2016-02-10 00:12:00 +01:00
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// Copyright (c) 2011-present, Facebook, Inc. All rights reserved.
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2017-07-16 01:03:42 +02:00
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// This source code is licensed under both the GPLv2 (found in the
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// COPYING file in the root directory) and Apache 2.0 License
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// (found in the LICENSE.Apache file in the root directory).
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2013-10-16 23:59:46 +02:00
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//
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2011-03-18 23:37:00 +01:00
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// Copyright (c) 2011 The LevelDB Authors. All rights reserved.
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// Use of this source code is governed by a BSD-style license that can be
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// found in the LICENSE file. See the AUTHORS file for names of contributors.
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#include "db/table_cache.h"
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2015-06-23 19:25:45 +02:00
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#include "db/dbformat.h"
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Use only "local" range tombstones during Get (#4449)
Summary:
Previously, range tombstones were accumulated from every level, which
was necessary if a range tombstone in a higher level covered a key in a lower
level. However, RangeDelAggregator::AddTombstones's complexity is based on
the number of tombstones that are currently stored in it, which is wasteful in
the Get case, where we only need to know the highest sequence number of range
tombstones that cover the key from higher levels, and compute the highest covering
sequence number at the current level. This change introduces this optimization, and
removes the use of RangeDelAggregator from the Get path.
In the benchmark results, the following command was used to initialize the database:
```
./db_bench -db=/dev/shm/5k-rts -use_existing_db=false -benchmarks=filluniquerandom -write_buffer_size=1048576 -compression_type=lz4 -target_file_size_base=1048576 -max_bytes_for_level_base=4194304 -value_size=112 -key_size=16 -block_size=4096 -level_compaction_dynamic_level_bytes=true -num=5000000 -max_background_jobs=12 -benchmark_write_rate_limit=20971520 -range_tombstone_width=100 -writes_per_range_tombstone=100 -max_num_range_tombstones=50000 -bloom_bits=8
```
...and the following command was used to measure read throughput:
```
./db_bench -db=/dev/shm/5k-rts/ -use_existing_db=true -benchmarks=readrandom -disable_auto_compactions=true -num=5000000 -reads=100000 -threads=32
```
The filluniquerandom command was only run once, and the resulting database was used
to measure read performance before and after the PR. Both binaries were compiled with
`DEBUG_LEVEL=0`.
Readrandom results before PR:
```
readrandom : 4.544 micros/op 220090 ops/sec; 16.9 MB/s (63103 of 100000 found)
```
Readrandom results after PR:
```
readrandom : 11.147 micros/op 89707 ops/sec; 6.9 MB/s (63103 of 100000 found)
```
So it's actually slower right now, but this PR paves the way for future optimizations (see #4493).
----
Pull Request resolved: https://github.com/facebook/rocksdb/pull/4449
Differential Revision: D10370575
Pulled By: abhimadan
fbshipit-source-id: 9a2e152be1ef36969055c0e9eb4beb0d96c11f4d
2018-10-24 21:29:29 +02:00
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#include "db/range_tombstone_fragmenter.h"
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2019-07-23 03:53:03 +02:00
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#include "db/snapshot_impl.h"
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2014-01-07 05:29:17 +01:00
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#include "db/version_edit.h"
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2019-05-30 05:44:08 +02:00
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#include "file/filename.h"
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2019-09-16 19:31:27 +02:00
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#include "file/random_access_file_reader.h"
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2017-04-06 04:02:00 +02:00
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#include "monitoring/perf_context_imp.h"
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2013-08-23 17:38:13 +02:00
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#include "rocksdb/statistics.h"
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2019-06-19 23:07:36 +02:00
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#include "table/block_based/block_based_table_reader.h"
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2017-04-06 04:02:00 +02:00
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#include "table/get_context.h"
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2015-10-13 00:06:38 +02:00
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#include "table/internal_iterator.h"
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In DB::NewIterator(), try to allocate the whole iterator tree in an arena
Summary:
In this patch, try to allocate the whole iterator tree starting from DBIter from an arena
1. ArenaWrappedDBIter is created when serves as the entry point of an iterator tree, with an arena in it.
2. Add an option to create iterator from arena for following iterators: DBIter, MergingIterator, MemtableIterator, all mem table's iterators, all table reader's iterators and two level iterator.
3. MergeIteratorBuilder is created to incrementally build the tree of internal iterators. It is passed to mem table list and version set and add iterators to it.
Limitations:
(1) Only DB::NewIterator() without tailing uses the arena. Other cases, including readonly DB and compactions are still from malloc
(2) Two level iterator itself is allocated in arena, but not iterators inside it.
Test Plan: make all check
Reviewers: ljin, haobo
Reviewed By: haobo
Subscribers: leveldb, dhruba, yhchiang, igor
Differential Revision: https://reviews.facebook.net/D18513
2014-06-03 01:38:00 +02:00
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#include "table/iterator_wrapper.h"
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Introduce a new MultiGet batching implementation (#5011)
Summary:
This PR introduces a new MultiGet() API, with the underlying implementation grouping keys based on SST file and batching lookups in a file. The reason for the new API is twofold - the definition allows callers to allocate storage for status and values on stack instead of std::vector, as well as return values as PinnableSlices in order to avoid copying, and it keeps the original MultiGet() implementation intact while we experiment with batching.
Batching is useful when there is some spatial locality to the keys being queries, as well as larger batch sizes. The main benefits are due to -
1. Fewer function calls, especially to BlockBasedTableReader::MultiGet() and FullFilterBlockReader::KeysMayMatch()
2. Bloom filter cachelines can be prefetched, hiding the cache miss latency
The next step is to optimize the binary searches in the level_storage_info, index blocks and data blocks, since we could reduce the number of key comparisons if the keys are relatively close to each other. The batching optimizations also need to be extended to other formats, such as PlainTable and filter formats. This also needs to be added to db_stress.
Benchmark results from db_bench for various batch size/locality of reference combinations are given below. Locality was simulated by offsetting the keys in a batch by a stride length. Each SST file is about 8.6MB uncompressed and key/value size is 16/100 uncompressed. To focus on the cpu benefit of batching, the runs were single threaded and bound to the same cpu to eliminate interference from other system events. The results show a 10-25% improvement in micros/op from smaller to larger batch sizes (4 - 32).
Batch Sizes
1 | 2 | 4 | 8 | 16 | 32
Random pattern (Stride length 0)
4.158 | 4.109 | 4.026 | 4.05 | 4.1 | 4.074 - Get
4.438 | 4.302 | 4.165 | 4.122 | 4.096 | 4.075 - MultiGet (no batching)
4.461 | 4.256 | 4.277 | 4.11 | 4.182 | 4.14 - MultiGet (w/ batching)
Good locality (Stride length 16)
4.048 | 3.659 | 3.248 | 2.99 | 2.84 | 2.753
4.429 | 3.728 | 3.406 | 3.053 | 2.911 | 2.781
4.452 | 3.45 | 2.833 | 2.451 | 2.233 | 2.135
Good locality (Stride length 256)
4.066 | 3.786 | 3.581 | 3.447 | 3.415 | 3.232
4.406 | 4.005 | 3.644 | 3.49 | 3.381 | 3.268
4.393 | 3.649 | 3.186 | 2.882 | 2.676 | 2.62
Medium locality (Stride length 4096)
4.012 | 3.922 | 3.768 | 3.61 | 3.582 | 3.555
4.364 | 4.057 | 3.791 | 3.65 | 3.57 | 3.465
4.479 | 3.758 | 3.316 | 3.077 | 2.959 | 2.891
dbbench command used (on a DB with 4 levels, 12 million keys)-
TEST_TMPDIR=/dev/shm numactl -C 10 ./db_bench.tmp -use_existing_db=true -benchmarks="readseq,multireadrandom" -write_buffer_size=4194304 -target_file_size_base=4194304 -max_bytes_for_level_base=16777216 -num=12000000 -reads=12000000 -duration=90 -threads=1 -compression_type=none -cache_size=4194304000 -batch_size=32 -disable_auto_compactions=true -bloom_bits=10 -cache_index_and_filter_blocks=true -pin_l0_filter_and_index_blocks_in_cache=true -multiread_batched=true -multiread_stride=4
Pull Request resolved: https://github.com/facebook/rocksdb/pull/5011
Differential Revision: D14348703
Pulled By: anand1976
fbshipit-source-id: 774406dab3776d979c809522a67bedac6c17f84b
2019-04-11 23:24:09 +02:00
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#include "table/multiget_context.h"
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2015-09-11 20:36:33 +02:00
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#include "table/table_builder.h"
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2014-01-28 06:58:46 +01:00
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#include "table/table_reader.h"
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2019-05-31 02:39:43 +02:00
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#include "test_util/sync_point.h"
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2019-07-23 03:53:03 +02:00
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#include "util/cast_util.h"
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2011-03-18 23:37:00 +01:00
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#include "util/coding.h"
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2013-06-07 19:02:28 +02:00
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#include "util/stop_watch.h"
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2011-03-18 23:37:00 +01:00
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2013-10-04 06:49:15 +02:00
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namespace rocksdb {
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2011-03-18 23:37:00 +01:00
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2015-06-23 19:25:45 +02:00
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namespace {
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template <class T>
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2018-03-05 22:08:17 +01:00
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static void DeleteEntry(const Slice& /*key*/, void* value) {
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2015-06-23 19:25:45 +02:00
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T* typed_value = reinterpret_cast<T*>(value);
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delete typed_value;
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2011-03-18 23:37:00 +01:00
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}
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static void UnrefEntry(void* arg1, void* arg2) {
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Cache* cache = reinterpret_cast<Cache*>(arg1);
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Cache::Handle* h = reinterpret_cast<Cache::Handle*>(arg2);
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cache->Release(h);
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}
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2014-06-14 00:54:19 +02:00
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static Slice GetSliceForFileNumber(const uint64_t* file_number) {
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2014-01-02 19:29:48 +01:00
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return Slice(reinterpret_cast<const char*>(file_number),
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sizeof(*file_number));
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2013-12-27 01:25:45 +01:00
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}
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2015-06-23 19:25:45 +02:00
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#ifndef ROCKSDB_LITE
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void AppendVarint64(IterKey* key, uint64_t v) {
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char buf[10];
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auto ptr = EncodeVarint64(buf, v);
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key->TrimAppend(key->Size(), buf, ptr - buf);
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}
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#endif // ROCKSDB_LITE
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} // namespace
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2014-09-05 01:18:36 +02:00
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TableCache::TableCache(const ImmutableCFOptions& ioptions,
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2019-06-14 00:39:52 +02:00
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const EnvOptions& env_options, Cache* const cache,
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BlockCacheTracer* const block_cache_tracer)
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2018-06-28 02:09:29 +02:00
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: ioptions_(ioptions),
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env_options_(env_options),
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cache_(cache),
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2019-06-14 00:39:52 +02:00
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immortal_tables_(false),
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block_cache_tracer_(block_cache_tracer) {
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2015-06-23 19:25:45 +02:00
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if (ioptions_.row_cache) {
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// If the same cache is shared by multiple instances, we need to
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// disambiguate its entries.
