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rocksdb/table/block_based/block_based_table_reader.cc

3343 lines
127 KiB

// 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>
#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
9 years ago
#include "db/pinned_iterators_manager.h"
#include "file/file_prefetch_buffer.h"
#include "file/random_access_file_reader.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
5 years ago
#include "rocksdb/file_system.h"
#include "rocksdb/filter_policy.h"
#include "rocksdb/iterator.h"
#include "rocksdb/options.h"
#include "rocksdb/statistics.h"
#include "rocksdb/table.h"
#include "rocksdb/table_properties.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"
#include "table/block_based/block_prefix_index.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
6 years ago
#include "table/multiget_context.h"
#include "table/persistent_cache_helper.h"
#include "table/sst_file_writer_collectors.h"
#include "table/two_level_iterator.h"
#include "monitoring/perf_context_imp.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"
#include "util/util.h"
#include "util/xxhash.h"
namespace ROCKSDB_NAMESPACE {
extern const uint64_t kBlockBasedTableMagicNumber;
extern const std::string kHashIndexPrefixesBlock;
extern const std::string kHashIndexPrefixesMetadataBlock;
typedef BlockBasedTable::IndexReader IndexReader;
// Found that 256 KB readahead size provides the best performance, based on
// experiments, for auto readahead. Experiment data is in PR #3282.
const size_t BlockBasedTable::kMaxAutoReadaheadSize = 256 * 1024;
BlockBasedTable::~BlockBasedTable() {
delete rep_;
}
std::atomic<uint64_t> BlockBasedTable::next_cache_key_id_(0);
template <typename TBlocklike>
class BlocklikeTraits;
template <>
class BlocklikeTraits<BlockContents> {
public:
static BlockContents* Create(BlockContents&& contents,
size_t /* read_amp_bytes_per_bit */,
Statistics* /* 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
5 years ago
bool /* using_zstd */,
const FilterPolicy* /* filter_policy */) {
return new BlockContents(std::move(contents));
}
static uint32_t GetNumRestarts(const BlockContents& /* contents */) {
return 0;
}
};
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
5 years ago
template <>
class BlocklikeTraits<ParsedFullFilterBlock> {
public:
static ParsedFullFilterBlock* Create(BlockContents&& contents,
size_t /* read_amp_bytes_per_bit */,
Statistics* /* statistics */,
bool /* using_zstd */,
const FilterPolicy* filter_policy) {
return new ParsedFullFilterBlock(filter_policy, std::move(contents));
}
static uint32_t GetNumRestarts(const ParsedFullFilterBlock& /* block */) {
return 0;
}
};
template <>
class BlocklikeTraits<Block> {
public:
static Block* Create(BlockContents&& contents, size_t read_amp_bytes_per_bit,
Statistics* statistics, bool /* using_zstd */,
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
5 years ago
const FilterPolicy* /* filter_policy */) {
return new Block(std::move(contents), read_amp_bytes_per_bit, statistics);
}
static uint32_t GetNumRestarts(const Block& block) {
return block.NumRestarts();
}
};
template <>
class BlocklikeTraits<UncompressionDict> {
public:
static UncompressionDict* Create(BlockContents&& contents,
size_t /* read_amp_bytes_per_bit */,
Statistics* /* 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
5 years ago
bool using_zstd,
const FilterPolicy* /* filter_policy */) {
return new UncompressionDict(contents.data, std::move(contents.allocation),
using_zstd);
}
static uint32_t GetNumRestarts(const UncompressionDict& /* dict */) {
return 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 ImmutableCFOptions& 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.statistics,
using_zstd, filter_policy));
}
return s;
}
// Delete the entry resided in the cache.
