rocksdb/table/table_reader.h

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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.
#pragma once
#include <memory>
#include "db/range_tombstone_fragmenter.h"
Multi file concurrency in MultiGet using coroutines and async IO (#9968) Summary: This PR implements a coroutine version of batched MultiGet in order to concurrently read from multiple SST files in a level using async IO, thus reducing the latency of the MultiGet. The API from the user perspective is still synchronous and single threaded, with the RocksDB part of the processing happening in the context of the caller's thread. In Version::MultiGet, the decision is made whether to call synchronous or coroutine code. A good way to review this PR is to review the first 4 commits in order - de773b3, 70c2f70, 10b50e1, and 377a597 - before reviewing the rest. TODO: 1. Figure out how to build it in CircleCI (requires some dependencies to be installed) 2. Do some stress testing with coroutines enabled No regression in synchronous MultiGet between this branch and main - ``` ./db_bench -use_existing_db=true --db=/data/mysql/rocksdb/prefix_scan -benchmarks="readseq,multireadrandom" -key_size=32 -value_size=512 -num=5000000 -batch_size=64 -multiread_batched=true -use_direct_reads=false -duration=60 -ops_between_duration_checks=1 -readonly=true -adaptive_readahead=true -threads=16 -cache_size=10485760000 -async_io=false -multiread_stride=40000 -statistics ``` Branch - ```multireadrandom : 4.025 micros/op 3975111 ops/sec 60.001 seconds 238509056 operations; 2062.3 MB/s (14767808 of 14767808 found)``` Main - ```multireadrandom : 3.987 micros/op 4013216 ops/sec 60.001 seconds 240795392 operations; 2082.1 MB/s (15231040 of 15231040 found)``` More benchmarks in various scenarios are given below. The measurements were taken with ```async_io=false``` (no coroutines) and ```async_io=true``` (use coroutines). For an IO bound workload (with every key requiring an IO), the coroutines version shows a clear benefit, being ~2.6X faster. For CPU bound workloads, the coroutines version has ~6-15% higher CPU utilization, depending on how many keys overlap an SST file. 1. Single thread IO bound workload on remote storage with sparse MultiGet batch keys (~1 key overlap/file) - No coroutines - ```multireadrandom : 831.774 micros/op 1202 ops/sec 60.001 seconds 72136 operations; 0.6 MB/s (72136 of 72136 found)``` Using coroutines - ```multireadrandom : 318.742 micros/op 3137 ops/sec 60.003 seconds 188248 operations; 1.6 MB/s (188248 of 188248 found)``` 2. Single thread CPU bound workload (all data cached) with ~1 key overlap/file - No coroutines - ```multireadrandom : 4.127 micros/op 242322 ops/sec 60.000 seconds 14539384 operations; 125.7 MB/s (14539384 of 14539384 found)``` Using coroutines - ```multireadrandom : 4.741 micros/op 210935 ops/sec 60.000 seconds 12656176 operations; 109.4 MB/s (12656176 of 12656176 found)``` 3. Single thread CPU bound workload with ~2 key overlap/file - No coroutines - ```multireadrandom : 3.717 micros/op 269000 ops/sec 60.000 seconds 16140024 operations; 139.6 MB/s (16140024 of 16140024 found)``` Using coroutines - ```multireadrandom : 4.146 micros/op 241204 ops/sec 60.000 seconds 14472296 operations; 125.1 MB/s (14472296 of 14472296 found)``` 4. CPU bound multi-threaded (16 threads) with ~4 key overlap/file - No coroutines - ```multireadrandom : 4.534 micros/op 3528792 ops/sec 60.000 seconds 211728728 operations; 1830.7 MB/s (12737024 of 12737024 found) ``` Using coroutines - ```multireadrandom : 4.872 micros/op 3283812 ops/sec 60.000 seconds 197030096 operations; 1703.6 MB/s (12548032 of 12548032 found) ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/9968 Reviewed By: akankshamahajan15 Differential Revision: D36348563 Pulled By: anand1976 fbshipit-source-id: c0ce85a505fd26ebfbb09786cbd7f25202038696
2022-05-19 22:36:27 +00:00
#if USE_COROUTINES
#include "folly/experimental/coro/Coroutine.h"
#include "folly/experimental/coro/Task.h"
#endif
#include "rocksdb/slice_transform.h"
Refactor block cache tracing APIs (#10811) Summary: Refactor the classes, APIs and data structures for block cache tracing to allow a user provided trace writer to be used. Currently, only a TraceWriter is supported, with a default built-in implementation of FileTraceWriter. The TraceWriter, however, takes a flat trace record and is thus only suitable for file tracing. This PR introduces an abstract BlockCacheTraceWriter class that takes a structured BlockCacheTraceRecord. The BlockCacheTraceWriter implementation can then format and log the record in whatever way it sees fit. The default BlockCacheTraceWriterImpl does file tracing using a user provided TraceWriter. `DB::StartBlockTrace` will internally redirect to changed `BlockCacheTrace::StartBlockCacheTrace`. New API `DB::StartBlockTrace` is also added that directly takes `BlockCacheTraceWriter` pointer. This same philosophy can be applied to KV and IO tracing as well. Pull Request resolved: https://github.com/facebook/rocksdb/pull/10811 Test Plan: existing unit tests Old API DB::StartBlockTrace checked with db_bench tool create database ``` ./db_bench --benchmarks="fillseq" \ --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 \ --cache_index_and_filter_blocks --cache_size=1048576 \ --disable_auto_compactions=1 --disable_wal=1 --compression_type=none \ --min_level_to_compress=-1 --compression_ratio=1 --num=10000000 ``` To trace block cache accesses when running readrandom benchmark: ``` ./db_bench --benchmarks="readrandom" --use_existing_db --duration=60 \ --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 \ --cache_index_and_filter_blocks --cache_size=1048576 \ --disable_auto_compactions=1 --disable_wal=1 --compression_type=none \ --min_level_to_compress=-1 --compression_ratio=1 --num=10000000 \ --threads=16 \ -block_cache_trace_file="/tmp/binary_trace_test_example" \ -block_cache_trace_max_trace_file_size_in_bytes=1073741824 \ -block_cache_trace_sampling_frequency=1 ``` Reviewed By: anand1976 Differential Revision: D40435289 Pulled By: akankshamahajan15 fbshipit-source-id: fa2755f4788185e19f4605e731641cfd21ab3282
2022-10-21 19:15:35 +00:00
#include "rocksdb/table_reader_caller.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/get_context.h"
#include "table/internal_iterator.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"
namespace ROCKSDB_NAMESPACE {
class Iterator;
struct ParsedInternalKey;
class Slice;
class Arena;
struct ReadOptions;
struct TableProperties;
class GetContext;
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
class MultiGetContext;
// A Table (also referred to as SST) is a sorted map from strings to strings.
// Tables are immutable and persistent. A Table may be safely accessed from
// multiple threads without external synchronization. Table readers are used
// for reading various types of table formats supported by rocksdb including
// BlockBasedTable, PlainTable and CuckooTable format.
class TableReader {
public:
virtual ~TableReader() {}
// Returns a new iterator over the table contents.
// The result of NewIterator() is initially invalid (caller must
// call one of the Seek methods on the iterator before using it).
//
// read_options: Must outlive the returned iterator.
// arena: If not null, the arena needs to be used to allocate the Iterator.
// When destroying the iterator, the caller will not call "delete"
// but Iterator::~Iterator() directly. The destructor needs to destroy
// all the states but those allocated in arena.
// skip_filters: disables checking the bloom filters even if they exist. This
// option is effective only for block-based table format.
// compaction_readahead_size: its value will only be used if caller =
// kCompaction
virtual InternalIterator* 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 = 0,
bool allow_unprepared_value = false) = 0;
Add new Iterator API Refresh(const snapshot*) (#10594) Summary: This PR resolves https://github.com/facebook/rocksdb/issues/10487 & https://github.com/facebook/rocksdb/issues/10536, user code needs to call Refresh() periodically. The main code change is to support range deletions. A range tombstone iterator uses a sequence number as upper bound to decide which range tombstones are effective. During Iterator refresh, this sequence number upper bound needs to be updated for all range tombstone iterators under DBIter and LevelIterator. LevelIterator may create new table iterators and range tombstone iterator during scanning, so it needs to be aware of iterator refresh. The code path that propagates this change is `db_iter_->set_sequence(read_seq) -> MergingIterator::SetRangeDelReadSeqno() -> TruncatedRangeDelIterator::SetRangeDelReadSeqno() and LevelIterator::SetRangeDelReadSeqno()`. This change also fixes an issue where range tombstone iterators created by LevelIterator may access ReadOptions::snapshot, even though we do not explicitly require users to keep a snapshot alive after creating an Iterator. Pull Request resolved: https://github.com/facebook/rocksdb/pull/10594 Test Plan: * New unit tests. * Add Iterator::Refresh(snapshot) to stress test. Note that this change only adds tests for refreshing to the same snapshot since this is the main target use case. TODO in a following PR: * Stress test Iterator::Refresh() to different snapshots or no snapshot. Reviewed By: ajkr Differential Revision: D48456896 Pulled By: cbi42 fbshipit-source-id: 2e642c04e91235cc9542ef4cd37b3c20823bd779
2023-09-15 17:44:43 +00:00
// read_options.snapshot needs to outlive this call.