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PutVarint64(&row_cache_id_, ioptions_.row_cache->NewId());
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}
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}
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2011-03-18 23:37:00 +01:00
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TableCache::~TableCache() {
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}
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2014-01-07 05:29:17 +01:00
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TableReader* TableCache::GetTableReaderFromHandle(Cache::Handle* handle) {
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return reinterpret_cast<TableReader*>(cache_->Value(handle));
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}
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void TableCache::ReleaseHandle(Cache::Handle* handle) {
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cache_->Release(handle);
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}
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2015-08-21 07:33:25 +02:00
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Status TableCache::GetTableReader(
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const EnvOptions& env_options,
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const InternalKeyComparator& internal_comparator, const FileDescriptor& fd,
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2019-06-19 23:07:36 +02:00
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bool sequential_mode, bool record_read_stats, HistogramImpl* file_read_hist,
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std::unique_ptr<TableReader>* table_reader,
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2018-05-21 23:33:55 +02:00
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const SliceTransform* prefix_extractor, bool skip_filters, int level,
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2019-07-17 01:27:32 +02:00
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bool prefetch_index_and_filter_in_cache) {
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2015-08-21 07:33:25 +02:00
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std::string fname =
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2018-04-06 04:49:06 +02:00
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TableFileName(ioptions_.cf_paths, fd.GetNumber(), fd.GetPathId());
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2018-11-09 20:17:34 +01:00
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std::unique_ptr<RandomAccessFile> file;
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2015-08-21 07:33:25 +02:00
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Status s = ioptions_.env->NewRandomAccessFile(fname, &file, env_options);
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2016-05-05 00:25:58 +02:00
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2015-08-21 07:33:25 +02:00
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RecordTick(ioptions_.statistics, NO_FILE_OPENS);
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if (s.ok()) {
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2015-08-27 00:25:59 +02:00
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if (!sequential_mode && ioptions_.advise_random_on_open) {
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2015-08-21 07:33:25 +02:00
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file->Hint(RandomAccessFile::RANDOM);
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}
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StopWatch sw(ioptions_.env, ioptions_.statistics, TABLE_OPEN_IO_MICROS);
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std::unique_ptr<RandomAccessFileReader> file_reader(
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2017-07-31 21:07:42 +02:00
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new RandomAccessFileReader(
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std::move(file), fname, ioptions_.env,
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record_read_stats ? ioptions_.statistics : nullptr, SST_READ_MICROS,
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2019-07-17 01:27:32 +02:00
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file_read_hist, ioptions_.rate_limiter, ioptions_.listeners));
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2015-08-21 07:33:25 +02:00
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s = ioptions_.table_factory->NewTableReader(
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2018-05-21 23:33:55 +02:00
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TableReaderOptions(ioptions_, prefix_extractor, env_options,
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2018-06-28 02:09:29 +02:00
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internal_comparator, skip_filters, immortal_tables_,
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2019-06-14 00:39:52 +02:00
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level, fd.largest_seqno, block_cache_tracer_),
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2016-07-20 20:23:31 +02:00
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std::move(file_reader), fd.GetFileSize(), table_reader,
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prefetch_index_and_filter_in_cache);
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2015-08-21 07:33:25 +02:00
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TEST_SYNC_POINT("TableCache::GetTableReader:0");
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}
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return s;
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}
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Adding pin_l0_filter_and_index_blocks_in_cache feature and related fixes.
Summary:
When a block based table file is opened, if prefetch_index_and_filter is true, it will prefetch the index and filter blocks, putting them into the block cache.
What this feature adds: when a L0 block based table file is opened, if pin_l0_filter_and_index_blocks_in_cache is true in the options (and prefetch_index_and_filter is true), then the filter and index blocks aren't released back to the block cache at the end of BlockBasedTableReader::Open(). Instead the table reader takes ownership of them, hence pinning them, ie. the LRU cache will never push them out. Meanwhile in the table reader, further accesses will not hit the block cache, thus avoiding lock contention.
Test Plan:
'export TEST_TMPDIR=/dev/shm/ && DISABLE_JEMALLOC=1 OPT=-g make all valgrind_check -j32' is OK.
I didn't run the Java tests, I don't have Java set up on my devserver.
Reviewers: sdong
Reviewed By: sdong
Subscribers: andrewkr, dhruba
Differential Revision: https://reviews.facebook.net/D56133
2016-04-01 19:42:39 +02:00
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void TableCache::EraseHandle(const FileDescriptor& fd, Cache::Handle* handle) {
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ReleaseHandle(handle);
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uint64_t number = fd.GetNumber();
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Slice key = GetSliceForFileNumber(&number);
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cache_->Erase(key);
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}
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2014-09-05 01:18:36 +02:00
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Status TableCache::FindTable(const EnvOptions& env_options,
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2014-01-27 22:53:22 +01:00
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const InternalKeyComparator& internal_comparator,
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2014-06-14 00:54:19 +02:00
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const FileDescriptor& fd, Cache::Handle** handle,
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2018-05-21 23:33:55 +02:00
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const SliceTransform* prefix_extractor,
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Measure file read latency histogram per level
Summary: In internal stats, remember read latency histogram, if statistics is enabled. It can be retrieved from DB::GetProperty() with "rocksdb.dbstats" property, if it is enabled.
Test Plan: Manually run db_bench and prints out "rocksdb.dbstats" by hand and make sure it prints out as expected
Reviewers: igor, IslamAbdelRahman, rven, kradhakrishnan, anthony, yhchiang
Reviewed By: yhchiang
Subscribers: MarkCallaghan, leveldb, dhruba
Differential Revision: https://reviews.facebook.net/D44193
2015-08-13 23:35:54 +02:00
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const bool no_io, bool record_read_stats,
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Adding pin_l0_filter_and_index_blocks_in_cache feature and related fixes.
Summary:
When a block based table file is opened, if prefetch_index_and_filter is true, it will prefetch the index and filter blocks, putting them into the block cache.
What this feature adds: when a L0 block based table file is opened, if pin_l0_filter_and_index_blocks_in_cache is true in the options (and prefetch_index_and_filter is true), then the filter and index blocks aren't released back to the block cache at the end of BlockBasedTableReader::Open(). Instead the table reader takes ownership of them, hence pinning them, ie. the LRU cache will never push them out. Meanwhile in the table reader, further accesses will not hit the block cache, thus avoiding lock contention.
Test Plan:
'export TEST_TMPDIR=/dev/shm/ && DISABLE_JEMALLOC=1 OPT=-g make all valgrind_check -j32' is OK.
I didn't run the Java tests, I don't have Java set up on my devserver.
Reviewers: sdong
Reviewed By: sdong
Subscribers: andrewkr, dhruba
Differential Revision: https://reviews.facebook.net/D56133
2016-04-01 19:42:39 +02:00
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HistogramImpl* file_read_hist, bool skip_filters,
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2016-07-20 20:23:31 +02:00
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int level,
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bool prefetch_index_and_filter_in_cache) {
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2018-12-20 21:00:40 +01:00
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PERF_TIMER_GUARD_WITH_ENV(find_table_nanos, ioptions_.env);
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2012-04-17 17:36:46 +02:00
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Status s;
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2014-07-02 18:54:20 +02:00
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uint64_t number = fd.GetNumber();
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Slice key = GetSliceForFileNumber(&number);
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2012-04-17 17:36:46 +02:00
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*handle = cache_->Lookup(key);
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2015-08-21 07:33:25 +02:00
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TEST_SYNC_POINT_CALLBACK("TableCache::FindTable:0",
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const_cast<bool*>(&no_io));
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2013-02-25 22:58:34 +01:00
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if (*handle == nullptr) {
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2015-08-26 19:10:26 +02:00
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if (no_io) { // Don't do IO and return a not-found status
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2013-08-25 07:48:51 +02:00
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return Status::Incomplete("Table not found in table_cache, no_io is set");
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2013-07-13 01:56:52 +02:00
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}
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2018-11-09 20:17:34 +01:00
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std::unique_ptr<TableReader> table_reader;
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2015-08-21 07:33:25 +02:00
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s = GetTableReader(env_options, internal_comparator, fd,
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2019-06-19 23:07:36 +02:00
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false /* sequential mode */, record_read_stats,
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file_read_hist, &table_reader, prefix_extractor,
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skip_filters, level, prefetch_index_and_filter_in_cache);
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2011-03-18 23:37:00 +01:00
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if (!s.ok()) {
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2013-10-30 18:52:33 +01:00
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assert(table_reader == nullptr);
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2014-09-05 01:18:36 +02:00
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RecordTick(ioptions_.statistics, NO_FILE_ERRORS);
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2011-03-18 23:37:00 +01:00
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// We do not cache error results so that if the error is transient,
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// or somebody repairs the file, we recover automatically.
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2012-04-17 17:36:46 +02:00
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} else {
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2016-03-11 02:35:19 +01:00
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s = cache_->Insert(key, table_reader.get(), 1, &DeleteEntry<TableReader>,
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handle);
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if (s.ok()) {
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// Release ownership of table reader.
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table_reader.release();
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|
}
|
2011-03-18 23:37:00 +01:00
|
|
|
}
|
2012-04-17 17:36:46 +02:00
|
|
|
}
|
|
|
|
return s;
|
|
|
|
}
|
2011-03-18 23:37:00 +01:00
|
|
|
|
2015-10-13 00:06:38 +02:00
|
|
|
InternalIterator* TableCache::NewIterator(
|
|
|
|
const ReadOptions& options, const EnvOptions& env_options,
|
2018-07-14 02:34:54 +02:00
|
|
|
const InternalKeyComparator& icomparator, const FileMetaData& file_meta,
|
2018-12-18 02:26:56 +01:00
|
|
|
RangeDelAggregator* range_del_agg, const SliceTransform* prefix_extractor,
|
2018-05-21 23:33:55 +02:00
|
|
|
TableReader** table_reader_ptr, HistogramImpl* file_read_hist,
|
2019-06-20 23:28:22 +02:00
|
|
|
TableReaderCaller caller, Arena* arena, bool skip_filters, int level,
|
2018-10-10 00:15:27 +02:00
|
|
|
const InternalKey* smallest_compaction_key,
|
|
|
|
const InternalKey* largest_compaction_key) {
|
2015-07-09 01:34:48 +02:00
|
|
|
PERF_TIMER_GUARD(new_table_iterator_nanos);
|
|
|
|
|
2016-11-17 23:30:11 +01:00
|
|
|
Status s;
|
2015-08-21 07:33:25 +02:00
|
|
|
TableReader* table_reader = nullptr;
|
2014-04-17 23:07:05 +02:00
|
|
|
Cache::Handle* handle = nullptr;
|
2017-11-03 22:34:10 +01:00
|
|
|
if (table_reader_ptr != nullptr) {
|
|
|
|
*table_reader_ptr = nullptr;
|
|
|
|
}
|
2019-06-20 23:28:22 +02:00
|
|
|
bool for_compaction = caller == TableReaderCaller::kCompaction;
|
2018-07-14 02:34:54 +02:00
|
|
|
auto& fd = file_meta.fd;
|
2019-06-19 23:07:36 +02:00
|
|
|
table_reader = fd.table_reader;
|
|
|
|
if (table_reader == nullptr) {
|
|
|
|
s = FindTable(env_options, icomparator, fd, &handle, prefix_extractor,
|
|
|
|
options.read_tier == kBlockCacheTier /* no_io */,
|
2019-07-24 00:30:59 +02:00
|
|
|
!for_compaction /* record_read_stats */, file_read_hist,
|
2019-06-19 23:07:36 +02:00
|
|
|
skip_filters, level);
|
2017-11-03 22:34:10 +01:00
|
|
|
if (s.ok()) {
|
2019-06-19 23:07:36 +02:00
|
|
|
table_reader = GetTableReaderFromHandle(handle);
|
2016-11-17 23:30:11 +01:00
|
|
|
}
|
2014-01-07 05:29:17 +01:00
|
|
|
}
|
2016-11-22 06:08:06 +01:00
|
|
|
InternalIterator* result = nullptr;
|
2016-11-17 23:30:11 +01:00
|
|
|
if (s.ok()) {
|
expose a hook to skip tables during iteration
Summary:
As discussed on the mailing list (["Skipping entire SSTs while iterating"](https://groups.google.com/forum/#!topic/rocksdb/ujHCJVLrHlU)), this patch adds a `table_filter` to `ReadOptions` that allows specifying a callback to be executed during iteration before each table in the database is scanned. The callback is passed the table's properties; the table is scanned iff the callback returns true.