template <class Entry>
void DeleteCachedEntry(const Slice& /*key*/, void* value) {
auto entry = reinterpret_cast<Entry*>(value);
delete entry;
}
// 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.statistics;
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.statistics;
// 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) const {
Statistics* const statistics = rep_->ioptions.statistics;
// TODO: introduce perf counters for block cache insertions
if (get_context) {
++get_context->get_context_stats_.num_cache_add;
get_context->get_context_stats_.num_cache_bytes_write += usage;
} else {
RecordTick(statistics, BLOCK_CACHE_ADD);
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;
get_context->get_context_stats_.num_cache_filter_bytes_insert += usage;
} else {
RecordTick(statistics, BLOCK_CACHE_FILTER_ADD);
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;
get_context->get_context_stats_
.num_cache_compression_dict_bytes_insert += usage;
} else {
RecordTick(statistics, BLOCK_CACHE_COMPRESSION_DICT_ADD);
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;
get_context->get_context_stats_.num_cache_index_bytes_insert += usage;
} else {
RecordTick(statistics, BLOCK_CACHE_INDEX_ADD);
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;
get_context->get_context_stats_.num_cache_data_bytes_insert += usage;
} else {
RecordTick(statistics, BLOCK_CACHE_DATA_ADD);
RecordTick(statistics, BLOCK_CACHE_DATA_BYTES_INSERT, usage);
}
break;
}
}
Cache::Handle* BlockBasedTable::GetEntryFromCache(
Cache* block_cache, const Slice& key, BlockType block_type,
GetContext* get_context) const {
auto cache_handle = block_cache->Lookup(key, rep_->ioptions.statistics);
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) {
assert(kMaxCacheKeyPrefixSize >= 10);
rep->cache_key_prefix_size = 0;
rep->compressed_cache_key_prefix_size = 0;
if (rep->table_options.block_cache != nullptr) {
GenerateCachePrefix(rep->table_options.block_cache.get(), rep->file->file(),
&rep->cache_key_prefix[0], &rep->cache_key_prefix_size);
}
if (rep->table_options.persistent_cache != nullptr) {
GenerateCachePrefix(/*cache=*/nullptr, rep->file->file(),
&rep->persistent_cache_key_prefix[0],
&rep->persistent_cache_key_prefix_size);
}
if (rep->table_options.block_cache_compressed != nullptr) {
GenerateCachePrefix(rep->table_options.block_cache_compressed.get(),
rep->file->file(), &rep->compressed_cache_key_prefix[0],
&rep->compressed_cache_key_prefix_size);
}
}
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
5 years ago
void BlockBasedTable::GenerateCachePrefix(Cache* cc, FSRandomAccessFile* file,
char* buffer, size_t* size) {
// generate an id from the file
*size = file->GetUniqueId(buffer, kMaxCacheKeyPrefixSize);
// If the prefix wasn't generated or was too long,
// create one from the cache.
if (cc != nullptr && *size == 0) {
char* end = EncodeVarint64(buffer, cc->NewId());
*size = static_cast<size_t>(end - buffer);
}
}
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
5 years ago
void BlockBasedTable::GenerateCachePrefix(Cache* cc, FSWritableFile* file,
char* buffer, size_t* size) {
// generate an id from the file
*size = file->GetUniqueId(buffer, kMaxCacheKeyPrefixSize);
// If the prefix wasn't generated or was too long,
// create one from the cache.
if (cc != nullptr && *size == 0) {
char* end = EncodeVarint64(buffer, cc->NewId());
*size = static_cast<size_t>(end - buffer);
}
}
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 ImmutableCFOptions& 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, TailPrefetchStats* tail_prefetch_stats,
BlockCacheTracer* const block_cache_tracer) {
table_reader->reset();
Status s;
Footer footer;
std::unique_ptr<FilePrefetchBuffer> prefetch_buffer;
// 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(file.get(), file_size, tail_prefetch_stats, prefetch_all,
preload_all, &prefetch_buffer);
} 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]
s = ReadFooterFromFile(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,
internal_comparator, skip_filters, 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));
}
SetupCacheKeyPrefix(rep);
std::unique_ptr<BlockBasedTable> new_table(
new BlockBasedTable(rep, block_cache_tracer));
// page cache options
rep->persistent_cache_options =
PersistentCacheOptions(rep->table_options.persistent_cache,
std::string(rep->persistent_cache_key_prefix,
rep->persistent_cache_key_prefix_size),
rep->ioptions.statistics);
// 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<Block> metaindex;
std::unique_ptr<InternalIterator> metaindex_iter;
s = new_table->ReadMetaIndexBlock(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
6 years ago
// Populates table_properties and some fields that depend on it,
// such as index_type.
s = new_table->ReadPropertiesBlock(prefetch_buffer.get(),
metaindex_iter.get(), largest_seqno);
if (!s.ok()) {
return s;
}
s = new_table->ReadRangeDelBlock(prefetch_buffer.get(), metaindex_iter.get(),
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
6 years ago
internal_comparator, &lookup_context);
if (!s.ok()) {
return s;
}
s = new_table->PrefetchIndexAndFilterBlocks(
prefetch_buffer.get(), metaindex_iter.get(), new_table.get(),
prefetch_all, table_options, level, &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(
RandomAccessFileReader* file, uint64_t file_size,
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);
Status s;
// TODO should not have this special logic in the future.
