is null, we can do combined read. Otherwise, extra memory copy is needed, which may cause extra overhead. In the current case, data blocks will be uncompressed to a new memory space. Also, in the case that 1) data block compression is enabled, and 2) compressed block cache is null, it is possible the data block is actually not compressed. In the current logic, these data blocks will not be added to the uncompressed_cache. So if memory buffer is shared and the data block is not compressed, the data block are copied to the head and fill the cache. Pull Request resolved: https://github.com/facebook/rocksdb/pull/6089 Test Plan: Added test case to ParallelIO.MultiGet. Pass make asan_check Differential Revision: D18734668 Pulled By: zhichao-cao fbshipit-source-id: 67c5615ed373e51e42635fd74b36f8f3a66d5da4
2019-12-16 23:55:33 +00:00
size_t valid_batch_idx = 0;
for (auto mget_iter = batch->begin(); mget_iter != batch->end();
++mget_iter, ++idx_in_batch) {
const BlockHandle& handle = (*handles)[idx_in_batch];
if (handle.IsNull()) {
continue;
}
Merge adjacent file block reads in RocksDB MultiGet() and Add uncompressed block to cache (#6089) Summary: In the current MultiGet, if the KV-pairs do not belong to the data blocks in the block cache, multiple blocks are read from a SST. It will trigger one block read for each block request and read them in parallel. In some cases, if some data blocks are adjacent in the SST, the reads for these blocks can be combined to a single large read, which can reduce the system calls and reduce the read latency if possible. Considering to fill the block cache, if multiple data blocks are in the same memory buffer, we need to copy them to the heap separately. Therefore, only in the case that 1) data block compression is enabled, and 2) compressed block cache is null, we can do combined read. Otherwise, extra memory copy is needed, which may cause extra overhead. In the current case, data blocks will be uncompressed to a new memory space. Also, in the case that 1) data block compression is enabled, and 2) compressed block cache is null, it is possible the data block is actually not compressed. In the current logic, these data blocks will not be added to the uncompressed_cache. So if memory buffer is shared and the data block is not compressed, the data block are copied to the head and fill the cache. Pull Request resolved: https://github.com/facebook/rocksdb/pull/6089 Test Plan: Added test case to ParallelIO.MultiGet. Pass make asan_check Differential Revision: D18734668 Pulled By: zhichao-cao fbshipit-source-id: 67c5615ed373e51e42635fd74b36f8f3a66d5da4
2019-12-16 23:55:33 +00:00
assert(valid_batch_idx < req_idx_for_block.size());
assert(valid_batch_idx < req_offset_for_block.size());
assert(req_idx_for_block[valid_batch_idx] < read_reqs.size());
size_t& req_idx = req_idx_for_block[valid_batch_idx];
size_t& req_offset = req_offset_for_block[valid_batch_idx];
valid_batch_idx++;
if (mget_iter->get_context) {
++(mget_iter->get_context->get_context_stats_.num_data_read);
}
Merge adjacent file block reads in RocksDB MultiGet() and Add uncompressed block to cache (#6089) Summary: In the current MultiGet, if the KV-pairs do not belong to the data blocks in the block cache, multiple blocks are read from a SST. It will trigger one block read for each block request and read them in parallel. In some cases, if some data blocks are adjacent in the SST, the reads for these blocks can be combined to a single large read, which can reduce the system calls and reduce the read latency if possible. Considering to fill the block cache, if multiple data blocks are in the same memory buffer, we need to copy them to the heap separately. Therefore, only in the case that 1) data block compression is enabled, and 2) compressed block cache is null, we can do combined read. Otherwise, extra memory copy is needed, which may cause extra overhead. In the current case, data blocks will be uncompressed to a new memory space. Also, in the case that 1) data block compression is enabled, and 2) compressed block cache is null, it is possible the data block is actually not compressed. In the current logic, these data blocks will not be added to the uncompressed_cache. So if memory buffer is shared and the data block is not compressed, the data block are copied to the head and fill the cache. Pull Request resolved: https://github.com/facebook/rocksdb/pull/6089 Test Plan: Added test case to ParallelIO.MultiGet. Pass make asan_check Differential Revision: D18734668 Pulled By: zhichao-cao fbshipit-source-id: 67c5615ed373e51e42635fd74b36f8f3a66d5da4
2019-12-16 23:55:33 +00:00
FSReadRequest& req = read_reqs[req_idx];
Status s = req.status;
if (s.ok()) {
if ((req.result.size() != req.len) ||
(req_offset + block_size(handle) > req.result.size())) {
Merge adjacent file block reads in RocksDB MultiGet() and Add uncompressed block to cache (#6089) Summary: In the current MultiGet, if the KV-pairs do not belong to the data blocks in the block cache, multiple blocks are read from a SST. It will trigger one block read for each block request and read them in parallel. In some cases, if some data blocks are adjacent in the SST, the reads for these blocks can be combined to a single large read, which can reduce the system calls and reduce the read latency if possible. Considering to fill the block cache, if multiple data blocks are in the same memory buffer, we need to copy them to the heap separately. Therefore, only in the case that 1) data block compression is enabled, and 2) compressed block cache is null, we can do combined read. Otherwise, extra memory copy is needed, which may cause extra overhead. In the current case, data blocks will be uncompressed to a new memory space. Also, in the case that 1) data block compression is enabled, and 2) compressed block cache is null, it is possible the data block is actually not compressed. In the current logic, these data blocks will not be added to the uncompressed_cache. So if memory buffer is shared and the data block is not compressed, the data block are copied to the head and fill the cache. Pull Request resolved: https://github.com/facebook/rocksdb/pull/6089 Test Plan: Added test case to ParallelIO.MultiGet. Pass make asan_check Differential Revision: D18734668 Pulled By: zhichao-cao fbshipit-source-id: 67c5615ed373e51e42635fd74b36f8f3a66d5da4
2019-12-16 23:55:33 +00:00
s = Status::Corruption(
"truncated block read from " + rep_->file->file_name() +
" offset " + ToString(handle.offset()) + ", expected " +
ToString(req.len) + " bytes, got " + ToString(req.result.size()));
}
}
BlockContents raw_block_contents;
if (s.ok()) {
if (!use_shared_buffer) {
// We allocated a buffer for this block. Give ownership of it to
// BlockContents so it can free the memory
assert(req.result.data() == req.scratch);
assert(req.result.size() == block_size(handle));
assert(req_offset == 0);
std::unique_ptr<char[]> raw_block(req.scratch);
raw_block_contents = BlockContents(std::move(raw_block), handle.size());
} else {
// We used the scratch buffer or direct io buffer
// which are shared by the blocks.
// raw_block_contents does not have the ownership.
raw_block_contents =
BlockContents(Slice(req.result.data() + req_offset, handle.size()));
}
#ifndef NDEBUG
raw_block_contents.is_raw_block = true;
#endif
if (options.verify_checksums) {
PERF_TIMER_GUARD(block_checksum_time);
const char* data = req.result.data();
// Since the scratch might be shared, the offset of the data block in
// the buffer might not be 0. req.result.data() only point to the
// begin address of each read request, we need to add the offset
// in each read request. Checksum is stored in the block trailer,
// beyond the payload size.
s = ROCKSDB_NAMESPACE::VerifyBlockChecksum(
footer.checksum(), data + req_offset, handle.size(),
rep_->file->file_name(), handle.offset());
TEST_SYNC_POINT_CALLBACK("RetrieveMultipleBlocks:VerifyChecksum", &s);
}
} else if (!use_shared_buffer) {
// Free the allocated scratch buffer.
delete[] req.scratch;
}
Merge adjacent file block reads in RocksDB MultiGet() and Add uncompressed block to cache (#6089) Summary: In the current MultiGet, if the KV-pairs do not belong to the data blocks in the block cache, multiple blocks are read from a SST. It will trigger one block read for each block request and read them in parallel. In some cases, if some data blocks are adjacent in the SST, the reads for these blocks can be combined to a single large read, which can reduce the system calls and reduce the read latency if possible. Considering to fill the block cache, if multiple data blocks are in the same memory buffer, we need to copy them to the heap separately. Therefore, only in the case that 1) data block compression is enabled, and 2) compressed block cache is null, we can do combined read. Otherwise, extra memory copy is needed, which may cause extra overhead. In the current case, data blocks will be uncompressed to a new memory space. Also, in the case that 1) data block compression is enabled, and 2) compressed block cache is null, it is possible the data block is actually not compressed. In the current logic, these data blocks will not be added to the uncompressed_cache. So if memory buffer is shared and the data block is not compressed, the data block are copied to the head and fill the cache. Pull Request resolved: https://github.com/facebook/rocksdb/pull/6089 Test Plan: Added test case to ParallelIO.MultiGet. Pass make asan_check Differential Revision: D18734668 Pulled By: zhichao-cao fbshipit-source-id: 67c5615ed373e51e42635fd74b36f8f3a66d5da4
2019-12-16 23:55:33 +00:00
if (s.ok()) {
// When the blocks share the same underlying buffer (scratch or direct io
// buffer), we may need to manually copy the block into heap if the raw
// block has to be inserted into a cache. That falls into th following
// cases -
// 1. Raw block is not compressed, it needs to be inserted into the
// uncompressed block cache if there is one
// 2. If the raw block is compressed, it needs to be inserted into the
// compressed block cache if there is one
//
// In all other cases, the raw block is either uncompressed into a heap
// buffer or there is no cache at all.
Merge adjacent file block reads in RocksDB MultiGet() and Add uncompressed block to cache (#6089) Summary: In the current MultiGet, if the KV-pairs do not belong to the data blocks in the block cache, multiple blocks are read from a SST. It will trigger one block read for each block request and read them in parallel. In some cases, if some data blocks are adjacent in the SST, the reads for these blocks can be combined to a single large read, which can reduce the system calls and reduce the read latency if possible. Considering to fill the block cache, if multiple data blocks are in the same memory buffer, we need to copy them to the heap separately. Therefore, only in the case that 1) data block compression is enabled, and 2) compressed block cache is null, we can do combined read. Otherwise, extra memory copy is needed, which may cause extra overhead. In the current case, data blocks will be uncompressed to a new memory space. Also, in the case that 1) data block compression is enabled, and 2) compressed block cache is null, it is possible the data block is actually not compressed. In the current logic, these data blocks will not be added to the uncompressed_cache. So if memory buffer is shared and the data block is not compressed, the data block are copied to the head and fill the cache. Pull Request resolved: https://github.com/facebook/rocksdb/pull/6089 Test Plan: Added test case to ParallelIO.MultiGet. Pass make asan_check Differential Revision: D18734668 Pulled By: zhichao-cao fbshipit-source-id: 67c5615ed373e51e42635fd74b36f8f3a66d5da4
2019-12-16 23:55:33 +00:00
CompressionType compression_type =
raw_block_contents.get_compression_type();
if (use_shared_buffer && (compression_type == kNoCompression ||
(compression_type != kNoCompression &&
rep_->table_options.block_cache_compressed))) {
Slice raw = Slice(req.result.data() + req_offset, block_size(handle));
Merge adjacent file block reads in RocksDB MultiGet() and Add uncompressed block to cache (#6089) Summary: In the current MultiGet, if the KV-pairs do not belong to the data blocks in the block cache, multiple blocks are read from a SST. It will trigger one block read for each block request and read them in parallel. In some cases, if some data blocks are adjacent in the SST, the reads for these blocks can be combined to a single large read, which can reduce the system calls and reduce the read latency if possible. Considering to fill the block cache, if multiple data blocks are in the same memory buffer, we need to copy them to the heap separately. Therefore, only in the case that 1) data block compression is enabled, and 2) compressed block cache is null, we can do combined read. Otherwise, extra memory copy is needed, which may cause extra overhead. In the current case, data blocks will be uncompressed to a new memory space. Also, in the case that 1) data block compression is enabled, and 2) compressed block cache is null, it is possible the data block is actually not compressed. In the current logic, these data blocks will not be added to the uncompressed_cache. So if memory buffer is shared and the data block is not compressed, the data block are copied to the head and fill the cache. Pull Request resolved: https://github.com/facebook/rocksdb/pull/6089 Test Plan: Added test case to ParallelIO.MultiGet. Pass make asan_check Differential Revision: D18734668 Pulled By: zhichao-cao fbshipit-source-id: 67c5615ed373e51e42635fd74b36f8f3a66d5da4
2019-12-16 23:55:33 +00:00
raw_block_contents = BlockContents(
CopyBufferToHeap(GetMemoryAllocator(rep_->table_options), raw),
handle.size());
#ifndef NDEBUG
raw_block_contents.is_raw_block = true;
#endif
}
}
if (s.ok()) {
if (options.fill_cache) {
BlockCacheLookupContext lookup_data_block_context(
TableReaderCaller::kUserMultiGet);
CachableEntry<Block>* block_entry = &(*results)[idx_in_batch];
// MaybeReadBlockAndLoadToCache will insert into the block caches if
// necessary. Since we're passing the raw block contents, it will
// avoid looking up the block cache
s = MaybeReadBlockAndLoadToCache(
Parallelize secondary cache lookup in MultiGet (#8405) Summary: Implement the ```WaitAll()``` interface in ```LRUCache``` to allow callers to issue multiple lookups in parallel and wait for all of them to complete. Modify ```MultiGet``` to use this to parallelize the secondary cache lookups in order to reduce the overall latency. A call to ```cache->Lookup()``` returns a handle that has an incomplete value (nullptr), and the caller can call ```cache->IsReady()``` to check whether the lookup is complete, and pass a vector of handles to ```WaitAll``` to wait for completion. If any of the lookups fail, ```MultiGet``` will read the block from the SST file. Another change in this PR is to rename ```SecondaryCacheHandle``` to ```SecondaryCacheResultHandle``` as it more accurately describes the return result of the secondary cache lookup, which is more like a future. Tests: 1. Add unit tests in lru_cache_test 2. Benchmark results with no secondary cache configured Master - ``` readrandom : 41.175 micros/op 388562 ops/sec; 106.7 MB/s (7277999 of 7277999 found) readrandom : 41.217 micros/op 388160 ops/sec; 106.6 MB/s (7274999 of 7274999 found) multireadrandom : 10.309 micros/op 1552082 ops/sec; (28908992 of 28908992 found) multireadrandom : 10.321 micros/op 1550218 ops/sec; (29081984 of 29081984 found) ``` This PR - ``` readrandom : 41.158 micros/op 388723 ops/sec; 106.8 MB/s (7290999 of 7290999 found) readrandom : 41.185 micros/op 388463 ops/sec; 106.7 MB/s (7287999 of 7287999 found) multireadrandom : 10.277 micros/op 1556801 ops/sec; (29346944 of 29346944 found) multireadrandom : 10.253 micros/op 1560539 ops/sec; (29274944 of 29274944 found) ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/8405 Reviewed By: zhichao-cao Differential Revision: D29190509 Pulled By: anand1976 fbshipit-source-id: 6f8eff6246712af8a297cfe22ea0d1c3b2a01bb0
2021-06-18 16:35:03 +00:00
nullptr, options, handle, uncompression_dict, /*wait=*/true,
block_entry, BlockType::kData, mget_iter->get_context,
&lookup_data_block_context, &raw_block_contents);
// block_entry value could be null if no block cache is present, i.e
// BlockBasedTableOptions::no_block_cache is true and no compressed
// block cache is configured. In that case, fall
// through and set up the block explicitly
if (block_entry->GetValue() != nullptr) {
s.PermitUncheckedError();
continue;
}
}
CompressionType compression_type =
raw_block_contents.get_compression_type();
BlockContents contents;
if (compression_type != kNoCompression) {
UncompressionContext context(compression_type);
UncompressionInfo info(context, uncompression_dict, compression_type);
Merge adjacent file block reads in RocksDB MultiGet() and Add uncompressed block to cache (#6089) Summary: In the current MultiGet, if the KV-pairs do not belong to the data blocks in the block cache, multiple blocks are read from a SST. It will trigger one block read for each block request and read them in parallel. In some cases, if some data blocks are adjacent in the SST, the reads for these blocks can be combined to a single large read, which can reduce the system calls and reduce the read latency if possible. Considering to fill the block cache, if multiple data blocks are in the same memory buffer, we need to copy them to the heap separately. Therefore, only in the case that 1) data block compression is enabled, and 2) compressed block cache is null, we can do combined read. Otherwise, extra memory copy is needed, which may cause extra overhead. In the current case, data blocks will be uncompressed to a new memory space. Also, in the case that 1) data block compression is enabled, and 2) compressed block cache is null, it is possible the data block is actually not compressed. In the current logic, these data blocks will not be added to the uncompressed_cache. So if memory buffer is shared and the data block is not compressed, the data block are copied to the head and fill the cache. Pull Request resolved: https://github.com/facebook/rocksdb/pull/6089 Test Plan: Added test case to ParallelIO.MultiGet. Pass make asan_check Differential Revision: D18734668 Pulled By: zhichao-cao fbshipit-source-id: 67c5615ed373e51e42635fd74b36f8f3a66d5da4
2019-12-16 23:55:33 +00:00
s = UncompressBlockContents(info, req.result.data() + req_offset,
handle.size(), &contents, footer.version(),
rep_->ioptions, memory_allocator);
} else {
// There are two cases here:
// 1) caller uses the shared buffer (scratch or direct io buffer);
// 2) we use the requst buffer.
// If scratch buffer or direct io buffer is used, we ensure that
Merge adjacent file block reads in RocksDB MultiGet() and Add uncompressed block to cache (#6089) Summary: In the current MultiGet, if the KV-pairs do not belong to the data blocks in the block cache, multiple blocks are read from a SST. It will trigger one block read for each block request and read them in parallel. In some cases, if some data blocks are adjacent in the SST, the reads for these blocks can be combined to a single large read, which can reduce the system calls and reduce the read latency if possible. Considering to fill the block cache, if multiple data blocks are in the same memory buffer, we need to copy them to the heap separately. Therefore, only in the case that 1) data block compression is enabled, and 2) compressed block cache is null, we can do combined read. Otherwise, extra memory copy is needed, which may cause extra overhead. In the current case, data blocks will be uncompressed to a new memory space. Also, in the case that 1) data block compression is enabled, and 2) compressed block cache is null, it is possible the data block is actually not compressed. In the current logic, these data blocks will not be added to the uncompressed_cache. So if memory buffer is shared and the data block is not compressed, the data block are copied to the head and fill the cache. Pull Request resolved: https://github.com/facebook/rocksdb/pull/6089 Test Plan: Added test case to ParallelIO.MultiGet. Pass make asan_check Differential Revision: D18734668 Pulled By: zhichao-cao fbshipit-source-id: 67c5615ed373e51e42635fd74b36f8f3a66d5da4
2019-12-16 23:55:33 +00:00
// all raw blocks are copyed to the heap as single blocks. If scratch
// buffer is not used, we also have no combined read, so the raw
// block can be used directly.
contents = std::move(raw_block_contents);
}
if (s.ok()) {
(*results)[idx_in_batch].SetOwnedValue(new Block(
std::move(contents), read_amp_bytes_per_bit, ioptions.stats));
}
}
(*statuses)[idx_in_batch] = s;
}
}
template <typename TBlocklike>
Status BlockBasedTable::RetrieveBlock(
FilePrefetchBuffer* prefetch_buffer, const ReadOptions& ro,
const BlockHandle& handle, const UncompressionDict& uncompression_dict,
CachableEntry<TBlocklike>* block_entry, BlockType block_type,
GetContext* get_context, BlockCacheLookupContext* lookup_context,
Parallelize secondary cache lookup in MultiGet (#8405) Summary: Implement the ```WaitAll()``` interface in ```LRUCache``` to allow callers to issue multiple lookups in parallel and wait for all of them to complete. Modify ```MultiGet``` to use this to parallelize the secondary cache lookups in order to reduce the overall latency. A call to ```cache->Lookup()``` returns a handle that has an incomplete value (nullptr), and the caller can call ```cache->IsReady()``` to check whether the lookup is complete, and pass a vector of handles to ```WaitAll``` to wait for completion. If any of the lookups fail, ```MultiGet``` will read the block from the SST file. Another change in this PR is to rename ```SecondaryCacheHandle``` to ```SecondaryCacheResultHandle``` as it more accurately describes the return result of the secondary cache lookup, which is more like a future. Tests: 1. Add unit tests in lru_cache_test 2. Benchmark results with no secondary cache configured Master - ``` readrandom : 41.175 micros/op 388562 ops/sec; 106.7 MB/s (7277999 of 7277999 found) readrandom : 41.217 micros/op 388160 ops/sec; 106.6 MB/s (7274999 of 7274999 found) multireadrandom : 10.309 micros/op 1552082 ops/sec; (28908992 of 28908992 found) multireadrandom : 10.321 micros/op 1550218 ops/sec; (29081984 of 29081984 found) ``` This PR - ``` readrandom : 41.158 micros/op 388723 ops/sec; 106.8 MB/s (7290999 of 7290999 found) readrandom : 41.185 micros/op 388463 ops/sec; 106.7 MB/s (7287999 of 7287999 found) multireadrandom : 10.277 micros/op 1556801 ops/sec; (29346944 of 29346944 found) multireadrandom : 10.253 micros/op 1560539 ops/sec; (29274944 of 29274944 found) ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/8405 Reviewed By: zhichao-cao Differential Revision: D29190509 Pulled By: anand1976 fbshipit-source-id: 6f8eff6246712af8a297cfe22ea0d1c3b2a01bb0
2021-06-18 16:35:03 +00:00
bool for_compaction, bool use_cache, bool wait_for_cache) const {
assert(block_entry);
assert(block_entry->IsEmpty());
Status s;
Fix regression affecting partitioned indexes/filters when cache_index_and_filter_blocks is false (#5705) Summary: PR https://github.com/facebook/rocksdb/issues/5298 (and subsequent related patches) unintentionally changed the semantics of cache_index_and_filter_blocks: historically, this option only affected the main index/filter block; with the changes, it affects index/filter partitions as well. This can cause performance issues when cache_index_and_filter_blocks is false since in this case, partitions are neither cached nor preloaded (i.e. they are loaded on demand upon each access). The patch reverts to the earlier behavior, that is, partitions are cached similarly to data blocks regardless of the value of the above option. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5705 Test Plan: make check ./db_bench -benchmarks=fillrandom --statistics --stats_interval_seconds=1 --duration=30 --num=500000000 --bloom_bits=20 --partition_index_and_filters=true --cache_index_and_filter_blocks=false ./db_bench -benchmarks=readrandom --use_existing_db --statistics --stats_interval_seconds=1 --duration=10 --num=500000000 --bloom_bits=20 --partition_index_and_filters=true --cache_index_and_filter_blocks=false --cache_size=8000000000 Relevant statistics from the readrandom benchmark with the old code: rocksdb.block.cache.index.miss COUNT : 0 rocksdb.block.cache.index.hit COUNT : 0 rocksdb.block.cache.index.add COUNT : 0 rocksdb.block.cache.index.bytes.insert COUNT : 0 rocksdb.block.cache.index.bytes.evict COUNT : 0 rocksdb.block.cache.filter.miss COUNT : 0 rocksdb.block.cache.filter.hit COUNT : 0 rocksdb.block.cache.filter.add COUNT : 0 rocksdb.block.cache.filter.bytes.insert COUNT : 0 rocksdb.block.cache.filter.bytes.evict COUNT : 0 With the new code: rocksdb.block.cache.index.miss COUNT : 2500 rocksdb.block.cache.index.hit COUNT : 42696 rocksdb.block.cache.index.add COUNT : 2500 rocksdb.block.cache.index.bytes.insert COUNT : 4050048 rocksdb.block.cache.index.bytes.evict COUNT : 0 rocksdb.block.cache.filter.miss COUNT : 2500 rocksdb.block.cache.filter.hit COUNT : 4550493 rocksdb.block.cache.filter.add COUNT : 2500 rocksdb.block.cache.filter.bytes.insert COUNT : 10331040 rocksdb.block.cache.filter.bytes.evict COUNT : 0 Differential Revision: D16817382 Pulled By: ltamasi fbshipit-source-id: 28a516b0da1f041a03313e0b70b28cf5cf205d00
2019-08-15 01:13:14 +00:00
if (use_cache) {
Parallelize secondary cache lookup in MultiGet (#8405) Summary: Implement the ```WaitAll()``` interface in ```LRUCache``` to allow callers to issue multiple lookups in parallel and wait for all of them to complete. Modify ```MultiGet``` to use this to parallelize the secondary cache lookups in order to reduce the overall latency. A call to ```cache->Lookup()``` returns a handle that has an incomplete value (nullptr), and the caller can call ```cache->IsReady()``` to check whether the lookup is complete, and pass a vector of handles to ```WaitAll``` to wait for completion. If any of the lookups fail, ```MultiGet``` will read the block from the SST file. Another change in this PR is to rename ```SecondaryCacheHandle``` to ```SecondaryCacheResultHandle``` as it more accurately describes the return result of the secondary cache lookup, which is more like a future. Tests: 1. Add unit tests in lru_cache_test 2. Benchmark results with no secondary cache configured Master - ``` readrandom : 41.175 micros/op 388562 ops/sec; 106.7 MB/s (7277999 of 7277999 found) readrandom : 41.217 micros/op 388160 ops/sec; 106.6 MB/s (7274999 of 7274999 found) multireadrandom : 10.309 micros/op 1552082 ops/sec; (28908992 of 28908992 found) multireadrandom : 10.321 micros/op 1550218 ops/sec; (29081984 of 29081984 found) ``` This PR - ``` readrandom : 41.158 micros/op 388723 ops/sec; 106.8 MB/s (7290999 of 7290999 found) readrandom : 41.185 micros/op 388463 ops/sec; 106.7 MB/s (7287999 of 7287999 found) multireadrandom : 10.277 micros/op 1556801 ops/sec; (29346944 of 29346944 found) multireadrandom : 10.253 micros/op 1560539 ops/sec; (29274944 of 29274944 found) ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/8405 Reviewed By: zhichao-cao Differential Revision: D29190509 Pulled By: anand1976 fbshipit-source-id: 6f8eff6246712af8a297cfe22ea0d1c3b2a01bb0
2021-06-18 16:35:03 +00:00
s = MaybeReadBlockAndLoadToCache(
prefetch_buffer, ro, handle, uncompression_dict, wait_for_cache,
block_entry, block_type, get_context, lookup_context,
/*contents=*/nullptr);
if (!s.ok()) {
return s;
}
Parallelize secondary cache lookup in MultiGet (#8405) Summary: Implement the ```WaitAll()``` interface in ```LRUCache``` to allow callers to issue multiple lookups in parallel and wait for all of them to complete. Modify ```MultiGet``` to use this to parallelize the secondary cache lookups in order to reduce the overall latency. A call to ```cache->Lookup()``` returns a handle that has an incomplete value (nullptr), and the caller can call ```cache->IsReady()``` to check whether the lookup is complete, and pass a vector of handles to ```WaitAll``` to wait for completion. If any of the lookups fail, ```MultiGet``` will read the block from the SST file. Another change in this PR is to rename ```SecondaryCacheHandle``` to ```SecondaryCacheResultHandle``` as it more accurately describes the return result of the secondary cache lookup, which is more like a future. Tests: 1. Add unit tests in lru_cache_test 2. Benchmark results with no secondary cache configured Master - ``` readrandom : 41.175 micros/op 388562 ops/sec; 106.7 MB/s (7277999 of 7277999 found) readrandom : 41.217 micros/op 388160 ops/sec; 106.6 MB/s (7274999 of 7274999 found) multireadrandom : 10.309 micros/op 1552082 ops/sec; (28908992 of 28908992 found) multireadrandom : 10.321 micros/op 1550218 ops/sec; (29081984 of 29081984 found) ``` This PR - ``` readrandom : 41.158 micros/op 388723 ops/sec; 106.8 MB/s (7290999 of 7290999 found) readrandom : 41.185 micros/op 388463 ops/sec; 106.7 MB/s (7287999 of 7287999 found) multireadrandom : 10.277 micros/op 1556801 ops/sec; (29346944 of 29346944 found) multireadrandom : 10.253 micros/op 1560539 ops/sec; (29274944 of 29274944 found) ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/8405 Reviewed By: zhichao-cao Differential Revision: D29190509 Pulled By: anand1976 fbshipit-source-id: 6f8eff6246712af8a297cfe22ea0d1c3b2a01bb0
2021-06-18 16:35:03 +00:00
if (block_entry->GetValue() != nullptr ||
block_entry->GetCacheHandle() != nullptr) {
assert(s.ok());
return s;
}
}
assert(block_entry->IsEmpty());
const bool no_io = ro.read_tier == kBlockCacheTier;
if (no_io) {
return Status::Incomplete("no blocking io");
}
const bool maybe_compressed =
block_type != BlockType::kFilter &&
block_type != BlockType::kCompressionDictionary &&
rep_->blocks_maybe_compressed;
const bool do_uncompress = maybe_compressed;
std::unique_ptr<TBlocklike> block;
{
StopWatch sw(rep_->ioptions.clock, rep_->ioptions.stats,
READ_BLOCK_GET_MICROS);
s = ReadBlockFromFile(
rep_->file.get(), prefetch_buffer, rep_->footer, ro, handle, &block,
rep_->ioptions, do_uncompress, maybe_compressed, block_type,
uncompression_dict, rep_->persistent_cache_options,
block_type == BlockType::kData
? rep_->table_options.read_amp_bytes_per_bit
: 0,
GetMemoryAllocator(rep_->table_options), for_compaction,
Store the filter bits reader alongside the filter block contents (#5936) Summary: Amongst other things, PR https://github.com/facebook/rocksdb/issues/5504 refactored the filter block readers so that only the filter block contents are stored in the block cache (as opposed to the earlier design where the cache stored the filter block reader itself, leading to potentially dangling pointers and concurrency bugs). However, this change introduced a performance hit since with the new code, the metadata fields are re-parsed upon every access. This patch reunites the block contents with the filter bits reader to eliminate this overhead; since this is still a self-contained pure data object, it is safe to store it in the cache. (Note: this is similar to how the zstd digest is handled.) Pull Request resolved: https://github.com/facebook/rocksdb/pull/5936 Test Plan: make asan_check filter_bench results for the old code: ``` $ ./filter_bench -quick WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 26.7153 Number of filters: 16669 Total memory (MB): 200.009 Bits/key actual: 10.0647 ---------------------------- Inside queries... Dry run (46b) ns/op: 33.4258 Single filter ns/op: 42.5974 Random filter ns/op: 217.861 ---------------------------- Outside queries... Dry run (25d) ns/op: 32.4217 Single filter ns/op: 50.9855 Random filter ns/op: 219.167 Average FP rate %: 1.13993 ---------------------------- Done. (For more info, run with -legend or -help.) $ ./filter_bench -quick -use_full_block_reader WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 26.5172 Number of filters: 16669 Total memory (MB): 200.009 Bits/key actual: 10.0647 ---------------------------- Inside queries... Dry run (46b) ns/op: 32.3556 Single filter ns/op: 83.2239 Random filter ns/op: 370.676 ---------------------------- Outside queries... Dry run (25d) ns/op: 32.2265 Single filter ns/op: 93.5651 Random filter ns/op: 408.393 Average FP rate %: 1.13993 ---------------------------- Done. (For more info, run with -legend or -help.) ``` With the new code: ``` $ ./filter_bench -quick WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 25.4285 Number of filters: 16669 Total memory (MB): 200.009 Bits/key actual: 10.0647 ---------------------------- Inside queries... Dry run (46b) ns/op: 31.0594 Single filter ns/op: 43.8974 Random filter ns/op: 226.075 ---------------------------- Outside queries... Dry run (25d) ns/op: 31.0295 Single filter ns/op: 50.3824 Random filter ns/op: 226.805 Average FP rate %: 1.13993 ---------------------------- Done. (For more info, run with -legend or -help.) $ ./filter_bench -quick -use_full_block_reader WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 26.5308 Number of filters: 16669 Total memory (MB): 200.009 Bits/key actual: 10.0647 ---------------------------- Inside queries... Dry run (46b) ns/op: 33.2968 Single filter ns/op: 58.6163 Random filter ns/op: 291.434 ---------------------------- Outside queries... Dry run (25d) ns/op: 32.1839 Single filter ns/op: 66.9039 Random filter ns/op: 292.828 Average FP rate %: 1.13993 ---------------------------- Done. (For more info, run with -legend or -help.) ``` Differential Revision: D17991712 Pulled By: ltamasi fbshipit-source-id: 7ea205550217bfaaa1d5158ebd658e5832e60f29
2019-10-19 02:30:47 +00:00
rep_->blocks_definitely_zstd_compressed,
rep_->table_options.filter_policy.get());
if (get_context) {
switch (block_type) {
case BlockType::kIndex:
++(get_context->get_context_stats_.num_index_read);
break;
case BlockType::kFilter:
++(get_context->get_context_stats_.num_filter_read);
break;
case BlockType::kData:
++(get_context->get_context_stats_.num_data_read);
break;
default:
break;
}
}
}
if (!s.ok()) {
return s;
}
block_entry->SetOwnedValue(block.release());
assert(s.ok());
return s;
}
// Explicitly instantiate templates for both "blocklike" types we use.