This can be used in conjunction with a `TablePropertiesCollector` to dramatically speed up scans by skipping tables that are known to contain irrelevant data for the scan at hand.
We're using this [downstream in CockroachDB](https://github.com/cockroachdb/cockroach/blob/master/pkg/storage/engine/db.cc#L2009-L2022) already. With this feature, under ideal conditions, we can reduce the time of an incremental backup in from hours to seconds.
FYI, the first commit in this PR fixes a segfault that I unfortunately have not figured out how to reproduce outside of CockroachDB. I'm hoping you accept it on the grounds that it is not correct to return 8-byte aligned memory from a call to `malloc` on some 64-bit platforms; one correct approach is to infer the necessary alignment from `std::max_align_t`, as done here. As noted in the first commit message, the bug is tickled by having a`std::function` in `struct ReadOptions`. That is, the following patch alone is enough to cause RocksDB to segfault when run from CockroachDB on Darwin.
```diff
--- a/include/rocksdb/options.h
+++ b/include/rocksdb/options.h
@@ -1546,6 +1546,13 @@ struct ReadOptions {
// Default: false
bool ignore_range_deletions;
+ // A callback to determine whether relevant keys for this scan exist in a
+ // given table based on the table's properties. The callback is passed the
+ // properties of each table during iteration. If the callback returns false,
+ // the table will not be scanned.
+ // Default: empty (every table will be scanned)
+ std::function<bool(const TableProperties&)> table_filter;
+
ReadOptions();
ReadOptions(bool cksum, bool cache);
};
```
/cc danhhz
Closes https://github.com/facebook/rocksdb/pull/2265
Differential Revision: D5054262
Pulled By: yiwu-arbug
fbshipit-source-id: dd6b28f2bba6cb8466250d8c5c542d3c92785476
2017-10-18 07:09:01 +02:00
|
|
|
if (options.table_filter &&
|
|
|
|
!options.table_filter(*table_reader->GetTableProperties())) {
|
2018-08-10 01:49:45 +02:00
|
|
|
result = NewEmptyInternalIterator<Slice>(arena);
|
expose a hook to skip tables during iteration
Summary:
As discussed on the mailing list (["Skipping entire SSTs while iterating"](https://groups.google.com/forum/#!topic/rocksdb/ujHCJVLrHlU)), this patch adds a `table_filter` to `ReadOptions` that allows specifying a callback to be executed during iteration before each table in the database is scanned. The callback is passed the table's properties; the table is scanned iff the callback returns true.
This can be used in conjunction with a `TablePropertiesCollector` to dramatically speed up scans by skipping tables that are known to contain irrelevant data for the scan at hand.
We're using this [downstream in CockroachDB](https://github.com/cockroachdb/cockroach/blob/master/pkg/storage/engine/db.cc#L2009-L2022) already. With this feature, under ideal conditions, we can reduce the time of an incremental backup in from hours to seconds.
FYI, the first commit in this PR fixes a segfault that I unfortunately have not figured out how to reproduce outside of CockroachDB. I'm hoping you accept it on the grounds that it is not correct to return 8-byte aligned memory from a call to `malloc` on some 64-bit platforms; one correct approach is to infer the necessary alignment from `std::max_align_t`, as done here. As noted in the first commit message, the bug is tickled by having a`std::function` in `struct ReadOptions`. That is, the following patch alone is enough to cause RocksDB to segfault when run from CockroachDB on Darwin.
```diff
--- a/include/rocksdb/options.h
+++ b/include/rocksdb/options.h
@@ -1546,6 +1546,13 @@ struct ReadOptions {
// Default: false
bool ignore_range_deletions;
+ // A callback to determine whether relevant keys for this scan exist in a
+ // given table based on the table's properties. The callback is passed the
+ // properties of each table during iteration. If the callback returns false,
+ // the table will not be scanned.
+ // Default: empty (every table will be scanned)
+ std::function<bool(const TableProperties&)> table_filter;
+
ReadOptions();
ReadOptions(bool cksum, bool cache);
};
```
/cc danhhz
Closes https://github.com/facebook/rocksdb/pull/2265
Differential Revision: D5054262
Pulled By: yiwu-arbug
fbshipit-source-id: dd6b28f2bba6cb8466250d8c5c542d3c92785476
2017-10-18 07:09:01 +02:00
|
|
|
} else {
|
2018-05-21 23:33:55 +02:00
|
|
|
result = table_reader->NewIterator(options, prefix_extractor, arena,
|
2019-06-20 23:28:22 +02:00
|
|
|
skip_filters, caller,
|
2019-06-19 23:07:36 +02:00
|
|
|
env_options.compaction_readahead_size);
|
expose a hook to skip tables during iteration
Summary:
As discussed on the mailing list (["Skipping entire SSTs while iterating"](https://groups.google.com/forum/#!topic/rocksdb/ujHCJVLrHlU)), this patch adds a `table_filter` to `ReadOptions` that allows specifying a callback to be executed during iteration before each table in the database is scanned. The callback is passed the table's properties; the table is scanned iff the callback returns true.
This can be used in conjunction with a `TablePropertiesCollector` to dramatically speed up scans by skipping tables that are known to contain irrelevant data for the scan at hand.
We're using this [downstream in CockroachDB](https://github.com/cockroachdb/cockroach/blob/master/pkg/storage/engine/db.cc#L2009-L2022) already. With this feature, under ideal conditions, we can reduce the time of an incremental backup in from hours to seconds.
FYI, the first commit in this PR fixes a segfault that I unfortunately have not figured out how to reproduce outside of CockroachDB. I'm hoping you accept it on the grounds that it is not correct to return 8-byte aligned memory from a call to `malloc` on some 64-bit platforms; one correct approach is to infer the necessary alignment from `std::max_align_t`, as done here. As noted in the first commit message, the bug is tickled by having a`std::function` in `struct ReadOptions`. That is, the following patch alone is enough to cause RocksDB to segfault when run from CockroachDB on Darwin.
```diff
--- a/include/rocksdb/options.h
+++ b/include/rocksdb/options.h
@@ -1546,6 +1546,13 @@ struct ReadOptions {
// Default: false
bool ignore_range_deletions;
+ // A callback to determine whether relevant keys for this scan exist in a
+ // given table based on the table's properties. The callback is passed the
+ // properties of each table during iteration. If the callback returns false,
+ // the table will not be scanned.
+ // Default: empty (every table will be scanned)
+ std::function<bool(const TableProperties&)> table_filter;
+
ReadOptions();
ReadOptions(bool cksum, bool cache);
};
```
/cc danhhz
Closes https://github.com/facebook/rocksdb/pull/2265
Differential Revision: D5054262
Pulled By: yiwu-arbug
fbshipit-source-id: dd6b28f2bba6cb8466250d8c5c542d3c92785476
2017-10-18 07:09:01 +02:00
|
|
|
}
|
2019-06-19 23:07:36 +02:00
|
|
|
if (handle != nullptr) {
|
2016-11-17 23:30:11 +01:00
|
|
|
result->RegisterCleanup(&UnrefEntry, cache_, handle);
|
2016-11-22 06:08:06 +01:00
|
|
|
handle = nullptr; // prevent from releasing below
|
2016-11-17 23:30:11 +01:00
|
|
|
}
|
2013-05-18 00:53:01 +02:00
|
|
|
|
2016-11-17 23:30:11 +01:00
|
|
|
if (for_compaction) {
|
|
|
|
table_reader->SetupForCompaction();
|
|
|
|
}
|
|
|
|
if (table_reader_ptr != nullptr) {
|
|
|
|
*table_reader_ptr = table_reader;
|
|
|
|
}
|
2016-11-16 02:18:32 +01:00
|
|
|
}
|
2016-11-22 06:08:06 +01:00
|
|
|
if (s.ok() && range_del_agg != nullptr && !options.ignore_range_deletions) {
|
2018-05-05 01:37:39 +02:00
|
|
|
if (range_del_agg->AddFile(fd.GetNumber())) {
|
2018-11-21 19:53:44 +01:00
|
|
|
std::unique_ptr<FragmentedRangeTombstoneIterator> range_del_iter(
|
|
|
|
static_cast<FragmentedRangeTombstoneIterator*>(
|
|
|
|
table_reader->NewRangeTombstoneIterator(options)));
|
2018-05-05 01:37:39 +02:00
|
|
|
if (range_del_iter != nullptr) {
|
|
|
|
s = range_del_iter->status();
|
|
|
|
}
|
|
|
|
if (s.ok()) {
|
2018-10-10 00:15:27 +02:00
|
|
|
const InternalKey* smallest = &file_meta.smallest;
|
|
|
|
const InternalKey* largest = &file_meta.largest;
|
|
|
|
if (smallest_compaction_key != nullptr) {
|
|
|
|
smallest = smallest_compaction_key;
|
|
|
|
}
|
|
|
|
if (largest_compaction_key != nullptr) {
|
|
|
|
largest = largest_compaction_key;
|
|
|
|
}
|
2018-11-21 19:53:44 +01:00
|
|
|
range_del_agg->AddTombstones(std::move(range_del_iter), smallest,
|
|
|
|
largest);
|
2018-05-05 01:37:39 +02:00
|
|
|
}
|
Compaction Support for Range Deletion
Summary:
This diff introduces RangeDelAggregator, which takes ownership of iterators
provided to it via AddTombstones(). The tombstones are organized in a two-level
map (snapshot stripe -> begin key -> tombstone). Tombstone creation avoids data
copy by holding Slices returned by the iterator, which remain valid thanks to pinning.
For compaction, we create a hierarchical range tombstone iterator with structure
matching the iterator over compaction input data. An aggregator based on that
iterator is used by CompactionIterator to determine which keys are covered by
range tombstones. In case of merge operand, the same aggregator is used by
MergeHelper. Upon finishing each file in the compaction, relevant range tombstones
are added to the output file's range tombstone metablock and file boundaries are
updated accordingly.
To check whether a key is covered by range tombstone, RangeDelAggregator::ShouldDelete()
considers tombstones in the key's snapshot stripe. When this function is used outside of
compaction, it also checks newer stripes, which can contain covering tombstones. Currently
the intra-stripe check involves a linear scan; however, in the future we plan to collapse ranges
within a stripe such that binary search can be used.
RangeDelAggregator::AddToBuilder() adds all range tombstones in the table's key-range
to a new table's range tombstone meta-block. Since range tombstones may fall in the gap
between files, we may need to extend some files' key-ranges. The strategy is (1) first file
extends as far left as possible and other files do not extend left, (2) all files extend right
until either the start of the next file or the end of the last range tombstone in the gap,
whichever comes first.
One other notable change is adding release/move semantics to ScopedArenaIterator
such that it can be used to transfer ownership of an arena-allocated iterator, similar to
how unique_ptr is used for malloc'd data.