virtual FragmentedRangeTombstoneIterator* NewRangeTombstoneIterator(
const ReadOptions& /*read_options*/) {
return nullptr;
}
Add new Iterator API Refresh(const snapshot*) (#10594) Summary: This PR resolves https://github.com/facebook/rocksdb/issues/10487 & https://github.com/facebook/rocksdb/issues/10536, user code needs to call Refresh() periodically. The main code change is to support range deletions. A range tombstone iterator uses a sequence number as upper bound to decide which range tombstones are effective. During Iterator refresh, this sequence number upper bound needs to be updated for all range tombstone iterators under DBIter and LevelIterator. LevelIterator may create new table iterators and range tombstone iterator during scanning, so it needs to be aware of iterator refresh. The code path that propagates this change is `db_iter_->set_sequence(read_seq) -> MergingIterator::SetRangeDelReadSeqno() -> TruncatedRangeDelIterator::SetRangeDelReadSeqno() and LevelIterator::SetRangeDelReadSeqno()`. This change also fixes an issue where range tombstone iterators created by LevelIterator may access ReadOptions::snapshot, even though we do not explicitly require users to keep a snapshot alive after creating an Iterator. Pull Request resolved: https://github.com/facebook/rocksdb/pull/10594 Test Plan: * New unit tests. * Add Iterator::Refresh(snapshot) to stress test. Note that this change only adds tests for refreshing to the same snapshot since this is the main target use case. TODO in a following PR: * Stress test Iterator::Refresh() to different snapshots or no snapshot. Reviewed By: ajkr Differential Revision: D48456896 Pulled By: cbi42 fbshipit-source-id: 2e642c04e91235cc9542ef4cd37b3c20823bd779
2023-09-15 17:44:43 +00:00
virtual FragmentedRangeTombstoneIterator* NewRangeTombstoneIterator(
SequenceNumber /* read_seqno */, const Slice* /* timestamp */) {
return nullptr;
}
// Given a key, return an approximate byte offset in the file where
// the data for that key begins (or would begin if the key were
// present in the file). The returned value is in terms of file
// bytes, and so includes effects like compression of the underlying data.
// E.g., the approximate offset of the last key in the table will
// be close to the file length.
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
// TODO(peterd): Since this function is only used for approximate size
// from beginning of file, reduce code duplication by removing this
// function and letting ApproximateSize take optional start and end, so
// that absolute start and end can be specified and optimized without
// key / index work.
Group rocksdb.sst.read.micros stat by IOActivity flush and compaction (#11288) Summary: **Context:** The existing stat rocksdb.sst.read.micros does not reflect each of compaction and flush cases but aggregate them, which is not so helpful for us to understand IO read behavior of each of them. **Summary** - Update `StopWatch` and `RandomAccessFileReader` to record `rocksdb.sst.read.micros` and `rocksdb.file.{flush/compaction}.read.micros` - Fixed the default histogram in `RandomAccessFileReader` - New field `ReadOptions/IOOptions::io_activity`; Pass `ReadOptions` through paths under db open, flush and compaction to where we can prepare `IOOptions` and pass it to `RandomAccessFileReader` - Use `thread_status_util` for assertion in `DbStressFSWrapper` for continuous testing on we are passing correct `io_activity` under db open, flush and compaction Pull Request resolved: https://github.com/facebook/rocksdb/pull/11288 Test Plan: - **Stress test** - **Db bench 1: rocksdb.sst.read.micros COUNT ≈ sum of rocksdb.file.read.flush.micros's and rocksdb.file.read.compaction.micros's.** (without blob) - May not be exactly the same due to `HistogramStat::Add` only guarantees atomic not accuracy across threads. ``` ./db_bench -db=/dev/shm/testdb/ -statistics=true -benchmarks="fillseq" -key_size=32 -value_size=512 -num=50000 -write_buffer_size=655 -target_file_size_base=655 -disable_auto_compactions=false -compression_type=none -bloom_bits=3 (-use_plain_table=1 -prefix_size=10) ``` ``` // BlockBasedTable rocksdb.sst.read.micros P50 : 2.009374 P95 : 4.968548 P99 : 8.110362 P100 : 43.000000 COUNT : 40456 SUM : 114805 rocksdb.file.read.flush.micros P50 : 1.871841 P95 : 3.872407 P99 : 5.540541 P100 : 43.000000 COUNT : 2250 SUM : 6116 rocksdb.file.read.compaction.micros P50 : 2.023109 P95 : 5.029149 P99 : 8.196910 P100 : 26.000000 COUNT : 38206 SUM : 108689 // PlainTable Does not apply ``` - **Db bench 2: performance** **Read** SETUP: db with 900 files ``` ./db_bench -db=/dev/shm/testdb/ -benchmarks="fillseq" -key_size=32 -value_size=512 -num=50000 -write_buffer_size=655 -disable_auto_compactions=true -target_file_size_base=655 -compression_type=none ```run till convergence ``` ./db_bench -seed=1678564177044286 -use_existing_db=true -db=/dev/shm/testdb -benchmarks=readrandom[-X60] -statistics=true -num=1000000 -disable_auto_compactions=true -compression_type=none -bloom_bits=3 ``` Pre-change `readrandom [AVG 60 runs] : 21568 (± 248) ops/sec` Post-change (no regression, -0.3%) `readrandom [AVG 60 runs] : 21486 (± 236) ops/sec` **Compaction/Flush**run till convergence ``` ./db_bench -db=/dev/shm/testdb2/ -seed=1678564177044286 -benchmarks="fillseq[-X60]" -key_size=32 -value_size=512 -num=50000 -write_buffer_size=655 -disable_auto_compactions=false -target_file_size_base=655 -compression_type=none rocksdb.sst.read.micros COUNT : 33820 rocksdb.sst.read.flush.micros COUNT : 1800 rocksdb.sst.read.compaction.micros COUNT : 32020 ``` Pre-change `fillseq [AVG 46 runs] : 1391 (± 214) ops/sec; 0.7 (± 0.1) MB/sec` Post-change (no regression, ~-0.4%) `fillseq [AVG 46 runs] : 1385 (± 216) ops/sec; 0.7 (± 0.1) MB/sec` Reviewed By: ajkr Differential Revision: D44007011 Pulled By: hx235 fbshipit-source-id: a54c89e4846dfc9a135389edf3f3eedfea257132
2023-04-21 16:07:18 +00:00
virtual uint64_t ApproximateOffsetOf(const ReadOptions& read_options,
const Slice& key,
TableReaderCaller caller) = 0;
// Given start and end keys, return the approximate data size in the file
// between the keys. The returned value is in terms of file bytes, and so
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
// includes effects like compression of the underlying data and applicable
// portions of metadata including filters and indexes. Nullptr for start or
// end (or both) indicates absolute start or end of the table.