// This makes it possible to keep the template definitions in the .cc file.
template Status BlockBasedTable::RetrieveBlock<BlockContents>(
FilePrefetchBuffer* prefetch_buffer, const ReadOptions& ro,
const BlockHandle& handle, const UncompressionDict& uncompression_dict,
CachableEntry<BlockContents>* block_entry, BlockType block_type,
GetContext* get_context, BlockCacheLookupContext* lookup_context,
Parallelize secondary cache lookup in MultiGet (#8405) Summary: Implement the ```WaitAll()``` interface in ```LRUCache``` to allow callers to issue multiple lookups in parallel and wait for all of them to complete. Modify ```MultiGet``` to use this to parallelize the secondary cache lookups in order to reduce the overall latency. A call to ```cache->Lookup()``` returns a handle that has an incomplete value (nullptr), and the caller can call ```cache->IsReady()``` to check whether the lookup is complete, and pass a vector of handles to ```WaitAll``` to wait for completion. If any of the lookups fail, ```MultiGet``` will read the block from the SST file. Another change in this PR is to rename ```SecondaryCacheHandle``` to ```SecondaryCacheResultHandle``` as it more accurately describes the return result of the secondary cache lookup, which is more like a future. Tests: 1. Add unit tests in lru_cache_test 2. Benchmark results with no secondary cache configured Master - ``` readrandom : 41.175 micros/op 388562 ops/sec; 106.7 MB/s (7277999 of 7277999 found) readrandom : 41.217 micros/op 388160 ops/sec; 106.6 MB/s (7274999 of 7274999 found) multireadrandom : 10.309 micros/op 1552082 ops/sec; (28908992 of 28908992 found) multireadrandom : 10.321 micros/op 1550218 ops/sec; (29081984 of 29081984 found) ``` This PR - ``` readrandom : 41.158 micros/op 388723 ops/sec; 106.8 MB/s (7290999 of 7290999 found) readrandom : 41.185 micros/op 388463 ops/sec; 106.7 MB/s (7287999 of 7287999 found) multireadrandom : 10.277 micros/op 1556801 ops/sec; (29346944 of 29346944 found) multireadrandom : 10.253 micros/op 1560539 ops/sec; (29274944 of 29274944 found) ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/8405 Reviewed By: zhichao-cao Differential Revision: D29190509 Pulled By: anand1976 fbshipit-source-id: 6f8eff6246712af8a297cfe22ea0d1c3b2a01bb0
2021-06-18 16:35:03 +00:00
bool for_compaction, bool use_cache, bool wait_for_cache) const;
Store the filter bits reader alongside the filter block contents (#5936) Summary: Amongst other things, PR https://github.com/facebook/rocksdb/issues/5504 refactored the filter block readers so that only the filter block contents are stored in the block cache (as opposed to the earlier design where the cache stored the filter block reader itself, leading to potentially dangling pointers and concurrency bugs). However, this change introduced a performance hit since with the new code, the metadata fields are re-parsed upon every access. This patch reunites the block contents with the filter bits reader to eliminate this overhead; since this is still a self-contained pure data object, it is safe to store it in the cache. (Note: this is similar to how the zstd digest is handled.) Pull Request resolved: https://github.com/facebook/rocksdb/pull/5936 Test Plan: make asan_check filter_bench results for the old code: ``` $ ./filter_bench -quick WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 26.7153 Number of filters: 16669 Total memory (MB): 200.009 Bits/key actual: 10.0647 ---------------------------- Inside queries... Dry run (46b) ns/op: 33.4258 Single filter ns/op: 42.5974 Random filter ns/op: 217.861 ---------------------------- Outside queries... Dry run (25d) ns/op: 32.4217 Single filter ns/op: 50.9855 Random filter ns/op: 219.167 Average FP rate %: 1.13993 ---------------------------- Done. (For more info, run with -legend or -help.) $ ./filter_bench -quick -use_full_block_reader WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 26.5172 Number of filters: 16669 Total memory (MB): 200.009 Bits/key actual: 10.0647 ---------------------------- Inside queries... Dry run (46b) ns/op: 32.3556 Single filter ns/op: 83.2239 Random filter ns/op: 370.676 ---------------------------- Outside queries... Dry run (25d) ns/op: 32.2265 Single filter ns/op: 93.5651 Random filter ns/op: 408.393 Average FP rate %: 1.13993 ---------------------------- Done. (For more info, run with -legend or -help.) ``` With the new code: ``` $ ./filter_bench -quick WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 25.4285 Number of filters: 16669 Total memory (MB): 200.009 Bits/key actual: 10.0647 ---------------------------- Inside queries... Dry run (46b) ns/op: 31.0594 Single filter ns/op: 43.8974 Random filter ns/op: 226.075 ---------------------------- Outside queries... Dry run (25d) ns/op: 31.0295 Single filter ns/op: 50.3824 Random filter ns/op: 226.805 Average FP rate %: 1.13993 ---------------------------- Done. (For more info, run with -legend or -help.) $ ./filter_bench -quick -use_full_block_reader WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 26.5308 Number of filters: 16669 Total memory (MB): 200.009 Bits/key actual: 10.0647 ---------------------------- Inside queries... Dry run (46b) ns/op: 33.2968 Single filter ns/op: 58.6163 Random filter ns/op: 291.434 ---------------------------- Outside queries... Dry run (25d) ns/op: 32.1839 Single filter ns/op: 66.9039 Random filter ns/op: 292.828 Average FP rate %: 1.13993 ---------------------------- Done. (For more info, run with -legend or -help.) ``` Differential Revision: D17991712 Pulled By: ltamasi fbshipit-source-id: 7ea205550217bfaaa1d5158ebd658e5832e60f29
2019-10-19 02:30:47 +00:00
template Status BlockBasedTable::RetrieveBlock<ParsedFullFilterBlock>(
FilePrefetchBuffer* prefetch_buffer, const ReadOptions& ro,
const BlockHandle& handle, const UncompressionDict& uncompression_dict,
CachableEntry<ParsedFullFilterBlock>* block_entry, BlockType block_type,
GetContext* get_context, BlockCacheLookupContext* lookup_context,
Parallelize secondary cache lookup in MultiGet (#8405) Summary: Implement the ```WaitAll()``` interface in ```LRUCache``` to allow callers to issue multiple lookups in parallel and wait for all of them to complete. Modify ```MultiGet``` to use this to parallelize the secondary cache lookups in order to reduce the overall latency. A call to ```cache->Lookup()``` returns a handle that has an incomplete value (nullptr), and the caller can call ```cache->IsReady()``` to check whether the lookup is complete, and pass a vector of handles to ```WaitAll``` to wait for completion. If any of the lookups fail, ```MultiGet``` will read the block from the SST file. Another change in this PR is to rename ```SecondaryCacheHandle``` to ```SecondaryCacheResultHandle``` as it more accurately describes the return result of the secondary cache lookup, which is more like a future. Tests: 1. Add unit tests in lru_cache_test 2. Benchmark results with no secondary cache configured Master - ``` readrandom : 41.175 micros/op 388562 ops/sec; 106.7 MB/s (7277999 of 7277999 found) readrandom : 41.217 micros/op 388160 ops/sec; 106.6 MB/s (7274999 of 7274999 found) multireadrandom : 10.309 micros/op 1552082 ops/sec; (28908992 of 28908992 found) multireadrandom : 10.321 micros/op 1550218 ops/sec; (29081984 of 29081984 found) ``` This PR - ``` readrandom : 41.158 micros/op 388723 ops/sec; 106.8 MB/s (7290999 of 7290999 found) readrandom : 41.185 micros/op 388463 ops/sec; 106.7 MB/s (7287999 of 7287999 found) multireadrandom : 10.277 micros/op 1556801 ops/sec; (29346944 of 29346944 found) multireadrandom : 10.253 micros/op 1560539 ops/sec; (29274944 of 29274944 found) ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/8405 Reviewed By: zhichao-cao Differential Revision: D29190509 Pulled By: anand1976 fbshipit-source-id: 6f8eff6246712af8a297cfe22ea0d1c3b2a01bb0
2021-06-18 16:35:03 +00:00
bool for_compaction, bool use_cache, bool wait_for_cache) const;
Store the filter bits reader alongside the filter block contents (#5936) Summary: Amongst other things, PR https://github.com/facebook/rocksdb/issues/5504 refactored the filter block readers so that only the filter block contents are stored in the block cache (as opposed to the earlier design where the cache stored the filter block reader itself, leading to potentially dangling pointers and concurrency bugs). However, this change introduced a performance hit since with the new code, the metadata fields are re-parsed upon every access. This patch reunites the block contents with the filter bits reader to eliminate this overhead; since this is still a self-contained pure data object, it is safe to store it in the cache. (Note: this is similar to how the zstd digest is handled.) Pull Request resolved: https://github.com/facebook/rocksdb/pull/5936 Test Plan: make asan_check filter_bench results for the old code: ``` $ ./filter_bench -quick WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 26.7153 Number of filters: 16669 Total memory (MB): 200.009 Bits/key actual: 10.0647 ---------------------------- Inside queries... Dry run (46b) ns/op: 33.4258 Single filter ns/op: 42.5974 Random filter ns/op: 217.861 ---------------------------- Outside queries... Dry run (25d) ns/op: 32.4217 Single filter ns/op: 50.9855 Random filter ns/op: 219.167 Average FP rate %: 1.13993 ---------------------------- Done. (For more info, run with -legend or -help.) $ ./filter_bench -quick -use_full_block_reader WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 26.5172 Number of filters: 16669 Total memory (MB): 200.009 Bits/key actual: 10.0647 ---------------------------- Inside queries... Dry run (46b) ns/op: 32.3556 Single filter ns/op: 83.2239 Random filter ns/op: 370.676 ---------------------------- Outside queries... Dry run (25d) ns/op: 32.2265 Single filter ns/op: 93.5651 Random filter ns/op: 408.393 Average FP rate %: 1.13993 ---------------------------- Done. (For more info, run with -legend or -help.) ``` With the new code: ``` $ ./filter_bench -quick WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 25.4285 Number of filters: 16669 Total memory (MB): 200.009 Bits/key actual: 10.0647 ---------------------------- Inside queries... Dry run (46b) ns/op: 31.0594 Single filter ns/op: 43.8974 Random filter ns/op: 226.075 ---------------------------- Outside queries... Dry run (25d) ns/op: 31.0295 Single filter ns/op: 50.3824 Random filter ns/op: 226.805 Average FP rate %: 1.13993 ---------------------------- Done. (For more info, run with -legend or -help.) $ ./filter_bench -quick -use_full_block_reader WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 26.5308 Number of filters: 16669 Total memory (MB): 200.009 Bits/key actual: 10.0647 ---------------------------- Inside queries... Dry run (46b) ns/op: 33.2968 Single filter ns/op: 58.6163 Random filter ns/op: 291.434 ---------------------------- Outside queries... Dry run (25d) ns/op: 32.1839 Single filter ns/op: 66.9039 Random filter ns/op: 292.828 Average FP rate %: 1.13993 ---------------------------- Done. (For more info, run with -legend or -help.) ``` Differential Revision: D17991712 Pulled By: ltamasi fbshipit-source-id: 7ea205550217bfaaa1d5158ebd658e5832e60f29
2019-10-19 02:30:47 +00:00
template Status BlockBasedTable::RetrieveBlock<Block>(
FilePrefetchBuffer* prefetch_buffer, const ReadOptions& ro,
const BlockHandle& handle, const UncompressionDict& uncompression_dict,
CachableEntry<Block>* block_entry, BlockType block_type,
GetContext* get_context, BlockCacheLookupContext* lookup_context,
Parallelize secondary cache lookup in MultiGet (#8405) Summary: Implement the ```WaitAll()``` interface in ```LRUCache``` to allow callers to issue multiple lookups in parallel and wait for all of them to complete. Modify ```MultiGet``` to use this to parallelize the secondary cache lookups in order to reduce the overall latency. A call to ```cache->Lookup()``` returns a handle that has an incomplete value (nullptr), and the caller can call ```cache->IsReady()``` to check whether the lookup is complete, and pass a vector of handles to ```WaitAll``` to wait for completion. If any of the lookups fail, ```MultiGet``` will read the block from the SST file. Another change in this PR is to rename ```SecondaryCacheHandle``` to ```SecondaryCacheResultHandle``` as it more accurately describes the return result of the secondary cache lookup, which is more like a future. Tests: 1. Add unit tests in lru_cache_test 2. Benchmark results with no secondary cache configured Master - ``` readrandom : 41.175 micros/op 388562 ops/sec; 106.7 MB/s (7277999 of 7277999 found) readrandom : 41.217 micros/op 388160 ops/sec; 106.6 MB/s (7274999 of 7274999 found) multireadrandom : 10.309 micros/op 1552082 ops/sec; (28908992 of 28908992 found) multireadrandom : 10.321 micros/op 1550218 ops/sec; (29081984 of 29081984 found) ``` This PR - ``` readrandom : 41.158 micros/op 388723 ops/sec; 106.8 MB/s (7290999 of 7290999 found) readrandom : 41.185 micros/op 388463 ops/sec; 106.7 MB/s (7287999 of 7287999 found) multireadrandom : 10.277 micros/op 1556801 ops/sec; (29346944 of 29346944 found) multireadrandom : 10.253 micros/op 1560539 ops/sec; (29274944 of 29274944 found) ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/8405 Reviewed By: zhichao-cao Differential Revision: D29190509 Pulled By: anand1976 fbshipit-source-id: 6f8eff6246712af8a297cfe22ea0d1c3b2a01bb0
2021-06-18 16:35:03 +00:00
bool for_compaction, bool use_cache, bool wait_for_cache) const;
template Status BlockBasedTable::RetrieveBlock<UncompressionDict>(
FilePrefetchBuffer* prefetch_buffer, const ReadOptions& ro,
const BlockHandle& handle, const UncompressionDict& uncompression_dict,
CachableEntry<UncompressionDict>* block_entry, BlockType block_type,
GetContext* get_context, BlockCacheLookupContext* lookup_context,
Parallelize secondary cache lookup in MultiGet (#8405) Summary: Implement the ```WaitAll()``` interface in ```LRUCache``` to allow callers to issue multiple lookups in parallel and wait for all of them to complete. Modify ```MultiGet``` to use this to parallelize the secondary cache lookups in order to reduce the overall latency. A call to ```cache->Lookup()``` returns a handle that has an incomplete value (nullptr), and the caller can call ```cache->IsReady()``` to check whether the lookup is complete, and pass a vector of handles to ```WaitAll``` to wait for completion. If any of the lookups fail, ```MultiGet``` will read the block from the SST file. Another change in this PR is to rename ```SecondaryCacheHandle``` to ```SecondaryCacheResultHandle``` as it more accurately describes the return result of the secondary cache lookup, which is more like a future. Tests: 1. Add unit tests in lru_cache_test 2. Benchmark results with no secondary cache configured Master - ``` readrandom : 41.175 micros/op 388562 ops/sec; 106.7 MB/s (7277999 of 7277999 found) readrandom : 41.217 micros/op 388160 ops/sec; 106.6 MB/s (7274999 of 7274999 found) multireadrandom : 10.309 micros/op 1552082 ops/sec; (28908992 of 28908992 found) multireadrandom : 10.321 micros/op 1550218 ops/sec; (29081984 of 29081984 found) ``` This PR - ``` readrandom : 41.158 micros/op 388723 ops/sec; 106.8 MB/s (7290999 of 7290999 found) readrandom : 41.185 micros/op 388463 ops/sec; 106.7 MB/s (7287999 of 7287999 found) multireadrandom : 10.277 micros/op 1556801 ops/sec; (29346944 of 29346944 found) multireadrandom : 10.253 micros/op 1560539 ops/sec; (29274944 of 29274944 found) ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/8405 Reviewed By: zhichao-cao Differential Revision: D29190509 Pulled By: anand1976 fbshipit-source-id: 6f8eff6246712af8a297cfe22ea0d1c3b2a01bb0
2021-06-18 16:35:03 +00:00
bool for_compaction, bool use_cache, bool wait_for_cache) const;
BlockBasedTable::PartitionedIndexIteratorState::PartitionedIndexIteratorState(
const BlockBasedTable* table,
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
std::unordered_map<uint64_t, CachableEntry<Block>>* block_map)
: table_(table), block_map_(block_map) {}
InternalIteratorBase<IndexValue>*
BlockBasedTable::PartitionedIndexIteratorState::NewSecondaryIterator(
const BlockHandle& handle) {
// Return a block iterator on the index partition
auto block = block_map_->find(handle.offset());
// This is a possible scenario since block cache might not have had space
// for the partition
if (block != block_map_->end()) {
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
const Rep* rep = table_->get_rep();
assert(rep);
Statistics* kNullStats = nullptr;
// We don't return pinned data from index blocks, so no need
// to set `block_contents_pinned`.
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
return block->second.GetValue()->NewIndexIterator(
Separate internal and user key comparators in `BlockIter` (#6944) Summary: Replace `BlockIter::comparator_` and `IndexBlockIter::user_comparator_wrapper_` with a concrete `UserComparatorWrapper` and `InternalKeyComparator`. The motivation for this change was the inconvenience of not knowing the concrete type of `BlockIter::comparator_`, which prevented calling specialized internal key comparison functions to optimize comparison of keys with global seqno applied. Pull Request resolved: https://github.com/facebook/rocksdb/pull/6944 Test Plan: benchmark setup -- single file DBs, in-memory, no compression. "normal_db" created by regular flush; "ingestion_db" created by ingesting a file. Both DBs have same contents. ``` $ TEST_TMPDIR=/dev/shm/normal_db/ ./db_bench -benchmarks=fillrandom,compact -write_buffer_size=10485760000 -disable_auto_compactions=true -compression_type=none -num=1000000 $ ./ldb write_extern_sst ./tmp.sst --db=/dev/shm/ingestion_db/dbbench/ --compression_type=no --hex --create_if_missing < <(./sst_dump --command=scan --output_hex --file=/dev/shm/normal_db/dbbench/000007.sst | awk 'began {print "0x" substr($1, 2, length($1) - 2), "==>", "0x" $5} ; /^Sst file format: block-based/ {began=1}') $ ./ldb ingest_extern_sst ./tmp.sst --db=/dev/shm/ingestion_db/dbbench/ ``` benchmark run command: ``` $ TEST_TMPDIR=/dev/shm/$DB/ ./db_bench -benchmarks=seekrandom -seek_nexts=$SEEK_NEXT -use_existing_db=true -cache_index_and_filter_blocks=false -num=1000000 -cache_size=0 -threads=1 -reads=200000000 -mmap_read=1 -verify_checksum=false ``` results: perf improved marginally for ingestion_db and did not change significantly for normal_db: SEEK_NEXT | DB | code | ops/sec | % change -- | -- | -- | -- | -- 0 | normal_db | master | 350880 |   0 | normal_db | PR6944 | 351040 | 0.0 0 | ingestion_db | master | 343255 |   0 | ingestion_db | PR6944 | 349424 | 1.8 10 | normal_db | master | 218711 |   10 | normal_db | PR6944 | 217892 | -0.4 10 | ingestion_db | master | 220334 |   10 | ingestion_db | PR6944 | 226437 | 2.8 Reviewed By: pdillinger Differential Revision: D21924676 Pulled By: ajkr fbshipit-source-id: ea4288a2eefa8112eb6c651a671c1de18c12e538
2020-07-08 00:25:08 +00:00
rep->internal_comparator.user_comparator(),
rep->get_global_seqno(BlockType::kIndex), nullptr, kNullStats, true,
rep->index_has_first_key, rep->index_key_includes_seq,
rep->index_value_is_full);
}
// Create an empty iterator
// TODO(ajkr): this is not the right way to handle an unpinned partition.
return new IndexBlockIter();
}
// This will be broken if the user specifies an unusual implementation
// of Options.comparator, or if the user specifies an unusual
// definition of prefixes in BlockBasedTableOptions.filter_policy.
// In particular, we require the following three properties:
//
// 1) key.starts_with(prefix(key))
// 2) Compare(prefix(key), key) <= 0.
// 3) If Compare(key1, key2) <= 0, then Compare(prefix(key1), prefix(key2)) <= 0
//
Fix iterator reading filter block despite read_tier == kBlockCacheTier (#6562) Summary: We're seeing iterators with `ReadOptions::read_tier == kBlockCacheTier` sometimes doing file reads. Stack trace: ``` rocksdb::RandomAccessFileReader::Read(unsigned long, unsigned long, rocksdb::Slice*, char*, bool) const rocksdb::BlockFetcher::ReadBlockContents() rocksdb::Status rocksdb::BlockBasedTable::MaybeReadBlockAndLoadToCache<rocksdb::ParsedFullFilterBlock>(rocksdb::FilePrefetchBuffer*, rocksdb::ReadOptions const&, rocksdb::BlockHandle const&, rocksdb::UncompressionDict const&, rocksdb::CachableEntry<rocksdb::ParsedFullFilterBlock>*, rocksdb::BlockType, rocksdb::GetContext*, rocksdb::BlockCacheLookupContext*, rocksdb::BlockContents*) const rocksdb::Status rocksdb::BlockBasedTable::RetrieveBlock<rocksdb::ParsedFullFilterBlock>(rocksdb::FilePrefetchBuffer*, rocksdb::ReadOptions const&, rocksdb::BlockHandle const&, rocksdb::UncompressionDict const&, rocksdb::CachableEntry<rocksdb::ParsedFullFilterBlock>*, rocksdb::BlockType, rocksdb::GetContext*, rocksdb::BlockCacheLookupContext*, bool, bool) const rocksdb::FilterBlockReaderCommon<rocksdb::ParsedFullFilterBlock>::ReadFilterBlock(rocksdb::BlockBasedTable const*, rocksdb::FilePrefetchBuffer*, rocksdb::ReadOptions const&, bool, rocksdb::GetContext*, rocksdb::BlockCacheLookupContext*, rocksdb::CachableEntry<rocksdb::ParsedFullFilterBlock>*) rocksdb::FilterBlockReaderCommon<rocksdb::ParsedFullFilterBlock>::GetOrReadFilterBlock(bool, rocksdb::GetContext*, rocksdb::BlockCacheLookupContext*, rocksdb::CachableEntry<rocksdb::ParsedFullFilterBlock>*) const rocksdb::FullFilterBlockReader::MayMatch(rocksdb::Slice const&, bool, rocksdb::GetContext*, rocksdb::BlockCacheLookupContext*) const rocksdb::FullFilterBlockReader::RangeMayExist(rocksdb::Slice const*, rocksdb::Slice const&, rocksdb::SliceTransform const*, rocksdb::Comparator const*, rocksdb::Slice const*, bool*, bool, rocksdb::BlockCacheLookupContext*) rocksdb::BlockBasedTable::PrefixMayMatch(rocksdb::Slice const&, rocksdb::ReadOptions const&, rocksdb::SliceTransform const*, bool, rocksdb::BlockCacheLookupContext*) const rocksdb::BlockBasedTableIterator<rocksdb::DataBlockIter, rocksdb::Slice>::SeekImpl(rocksdb::Slice const*) rocksdb::ForwardIterator::SeekInternal(rocksdb::Slice const&, bool) rocksdb::DBIter::Seek(rocksdb::Slice const&) ``` `BlockBasedTableIterator::CheckPrefixMayMatch` was missing a check for `kBlockCacheTier`. This PR adds it. Pull Request resolved: https://github.com/facebook/rocksdb/pull/6562 Test Plan: deployed it to a logdevice test cluster and looked at logdevice's IO tracing. Reviewed By: siying Differential Revision: D20529368 Pulled By: al13n321 fbshipit-source-id: 65bf33964b1951464415c900336635fb20919611
2020-03-26 22:18:03 +00:00
// If read_options.read_tier == kBlockCacheTier, this method will do no I/O and
// will return true if the filter block is not in memory and not found in block
// cache.