if (!file->use_direct_io()) {
prefetch_buffer->reset(new FilePrefetchBuffer(
nullptr, 0, 0, false /* enable */, true /* track_min_offset */));
s = file->Prefetch(prefetch_off, prefetch_len);
} else {
prefetch_buffer->reset(new FilePrefetchBuffer(
nullptr, 0, 0, true /* enable */, true /* track_min_offset */));
s = (*prefetch_buffer)->Prefetch(file, prefetch_off, prefetch_len);
}
return s;
}
Status BlockBasedTable::TryReadPropertiesWithGlobalSeqno(
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(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);
}
uint32_t value = DecodeFixed32(tmp_buf.get() + block_size + 1);
s = ROCKSDB_NAMESPACE::VerifyChecksum(rep_->footer.checksum(),
tmp_buf.get(), block_size + 1, value);
}
return s;
}
Status BlockBasedTable::ReadPropertiesBlock(
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.info_log,
"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(
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 */);
}
if (s.IsCorruption()) {
s = TryReadPropertiesWithGlobalSeqno(prefetch_buffer, meta_iter->value(),
&table_properties);
}
std::unique_ptr<TableProperties> props_guard;
if (table_properties != nullptr) {
props_guard.reset(table_properties);
}
if (!s.ok()) {
ROCKS_LOG_WARN(rep_->ioptions.info_log,
"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.info_log,
"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.info_log);
rep_->prefix_filtering &=
IsFeatureSupported(*(rep_->table_properties),
BlockBasedTablePropertyNames::kPrefixFiltering,
rep_->ioptions.info_log);
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
6 years ago
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.info_log, "%s", s.ToString().c_str());
}
}
return s;
}
Status BlockBasedTable::ReadRangeDelBlock(
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
6 years ago
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.info_log,
"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()) {
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
6 years ago
ReadOptions read_options;
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
6 years ago
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
6 years ago
if (!s.ok()) {
ROCKS_LOG_WARN(
rep_->ioptions.info_log,
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
6 years ago
"Encountered error while reading data from range del block %s",
s.ToString().c_str());
} else {
rep_->fragmented_range_dels =
std::make_shared<FragmentedRangeTombstoneList>(std::move(iter),
internal_comparator);
}
}
return s;
}
Status BlockBasedTable::PrefetchIndexAndFilterBlocks(
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
6 years ago
const BlockBasedTableOptions& table_options, const int level,
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;
}
}
}
// 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
6 years ago
BlockBasedTableOptions::IndexType index_type = rep_->index_type;
const bool use_cache = table_options.cache_index_and_filter_blocks;
// pin both index and filters, down to all partitions
const bool pin_all =
rep_->table_options.pin_l0_filter_and_index_blocks_in_cache && level == 0;
// prefetch the first level of index
const bool prefetch_index =
prefetch_all ||
(table_options.pin_top_level_index_and_filter &&
index_type == BlockBasedTableOptions::kTwoLevelIndexSearch);
// pin the first level of index
const bool pin_index =
pin_all || (table_options.pin_top_level_index_and_filter &&
index_type == BlockBasedTableOptions::kTwoLevelIndexSearch);
std::unique_ptr<IndexReader> index_reader;
s = new_table->CreateIndexReader(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) {
rep_->index_reader->CacheDependencies(pin_all);
}
// prefetch the first level of filter
const bool prefetch_filter =
prefetch_all ||
(table_options.pin_top_level_index_and_filter &&
rep_->filter_type == Rep::FilterType::kPartitionedFilter);
// Partition fitlers cannot be enabled without partition indexes
assert(!prefetch_filter || prefetch_index);
// pin the first level of filter
const bool pin_filter =
pin_all || (table_options.pin_top_level_index_and_filter &&
rep_->filter_type == Rep::FilterType::kPartitionedFilter);
if (rep_->filter_policy) {
auto filter = new_table->CreateFilterBlockReader(
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) {
filter->CacheDependencies(pin_all);
}
rep_->filter = std::move(filter);
}
}
if (!rep_->compression_dict_handle.IsNull()) {
std::unique_ptr<UncompressionDictReader> uncompression_dict_reader;
s = UncompressionDictReader::Create(this, prefetch_buffer, use_cache,
prefetch_all, pin_all, 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
5 years ago
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
5 years ago
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
5 years ago
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(
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, ReadOptions(),
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.info_log,
"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(
BytewiseComparator(), 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,
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());
Status s;
BlockContents* compressed_block = nullptr;
Cache::Handle* block_cache_compressed_handle = nullptr;
// Lookup uncompressed cache first
if (block_cache != nullptr) {
auto cache_handle = GetEntryFromCache(block_cache, block_cache_key,
block_type, get_context);
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());
block_cache_compressed_handle =
block_cache_compressed->Lookup(compressed_block_cache_key);
Statistics* statistics = rep_->ioptions.statistics;
// 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
BlockContents contents;
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));
// Insert uncompressed block into block cache
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
5 years ago
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;
s = block_cache->Insert(block_cache_key, block_holder.get(), charge,
&DeleteCachedEntry<TBlocklike>, &cache_handle);
if (s.ok()) {
assert(cache_handle != nullptr);
block->SetCachedValue(block_holder.release(), block_cache,
cache_handle);
UpdateCacheInsertionMetrics(block_type, get_context, charge);