Depends on D61473
Test Plan: compaction_iterator_test, mock_table, end-to-end tests in D63927
Reviewers: sdong, IslamAbdelRahman, wanning, yhchiang, lightmark
Reviewed By: lightmark
Subscribers: andrewkr, dhruba, leveldb
Differential Revision: https://reviews.facebook.net/D62205
2016-10-18 21:04:56 +02:00
|
|
|
}
|
2016-11-16 02:18:32 +01:00
|
|
|
}
|
2016-11-22 06:08:06 +01:00
|
|
|
|
|
|
|
if (handle != nullptr) {
|
|
|
|
ReleaseHandle(handle);
|
|
|
|
}
|
|
|
|
if (!s.ok()) {
|
|
|
|
assert(result == nullptr);
|
2018-08-10 01:49:45 +02:00
|
|
|
result = NewErrorInternalIterator<Slice>(s, arena);
|
Compaction Support for Range Deletion
Summary:
This diff introduces RangeDelAggregator, which takes ownership of iterators
provided to it via AddTombstones(). The tombstones are organized in a two-level
map (snapshot stripe -> begin key -> tombstone). Tombstone creation avoids data
copy by holding Slices returned by the iterator, which remain valid thanks to pinning.
For compaction, we create a hierarchical range tombstone iterator with structure
matching the iterator over compaction input data. An aggregator based on that
iterator is used by CompactionIterator to determine which keys are covered by
range tombstones. In case of merge operand, the same aggregator is used by
MergeHelper. Upon finishing each file in the compaction, relevant range tombstones
are added to the output file's range tombstone metablock and file boundaries are
updated accordingly.
To check whether a key is covered by range tombstone, RangeDelAggregator::ShouldDelete()
considers tombstones in the key's snapshot stripe. When this function is used outside of
compaction, it also checks newer stripes, which can contain covering tombstones. Currently
the intra-stripe check involves a linear scan; however, in the future we plan to collapse ranges
within a stripe such that binary search can be used.
RangeDelAggregator::AddToBuilder() adds all range tombstones in the table's key-range
to a new table's range tombstone meta-block. Since range tombstones may fall in the gap
between files, we may need to extend some files' key-ranges. The strategy is (1) first file
extends as far left as possible and other files do not extend left, (2) all files extend right
until either the start of the next file or the end of the last range tombstone in the gap,
whichever comes first.
One other notable change is adding release/move semantics to ScopedArenaIterator
such that it can be used to transfer ownership of an arena-allocated iterator, similar to
how unique_ptr is used for malloc'd data.
Depends on D61473
Test Plan: compaction_iterator_test, mock_table, end-to-end tests in D63927
Reviewers: sdong, IslamAbdelRahman, wanning, yhchiang, lightmark
Reviewed By: lightmark
Subscribers: andrewkr, dhruba, leveldb
Differential Revision: https://reviews.facebook.net/D62205
2016-10-18 21:04:56 +02:00
|
|
|
}
|
2016-11-22 06:08:06 +01:00
|
|
|
return result;
|
2011-03-18 23:37:00 +01:00
|
|
|
}
|
|
|
|
|
2019-08-16 01:59:42 +02:00
|
|
|
Status TableCache::GetRangeTombstoneIterator(
|
|
|
|
const ReadOptions& options,
|
|
|
|
const InternalKeyComparator& internal_comparator,
|
|
|
|
const FileMetaData& file_meta,
|
|
|
|
std::unique_ptr<FragmentedRangeTombstoneIterator>* out_iter) {
|
|
|
|
const FileDescriptor& fd = file_meta.fd;
|
|
|
|
Status s;
|
|
|
|
TableReader* t = fd.table_reader;
|
|
|
|
Cache::Handle* handle = nullptr;
|
|
|
|
if (t == nullptr) {
|
|
|
|
s = FindTable(env_options_, internal_comparator, fd, &handle);
|
|
|
|
if (s.ok()) {
|
|
|
|
t = GetTableReaderFromHandle(handle);
|
|
|
|
}
|
|
|
|
}
|
|
|
|
if (s.ok()) {
|
|
|
|
out_iter->reset(t->NewRangeTombstoneIterator(options));
|
|
|
|
assert(out_iter);
|
|
|
|
}
|
|
|
|
return s;
|
|
|
|
}
|
|
|
|
|
2019-08-29 01:10:38 +02:00
|
|
|
#ifndef ROCKSDB_LITE
|
2019-09-20 21:00:55 +02:00
|
|
|
void TableCache::CreateRowCacheKeyPrefix(const ReadOptions& options,
|
|
|
|
const FileDescriptor& fd,
|
|
|
|
const Slice& internal_key,
|
|
|
|
GetContext* get_context,
|
|
|
|
IterKey& row_cache_key) {
|
2019-08-29 01:10:38 +02:00
|
|
|
uint64_t fd_number = fd.GetNumber();
|
|
|
|
// We use the user key as cache key instead of the internal key,
|
|
|
|
// otherwise the whole cache would be invalidated every time the
|
|
|
|
// sequence key increases. However, to support caching snapshot
|
|
|
|
// reads, we append the sequence number (incremented by 1 to
|
|
|
|
// distinguish from 0) only in this case.
|
|
|
|
// If the snapshot is larger than the largest seqno in the file,
|
|
|
|
// all data should be exposed to the snapshot, so we treat it
|
|
|
|
// the same as there is no snapshot. The exception is that if
|
|
|
|
// a seq-checking callback is registered, some internal keys
|
|
|
|
// may still be filtered out.
|
|
|
|
uint64_t seq_no = 0;
|
|
|
|
// Maybe we can include the whole file ifsnapshot == fd.largest_seqno.
|
|
|
|
if (options.snapshot != nullptr &&
|
|
|
|
(get_context->has_callback() ||
|
|
|
|
static_cast_with_check<const SnapshotImpl, const Snapshot>(
|
|
|
|
options.snapshot)
|
|
|
|
->GetSequenceNumber() <= fd.largest_seqno)) {
|
|
|
|
// We should consider to use options.snapshot->GetSequenceNumber()
|
|
|
|
// instead of GetInternalKeySeqno(k), which will make the code
|
|
|
|
// easier to understand.
|
|
|
|
seq_no = 1 + GetInternalKeySeqno(internal_key);
|
|
|
|
}
|
|
|
|
|
|
|
|
// Compute row cache key.
|
|
|
|
row_cache_key.TrimAppend(row_cache_key.Size(), row_cache_id_.data(),
|
|
|
|
row_cache_id_.size());
|
|
|
|
AppendVarint64(&row_cache_key, fd_number);
|
|
|
|
AppendVarint64(&row_cache_key, seq_no);
|
|
|
|
}
|
|
|
|
|
2019-09-20 21:00:55 +02:00
|
|
|
bool TableCache::GetFromRowCache(const Slice& user_key, IterKey& row_cache_key,
|
|
|
|
size_t prefix_size, GetContext* get_context) {
|
2019-08-29 01:10:38 +02:00
|
|
|
bool found = false;
|
|
|
|
|
2019-09-20 21:00:55 +02:00
|
|
|
row_cache_key.TrimAppend(prefix_size, user_key.data(), user_key.size());
|
2019-08-29 01:10:38 +02:00
|
|
|
if (auto row_handle =
|
|
|
|
ioptions_.row_cache->Lookup(row_cache_key.GetUserKey())) {
|
|
|
|
// Cleanable routine to release the cache entry
|
|
|
|
Cleanable value_pinner;
|
|
|
|
auto release_cache_entry_func = [](void* cache_to_clean,
|
|
|
|
void* cache_handle) {
|
|
|
|
((Cache*)cache_to_clean)->Release((Cache::Handle*)cache_handle);
|
|
|
|
};
|
2019-09-20 21:00:55 +02:00
|
|
|
auto found_row_cache_entry =
|
|
|
|
static_cast<const std::string*>(ioptions_.row_cache->Value(row_handle));
|
2019-08-29 01:10:38 +02:00
|
|
|
// If it comes here value is located on the cache.
|
|
|
|
// found_row_cache_entry points to the value on cache,
|
|
|
|
// and value_pinner has cleanup procedure for the cached entry.
|
|
|
|
// After replayGetContextLog() returns, get_context.pinnable_slice_
|
|
|
|
// will point to cache entry buffer (or a copy based on that) and
|
|
|
|
// cleanup routine under value_pinner will be delegated to
|
|
|
|
// get_context.pinnable_slice_. Cache entry is released when
|
|
|
|
// get_context.pinnable_slice_ is reset.
|
|
|
|
value_pinner.RegisterCleanup(release_cache_entry_func,
|
|
|
|
ioptions_.row_cache.get(), row_handle);
|
|
|
|
replayGetContextLog(*found_row_cache_entry, user_key, get_context,
|
|
|
|
&value_pinner);
|
|
|
|
RecordTick(ioptions_.statistics, ROW_CACHE_HIT);
|
|
|
|
found = true;
|
|
|
|
} else {
|
|
|
|
RecordTick(ioptions_.statistics, ROW_CACHE_MISS);
|
|
|
|
}
|
|
|
|
return found;
|
|
|
|
}
|
|
|
|
#endif // ROCKSDB_LITE
|
|
|
|
|
2012-04-17 17:36:46 +02:00
|
|
|
Status TableCache::Get(const ReadOptions& options,
|
2014-01-27 22:53:22 +01:00
|
|
|
const InternalKeyComparator& internal_comparator,
|
2018-07-14 02:34:54 +02:00
|
|
|
const FileMetaData& file_meta, const Slice& k,
|
2018-05-21 23:33:55 +02:00
|
|
|
GetContext* get_context,
|
|
|
|
const SliceTransform* prefix_extractor,
|
|
|
|
HistogramImpl* file_read_hist, bool skip_filters,
|
|
|
|
int level) {
|
2018-07-14 02:34:54 +02:00
|
|
|
auto& fd = file_meta.fd;
|
2015-06-23 19:25:45 +02:00
|
|
|
std::string* row_cache_entry = nullptr;
|
2016-11-17 23:30:11 +01:00
|
|
|
bool done = false;
|
2015-06-23 19:25:45 +02:00
|
|
|
#ifndef ROCKSDB_LITE
|
|
|
|
IterKey row_cache_key;
|
|
|
|
std::string row_cache_entry_buffer;
|
2017-07-17 23:53:15 +02:00
|
|
|
|
2016-11-22 06:08:06 +01:00
|
|
|
// Check row cache if enabled. Since row cache does not currently store
|
|
|
|
// sequence numbers, we cannot use it if we need to fetch the sequence.
|
|
|
|
if (ioptions_.row_cache && !get_context->NeedToReadSequence()) {
|
|
|
|
auto user_key = ExtractUserKey(k);
|
2019-08-29 01:10:38 +02:00
|
|
|
CreateRowCacheKeyPrefix(options, fd, k, get_context, row_cache_key);
|
|
|
|
done = GetFromRowCache(user_key, row_cache_key, row_cache_key.Size(),
|
|
|
|
get_context);
|
|
|
|
if (!done) {
|
2016-11-22 06:08:06 +01:00
|
|
|
row_cache_entry = &row_cache_entry_buffer;
|
2015-06-23 19:25:45 +02:00
|
|
|
}
|
|
|
|
}
|
|
|
|
#endif // ROCKSDB_LITE
|
2016-11-22 06:08:06 +01:00
|
|
|
Status s;
|
|
|
|
TableReader* t = fd.table_reader;
|
|
|
|
Cache::Handle* handle = nullptr;
|
2016-11-17 23:30:11 +01:00
|
|
|
if (!done && s.ok()) {
|
2016-11-22 06:08:06 +01:00
|
|
|
if (t == nullptr) {
|
2018-05-21 23:33:55 +02:00
|
|
|
s = FindTable(
|
|
|
|
env_options_, internal_comparator, fd, &handle, prefix_extractor,
|
|
|
|
options.read_tier == kBlockCacheTier /* no_io */,
|
|
|
|
true /* record_read_stats */, file_read_hist, skip_filters, level);
|
2016-11-17 23:30:11 +01:00
|
|
|
if (s.ok()) {
|
|
|
|
t = GetTableReaderFromHandle(handle);
|
|
|
|
}
|
2014-04-18 00:14:04 +02:00
|
|
|
}
|
Use only "local" range tombstones during Get (#4449)
Summary:
Previously, range tombstones were accumulated from every level, which
was necessary if a range tombstone in a higher level covered a key in a lower
level. However, RangeDelAggregator::AddTombstones's complexity is based on
the number of tombstones that are currently stored in it, which is wasteful in
the Get case, where we only need to know the highest sequence number of range
tombstones that cover the key from higher levels, and compute the highest covering
sequence number at the current level. This change introduces this optimization, and
removes the use of RangeDelAggregator from the Get path.