//
// REQUIRES: this method shouldn't be called while the DB lock is held.
bool BlockBasedTable::PrefixMayMatch(
const Slice& internal_key, const ReadOptions& read_options,
const SliceTransform* options_prefix_extractor,
Create a BlockCacheLookupContext to enable fine-grained block cache tracing. (#5421) Summary: BlockCacheLookupContext only contains the caller for now. We will trace block accesses at five places: 1. BlockBasedTable::GetFilter. 2. BlockBasedTable::GetUncompressedDict. 3. BlockBasedTable::MaybeReadAndLoadToCache. (To trace access on data, index, and range deletion block.) 4. BlockBasedTable::Get. (To trace the referenced key and whether the referenced key exists in a fetched data block.) 5. BlockBasedTable::MultiGet. (To trace the referenced key and whether the referenced key exists in a fetched data block.) We create the context at: 1. BlockBasedTable::Get. (kUserGet) 2. BlockBasedTable::MultiGet. (kUserMGet) 3. BlockBasedTable::NewIterator. (either kUserIterator, kCompaction, or external SST ingestion calls this function.) 4. BlockBasedTable::Open. (kPrefetch) 5. Index/Filter::CacheDependencies. (kPrefetch) 6. BlockBasedTable::ApproximateOffsetOf. (kCompaction or kUserApproximateSize). I loaded 1 million key-value pairs into the database and ran the readrandom benchmark with a single thread. I gave the block cache 10 GB to make sure all reads hit the block cache after warmup. The throughput is comparable. Throughput of this PR: 231334 ops/s. Throughput of the master branch: 238428 ops/s. Experiment setup: RocksDB: version 6.2 Date: Mon Jun 10 10:42:51 2019 CPU: 24 * Intel Core Processor (Skylake) CPUCache: 16384 KB Keys: 20 bytes each Values: 100 bytes each (100 bytes after compression) Entries: 1000000 Prefix: 20 bytes Keys per prefix: 0 RawSize: 114.4 MB (estimated) FileSize: 114.4 MB (estimated) Write rate: 0 bytes/second Read rate: 0 ops/second Compression: NoCompression Compression sampling rate: 0 Memtablerep: skip_list Perf Level: 1 Load command: ./db_bench --benchmarks="fillseq" --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --statistics --cache_index_and_filter_blocks --cache_size=10737418240 --disable_auto_compactions=1 --disable_wal=1 --compression_type=none --min_level_to_compress=-1 --compression_ratio=1 --num=1000000 Run command: ./db_bench --benchmarks="readrandom,stats" --use_existing_db --threads=1 --duration=120 --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --statistics --cache_index_and_filter_blocks --cache_size=10737418240 --disable_auto_compactions=1 --disable_wal=1 --compression_type=none --min_level_to_compress=-1 --compression_ratio=1 --num=1000000 --duration=120 TODOs: 1. Create a caller for external SST file ingestion and differentiate the callers for iterator. 2. Integrate tracer to trace block cache accesses. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5421 Differential Revision: D15704258 Pulled By: HaoyuHuang fbshipit-source-id: 4aa8a55f8cb1576ffb367bfa3186a91d8f06d93a
2019-06-10 22:30:05 +00:00
const bool need_upper_bound_check,
BlockCacheLookupContext* lookup_context) const {
if (!rep_->filter_policy) {
return true;
}
const SliceTransform* prefix_extractor;
if (rep_->table_prefix_extractor == nullptr) {
if (need_upper_bound_check) {
return true;
}
prefix_extractor = options_prefix_extractor;
} else {
prefix_extractor = rep_->table_prefix_extractor.get();
}
auto ts_sz = rep_->internal_comparator.user_comparator()->timestamp_size();
auto user_key_without_ts =
ExtractUserKeyAndStripTimestamp(internal_key, ts_sz);
if (!prefix_extractor->InDomain(user_key_without_ts)) {
return true;
}
bool may_match = true;
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
// First, try check with full filter
FilterBlockReader* const filter = rep_->filter.get();
bool filter_checked = true;
if (filter != nullptr) {
Fix iterator reading filter block despite read_tier == kBlockCacheTier (#6562) Summary: We're seeing iterators with `ReadOptions::read_tier == kBlockCacheTier` sometimes doing file reads. Stack trace: ``` rocksdb::RandomAccessFileReader::Read(unsigned long, unsigned long, rocksdb::Slice*, char*, bool) const rocksdb::BlockFetcher::ReadBlockContents() rocksdb::Status rocksdb::BlockBasedTable::MaybeReadBlockAndLoadToCache<rocksdb::ParsedFullFilterBlock>(rocksdb::FilePrefetchBuffer*, rocksdb::ReadOptions const&, rocksdb::BlockHandle const&, rocksdb::UncompressionDict const&, rocksdb::CachableEntry<rocksdb::ParsedFullFilterBlock>*, rocksdb::BlockType, rocksdb::GetContext*, rocksdb::BlockCacheLookupContext*, rocksdb::BlockContents*) const rocksdb::Status rocksdb::BlockBasedTable::RetrieveBlock<rocksdb::ParsedFullFilterBlock>(rocksdb::FilePrefetchBuffer*, rocksdb::ReadOptions const&, rocksdb::BlockHandle const&, rocksdb::UncompressionDict const&, rocksdb::CachableEntry<rocksdb::ParsedFullFilterBlock>*, rocksdb::BlockType, rocksdb::GetContext*, rocksdb::BlockCacheLookupContext*, bool, bool) const rocksdb::FilterBlockReaderCommon<rocksdb::ParsedFullFilterBlock>::ReadFilterBlock(rocksdb::BlockBasedTable const*, rocksdb::FilePrefetchBuffer*, rocksdb::ReadOptions const&, bool, rocksdb::GetContext*, rocksdb::BlockCacheLookupContext*, rocksdb::CachableEntry<rocksdb::ParsedFullFilterBlock>*) rocksdb::FilterBlockReaderCommon<rocksdb::ParsedFullFilterBlock>::GetOrReadFilterBlock(bool, rocksdb::GetContext*, rocksdb::BlockCacheLookupContext*, rocksdb::CachableEntry<rocksdb::ParsedFullFilterBlock>*) const rocksdb::FullFilterBlockReader::MayMatch(rocksdb::Slice const&, bool, rocksdb::GetContext*, rocksdb::BlockCacheLookupContext*) const rocksdb::FullFilterBlockReader::RangeMayExist(rocksdb::Slice const*, rocksdb::Slice const&, rocksdb::SliceTransform const*, rocksdb::Comparator const*, rocksdb::Slice const*, bool*, bool, rocksdb::BlockCacheLookupContext*) rocksdb::BlockBasedTable::PrefixMayMatch(rocksdb::Slice const&, rocksdb::ReadOptions const&, rocksdb::SliceTransform const*, bool, rocksdb::BlockCacheLookupContext*) const rocksdb::BlockBasedTableIterator<rocksdb::DataBlockIter, rocksdb::Slice>::SeekImpl(rocksdb::Slice const*) rocksdb::ForwardIterator::SeekInternal(rocksdb::Slice const&, bool) rocksdb::DBIter::Seek(rocksdb::Slice const&) ``` `BlockBasedTableIterator::CheckPrefixMayMatch` was missing a check for `kBlockCacheTier`. This PR adds it. Pull Request resolved: https://github.com/facebook/rocksdb/pull/6562 Test Plan: deployed it to a logdevice test cluster and looked at logdevice's IO tracing. Reviewed By: siying Differential Revision: D20529368 Pulled By: al13n321 fbshipit-source-id: 65bf33964b1951464415c900336635fb20919611
2020-03-26 22:18:03 +00:00
const bool no_io = read_options.read_tier == kBlockCacheTier;
if (!filter->IsBlockBased()) {
const Slice* const const_ikey_ptr = &internal_key;
may_match = filter->RangeMayExist(
read_options.iterate_upper_bound, user_key_without_ts,
prefix_extractor, rep_->internal_comparator.user_comparator(),
const_ikey_ptr, &filter_checked, need_upper_bound_check, no_io,
lookup_context);
} else {
// if prefix_extractor changed for block based filter, skip filter
if (need_upper_bound_check) {
return true;
}
auto prefix = prefix_extractor->Transform(user_key_without_ts);
InternalKey internal_key_prefix(prefix, kMaxSequenceNumber, kTypeValue);
auto internal_prefix = internal_key_prefix.Encode();
// To prevent any io operation in this method, we set `read_tier` to make
// sure we always read index or filter only when they have already been
// loaded to memory.
ReadOptions no_io_read_options;
no_io_read_options.read_tier = kBlockCacheTier;
// Then, try find it within each block
// we already know prefix_extractor and prefix_extractor_name must match
// because `CheckPrefixMayMatch` first checks `check_filter_ == true`
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
std::unique_ptr<InternalIteratorBase<IndexValue>> iiter(NewIndexIterator(
Create a BlockCacheLookupContext to enable fine-grained block cache tracing. (#5421) Summary: BlockCacheLookupContext only contains the caller for now. We will trace block accesses at five places: 1. BlockBasedTable::GetFilter. 2. BlockBasedTable::GetUncompressedDict. 3. BlockBasedTable::MaybeReadAndLoadToCache. (To trace access on data, index, and range deletion block.) 4. BlockBasedTable::Get. (To trace the referenced key and whether the referenced key exists in a fetched data block.) 5. BlockBasedTable::MultiGet. (To trace the referenced key and whether the referenced key exists in a fetched data block.) We create the context at: 1. BlockBasedTable::Get. (kUserGet) 2. BlockBasedTable::MultiGet. (kUserMGet) 3. BlockBasedTable::NewIterator. (either kUserIterator, kCompaction, or external SST ingestion calls this function.) 4. BlockBasedTable::Open. (kPrefetch) 5. Index/Filter::CacheDependencies. (kPrefetch) 6. BlockBasedTable::ApproximateOffsetOf. (kCompaction or kUserApproximateSize). I loaded 1 million key-value pairs into the database and ran the readrandom benchmark with a single thread. I gave the block cache 10 GB to make sure all reads hit the block cache after warmup. The throughput is comparable. Throughput of this PR: 231334 ops/s. Throughput of the master branch: 238428 ops/s. Experiment setup: RocksDB: version 6.2 Date: Mon Jun 10 10:42:51 2019 CPU: 24 * Intel Core Processor (Skylake) CPUCache: 16384 KB Keys: 20 bytes each Values: 100 bytes each (100 bytes after compression) Entries: 1000000 Prefix: 20 bytes Keys per prefix: 0 RawSize: 114.4 MB (estimated) FileSize: 114.4 MB (estimated) Write rate: 0 bytes/second Read rate: 0 ops/second Compression: NoCompression Compression sampling rate: 0 Memtablerep: skip_list Perf Level: 1 Load command: ./db_bench --benchmarks="fillseq" --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --statistics --cache_index_and_filter_blocks --cache_size=10737418240 --disable_auto_compactions=1 --disable_wal=1 --compression_type=none --min_level_to_compress=-1 --compression_ratio=1 --num=1000000 Run command: ./db_bench --benchmarks="readrandom,stats" --use_existing_db --threads=1 --duration=120 --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --statistics --cache_index_and_filter_blocks --cache_size=10737418240 --disable_auto_compactions=1 --disable_wal=1 --compression_type=none --min_level_to_compress=-1 --compression_ratio=1 --num=1000000 --duration=120 TODOs: 1. Create a caller for external SST file ingestion and differentiate the callers for iterator. 2. Integrate tracer to trace block cache accesses. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5421 Differential Revision: D15704258 Pulled By: HaoyuHuang fbshipit-source-id: 4aa8a55f8cb1576ffb367bfa3186a91d8f06d93a
2019-06-10 22:30:05 +00:00
no_io_read_options,
/*need_upper_bound_check=*/false, /*input_iter=*/nullptr,
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
/*get_context=*/nullptr, lookup_context));
iiter->Seek(internal_prefix);
if (!iiter->Valid()) {
// we're past end of file
// if it's incomplete, it means that we avoided I/O
// and we're not really sure that we're past the end
// of the file
may_match = iiter->status().IsIncomplete();
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
} else if ((rep_->index_key_includes_seq ? ExtractUserKey(iiter->key())
: iiter->key())
.starts_with(ExtractUserKey(internal_prefix))) {
// we need to check for this subtle case because our only
// guarantee is that "the key is a string >= last key in that data
// block" according to the doc/table_format.txt spec.
//
// Suppose iiter->key() starts with the desired prefix; it is not
// necessarily the case that the corresponding data block will
// contain the prefix, since iiter->key() need not be in the
// block. However, the next data block may contain the prefix, so
// we return true to play it safe.
may_match = true;
} else if (filter->IsBlockBased()) {
// iiter->key() does NOT start with the desired prefix. Because
// Seek() finds the first key that is >= the seek target, this
// means that iiter->key() > prefix. Thus, any data blocks coming
// after the data block corresponding to iiter->key() cannot
// possibly contain the key. Thus, the corresponding data block
// is the only on could potentially contain the prefix.
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
BlockHandle handle = iiter->value().handle;
Create a BlockCacheLookupContext to enable fine-grained block cache tracing. (#5421) Summary: BlockCacheLookupContext only contains the caller for now. We will trace block accesses at five places: 1. BlockBasedTable::GetFilter. 2. BlockBasedTable::GetUncompressedDict. 3. BlockBasedTable::MaybeReadAndLoadToCache. (To trace access on data, index, and range deletion block.) 4. BlockBasedTable::Get. (To trace the referenced key and whether the referenced key exists in a fetched data block.) 5. BlockBasedTable::MultiGet. (To trace the referenced key and whether the referenced key exists in a fetched data block.) We create the context at: 1. BlockBasedTable::Get. (kUserGet) 2. BlockBasedTable::MultiGet. (kUserMGet) 3. BlockBasedTable::NewIterator. (either kUserIterator, kCompaction, or external SST ingestion calls this function.) 4. BlockBasedTable::Open. (kPrefetch) 5. Index/Filter::CacheDependencies. (kPrefetch) 6. BlockBasedTable::ApproximateOffsetOf. (kCompaction or kUserApproximateSize). I loaded 1 million key-value pairs into the database and ran the readrandom benchmark with a single thread. I gave the block cache 10 GB to make sure all reads hit the block cache after warmup. The throughput is comparable. Throughput of this PR: 231334 ops/s. Throughput of the master branch: 238428 ops/s. Experiment setup: RocksDB: version 6.2 Date: Mon Jun 10 10:42:51 2019 CPU: 24 * Intel Core Processor (Skylake) CPUCache: 16384 KB Keys: 20 bytes each Values: 100 bytes each (100 bytes after compression) Entries: 1000000 Prefix: 20 bytes Keys per prefix: 0 RawSize: 114.4 MB (estimated) FileSize: 114.4 MB (estimated) Write rate: 0 bytes/second Read rate: 0 ops/second Compression: NoCompression Compression sampling rate: 0 Memtablerep: skip_list Perf Level: 1 Load command: ./db_bench --benchmarks="fillseq" --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --statistics --cache_index_and_filter_blocks --cache_size=10737418240 --disable_auto_compactions=1 --disable_wal=1 --compression_type=none --min_level_to_compress=-1 --compression_ratio=1 --num=1000000 Run command: ./db_bench --benchmarks="readrandom,stats" --use_existing_db --threads=1 --duration=120 --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --statistics --cache_index_and_filter_blocks --cache_size=10737418240 --disable_auto_compactions=1 --disable_wal=1 --compression_type=none --min_level_to_compress=-1 --compression_ratio=1 --num=1000000 --duration=120 TODOs: 1. Create a caller for external SST file ingestion and differentiate the callers for iterator. 2. Integrate tracer to trace block cache accesses. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5421 Differential Revision: D15704258 Pulled By: HaoyuHuang fbshipit-source-id: 4aa8a55f8cb1576ffb367bfa3186a91d8f06d93a
2019-06-10 22:30:05 +00:00
may_match = filter->PrefixMayMatch(
Fix iterator reading filter block despite read_tier == kBlockCacheTier (#6562) Summary: We're seeing iterators with `ReadOptions::read_tier == kBlockCacheTier` sometimes doing file reads. Stack trace: ``` rocksdb::RandomAccessFileReader::Read(unsigned long, unsigned long, rocksdb::Slice*, char*, bool) const rocksdb::BlockFetcher::ReadBlockContents() rocksdb::Status rocksdb::BlockBasedTable::MaybeReadBlockAndLoadToCache<rocksdb::ParsedFullFilterBlock>(rocksdb::FilePrefetchBuffer*, rocksdb::ReadOptions const&, rocksdb::BlockHandle const&, rocksdb::UncompressionDict const&, rocksdb::CachableEntry<rocksdb::ParsedFullFilterBlock>*, rocksdb::BlockType, rocksdb::GetContext*, rocksdb::BlockCacheLookupContext*, rocksdb::BlockContents*) const rocksdb::Status rocksdb::BlockBasedTable::RetrieveBlock<rocksdb::ParsedFullFilterBlock>(rocksdb::FilePrefetchBuffer*, rocksdb::ReadOptions const&, rocksdb::BlockHandle const&, rocksdb::UncompressionDict const&, rocksdb::CachableEntry<rocksdb::ParsedFullFilterBlock>*, rocksdb::BlockType, rocksdb::GetContext*, rocksdb::BlockCacheLookupContext*, bool, bool) const rocksdb::FilterBlockReaderCommon<rocksdb::ParsedFullFilterBlock>::ReadFilterBlock(rocksdb::BlockBasedTable const*, rocksdb::FilePrefetchBuffer*, rocksdb::ReadOptions const&, bool, rocksdb::GetContext*, rocksdb::BlockCacheLookupContext*, rocksdb::CachableEntry<rocksdb::ParsedFullFilterBlock>*) rocksdb::FilterBlockReaderCommon<rocksdb::ParsedFullFilterBlock>::GetOrReadFilterBlock(bool, rocksdb::GetContext*, rocksdb::BlockCacheLookupContext*, rocksdb::CachableEntry<rocksdb::ParsedFullFilterBlock>*) const rocksdb::FullFilterBlockReader::MayMatch(rocksdb::Slice const&, bool, rocksdb::GetContext*, rocksdb::BlockCacheLookupContext*) const rocksdb::FullFilterBlockReader::RangeMayExist(rocksdb::Slice const*, rocksdb::Slice const&, rocksdb::SliceTransform const*, rocksdb::Comparator const*, rocksdb::Slice const*, bool*, bool, rocksdb::BlockCacheLookupContext*) rocksdb::BlockBasedTable::PrefixMayMatch(rocksdb::Slice const&, rocksdb::ReadOptions const&, rocksdb::SliceTransform const*, bool, rocksdb::BlockCacheLookupContext*) const rocksdb::BlockBasedTableIterator<rocksdb::DataBlockIter, rocksdb::Slice>::SeekImpl(rocksdb::Slice const*) rocksdb::ForwardIterator::SeekInternal(rocksdb::Slice const&, bool) rocksdb::DBIter::Seek(rocksdb::Slice const&) ``` `BlockBasedTableIterator::CheckPrefixMayMatch` was missing a check for `kBlockCacheTier`. This PR adds it. Pull Request resolved: https://github.com/facebook/rocksdb/pull/6562 Test Plan: deployed it to a logdevice test cluster and looked at logdevice's IO tracing. Reviewed By: siying Differential Revision: D20529368 Pulled By: al13n321 fbshipit-source-id: 65bf33964b1951464415c900336635fb20919611
2020-03-26 22:18:03 +00:00
prefix, prefix_extractor, handle.offset(), no_io,
/*const_key_ptr=*/nullptr, /*get_context=*/nullptr, lookup_context);
}
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
}
}
if (filter_checked) {
Statistics* statistics = rep_->ioptions.stats;
RecordTick(statistics, BLOOM_FILTER_PREFIX_CHECKED);
if (!may_match) {
RecordTick(statistics, BLOOM_FILTER_PREFIX_USEFUL);
}
}
return may_match;
}
InternalIterator* BlockBasedTable::NewIterator(
const ReadOptions& read_options, const SliceTransform* prefix_extractor,
Arena* arena, bool skip_filters, TableReaderCaller caller,
Properly report IO errors when IndexType::kBinarySearchWithFirstKey is used (#6621) Summary: Context: Index type `kBinarySearchWithFirstKey` added the ability for sst file iterator to sometimes report a key from index without reading the corresponding data block. This is useful when sst blocks are cut at some meaningful boundaries (e.g. one block per key prefix), and many seeks land between blocks (e.g. for each prefix, the ranges of keys in different sst files are nearly disjoint, so a typical seek needs to read a data block from only one file even if all files have the prefix). But this added a new error condition, which rocksdb code was really not equipped to deal with: `InternalIterator::value()` may fail with an IO error or Status::Incomplete, but it's just a method returning a Slice, with no way to report error instead. Before this PR, this type of error wasn't handled at all (an empty slice was returned), and kBinarySearchWithFirstKey implementation was considered a prototype. Now that we (LogDevice) have experimented with kBinarySearchWithFirstKey for a while and confirmed that it's really useful, this PR is adding the missing error handling. It's a pretty inconvenient situation implementation-wise. The error needs to be reported from InternalIterator when trying to access value. But there are ~700 call sites of `InternalIterator::value()`, most of which either can't hit the error condition (because the iterator is reading from memtable or from index or something) or wouldn't benefit from the deferred loading of the value (e.g. compaction iterator that reads all values anyway). Adding error handling to all these call sites would needlessly bloat the code. So instead I made the deferred value loading optional: only the call sites that may use deferred loading have to call the new method `PrepareValue()` before calling `value()`. The feature is enabled with a new bool argument `allow_unprepared_value` to a bunch of methods that create iterators (it wouldn't make sense to put it in ReadOptions because it's completely internal to iterators, with virtually no user-visible effect). Lmk if you have better ideas. Note that the deferred value loading only happens for *internal* iterators. The user-visible iterator (DBIter) always prepares the value before returning from Seek/Next/etc. We could go further and add an API to defer that value loading too, but that's most likely not useful for LogDevice, so it doesn't seem worth the complexity for now. Pull Request resolved: https://github.com/facebook/rocksdb/pull/6621 Test Plan: make -j5 check . Will also deploy to some logdevice test clusters and look at stats. Reviewed By: siying Differential Revision: D20786930 Pulled By: al13n321 fbshipit-source-id: 6da77d918bad3780522e918f17f4d5513d3e99ee
2020-04-16 00:37:23 +00:00
size_t compaction_readahead_size, bool allow_unprepared_value) {
BlockCacheLookupContext lookup_context{caller};
bool need_upper_bound_check =
read_options.auto_prefix_mode ||
PrefixExtractorChanged(rep_->table_properties.get(), prefix_extractor);
De-template block based table iterator (#6531) Summary: Right now block based table iterator is used as both of iterating data for block based table, and for the index iterator for partitioend index. This was initially convenient for introducing a new iterator and block type for new index format, while reducing code change. However, these two usage doesn't go with each other very well. For example, Prev() is never called for partitioned index iterator, and some other complexity is maintained in block based iterators, which is not needed for index iterator but maintainers will always need to reason about it. Furthermore, the template usage is not following Google C++ Style which we are following, and makes a large chunk of code tangled together. This commit separate the two iterators. Right now, here is what it is done: 1. Copy the block based iterator code into partitioned index iterator, and de-template them. 2. Remove some code not needed for partitioned index. The upper bound check and tricks are removed. We never tested performance for those tricks when partitioned index is enabled in the first place. It's unlikelyl to generate performance regression, as creating new partitioned index block is much rarer than data blocks. 3. Separate out the prefetch logic to a helper class and both classes call them. This commit will enable future follow-ups. One direction is that we might separate index iterator interface for data blocks and index blocks, as they are quite different. Pull Request resolved: https://github.com/facebook/rocksdb/pull/6531 Test Plan: build using make and cmake. And build release Differential Revision: D20473108 fbshipit-source-id: e48011783b339a4257c204cc07507b171b834b0f
2020-03-16 19:17:34 +00:00
std::unique_ptr<InternalIteratorBase<IndexValue>> index_iter(NewIndexIterator(
read_options,
need_upper_bound_check &&
rep_->index_type == BlockBasedTableOptions::kHashSearch,
/*input_iter=*/nullptr, /*get_context=*/nullptr, &lookup_context));
if (arena == nullptr) {
De-template block based table iterator (#6531) Summary: Right now block based table iterator is used as both of iterating data for block based table, and for the index iterator for partitioend index. This was initially convenient for introducing a new iterator and block type for new index format, while reducing code change. However, these two usage doesn't go with each other very well. For example, Prev() is never called for partitioned index iterator, and some other complexity is maintained in block based iterators, which is not needed for index iterator but maintainers will always need to reason about it. Furthermore, the template usage is not following Google C++ Style which we are following, and makes a large chunk of code tangled together. This commit separate the two iterators. Right now, here is what it is done: 1. Copy the block based iterator code into partitioned index iterator, and de-template them. 2. Remove some code not needed for partitioned index. The upper bound check and tricks are removed. We never tested performance for those tricks when partitioned index is enabled in the first place. It's unlikelyl to generate performance regression, as creating new partitioned index block is much rarer than data blocks. 3. Separate out the prefetch logic to a helper class and both classes call them. This commit will enable future follow-ups. One direction is that we might separate index iterator interface for data blocks and index blocks, as they are quite different. Pull Request resolved: https://github.com/facebook/rocksdb/pull/6531 Test Plan: build using make and cmake. And build release Differential Revision: D20473108 fbshipit-source-id: e48011783b339a4257c204cc07507b171b834b0f
2020-03-16 19:17:34 +00:00
return new BlockBasedTableIterator(
this, read_options, rep_->internal_comparator, std::move(index_iter),
!skip_filters && !read_options.total_order_seek &&
prefix_extractor != nullptr,
De-template block based table iterator (#6531) Summary: Right now block based table iterator is used as both of iterating data for block based table, and for the index iterator for partitioend index. This was initially convenient for introducing a new iterator and block type for new index format, while reducing code change. However, these two usage doesn't go with each other very well. For example, Prev() is never called for partitioned index iterator, and some other complexity is maintained in block based iterators, which is not needed for index iterator but maintainers will always need to reason about it. Furthermore, the template usage is not following Google C++ Style which we are following, and makes a large chunk of code tangled together. This commit separate the two iterators. Right now, here is what it is done: 1. Copy the block based iterator code into partitioned index iterator, and de-template them. 2. Remove some code not needed for partitioned index. The upper bound check and tricks are removed. We never tested performance for those tricks when partitioned index is enabled in the first place. It's unlikelyl to generate performance regression, as creating new partitioned index block is much rarer than data blocks. 3. Separate out the prefetch logic to a helper class and both classes call them. This commit will enable future follow-ups. One direction is that we might separate index iterator interface for data blocks and index blocks, as they are quite different. Pull Request resolved: https://github.com/facebook/rocksdb/pull/6531 Test Plan: build using make and cmake. And build release Differential Revision: D20473108 fbshipit-source-id: e48011783b339a4257c204cc07507b171b834b0f
2020-03-16 19:17:34 +00:00
need_upper_bound_check, prefix_extractor, caller,
Properly report IO errors when IndexType::kBinarySearchWithFirstKey is used (#6621) Summary: Context: Index type `kBinarySearchWithFirstKey` added the ability for sst file iterator to sometimes report a key from index without reading the corresponding data block. This is useful when sst blocks are cut at some meaningful boundaries (e.g. one block per key prefix), and many seeks land between blocks (e.g. for each prefix, the ranges of keys in different sst files are nearly disjoint, so a typical seek needs to read a data block from only one file even if all files have the prefix). But this added a new error condition, which rocksdb code was really not equipped to deal with: `InternalIterator::value()` may fail with an IO error or Status::Incomplete, but it's just a method returning a Slice, with no way to report error instead. Before this PR, this type of error wasn't handled at all (an empty slice was returned), and kBinarySearchWithFirstKey implementation was considered a prototype. Now that we (LogDevice) have experimented with kBinarySearchWithFirstKey for a while and confirmed that it's really useful, this PR is adding the missing error handling. It's a pretty inconvenient situation implementation-wise. The error needs to be reported from InternalIterator when trying to access value. But there are ~700 call sites of `InternalIterator::value()`, most of which either can't hit the error condition (because the iterator is reading from memtable or from index or something) or wouldn't benefit from the deferred loading of the value (e.g. compaction iterator that reads all values anyway). Adding error handling to all these call sites would needlessly bloat the code. So instead I made the deferred value loading optional: only the call sites that may use deferred loading have to call the new method `PrepareValue()` before calling `value()`. The feature is enabled with a new bool argument `allow_unprepared_value` to a bunch of methods that create iterators (it wouldn't make sense to put it in ReadOptions because it's completely internal to iterators, with virtually no user-visible effect). Lmk if you have better ideas. Note that the deferred value loading only happens for *internal* iterators. The user-visible iterator (DBIter) always prepares the value before returning from Seek/Next/etc. We could go further and add an API to defer that value loading too, but that's most likely not useful for LogDevice, so it doesn't seem worth the complexity for now. Pull Request resolved: https://github.com/facebook/rocksdb/pull/6621 Test Plan: make -j5 check . Will also deploy to some logdevice test clusters and look at stats. Reviewed By: siying Differential Revision: D20786930 Pulled By: al13n321 fbshipit-source-id: 6da77d918bad3780522e918f17f4d5513d3e99ee