} 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 ImmutableCFOptions& 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.statistics;
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
5 years ago
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
5 years ago
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,
block_cont_for_comp_cache->ApproximateMemoryUsage(),
&DeleteCachedEntry<BlockContents>);
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;
s = block_cache->Insert(block_cache_key, block_holder.get(), charge,
&DeleteCachedEntry<TBlocklike>, &cache_handle,
priority);
if (s.ok()) {
assert(cache_handle != nullptr);
cached_block->SetCachedValue(block_holder.release(), block_cache,
cache_handle);
UpdateCacheInsertionMetrics(block_type, get_context, charge);
} else {
RecordTick(statistics, BLOCK_CACHE_ADD_FAILURES);
}
} else {
cached_block->SetOwnedValue(block_holder.release());
}
return s;
}
std::unique_ptr<FilterBlockReader> BlockBasedTable::CreateFilterBlockReader(
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, prefetch_buffer, use_cache, prefetch, pin, lookup_context);
case Rep::FilterType::kBlockFilter:
return BlockBasedFilterBlockReader::Create(
this, prefetch_buffer, use_cache, prefetch, pin, lookup_context);
case Rep::FilterType::kFullFilter:
return FullFilterBlockReader::Create(this, 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
6 years ago
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
6 years ago
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
6 years ago
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
6 years ago
template <>
DataBlockIter* BlockBasedTable::InitBlockIterator<DataBlockIter>(
const Rep* rep, Block* block, BlockType block_type,
DataBlockIter* 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
6 years ago
return block->NewDataIterator(
&rep->internal_comparator, rep->internal_comparator.user_comparator(),
rep->get_global_seqno(block_type), input_iter, rep->ioptions.statistics,
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
6 years ago
}
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
6 years ago
return block->NewIndexIterator(
&rep->internal_comparator, rep->internal_comparator.user_comparator(),
rep->get_global_seqno(block_type), input_iter, rep->ioptions.statistics,
/* 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
6 years ago
}
// 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,
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();
// No point to cache compressed blocks if it never goes away
Cache* block_cache_compressed =
rep_->immortal_table ? nullptr
: 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
10 years ago
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,
get_context);
if (block_entry->GetValue()) {
// TODO(haoyu): Differentiate cache hit on uncompressed block cache and
// compressed block cache.
is_cache_hit = true;
}
}
// Can't find the block from the cache. If I/O is allowed, read from the
// file.
if (block_entry->GetValue() == nullptr && !no_io && ro.fill_cache) {
Statistics* statistics = rep_->ioptions.statistics;
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.env, 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;
} 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.env->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=*/"");
block_cache_tracer_->WriteBlockAccess(access_record, key,
rep_->cf_name_for_tracing(),
lookup_context->referenced_key);
}
}
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
5 years ago
// 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 ImmutableCFOptions& 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 (file->use_direct_io() || 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
5 years ago
(*statuses)[idx_in_batch] =
RetrieveBlock(nullptr, options, handle, uncompression_dict,
&(*results)[idx_in_batch], BlockType::kData,
mget_iter->get_context, &lookup_data_block_context,
/* for_compaction */ false, /* use_cache */ true);
}
return;
}
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
5 years ago
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
5 years ago
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
5 years ago
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.
if (scratch != nullptr && prev_end == handle.offset()) {
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 (scratch == nullptr) {
req.scratch = new char[req.len];
} else {
req.scratch = scratch + buf_offset;
buf_offset += req.len;
}
req.status = IOStatus::OK();
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());
}
// 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
5 years ago
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
5 years ago
req.offset = prev_offset;
req.len = prev_len;
if (scratch == nullptr) {
req.scratch = new char[req.len];
} else {
req.scratch = scratch + buf_offset;
}
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
5 years ago
req.status = IOStatus::OK();
read_reqs.emplace_back(req);
}
file->MultiRead(&read_reqs[0], read_reqs.size());
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
5 years ago
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
5 years ago
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++;
FSReadRequest& req = read_reqs[req_idx];
Status s = req.status;
if (s.ok()) {
if (req.result.size() != 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
5 years ago
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;
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
5 years ago
size_t cur_read_end = req_offset + block_size(handle);
if (cur_read_end > req.result.size()) {
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()));
}
bool blocks_share_read_buffer = (req.result.size() != block_size(handle));
if (s.ok()) {
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
5 years ago
if (scratch == nullptr && !blocks_share_read_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);
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
5 years ago
std::unique_ptr<char[]> raw_block(req.scratch + req_offset);
raw_block_contents = BlockContents(std::move(raw_block), handle.size());
} else {
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
5 years ago
// We used the scratch buffer which are shared by the blocks.