In the benchmark results, the following command was used to initialize the database:
```
./db_bench -db=/dev/shm/5k-rts -use_existing_db=false -benchmarks=filluniquerandom -write_buffer_size=1048576 -compression_type=lz4 -target_file_size_base=1048576 -max_bytes_for_level_base=4194304 -value_size=112 -key_size=16 -block_size=4096 -level_compaction_dynamic_level_bytes=true -num=5000000 -max_background_jobs=12 -benchmark_write_rate_limit=20971520 -range_tombstone_width=100 -writes_per_range_tombstone=100 -max_num_range_tombstones=50000 -bloom_bits=8
```
...and the following command was used to measure read throughput:
```
./db_bench -db=/dev/shm/5k-rts/ -use_existing_db=true -benchmarks=readrandom -disable_auto_compactions=true -num=5000000 -reads=100000 -threads=32
```
The filluniquerandom command was only run once, and the resulting database was used
to measure read performance before and after the PR. Both binaries were compiled with
`DEBUG_LEVEL=0`.
Readrandom results before PR:
```
readrandom : 4.544 micros/op 220090 ops/sec; 16.9 MB/s (63103 of 100000 found)
```
Readrandom results after PR:
```
readrandom : 11.147 micros/op 89707 ops/sec; 6.9 MB/s (63103 of 100000 found)
```
So it's actually slower right now, but this PR paves the way for future optimizations (see #4493).
----
Pull Request resolved: https://github.com/facebook/rocksdb/pull/4449
Differential Revision: D10370575
Pulled By: abhimadan
fbshipit-source-id: 9a2e152be1ef36969055c0e9eb4beb0d96c11f4d
2018-10-24 21:29:29 +02:00
|
|
|
SequenceNumber* max_covering_tombstone_seq =
|
|
|
|
get_context->max_covering_tombstone_seq();
|
|
|
|
if (s.ok() && max_covering_tombstone_seq != nullptr &&
|
2016-11-29 07:45:35 +01:00
|
|
|
!options.ignore_range_deletions) {
|
2018-11-15 01:18:16 +01:00
|
|
|
std::unique_ptr<FragmentedRangeTombstoneIterator> range_del_iter(
|
2018-11-29 00:26:56 +01:00
|
|
|
t->NewRangeTombstoneIterator(options));
|
2018-11-15 01:18:16 +01:00
|
|
|
if (range_del_iter != nullptr) {
|
|
|
|
*max_covering_tombstone_seq = std::max(
|
|
|
|
*max_covering_tombstone_seq,
|
|
|
|
range_del_iter->MaxCoveringTombstoneSeqnum(ExtractUserKey(k)));
|
|
|
|
}
|
2016-11-29 07:45:35 +01:00
|
|
|
}
|
2016-11-17 23:30:11 +01:00
|
|
|
if (s.ok()) {
|
|
|
|
get_context->SetReplayLog(row_cache_entry); // nullptr if no cache.
|
2018-05-21 23:33:55 +02:00
|
|
|
s = t->Get(options, k, get_context, prefix_extractor, skip_filters);
|
2016-11-17 23:30:11 +01:00
|
|
|
get_context->SetReplayLog(nullptr);
|
|
|
|
} else if (options.read_tier == kBlockCacheTier && s.IsIncomplete()) {
|
|
|
|
// Couldn't find Table in cache but treat as kFound if no_io set
|
|
|
|
get_context->MarkKeyMayExist();
|
|
|
|
s = Status::OK();
|
|
|
|
done = true;
|
2014-01-07 05:29:17 +01:00
|
|
|
}
|
2012-04-17 17:36:46 +02:00
|
|
|
}
|
2016-11-22 06:08:06 +01:00
|
|
|
|
2015-06-23 19:25:45 +02:00
|
|
|
#ifndef ROCKSDB_LITE
|
|
|
|
// Put the replay log in row cache only if something was found.
|
2016-11-17 23:30:11 +01:00
|
|
|
if (!done && s.ok() && row_cache_entry && !row_cache_entry->empty()) {
|
2015-06-23 19:25:45 +02:00
|
|
|
size_t charge =
|
|
|
|
row_cache_key.Size() + row_cache_entry->size() + sizeof(std::string);
|
|
|
|
void* row_ptr = new std::string(std::move(*row_cache_entry));
|
2017-04-04 23:17:16 +02:00
|
|
|
ioptions_.row_cache->Insert(row_cache_key.GetUserKey(), row_ptr, charge,
|
2016-03-11 02:35:19 +01:00
|
|
|
&DeleteEntry<std::string>);
|
2015-06-23 19:25:45 +02:00
|
|
|
}
|
|
|
|
#endif // ROCKSDB_LITE
|
|
|
|
|
2016-11-17 23:30:11 +01:00
|
|
|
if (handle != nullptr) {
|
|
|
|
ReleaseHandle(handle);
|
|
|
|
}
|
2012-04-17 17:36:46 +02:00
|
|
|
return s;
|
|
|
|
}
|
2014-09-05 01:18:36 +02:00
|
|
|
|
Introduce a new MultiGet batching implementation (#5011)
Summary:
This PR introduces a new MultiGet() API, with the underlying implementation grouping keys based on SST file and batching lookups in a file. The reason for the new API is twofold - the definition allows callers to allocate storage for status and values on stack instead of std::vector, as well as return values as PinnableSlices in order to avoid copying, and it keeps the original MultiGet() implementation intact while we experiment with batching.
Batching is useful when there is some spatial locality to the keys being queries, as well as larger batch sizes. The main benefits are due to -
1. Fewer function calls, especially to BlockBasedTableReader::MultiGet() and FullFilterBlockReader::KeysMayMatch()
2. Bloom filter cachelines can be prefetched, hiding the cache miss latency
The next step is to optimize the binary searches in the level_storage_info, index blocks and data blocks, since we could reduce the number of key comparisons if the keys are relatively close to each other. The batching optimizations also need to be extended to other formats, such as PlainTable and filter formats. This also needs to be added to db_stress.
Benchmark results from db_bench for various batch size/locality of reference combinations are given below. Locality was simulated by offsetting the keys in a batch by a stride length. Each SST file is about 8.6MB uncompressed and key/value size is 16/100 uncompressed. To focus on the cpu benefit of batching, the runs were single threaded and bound to the same cpu to eliminate interference from other system events. The results show a 10-25% improvement in micros/op from smaller to larger batch sizes (4 - 32).
Batch Sizes
1 | 2 | 4 | 8 | 16 | 32
Random pattern (Stride length 0)
4.158 | 4.109 | 4.026 | 4.05 | 4.1 | 4.074 - Get
4.438 | 4.302 | 4.165 | 4.122 | 4.096 | 4.075 - MultiGet (no batching)
4.461 | 4.256 | 4.277 | 4.11 | 4.182 | 4.14 - MultiGet (w/ batching)
Good locality (Stride length 16)
4.048 | 3.659 | 3.248 | 2.99 | 2.84 | 2.753
4.429 | 3.728 | 3.406 | 3.053 | 2.911 | 2.781
4.452 | 3.45 | 2.833 | 2.451 | 2.233 | 2.135
Good locality (Stride length 256)
4.066 | 3.786 | 3.581 | 3.447 | 3.415 | 3.232
4.406 | 4.005 | 3.644 | 3.49 | 3.381 | 3.268
4.393 | 3.649 | 3.186 | 2.882 | 2.676 | 2.62
Medium locality (Stride length 4096)
4.012 | 3.922 | 3.768 | 3.61 | 3.582 | 3.555
4.364 | 4.057 | 3.791 | 3.65 | 3.57 | 3.465
4.479 | 3.758 | 3.316 | 3.077 | 2.959 | 2.891
dbbench command used (on a DB with 4 levels, 12 million keys)-
TEST_TMPDIR=/dev/shm numactl -C 10 ./db_bench.tmp -use_existing_db=true -benchmarks="readseq,multireadrandom" -write_buffer_size=4194304 -target_file_size_base=4194304 -max_bytes_for_level_base=16777216 -num=12000000 -reads=12000000 -duration=90 -threads=1 -compression_type=none -cache_size=4194304000 -batch_size=32 -disable_auto_compactions=true -bloom_bits=10 -cache_index_and_filter_blocks=true -pin_l0_filter_and_index_blocks_in_cache=true -multiread_batched=true -multiread_stride=4
Pull Request resolved: https://github.com/facebook/rocksdb/pull/5011
Differential Revision: D14348703
Pulled By: anand1976
fbshipit-source-id: 774406dab3776d979c809522a67bedac6c17f84b
2019-04-11 23:24:09 +02:00
|
|
|
// Batched version of TableCache::MultiGet.
|
|
|
|
Status TableCache::MultiGet(const ReadOptions& options,
|
|
|
|
const InternalKeyComparator& internal_comparator,
|
|
|
|
const FileMetaData& file_meta,
|
|
|
|
const MultiGetContext::Range* mget_range,
|
|
|
|
const SliceTransform* prefix_extractor,
|
|
|
|
HistogramImpl* file_read_hist, bool skip_filters,
|
|
|
|
int level) {
|
|
|
|
auto& fd = file_meta.fd;
|
|
|
|
Status s;
|
|
|
|
TableReader* t = fd.table_reader;
|
|
|
|
Cache::Handle* handle = nullptr;
|
2019-09-20 21:00:55 +02:00
|
|
|
MultiGetRange table_range(*mget_range, mget_range->begin(),
|
|
|
|
mget_range->end());
|
2019-08-29 01:10:38 +02:00
|
|
|
#ifndef ROCKSDB_LITE
|
|
|
|
autovector<std::string, MultiGetContext::MAX_BATCH_SIZE> row_cache_entries;
|
|
|
|
IterKey row_cache_key;
|
|
|
|
size_t row_cache_key_prefix_size = 0;
|
|
|
|
KeyContext& first_key = *table_range.begin();
|
2019-09-20 21:00:55 +02:00
|
|
|
bool lookup_row_cache =
|
|
|
|
ioptions_.row_cache && !first_key.get_context->NeedToReadSequence();
|
2019-08-29 01:10:38 +02:00
|
|
|
|
|
|
|
// Check row cache if enabled. Since row cache does not currently store
|
|
|
|
// sequence numbers, we cannot use it if we need to fetch the sequence.