2020-04-16 00:37:23 +00:00
compaction_readahead_size, allow_unprepared_value);
} else {
De-template block based table iterator (#6531) Summary: Right now block based table iterator is used as both of iterating data for block based table, and for the index iterator for partitioend index. This was initially convenient for introducing a new iterator and block type for new index format, while reducing code change. However, these two usage doesn't go with each other very well. For example, Prev() is never called for partitioned index iterator, and some other complexity is maintained in block based iterators, which is not needed for index iterator but maintainers will always need to reason about it. Furthermore, the template usage is not following Google C++ Style which we are following, and makes a large chunk of code tangled together. This commit separate the two iterators. Right now, here is what it is done: 1. Copy the block based iterator code into partitioned index iterator, and de-template them. 2. Remove some code not needed for partitioned index. The upper bound check and tricks are removed. We never tested performance for those tricks when partitioned index is enabled in the first place. It's unlikelyl to generate performance regression, as creating new partitioned index block is much rarer than data blocks. 3. Separate out the prefetch logic to a helper class and both classes call them. This commit will enable future follow-ups. One direction is that we might separate index iterator interface for data blocks and index blocks, as they are quite different. Pull Request resolved: https://github.com/facebook/rocksdb/pull/6531 Test Plan: build using make and cmake. And build release Differential Revision: D20473108 fbshipit-source-id: e48011783b339a4257c204cc07507b171b834b0f
2020-03-16 19:17:34 +00:00
auto* mem = arena->AllocateAligned(sizeof(BlockBasedTableIterator));
return new (mem) BlockBasedTableIterator(
this, read_options, rep_->internal_comparator, std::move(index_iter),
!skip_filters && !read_options.total_order_seek &&
prefix_extractor != nullptr,
De-template block based table iterator (#6531) Summary: Right now block based table iterator is used as both of iterating data for block based table, and for the index iterator for partitioend index. This was initially convenient for introducing a new iterator and block type for new index format, while reducing code change. However, these two usage doesn't go with each other very well. For example, Prev() is never called for partitioned index iterator, and some other complexity is maintained in block based iterators, which is not needed for index iterator but maintainers will always need to reason about it. Furthermore, the template usage is not following Google C++ Style which we are following, and makes a large chunk of code tangled together. This commit separate the two iterators. Right now, here is what it is done: 1. Copy the block based iterator code into partitioned index iterator, and de-template them. 2. Remove some code not needed for partitioned index. The upper bound check and tricks are removed. We never tested performance for those tricks when partitioned index is enabled in the first place. It's unlikelyl to generate performance regression, as creating new partitioned index block is much rarer than data blocks. 3. Separate out the prefetch logic to a helper class and both classes call them. This commit will enable future follow-ups. One direction is that we might separate index iterator interface for data blocks and index blocks, as they are quite different. Pull Request resolved: https://github.com/facebook/rocksdb/pull/6531 Test Plan: build using make and cmake. And build release Differential Revision: D20473108 fbshipit-source-id: e48011783b339a4257c204cc07507b171b834b0f
2020-03-16 19:17:34 +00:00
need_upper_bound_check, prefix_extractor, caller,
Properly report IO errors when IndexType::kBinarySearchWithFirstKey is used (#6621) Summary: Context: Index type `kBinarySearchWithFirstKey` added the ability for sst file iterator to sometimes report a key from index without reading the corresponding data block. This is useful when sst blocks are cut at some meaningful boundaries (e.g. one block per key prefix), and many seeks land between blocks (e.g. for each prefix, the ranges of keys in different sst files are nearly disjoint, so a typical seek needs to read a data block from only one file even if all files have the prefix). But this added a new error condition, which rocksdb code was really not equipped to deal with: `InternalIterator::value()` may fail with an IO error or Status::Incomplete, but it's just a method returning a Slice, with no way to report error instead. Before this PR, this type of error wasn't handled at all (an empty slice was returned), and kBinarySearchWithFirstKey implementation was considered a prototype. Now that we (LogDevice) have experimented with kBinarySearchWithFirstKey for a while and confirmed that it's really useful, this PR is adding the missing error handling. It's a pretty inconvenient situation implementation-wise. The error needs to be reported from InternalIterator when trying to access value. But there are ~700 call sites of `InternalIterator::value()`, most of which either can't hit the error condition (because the iterator is reading from memtable or from index or something) or wouldn't benefit from the deferred loading of the value (e.g. compaction iterator that reads all values anyway). Adding error handling to all these call sites would needlessly bloat the code. So instead I made the deferred value loading optional: only the call sites that may use deferred loading have to call the new method `PrepareValue()` before calling `value()`. The feature is enabled with a new bool argument `allow_unprepared_value` to a bunch of methods that create iterators (it wouldn't make sense to put it in ReadOptions because it's completely internal to iterators, with virtually no user-visible effect). Lmk if you have better ideas. Note that the deferred value loading only happens for *internal* iterators. The user-visible iterator (DBIter) always prepares the value before returning from Seek/Next/etc. We could go further and add an API to defer that value loading too, but that's most likely not useful for LogDevice, so it doesn't seem worth the complexity for now. Pull Request resolved: https://github.com/facebook/rocksdb/pull/6621 Test Plan: make -j5 check . Will also deploy to some logdevice test clusters and look at stats. Reviewed By: siying Differential Revision: D20786930 Pulled By: al13n321 fbshipit-source-id: 6da77d918bad3780522e918f17f4d5513d3e99ee
2020-04-16 00:37:23 +00:00
compaction_readahead_size, allow_unprepared_value);
}
}
FragmentedRangeTombstoneIterator* BlockBasedTable::NewRangeTombstoneIterator(
const ReadOptions& read_options) {
Cache fragmented range tombstones in BlockBasedTableReader (#4493) Summary: This allows tombstone fragmenting to only be performed when the table is opened, and cached for subsequent accesses. On the same DB used in #4449, running `readrandom` results in the following: ``` readrandom : 0.983 micros/op 1017076 ops/sec; 78.3 MB/s (63103 of 100000 found) ``` Now that Get performance in the presence of range tombstones is reasonable, I also compared the performance between a DB with range tombstones, "expanded" range tombstones (several point tombstones that cover the same keys the equivalent range tombstone would cover, a common workaround for DeleteRange), and no range tombstones. The created DBs had 5 million keys each, and DeleteRange was called at regular intervals (depending on the total number of range tombstones being written) after 4.5 million Puts. The table below summarizes the results of a `readwhilewriting` benchmark (in order to provide somewhat more realistic results): ``` Tombstones? | avg micros/op | stddev micros/op | avg ops/s | stddev ops/s ----------------- | ------------- | ---------------- | ------------ | ------------ None | 0.6186 | 0.04637 | 1,625,252.90 | 124,679.41 500 Expanded | 0.6019 | 0.03628 | 1,666,670.40 | 101,142.65 500 Unexpanded | 0.6435 | 0.03994 | 1,559,979.40 | 104,090.52 1k Expanded | 0.6034 | 0.04349 | 1,665,128.10 | 125,144.57 1k Unexpanded | 0.6261 | 0.03093 | 1,600,457.50 | 79,024.94 5k Expanded | 0.6163 | 0.05926 | 1,636,668.80 | 154,888.85 5k Unexpanded | 0.6402 | 0.04002 | 1,567,804.70 | 100,965.55 10k Expanded | 0.6036 | 0.05105 | 1,667,237.70 | 142,830.36 10k Unexpanded | 0.6128 | 0.02598 | 1,634,633.40 | 72,161.82 25k Expanded | 0.6198 | 0.04542 | 1,620,980.50 | 116,662.93 25k Unexpanded | 0.5478 | 0.0362 | 1,833,059.10 | 121,233.81 50k Expanded | 0.5104 | 0.04347 | 1,973,107.90 | 184,073.49 50k Unexpanded | 0.4528 | 0.03387 | 2,219,034.50 | 170,984.32 ``` After a large enough quantity of range tombstones are written, range tombstone Gets can become faster than reading from an equivalent DB with several point tombstones. Pull Request resolved: https://github.com/facebook/rocksdb/pull/4493 Differential Revision: D10842844 Pulled By: abhimadan fbshipit-source-id: a7d44534f8120e6aabb65779d26c6b9df954c509
2018-10-26 02:25:00 +00:00
if (rep_->fragmented_range_dels == nullptr) {
return nullptr;
}
SequenceNumber snapshot = kMaxSequenceNumber;
if (read_options.snapshot != nullptr) {
snapshot = read_options.snapshot->GetSequenceNumber();
}
return new FragmentedRangeTombstoneIterator(
rep_->fragmented_range_dels, rep_->internal_comparator, snapshot);
Cache fragmented range tombstones in BlockBasedTableReader (#4493) Summary: This allows tombstone fragmenting to only be performed when the table is opened, and cached for subsequent accesses. On the same DB used in #4449, running `readrandom` results in the following: ``` readrandom : 0.983 micros/op 1017076 ops/sec; 78.3 MB/s (63103 of 100000 found) ``` Now that Get performance in the presence of range tombstones is reasonable, I also compared the performance between a DB with range tombstones, "expanded" range tombstones (several point tombstones that cover the same keys the equivalent range tombstone would cover, a common workaround for DeleteRange), and no range tombstones. The created DBs had 5 million keys each, and DeleteRange was called at regular intervals (depending on the total number of range tombstones being written) after 4.5 million Puts. The table below summarizes the results of a `readwhilewriting` benchmark (in order to provide somewhat more realistic results): ``` Tombstones? | avg micros/op | stddev micros/op | avg ops/s | stddev ops/s ----------------- | ------------- | ---------------- | ------------ | ------------ None | 0.6186 | 0.04637 | 1,625,252.90 | 124,679.41 500 Expanded | 0.6019 | 0.03628 | 1,666,670.40 | 101,142.65 500 Unexpanded | 0.6435 | 0.03994 | 1,559,979.40 | 104,090.52 1k Expanded | 0.6034 | 0.04349 | 1,665,128.10 | 125,144.57 1k Unexpanded | 0.6261 | 0.03093 | 1,600,457.50 | 79,024.94 5k Expanded | 0.6163 | 0.05926 | 1,636,668.80 | 154,888.85 5k Unexpanded | 0.6402 | 0.04002 | 1,567,804.70 | 100,965.55 10k Expanded | 0.6036 | 0.05105 | 1,667,237.70 | 142,830.36 10k Unexpanded | 0.6128 | 0.02598 | 1,634,633.40 | 72,161.82 25k Expanded | 0.6198 | 0.04542 | 1,620,980.50 | 116,662.93 25k Unexpanded | 0.5478 | 0.0362 | 1,833,059.10 | 121,233.81 50k Expanded | 0.5104 | 0.04347 | 1,973,107.90 | 184,073.49 50k Unexpanded | 0.4528 | 0.03387 | 2,219,034.50 | 170,984.32 ``` After a large enough quantity of range tombstones are written, range tombstone Gets can become faster than reading from an equivalent DB with several point tombstones. Pull Request resolved: https://github.com/facebook/rocksdb/pull/4493 Differential Revision: D10842844 Pulled By: abhimadan fbshipit-source-id: a7d44534f8120e6aabb65779d26c6b9df954c509
2018-10-26 02:25:00 +00:00
}
bool BlockBasedTable::FullFilterKeyMayMatch(
const ReadOptions& read_options, FilterBlockReader* filter,
const Slice& internal_key, const bool no_io,
const SliceTransform* prefix_extractor, GetContext* get_context,
Create a BlockCacheLookupContext to enable fine-grained block cache tracing. (#5421) Summary: BlockCacheLookupContext only contains the caller for now. We will trace block accesses at five places: 1. BlockBasedTable::GetFilter. 2. BlockBasedTable::GetUncompressedDict. 3. BlockBasedTable::MaybeReadAndLoadToCache. (To trace access on data, index, and range deletion block.) 4. BlockBasedTable::Get. (To trace the referenced key and whether the referenced key exists in a fetched data block.) 5. BlockBasedTable::MultiGet. (To trace the referenced key and whether the referenced key exists in a fetched data block.) We create the context at: 1. BlockBasedTable::Get. (kUserGet) 2. BlockBasedTable::MultiGet. (kUserMGet) 3. BlockBasedTable::NewIterator. (either kUserIterator, kCompaction, or external SST ingestion calls this function.) 4. BlockBasedTable::Open. (kPrefetch) 5. Index/Filter::CacheDependencies. (kPrefetch) 6. BlockBasedTable::ApproximateOffsetOf. (kCompaction or kUserApproximateSize). I loaded 1 million key-value pairs into the database and ran the readrandom benchmark with a single thread. I gave the block cache 10 GB to make sure all reads hit the block cache after warmup. The throughput is comparable. Throughput of this PR: 231334 ops/s. Throughput of the master branch: 238428 ops/s. Experiment setup: RocksDB: version 6.2 Date: Mon Jun 10 10:42:51 2019 CPU: 24 * Intel Core Processor (Skylake) CPUCache: 16384 KB Keys: 20 bytes each Values: 100 bytes each (100 bytes after compression) Entries: 1000000 Prefix: 20 bytes Keys per prefix: 0 RawSize: 114.4 MB (estimated) FileSize: 114.4 MB (estimated) Write rate: 0 bytes/second Read rate: 0 ops/second Compression: NoCompression Compression sampling rate: 0 Memtablerep: skip_list Perf Level: 1 Load command: ./db_bench --benchmarks="fillseq" --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --statistics --cache_index_and_filter_blocks --cache_size=10737418240 --disable_auto_compactions=1 --disable_wal=1 --compression_type=none --min_level_to_compress=-1 --compression_ratio=1 --num=1000000 Run command: ./db_bench --benchmarks="readrandom,stats" --use_existing_db --threads=1 --duration=120 --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --statistics --cache_index_and_filter_blocks --cache_size=10737418240 --disable_auto_compactions=1 --disable_wal=1 --compression_type=none --min_level_to_compress=-1 --compression_ratio=1 --num=1000000 --duration=120 TODOs: 1. Create a caller for external SST file ingestion and differentiate the callers for iterator. 2. Integrate tracer to trace block cache accesses. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5421 Differential Revision: D15704258 Pulled By: HaoyuHuang fbshipit-source-id: 4aa8a55f8cb1576ffb367bfa3186a91d8f06d93a
2019-06-10 22:30:05 +00:00
BlockCacheLookupContext* lookup_context) const {
if (filter == nullptr || filter->IsBlockBased()) {
return true;
}
Slice user_key = ExtractUserKey(internal_key);
const Slice* const const_ikey_ptr = &internal_key;
bool may_match = true;
size_t ts_sz = rep_->internal_comparator.user_comparator()->timestamp_size();
Slice user_key_without_ts = StripTimestampFromUserKey(user_key, ts_sz);
if (rep_->whole_key_filtering) {
Create a BlockCacheLookupContext to enable fine-grained block cache tracing. (#5421) Summary: BlockCacheLookupContext only contains the caller for now. We will trace block accesses at five places: 1. BlockBasedTable::GetFilter. 2. BlockBasedTable::GetUncompressedDict. 3. BlockBasedTable::MaybeReadAndLoadToCache. (To trace access on data, index, and range deletion block.) 4. BlockBasedTable::Get. (To trace the referenced key and whether the referenced key exists in a fetched data block.) 5. BlockBasedTable::MultiGet. (To trace the referenced key and whether the referenced key exists in a fetched data block.) We create the context at: 1. BlockBasedTable::Get. (kUserGet) 2. BlockBasedTable::MultiGet. (kUserMGet) 3. BlockBasedTable::NewIterator. (either kUserIterator, kCompaction, or external SST ingestion calls this function.) 4. BlockBasedTable::Open. (kPrefetch) 5. Index/Filter::CacheDependencies. (kPrefetch) 6. BlockBasedTable::ApproximateOffsetOf. (kCompaction or kUserApproximateSize). I loaded 1 million key-value pairs into the database and ran the readrandom benchmark with a single thread. I gave the block cache 10 GB to make sure all reads hit the block cache after warmup. The throughput is comparable. Throughput of this PR: 231334 ops/s. Throughput of the master branch: 238428 ops/s. Experiment setup: RocksDB: version 6.2 Date: Mon Jun 10 10:42:51 2019 CPU: 24 * Intel Core Processor (Skylake) CPUCache: 16384 KB Keys: 20 bytes each Values: 100 bytes each (100 bytes after compression) Entries: 1000000 Prefix: 20 bytes Keys per prefix: 0 RawSize: 114.4 MB (estimated) FileSize: 114.4 MB (estimated) Write rate: 0 bytes/second Read rate: 0 ops/second Compression: NoCompression Compression sampling rate: 0 Memtablerep: skip_list Perf Level: 1 Load command: ./db_bench --benchmarks="fillseq" --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --statistics --cache_index_and_filter_blocks --cache_size=10737418240 --disable_auto_compactions=1 --disable_wal=1 --compression_type=none --min_level_to_compress=-1 --compression_ratio=1 --num=1000000 Run command: ./db_bench --benchmarks="readrandom,stats" --use_existing_db --threads=1 --duration=120 --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --statistics --cache_index_and_filter_blocks --cache_size=10737418240 --disable_auto_compactions=1 --disable_wal=1 --compression_type=none --min_level_to_compress=-1 --compression_ratio=1 --num=1000000 --duration=120 TODOs: 1. Create a caller for external SST file ingestion and differentiate the callers for iterator. 2. Integrate tracer to trace block cache accesses. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5421 Differential Revision: D15704258 Pulled By: HaoyuHuang fbshipit-source-id: 4aa8a55f8cb1576ffb367bfa3186a91d8f06d93a
2019-06-10 22:30:05 +00:00
may_match =
filter->KeyMayMatch(user_key_without_ts, prefix_extractor, kNotValid,
no_io, const_ikey_ptr, get_context, lookup_context);
} else if (!read_options.total_order_seek && prefix_extractor &&
rep_->table_properties->prefix_extractor_name.compare(
prefix_extractor->Name()) == 0 &&
prefix_extractor->InDomain(user_key_without_ts) &&
!filter->PrefixMayMatch(
prefix_extractor->Transform(user_key_without_ts),
prefix_extractor, kNotValid, no_io, const_ikey_ptr,
get_context, lookup_context)) {
may_match = false;
}
if (may_match) {
RecordTick(rep_->ioptions.stats, BLOOM_FILTER_FULL_POSITIVE);
PERF_COUNTER_BY_LEVEL_ADD(bloom_filter_full_positive, 1, rep_->level);
}
return may_match;
}
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
void BlockBasedTable::FullFilterKeysMayMatch(
const ReadOptions& read_options, FilterBlockReader* filter,
MultiGetRange* range, const bool no_io,
Create a BlockCacheLookupContext to enable fine-grained block cache tracing. (#5421) Summary: BlockCacheLookupContext only contains the caller for now. We will trace block accesses at five places: 1. BlockBasedTable::GetFilter. 2. BlockBasedTable::GetUncompressedDict. 3. BlockBasedTable::MaybeReadAndLoadToCache. (To trace access on data, index, and range deletion block.) 4. BlockBasedTable::Get. (To trace the referenced key and whether the referenced key exists in a fetched data block.) 5. BlockBasedTable::MultiGet. (To trace the referenced key and whether the referenced key exists in a fetched data block.) We create the context at: 1. BlockBasedTable::Get. (kUserGet) 2. BlockBasedTable::MultiGet. (kUserMGet) 3. BlockBasedTable::NewIterator. (either kUserIterator, kCompaction, or external SST ingestion calls this function.) 4. BlockBasedTable::Open. (kPrefetch) 5. Index/Filter::CacheDependencies. (kPrefetch) 6. BlockBasedTable::ApproximateOffsetOf. (kCompaction or kUserApproximateSize). I loaded 1 million key-value pairs into the database and ran the readrandom benchmark with a single thread. I gave the block cache 10 GB to make sure all reads hit the block cache after warmup. The throughput is comparable. Throughput of this PR: 231334 ops/s. Throughput of the master branch: 238428 ops/s. Experiment setup: RocksDB: version 6.2 Date: Mon Jun 10 10:42:51 2019 CPU: 24 * Intel Core Processor (Skylake) CPUCache: 16384 KB Keys: 20 bytes each Values: 100 bytes each (100 bytes after compression) Entries: 1000000 Prefix: 20 bytes Keys per prefix: 0 RawSize: 114.4 MB (estimated) FileSize: 114.4 MB (estimated) Write rate: 0 bytes/second Read rate: 0 ops/second Compression: NoCompression Compression sampling rate: 0 Memtablerep: skip_list Perf Level: 1 Load command: ./db_bench --benchmarks="fillseq" --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --statistics --cache_index_and_filter_blocks --cache_size=10737418240 --disable_auto_compactions=1 --disable_wal=1 --compression_type=none --min_level_to_compress=-1 --compression_ratio=1 --num=1000000 Run command: ./db_bench --benchmarks="readrandom,stats" --use_existing_db --threads=1 --duration=120 --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --statistics --cache_index_and_filter_blocks --cache_size=10737418240 --disable_auto_compactions=1 --disable_wal=1 --compression_type=none --min_level_to_compress=-1 --compression_ratio=1 --num=1000000 --duration=120 TODOs: 1. Create a caller for external SST file ingestion and differentiate the callers for iterator. 2. Integrate tracer to trace block cache accesses. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5421 Differential Revision: D15704258 Pulled By: HaoyuHuang fbshipit-source-id: 4aa8a55f8cb1576ffb367bfa3186a91d8f06d93a
2019-06-10 22:30:05 +00:00
const SliceTransform* prefix_extractor,
BlockCacheLookupContext* lookup_context) const {
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 (filter == nullptr || filter->IsBlockBased()) {
return;
}
Basic MultiGet support for partitioned filters (#6757) Summary: In MultiGet, access each applicable filter partition only once per batch, rather than for each applicable key. Also, * Fix Bloom stats for MultiGet * Fix/refactor MultiGetContext::Range::KeysLeft, including * Add efficient BitsSetToOne implementation * Assert that MultiGetContext::Range does not go beyond shift range Performance test: Generate db: $ ./db_bench --benchmarks=fillrandom --num=15000000 --cache_index_and_filter_blocks -bloom_bits=10 -partition_index_and_filters=true ... Before (middle performing run of three; note some missing Bloom stats): $ ./db_bench --use-existing-db --benchmarks=multireadrandom --num=15000000 --cache_index_and_filter_blocks --bloom_bits=10 --threads=16 --cache_size=20000000 -partition_index_and_filters -batch_size=32 -multiread_batched -statistics --duration=20 2>&1 | egrep 'micros/op|block.cache.filter.hit|bloom.filter.(full|use)|number.multiget' multireadrandom : 26.403 micros/op 597517 ops/sec; (548427 of 671968 found) rocksdb.block.cache.filter.hit COUNT : 83443275 rocksdb.bloom.filter.useful COUNT : 0 rocksdb.bloom.filter.full.positive COUNT : 0 rocksdb.bloom.filter.full.true.positive COUNT : 7931450 rocksdb.number.multiget.get COUNT : 385984 rocksdb.number.multiget.keys.read COUNT : 12351488 rocksdb.number.multiget.bytes.read COUNT : 793145000 rocksdb.number.multiget.keys.found COUNT : 7931450 After (middle performing run of three): $ ./db_bench_new --use-existing-db --benchmarks=multireadrandom --num=15000000 --cache_index_and_filter_blocks --bloom_bits=10 --threads=16 --cache_size=20000000 -partition_index_and_filters -batch_size=32 -multiread_batched -statistics --duration=20 2>&1 | egrep 'micros/op|block.cache.filter.hit|bloom.filter.(full|use)|number.multiget' multireadrandom : 21.024 micros/op 752963 ops/sec; (705188 of 863968 found) rocksdb.block.cache.filter.hit COUNT : 49856682 rocksdb.bloom.filter.useful COUNT : 45684579 rocksdb.bloom.filter.full.positive COUNT : 10395458 rocksdb.bloom.filter.full.true.positive COUNT : 9908456 rocksdb.number.multiget.get COUNT : 481984 rocksdb.number.multiget.keys.read COUNT : 15423488 rocksdb.number.multiget.bytes.read COUNT : 990845600 rocksdb.number.multiget.keys.found COUNT : 9908456 So that's about 25% higher throughput even for random keys Pull Request resolved: https://github.com/facebook/rocksdb/pull/6757 Test Plan: unit test included Reviewed By: anand1976 Differential Revision: D21243256 Pulled By: pdillinger fbshipit-source-id: 5644a1468d9e8c8575be02f4e04bc5d62dbbb57f
2020-04-28 21:46:13 +00:00
uint64_t before_keys = range->KeysLeft();
assert(before_keys > 0); // Caller should ensure
if (rep_->whole_key_filtering) {
Create a BlockCacheLookupContext to enable fine-grained block cache tracing. (#5421) Summary: BlockCacheLookupContext only contains the caller for now. We will trace block accesses at five places: 1. BlockBasedTable::GetFilter. 2. BlockBasedTable::GetUncompressedDict. 3. BlockBasedTable::MaybeReadAndLoadToCache. (To trace access on data, index, and range deletion block.) 4. BlockBasedTable::Get. (To trace the referenced key and whether the referenced key exists in a fetched data block.) 5. BlockBasedTable::MultiGet. (To trace the referenced key and whether the referenced key exists in a fetched data block.) We create the context at: 1. BlockBasedTable::Get. (kUserGet) 2. BlockBasedTable::MultiGet. (kUserMGet) 3. BlockBasedTable::NewIterator. (either kUserIterator, kCompaction, or external SST ingestion calls this function.) 4. BlockBasedTable::Open. (kPrefetch) 5. Index/Filter::CacheDependencies. (kPrefetch) 6. BlockBasedTable::ApproximateOffsetOf. (kCompaction or kUserApproximateSize). I loaded 1 million key-value pairs into the database and ran the readrandom benchmark with a single thread. I gave the block cache 10 GB to make sure all reads hit the block cache after warmup. The throughput is comparable. Throughput of this PR: 231334 ops/s. Throughput of the master branch: 238428 ops/s. Experiment setup: RocksDB: version 6.2 Date: Mon Jun 10 10:42:51 2019 CPU: 24 * Intel Core Processor (Skylake) CPUCache: 16384 KB Keys: 20 bytes each Values: 100 bytes each (100 bytes after compression) Entries: 1000000 Prefix: 20 bytes Keys per prefix: 0 RawSize: 114.4 MB (estimated) FileSize: 114.4 MB (estimated) Write rate: 0 bytes/second Read rate: 0 ops/second Compression: NoCompression Compression sampling rate: 0 Memtablerep: skip_list Perf Level: 1 Load command: ./db_bench --benchmarks="fillseq" --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --statistics --cache_index_and_filter_blocks --cache_size=10737418240 --disable_auto_compactions=1 --disable_wal=1 --compression_type=none --min_level_to_compress=-1 --compression_ratio=1 --num=1000000 Run command: ./db_bench --benchmarks="readrandom,stats" --use_existing_db --threads=1 --duration=120 --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --statistics --cache_index_and_filter_blocks --cache_size=10737418240 --disable_auto_compactions=1 --disable_wal=1 --compression_type=none --min_level_to_compress=-1 --compression_ratio=1 --num=1000000 --duration=120 TODOs: 1. Create a caller for external SST file ingestion and differentiate the callers for iterator. 2. Integrate tracer to trace block cache accesses. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5421 Differential Revision: D15704258 Pulled By: HaoyuHuang fbshipit-source-id: 4aa8a55f8cb1576ffb367bfa3186a91d8f06d93a
2019-06-10 22:30:05 +00:00
filter->KeysMayMatch(range, prefix_extractor, kNotValid, no_io,
lookup_context);