Group rocksdb.sst.read.micros stat by IOActivity flush and compaction (#11288) Summary: **Context:** The existing stat rocksdb.sst.read.micros does not reflect each of compaction and flush cases but aggregate them, which is not so helpful for us to understand IO read behavior of each of them. **Summary** - Update `StopWatch` and `RandomAccessFileReader` to record `rocksdb.sst.read.micros` and `rocksdb.file.{flush/compaction}.read.micros` - Fixed the default histogram in `RandomAccessFileReader` - New field `ReadOptions/IOOptions::io_activity`; Pass `ReadOptions` through paths under db open, flush and compaction to where we can prepare `IOOptions` and pass it to `RandomAccessFileReader` - Use `thread_status_util` for assertion in `DbStressFSWrapper` for continuous testing on we are passing correct `io_activity` under db open, flush and compaction Pull Request resolved: https://github.com/facebook/rocksdb/pull/11288 Test Plan: - **Stress test** - **Db bench 1: rocksdb.sst.read.micros COUNT ≈ sum of rocksdb.file.read.flush.micros's and rocksdb.file.read.compaction.micros's.** (without blob) - May not be exactly the same due to `HistogramStat::Add` only guarantees atomic not accuracy across threads. ``` ./db_bench -db=/dev/shm/testdb/ -statistics=true -benchmarks="fillseq" -key_size=32 -value_size=512 -num=50000 -write_buffer_size=655 -target_file_size_base=655 -disable_auto_compactions=false -compression_type=none -bloom_bits=3 (-use_plain_table=1 -prefix_size=10) ``` ``` // BlockBasedTable rocksdb.sst.read.micros P50 : 2.009374 P95 : 4.968548 P99 : 8.110362 P100 : 43.000000 COUNT : 40456 SUM : 114805 rocksdb.file.read.flush.micros P50 : 1.871841 P95 : 3.872407 P99 : 5.540541 P100 : 43.000000 COUNT : 2250 SUM : 6116 rocksdb.file.read.compaction.micros P50 : 2.023109 P95 : 5.029149 P99 : 8.196910 P100 : 26.000000 COUNT : 38206 SUM : 108689 // PlainTable Does not apply ``` - **Db bench 2: performance** **Read** SETUP: db with 900 files ``` ./db_bench -db=/dev/shm/testdb/ -benchmarks="fillseq" -key_size=32 -value_size=512 -num=50000 -write_buffer_size=655 -disable_auto_compactions=true -target_file_size_base=655 -compression_type=none ```run till convergence ``` ./db_bench -seed=1678564177044286 -use_existing_db=true -db=/dev/shm/testdb -benchmarks=readrandom[-X60] -statistics=true -num=1000000 -disable_auto_compactions=true -compression_type=none -bloom_bits=3 ``` Pre-change `readrandom [AVG 60 runs] : 21568 (± 248) ops/sec` Post-change (no regression, -0.3%) `readrandom [AVG 60 runs] : 21486 (± 236) ops/sec` **Compaction/Flush**run till convergence ``` ./db_bench -db=/dev/shm/testdb2/ -seed=1678564177044286 -benchmarks="fillseq[-X60]" -key_size=32 -value_size=512 -num=50000 -write_buffer_size=655 -disable_auto_compactions=false -target_file_size_base=655 -compression_type=none rocksdb.sst.read.micros COUNT : 33820 rocksdb.sst.read.flush.micros COUNT : 1800 rocksdb.sst.read.compaction.micros COUNT : 32020 ``` Pre-change `fillseq [AVG 46 runs] : 1391 (± 214) ops/sec; 0.7 (± 0.1) MB/sec` Post-change (no regression, ~-0.4%) `fillseq [AVG 46 runs] : 1385 (± 216) ops/sec; 0.7 (± 0.1) MB/sec` Reviewed By: ajkr Differential Revision: D44007011 Pulled By: hx235 fbshipit-source-id: a54c89e4846dfc9a135389edf3f3eedfea257132
2023-04-21 16:07:18 +00:00
virtual uint64_t ApproximateSize(const ReadOptions& read_options,
const Slice& start, const Slice& end,
TableReaderCaller caller) = 0;
struct Anchor {
Anchor(const Slice& _user_key, size_t _range_size)
: user_key(_user_key.ToStringView()), range_size(_range_size) {}
std::string user_key;
size_t range_size;
};
// Now try to return approximately 128 anchor keys.