|
|
|
|
if (lookup_row_cache) {
|
|
|
|
GetContext* first_context = first_key.get_context;
|
|
|
|
CreateRowCacheKeyPrefix(options, fd, first_key.ikey, first_context,
|
|
|
|
row_cache_key);
|
|
|
|
row_cache_key_prefix_size = row_cache_key.Size();
|
|
|
|
|
2019-09-20 21:00:55 +02:00
|
|
|
for (auto miter = table_range.begin(); miter != table_range.end();
|
|
|
|
++miter) {
|
|
|
|
const Slice& user_key = miter->ukey;
|
|
|
|
;
|
2019-08-29 01:10:38 +02:00
|
|
|
GetContext* get_context = miter->get_context;
|
|
|
|
|
|
|
|
if (GetFromRowCache(user_key, row_cache_key, row_cache_key_prefix_size,
|
|
|
|
get_context)) {
|
|
|
|
table_range.SkipKey(miter);
|
|
|
|
} else {
|
|
|
|
row_cache_entries.emplace_back();
|
|
|
|
get_context->SetReplayLog(&(row_cache_entries.back()));
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
#endif // ROCKSDB_LITE
|
|
|
|
|
|
|
|
// Check that table_range is not empty. Its possible all keys may have been
|
|
|
|
// found in the row cache and thus the range may now be empty
|
|
|
|
if (s.ok() && !table_range.empty()) {
|
Introduce a new MultiGet batching implementation (#5011)
Summary:
This PR introduces a new MultiGet() API, with the underlying implementation grouping keys based on SST file and batching lookups in a file. The reason for the new API is twofold - the definition allows callers to allocate storage for status and values on stack instead of std::vector, as well as return values as PinnableSlices in order to avoid copying, and it keeps the original MultiGet() implementation intact while we experiment with batching.
Batching is useful when there is some spatial locality to the keys being queries, as well as larger batch sizes. The main benefits are due to -
1. Fewer function calls, especially to BlockBasedTableReader::MultiGet() and FullFilterBlockReader::KeysMayMatch()
2. Bloom filter cachelines can be prefetched, hiding the cache miss latency
The next step is to optimize the binary searches in the level_storage_info, index blocks and data blocks, since we could reduce the number of key comparisons if the keys are relatively close to each other. The batching optimizations also need to be extended to other formats, such as PlainTable and filter formats. This also needs to be added to db_stress.
Benchmark results from db_bench for various batch size/locality of reference combinations are given below. Locality was simulated by offsetting the keys in a batch by a stride length. Each SST file is about 8.6MB uncompressed and key/value size is 16/100 uncompressed. To focus on the cpu benefit of batching, the runs were single threaded and bound to the same cpu to eliminate interference from other system events. The results show a 10-25% improvement in micros/op from smaller to larger batch sizes (4 - 32).
Batch Sizes
1 | 2 | 4 | 8 | 16 | 32
Random pattern (Stride length 0)
4.158 | 4.109 | 4.026 | 4.05 | 4.1 | 4.074 - Get
4.438 | 4.302 | 4.165 | 4.122 | 4.096 | 4.075 - MultiGet (no batching)
4.461 | 4.256 | 4.277 | 4.11 | 4.182 | 4.14 - MultiGet (w/ batching)
Good locality (Stride length 16)
4.048 | 3.659 | 3.248 | 2.99 | 2.84 | 2.753
4.429 | 3.728 | 3.406 | 3.053 | 2.911 | 2.781
4.452 | 3.45 | 2.833 | 2.451 | 2.233 | 2.135
Good locality (Stride length 256)
4.066 | 3.786 | 3.581 | 3.447 | 3.415 | 3.232
4.406 | 4.005 | 3.644 | 3.49 | 3.381 | 3.268
4.393 | 3.649 | 3.186 | 2.882 | 2.676 | 2.62
Medium locality (Stride length 4096)
4.012 | 3.922 | 3.768 | 3.61 | 3.582 | 3.555
4.364 | 4.057 | 3.791 | 3.65 | 3.57 | 3.465
4.479 | 3.758 | 3.316 | 3.077 | 2.959 | 2.891
dbbench command used (on a DB with 4 levels, 12 million keys)-
TEST_TMPDIR=/dev/shm numactl -C 10 ./db_bench.tmp -use_existing_db=true -benchmarks="readseq,multireadrandom" -write_buffer_size=4194304 -target_file_size_base=4194304 -max_bytes_for_level_base=16777216 -num=12000000 -reads=12000000 -duration=90 -threads=1 -compression_type=none -cache_size=4194304000 -batch_size=32 -disable_auto_compactions=true -bloom_bits=10 -cache_index_and_filter_blocks=true -pin_l0_filter_and_index_blocks_in_cache=true -multiread_batched=true -multiread_stride=4
Pull Request resolved: https://github.com/facebook/rocksdb/pull/5011
Differential Revision: D14348703
Pulled By: anand1976
fbshipit-source-id: 774406dab3776d979c809522a67bedac6c17f84b
2019-04-11 23:24:09 +02:00
|
|
|
if (t == nullptr) {
|
|
|
|
s = FindTable(
|
|
|
|
env_options_, internal_comparator, fd, &handle, prefix_extractor,
|
|
|
|
options.read_tier == kBlockCacheTier /* no_io */,
|
|
|
|
true /* record_read_stats */, file_read_hist, skip_filters, level);
|
|
|
|
if (s.ok()) {
|
|
|
|
t = GetTableReaderFromHandle(handle);
|
|
|
|
assert(t);
|
|
|
|
}
|
|
|
|
}
|
|
|
|
if (s.ok() && !options.ignore_range_deletions) {
|
|
|
|
std::unique_ptr<FragmentedRangeTombstoneIterator> range_del_iter(
|
|
|
|
t->NewRangeTombstoneIterator(options));
|
|
|
|
if (range_del_iter != nullptr) {
|
2019-08-29 01:10:38 +02:00
|
|
|
for (auto iter = table_range.begin(); iter != table_range.end();
|
Introduce a new MultiGet batching implementation (#5011)
Summary:
This PR introduces a new MultiGet() API, with the underlying implementation grouping keys based on SST file and batching lookups in a file. The reason for the new API is twofold - the definition allows callers to allocate storage for status and values on stack instead of std::vector, as well as return values as PinnableSlices in order to avoid copying, and it keeps the original MultiGet() implementation intact while we experiment with batching.
Batching is useful when there is some spatial locality to the keys being queries, as well as larger batch sizes. The main benefits are due to -
1. Fewer function calls, especially to BlockBasedTableReader::MultiGet() and FullFilterBlockReader::KeysMayMatch()
2. Bloom filter cachelines can be prefetched, hiding the cache miss latency
The next step is to optimize the binary searches in the level_storage_info, index blocks and data blocks, since we could reduce the number of key comparisons if the keys are relatively close to each other. The batching optimizations also need to be extended to other formats, such as PlainTable and filter formats. This also needs to be added to db_stress.
Benchmark results from db_bench for various batch size/locality of reference combinations are given below. Locality was simulated by offsetting the keys in a batch by a stride length. Each SST file is about 8.6MB uncompressed and key/value size is 16/100 uncompressed. To focus on the cpu benefit of batching, the runs were single threaded and bound to the same cpu to eliminate interference from other system events. The results show a 10-25% improvement in micros/op from smaller to larger batch sizes (4 - 32).
Batch Sizes
1 | 2 | 4 | 8 | 16 | 32
Random pattern (Stride length 0)
4.158 | 4.109 | 4.026 | 4.05 | 4.1 | 4.074 - Get
4.438 | 4.302 | 4.165 | 4.122 | 4.096 | 4.075 - MultiGet (no batching)
4.461 | 4.256 | 4.277 | 4.11 | 4.182 | 4.14 - MultiGet (w/ batching)
Good locality (Stride length 16)
4.048 | 3.659 | 3.248 | 2.99 | 2.84 | 2.753
4.429 | 3.728 | 3.406 | 3.053 | 2.911 | 2.781
4.452 | 3.45 | 2.833 | 2.451 | 2.233 | 2.135
Good locality (Stride length 256)
4.066 | 3.786 | 3.581 | 3.447 | 3.415 | 3.232
4.406 | 4.005 | 3.644 | 3.49 | 3.381 | 3.268
4.393 | 3.649 | 3.186 | 2.882 | 2.676 | 2.62
Medium locality (Stride length 4096)
4.012 | 3.922 | 3.768 | 3.61 | 3.582 | 3.555
4.364 | 4.057 | 3.791 | 3.65 | 3.57 | 3.465
4.479 | 3.758 | 3.316 | 3.077 | 2.959 | 2.891
dbbench command used (on a DB with 4 levels, 12 million keys)-
TEST_TMPDIR=/dev/shm numactl -C 10 ./db_bench.tmp -use_existing_db=true -benchmarks="readseq,multireadrandom" -write_buffer_size=4194304 -target_file_size_base=4194304 -max_bytes_for_level_base=16777216 -num=12000000 -reads=12000000 -duration=90 -threads=1 -compression_type=none -cache_size=4194304000 -batch_size=32 -disable_auto_compactions=true -bloom_bits=10 -cache_index_and_filter_blocks=true -pin_l0_filter_and_index_blocks_in_cache=true -multiread_batched=true -multiread_stride=4
Pull Request resolved: https://github.com/facebook/rocksdb/pull/5011
Differential Revision: D14348703
Pulled By: anand1976
fbshipit-source-id: 774406dab3776d979c809522a67bedac6c17f84b
2019-04-11 23:24:09 +02:00
|
|
|
++iter) {
|
|
|
|
SequenceNumber* max_covering_tombstone_seq =
|
|
|
|
iter->get_context->max_covering_tombstone_seq();
|
2019-08-29 01:10:38 +02:00
|
|
|
*max_covering_tombstone_seq =
|
|
|
|
std::max(*max_covering_tombstone_seq,
|
|
|
|
range_del_iter->MaxCoveringTombstoneSeqnum(iter->ukey));
|
Introduce a new MultiGet batching implementation (#5011)
Summary:
This PR introduces a new MultiGet() API, with the underlying implementation grouping keys based on SST file and batching lookups in a file. The reason for the new API is twofold - the definition allows callers to allocate storage for status and values on stack instead of std::vector, as well as return values as PinnableSlices in order to avoid copying, and it keeps the original MultiGet() implementation intact while we experiment with batching.
Batching is useful when there is some spatial locality to the keys being queries, as well as larger batch sizes. The main benefits are due to -
1. Fewer function calls, especially to BlockBasedTableReader::MultiGet() and FullFilterBlockReader::KeysMayMatch()
2. Bloom filter cachelines can be prefetched, hiding the cache miss latency
The next step is to optimize the binary searches in the level_storage_info, index blocks and data blocks, since we could reduce the number of key comparisons if the keys are relatively close to each other. The batching optimizations also need to be extended to other formats, such as PlainTable and filter formats. This also needs to be added to db_stress.
Benchmark results from db_bench for various batch size/locality of reference combinations are given below. Locality was simulated by offsetting the keys in a batch by a stride length. Each SST file is about 8.6MB uncompressed and key/value size is 16/100 uncompressed. To focus on the cpu benefit of batching, the runs were single threaded and bound to the same cpu to eliminate interference from other system events. The results show a 10-25% improvement in micros/op from smaller to larger batch sizes (4 - 32).