Basic MultiGet support for partitioned filters (#6757) Summary: In MultiGet, access each applicable filter partition only once per batch, rather than for each applicable key. Also, * Fix Bloom stats for MultiGet * Fix/refactor MultiGetContext::Range::KeysLeft, including * Add efficient BitsSetToOne implementation * Assert that MultiGetContext::Range does not go beyond shift range Performance test: Generate db: $ ./db_bench --benchmarks=fillrandom --num=15000000 --cache_index_and_filter_blocks -bloom_bits=10 -partition_index_and_filters=true ... Before (middle performing run of three; note some missing Bloom stats): $ ./db_bench --use-existing-db --benchmarks=multireadrandom --num=15000000 --cache_index_and_filter_blocks --bloom_bits=10 --threads=16 --cache_size=20000000 -partition_index_and_filters -batch_size=32 -multiread_batched -statistics --duration=20 2>&1 | egrep 'micros/op|block.cache.filter.hit|bloom.filter.(full|use)|number.multiget' multireadrandom : 26.403 micros/op 597517 ops/sec; (548427 of 671968 found) rocksdb.block.cache.filter.hit COUNT : 83443275 rocksdb.bloom.filter.useful COUNT : 0 rocksdb.bloom.filter.full.positive COUNT : 0 rocksdb.bloom.filter.full.true.positive COUNT : 7931450 rocksdb.number.multiget.get COUNT : 385984 rocksdb.number.multiget.keys.read COUNT : 12351488 rocksdb.number.multiget.bytes.read COUNT : 793145000 rocksdb.number.multiget.keys.found COUNT : 7931450 After (middle performing run of three): $ ./db_bench_new --use-existing-db --benchmarks=multireadrandom --num=15000000 --cache_index_and_filter_blocks --bloom_bits=10 --threads=16 --cache_size=20000000 -partition_index_and_filters -batch_size=32 -multiread_batched -statistics --duration=20 2>&1 | egrep 'micros/op|block.cache.filter.hit|bloom.filter.(full|use)|number.multiget' multireadrandom : 21.024 micros/op 752963 ops/sec; (705188 of 863968 found) rocksdb.block.cache.filter.hit COUNT : 49856682 rocksdb.bloom.filter.useful COUNT : 45684579 rocksdb.bloom.filter.full.positive COUNT : 10395458 rocksdb.bloom.filter.full.true.positive COUNT : 9908456 rocksdb.number.multiget.get COUNT : 481984 rocksdb.number.multiget.keys.read COUNT : 15423488 rocksdb.number.multiget.bytes.read COUNT : 990845600 rocksdb.number.multiget.keys.found COUNT : 9908456 So that's about 25% higher throughput even for random keys Pull Request resolved: https://github.com/facebook/rocksdb/pull/6757 Test Plan: unit test included Reviewed By: anand1976 Differential Revision: D21243256 Pulled By: pdillinger fbshipit-source-id: 5644a1468d9e8c8575be02f4e04bc5d62dbbb57f
2020-04-28 21:46:13 +00:00
uint64_t after_keys = range->KeysLeft();
if (after_keys) {
RecordTick(rep_->ioptions.stats, BLOOM_FILTER_FULL_POSITIVE, after_keys);
Basic MultiGet support for partitioned filters (#6757) Summary: In MultiGet, access each applicable filter partition only once per batch, rather than for each applicable key. Also, * Fix Bloom stats for MultiGet * Fix/refactor MultiGetContext::Range::KeysLeft, including * Add efficient BitsSetToOne implementation * Assert that MultiGetContext::Range does not go beyond shift range Performance test: Generate db: $ ./db_bench --benchmarks=fillrandom --num=15000000 --cache_index_and_filter_blocks -bloom_bits=10 -partition_index_and_filters=true ... Before (middle performing run of three; note some missing Bloom stats): $ ./db_bench --use-existing-db --benchmarks=multireadrandom --num=15000000 --cache_index_and_filter_blocks --bloom_bits=10 --threads=16 --cache_size=20000000 -partition_index_and_filters -batch_size=32 -multiread_batched -statistics --duration=20 2>&1 | egrep 'micros/op|block.cache.filter.hit|bloom.filter.(full|use)|number.multiget' multireadrandom : 26.403 micros/op 597517 ops/sec; (548427 of 671968 found) rocksdb.block.cache.filter.hit COUNT : 83443275 rocksdb.bloom.filter.useful COUNT : 0 rocksdb.bloom.filter.full.positive COUNT : 0 rocksdb.bloom.filter.full.true.positive COUNT : 7931450 rocksdb.number.multiget.get COUNT : 385984 rocksdb.number.multiget.keys.read COUNT : 12351488 rocksdb.number.multiget.bytes.read COUNT : 793145000 rocksdb.number.multiget.keys.found COUNT : 7931450 After (middle performing run of three): $ ./db_bench_new --use-existing-db --benchmarks=multireadrandom --num=15000000 --cache_index_and_filter_blocks --bloom_bits=10 --threads=16 --cache_size=20000000 -partition_index_and_filters -batch_size=32 -multiread_batched -statistics --duration=20 2>&1 | egrep 'micros/op|block.cache.filter.hit|bloom.filter.(full|use)|number.multiget' multireadrandom : 21.024 micros/op 752963 ops/sec; (705188 of 863968 found) rocksdb.block.cache.filter.hit COUNT : 49856682 rocksdb.bloom.filter.useful COUNT : 45684579 rocksdb.bloom.filter.full.positive COUNT : 10395458 rocksdb.bloom.filter.full.true.positive COUNT : 9908456 rocksdb.number.multiget.get COUNT : 481984 rocksdb.number.multiget.keys.read COUNT : 15423488 rocksdb.number.multiget.bytes.read COUNT : 990845600 rocksdb.number.multiget.keys.found COUNT : 9908456 So that's about 25% higher throughput even for random keys Pull Request resolved: https://github.com/facebook/rocksdb/pull/6757 Test Plan: unit test included Reviewed By: anand1976 Differential Revision: D21243256 Pulled By: pdillinger fbshipit-source-id: 5644a1468d9e8c8575be02f4e04bc5d62dbbb57f
2020-04-28 21:46:13 +00:00
PERF_COUNTER_BY_LEVEL_ADD(bloom_filter_full_positive, after_keys,
rep_->level);
}
uint64_t filtered_keys = before_keys - after_keys;
if (filtered_keys) {
RecordTick(rep_->ioptions.stats, BLOOM_FILTER_USEFUL, filtered_keys);
Basic MultiGet support for partitioned filters (#6757) Summary: In MultiGet, access each applicable filter partition only once per batch, rather than for each applicable key. Also, * Fix Bloom stats for MultiGet * Fix/refactor MultiGetContext::Range::KeysLeft, including * Add efficient BitsSetToOne implementation * Assert that MultiGetContext::Range does not go beyond shift range Performance test: Generate db: $ ./db_bench --benchmarks=fillrandom --num=15000000 --cache_index_and_filter_blocks -bloom_bits=10 -partition_index_and_filters=true ... Before (middle performing run of three; note some missing Bloom stats): $ ./db_bench --use-existing-db --benchmarks=multireadrandom --num=15000000 --cache_index_and_filter_blocks --bloom_bits=10 --threads=16 --cache_size=20000000 -partition_index_and_filters -batch_size=32 -multiread_batched -statistics --duration=20 2>&1 | egrep 'micros/op|block.cache.filter.hit|bloom.filter.(full|use)|number.multiget' multireadrandom : 26.403 micros/op 597517 ops/sec; (548427 of 671968 found) rocksdb.block.cache.filter.hit COUNT : 83443275 rocksdb.bloom.filter.useful COUNT : 0 rocksdb.bloom.filter.full.positive COUNT : 0 rocksdb.bloom.filter.full.true.positive COUNT : 7931450 rocksdb.number.multiget.get COUNT : 385984 rocksdb.number.multiget.keys.read COUNT : 12351488 rocksdb.number.multiget.bytes.read COUNT : 793145000 rocksdb.number.multiget.keys.found COUNT : 7931450 After (middle performing run of three): $ ./db_bench_new --use-existing-db --benchmarks=multireadrandom --num=15000000 --cache_index_and_filter_blocks --bloom_bits=10 --threads=16 --cache_size=20000000 -partition_index_and_filters -batch_size=32 -multiread_batched -statistics --duration=20 2>&1 | egrep 'micros/op|block.cache.filter.hit|bloom.filter.(full|use)|number.multiget' multireadrandom : 21.024 micros/op 752963 ops/sec; (705188 of 863968 found) rocksdb.block.cache.filter.hit COUNT : 49856682 rocksdb.bloom.filter.useful COUNT : 45684579 rocksdb.bloom.filter.full.positive COUNT : 10395458 rocksdb.bloom.filter.full.true.positive COUNT : 9908456 rocksdb.number.multiget.get COUNT : 481984 rocksdb.number.multiget.keys.read COUNT : 15423488 rocksdb.number.multiget.bytes.read COUNT : 990845600 rocksdb.number.multiget.keys.found COUNT : 9908456 So that's about 25% higher throughput even for random keys Pull Request resolved: https://github.com/facebook/rocksdb/pull/6757 Test Plan: unit test included Reviewed By: anand1976 Differential Revision: D21243256 Pulled By: pdillinger fbshipit-source-id: 5644a1468d9e8c8575be02f4e04bc5d62dbbb57f
2020-04-28 21:46:13 +00:00
PERF_COUNTER_BY_LEVEL_ADD(bloom_filter_useful, filtered_keys,
rep_->level);
}
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
} else if (!read_options.total_order_seek && prefix_extractor &&
rep_->table_properties->prefix_extractor_name.compare(
prefix_extractor->Name()) == 0) {
Create a BlockCacheLookupContext to enable fine-grained block cache tracing. (#5421) Summary: BlockCacheLookupContext only contains the caller for now. We will trace block accesses at five places: 1. BlockBasedTable::GetFilter. 2. BlockBasedTable::GetUncompressedDict. 3. BlockBasedTable::MaybeReadAndLoadToCache. (To trace access on data, index, and range deletion block.) 4. BlockBasedTable::Get. (To trace the referenced key and whether the referenced key exists in a fetched data block.) 5. BlockBasedTable::MultiGet. (To trace the referenced key and whether the referenced key exists in a fetched data block.) We create the context at: 1. BlockBasedTable::Get. (kUserGet) 2. BlockBasedTable::MultiGet. (kUserMGet) 3. BlockBasedTable::NewIterator. (either kUserIterator, kCompaction, or external SST ingestion calls this function.) 4. BlockBasedTable::Open. (kPrefetch) 5. Index/Filter::CacheDependencies. (kPrefetch) 6. BlockBasedTable::ApproximateOffsetOf. (kCompaction or kUserApproximateSize). I loaded 1 million key-value pairs into the database and ran the readrandom benchmark with a single thread. I gave the block cache 10 GB to make sure all reads hit the block cache after warmup. The throughput is comparable. Throughput of this PR: 231334 ops/s. Throughput of the master branch: 238428 ops/s. Experiment setup: RocksDB: version 6.2 Date: Mon Jun 10 10:42:51 2019 CPU: 24 * Intel Core Processor (Skylake) CPUCache: 16384 KB Keys: 20 bytes each Values: 100 bytes each (100 bytes after compression) Entries: 1000000 Prefix: 20 bytes Keys per prefix: 0 RawSize: 114.4 MB (estimated) FileSize: 114.4 MB (estimated) Write rate: 0 bytes/second Read rate: 0 ops/second Compression: NoCompression Compression sampling rate: 0 Memtablerep: skip_list Perf Level: 1 Load command: ./db_bench --benchmarks="fillseq" --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --statistics --cache_index_and_filter_blocks --cache_size=10737418240 --disable_auto_compactions=1 --disable_wal=1 --compression_type=none --min_level_to_compress=-1 --compression_ratio=1 --num=1000000 Run command: ./db_bench --benchmarks="readrandom,stats" --use_existing_db --threads=1 --duration=120 --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --statistics --cache_index_and_filter_blocks --cache_size=10737418240 --disable_auto_compactions=1 --disable_wal=1 --compression_type=none --min_level_to_compress=-1 --compression_ratio=1 --num=1000000 --duration=120 TODOs: 1. Create a caller for external SST file ingestion and differentiate the callers for iterator. 2. Integrate tracer to trace block cache accesses. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5421 Differential Revision: D15704258 Pulled By: HaoyuHuang fbshipit-source-id: 4aa8a55f8cb1576ffb367bfa3186a91d8f06d93a
2019-06-10 22:30:05 +00:00
filter->PrefixesMayMatch(range, prefix_extractor, kNotValid, false,
lookup_context);
RecordTick(rep_->ioptions.stats, BLOOM_FILTER_PREFIX_CHECKED, before_keys);
Basic MultiGet support for partitioned filters (#6757) Summary: In MultiGet, access each applicable filter partition only once per batch, rather than for each applicable key. Also, * Fix Bloom stats for MultiGet * Fix/refactor MultiGetContext::Range::KeysLeft, including * Add efficient BitsSetToOne implementation * Assert that MultiGetContext::Range does not go beyond shift range Performance test: Generate db: $ ./db_bench --benchmarks=fillrandom --num=15000000 --cache_index_and_filter_blocks -bloom_bits=10 -partition_index_and_filters=true ... Before (middle performing run of three; note some missing Bloom stats): $ ./db_bench --use-existing-db --benchmarks=multireadrandom --num=15000000 --cache_index_and_filter_blocks --bloom_bits=10 --threads=16 --cache_size=20000000 -partition_index_and_filters -batch_size=32 -multiread_batched -statistics --duration=20 2>&1 | egrep 'micros/op|block.cache.filter.hit|bloom.filter.(full|use)|number.multiget' multireadrandom : 26.403 micros/op 597517 ops/sec; (548427 of 671968 found) rocksdb.block.cache.filter.hit COUNT : 83443275 rocksdb.bloom.filter.useful COUNT : 0 rocksdb.bloom.filter.full.positive COUNT : 0 rocksdb.bloom.filter.full.true.positive COUNT : 7931450 rocksdb.number.multiget.get COUNT : 385984 rocksdb.number.multiget.keys.read COUNT : 12351488 rocksdb.number.multiget.bytes.read COUNT : 793145000 rocksdb.number.multiget.keys.found COUNT : 7931450 After (middle performing run of three): $ ./db_bench_new --use-existing-db --benchmarks=multireadrandom --num=15000000 --cache_index_and_filter_blocks --bloom_bits=10 --threads=16 --cache_size=20000000 -partition_index_and_filters -batch_size=32 -multiread_batched -statistics --duration=20 2>&1 | egrep 'micros/op|block.cache.filter.hit|bloom.filter.(full|use)|number.multiget' multireadrandom : 21.024 micros/op 752963 ops/sec; (705188 of 863968 found) rocksdb.block.cache.filter.hit COUNT : 49856682 rocksdb.bloom.filter.useful COUNT : 45684579 rocksdb.bloom.filter.full.positive COUNT : 10395458 rocksdb.bloom.filter.full.true.positive COUNT : 9908456 rocksdb.number.multiget.get COUNT : 481984 rocksdb.number.multiget.keys.read COUNT : 15423488 rocksdb.number.multiget.bytes.read COUNT : 990845600 rocksdb.number.multiget.keys.found COUNT : 9908456 So that's about 25% higher throughput even for random keys Pull Request resolved: https://github.com/facebook/rocksdb/pull/6757 Test Plan: unit test included Reviewed By: anand1976 Differential Revision: D21243256 Pulled By: pdillinger fbshipit-source-id: 5644a1468d9e8c8575be02f4e04bc5d62dbbb57f
2020-04-28 21:46:13 +00:00
uint64_t after_keys = range->KeysLeft();
uint64_t filtered_keys = before_keys - after_keys;
if (filtered_keys) {
RecordTick(rep_->ioptions.stats, BLOOM_FILTER_PREFIX_USEFUL,
Basic MultiGet support for partitioned filters (#6757) Summary: In MultiGet, access each applicable filter partition only once per batch, rather than for each applicable key. Also, * Fix Bloom stats for MultiGet * Fix/refactor MultiGetContext::Range::KeysLeft, including * Add efficient BitsSetToOne implementation * Assert that MultiGetContext::Range does not go beyond shift range Performance test: Generate db: $ ./db_bench --benchmarks=fillrandom --num=15000000 --cache_index_and_filter_blocks -bloom_bits=10 -partition_index_and_filters=true ... Before (middle performing run of three; note some missing Bloom stats): $ ./db_bench --use-existing-db --benchmarks=multireadrandom --num=15000000 --cache_index_and_filter_blocks --bloom_bits=10 --threads=16 --cache_size=20000000 -partition_index_and_filters -batch_size=32 -multiread_batched -statistics --duration=20 2>&1 | egrep 'micros/op|block.cache.filter.hit|bloom.filter.(full|use)|number.multiget' multireadrandom : 26.403 micros/op 597517 ops/sec; (548427 of 671968 found) rocksdb.block.cache.filter.hit COUNT : 83443275 rocksdb.bloom.filter.useful COUNT : 0 rocksdb.bloom.filter.full.positive COUNT : 0 rocksdb.bloom.filter.full.true.positive COUNT : 7931450 rocksdb.number.multiget.get COUNT : 385984 rocksdb.number.multiget.keys.read COUNT : 12351488 rocksdb.number.multiget.bytes.read COUNT : 793145000 rocksdb.number.multiget.keys.found COUNT : 7931450 After (middle performing run of three): $ ./db_bench_new --use-existing-db --benchmarks=multireadrandom --num=15000000 --cache_index_and_filter_blocks --bloom_bits=10 --threads=16 --cache_size=20000000 -partition_index_and_filters -batch_size=32 -multiread_batched -statistics --duration=20 2>&1 | egrep 'micros/op|block.cache.filter.hit|bloom.filter.(full|use)|number.multiget' multireadrandom : 21.024 micros/op 752963 ops/sec; (705188 of 863968 found) rocksdb.block.cache.filter.hit COUNT : 49856682 rocksdb.bloom.filter.useful COUNT : 45684579 rocksdb.bloom.filter.full.positive COUNT : 10395458 rocksdb.bloom.filter.full.true.positive COUNT : 9908456 rocksdb.number.multiget.get COUNT : 481984 rocksdb.number.multiget.keys.read COUNT : 15423488 rocksdb.number.multiget.bytes.read COUNT : 990845600 rocksdb.number.multiget.keys.found COUNT : 9908456 So that's about 25% higher throughput even for random keys Pull Request resolved: https://github.com/facebook/rocksdb/pull/6757 Test Plan: unit test included Reviewed By: anand1976 Differential Revision: D21243256 Pulled By: pdillinger fbshipit-source-id: 5644a1468d9e8c8575be02f4e04bc5d62dbbb57f
2020-04-28 21:46:13 +00:00
filtered_keys);
}
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
}
}
Status BlockBasedTable::Get(const ReadOptions& read_options, const Slice& key,
GetContext* get_context,
const SliceTransform* prefix_extractor,
bool skip_filters) {
assert(key.size() >= 8); // key must be internal key
assert(get_context != nullptr);
Status s;
const bool no_io = read_options.read_tier == kBlockCacheTier;
FilterBlockReader* const filter =
!skip_filters ? rep_->filter.get() : nullptr;
// First check the full filter
// If full filter not useful, Then go into each block
uint64_t tracing_get_id = get_context->get_tracing_get_id();
BlockCacheLookupContext lookup_context{
TableReaderCaller::kUserGet, tracing_get_id,
/*get_from_user_specified_snapshot=*/read_options.snapshot != nullptr};
if (block_cache_tracer_ && block_cache_tracer_->is_tracing_enabled()) {
// Trace the key since it contains both user key and sequence number.
lookup_context.referenced_key = key.ToString();
lookup_context.get_from_user_specified_snapshot =
read_options.snapshot != nullptr;
}
TEST_SYNC_POINT("BlockBasedTable::Get:BeforeFilterMatch");
const bool may_match =
FullFilterKeyMayMatch(read_options, filter, key, no_io, prefix_extractor,
get_context, &lookup_context);
TEST_SYNC_POINT("BlockBasedTable::Get:AfterFilterMatch");
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 (!may_match) {
RecordTick(rep_->ioptions.stats, BLOOM_FILTER_USEFUL);
PERF_COUNTER_BY_LEVEL_ADD(bloom_filter_useful, 1, rep_->level);
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
} else {
IndexBlockIter iiter_on_stack;
// if prefix_extractor found in block differs from options, disable
// BlockPrefixIndex. Only do this check when index_type is kHashSearch.
bool need_upper_bound_check = false;
if (rep_->index_type == BlockBasedTableOptions::kHashSearch) {
need_upper_bound_check = PrefixExtractorChanged(
rep_->table_properties.get(), prefix_extractor);
}
Create a BlockCacheLookupContext to enable fine-grained block cache tracing. (#5421) Summary: BlockCacheLookupContext only contains the caller for now. We will trace block accesses at five places: 1. BlockBasedTable::GetFilter. 2. BlockBasedTable::GetUncompressedDict. 3. BlockBasedTable::MaybeReadAndLoadToCache. (To trace access on data, index, and range deletion block.) 4. BlockBasedTable::Get. (To trace the referenced key and whether the referenced key exists in a fetched data block.) 5. BlockBasedTable::MultiGet. (To trace the referenced key and whether the referenced key exists in a fetched data block.) We create the context at: 1. BlockBasedTable::Get. (kUserGet) 2. BlockBasedTable::MultiGet. (kUserMGet) 3. BlockBasedTable::NewIterator. (either kUserIterator, kCompaction, or external SST ingestion calls this function.) 4. BlockBasedTable::Open. (kPrefetch) 5. Index/Filter::CacheDependencies. (kPrefetch) 6. BlockBasedTable::ApproximateOffsetOf. (kCompaction or kUserApproximateSize). I loaded 1 million key-value pairs into the database and ran the readrandom benchmark with a single thread. I gave the block cache 10 GB to make sure all reads hit the block cache after warmup. The throughput is comparable. Throughput of this PR: 231334 ops/s. Throughput of the master branch: 238428 ops/s. Experiment setup: RocksDB: version 6.2 Date: Mon Jun 10 10:42:51 2019 CPU: 24 * Intel Core Processor (Skylake) CPUCache: 16384 KB Keys: 20 bytes each Values: 100 bytes each (100 bytes after compression) Entries: 1000000 Prefix: 20 bytes Keys per prefix: 0 RawSize: 114.4 MB (estimated) FileSize: 114.4 MB (estimated) Write rate: 0 bytes/second Read rate: 0 ops/second Compression: NoCompression Compression sampling rate: 0 Memtablerep: skip_list Perf Level: 1 Load command: ./db_bench --benchmarks="fillseq" --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --statistics --cache_index_and_filter_blocks --cache_size=10737418240 --disable_auto_compactions=1 --disable_wal=1 --compression_type=none --min_level_to_compress=-1 --compression_ratio=1 --num=1000000 Run command: ./db_bench --benchmarks="readrandom,stats" --use_existing_db --threads=1 --duration=120 --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --statistics --cache_index_and_filter_blocks --cache_size=10737418240 --disable_auto_compactions=1 --disable_wal=1 --compression_type=none --min_level_to_compress=-1 --compression_ratio=1 --num=1000000 --duration=120 TODOs: 1. Create a caller for external SST file ingestion and differentiate the callers for iterator. 2. Integrate tracer to trace block cache accesses. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5421 Differential Revision: D15704258 Pulled By: HaoyuHuang fbshipit-source-id: 4aa8a55f8cb1576ffb367bfa3186a91d8f06d93a
2019-06-10 22:30:05 +00:00
auto iiter =
NewIndexIterator(read_options, need_upper_bound_check, &iiter_on_stack,
get_context, &lookup_context);
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
std::unique_ptr<InternalIteratorBase<IndexValue>> iiter_unique_ptr;
if (iiter != &iiter_on_stack) {
iiter_unique_ptr.reset(iiter);
}
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
Add support for timestamp in Get/Put (#5079) Summary: It's useful to be able to (optionally) associate key-value pairs with user-provided timestamps. This PR is an early effort towards this goal and continues the work of facebook#4942. A suite of new unit tests exist in DBBasicTestWithTimestampWithParam. Support for timestamp requires the user to provide timestamp as a slice in `ReadOptions` and `WriteOptions`. All timestamps of the same database must share the same length, format, etc. The format of the timestamp is the same throughout the same database, and the user is responsible for providing a comparator function (Comparator) to order the <key, timestamp> tuples. Once created, the format and length of the timestamp cannot change (at least for now). Test plan (on devserver): ``` $COMPILE_WITH_ASAN=1 make -j32 all $./db_basic_test --gtest_filter=Timestamp/DBBasicTestWithTimestampWithParam.PutAndGet/* $make check ``` All tests must pass. We also run the following db_bench tests to verify whether there is regression on Get/Put while timestamp is not enabled. ``` $TEST_TMPDIR=/dev/shm ./db_bench -benchmarks=fillseq,readrandom -num=1000000 $TEST_TMPDIR=/dev/shm ./db_bench -benchmarks=fillrandom -num=1000000 ``` Repeat for 6 times for both versions. Results are as follows: ``` | | readrandom | fillrandom | | master | 16.77 MB/s | 47.05 MB/s | | PR5079 | 16.44 MB/s | 47.03 MB/s | ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/5079 Differential Revision: D15132946 Pulled By: riversand963 fbshipit-source-id: 833a0d657eac21182f0f206c910a6438154c742c
2019-06-06 06:07:28 +00:00
size_t ts_sz =
rep_->internal_comparator.user_comparator()->timestamp_size();
bool matched = false; // if such user key matched a key in SST
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
bool done = false;
for (iiter->Seek(key); iiter->Valid() && !done; iiter->Next()) {
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
IndexValue v = iiter->value();
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
bool not_exist_in_filter =
filter != nullptr && filter->IsBlockBased() == true &&
Add support for timestamp in Get/Put (#5079) Summary: It's useful to be able to (optionally) associate key-value pairs with user-provided timestamps. This PR is an early effort towards this goal and continues the work of facebook#4942. A suite of new unit tests exist in DBBasicTestWithTimestampWithParam. Support for timestamp requires the user to provide timestamp as a slice in `ReadOptions` and `WriteOptions`. All timestamps of the same database must share the same length, format, etc. The format of the timestamp is the same throughout the same database, and the user is responsible for providing a comparator function (Comparator) to order the <key, timestamp> tuples. Once created, the format and length of the timestamp cannot change (at least for now). Test plan (on devserver): ``` $COMPILE_WITH_ASAN=1 make -j32 all $./db_basic_test --gtest_filter=Timestamp/DBBasicTestWithTimestampWithParam.PutAndGet/* $make check ``` All tests must pass. We also run the following db_bench tests to verify whether there is regression on Get/Put while timestamp is not enabled. ``` $TEST_TMPDIR=/dev/shm ./db_bench -benchmarks=fillseq,readrandom -num=1000000 $TEST_TMPDIR=/dev/shm ./db_bench -benchmarks=fillrandom -num=1000000 ``` Repeat for 6 times for both versions. Results are as follows: ``` | | readrandom | fillrandom | | master | 16.77 MB/s | 47.05 MB/s | | PR5079 | 16.44 MB/s | 47.03 MB/s | ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/5079 Differential Revision: D15132946 Pulled By: riversand963 fbshipit-source-id: 833a0d657eac21182f0f206c910a6438154c742c
2019-06-06 06:07:28 +00:00
!filter->KeyMayMatch(ExtractUserKeyAndStripTimestamp(key, ts_sz),
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
prefix_extractor, v.handle.offset(), no_io,
/*const_ikey_ptr=*/nullptr, get_context,
&lookup_context);
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
if (not_exist_in_filter) {
// Not found
// TODO: think about interaction with Merge. If a user key cannot
// cross one data block, we should be fine.
RecordTick(rep_->ioptions.stats, BLOOM_FILTER_USEFUL);
PERF_COUNTER_BY_LEVEL_ADD(bloom_filter_useful, 1, rep_->level);
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
break;
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
}
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
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
if (!v.first_internal_key.empty() && !skip_filters &&
UserComparatorWrapper(rep_->internal_comparator.user_comparator())
.CompareWithoutTimestamp(
ExtractUserKey(key),
ExtractUserKey(v.first_internal_key)) < 0) {
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
// The requested key falls between highest key in previous block and
// lowest key in current block.
break;
}
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
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
BlockCacheLookupContext lookup_data_block_context{
TableReaderCaller::kUserGet, tracing_get_id,
/*get_from_user_specified_snapshot=*/read_options.snapshot !=
nullptr};
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
bool does_referenced_key_exist = false;
DataBlockIter biter;
uint64_t referenced_data_size = 0;
NewDataBlockIterator<DataBlockIter>(
read_options, v.handle, &biter, BlockType::kData, get_context,
&lookup_data_block_context,
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
/*s=*/Status(), /*prefetch_buffer*/ nullptr);
if (no_io && biter.status().IsIncomplete()) {
// couldn't get block from block_cache
// Update Saver.state to Found because we are only looking for
// whether we can guarantee the key is not there when "no_io" is set
get_context->MarkKeyMayExist();
s = biter.status();
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
break;
}
if (!biter.status().ok()) {
s = biter.status();
break;
}
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
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
bool may_exist = biter.SeekForGet(key);
// If user-specified timestamp is supported, we cannot end the search
// just because hash index lookup indicates the key+ts does not exist.
if (!may_exist && ts_sz == 0) {
// HashSeek cannot find the key this block and the the iter is not
// the end of the block, i.e. cannot be in the following blocks
// either. In this case, the seek_key cannot be found, so we break
// from the top level for-loop.
done = true;
} else {
// Call the *saver function on each entry/block until it returns false
for (; biter.Valid(); biter.Next()) {
ParsedInternalKey parsed_key;
Status pik_status = ParseInternalKey(
biter.key(), &parsed_key, false /* log_err_key */); // TODO
if (!pik_status.ok()) {
s = pik_status;
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
}
if (!get_context->SaveValue(
parsed_key, biter.value(), &matched,
biter.IsValuePinned() ? &biter : nullptr)) {
if (get_context->State() == GetContext::GetState::kFound) {
does_referenced_key_exist = true;
referenced_data_size = biter.key().size() + biter.value().size();
}
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
done = true;
break;
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
}
}
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
s = biter.status();
}
// Write the block cache access record.
if (block_cache_tracer_ && block_cache_tracer_->is_tracing_enabled()) {
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
// Avoid making copy of block_key, cf_name, and referenced_key when
// constructing the access record.