// The last one tends to be the largest key.
virtual Status ApproximateKeyAnchors(const ReadOptions& /*read_options*/,
std::vector<Anchor>& /*anchors*/) {
return Status::NotSupported("ApproximateKeyAnchors() not supported.");
}
// Set up the table for Compaction. Might change some parameters with
// posix_fadvise
virtual void SetupForCompaction() = 0;
virtual std::shared_ptr<const TableProperties> GetTableProperties() const = 0;
// Prepare work that can be done before the real Get()
virtual void Prepare(const Slice& /*target*/) {}
// Report an approximation of how much memory has been used.
virtual size_t ApproximateMemoryUsage() const = 0;
// Calls get_context->SaveValue() repeatedly, starting with
// the entry found after a call to Seek(key), until it returns false.
// May not make such a call if filter policy says that key is not present.
//
// get_context->MarkKeyMayExist needs to be called when it is configured to be
// memory only and the key is not found in the block cache.
//
// readOptions is the options for the read
// key is the key to search for
// skip_filters: disables checking the bloom filters even if they exist. This
// option is effective only for block-based table format.
virtual Status Get(const ReadOptions& readOptions, const Slice& key,
GetContext* get_context,
const SliceTransform* prefix_extractor,
bool skip_filters = false) = 0;
// Use bloom filters in the table file, if present, to filter out keys. The
// mget_range will be updated to skip keys that get a negative result from
// the filter lookup.
virtual Status MultiGetFilter(const ReadOptions& /*readOptions*/,
const SliceTransform* /*prefix_extractor*/,
MultiGetContext::Range* /*mget_range*/) {
return Status::NotSupported();
}
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
virtual void MultiGet(const ReadOptions& readOptions,
const MultiGetContext::Range* mget_range,
const SliceTransform* prefix_extractor,
bool skip_filters = false) {
for (auto iter = mget_range->begin(); iter != mget_range->end(); ++iter) {
*iter->s = Get(readOptions, iter->ikey, iter->get_context,
prefix_extractor, skip_filters);
}
}
Multi file concurrency in MultiGet using coroutines and async IO (#9968) Summary: This PR implements a coroutine version of batched MultiGet in order to concurrently read from multiple SST files in a level using async IO, thus reducing the latency of the MultiGet. The API from the user perspective is still synchronous and single threaded, with the RocksDB part of the processing happening in the context of the caller's thread. In Version::MultiGet, the decision is made whether to call synchronous or coroutine code. A good way to review this PR is to review the first 4 commits in order - de773b3, 70c2f70, 10b50e1, and 377a597 - before reviewing the rest. TODO: 1. Figure out how to build it in CircleCI (requires some dependencies to be installed) 2. Do some stress testing with coroutines enabled No regression in synchronous MultiGet between this branch and main - ``` ./db_bench -use_existing_db=true --db=/data/mysql/rocksdb/prefix_scan -benchmarks="readseq,multireadrandom" -key_size=32 -value_size=512 -num=5000000 -batch_size=64 -multiread_batched=true -use_direct_reads=false -duration=60 -ops_between_duration_checks=1 -readonly=true -adaptive_readahead=true -threads=16 -cache_size=10485760000 -async_io=false -multiread_stride=40000 -statistics ``` Branch - ```multireadrandom : 4.025 micros/op 3975111 ops/sec 60.001 seconds 238509056 operations; 2062.3 MB/s (14767808 of 14767808 found)``` Main - ```multireadrandom : 3.987 micros/op 4013216 ops/sec 60.001 seconds 240795392 operations; 2082.1 MB/s (15231040 of 15231040 found)``` More benchmarks in various scenarios are given below. The measurements were taken with ```async_io=false``` (no coroutines) and ```async_io=true``` (use coroutines). For an IO bound workload (with every key requiring an IO), the coroutines version shows a clear benefit, being ~2.6X faster. For CPU bound workloads, the coroutines version has ~6-15% higher CPU utilization, depending on how many keys overlap an SST file. 1. Single thread IO bound workload on remote storage with sparse MultiGet batch keys (~1 key overlap/file) - No coroutines - ```multireadrandom : 831.774 micros/op 1202 ops/sec 60.001 seconds 72136 operations; 0.6 MB/s (72136 of 72136 found)``` Using coroutines - ```multireadrandom : 318.742 micros/op 3137 ops/sec 60.003 seconds 188248 operations; 1.6 MB/s (188248 of 188248 found)``` 2. Single thread CPU bound workload (all data cached) with ~1 key overlap/file - No coroutines - ```multireadrandom : 4.127 micros/op 242322 ops/sec 60.000 seconds 14539384 operations; 125.7 MB/s (14539384 of 14539384 found)``` Using coroutines - ```multireadrandom : 4.741 micros/op 210935 ops/sec 60.000 seconds 12656176 operations; 109.4 MB/s (12656176 of 12656176 found)``` 3. Single thread CPU bound workload with ~2 key