// raw_block_contents does not have the ownership.
raw_block_contents =
BlockContents(Slice(req.scratch + req_offset, handle.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
5 years ago
#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();
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
5 years ago
uint32_t expected =
DecodeFixed32(data + req_offset + handle.size() + 1);
// 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,
// which is handle.size() + 1.
s = ROCKSDB_NAMESPACE::VerifyChecksum(footer.checksum(),
req.result.data() + req_offset,
handle.size() + 1, expected);
TEST_SYNC_POINT_CALLBACK("RetrieveMultipleBlocks:VerifyChecksum", &s);
}
}
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
5 years ago
if (s.ok()) {
// It handles a rare case: compression is set and these is no compressed
// cache (enable combined read). In this case, the scratch != nullptr.
// At the same time, some blocks are actually not compressed,
// since its compression space saving is smaller than the threshold. In
// this case, if the block shares the scratch memory, we need to copy it
// to the heap such that it can be added to the regular block cache.
CompressionType compression_type =
raw_block_contents.get_compression_type();
if (scratch != nullptr && compression_type == kNoCompression) {
Slice raw = Slice(req.scratch + req_offset, block_size(handle));
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(
nullptr, options, handle, uncompression_dict, 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) {
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
5 years ago
s = UncompressBlockContents(info, req.result.data() + req_offset,
handle.size(), &contents, footer.version(),
rep_->ioptions, memory_allocator);
} else {
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
5 years ago
// There are two cases here: 1) caller uses the scratch buffer; 2) we
// use the requst buffer. If scratch buffer is used, we ensure that
// 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.statistics));
}
}
(*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,
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
5 years ago
bool for_compaction, bool use_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
5 years ago
if (use_cache) {
s = MaybeReadBlockAndLoadToCache(prefetch_buffer, ro, handle,
uncompression_dict, block_entry,
block_type, get_context, lookup_context,
/*contents=*/nullptr);
if (!s.ok()) {
return s;
}
if (block_entry->GetValue() != 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.env, rep_->ioptions.statistics,
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
5 years ago
rep_->blocks_definitely_zstd_compressed,
rep_->table_options.filter_policy.get());
}
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,
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
5 years ago
bool for_compaction, bool use_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
5 years ago
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,
bool for_compaction, bool use_cache) const;
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,
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
5 years ago
bool for_compaction, bool use_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,
bool for_compaction, bool use_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
6 years ago
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
6 years ago
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
6 years ago
return block->second.GetValue()->NewIndexIterator(
&rep->internal_comparator, 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
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
//
// Otherwise, this method guarantees no I/O will be incurred.
//
// 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
6 years ago
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 user_key = ExtractUserKey(internal_key);
if (!prefix_extractor->InDomain(user_key)) {
return true;
}
bool may_match = true;
Status s;
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
10 years ago
// First, try check with full filter
FilterBlockReader* const filter = rep_->filter.get();
bool filter_checked = true;
if (filter != nullptr) {
if (!filter->IsBlockBased()) {
const Slice* const const_ikey_ptr = &internal_key;
may_match = filter->RangeMayExist(
read_options.iterate_upper_bound, user_key, prefix_extractor,
rep_->internal_comparator.user_comparator(), const_ikey_ptr,
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
6 years ago
&filter_checked, need_upper_bound_check, 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);
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
6 years ago
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
6 years ago
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
6 years ago
/*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
6 years ago
} 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
6 years ago
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
6 years ago
may_match = filter->PrefixMayMatch(
prefix, prefix_extractor, handle.offset(), /*no_io=*/false,
/*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
10 years ago
}
}
if (filter_checked) {
Statistics* statistics = rep_->ioptions.statistics;
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,
size_t compaction_readahead_size) {
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
5 years ago
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
5 years ago
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
5 years ago
need_upper_bound_check, prefix_extractor, caller,
compaction_readahead_size);
} 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
5 years ago
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
5 years ago
need_upper_bound_check, prefix_extractor, caller,
compaction_readahead_size);
}
}
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
6 years ago
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
6 years ago
}
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
6 years ago
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;
if (rep_->whole_key_filtering) {
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
6 years ago
size_t ts_sz =
rep_->internal_comparator.user_comparator()->timestamp_size();
Slice user_key_without_ts = StripTimestampFromUserKey(user_key, ts_sz);
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
6 years ago
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) &&
!filter->PrefixMayMatch(prefix_extractor->Transform(user_key),
prefix_extractor, kNotValid, no_io,
const_ikey_ptr, get_context,
lookup_context)) {
may_match = false;
}
if (may_match) {
RecordTick(rep_->ioptions.statistics, 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
6 years ago
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
6 years ago
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
6 years ago
if (filter == nullptr || filter->IsBlockBased()) {
return;
}
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
6 years ago
filter->KeysMayMatch(range, prefix_extractor, kNotValid, no_io,
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
6 years ago
} 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
6 years ago
filter->PrefixesMayMatch(range, prefix_extractor, kNotValid, false,
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
6 years ago
}
}
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;
}
const bool may_match =
FullFilterKeyMayMatch(read_options, filter, key, no_io, prefix_extractor,
get_context, &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
6 years ago
if (!may_match) {
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