Slice referenced_key;
if (does_referenced_key_exist) {
referenced_key = biter.key();
} else {
referenced_key = key;
}
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
BlockCacheTraceRecord access_record(
rep_->ioptions.clock->NowMicros(),
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
/*block_key=*/"", lookup_data_block_context.block_type,
lookup_data_block_context.block_size, rep_->cf_id_for_tracing(),
/*cf_name=*/"", rep_->level_for_tracing(),
rep_->sst_number_for_tracing(), lookup_data_block_context.caller,
lookup_data_block_context.is_cache_hit,
lookup_data_block_context.no_insert,
lookup_data_block_context.get_id,
lookup_data_block_context.get_from_user_specified_snapshot,
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
/*referenced_key=*/"", referenced_data_size,
lookup_data_block_context.num_keys_in_block,
does_referenced_key_exist);
// TODO: Should handle status here?
block_cache_tracer_
->WriteBlockAccess(access_record,
lookup_data_block_context.block_key,
rep_->cf_name_for_tracing(), referenced_key)
.PermitUncheckedError();
}
if (done) {
// Avoid the extra Next which is expensive in two-level indexes
break;
}
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
}
if (matched && filter != nullptr && !filter->IsBlockBased()) {
RecordTick(rep_->ioptions.stats, BLOOM_FILTER_FULL_TRUE_POSITIVE);
PERF_COUNTER_BY_LEVEL_ADD(bloom_filter_full_true_positive, 1,
rep_->level);
}
if (s.ok() && !iiter->status().IsNotFound()) {
s = iiter->status();
}
}
return s;
}
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
using MultiGetRange = MultiGetContext::Range;
void BlockBasedTable::MultiGet(const ReadOptions& read_options,
const MultiGetRange* mget_range,
const SliceTransform* prefix_extractor,
bool skip_filters) {
Basic MultiGet support for partitioned filters (#6757) Summary: In MultiGet, access each applicable filter partition only once per batch, rather than for each applicable key. Also, * Fix Bloom stats for MultiGet * Fix/refactor MultiGetContext::Range::KeysLeft, including * Add efficient BitsSetToOne implementation * Assert that MultiGetContext::Range does not go beyond shift range Performance test: Generate db: $ ./db_bench --benchmarks=fillrandom --num=15000000 --cache_index_and_filter_blocks -bloom_bits=10 -partition_index_and_filters=true ... Before (middle performing run of three; note some missing Bloom stats): $ ./db_bench --use-existing-db --benchmarks=multireadrandom --num=15000000 --cache_index_and_filter_blocks --bloom_bits=10 --threads=16 --cache_size=20000000 -partition_index_and_filters -batch_size=32 -multiread_batched -statistics --duration=20 2>&1 | egrep 'micros/op|block.cache.filter.hit|bloom.filter.(full|use)|number.multiget' multireadrandom : 26.403 micros/op 597517 ops/sec; (548427 of 671968 found) rocksdb.block.cache.filter.hit COUNT : 83443275 rocksdb.bloom.filter.useful COUNT : 0 rocksdb.bloom.filter.full.positive COUNT : 0 rocksdb.bloom.filter.full.true.positive COUNT : 7931450 rocksdb.number.multiget.get COUNT : 385984 rocksdb.number.multiget.keys.read COUNT : 12351488 rocksdb.number.multiget.bytes.read COUNT : 793145000 rocksdb.number.multiget.keys.found COUNT : 7931450 After (middle performing run of three): $ ./db_bench_new --use-existing-db --benchmarks=multireadrandom --num=15000000 --cache_index_and_filter_blocks --bloom_bits=10 --threads=16 --cache_size=20000000 -partition_index_and_filters -batch_size=32 -multiread_batched -statistics --duration=20 2>&1 | egrep 'micros/op|block.cache.filter.hit|bloom.filter.(full|use)|number.multiget' multireadrandom : 21.024 micros/op 752963 ops/sec; (705188 of 863968 found) rocksdb.block.cache.filter.hit COUNT : 49856682 rocksdb.bloom.filter.useful COUNT : 45684579 rocksdb.bloom.filter.full.positive COUNT : 10395458 rocksdb.bloom.filter.full.true.positive COUNT : 9908456 rocksdb.number.multiget.get COUNT : 481984 rocksdb.number.multiget.keys.read COUNT : 15423488 rocksdb.number.multiget.bytes.read COUNT : 990845600 rocksdb.number.multiget.keys.found COUNT : 9908456 So that's about 25% higher throughput even for random keys Pull Request resolved: https://github.com/facebook/rocksdb/pull/6757 Test Plan: unit test included Reviewed By: anand1976 Differential Revision: D21243256 Pulled By: pdillinger fbshipit-source-id: 5644a1468d9e8c8575be02f4e04bc5d62dbbb57f
2020-04-28 21:46:13 +00:00
if (mget_range->empty()) {
// Caller should ensure non-empty (performance bug)
assert(false);
return; // Nothing to do
}
FilterBlockReader* const filter =
!skip_filters ? rep_->filter.get() : nullptr;
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
MultiGetRange sst_file_range(*mget_range, mget_range->begin(),
mget_range->end());
// First check the full filter
// If full filter not useful, Then go into each block
const bool no_io = read_options.read_tier == kBlockCacheTier;
uint64_t tracing_mget_id = BlockCacheTraceHelper::kReservedGetId;
Basic MultiGet support for partitioned filters (#6757) Summary: In MultiGet, access each applicable filter partition only once per batch, rather than for each applicable key. Also, * Fix Bloom stats for MultiGet * Fix/refactor MultiGetContext::Range::KeysLeft, including * Add efficient BitsSetToOne implementation * Assert that MultiGetContext::Range does not go beyond shift range Performance test: Generate db: $ ./db_bench --benchmarks=fillrandom --num=15000000 --cache_index_and_filter_blocks -bloom_bits=10 -partition_index_and_filters=true ... Before (middle performing run of three; note some missing Bloom stats): $ ./db_bench --use-existing-db --benchmarks=multireadrandom --num=15000000 --cache_index_and_filter_blocks --bloom_bits=10 --threads=16 --cache_size=20000000 -partition_index_and_filters -batch_size=32 -multiread_batched -statistics --duration=20 2>&1 | egrep 'micros/op|block.cache.filter.hit|bloom.filter.(full|use)|number.multiget' multireadrandom : 26.403 micros/op 597517 ops/sec; (548427 of 671968 found) rocksdb.block.cache.filter.hit COUNT : 83443275 rocksdb.bloom.filter.useful COUNT : 0 rocksdb.bloom.filter.full.positive COUNT : 0 rocksdb.bloom.filter.full.true.positive COUNT : 7931450 rocksdb.number.multiget.get COUNT : 385984 rocksdb.number.multiget.keys.read COUNT : 12351488 rocksdb.number.multiget.bytes.read COUNT : 793145000 rocksdb.number.multiget.keys.found COUNT : 7931450 After (middle performing run of three): $ ./db_bench_new --use-existing-db --benchmarks=multireadrandom --num=15000000 --cache_index_and_filter_blocks --bloom_bits=10 --threads=16 --cache_size=20000000 -partition_index_and_filters -batch_size=32 -multiread_batched -statistics --duration=20 2>&1 | egrep 'micros/op|block.cache.filter.hit|bloom.filter.(full|use)|number.multiget' multireadrandom : 21.024 micros/op 752963 ops/sec; (705188 of 863968 found) rocksdb.block.cache.filter.hit COUNT : 49856682 rocksdb.bloom.filter.useful COUNT : 45684579 rocksdb.bloom.filter.full.positive COUNT : 10395458 rocksdb.bloom.filter.full.true.positive COUNT : 9908456 rocksdb.number.multiget.get COUNT : 481984 rocksdb.number.multiget.keys.read COUNT : 15423488 rocksdb.number.multiget.bytes.read COUNT : 990845600 rocksdb.number.multiget.keys.found COUNT : 9908456 So that's about 25% higher throughput even for random keys Pull Request resolved: https://github.com/facebook/rocksdb/pull/6757 Test Plan: unit test included Reviewed By: anand1976 Differential Revision: D21243256 Pulled By: pdillinger fbshipit-source-id: 5644a1468d9e8c8575be02f4e04bc5d62dbbb57f
2020-04-28 21:46:13 +00:00
if (sst_file_range.begin()->get_context) {
tracing_mget_id = sst_file_range.begin()->get_context->get_tracing_get_id();
}
BlockCacheLookupContext lookup_context{
TableReaderCaller::kUserMultiGet, tracing_mget_id,
/*get_from_user_specified_snapshot=*/read_options.snapshot != nullptr};
FullFilterKeysMayMatch(read_options, filter, &sst_file_range, no_io,
prefix_extractor, &lookup_context);
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
Basic MultiGet support for partitioned filters (#6757) Summary: In MultiGet, access each applicable filter partition only once per batch, rather than for each applicable key. Also, * Fix Bloom stats for MultiGet * Fix/refactor MultiGetContext::Range::KeysLeft, including * Add efficient BitsSetToOne implementation * Assert that MultiGetContext::Range does not go beyond shift range Performance test: Generate db: $ ./db_bench --benchmarks=fillrandom --num=15000000 --cache_index_and_filter_blocks -bloom_bits=10 -partition_index_and_filters=true ... Before (middle performing run of three; note some missing Bloom stats): $ ./db_bench --use-existing-db --benchmarks=multireadrandom --num=15000000 --cache_index_and_filter_blocks --bloom_bits=10 --threads=16 --cache_size=20000000 -partition_index_and_filters -batch_size=32 -multiread_batched -statistics --duration=20 2>&1 | egrep 'micros/op|block.cache.filter.hit|bloom.filter.(full|use)|number.multiget' multireadrandom : 26.403 micros/op 597517 ops/sec; (548427 of 671968 found) rocksdb.block.cache.filter.hit COUNT : 83443275 rocksdb.bloom.filter.useful COUNT : 0 rocksdb.bloom.filter.full.positive COUNT : 0 rocksdb.bloom.filter.full.true.positive COUNT : 7931450 rocksdb.number.multiget.get COUNT : 385984 rocksdb.number.multiget.keys.read COUNT : 12351488 rocksdb.number.multiget.bytes.read COUNT : 793145000 rocksdb.number.multiget.keys.found COUNT : 7931450 After (middle performing run of three): $ ./db_bench_new --use-existing-db --benchmarks=multireadrandom --num=15000000 --cache_index_and_filter_blocks --bloom_bits=10 --threads=16 --cache_size=20000000 -partition_index_and_filters -batch_size=32 -multiread_batched -statistics --duration=20 2>&1 | egrep 'micros/op|block.cache.filter.hit|bloom.filter.(full|use)|number.multiget' multireadrandom : 21.024 micros/op 752963 ops/sec; (705188 of 863968 found) rocksdb.block.cache.filter.hit COUNT : 49856682 rocksdb.bloom.filter.useful COUNT : 45684579 rocksdb.bloom.filter.full.positive COUNT : 10395458 rocksdb.bloom.filter.full.true.positive COUNT : 9908456 rocksdb.number.multiget.get COUNT : 481984 rocksdb.number.multiget.keys.read COUNT : 15423488 rocksdb.number.multiget.bytes.read COUNT : 990845600 rocksdb.number.multiget.keys.found COUNT : 9908456 So that's about 25% higher throughput even for random keys Pull Request resolved: https://github.com/facebook/rocksdb/pull/6757 Test Plan: unit test included Reviewed By: anand1976 Differential Revision: D21243256 Pulled By: pdillinger fbshipit-source-id: 5644a1468d9e8c8575be02f4e04bc5d62dbbb57f
2020-04-28 21:46:13 +00:00
if (!sst_file_range.empty()) {
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
IndexBlockIter iiter_on_stack;
// if prefix_extractor found in block differs from options, disable
// BlockPrefixIndex. Only do this check when index_type is kHashSearch.
bool need_upper_bound_check = false;
if (rep_->index_type == BlockBasedTableOptions::kHashSearch) {
need_upper_bound_check = PrefixExtractorChanged(
rep_->table_properties.get(), prefix_extractor);
}
auto iiter =
NewIndexIterator(read_options, need_upper_bound_check, &iiter_on_stack,
Create a BlockCacheLookupContext to enable fine-grained block cache tracing. (#5421) Summary: BlockCacheLookupContext only contains the caller for now. We will trace block accesses at five places: 1. BlockBasedTable::GetFilter. 2. BlockBasedTable::GetUncompressedDict. 3. BlockBasedTable::MaybeReadAndLoadToCache. (To trace access on data, index, and range deletion block.) 4. BlockBasedTable::Get. (To trace the referenced key and whether the referenced key exists in a fetched data block.) 5. BlockBasedTable::MultiGet. (To trace the referenced key and whether the referenced key exists in a fetched data block.) We create the context at: 1. BlockBasedTable::Get. (kUserGet) 2. BlockBasedTable::MultiGet. (kUserMGet) 3. BlockBasedTable::NewIterator. (either kUserIterator, kCompaction, or external SST ingestion calls this function.) 4. BlockBasedTable::Open. (kPrefetch) 5. Index/Filter::CacheDependencies. (kPrefetch) 6. BlockBasedTable::ApproximateOffsetOf. (kCompaction or kUserApproximateSize). I loaded 1 million key-value pairs into the database and ran the readrandom benchmark with a single thread. I gave the block cache 10 GB to make sure all reads hit the block cache after warmup. The throughput is comparable. Throughput of this PR: 231334 ops/s. Throughput of the master branch: 238428 ops/s. Experiment setup: RocksDB: version 6.2 Date: Mon Jun 10 10:42:51 2019 CPU: 24 * Intel Core Processor (Skylake) CPUCache: 16384 KB Keys: 20 bytes each Values: 100 bytes each (100 bytes after compression) Entries: 1000000 Prefix: 20 bytes Keys per prefix: 0 RawSize: 114.4 MB (estimated) FileSize: 114.4 MB (estimated) Write rate: 0 bytes/second Read rate: 0 ops/second Compression: NoCompression Compression sampling rate: 0 Memtablerep: skip_list Perf Level: 1 Load command: ./db_bench --benchmarks="fillseq" --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --statistics --cache_index_and_filter_blocks --cache_size=10737418240 --disable_auto_compactions=1 --disable_wal=1 --compression_type=none --min_level_to_compress=-1 --compression_ratio=1 --num=1000000 Run command: ./db_bench --benchmarks="readrandom,stats" --use_existing_db --threads=1 --duration=120 --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --statistics --cache_index_and_filter_blocks --cache_size=10737418240 --disable_auto_compactions=1 --disable_wal=1 --compression_type=none --min_level_to_compress=-1 --compression_ratio=1 --num=1000000 --duration=120 TODOs: 1. Create a caller for external SST file ingestion and differentiate the callers for iterator. 2. Integrate tracer to trace block cache accesses. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5421 Differential Revision: D15704258 Pulled By: HaoyuHuang fbshipit-source-id: 4aa8a55f8cb1576ffb367bfa3186a91d8f06d93a
2019-06-10 22:30:05 +00:00
sst_file_range.begin()->get_context, &lookup_context);
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
std::unique_ptr<InternalIteratorBase<IndexValue>> iiter_unique_ptr;
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 (iiter != &iiter_on_stack) {
iiter_unique_ptr.reset(iiter);
}
uint64_t offset = std::numeric_limits<uint64_t>::max();
autovector<BlockHandle, MultiGetContext::MAX_BATCH_SIZE> block_handles;
autovector<CachableEntry<Block>, MultiGetContext::MAX_BATCH_SIZE> results;
autovector<Status, MultiGetContext::MAX_BATCH_SIZE> statuses;
char stack_buf[kMultiGetReadStackBufSize];
std::unique_ptr<char[]> block_buf;
{
MultiGetRange data_block_range(sst_file_range, sst_file_range.begin(),
sst_file_range.end());
Parallelize secondary cache lookup in MultiGet (#8405) Summary: Implement the ```WaitAll()``` interface in ```LRUCache``` to allow callers to issue multiple lookups in parallel and wait for all of them to complete. Modify ```MultiGet``` to use this to parallelize the secondary cache lookups in order to reduce the overall latency. A call to ```cache->Lookup()``` returns a handle that has an incomplete value (nullptr), and the caller can call ```cache->IsReady()``` to check whether the lookup is complete, and pass a vector of handles to ```WaitAll``` to wait for completion. If any of the lookups fail, ```MultiGet``` will read the block from the SST file. Another change in this PR is to rename ```SecondaryCacheHandle``` to ```SecondaryCacheResultHandle``` as it more accurately describes the return result of the secondary cache lookup, which is more like a future. Tests: 1. Add unit tests in lru_cache_test 2. Benchmark results with no secondary cache configured Master - ``` readrandom : 41.175 micros/op 388562 ops/sec; 106.7 MB/s (7277999 of 7277999 found) readrandom : 41.217 micros/op 388160 ops/sec; 106.6 MB/s (7274999 of 7274999 found) multireadrandom : 10.309 micros/op 1552082 ops/sec; (28908992 of 28908992 found) multireadrandom : 10.321 micros/op 1550218 ops/sec; (29081984 of 29081984 found) ``` This PR - ``` readrandom : 41.158 micros/op 388723 ops/sec; 106.8 MB/s (7290999 of 7290999 found) readrandom : 41.185 micros/op 388463 ops/sec; 106.7 MB/s (7287999 of 7287999 found) multireadrandom : 10.277 micros/op 1556801 ops/sec; (29346944 of 29346944 found) multireadrandom : 10.253 micros/op 1560539 ops/sec; (29274944 of 29274944 found) ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/8405 Reviewed By: zhichao-cao Differential Revision: D29190509 Pulled By: anand1976 fbshipit-source-id: 6f8eff6246712af8a297cfe22ea0d1c3b2a01bb0
2021-06-18 16:35:03 +00:00
std::vector<Cache::Handle*> cache_handles;
bool wait_for_cache_results = false;
CachableEntry<UncompressionDict> uncompression_dict;
Status uncompression_dict_status;
uncompression_dict_status.PermitUncheckedError();
bool uncompression_dict_inited = false;
size_t total_len = 0;
ReadOptions ro = read_options;
ro.read_tier = kBlockCacheTier;
for (auto miter = data_block_range.begin();
miter != data_block_range.end(); ++miter) {
const Slice& key = miter->ikey;
iiter->Seek(miter->ikey);
IndexValue v;
if (iiter->Valid()) {
v = iiter->value();
}
if (!iiter->Valid() ||
(!v.first_internal_key.empty() && !skip_filters &&
UserComparatorWrapper(rep_->internal_comparator.user_comparator())
.CompareWithoutTimestamp(
ExtractUserKey(key),
ExtractUserKey(v.first_internal_key)) < 0)) {
// The requested key falls between highest key in previous block and
// lowest key in current block.
if (!iiter->status().IsNotFound()) {
*(miter->s) = iiter->status();
}
data_block_range.SkipKey(miter);
sst_file_range.SkipKey(miter);
continue;
}
if (!uncompression_dict_inited && rep_->uncompression_dict_reader) {
uncompression_dict_status =
rep_->uncompression_dict_reader->GetOrReadUncompressionDictionary(
nullptr /* prefetch_buffer */, no_io,
sst_file_range.begin()->get_context, &lookup_context,
&uncompression_dict);
uncompression_dict_inited = true;
}
if (!uncompression_dict_status.ok()) {
assert(!uncompression_dict_status.IsNotFound());
*(miter->s) = uncompression_dict_status;
data_block_range.SkipKey(miter);
sst_file_range.SkipKey(miter);
continue;
}
statuses.emplace_back();
results.emplace_back();
if (v.handle.offset() == offset) {
// We're going to reuse the block for this key later on. No need to
// look it up now. Place a null handle
block_handles.emplace_back(BlockHandle::NullBlockHandle());
continue;
}
// Lookup the cache for the given data block referenced by an index
// iterator value (i.e BlockHandle). If it exists in the cache,
// initialize block to the contents of the data block.
offset = v.handle.offset();
BlockHandle handle = v.handle;
BlockCacheLookupContext lookup_data_block_context(
TableReaderCaller::kUserMultiGet);
const UncompressionDict& dict = uncompression_dict.GetValue()
? *uncompression_dict.GetValue()
: UncompressionDict::GetEmptyDict();
Status s = RetrieveBlock(
nullptr, ro, handle, dict, &(results.back()), BlockType::kData,
miter->get_context, &lookup_data_block_context,
Parallelize secondary cache lookup in MultiGet (#8405) Summary: Implement the ```WaitAll()``` interface in ```LRUCache``` to allow callers to issue multiple lookups in parallel and wait for all of them to complete. Modify ```MultiGet``` to use this to parallelize the secondary cache lookups in order to reduce the overall latency. A call to ```cache->Lookup()``` returns a handle that has an incomplete value (nullptr), and the caller can call ```cache->IsReady()``` to check whether the lookup is complete, and pass a vector of handles to ```WaitAll``` to wait for completion. If any of the lookups fail, ```MultiGet``` will read the block from the SST file. Another change in this PR is to rename ```SecondaryCacheHandle``` to ```SecondaryCacheResultHandle``` as it more accurately describes the return result of the secondary cache lookup, which is more like a future. Tests: 1. Add unit tests in lru_cache_test 2. Benchmark results with no secondary cache configured Master - ``` readrandom : 41.175 micros/op 388562 ops/sec; 106.7 MB/s (7277999 of 7277999 found) readrandom : 41.217 micros/op 388160 ops/sec; 106.6 MB/s (7274999 of 7274999 found) multireadrandom : 10.309 micros/op 1552082 ops/sec; (28908992 of 28908992 found) multireadrandom : 10.321 micros/op 1550218 ops/sec; (29081984 of 29081984 found) ``` This PR - ``` readrandom : 41.158 micros/op 388723 ops/sec; 106.8 MB/s (7290999 of 7290999 found) readrandom : 41.185 micros/op 388463 ops/sec; 106.7 MB/s (7287999 of 7287999 found) multireadrandom : 10.277 micros/op 1556801 ops/sec; (29346944 of 29346944 found) multireadrandom : 10.253 micros/op 1560539 ops/sec; (29274944 of 29274944 found) ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/8405 Reviewed By: zhichao-cao Differential Revision: D29190509 Pulled By: anand1976 fbshipit-source-id: 6f8eff6246712af8a297cfe22ea0d1c3b2a01bb0
2021-06-18 16:35:03 +00:00
/* for_compaction */ false, /* use_cache */ true,
/* wait_for_cache */ false);
if (s.IsIncomplete()) {
s = Status::OK();
}
if (s.ok() && !results.back().IsEmpty()) {
// Since we have a valid handle, check the value. If its nullptr,
// it means the cache is waiting for the final result and we're
// supposed to call WaitAll() to wait for the result.
if (results.back().GetValue() != nullptr) {
Parallelize secondary cache lookup in MultiGet (#8405) Summary: Implement the ```WaitAll()``` interface in ```LRUCache``` to allow callers to issue multiple lookups in parallel and wait for all of them to complete. Modify ```MultiGet``` to use this to parallelize the secondary cache lookups in order to reduce the overall latency. A call to ```cache->Lookup()``` returns a handle that has an incomplete value (nullptr), and the caller can call ```cache->IsReady()``` to check whether the lookup is complete, and pass a vector of handles to ```WaitAll``` to wait for completion. If any of the lookups fail, ```MultiGet``` will read the block from the SST file. Another change in this PR is to rename ```SecondaryCacheHandle``` to ```SecondaryCacheResultHandle``` as it more accurately describes the return result of the secondary cache lookup, which is more like a future. Tests: 1. Add unit tests in lru_cache_test 2. Benchmark results with no secondary cache configured Master - ``` readrandom : 41.175 micros/op 388562 ops/sec; 106.7 MB/s (7277999 of 7277999 found) readrandom : 41.217 micros/op 388160 ops/sec; 106.6 MB/s (7274999 of 7274999 found) multireadrandom : 10.309 micros/op 1552082 ops/sec; (28908992 of 28908992 found) multireadrandom : 10.321 micros/op 1550218 ops/sec; (29081984 of 29081984 found) ``` This PR - ``` readrandom : 41.158 micros/op 388723 ops/sec; 106.8 MB/s (7290999 of 7290999 found) readrandom : 41.185 micros/op 388463 ops/sec; 106.7 MB/s (7287999 of 7287999 found) multireadrandom : 10.277 micros/op 1556801 ops/sec; (29346944 of 29346944 found) multireadrandom : 10.253 micros/op 1560539 ops/sec; (29274944 of 29274944 found) ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/8405 Reviewed By: zhichao-cao Differential Revision: D29190509 Pulled By: anand1976 fbshipit-source-id: 6f8eff6246712af8a297cfe22ea0d1c3b2a01bb0
2021-06-18 16:35:03 +00:00
// Found it in the cache. Add NULL handle to indicate there is
// nothing to read from disk.
if (results.back().GetCacheHandle()) {
results.back().UpdateCachedValue();
}
block_handles.emplace_back(BlockHandle::NullBlockHandle());
Parallelize secondary cache lookup in MultiGet (#8405) Summary: Implement the ```WaitAll()``` interface in ```LRUCache``` to allow callers to issue multiple lookups in parallel and wait for all of them to complete. Modify ```MultiGet``` to use this to parallelize the secondary cache lookups in order to reduce the overall latency. A call to ```cache->Lookup()``` returns a handle that has an incomplete value (nullptr), and the caller can call ```cache->IsReady()``` to check whether the lookup is complete, and pass a vector of handles to ```WaitAll``` to wait for completion. If any of the lookups fail, ```MultiGet``` will read the block from the SST file. Another change in this PR is to rename ```SecondaryCacheHandle``` to ```SecondaryCacheResultHandle``` as it more accurately describes the return result of the secondary cache lookup, which is more like a future. Tests: 1. Add unit tests in lru_cache_test 2. Benchmark results with no secondary cache configured Master - ``` readrandom : 41.175 micros/op 388562 ops/sec; 106.7 MB/s (7277999 of 7277999 found) readrandom : 41.217 micros/op 388160 ops/sec; 106.6 MB/s (7274999 of 7274999 found) multireadrandom : 10.309 micros/op 1552082 ops/sec; (28908992 of 28908992 found) multireadrandom : 10.321 micros/op 1550218 ops/sec; (29081984 of 29081984 found) ``` This PR - ``` readrandom : 41.158 micros/op 388723 ops/sec; 106.8 MB/s (7290999 of 7290999 found) readrandom : 41.185 micros/op 388463 ops/sec; 106.7 MB/s (7287999 of 7287999 found) multireadrandom : 10.277 micros/op 1556801 ops/sec; (29346944 of 29346944 found) multireadrandom : 10.253 micros/op 1560539 ops/sec; (29274944 of 29274944 found) ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/8405 Reviewed By: zhichao-cao Differential Revision: D29190509 Pulled By: anand1976 fbshipit-source-id: 6f8eff6246712af8a297cfe22ea0d1c3b2a01bb0
2021-06-18 16:35:03 +00:00
} else {
// We have to wait for the cache lookup to finish in the
// background, and then we may have to read the block from disk
// anyway
assert(results.back().GetCacheHandle());
Parallelize secondary cache lookup in MultiGet (#8405) Summary: Implement the ```WaitAll()``` interface in ```LRUCache``` to allow callers to issue multiple lookups in parallel and wait for all of them to complete. Modify ```MultiGet``` to use this to parallelize the secondary cache lookups in order to reduce the overall latency. A call to ```cache->Lookup()``` returns a handle that has an incomplete value (nullptr), and the caller can call ```cache->IsReady()``` to check whether the lookup is complete, and pass a vector of handles to ```WaitAll``` to wait for completion. If any of the lookups fail, ```MultiGet``` will read the block from the SST file. Another change in this PR is to rename ```SecondaryCacheHandle``` to ```SecondaryCacheResultHandle``` as it more accurately describes the return result of the secondary cache lookup, which is more like a future. Tests: 1. Add unit tests in lru_cache_test 2. Benchmark results with no secondary cache configured Master - ``` readrandom : 41.175 micros/op 388562 ops/sec; 106.7 MB/s (7277999 of 7277999 found) readrandom : 41.217 micros/op 388160 ops/sec; 106.6 MB/s (7274999 of 7274999 found) multireadrandom : 10.309 micros/op 1552082 ops/sec; (28908992 of 28908992 found) multireadrandom : 10.321 micros/op 1550218 ops/sec; (29081984 of 29081984 found) ``` This PR - ``` readrandom : 41.158 micros/op 388723 ops/sec; 106.8 MB/s (7290999 of 7290999 found) readrandom : 41.185 micros/op 388463 ops/sec; 106.7 MB/s (7287999 of 7287999 found) multireadrandom : 10.277 micros/op 1556801 ops/sec; (29346944 of 29346944 found) multireadrandom : 10.253 micros/op 1560539 ops/sec; (29274944 of 29274944 found) ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/8405 Reviewed By: zhichao-cao Differential Revision: D29190509 Pulled By: anand1976 fbshipit-source-id: 6f8eff6246712af8a297cfe22ea0d1c3b2a01bb0
2021-06-18 16:35:03 +00:00
wait_for_cache_results = true;
block_handles.emplace_back(handle);
cache_handles.emplace_back(results.back().GetCacheHandle());
}
} else {
block_handles.emplace_back(handle);
total_len += block_size(handle);
}
}
Parallelize secondary cache lookup in MultiGet (#8405) Summary: Implement the ```WaitAll()``` interface in ```LRUCache``` to allow callers to issue multiple lookups in parallel and wait for all of them to complete. Modify ```MultiGet``` to use this to parallelize the secondary cache lookups in order to reduce the overall latency. A call to ```cache->Lookup()``` returns a handle that has an incomplete value (nullptr), and the caller can call ```cache->IsReady()``` to check whether the lookup is complete, and pass a vector of handles to ```WaitAll``` to wait for completion. If any of the lookups fail, ```MultiGet``` will read the block from the SST file. Another change in this PR is to rename ```SecondaryCacheHandle``` to ```SecondaryCacheResultHandle``` as it more accurately describes the return result of the secondary cache lookup, which is more like a future. Tests: 1. Add unit tests in lru_cache_test 2. Benchmark results with no secondary cache configured Master - ``` readrandom : 41.175 micros/op 388562 ops/sec; 106.7 MB/s (7277999 of 7277999 found) readrandom : 41.217 micros/op 388160 ops/sec; 106.6 MB/s (7274999 of 7274999 found) multireadrandom : 10.309 micros/op 1552082 ops/sec; (28908992 of 28908992 found) multireadrandom : 10.321 micros/op 1550218 ops/sec; (29081984 of 29081984 found) ``` This PR - ``` readrandom : 41.158 micros/op 388723 ops/sec; 106.8 MB/s (7290999 of 7290999 found) readrandom : 41.185 micros/op 388463 ops/sec; 106.7 MB/s (7287999 of 7287999 found) multireadrandom : 10.277 micros/op 1556801 ops/sec; (29346944 of 29346944 found) multireadrandom : 10.253 micros/op 1560539 ops/sec; (29274944 of 29274944 found) ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/8405 Reviewed By: zhichao-cao Differential Revision: D29190509 Pulled By: anand1976 fbshipit-source-id: 6f8eff6246712af8a297cfe22ea0d1c3b2a01bb0
2021-06-18 16:35:03 +00:00
if (wait_for_cache_results) {
Cache* block_cache = rep_->table_options.block_cache.get();
block_cache->WaitAll(cache_handles);
for (size_t i = 0; i < block_handles.size(); ++i) {
// If this block was a success or failure or not needed because
// the corresponding key is in the same block as a prior key, skip
if (block_handles[i] == BlockHandle::NullBlockHandle() ||
results[i].IsEmpty()) {
continue;
}
results[i].UpdateCachedValue();
void* val = results[i].GetValue();
if (!val) {
// The async cache lookup failed - could be due to an error
// or a false positive. We need to read the data block from
// the SST file
results[i].Reset();
total_len += block_size(block_handles[i]);
} else {
block_handles[i] = BlockHandle::NullBlockHandle();
}
}
}
if (total_len) {
char* scratch = nullptr;
const UncompressionDict& dict = uncompression_dict.GetValue()
? *uncompression_dict.GetValue()
: UncompressionDict::GetEmptyDict();
assert(uncompression_dict_inited || !rep_->uncompression_dict_reader);
assert(uncompression_dict_status.ok());
// If using direct IO, then scratch is not used, so keep it nullptr.