overlap/file - No coroutines - ```multireadrandom : 3.717 micros/op 269000 ops/sec 60.000 seconds 16140024 operations; 139.6 MB/s (16140024 of 16140024 found)``` Using coroutines - ```multireadrandom : 4.146 micros/op 241204 ops/sec 60.000 seconds 14472296 operations; 125.1 MB/s (14472296 of 14472296 found)``` 4. CPU bound multi-threaded (16 threads) with ~4 key overlap/file - No coroutines - ```multireadrandom : 4.534 micros/op 3528792 ops/sec 60.000 seconds 211728728 operations; 1830.7 MB/s (12737024 of 12737024 found) ``` Using coroutines - ```multireadrandom : 4.872 micros/op 3283812 ops/sec 60.000 seconds 197030096 operations; 1703.6 MB/s (12548032 of 12548032 found) ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/9968 Reviewed By: akankshamahajan15 Differential Revision: D36348563 Pulled By: anand1976 fbshipit-source-id: c0ce85a505fd26ebfbb09786cbd7f25202038696
2022-05-19 22:36:27 +00:00
#if USE_COROUTINES
virtual folly::coro::Task<void> MultiGetCoroutine(
const ReadOptions& readOptions, const MultiGetContext::Range* mget_range,
const SliceTransform* prefix_extractor, bool skip_filters = false) {
MultiGet(readOptions, mget_range, prefix_extractor, skip_filters);
co_return;
}
#endif // USE_COROUTINES
// Prefetch data corresponding to a give range of keys
// Typically this functionality is required for table implementations that
// persists the data on a non volatile storage medium like disk/SSD
Group rocksdb.sst.read.micros stat by IOActivity flush and compaction (#11288) Summary: **Context:** The existing stat rocksdb.sst.read.micros does not reflect each of compaction and flush cases but aggregate them, which is not so helpful for us to understand IO read behavior of each of them. **Summary** - Update `StopWatch` and `RandomAccessFileReader` to record `rocksdb.sst.read.micros` and `rocksdb.file.{flush/compaction}.read.micros` - Fixed the default histogram in `RandomAccessFileReader` - New field `ReadOptions/IOOptions::io_activity`; Pass `ReadOptions` through paths under db open, flush and compaction to where we can prepare `IOOptions` and pass it to `RandomAccessFileReader` - Use `thread_status_util` for assertion in `DbStressFSWrapper` for continuous testing on we are passing correct `io_activity` under db open, flush and compaction Pull Request resolved: https://github.com/facebook/rocksdb/pull/11288 Test Plan: - **Stress test** - **Db bench 1: rocksdb.sst.read.micros COUNT ≈ sum of rocksdb.file.read.flush.micros's and rocksdb.file.read.compaction.micros's.** (without blob) - May not be exactly the same due to `HistogramStat::Add` only guarantees atomic not accuracy across threads. ``` ./db_bench -db=/dev/shm/testdb/ -statistics=true -benchmarks="fillseq" -key_size=32 -value_size=512 -num=50000 -write_buffer_size=655 -target_file_size_base=655 -disable_auto_compactions=false -compression_type=none -bloom_bits=3 (-use_plain_table=1 -prefix_size=10) ``` ``` // BlockBasedTable rocksdb.sst.read.micros P50 : 2.009374 P95 : 4.968548 P99 : 8.110362 P100 : 43.000000 COUNT : 40456 SUM : 114805 rocksdb.file.read.flush.micros P50 : 1.871841 P95 : 3.872407 P99 : 5.540541 P100 : 43.000000 COUNT : 2250 SUM : 6116 rocksdb.file.read.compaction.micros P50 : 2.023109 P95 : 5.029149 P99 : 8.196910 P100 : 26.000000 COUNT : 38206 SUM : 108689 // PlainTable Does not apply ``` - **Db bench 2: performance** **Read** SETUP: db with 900 files ``` ./db_bench -db=/dev/shm/testdb/ -benchmarks="fillseq" -key_size=32 -value_size=512 -num=50000 -write_buffer_size=655 -disable_auto_compactions=true -target_file_size_base=655 -compression_type=none ```run till convergence ``` ./db_bench -seed=1678564177044286 -use_existing_db=true -db=/dev/shm/testdb -benchmarks=readrandom[-X60] -statistics=true -num=1000000 -disable_auto_compactions=true -compression_type=none -bloom_bits=3 ``` Pre-change `readrandom [AVG 60 runs] : 21568 (± 248) ops/sec` Post-change (no regression, -0.3%) `readrandom [AVG 60 runs] : 21486 (± 236) ops/sec` **Compaction/Flush**run till convergence ``` ./db_bench -db=/dev/shm/testdb2/ -seed=1678564177044286 -benchmarks="fillseq[-X60]" -key_size=32 -value_size=512 -num=50000 -write_buffer_size=655 -disable_auto_compactions=false -target_file_size_base=655 -compression_type=none rocksdb.sst.read.micros COUNT : 33820 rocksdb.sst.read.flush.micros COUNT : 1800 rocksdb.sst.read.compaction.micros COUNT : 32020 ``` Pre-change `fillseq [AVG 46 runs] : 1391 (± 214) ops/sec; 0.7 (± 0.1) MB/sec` Post-change (no regression, ~-0.4%) `fillseq [AVG 46 runs] : 1385 (± 216) ops/sec; 0.7 (± 0.1) MB/sec` Reviewed By: ajkr Differential Revision: D44007011 Pulled By: hx235 fbshipit-source-id: a54c89e4846dfc9a135389edf3f3eedfea257132
2023-04-21 16:07:18 +00:00
virtual Status Prefetch(const ReadOptions& /* read_options */,
const Slice* begin = nullptr,
const Slice* end = nullptr) {
(void)begin;
(void)end;
// Default implementation is NOOP.