Batch Sizes
1 | 2 | 4 | 8 | 16 | 32
Random pattern (Stride length 0)
4.158 | 4.109 | 4.026 | 4.05 | 4.1 | 4.074 - Get
4.438 | 4.302 | 4.165 | 4.122 | 4.096 | 4.075 - MultiGet (no batching)
4.461 | 4.256 | 4.277 | 4.11 | 4.182 | 4.14 - MultiGet (w/ batching)
Good locality (Stride length 16)
4.048 | 3.659 | 3.248 | 2.99 | 2.84 | 2.753
4.429 | 3.728 | 3.406 | 3.053 | 2.911 | 2.781
4.452 | 3.45 | 2.833 | 2.451 | 2.233 | 2.135
Good locality (Stride length 256)
4.066 | 3.786 | 3.581 | 3.447 | 3.415 | 3.232
4.406 | 4.005 | 3.644 | 3.49 | 3.381 | 3.268
4.393 | 3.649 | 3.186 | 2.882 | 2.676 | 2.62
Medium locality (Stride length 4096)
4.012 | 3.922 | 3.768 | 3.61 | 3.582 | 3.555
4.364 | 4.057 | 3.791 | 3.65 | 3.57 | 3.465
4.479 | 3.758 | 3.316 | 3.077 | 2.959 | 2.891
dbbench command used (on a DB with 4 levels, 12 million keys)-
TEST_TMPDIR=/dev/shm numactl -C 10 ./db_bench.tmp -use_existing_db=true -benchmarks="readseq,multireadrandom" -write_buffer_size=4194304 -target_file_size_base=4194304 -max_bytes_for_level_base=16777216 -num=12000000 -reads=12000000 -duration=90 -threads=1 -compression_type=none -cache_size=4194304000 -batch_size=32 -disable_auto_compactions=true -bloom_bits=10 -cache_index_and_filter_blocks=true -pin_l0_filter_and_index_blocks_in_cache=true -multiread_batched=true -multiread_stride=4
Pull Request resolved: https://github.com/facebook/rocksdb/pull/5011
Differential Revision: D14348703
Pulled By: anand1976
fbshipit-source-id: 774406dab3776d979c809522a67bedac6c17f84b
2019-04-11 23:24:09 +02:00
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
if (s.ok()) {
|
2019-08-29 01:10:38 +02:00
|
|
|
t->MultiGet(options, &table_range, prefix_extractor, skip_filters);
|
Introduce a new MultiGet batching implementation (#5011)
Summary:
This PR introduces a new MultiGet() API, with the underlying implementation grouping keys based on SST file and batching lookups in a file. The reason for the new API is twofold - the definition allows callers to allocate storage for status and values on stack instead of std::vector, as well as return values as PinnableSlices in order to avoid copying, and it keeps the original MultiGet() implementation intact while we experiment with batching.
Batching is useful when there is some spatial locality to the keys being queries, as well as larger batch sizes. The main benefits are due to -
1. Fewer function calls, especially to BlockBasedTableReader::MultiGet() and FullFilterBlockReader::KeysMayMatch()
2. Bloom filter cachelines can be prefetched, hiding the cache miss latency
The next step is to optimize the binary searches in the level_storage_info, index blocks and data blocks, since we could reduce the number of key comparisons if the keys are relatively close to each other. The batching optimizations also need to be extended to other formats, such as PlainTable and filter formats. This also needs to be added to db_stress.
Benchmark results from db_bench for various batch size/locality of reference combinations are given below. Locality was simulated by offsetting the keys in a batch by a stride length. Each SST file is about 8.6MB uncompressed and key/value size is 16/100 uncompressed. To focus on the cpu benefit of batching, the runs were single threaded and bound to the same cpu to eliminate interference from other system events. The results show a 10-25% improvement in micros/op from smaller to larger batch sizes (4 - 32).
Batch Sizes
1 | 2 | 4 | 8 | 16 | 32
Random pattern (Stride length 0)
4.158 | 4.109 | 4.026 | 4.05 | 4.1 | 4.074 - Get
4.438 | 4.302 | 4.165 | 4.122 | 4.096 | 4.075 - MultiGet (no batching)
4.461 | 4.256 | 4.277 | 4.11 | 4.182 | 4.14 - MultiGet (w/ batching)
Good locality (Stride length 16)
4.048 | 3.659 | 3.248 | 2.99 | 2.84 | 2.753
4.429 | 3.728 | 3.406 | 3.053 | 2.911 | 2.781
4.452 | 3.45 | 2.833 | 2.451 | 2.233 | 2.135
Good locality (Stride length 256)
4.066 | 3.786 | 3.581 | 3.447 | 3.415 | 3.232
4.406 | 4.005 | 3.644 | 3.49 | 3.381 | 3.268
4.393 | 3.649 | 3.186 | 2.882 | 2.676 | 2.62
Medium locality (Stride length 4096)
4.012 | 3.922 | 3.768 | 3.61 | 3.582 | 3.555
4.364 | 4.057 | 3.791 | 3.65 | 3.57 | 3.465
4.479 | 3.758 | 3.316 | 3.077 | 2.959 | 2.891
dbbench command used (on a DB with 4 levels, 12 million keys)-
TEST_TMPDIR=/dev/shm numactl -C 10 ./db_bench.tmp -use_existing_db=true -benchmarks="readseq,multireadrandom" -write_buffer_size=4194304 -target_file_size_base=4194304 -max_bytes_for_level_base=16777216 -num=12000000 -reads=12000000 -duration=90 -threads=1 -compression_type=none -cache_size=4194304000 -batch_size=32 -disable_auto_compactions=true -bloom_bits=10 -cache_index_and_filter_blocks=true -pin_l0_filter_and_index_blocks_in_cache=true -multiread_batched=true -multiread_stride=4
Pull Request resolved: https://github.com/facebook/rocksdb/pull/5011
Differential Revision: D14348703
Pulled By: anand1976
fbshipit-source-id: 774406dab3776d979c809522a67bedac6c17f84b
2019-04-11 23:24:09 +02:00
|
|
|
} else if (options.read_tier == kBlockCacheTier && s.IsIncomplete()) {
|
2019-08-29 01:10:38 +02:00
|
|
|
for (auto iter = table_range.begin(); iter != table_range.end(); ++iter) {
|
Introduce a new MultiGet batching implementation (#5011)
Summary:
This PR introduces a new MultiGet() API, with the underlying implementation grouping keys based on SST file and batching lookups in a file. The reason for the new API is twofold - the definition allows callers to allocate storage for status and values on stack instead of std::vector, as well as return values as PinnableSlices in order to avoid copying, and it keeps the original MultiGet() implementation intact while we experiment with batching.
Batching is useful when there is some spatial locality to the keys being queries, as well as larger batch sizes. The main benefits are due to -
1. Fewer function calls, especially to BlockBasedTableReader::MultiGet() and FullFilterBlockReader::KeysMayMatch()
2. Bloom filter cachelines can be prefetched, hiding the cache miss latency
The next step is to optimize the binary searches in the level_storage_info, index blocks and data blocks, since we could reduce the number of key comparisons if the keys are relatively close to each other. The batching optimizations also need to be extended to other formats, such as PlainTable and filter formats. This also needs to be added to db_stress.
Benchmark results from db_bench for various batch size/locality of reference combinations are given below. Locality was simulated by offsetting the keys in a batch by a stride length. Each SST file is about 8.6MB uncompressed and key/value size is 16/100 uncompressed. To focus on the cpu benefit of batching, the runs were single threaded and bound to the same cpu to eliminate interference from other system events. The results show a 10-25% improvement in micros/op from smaller to larger batch sizes (4 - 32).
Batch Sizes
1 | 2 | 4 | 8 | 16 | 32
Random pattern (Stride length 0)
4.158 | 4.109 | 4.026 | 4.05 | 4.1 | 4.074 - Get
4.438 | 4.302 | 4.165 | 4.122 | 4.096 | 4.075 - MultiGet (no batching)
4.461 | 4.256 | 4.277 | 4.11 | 4.182 | 4.14 - MultiGet (w/ batching)
Good locality (Stride length 16)
4.048 | 3.659 | 3.248 | 2.99 | 2.84 | 2.753
4.429 | 3.728 | 3.406 | 3.053 | 2.911 | 2.781
4.452 | 3.45 | 2.833 | 2.451 | 2.233 | 2.135
Good locality (Stride length 256)
4.066 | 3.786 | 3.581 | 3.447 | 3.415 | 3.232
4.406 | 4.005 | 3.644 | 3.49 | 3.381 | 3.268
4.393 | 3.649 | 3.186 | 2.882 | 2.676 | 2.62
Medium locality (Stride length 4096)
4.012 | 3.922 | 3.768 | 3.61 | 3.582 | 3.555
4.364 | 4.057 | 3.791 | 3.65 | 3.57 | 3.465
4.479 | 3.758 | 3.316 | 3.077 | 2.959 | 2.891
dbbench command used (on a DB with 4 levels, 12 million keys)-
TEST_TMPDIR=/dev/shm numactl -C 10 ./db_bench.tmp -use_existing_db=true -benchmarks="readseq,multireadrandom" -write_buffer_size=4194304 -target_file_size_base=4194304 -max_bytes_for_level_base=16777216 -num=12000000 -reads=12000000 -duration=90 -threads=1 -compression_type=none -cache_size=4194304000 -batch_size=32 -disable_auto_compactions=true -bloom_bits=10 -cache_index_and_filter_blocks=true -pin_l0_filter_and_index_blocks_in_cache=true -multiread_batched=true -multiread_stride=4
Pull Request resolved: https://github.com/facebook/rocksdb/pull/5011
Differential Revision: D14348703
Pulled By: anand1976
fbshipit-source-id: 774406dab3776d979c809522a67bedac6c17f84b
2019-04-11 23:24:09 +02:00
|
|
|
Status* status = iter->s;
|
|
|
|
if (status->IsIncomplete()) {
|
|
|
|
// Couldn't find Table in cache but treat as kFound if no_io set
|
|
|
|
iter->get_context->MarkKeyMayExist();
|
|
|
|
s = Status::OK();
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
2019-08-29 01:10:38 +02:00
|
|
|
#ifndef ROCKSDB_LITE
|
|
|
|
if (lookup_row_cache) {
|
|
|
|
size_t row_idx = 0;
|
|
|
|
|
2019-09-20 21:00:55 +02:00
|
|
|
for (auto miter = table_range.begin(); miter != table_range.end();
|
|
|
|
++miter) {
|
2019-08-29 01:10:38 +02:00
|
|
|
std::string& row_cache_entry = row_cache_entries[row_idx++];
|
2019-09-20 21:00:55 +02:00
|
|
|
const Slice& user_key = miter->ukey;
|
|
|
|
;
|
2019-08-29 01:10:38 +02:00
|
|
|
GetContext* get_context = miter->get_context;
|
|
|
|
|
|
|
|
get_context->SetReplayLog(nullptr);
|
|
|
|
// Compute row cache key.
|
|
|
|
row_cache_key.TrimAppend(row_cache_key_prefix_size, user_key.data(),
|
|
|
|
user_key.size());
|
|
|
|
// Put the replay log in row cache only if something was found.
|
|
|
|
if (s.ok() && !row_cache_entry.empty()) {
|
|
|
|
size_t charge =
|
|
|
|
row_cache_key.Size() + row_cache_entry.size() + sizeof(std::string);
|
|
|
|
void* row_ptr = new std::string(std::move(row_cache_entry));
|
|
|
|
ioptions_.row_cache->Insert(row_cache_key.GetUserKey(), row_ptr, charge,
|
|
|
|
&DeleteEntry<std::string>);
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
#endif // ROCKSDB_LITE
|
|
|
|
|
Introduce a new MultiGet batching implementation (#5011)
Summary:
This PR introduces a new MultiGet() API, with the underlying implementation grouping keys based on SST file and batching lookups in a file. The reason for the new API is twofold - the definition allows callers to allocate storage for status and values on stack instead of std::vector, as well as return values as PinnableSlices in order to avoid copying, and it keeps the original MultiGet() implementation intact while we experiment with batching.