10 years ago
RecordTick(rep_->ioptions.statistics, 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
10 years ago
} 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
6 years ago
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
6 years ago
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
10 years ago
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
6 years ago
size_t ts_sz =
rep_->internal_comparator.user_comparator()->timestamp_size();
bool matched = false; // if such user key mathced 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
10 years ago
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
6 years ago
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
10 years ago
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
6 years ago
!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
6 years ago
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
10 years ago
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.statistics, 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
10 years ago
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
6 years ago
}
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
10 years ago
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
6 years ago
if (!v.first_internal_key.empty() && !skip_filters &&
UserComparatorWrapper(rep_->internal_comparator.user_comparator())
.Compare(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;
}
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
10 years ago
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
6 years ago
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
6 years ago
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
6 years ago
/*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();
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
10 years ago
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
6 years ago
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;
if (!ParseInternalKey(biter.key(), &parsed_key)) {
s = Status::Corruption(Slice());
}
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
6 years ago
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
10 years ago
}
}
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
6 years ago
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
6 years ago
// 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
6 years ago
BlockCacheTraceRecord access_record(
rep_->ioptions.env->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,
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
6 years ago
/*referenced_key=*/"", referenced_data_size,
lookup_data_block_context.num_keys_in_block,
does_referenced_key_exist);
block_cache_tracer_->WriteBlockAccess(
access_record, lookup_data_block_context.block_key,
rep_->cf_name_for_tracing(), referenced_key);
}
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
10 years ago
}
if (matched && filter != nullptr && !filter->IsBlockBased()) {
RecordTick(rep_->ioptions.statistics, 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
6 years ago
using MultiGetRange = MultiGetContext::Range;
void BlockBasedTable::MultiGet(const ReadOptions& read_options,
const MultiGetRange* mget_range,
const SliceTransform* prefix_extractor,
bool skip_filters) {
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
6 years ago
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;
if (!sst_file_range.empty() && 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
6 years ago
if (skip_filters || !sst_file_range.empty()) {
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
6 years ago
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
6 years ago
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
6 years ago
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());
CachableEntry<UncompressionDict> uncompression_dict;
Status uncompression_dict_status;
if (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);
}
const UncompressionDict& dict = uncompression_dict.GetValue()
? *uncompression_dict.GetValue()
: UncompressionDict::GetEmptyDict();
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())
.Compare(ExtractUserKey(key),
ExtractUserKey(v.first_internal_key)) < 0)) {
// The requested key falls between highest key in previous block and
// lowest key in current block.
*(miter->s) = iiter->status();
data_block_range.SkipKey(miter);
sst_file_range.SkipKey(miter);
continue;
}
if (!uncompression_dict_status.ok()) {
*(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);
Status s = RetrieveBlock(
nullptr, ro, handle, dict, &(results.back()), BlockType::kData,
miter->get_context, &lookup_data_block_context,
/* for_compaction */ false, /* use_cache */ true);
if (s.IsIncomplete()) {
s = Status::OK();
}
if (s.ok() && !results.back().IsEmpty()) {
// Found it in the cache. Add NULL handle to indicate there is
// nothing to read from disk
block_handles.emplace_back(BlockHandle::NullBlockHandle());
} else {
block_handles.emplace_back(handle);
total_len += block_size(handle);
}
}
if (total_len) {
char* scratch = 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_->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);
}
}
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
6 years ago
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;
}
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())
.Compare(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
6 years ago
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
6 years ago
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
6 years ago
// 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
6 years ago
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
6 years ago
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
6 years ago
// 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;
if (!ParseInternalKey(biter->key(), &parsed_key)) {
s = Status::Corruption(Slice());
}
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
6 years ago
}
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.env->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);
block_cache_tracer_->WriteBlockAccess(
access_record, lookup_data_block_context.block_key,
rep_->cf_name_for_tracing(), referenced_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
6 years ago
}
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
6 years ago
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
6 years ago
if (matched && filter != nullptr && !filter->IsBlockBased()) {
RecordTick(rep_->ioptions.statistics, BLOOM_FILTER_FULL_TRUE_POSITIVE);
PERF_COUNTER_BY_LEVEL_ADD(bloom_filter_full_true_positive, 1,
rep_->level);
}
if (s.ok()) {
s = iiter->status();
}
*(miter->s) = s;
}
}
}
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
6 years ago
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
6 years ago
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
6 years ago
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
6 years ago
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
6 years ago
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
6 years ago
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
6 years ago
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;
s = ReadMetaIndexBlock(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
6 years ago
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
6 years ago
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
6 years ago
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
6 years ago
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
: kMaxAutoReadaheadSize;
// FilePrefetchBuffer doesn't work in mmap mode and readahead is not
// needed there.