// The child class should implement functionality when applicable
return Status::OK();
}
// convert db file to a human readable form
virtual Status DumpTable(WritableFile* /*out_file*/) {
return Status::NotSupported("DumpTable() not supported");
}
// check whether there is corruption in this db file
virtual Status VerifyChecksum(const ReadOptions& /*read_options*/,
TableReaderCaller /*caller*/) {
return Status::NotSupported("VerifyChecksum() not supported");
}
Support pro-actively erasing obsolete block cache entries (#12694) Summary: Currently, when files become obsolete, the block cache entries associated with them just age out naturally. With pure LRU, this is not too bad, as once you "use" enough cache entries to (re-)fill the cache, you are guranteed to have purged the obsolete entries. However, HyperClockCache is a counting clock cache with a somewhat longer memory, so could be more negatively impacted by previously-hot cache entries becoming obsolete, and taking longer to age out than newer single-hit entries. Part of the reason we still have this natural aging-out is that there's almost no connection between block cache entries and the file they are associated with. Everything is hashed into the same pool(s) of entries with nothing like a secondary index based on file. Keeping track of such an index could be expensive. This change adds a new, mutable CF option `uncache_aggressiveness` for erasing obsolete block cache entries. The process can be speculative, lossy, or unproductive because not all potential block cache entries associated with files will be resident in memory, and attempting to remove them all could be wasted CPU time. Rather than a simple on/off switch, `uncache_aggressiveness` basically tells RocksDB how much CPU you're willing to burn trying to purge obsolete block cache entries. When such efforts are not sufficiently productive for a file, we stop and move on. The option is in ColumnFamilyOptions so that it is dynamically changeable for already-open files, and customizeable by CF. Note that this block cache removal happens as part of the process of purging obsolete files, which is often in a background thread (depending on `background_purge_on_iterator_cleanup` and `avoid_unnecessary_blocking_io` options) rather than along CPU critical paths. Notable auxiliary code details: * Possibly fixing some issues with trivial moves with `only_delete_metadata`: unnecessary TableCache::Evict in that case and missing from the ObsoleteFileInfo move operator. (Not able to reproduce an current failure.) * Remove suspicious TableCache::Erase() from VersionSet::AddObsoleteBlobFile() (TODO follow-up item) Marked EXPERIMENTAL until more thorough validation is complete. Direct stats of this functionality are omitted because they could be misleading. Block cache hit rate is a better indicator of benefit, and CPU profiling a better indicator of cost. Pull Request resolved: https://github.com/facebook/rocksdb/pull/12694 Test Plan: * Unit tests added, including refactoring an existing test to make better use of parameterized tests. * Added to crash test. * Performance, sample command: ``` for I in `seq 1 10`; do for UA in 300; do for CT in lru_cache fixed_hyper_clock_cache auto_hyper_clock_cache; do rm -rf /dev/shm/test3; TEST_TMPDIR=/dev/shm/test3 /usr/bin/time ./db_bench -benchmarks=readwhilewriting -num=13000000 -read_random_exp_range=6 -write_buffer_size=10000000 -bloom_bits=10 -cache_type=$CT -cache_size=390000000 -cache_index_and_filter_blocks=1 -disable_wal=1 -duration=60 -statistics -uncache_aggressiveness=$UA 2>&1 | grep -E 'micros/op|rocksdb.block.cache.data.(hit|miss)|rocksdb.number.keys.(read|written)|maxresident' | awk '/rocksdb.block.cache.data.miss/ { miss = $4 } /rocksdb.block.cache.data.hit/ { hit = $4 } { print } END { print "hit rate = " ((hit * 1.0) / (miss + hit)) }' | tee -a results-$CT-$UA; done; done; done ``` Averaging 10 runs each case, block cache data block hit rates ``` lru_cache UA=0 -> hit rate = 0.327, ops/s = 87668, user CPU sec = 139.0 UA=300 -> hit rate = 0.336, ops/s = 87960, user CPU sec = 139.0 fixed_hyper_clock_cache UA=0 -> hit rate = 0.336, ops/s = 100069, user CPU sec = 139.9 UA=300 -> hit rate = 0.343, ops/s = 100104, user CPU sec = 140.2 auto_hyper_clock_cache UA=0 -> hit rate = 0.336, ops/s = 97580, user CPU sec = 140.5 UA=300 -> hit rate = 0.345, ops/s = 97972, user CPU sec = 139.8 ``` Conclusion: up to roughly 1 percentage point of improved block cache hit rate, likely leading to overall improved efficiency (because the foreground CPU cost of cache misses likely outweighs the background CPU cost of erasure, let alone I/O savings). Reviewed By: ajkr Differential Revision: D57932442 Pulled By: pdillinger fbshipit-source-id: 84a243ca5f965f731f346a4853009780a904af6c
2024-06-07 15:57:11 +00:00
// Tell the reader that the file should now be obsolete, e.g. as a hint
// to delete relevant cache entries on destruction. (It might not be safe
// to "unpin" cache entries until destruction time.) NOTE: must be thread
// safe because multiple table cache references might all mark this file as
// obsolete when they are released (the last of which destroys this reader).