// If the blocks need to be uncompressed and we don't need the
// compressed blocks, then we can use a contiguous block of
// memory to read in all the blocks as it will be temporary
// storage
// 1. If blocks are compressed and compressed block cache is there,
// alloc heap bufs
// 2. If blocks are uncompressed, alloc heap bufs
// 3. If blocks are compressed and no compressed block cache, use
// stack buf
if (!rep_->file->use_direct_io() &&
rep_->table_options.block_cache_compressed == nullptr &&
rep_->blocks_maybe_compressed) {
if (total_len <= kMultiGetReadStackBufSize) {
scratch = stack_buf;
} else {
scratch = new char[total_len];
block_buf.reset(scratch);
}
}
RetrieveMultipleBlocks(read_options, &data_block_range, &block_handles,
&statuses, &results, scratch, dict);
if (sst_file_range.begin()->get_context) {
++(sst_file_range.begin()
->get_context->get_context_stats_.num_sst_read);
}
}
}
DataBlockIter first_biter;
DataBlockIter next_biter;
size_t idx_in_batch = 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
for (auto miter = sst_file_range.begin(); miter != sst_file_range.end();
++miter) {
Status s;
GetContext* get_context = miter->get_context;
const Slice& key = miter->ikey;
bool matched = false; // if such user key matched a key in SST
bool done = false;
bool first_block = true;
do {
DataBlockIter* biter = nullptr;
bool reusing_block = true;
uint64_t referenced_data_size = 0;
bool does_referenced_key_exist = false;
BlockCacheLookupContext lookup_data_block_context(
TableReaderCaller::kUserMultiGet, tracing_mget_id,
/*get_from_user_specified_snapshot=*/read_options.snapshot !=
nullptr);
if (first_block) {
if (!block_handles[idx_in_batch].IsNull() ||
!results[idx_in_batch].IsEmpty()) {
first_biter.Invalidate(Status::OK());
NewDataBlockIterator<DataBlockIter>(
read_options, results[idx_in_batch], &first_biter,
statuses[idx_in_batch]);
reusing_block = false;
} else {
// If handler is null and result is empty, then the status is never
// set, which should be the initial value: ok().
assert(statuses[idx_in_batch].ok());
}
biter = &first_biter;
idx_in_batch++;
} else {
IndexValue v = iiter->value();
if (!v.first_internal_key.empty() && !skip_filters &&
UserComparatorWrapper(rep_->internal_comparator.user_comparator())
.CompareWithoutTimestamp(
ExtractUserKey(key),
ExtractUserKey(v.first_internal_key)) < 0) {
// The requested key falls between highest key in previous block and
// lowest key in current block.
break;
}
next_biter.Invalidate(Status::OK());
NewDataBlockIterator<DataBlockIter>(
read_options, iiter->value().handle, &next_biter,
BlockType::kData, get_context, &lookup_data_block_context,
Status(), nullptr);
biter = &next_biter;
reusing_block = false;
}
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
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 (read_options.read_tier == kBlockCacheTier &&
biter->status().IsIncomplete()) {
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
// couldn't get block from block_cache
// Update Saver.state to Found because we are only looking for
// whether we can guarantee the key is not there when "no_io" is set
get_context->MarkKeyMayExist();
break;
}
if (!biter->status().ok()) {
s = biter->status();
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
break;
}
bool may_exist = biter->SeekForGet(key);
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 (!may_exist) {
// HashSeek cannot find the key this block and the the iter is not
// the end of the block, i.e. cannot be in the following blocks
// either. In this case, the seek_key cannot be found, so we break
// from the top level for-loop.
break;
}
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
// Call the *saver function on each entry/block until it returns false
for (; biter->Valid(); biter->Next()) {
ParsedInternalKey parsed_key;
Cleanable dummy;
Cleanable* value_pinner = nullptr;
Status pik_status = ParseInternalKey(
biter->key(), &parsed_key, false /* log_err_key */); // TODO
if (!pik_status.ok()) {
s = pik_status;
}
if (biter->IsValuePinned()) {
if (reusing_block) {
Cache* block_cache = rep_->table_options.block_cache.get();
assert(biter->cache_handle() != nullptr);
block_cache->Ref(biter->cache_handle());
dummy.RegisterCleanup(&ReleaseCachedEntry, block_cache,
biter->cache_handle());
value_pinner = &dummy;
} else {
value_pinner = biter;
}
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 (!get_context->SaveValue(parsed_key, biter->value(), &matched,
value_pinner)) {
if (get_context->State() == GetContext::GetState::kFound) {
does_referenced_key_exist = true;
referenced_data_size =
biter->key().size() + biter->value().size();
}
done = true;
break;
}
s = biter->status();
}
// Write the block cache access.
if (block_cache_tracer_ && block_cache_tracer_->is_tracing_enabled()) {
// Avoid making copy of block_key, cf_name, and referenced_key when
// constructing the access record.
Slice referenced_key;
if (does_referenced_key_exist) {
referenced_key = biter->key();
} else {
referenced_key = key;
}
BlockCacheTraceRecord access_record(
rep_->ioptions.clock->NowMicros(),
/*block_key=*/"", lookup_data_block_context.block_type,
lookup_data_block_context.block_size, rep_->cf_id_for_tracing(),
/*cf_name=*/"", rep_->level_for_tracing(),
rep_->sst_number_for_tracing(), lookup_data_block_context.caller,
lookup_data_block_context.is_cache_hit,
lookup_data_block_context.no_insert,
lookup_data_block_context.get_id,
lookup_data_block_context.get_from_user_specified_snapshot,
/*referenced_key=*/"", referenced_data_size,
lookup_data_block_context.num_keys_in_block,
does_referenced_key_exist);
// TODO: Should handle status here?
block_cache_tracer_
->WriteBlockAccess(access_record,
lookup_data_block_context.block_key,
rep_->cf_name_for_tracing(), referenced_key)
.PermitUncheckedError();
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
}
s = biter->status();
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 (done) {
// Avoid the extra Next which is expensive in two-level indexes
break;
}
if (first_block) {
iiter->Seek(key);
}
first_block = false;
iiter->Next();
} while (iiter->Valid());
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 (matched && filter != nullptr && !filter->IsBlockBased()) {
RecordTick(rep_->ioptions.stats, BLOOM_FILTER_FULL_TRUE_POSITIVE);
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
PERF_COUNTER_BY_LEVEL_ADD(bloom_filter_full_true_positive, 1,
rep_->level);
}
if (s.ok() && !iiter->status().IsNotFound()) {
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
s = iiter->status();
}
*(miter->s) = s;
}
#ifdef ROCKSDB_ASSERT_STATUS_CHECKED
// Not sure why we need to do it. Should investigate more.
for (auto& st : statuses) {
st.PermitUncheckedError();
}
#endif // ROCKSDB_ASSERT_STATUS_CHECKED
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
}
}
Status BlockBasedTable::Prefetch(const Slice* const begin,
const Slice* const end) {
auto& comparator = rep_->internal_comparator;
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
UserComparatorWrapper user_comparator(comparator.user_comparator());
// pre-condition
if (begin && end && comparator.Compare(*begin, *end) > 0) {
return Status::InvalidArgument(*begin, *end);
}
BlockCacheLookupContext lookup_context{TableReaderCaller::kPrefetch};
IndexBlockIter iiter_on_stack;
Create a BlockCacheLookupContext to enable fine-grained block cache tracing. (#5421) Summary: BlockCacheLookupContext only contains the caller for now. We will trace block accesses at five places: 1. BlockBasedTable::GetFilter. 2. BlockBasedTable::GetUncompressedDict. 3. BlockBasedTable::MaybeReadAndLoadToCache. (To trace access on data, index, and range deletion block.) 4. BlockBasedTable::Get. (To trace the referenced key and whether the referenced key exists in a fetched data block.) 5. BlockBasedTable::MultiGet. (To trace the referenced key and whether the referenced key exists in a fetched data block.) We create the context at: 1. BlockBasedTable::Get. (kUserGet) 2. BlockBasedTable::MultiGet. (kUserMGet) 3. BlockBasedTable::NewIterator. (either kUserIterator, kCompaction, or external SST ingestion calls this function.) 4. BlockBasedTable::Open. (kPrefetch) 5. Index/Filter::CacheDependencies. (kPrefetch) 6. BlockBasedTable::ApproximateOffsetOf. (kCompaction or kUserApproximateSize). I loaded 1 million key-value pairs into the database and ran the readrandom benchmark with a single thread. I gave the block cache 10 GB to make sure all reads hit the block cache after warmup. The throughput is comparable. Throughput of this PR: 231334 ops/s. Throughput of the master branch: 238428 ops/s. Experiment setup: RocksDB: version 6.2 Date: Mon Jun 10 10:42:51 2019 CPU: 24 * Intel Core Processor (Skylake) CPUCache: 16384 KB Keys: 20 bytes each Values: 100 bytes each (100 bytes after compression) Entries: 1000000 Prefix: 20 bytes Keys per prefix: 0 RawSize: 114.4 MB (estimated) FileSize: 114.4 MB (estimated) Write rate: 0 bytes/second Read rate: 0 ops/second Compression: NoCompression Compression sampling rate: 0 Memtablerep: skip_list Perf Level: 1 Load command: ./db_bench --benchmarks="fillseq" --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --statistics --cache_index_and_filter_blocks --cache_size=10737418240 --disable_auto_compactions=1 --disable_wal=1 --compression_type=none --min_level_to_compress=-1 --compression_ratio=1 --num=1000000 Run command: ./db_bench --benchmarks="readrandom,stats" --use_existing_db --threads=1 --duration=120 --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --statistics --cache_index_and_filter_blocks --cache_size=10737418240 --disable_auto_compactions=1 --disable_wal=1 --compression_type=none --min_level_to_compress=-1 --compression_ratio=1 --num=1000000 --duration=120 TODOs: 1. Create a caller for external SST file ingestion and differentiate the callers for iterator. 2. Integrate tracer to trace block cache accesses. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5421 Differential Revision: D15704258 Pulled By: HaoyuHuang fbshipit-source-id: 4aa8a55f8cb1576ffb367bfa3186a91d8f06d93a
2019-06-10 22:30:05 +00:00
auto iiter = NewIndexIterator(ReadOptions(), /*need_upper_bound_check=*/false,
&iiter_on_stack, /*get_context=*/nullptr,
&lookup_context);
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
std::unique_ptr<InternalIteratorBase<IndexValue>> iiter_unique_ptr;
if (iiter != &iiter_on_stack) {
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
iiter_unique_ptr = std::unique_ptr<InternalIteratorBase<IndexValue>>(iiter);
}
if (!iiter->status().ok()) {
// error opening index iterator
return iiter->status();
}
// indicates if we are on the last page that need to be pre-fetched
bool prefetching_boundary_page = false;
for (begin ? iiter->Seek(*begin) : iiter->SeekToFirst(); iiter->Valid();
iiter->Next()) {
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
BlockHandle block_handle = iiter->value().handle;
const bool is_user_key = !rep_->index_key_includes_seq;
if (end &&
((!is_user_key && comparator.Compare(iiter->key(), *end) >= 0) ||
(is_user_key &&
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
user_comparator.Compare(iiter->key(), ExtractUserKey(*end)) >= 0))) {
if (prefetching_boundary_page) {
break;
}
// The index entry represents the last key in the data block.
// We should load this page into memory as well, but no more
prefetching_boundary_page = true;
}
// Load the block specified by the block_handle into the block cache
DataBlockIter biter;
Create a BlockCacheLookupContext to enable fine-grained block cache tracing. (#5421) Summary: BlockCacheLookupContext only contains the caller for now. We will trace block accesses at five places: 1. BlockBasedTable::GetFilter. 2. BlockBasedTable::GetUncompressedDict. 3. BlockBasedTable::MaybeReadAndLoadToCache. (To trace access on data, index, and range deletion block.) 4. BlockBasedTable::Get. (To trace the referenced key and whether the referenced key exists in a fetched data block.) 5. BlockBasedTable::MultiGet. (To trace the referenced key and whether the referenced key exists in a fetched data block.) We create the context at: 1. BlockBasedTable::Get. (kUserGet) 2. BlockBasedTable::MultiGet. (kUserMGet) 3. BlockBasedTable::NewIterator. (either kUserIterator, kCompaction, or external SST ingestion calls this function.) 4. BlockBasedTable::Open. (kPrefetch) 5. Index/Filter::CacheDependencies. (kPrefetch) 6. BlockBasedTable::ApproximateOffsetOf. (kCompaction or kUserApproximateSize). I loaded 1 million key-value pairs into the database and ran the readrandom benchmark with a single thread. I gave the block cache 10 GB to make sure all reads hit the block cache after warmup. The throughput is comparable. Throughput of this PR: 231334 ops/s. Throughput of the master branch: 238428 ops/s. Experiment setup: RocksDB: version 6.2 Date: Mon Jun 10 10:42:51 2019 CPU: 24 * Intel Core Processor (Skylake) CPUCache: 16384 KB Keys: 20 bytes each Values: 100 bytes each (100 bytes after compression) Entries: 1000000 Prefix: 20 bytes Keys per prefix: 0 RawSize: 114.4 MB (estimated) FileSize: 114.4 MB (estimated) Write rate: 0 bytes/second Read rate: 0 ops/second Compression: NoCompression Compression sampling rate: 0 Memtablerep: skip_list Perf Level: 1 Load command: ./db_bench --benchmarks="fillseq" --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --statistics --cache_index_and_filter_blocks --cache_size=10737418240 --disable_auto_compactions=1 --disable_wal=1 --compression_type=none --min_level_to_compress=-1 --compression_ratio=1 --num=1000000 Run command: ./db_bench --benchmarks="readrandom,stats" --use_existing_db --threads=1 --duration=120 --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --statistics --cache_index_and_filter_blocks --cache_size=10737418240 --disable_auto_compactions=1 --disable_wal=1 --compression_type=none --min_level_to_compress=-1 --compression_ratio=1 --num=1000000 --duration=120 TODOs: 1. Create a caller for external SST file ingestion and differentiate the callers for iterator. 2. Integrate tracer to trace block cache accesses. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5421 Differential Revision: D15704258 Pulled By: HaoyuHuang fbshipit-source-id: 4aa8a55f8cb1576ffb367bfa3186a91d8f06d93a
2019-06-10 22:30:05 +00:00
NewDataBlockIterator<DataBlockIter>(
ReadOptions(), block_handle, &biter, /*type=*/BlockType::kData,
/*get_context=*/nullptr, &lookup_context, Status(),
/*prefetch_buffer=*/nullptr);
if (!biter.status().ok()) {
// there was an unexpected error while pre-fetching
return biter.status();
}
}
return Status::OK();
}
Status BlockBasedTable::VerifyChecksum(const ReadOptions& read_options,
TableReaderCaller caller) {
Status s;
// Check Meta blocks
std::unique_ptr<Block> metaindex;
std::unique_ptr<InternalIterator> metaindex_iter;
ReadOptions ro;
s = ReadMetaIndexBlock(ro, nullptr /* prefetch buffer */, &metaindex,
&metaindex_iter);
if (s.ok()) {
s = VerifyChecksumInMetaBlocks(metaindex_iter.get());
if (!s.ok()) {
return s;
}
} else {
return s;
}
// Check Data blocks
IndexBlockIter iiter_on_stack;
BlockCacheLookupContext context{caller};
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
InternalIteratorBase<IndexValue>* iiter = NewIndexIterator(
read_options, /*disable_prefix_seek=*/false, &iiter_on_stack,
/*get_context=*/nullptr, &context);
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
std::unique_ptr<InternalIteratorBase<IndexValue>> iiter_unique_ptr;
if (iiter != &iiter_on_stack) {
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
iiter_unique_ptr = std::unique_ptr<InternalIteratorBase<IndexValue>>(iiter);
}
if (!iiter->status().ok()) {
// error opening index iterator
return iiter->status();
}
s = VerifyChecksumInBlocks(read_options, iiter);
return s;
}
Status BlockBasedTable::VerifyChecksumInBlocks(
const ReadOptions& read_options,
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
InternalIteratorBase<IndexValue>* index_iter) {
Status s;
// We are scanning the whole file, so no need to do exponential
// increasing of the buffer size.
size_t readahead_size = (read_options.readahead_size != 0)
? read_options.readahead_size
: rep_->table_options.max_auto_readahead_size;
// FilePrefetchBuffer doesn't work in mmap mode and readahead is not
// needed there.
FilePrefetchBuffer prefetch_buffer(
rep_->file.get(), readahead_size /* readahead_size */,
readahead_size /* max_readahead_size */,
!rep_->ioptions.allow_mmap_reads /* enable */);
for (index_iter->SeekToFirst(); index_iter->Valid(); index_iter->Next()) {
s = index_iter->status();
if (!s.ok()) {
break;
}
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
BlockHandle handle = index_iter->value().handle;
BlockContents contents;
BlockFetcher block_fetcher(
rep_->file.get(), &prefetch_buffer, rep_->footer, ReadOptions(), handle,
&contents, rep_->ioptions, false /* decompress */,
false /*maybe_compressed*/, BlockType::kData,
UncompressionDict::GetEmptyDict(), rep_->persistent_cache_options);
s = block_fetcher.ReadBlockContents();
if (!s.ok()) {
break;
}
}
if (s.ok()) {
// In the case of two level indexes, we would have exited the above loop
// by checking index_iter->Valid(), but Valid() might have returned false
// due to an IO error. So check the index_iter status
s = index_iter->status();
}
return s;
}
BlockType BlockBasedTable::GetBlockTypeForMetaBlockByName(
const Slice& meta_block_name) {
if (meta_block_name.starts_with(kFilterBlockPrefix) ||
meta_block_name.starts_with(kFullFilterBlockPrefix) ||
meta_block_name.starts_with(kPartitionedFilterBlockPrefix)) {
return BlockType::kFilter;
}
if (meta_block_name == kPropertiesBlock) {
return BlockType::kProperties;
}
if (meta_block_name == kCompressionDictBlock) {
return BlockType::kCompressionDictionary;
}
if (meta_block_name == kRangeDelBlock) {
return BlockType::kRangeDeletion;
}
if (meta_block_name == kHashIndexPrefixesBlock) {
return BlockType::kHashIndexPrefixes;
}
if (meta_block_name == kHashIndexPrefixesMetadataBlock) {
return BlockType::kHashIndexMetadata;
}
assert(false);
return BlockType::kInvalid;
}
Status BlockBasedTable::VerifyChecksumInMetaBlocks(
InternalIteratorBase<Slice>* index_iter) {
Status s;
for (index_iter->SeekToFirst(); index_iter->Valid(); index_iter->Next()) {
s = index_iter->status();
if (!s.ok()) {
break;
}
BlockHandle handle;
Slice input = index_iter->value();
s = handle.DecodeFrom(&input);
BlockContents contents;
const Slice meta_block_name = index_iter->key();
BlockFetcher block_fetcher(
rep_->file.get(), nullptr /* prefetch buffer */, rep_->footer,
ReadOptions(), handle, &contents, rep_->ioptions,
false /* decompress */, false /*maybe_compressed*/,
GetBlockTypeForMetaBlockByName(meta_block_name),
UncompressionDict::GetEmptyDict(), rep_->persistent_cache_options);
s = block_fetcher.ReadBlockContents();
if (s.IsCorruption() && meta_block_name == kPropertiesBlock) {
TableProperties* table_properties;
ReadOptions ro;
s = TryReadPropertiesWithGlobalSeqno(ro, nullptr /* prefetch_buffer */,
index_iter->value(),
&table_properties);
delete table_properties;
}
if (!s.ok()) {
break;
}
}
return s;
}
bool BlockBasedTable::TEST_BlockInCache(const BlockHandle& handle) const {
assert(rep_ != nullptr);
Cache* const cache = rep_->table_options.block_cache.get();
if (cache == nullptr) {
return false;
}
char cache_key_storage[kMaxCacheKeyPrefixSize + kMaxVarint64Length];
Slice cache_key =
GetCacheKey(rep_->cache_key_prefix, rep_->cache_key_prefix_size, handle,
cache_key_storage);
Cache::Handle* const cache_handle = cache->Lookup(cache_key);
if (cache_handle == nullptr) {
return false;
}
cache->Release(cache_handle);
return true;
}
bool BlockBasedTable::TEST_KeyInCache(const ReadOptions& options,
const Slice& key) {
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
std::unique_ptr<InternalIteratorBase<IndexValue>> iiter(NewIndexIterator(
Create a BlockCacheLookupContext to enable fine-grained block cache tracing. (#5421) Summary: BlockCacheLookupContext only contains the caller for now. We will trace block accesses at five places: 1. BlockBasedTable::GetFilter. 2. BlockBasedTable::GetUncompressedDict. 3. BlockBasedTable::MaybeReadAndLoadToCache. (To trace access on data, index, and range deletion block.) 4. BlockBasedTable::Get. (To trace the referenced key and whether the referenced key exists in a fetched data block.) 5. BlockBasedTable::MultiGet. (To trace the referenced key and whether the referenced key exists in a fetched data block.) We create the context at: 1. BlockBasedTable::Get. (kUserGet) 2. BlockBasedTable::MultiGet. (kUserMGet) 3. BlockBasedTable::NewIterator. (either kUserIterator, kCompaction, or external SST ingestion calls this function.) 4. BlockBasedTable::Open. (kPrefetch) 5. Index/Filter::CacheDependencies. (kPrefetch) 6. BlockBasedTable::ApproximateOffsetOf. (kCompaction or kUserApproximateSize). I loaded 1 million key-value pairs into the database and ran the readrandom benchmark with a single thread. I gave the block cache 10 GB to make sure all reads hit the block cache after warmup. The throughput is comparable. Throughput of this PR: 231334 ops/s. Throughput of the master branch: 238428 ops/s. Experiment setup: RocksDB: version 6.2 Date: Mon Jun 10 10:42:51 2019 CPU: 24 * Intel Core Processor (Skylake) CPUCache: 16384 KB Keys: 20 bytes each Values: 100 bytes each (100 bytes after compression) Entries: 1000000 Prefix: 20 bytes Keys per prefix: 0 RawSize: 114.4 MB (estimated) FileSize: 114.4 MB (estimated) Write rate: 0 bytes/second Read rate: 0 ops/second Compression: NoCompression Compression sampling rate: 0 Memtablerep: skip_list Perf Level: 1 Load command: ./db_bench --benchmarks="fillseq" --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --statistics --cache_index_and_filter_blocks --cache_size=10737418240 --disable_auto_compactions=1 --disable_wal=1 --compression_type=none --min_level_to_compress=-1 --compression_ratio=1 --num=1000000 Run command: ./db_bench --benchmarks="readrandom,stats" --use_existing_db --threads=1 --duration=120 --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --statistics --cache_index_and_filter_blocks --cache_size=10737418240 --disable_auto_compactions=1 --disable_wal=1 --compression_type=none --min_level_to_compress=-1 --compression_ratio=1 --num=1000000 --duration=120 TODOs: 1. Create a caller for external SST file ingestion and differentiate the callers for iterator. 2. Integrate tracer to trace block cache accesses. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5421 Differential Revision: D15704258 Pulled By: HaoyuHuang fbshipit-source-id: 4aa8a55f8cb1576ffb367bfa3186a91d8f06d93a
2019-06-10 22:30:05 +00:00
options, /*need_upper_bound_check=*/false, /*input_iter=*/nullptr,
/*get_context=*/nullptr, /*lookup_context=*/nullptr));
iiter->Seek(key);
assert(iiter->Valid());
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
return TEST_BlockInCache(iiter->value().handle);
}
// REQUIRES: The following fields of rep_ should have already been populated:
// 1. file
// 2. index_handle,
// 3. options
// 4. internal_comparator
// 5. index_type
Status BlockBasedTable::CreateIndexReader(
const ReadOptions& ro, FilePrefetchBuffer* prefetch_buffer,
InternalIterator* preloaded_meta_index_iter, bool use_cache, bool prefetch,
bool pin, BlockCacheLookupContext* lookup_context,
std::unique_ptr<IndexReader>* index_reader) {
// kHashSearch requires non-empty prefix_extractor but bypass checking
// prefix_extractor here since we have no access to MutableCFOptions.
// Add need_upper_bound_check flag in BlockBasedTable::NewIndexIterator.
// If prefix_extractor does not match prefix_extractor_name from table
// properties, turn off Hash Index by setting total_order_seek to true
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
switch (rep_->index_type) {
case BlockBasedTableOptions::kTwoLevelIndexSearch: {
return PartitionIndexReader::Create(this, ro, prefetch_buffer, use_cache,
prefetch, pin, lookup_context,
index_reader);
}
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
case BlockBasedTableOptions::kBinarySearch:
FALLTHROUGH_INTENDED;
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
case BlockBasedTableOptions::kBinarySearchWithFirstKey: {
return BinarySearchIndexReader::Create(this, ro, prefetch_buffer,
use_cache, prefetch, pin,
lookup_context, index_reader);
}
case BlockBasedTableOptions::kHashSearch: {
std::unique_ptr<Block> metaindex_guard;
std::unique_ptr<InternalIterator> metaindex_iter_guard;
auto meta_index_iter = preloaded_meta_index_iter;
bool should_fallback = false;
if (rep_->internal_prefix_transform.get() == nullptr) {
ROCKS_LOG_WARN(rep_->ioptions.logger,
"No prefix extractor passed in. Fall back to binary"
" search index.");
should_fallback = true;
} else if (meta_index_iter == nullptr) {
auto s = ReadMetaIndexBlock(ro, prefetch_buffer, &metaindex_guard,
&metaindex_iter_guard);
if (!s.ok()) {
// we simply fall back to binary search in case there is any
// problem with prefix hash index loading.
ROCKS_LOG_WARN(rep_->ioptions.logger,
"Unable to read the metaindex block."