FilePrefetchBuffer prefetch_buffer(
rep_->file.get(), readahead_size /* readadhead_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
6 years ago
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;
}
}
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;
s = TryReadPropertiesWithGlobalSeqno(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
6 years ago
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
6 years ago
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
6 years ago
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(
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
6 years ago
switch (rep_->index_type) {
case BlockBasedTableOptions::kTwoLevelIndexSearch: {
return PartitionIndexReader::Create(this, 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
6 years ago
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
6 years ago
case BlockBasedTableOptions::kBinarySearchWithFirstKey: {
return BinarySearchIndexReader::Create(this, 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.info_log,
"No prefix extractor passed in. Fall back to binary"
" search index.");
should_fallback = true;
} else if (meta_index_iter == nullptr) {
auto s = ReadMetaIndexBlock(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.info_log,
"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, prefetch_buffer, use_cache,
prefetch, pin, lookup_context,
index_reader);
} else {
return HashIndexReader::Create(this, 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
6 years ago
"Unrecognized index type: " + ToString(rep_->index_type);
return Status::InvalidArgument(error_message.c_str());
}
}
}
uint64_t BlockBasedTable::ApproximateOffsetOf(
const InternalIteratorBase<IndexValue>& index_iter) const {
uint64_t result = 0;
if (index_iter.Valid()) {
BlockHandle handle = index_iter.value().handle;
result = handle.offset();
} else {
// The iterator is past the last key in the file. If table_properties is not
// available, approximate the offset by returning the offset of the
// metaindex block (which is right near the end of the file).
if (rep_->table_properties) {
result = rep_->table_properties->data_size;
}
// table_properties is not present in the table.
if (result == 0) {
result = rep_->footer.metaindex_handle().offset();
}
}
return result;
}
uint64_t BlockBasedTable::ApproximateOffsetOf(const Slice& key,
TableReaderCaller caller) {
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);
return ApproximateOffsetOf(*index_iter);
}
uint64_t BlockBasedTable::ApproximateSize(const Slice& start, const Slice& end,
TableReaderCaller caller) {
assert(rep_->internal_comparator.Compare(start, end) <= 0);
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);
uint64_t start_offset = ApproximateOffsetOf(*index_iter);
index_iter->Seek(end);
uint64_t end_offset = ApproximateOffsetOf(*index_iter);
assert(end_offset >= start_offset);
return end_offset - start_offset;
}
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
6 years ago
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
6 years ago
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
6 years ago
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
6 years ago
/*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) {
// Output Footer
out_file->Append(
"Footer Details:\n"
"--------------------------------------\n"
" ");
out_file->Append(rep_->footer.ToString().c_str());
out_file->Append("\n");
// Output MetaIndex
out_file->Append(
"Metaindex Details:\n"
"--------------------------------------\n");
std::unique_ptr<Block> metaindex;
std::unique_ptr<InternalIterator> metaindex_iter;
Status s = ReadMetaIndexBlock(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() == ROCKSDB_NAMESPACE::kPropertiesBlock) {
out_file->Append(" Properties block handle: ");
out_file->Append(metaindex_iter->value().ToString(true).c_str());
out_file->Append("\n");
} else if (metaindex_iter->key() ==
ROCKSDB_NAMESPACE::kCompressionDictBlock) {
out_file->Append(" Compression dictionary block handle: ");
out_file->Append(metaindex_iter->value().ToString(true).c_str());
out_file->Append("\n");
} else if (strstr(metaindex_iter->key().ToString().c_str(),
"filter.rocksdb.") != nullptr) {
out_file->Append(" Filter block handle: ");
out_file->Append(metaindex_iter->value().ToString(true).c_str());
out_file->Append("\n");
} else if (metaindex_iter->key() == ROCKSDB_NAMESPACE::kRangeDelBlock) {
out_file->Append(" Range deletion block handle: ");
out_file->Append(metaindex_iter->value().ToString(true).c_str());
out_file->Append("\n");
}
}
out_file->Append("\n");
} else {
return s;
}
// Output TableProperties
const ROCKSDB_NAMESPACE::TableProperties* table_properties;
table_properties = rep_->table_properties.get();
if (table_properties != nullptr) {