Support pro-actively erasing obsolete block cache entries (#12694) Summary: Currently, when files become obsolete, the block cache entries associated with them just age out naturally. With pure LRU, this is not too bad, as once you "use" enough cache entries to (re-)fill the cache, you are guranteed to have purged the obsolete entries. However, HyperClockCache is a counting clock cache with a somewhat longer memory, so could be more negatively impacted by previously-hot cache entries becoming obsolete, and taking longer to age out than newer single-hit entries. Part of the reason we still have this natural aging-out is that there's almost no connection between block cache entries and the file they are associated with. Everything is hashed into the same pool(s) of entries with nothing like a secondary index based on file. Keeping track of such an index could be expensive. This change adds a new, mutable CF option `uncache_aggressiveness` for erasing obsolete block cache entries. The process can be speculative, lossy, or unproductive because not all potential block cache entries associated with files will be resident in memory, and attempting to remove them all could be wasted CPU time. Rather than a simple on/off switch, `uncache_aggressiveness` basically tells RocksDB how much CPU you're willing to burn trying to purge obsolete block cache entries. When such efforts are not sufficiently productive for a file, we stop and move on. The option is in ColumnFamilyOptions so that it is dynamically changeable for already-open files, and customizeable by CF. Note that this block cache removal happens as part of the process of purging obsolete files, which is often in a background thread (depending on `background_purge_on_iterator_cleanup` and `avoid_unnecessary_blocking_io` options) rather than along CPU critical paths. Notable auxiliary code details: * Possibly fixing some issues with trivial moves with `only_delete_metadata`: unnecessary TableCache::Evict in that case and missing from the ObsoleteFileInfo move operator. (Not able to reproduce an current failure.) * Remove suspicious TableCache::Erase() from VersionSet::AddObsoleteBlobFile() (TODO follow-up item) Marked EXPERIMENTAL until more thorough validation is complete. Direct stats of this functionality are omitted because they could be misleading. Block cache hit rate is a better indicator of benefit, and CPU profiling a better indicator of cost. Pull Request resolved: https://github.com/facebook/rocksdb/pull/12694 Test Plan: * Unit tests added, including refactoring an existing test to make better use of parameterized tests. * Added to crash test. * Performance, sample command: ``` for I in `seq 1 10`; do for UA in 300; do for CT in lru_cache fixed_hyper_clock_cache auto_hyper_clock_cache; do rm -rf /dev/shm/test3; TEST_TMPDIR=/dev/shm/test3 /usr/bin/time ./db_bench -benchmarks=readwhilewriting -num=13000000 -read_random_exp_range=6 -write_buffer_size=10000000 -bloom_bits=10 -cache_type=$CT -cache_size=390000000 -cache_index_and_filter_blocks=1 -disable_wal=1 -duration=60 -statistics -uncache_aggressiveness=$UA 2>&1 | grep -E 'micros/op|rocksdb.block.cache.data.(hit|miss)|rocksdb.number.keys.(read|written)|maxresident' | awk '/rocksdb.block.cache.data.miss/ { miss = $4 } /rocksdb.block.cache.data.hit/ { hit = $4 } { print } END { print "hit rate = " ((hit * 1.0) / (miss + hit)) }' | tee -a results-$CT-$UA; done; done; done ``` Averaging 10 runs each case, block cache data block hit rates ``` lru_cache UA=0 -> hit rate = 0.327, ops/s = 87668, user CPU sec = 139.0 UA=300 -> hit rate = 0.336, ops/s = 87960, user CPU sec = 139.0 fixed_hyper_clock_cache UA=0 -> hit rate = 0.336, ops/s = 100069, user CPU sec = 139.9 UA=300 -> hit rate = 0.343, ops/s = 100104, user CPU sec = 140.2 auto_hyper_clock_cache UA=0 -> hit rate = 0.336, ops/s = 97580, user CPU sec = 140.5 UA=300 -> hit rate = 0.345, ops/s = 97972, user CPU sec = 139.8 ``` Conclusion: up to roughly 1 percentage point of improved block cache hit rate, likely leading to overall improved efficiency (because the foreground CPU cost of cache misses likely outweighs the background CPU cost of erasure, let alone I/O savings). Reviewed By: ajkr Differential Revision: D57932442 Pulled By: pdillinger fbshipit-source-id: 84a243ca5f965f731f346a4853009780a904af6c
2024-06-07 15:57:11 +00:00
virtual void MarkObsolete(uint32_t /*uncache_aggressiveness*/) {
// no-op as default
}
};
} // namespace ROCKSDB_NAMESPACE