" Fall back to binary search index.");
should_fallback = true;
}
meta_index_iter = metaindex_iter_guard.get();
}
if (should_fallback) {
return BinarySearchIndexReader::Create(this, ro, prefetch_buffer,
use_cache, prefetch, pin,
lookup_context, index_reader);
} else {
return HashIndexReader::Create(this, ro, prefetch_buffer,
meta_index_iter, use_cache, prefetch,
pin, lookup_context, index_reader);
}
}
default: {
std::string error_message =
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
"Unrecognized index type: " + ToString(rep_->index_type);
return Status::InvalidArgument(error_message.c_str());
}
}
}
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
uint64_t BlockBasedTable::ApproximateDataOffsetOf(
const InternalIteratorBase<IndexValue>& index_iter,
uint64_t data_size) const {
if (index_iter.Valid()) {
BlockHandle handle = index_iter.value().handle;
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
return handle.offset();
} else {
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
// The iterator is past the last key in the file.
return data_size;
}
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
}
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
uint64_t BlockBasedTable::GetApproximateDataSize() {
// Should be in table properties unless super old version
if (rep_->table_properties) {
return rep_->table_properties->data_size;
}
// Fall back to rough estimate from footer
return rep_->footer.metaindex_handle().offset();
}
uint64_t BlockBasedTable::ApproximateOffsetOf(const Slice& key,
TableReaderCaller caller) {
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
uint64_t data_size = GetApproximateDataSize();
if (UNLIKELY(data_size == 0)) {
// Hmm. Let's just split in half to avoid skewing one way or another,
// since we don't know whether we're operating on lower bound or
// upper bound.
return rep_->file_size / 2;
}
BlockCacheLookupContext context(caller);
IndexBlockIter iiter_on_stack;
ReadOptions ro;
ro.total_order_seek = true;
auto index_iter =
NewIndexIterator(ro, /*disable_prefix_seek=*/true,
/*input_iter=*/&iiter_on_stack, /*get_context=*/nullptr,
/*lookup_context=*/&context);
std::unique_ptr<InternalIteratorBase<IndexValue>> iiter_unique_ptr;
if (index_iter != &iiter_on_stack) {
iiter_unique_ptr.reset(index_iter);
}
index_iter->Seek(key);
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
uint64_t offset = ApproximateDataOffsetOf(*index_iter, data_size);
// Pro-rate file metadata (incl filters) size-proportionally across data
// blocks.
double size_ratio =
static_cast<double>(offset) / static_cast<double>(data_size);
return static_cast<uint64_t>(size_ratio *
static_cast<double>(rep_->file_size));
}
uint64_t BlockBasedTable::ApproximateSize(const Slice& start, const Slice& end,
TableReaderCaller caller) {
assert(rep_->internal_comparator.Compare(start, end) <= 0);
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
uint64_t data_size = GetApproximateDataSize();
if (UNLIKELY(data_size == 0)) {
// Hmm. Assume whole file is involved, since we have lower and upper
// bound.
return rep_->file_size;
}
BlockCacheLookupContext context(caller);
IndexBlockIter iiter_on_stack;
ReadOptions ro;
ro.total_order_seek = true;
auto index_iter =
NewIndexIterator(ro, /*disable_prefix_seek=*/true,
/*input_iter=*/&iiter_on_stack, /*get_context=*/nullptr,
/*lookup_context=*/&context);
std::unique_ptr<InternalIteratorBase<IndexValue>> iiter_unique_ptr;
if (index_iter != &iiter_on_stack) {
iiter_unique_ptr.reset(index_iter);
}
index_iter->Seek(start);
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
uint64_t start_offset = ApproximateDataOffsetOf(*index_iter, data_size);
index_iter->Seek(end);
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
uint64_t end_offset = ApproximateDataOffsetOf(*index_iter, data_size);
assert(end_offset >= start_offset);
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
// Pro-rate file metadata (incl filters) size-proportionally across data
// blocks.
double size_ratio = static_cast<double>(end_offset - start_offset) /
static_cast<double>(data_size);
return static_cast<uint64_t>(size_ratio *
static_cast<double>(rep_->file_size));
}
bool BlockBasedTable::TEST_FilterBlockInCache() const {
assert(rep_ != nullptr);
return TEST_BlockInCache(rep_->filter_handle);
}
bool BlockBasedTable::TEST_IndexBlockInCache() const {
assert(rep_ != nullptr);
return TEST_BlockInCache(rep_->footer.index_handle());
}
Status BlockBasedTable::GetKVPairsFromDataBlocks(
std::vector<KVPairBlock>* kv_pair_blocks) {
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
std::unique_ptr<InternalIteratorBase<IndexValue>> blockhandles_iter(
Create a BlockCacheLookupContext to enable fine-grained block cache tracing. (#5421) Summary: BlockCacheLookupContext only contains the caller for now. We will trace block accesses at five places: 1. BlockBasedTable::GetFilter. 2. BlockBasedTable::GetUncompressedDict. 3. BlockBasedTable::MaybeReadAndLoadToCache. (To trace access on data, index, and range deletion block.) 4. BlockBasedTable::Get. (To trace the referenced key and whether the referenced key exists in a fetched data block.) 5. BlockBasedTable::MultiGet. (To trace the referenced key and whether the referenced key exists in a fetched data block.) We create the context at: 1. BlockBasedTable::Get. (kUserGet) 2. BlockBasedTable::MultiGet. (kUserMGet) 3. BlockBasedTable::NewIterator. (either kUserIterator, kCompaction, or external SST ingestion calls this function.) 4. BlockBasedTable::Open. (kPrefetch) 5. Index/Filter::CacheDependencies. (kPrefetch) 6. BlockBasedTable::ApproximateOffsetOf. (kCompaction or kUserApproximateSize). I loaded 1 million key-value pairs into the database and ran the readrandom benchmark with a single thread. I gave the block cache 10 GB to make sure all reads hit the block cache after warmup. The throughput is comparable. Throughput of this PR: 231334 ops/s. Throughput of the master branch: 238428 ops/s. Experiment setup: RocksDB: version 6.2 Date: Mon Jun 10 10:42:51 2019 CPU: 24 * Intel Core Processor (Skylake) CPUCache: 16384 KB Keys: 20 bytes each Values: 100 bytes each (100 bytes after compression) Entries: 1000000 Prefix: 20 bytes Keys per prefix: 0 RawSize: 114.4 MB (estimated) FileSize: 114.4 MB (estimated) Write rate: 0 bytes/second Read rate: 0 ops/second Compression: NoCompression Compression sampling rate: 0 Memtablerep: skip_list Perf Level: 1 Load command: ./db_bench --benchmarks="fillseq" --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --statistics --cache_index_and_filter_blocks --cache_size=10737418240 --disable_auto_compactions=1 --disable_wal=1 --compression_type=none --min_level_to_compress=-1 --compression_ratio=1 --num=1000000 Run command: ./db_bench --benchmarks="readrandom,stats" --use_existing_db --threads=1 --duration=120 --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --statistics --cache_index_and_filter_blocks --cache_size=10737418240 --disable_auto_compactions=1 --disable_wal=1 --compression_type=none --min_level_to_compress=-1 --compression_ratio=1 --num=1000000 --duration=120 TODOs: 1. Create a caller for external SST file ingestion and differentiate the callers for iterator. 2. Integrate tracer to trace block cache accesses. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5421 Differential Revision: D15704258 Pulled By: HaoyuHuang fbshipit-source-id: 4aa8a55f8cb1576ffb367bfa3186a91d8f06d93a
2019-06-10 22:30:05 +00:00
NewIndexIterator(ReadOptions(), /*need_upper_bound_check=*/false,
/*input_iter=*/nullptr, /*get_context=*/nullptr,
/*lookup_contex=*/nullptr));
Status s = blockhandles_iter->status();
if (!s.ok()) {
// Cannot read Index Block
return s;
}
for (blockhandles_iter->SeekToFirst(); blockhandles_iter->Valid();
blockhandles_iter->Next()) {
s = blockhandles_iter->status();
if (!s.ok()) {
break;
}
std::unique_ptr<InternalIterator> datablock_iter;
datablock_iter.reset(NewDataBlockIterator<DataBlockIter>(
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
ReadOptions(), blockhandles_iter->value().handle,
/*input_iter=*/nullptr, /*type=*/BlockType::kData,
Create a BlockCacheLookupContext to enable fine-grained block cache tracing. (#5421) Summary: BlockCacheLookupContext only contains the caller for now. We will trace block accesses at five places: 1. BlockBasedTable::GetFilter. 2. BlockBasedTable::GetUncompressedDict. 3. BlockBasedTable::MaybeReadAndLoadToCache. (To trace access on data, index, and range deletion block.) 4. BlockBasedTable::Get. (To trace the referenced key and whether the referenced key exists in a fetched data block.) 5. BlockBasedTable::MultiGet. (To trace the referenced key and whether the referenced key exists in a fetched data block.) We create the context at: 1. BlockBasedTable::Get. (kUserGet) 2. BlockBasedTable::MultiGet. (kUserMGet) 3. BlockBasedTable::NewIterator. (either kUserIterator, kCompaction, or external SST ingestion calls this function.) 4. BlockBasedTable::Open. (kPrefetch) 5. Index/Filter::CacheDependencies. (kPrefetch) 6. BlockBasedTable::ApproximateOffsetOf. (kCompaction or kUserApproximateSize). I loaded 1 million key-value pairs into the database and ran the readrandom benchmark with a single thread. I gave the block cache 10 GB to make sure all reads hit the block cache after warmup. The throughput is comparable. Throughput of this PR: 231334 ops/s. Throughput of the master branch: 238428 ops/s. Experiment setup: RocksDB: version 6.2 Date: Mon Jun 10 10:42:51 2019 CPU: 24 * Intel Core Processor (Skylake) CPUCache: 16384 KB Keys: 20 bytes each Values: 100 bytes each (100 bytes after compression) Entries: 1000000 Prefix: 20 bytes Keys per prefix: 0 RawSize: 114.4 MB (estimated) FileSize: 114.4 MB (estimated) Write rate: 0 bytes/second Read rate: 0 ops/second Compression: NoCompression Compression sampling rate: 0 Memtablerep: skip_list Perf Level: 1 Load command: ./db_bench --benchmarks="fillseq" --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --statistics --cache_index_and_filter_blocks --cache_size=10737418240 --disable_auto_compactions=1 --disable_wal=1 --compression_type=none --min_level_to_compress=-1 --compression_ratio=1 --num=1000000 Run command: ./db_bench --benchmarks="readrandom,stats" --use_existing_db --threads=1 --duration=120 --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --statistics --cache_index_and_filter_blocks --cache_size=10737418240 --disable_auto_compactions=1 --disable_wal=1 --compression_type=none --min_level_to_compress=-1 --compression_ratio=1 --num=1000000 --duration=120 TODOs: 1. Create a caller for external SST file ingestion and differentiate the callers for iterator. 2. Integrate tracer to trace block cache accesses. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5421 Differential Revision: D15704258 Pulled By: HaoyuHuang fbshipit-source-id: 4aa8a55f8cb1576ffb367bfa3186a91d8f06d93a
2019-06-10 22:30:05 +00:00
/*get_context=*/nullptr, /*lookup_context=*/nullptr, Status(),
/*prefetch_buffer=*/nullptr));
s = datablock_iter->status();
if (!s.ok()) {
// Error reading the block - Skipped
continue;
}
KVPairBlock kv_pair_block;
for (datablock_iter->SeekToFirst(); datablock_iter->Valid();
datablock_iter->Next()) {
s = datablock_iter->status();
if (!s.ok()) {
// Error reading the block - Skipped
break;
}
const Slice& key = datablock_iter->key();
const Slice& value = datablock_iter->value();
std::string key_copy = std::string(key.data(), key.size());
std::string value_copy = std::string(value.data(), value.size());
kv_pair_block.push_back(
std::make_pair(std::move(key_copy), std::move(value_copy)));
}
kv_pair_blocks->push_back(std::move(kv_pair_block));
}
return Status::OK();
}
Status BlockBasedTable::DumpTable(WritableFile* out_file) {
WritableFileStringStreamAdapter out_file_wrapper(out_file);
std::ostream out_stream(&out_file_wrapper);
// Output Footer
out_stream << "Footer Details:\n"
"--------------------------------------\n";
out_stream << " " << rep_->footer.ToString() << "\n";
// Output MetaIndex
out_stream << "Metaindex Details:\n"
"--------------------------------------\n";
std::unique_ptr<Block> metaindex;
std::unique_ptr<InternalIterator> metaindex_iter;
ReadOptions ro;
Status s = ReadMetaIndexBlock(ro, nullptr /* prefetch_buffer */, &metaindex,
&metaindex_iter);
if (s.ok()) {
for (metaindex_iter->SeekToFirst(); metaindex_iter->Valid();
metaindex_iter->Next()) {
s = metaindex_iter->status();
if (!s.ok()) {
return s;
}
if (metaindex_iter->key() == kPropertiesBlock) {
out_stream << " Properties block handle: "
<< metaindex_iter->value().ToString(true) << "\n";
} else if (metaindex_iter->key() == kCompressionDictBlock) {
out_stream << " Compression dictionary block handle: "
<< metaindex_iter->value().ToString(true) << "\n";
} else if (strstr(metaindex_iter->key().ToString().c_str(),
"filter.rocksdb.") != nullptr) {
out_stream << " Filter block handle: "
<< metaindex_iter->value().ToString(true) << "\n";
} else if (metaindex_iter->key() == kRangeDelBlock) {
out_stream << " Range deletion block handle: "
<< metaindex_iter->value().ToString(true) << "\n";
}
}
out_stream << "\n";
} else {
return s;
}
// Output TableProperties
const ROCKSDB_NAMESPACE::TableProperties* table_properties;
table_properties = rep_->table_properties.get();
if (table_properties != nullptr) {
out_stream << "Table Properties:\n"
"--------------------------------------\n";
out_stream << " " << table_properties->ToString("\n ", ": ") << "\n";
}
if (rep_->filter) {
out_stream << "Filter Details:\n"
"--------------------------------------\n";
out_stream << " " << rep_->filter->ToString() << "\n";
}
// Output Index block
s = DumpIndexBlock(out_stream);
if (!s.ok()) {
return s;
}
// Output compression dictionary
if (rep_->uncompression_dict_reader) {
CachableEntry<UncompressionDict> uncompression_dict;
s = rep_->uncompression_dict_reader->GetOrReadUncompressionDictionary(
nullptr /* prefetch_buffer */, false /* no_io */,
nullptr /* get_context */, nullptr /* lookup_context */,
&uncompression_dict);
if (!s.ok()) {
return s;
}
assert(uncompression_dict.GetValue());
const Slice& raw_dict = uncompression_dict.GetValue()->GetRawDict();
out_stream << "Compression Dictionary:\n"
"--------------------------------------\n";
out_stream << " size (bytes): " << raw_dict.size() << "\n\n";
out_stream << " HEX " << raw_dict.ToString(true) << "\n\n";
}
// Output range deletions block
auto* range_del_iter = NewRangeTombstoneIterator(ReadOptions());
if (range_del_iter != nullptr) {
range_del_iter->SeekToFirst();
if (range_del_iter->Valid()) {
out_stream << "Range deletions:\n"
"--------------------------------------\n";
for (; range_del_iter->Valid(); range_del_iter->Next()) {
DumpKeyValue(range_del_iter->key(), range_del_iter->value(),
out_stream);
}
out_stream << "\n";
}
delete range_del_iter;
}
// Output Data blocks
s = DumpDataBlocks(out_stream);
if (!s.ok()) {
return s;
}
if (!out_stream.good()) {
return Status::IOError("Failed to write to output file");
}
return Status::OK();
}
Status BlockBasedTable::DumpIndexBlock(std::ostream& out_stream) {
out_stream << "Index Details:\n"
"--------------------------------------\n";
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
std::unique_ptr<InternalIteratorBase<IndexValue>> blockhandles_iter(
Create a BlockCacheLookupContext to enable fine-grained block cache tracing. (#5421) Summary: BlockCacheLookupContext only contains the caller for now. We will trace block accesses at five places: 1. BlockBasedTable::GetFilter. 2. BlockBasedTable::GetUncompressedDict. 3. BlockBasedTable::MaybeReadAndLoadToCache. (To trace access on data, index, and range deletion block.) 4. BlockBasedTable::Get. (To trace the referenced key and whether the referenced key exists in a fetched data block.) 5. BlockBasedTable::MultiGet. (To trace the referenced key and whether the referenced key exists in a fetched data block.) We create the context at: 1. BlockBasedTable::Get. (kUserGet) 2. BlockBasedTable::MultiGet. (kUserMGet) 3. BlockBasedTable::NewIterator. (either kUserIterator, kCompaction, or external SST ingestion calls this function.) 4. BlockBasedTable::Open. (kPrefetch) 5. Index/Filter::CacheDependencies. (kPrefetch) 6. BlockBasedTable::ApproximateOffsetOf. (kCompaction or kUserApproximateSize). I loaded 1 million key-value pairs into the database and ran the readrandom benchmark with a single thread. I gave the block cache 10 GB to make sure all reads hit the block cache after warmup. The throughput is comparable. Throughput of this PR: 231334 ops/s. Throughput of the master branch: 238428 ops/s. Experiment setup: RocksDB: version 6.2 Date: Mon Jun 10 10:42:51 2019 CPU: 24 * Intel Core Processor (Skylake) CPUCache: 16384 KB Keys: 20 bytes each Values: 100 bytes each (100 bytes after compression) Entries: 1000000 Prefix: 20 bytes Keys per prefix: 0 RawSize: 114.4 MB (estimated) FileSize: 114.4 MB (estimated) Write rate: 0 bytes/second Read rate: 0 ops/second Compression: NoCompression Compression sampling rate: 0 Memtablerep: skip_list Perf Level: 1 Load command: ./db_bench --benchmarks="fillseq" --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --statistics --cache_index_and_filter_blocks --cache_size=10737418240 --disable_auto_compactions=1 --disable_wal=1 --compression_type=none --min_level_to_compress=-1 --compression_ratio=1 --num=1000000 Run command: ./db_bench --benchmarks="readrandom,stats" --use_existing_db --threads=1 --duration=120 --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --statistics --cache_index_and_filter_blocks --cache_size=10737418240 --disable_auto_compactions=1 --disable_wal=1 --compression_type=none --min_level_to_compress=-1 --compression_ratio=1 --num=1000000 --duration=120 TODOs: 1. Create a caller for external SST file ingestion and differentiate the callers for iterator. 2. Integrate tracer to trace block cache accesses. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5421 Differential Revision: D15704258 Pulled By: HaoyuHuang fbshipit-source-id: 4aa8a55f8cb1576ffb367bfa3186a91d8f06d93a
2019-06-10 22:30:05 +00:00
NewIndexIterator(ReadOptions(), /*need_upper_bound_check=*/false,
/*input_iter=*/nullptr, /*get_context=*/nullptr,
/*lookup_contex=*/nullptr));
Status s = blockhandles_iter->status();
if (!s.ok()) {
out_stream << "Can not read Index Block \n\n";
return s;
}
out_stream << " Block key hex dump: Data block handle\n";
out_stream << " Block key ascii\n\n";
for (blockhandles_iter->SeekToFirst(); blockhandles_iter->Valid();
blockhandles_iter->Next()) {
s = blockhandles_iter->status();
if (!s.ok()) {
break;
}
Slice key = blockhandles_iter->key();
Slice user_key;
InternalKey ikey;
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
if (!rep_->index_key_includes_seq) {
user_key = key;
} else {
ikey.DecodeFrom(key);
user_key = ikey.user_key();
}
out_stream << " HEX " << user_key.ToString(true) << ": "
<< blockhandles_iter->value().ToString(true,
rep_->index_has_first_key)
<< "\n";
std::string str_key = user_key.ToString();
std::string res_key("");
char cspace = ' ';
for (size_t i = 0; i < str_key.size(); i++) {
res_key.append(&str_key[i], 1);
res_key.append(1, cspace);
}
out_stream << " ASCII " << res_key << "\n";
out_stream << " ------\n";
}
out_stream << "\n";
return Status::OK();
}
Status BlockBasedTable::DumpDataBlocks(std::ostream& out_stream) {
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
std::unique_ptr<InternalIteratorBase<IndexValue>> blockhandles_iter(
Create a BlockCacheLookupContext to enable fine-grained block cache tracing. (#5421) Summary: BlockCacheLookupContext only contains the caller for now. We will trace block accesses at five places: 1. BlockBasedTable::GetFilter. 2. BlockBasedTable::GetUncompressedDict. 3. BlockBasedTable::MaybeReadAndLoadToCache. (To trace access on data, index, and range deletion block.) 4. BlockBasedTable::Get. (To trace the referenced key and whether the referenced key exists in a fetched data block.) 5. BlockBasedTable::MultiGet. (To trace the referenced key and whether the referenced key exists in a fetched data block.) We create the context at: 1. BlockBasedTable::Get. (kUserGet) 2. BlockBasedTable::MultiGet. (kUserMGet) 3. BlockBasedTable::NewIterator. (either kUserIterator, kCompaction, or external SST ingestion calls this function.) 4. BlockBasedTable::Open. (kPrefetch) 5. Index/Filter::CacheDependencies. (kPrefetch) 6. BlockBasedTable::ApproximateOffsetOf. (kCompaction or kUserApproximateSize). I loaded 1 million key-value pairs into the database and ran the readrandom benchmark with a single thread. I gave the block cache 10 GB to make sure all reads hit the block cache after warmup. The throughput is comparable. Throughput of this PR: 231334 ops/s. Throughput of the master branch: 238428 ops/s. Experiment setup: RocksDB: version 6.2 Date: Mon Jun 10 10:42:51 2019 CPU: 24 * Intel Core Processor (Skylake) CPUCache: 16384 KB Keys: 20 bytes each Values: 100 bytes each (100 bytes after compression) Entries: 1000000 Prefix: 20 bytes Keys per prefix: 0 RawSize: 114.4 MB (estimated) FileSize: 114.4 MB (estimated) Write rate: 0 bytes/second Read rate: 0 ops/second Compression: NoCompression Compression sampling rate: 0 Memtablerep: skip_list Perf Level: 1 Load command: ./db_bench --benchmarks="fillseq" --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --statistics --cache_index_and_filter_blocks --cache_size=10737418240 --disable_auto_compactions=1 --disable_wal=1 --compression_type=none --min_level_to_compress=-1 --compression_ratio=1 --num=1000000 Run command: ./db_bench --benchmarks="readrandom,stats" --use_existing_db --threads=1 --duration=120 --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --statistics --cache_index_and_filter_blocks --cache_size=10737418240 --disable_auto_compactions=1 --disable_wal=1 --compression_type=none --min_level_to_compress=-1 --compression_ratio=1 --num=1000000 --duration=120 TODOs: 1. Create a caller for external SST file ingestion and differentiate the callers for iterator. 2. Integrate tracer to trace block cache accesses. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5421 Differential Revision: D15704258 Pulled By: HaoyuHuang fbshipit-source-id: 4aa8a55f8cb1576ffb367bfa3186a91d8f06d93a
2019-06-10 22:30:05 +00:00
NewIndexIterator(ReadOptions(), /*need_upper_bound_check=*/false,
/*input_iter=*/nullptr, /*get_context=*/nullptr,
/*lookup_contex=*/nullptr));
Status s = blockhandles_iter->status();
if (!s.ok()) {
out_stream << "Can not read Index Block \n\n";
return s;
}
uint64_t datablock_size_min = std::numeric_limits<uint64_t>::max();
uint64_t datablock_size_max = 0;
uint64_t datablock_size_sum = 0;
size_t block_id = 1;
for (blockhandles_iter->SeekToFirst(); blockhandles_iter->Valid();
block_id++, blockhandles_iter->Next()) {
s = blockhandles_iter->status();
if (!s.ok()) {
break;
}
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
BlockHandle bh = blockhandles_iter->value().handle;
uint64_t datablock_size = bh.size();
datablock_size_min = std::min(datablock_size_min, datablock_size);
datablock_size_max = std::max(datablock_size_max, datablock_size);
datablock_size_sum += datablock_size;
out_stream << "Data Block # " << block_id << " @ "
<< blockhandles_iter->value().handle.ToString(true) << "\n";
out_stream << "--------------------------------------\n";
std::unique_ptr<InternalIterator> datablock_iter;
datablock_iter.reset(NewDataBlockIterator<DataBlockIter>(
Add an option to put first key of each sst block in the index (#5289) Summary: The first key is used to defer reading the data block until this file gets to the top of merging iterator's heap. For short range scans, most files never make it to the top of the heap, so this change can reduce read amplification by a lot sometimes. Consider the following workload. There are a few data streams (we'll be calling them "logs"), each stream consisting of a sequence of blobs (we'll be calling them "records"). Each record is identified by log ID and a sequence number within the log. RocksDB key is concatenation of log ID and sequence number (big endian). Reads are mostly relatively short range scans, each within a single log. Writes are mostly sequential for each log, but writes to different logs are randomly interleaved. Compactions are disabled; instead, when we accumulate a few tens of sst files, we create a new column family and start writing to it. So, a typical sst file consists of a few ranges of blocks, each range corresponding to one log ID (we use FlushBlockPolicy to cut blocks at log boundaries). A typical read would go like this. First, iterator Seek() reads one block from each sst file. Then a series of Next()s move through one sst file (since writes to each log are mostly sequential) until the subiterator reaches the end of this log in this sst file; then Next() switches to the next sst file and reads sequentially from that, and so on. Often a range scan will only return records from a small number of blocks in small number of sst files; in this case, the cost of initial Seek() reading one block from each file may be bigger than the cost of reading the actually useful blocks. Neither iterate_upper_bound nor bloom filters can prevent reading one block from each file in Seek(). But this PR can: if the index contains first key from each block, we don't have to read the block until this block actually makes it to the top of merging iterator's heap, so for short range scans we won't read any blocks from most of the sst files. This PR does the deferred block loading inside value() call. This is not ideal: there's no good way to report an IO error from inside value(). As discussed with siying offline, it would probably be better to change InternalIterator's interface to explicitly fetch deferred value and get status. I'll do it in a separate PR. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5289 Differential Revision: D15256423 Pulled By: al13n321 fbshipit-source-id: 750e4c39ce88e8d41662f701cf6275d9388ba46a
2019-06-25 03:50:35 +00:00
ReadOptions(), blockhandles_iter->value().handle,
/*input_iter=*/nullptr, /*type=*/BlockType::kData,
Create a BlockCacheLookupContext to enable fine-grained block cache tracing. (#5421) Summary: BlockCacheLookupContext only contains the caller for now. We will trace block accesses at five places: 1. BlockBasedTable::GetFilter. 2. BlockBasedTable::GetUncompressedDict. 3. BlockBasedTable::MaybeReadAndLoadToCache. (To trace access on data, index, and range deletion block.) 4. BlockBasedTable::Get. (To trace the referenced key and whether the referenced key exists in a fetched data block.) 5. BlockBasedTable::MultiGet. (To trace the referenced key and whether the referenced key exists in a fetched data block.) We create the context at: 1. BlockBasedTable::Get. (kUserGet) 2. BlockBasedTable::MultiGet. (kUserMGet) 3. BlockBasedTable::NewIterator. (either kUserIterator, kCompaction, or external SST ingestion calls this function.) 4. BlockBasedTable::Open. (kPrefetch) 5. Index/Filter::CacheDependencies. (kPrefetch) 6. BlockBasedTable::ApproximateOffsetOf. (kCompaction or kUserApproximateSize). I loaded 1 million key-value pairs into the database and ran the readrandom benchmark with a single thread. I gave the block cache 10 GB to make sure all reads hit the block cache after warmup. The throughput is comparable. Throughput of this PR: 231334 ops/s. Throughput of the master branch: 238428 ops/s. Experiment setup: RocksDB: version 6.2 Date: Mon Jun 10 10:42:51 2019 CPU: 24 * Intel Core Processor (Skylake) CPUCache: 16384 KB Keys: 20 bytes each Values: 100 bytes each (100 bytes after compression) Entries: 1000000 Prefix: 20 bytes Keys per prefix: 0 RawSize: 114.4 MB (estimated) FileSize: 114.4 MB (estimated) Write rate: 0 bytes/second Read rate: 0 ops/second Compression: NoCompression Compression sampling rate: 0 Memtablerep: skip_list Perf Level: 1 Load command: ./db_bench --benchmarks="fillseq" --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --statistics --cache_index_and_filter_blocks --cache_size=10737418240 --disable_auto_compactions=1 --disable_wal=1 --compression_type=none --min_level_to_compress=-1 --compression_ratio=1 --num=1000000 Run command: ./db_bench --benchmarks="readrandom,stats" --use_existing_db --threads=1 --duration=120 --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --statistics --cache_index_and_filter_blocks --cache_size=10737418240 --disable_auto_compactions=1 --disable_wal=1 --compression_type=none --min_level_to_compress=-1 --compression_ratio=1 --num=1000000 --duration=120 TODOs: 1. Create a caller for external SST file ingestion and differentiate the callers for iterator. 2. Integrate tracer to trace block cache accesses. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5421 Differential Revision: D15704258 Pulled By: HaoyuHuang fbshipit-source-id: 4aa8a55f8cb1576ffb367bfa3186a91d8f06d93a
2019-06-10 22:30:05 +00:00
/*get_context=*/nullptr, /*lookup_context=*/nullptr, Status(),
/*prefetch_buffer=*/nullptr));
s = datablock_iter->status();
if (!s.ok()) {
out_stream << "Error reading the block - Skipped \n\n";
continue;
}
for (datablock_iter->SeekToFirst(); datablock_iter->Valid();
datablock_iter->Next()) {
s = datablock_iter->status();
if (!s.ok()) {
out_stream << "Error reading the block - Skipped \n";
break;
}
DumpKeyValue(datablock_iter->key(), datablock_iter->value(), out_stream);
}
out_stream << "\n";
}
uint64_t num_datablocks = block_id - 1;
if (num_datablocks) {
double datablock_size_avg =
static_cast<double>(datablock_size_sum) / num_datablocks;
out_stream << "Data Block Summary:\n";
out_stream << "--------------------------------------\n";
out_stream << " # data blocks: " << num_datablocks << "\n";
out_stream << " min data block size: " << datablock_size_min << "\n";
out_stream << " max data block size: " << datablock_size_max << "\n";
out_stream << " avg data block size: " << ToString(datablock_size_avg)
<< "\n";
}
return Status::OK();
}
void BlockBasedTable::DumpKeyValue(const Slice& key, const Slice& value,
std::ostream& out_stream) {
InternalKey ikey;
ikey.DecodeFrom(key);
out_stream << " HEX " << ikey.user_key().ToString(true) << ": "
<< value.ToString(true) << "\n";
std::string str_key = ikey.user_key().ToString();
std::string str_value = value.ToString();
std::string res_key(""), res_value("");
char cspace = ' ';
for (size_t i = 0; i < str_key.size(); i++) {
if (str_key[i] == '\0') {
res_key.append("\\0", 2);
} else {
res_key.append(&str_key[i], 1);
}
res_key.append(1, cspace);
}
for (size_t i = 0; i < str_value.size(); i++) {
if (str_value[i] == '\0') {
res_value.append("\\0", 2);
} else {
res_value.append(&str_value[i], 1);
}
res_value.append(1, cspace);
}
out_stream << " ASCII " << res_key << ": " << res_value << "\n";
out_stream << " ------\n";
}
} // namespace ROCKSDB_NAMESPACE