out_file->Append(
"Table Properties:\n"
"--------------------------------------\n"
" ");
out_file->Append(table_properties->ToString("\n ", ": ").c_str());
out_file->Append("\n");
}
if (rep_->filter) {
out_file->Append(
"Filter Details:\n"
"--------------------------------------\n"
" ");
out_file->Append(rep_->filter->ToString().c_str());
out_file->Append("\n");
}
// Output Index block
s = DumpIndexBlock(out_file);
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_file->Append(
"Compression Dictionary:\n"
"--------------------------------------\n");
out_file->Append(" size (bytes): ");
out_file->Append(ROCKSDB_NAMESPACE::ToString(raw_dict.size()));
out_file->Append("\n\n");
out_file->Append(" HEX ");
out_file->Append(raw_dict.ToString(true).c_str());
out_file->Append("\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_file->Append(
"Range deletions:\n"
"--------------------------------------\n"
" ");
for (; range_del_iter->Valid(); range_del_iter->Next()) {
DumpKeyValue(range_del_iter->key(), range_del_iter->value(), out_file);
}
out_file->Append("\n");
}
delete range_del_iter;
}
// Output Data blocks
s = DumpDataBlocks(out_file);
return s;
}
Status BlockBasedTable::DumpIndexBlock(WritableFile* out_file) {
out_file->Append(
"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
6 years ago
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
6 years ago
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_file->Append("Can not read Index Block \n\n");
return s;
}
out_file->Append(" Block key hex dump: Data block handle\n");
out_file->Append(" 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
6 years ago
if (!rep_->index_key_includes_seq) {
user_key = key;
} else {
ikey.DecodeFrom(key);
user_key = ikey.user_key();
}
out_file->Append(" HEX ");
out_file->Append(user_key.ToString(true).c_str());
out_file->Append(": ");
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
6 years ago
out_file->Append(blockhandles_iter->value()
.ToString(true, rep_->index_has_first_key)
.c_str());
out_file->Append("\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_file->Append(" ASCII ");
out_file->Append(res_key.c_str());
out_file->Append("\n ------\n");
}
out_file->Append("\n");
return Status::OK();
}
Status BlockBasedTable::DumpDataBlocks(WritableFile* out_file) {
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
6 years ago
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
6 years ago
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_file->Append("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
6 years ago
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_file->Append("Data Block # ");
out_file->Append(ROCKSDB_NAMESPACE::ToString(block_id));
out_file->Append(" @ ");
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
6 years ago
out_file->Append(blockhandles_iter->value().handle.ToString(true).c_str());
out_file->Append("\n");
out_file->Append("--------------------------------------\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
6 years ago
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
6 years ago
/*get_context=*/nullptr, /*lookup_context=*/nullptr, Status(),
/*prefetch_buffer=*/nullptr));
s = datablock_iter->status();
if (!s.ok()) {
out_file->Append("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_file->Append("Error reading the block - Skipped \n");
break;
}
DumpKeyValue(datablock_iter->key(), datablock_iter->value(), out_file);
}
out_file->Append("\n");
}
uint64_t num_datablocks = block_id - 1;
if (num_datablocks) {
double datablock_size_avg =
static_cast<double>(datablock_size_sum) / num_datablocks;
out_file->Append("Data Block Summary:\n");
out_file->Append("--------------------------------------");
out_file->Append("\n # data blocks: ");
out_file->Append(ROCKSDB_NAMESPACE::ToString(num_datablocks));
out_file->Append("\n min data block size: ");
out_file->Append(ROCKSDB_NAMESPACE::ToString(datablock_size_min));
out_file->Append("\n max data block size: ");
out_file->Append(ROCKSDB_NAMESPACE::ToString(datablock_size_max));
out_file->Append("\n avg data block size: ");
out_file->Append(ROCKSDB_NAMESPACE::ToString(datablock_size_avg));
out_file->Append("\n");
}
return Status::OK();
}
void BlockBasedTable::DumpKeyValue(const Slice& key, const Slice& value,
WritableFile* out_file) {
InternalKey ikey;
ikey.DecodeFrom(key);
out_file->Append(" HEX ");
out_file->Append(ikey.user_key().ToString(true).c_str());
out_file->Append(": ");
out_file->Append(value.ToString(true).c_str());
out_file->Append("\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_file->Append(" ASCII ");
out_file->Append(res_key.c_str());
out_file->Append(": ");
out_file->Append(res_value.c_str());
out_file->Append("\n ------\n");
}
} // namespace ROCKSDB_NAMESPACE