Batching is useful when there is some spatial locality to the keys being queries, as well as larger batch sizes. The main benefits are due to -
1. Fewer function calls, especially to BlockBasedTableReader::MultiGet() and FullFilterBlockReader::KeysMayMatch()
2. Bloom filter cachelines can be prefetched, hiding the cache miss latency
The next step is to optimize the binary searches in the level_storage_info, index blocks and data blocks, since we could reduce the number of key comparisons if the keys are relatively close to each other. The batching optimizations also need to be extended to other formats, such as PlainTable and filter formats. This also needs to be added to db_stress.
Benchmark results from db_bench for various batch size/locality of reference combinations are given below. Locality was simulated by offsetting the keys in a batch by a stride length. Each SST file is about 8.6MB uncompressed and key/value size is 16/100 uncompressed. To focus on the cpu benefit of batching, the runs were single threaded and bound to the same cpu to eliminate interference from other system events. The results show a 10-25% improvement in micros/op from smaller to larger batch sizes (4 - 32).
Batch Sizes
1 | 2 | 4 | 8 | 16 | 32
Random pattern (Stride length 0)
4.158 | 4.109 | 4.026 | 4.05 | 4.1 | 4.074 - Get
4.438 | 4.302 | 4.165 | 4.122 | 4.096 | 4.075 - MultiGet (no batching)
4.461 | 4.256 | 4.277 | 4.11 | 4.182 | 4.14 - MultiGet (w/ batching)
Good locality (Stride length 16)
4.048 | 3.659 | 3.248 | 2.99 | 2.84 | 2.753
4.429 | 3.728 | 3.406 | 3.053 | 2.911 | 2.781
4.452 | 3.45 | 2.833 | 2.451 | 2.233 | 2.135
Good locality (Stride length 256)
4.066 | 3.786 | 3.581 | 3.447 | 3.415 | 3.232
4.406 | 4.005 | 3.644 | 3.49 | 3.381 | 3.268
4.393 | 3.649 | 3.186 | 2.882 | 2.676 | 2.62
Medium locality (Stride length 4096)
4.012 | 3.922 | 3.768 | 3.61 | 3.582 | 3.555
4.364 | 4.057 | 3.791 | 3.65 | 3.57 | 3.465
4.479 | 3.758 | 3.316 | 3.077 | 2.959 | 2.891
dbbench command used (on a DB with 4 levels, 12 million keys)-
TEST_TMPDIR=/dev/shm numactl -C 10 ./db_bench.tmp -use_existing_db=true -benchmarks="readseq,multireadrandom" -write_buffer_size=4194304 -target_file_size_base=4194304 -max_bytes_for_level_base=16777216 -num=12000000 -reads=12000000 -duration=90 -threads=1 -compression_type=none -cache_size=4194304000 -batch_size=32 -disable_auto_compactions=true -bloom_bits=10 -cache_index_and_filter_blocks=true -pin_l0_filter_and_index_blocks_in_cache=true -multiread_batched=true -multiread_stride=4
Pull Request resolved: https://github.com/facebook/rocksdb/pull/5011
Differential Revision: D14348703
Pulled By: anand1976
fbshipit-source-id: 774406dab3776d979c809522a67bedac6c17f84b
2019-04-11 23:24:09 +02:00
|
|
|
if (handle != nullptr) {
|
|
|
|
ReleaseHandle(handle);
|
|
|
|
}
|
|
|
|
return s;
|
|
|
|
}
|
|
|
|
|
2014-02-14 01:28:21 +01:00
|
|
|
Status TableCache::GetTableProperties(
|
2014-09-05 01:18:36 +02:00
|
|
|
const EnvOptions& env_options,
|
2014-06-14 00:54:19 +02:00
|
|
|
const InternalKeyComparator& internal_comparator, const FileDescriptor& fd,
|
2018-05-21 23:33:55 +02:00
|
|
|
std::shared_ptr<const TableProperties>* properties,
|
|
|
|
const SliceTransform* prefix_extractor, bool no_io) {
|
2014-02-14 01:28:21 +01:00
|
|
|
Status s;
|
2014-06-14 00:54:19 +02:00
|
|
|
auto table_reader = fd.table_reader;
|
2014-02-14 01:28:21 +01:00
|
|
|
// table already been pre-loaded?
|
2014-04-17 23:07:05 +02:00
|
|
|
if (table_reader) {
|
|
|
|
*properties = table_reader->GetTableProperties();
|
|
|
|
|
2014-02-14 01:28:21 +01:00
|
|
|
return s;
|
|
|
|
}
|
|
|
|
|
2014-04-17 23:07:05 +02:00
|
|
|
Cache::Handle* table_handle = nullptr;
|
2018-05-21 23:33:55 +02:00
|
|
|
s = FindTable(env_options, internal_comparator, fd, &table_handle,
|
|
|
|
prefix_extractor, no_io);
|
2014-02-14 01:28:21 +01:00
|
|
|
if (!s.ok()) {
|
|
|
|
return s;
|
|
|
|
}
|
|
|
|
assert(table_handle);
|
|
|
|
auto table = GetTableReaderFromHandle(table_handle);
|
|
|
|
*properties = table->GetTableProperties();
|
|
|
|
ReleaseHandle(table_handle);
|
|
|
|
return s;
|
|
|
|
}
|
2012-04-17 17:36:46 +02:00
|
|
|
|
2014-08-05 20:27:34 +02:00
|
|
|
size_t TableCache::GetMemoryUsageByTableReader(
|
2014-09-05 01:18:36 +02:00
|
|
|
const EnvOptions& env_options,
|
2018-05-21 23:33:55 +02:00
|
|
|
const InternalKeyComparator& internal_comparator, const FileDescriptor& fd,
|
|
|
|
const SliceTransform* prefix_extractor) {
|
2014-08-05 20:27:34 +02:00
|
|
|
Status s;
|
|
|
|
auto table_reader = fd.table_reader;
|
|
|
|
// table already been pre-loaded?
|
|
|
|
if (table_reader) {
|
|
|
|
return table_reader->ApproximateMemoryUsage();
|
|
|
|
}
|
|
|
|
|
|
|
|
Cache::Handle* table_handle = nullptr;
|
2018-05-21 23:33:55 +02:00
|
|
|
s = FindTable(env_options, internal_comparator, fd, &table_handle,
|
|
|
|
prefix_extractor, true);
|
2014-08-05 20:27:34 +02:00
|
|
|
if (!s.ok()) {
|
|
|
|
return 0;
|
|
|
|
}
|
|
|
|
assert(table_handle);
|
|
|
|
auto table = GetTableReaderFromHandle(table_handle);
|
|
|
|
auto ret = table->ApproximateMemoryUsage();
|
|
|
|
ReleaseHandle(table_handle);
|
|
|
|
return ret;
|
|
|
|
}
|
|
|
|
|
[CF] Rethink table cache
Summary:
Adapting table cache to column families is interesting. We want table cache to be global LRU, so if some column families are use not as often as others, we want them to be evicted from cache. However, current TableCache object also constructs tables on its own. If table is not found in the cache, TableCache automatically creates new table. We want each column family to be able to specify different table factory.
To solve the problem, we still have a single LRU, but we provide the LRUCache object to TableCache on construction. We have one TableCache per column family, but the underyling cache is shared by all TableCache objects.
This allows us to have a global LRU, but still be able to support different table factories for different column families. Also, in the future it will also be able to support different directories for different column families.
Test Plan: make check
Reviewers: dhruba, haobo, kailiu, sdong
CC: leveldb
Differential Revision: https://reviews.facebook.net/D15915
2014-02-05 18:07:55 +01:00
|
|
|
void TableCache::Evict(Cache* cache, uint64_t file_number) {
|
|
|
|
cache->Erase(GetSliceForFileNumber(&file_number));
|
2011-03-18 23:37:00 +01:00
|
|
|
}
|
|
|
|
|
2019-07-24 00:30:59 +02:00
|
|
|
uint64_t TableCache::ApproximateOffsetOf(
|
|
|
|
const Slice& key, const FileDescriptor& fd, TableReaderCaller caller,
|
|
|
|
const InternalKeyComparator& internal_comparator,
|
|
|
|
const SliceTransform* prefix_extractor) {
|
|
|
|
uint64_t result = 0;
|
|
|
|
TableReader* table_reader = fd.table_reader;
|
|
|
|
Cache::Handle* table_handle = nullptr;
|
|
|
|
if (table_reader == nullptr) {
|
|
|
|
const bool for_compaction = (caller == TableReaderCaller::kCompaction);
|
|
|
|
Status s = FindTable(env_options_, internal_comparator, fd, &table_handle,
|
|
|
|
prefix_extractor, false /* no_io */,
|
|
|
|
!for_compaction /* record_read_stats */);
|
|
|
|
if (s.ok()) {
|
|
|
|
table_reader = GetTableReaderFromHandle(table_handle);
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
if (table_reader != nullptr) {
|
|
|
|
result = table_reader->ApproximateOffsetOf(key, caller);
|
|
|
|
}
|
|
|
|
if (table_handle != nullptr) {
|
|
|
|
ReleaseHandle(table_handle);
|
|
|
|
}
|
|
|
|
|
|
|
|
return result;
|
|
|
|
}
|
2019-08-16 23:16:49 +02:00
|
|
|
|
|
|
|
uint64_t TableCache::ApproximateSize(
|
|
|
|
const Slice& start, const Slice& end, const FileDescriptor& fd,
|
|
|
|
TableReaderCaller caller, const InternalKeyComparator& internal_comparator,
|
|
|
|
const SliceTransform* prefix_extractor) {
|
|
|
|
uint64_t result = 0;
|
|
|
|
TableReader* table_reader = fd.table_reader;
|
|
|
|
Cache::Handle* table_handle = nullptr;
|
|
|
|
if (table_reader == nullptr) {
|
|
|
|
const bool for_compaction = (caller == TableReaderCaller::kCompaction);
|
|
|
|
Status s = FindTable(env_options_, internal_comparator, fd, &table_handle,
|
|
|
|
prefix_extractor, false /* no_io */,
|
|
|
|
!for_compaction /* record_read_stats */);
|
|
|
|
if (s.ok()) {
|
|
|
|
table_reader = GetTableReaderFromHandle(table_handle);
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
if (table_reader != nullptr) {
|
|
|
|
result = table_reader->ApproximateSize(start, end, caller);
|
|
|
|
}
|
|
|
|
if (table_handle != nullptr) {
|
|
|
|
ReleaseHandle(table_handle);
|
|
|
|
}
|
|
|
|
|
|
|
|
return result;
|
|
|
|
}
|
2013-10-04 06:49:15 +02:00
|
|
|
} // namespace rocksdb
|