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rocksdb/table/cuckoo/cuckoo_table_reader.h

102 lines
3.4 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.
#pragma once
#ifndef ROCKSDB_LITE
#include <string>
#include <memory>
#include <utility>
#include <vector>
#include "db/dbformat.h"
#include "file/random_access_file_reader.h"
#include "rocksdb/env.h"
#include "rocksdb/options.h"
#include "table/table_reader.h"
namespace ROCKSDB_NAMESPACE {
class Arena;
class TableReader;
struct ImmutableOptions;
class CuckooTableReader: public TableReader {
public:
CuckooTableReader(const ImmutableOptions& ioptions,
std::unique_ptr<RandomAccessFileReader>&& file,
uint64_t file_size, const Comparator* user_comparator,
uint64_t (*get_slice_hash)(const Slice&, uint32_t,
uint64_t));
~CuckooTableReader() {}
std::shared_ptr<const TableProperties> GetTableProperties() const override {
return table_props_;
}
Status status() const { return status_; }
Status Get(const ReadOptions& readOptions, const Slice& key,
GetContext* get_context, const SliceTransform* prefix_extractor,
bool skip_filters = false) override;
// Returns a new iterator over table contents
// compaction_readahead_size: its value will only be used if for_compaction =
// true
InternalIterator* NewIterator(const ReadOptions&,
const SliceTransform* prefix_extractor,
Arena* arena, bool skip_filters,
TableReaderCaller caller,
Properly report IO errors when IndexType::kBinarySearchWithFirstKey is used (#6621) Summary: Context: Index type `kBinarySearchWithFirstKey` added the ability for sst file iterator to sometimes report a key from index without reading the corresponding data block. This is useful when sst blocks are cut at some meaningful boundaries (e.g. one block per key prefix), and many seeks land between blocks (e.g. for each prefix, the ranges of keys in different sst files are nearly disjoint, so a typical seek needs to read a data block from only one file even if all files have the prefix). But this added a new error condition, which rocksdb code was really not equipped to deal with: `InternalIterator::value()` may fail with an IO error or Status::Incomplete, but it's just a method returning a Slice, with no way to report error instead. Before this PR, this type of error wasn't handled at all (an empty slice was returned), and kBinarySearchWithFirstKey implementation was considered a prototype. Now that we (LogDevice) have experimented with kBinarySearchWithFirstKey for a while and confirmed that it's really useful, this PR is adding the missing error handling. It's a pretty inconvenient situation implementation-wise. The error needs to be reported from InternalIterator when trying to access value. But there are ~700 call sites of `InternalIterator::value()`, most of which either can't hit the error condition (because the iterator is reading from memtable or from index or something) or wouldn't benefit from the deferred loading of the value (e.g. compaction iterator that reads all values anyway). Adding error handling to all these call sites would needlessly bloat the code. So instead I made the deferred value loading optional: only the call sites that may use deferred loading have to call the new method `PrepareValue()` before calling `value()`. The feature is enabled with a new bool argument `allow_unprepared_value` to a bunch of methods that create iterators (it wouldn't make sense to put it in ReadOptions because it's completely internal to iterators, with virtually no user-visible effect). Lmk if you have better ideas. Note that the deferred value loading only happens for *internal* iterators. The user-visible iterator (DBIter) always prepares the value before returning from Seek/Next/etc. We could go further and add an API to defer that value loading too, but that's most likely not useful for LogDevice, so it doesn't seem worth the complexity for now. Pull Request resolved: https://github.com/facebook/rocksdb/pull/6621 Test Plan: make -j5 check . Will also deploy to some logdevice test clusters and look at stats. Reviewed By: siying Differential Revision: D20786930 Pulled By: al13n321 fbshipit-source-id: 6da77d918bad3780522e918f17f4d5513d3e99ee
5 years ago
size_t compaction_readahead_size = 0,
bool allow_unprepared_value = false) override;
Implement Prepare method in CuckooTableReader Summary: - Implement Prepare method - Rewrite performance tests in cuckoo_table_reader_test to write new file only if one doesn't already exist. - Add performance tests for batch lookup along with prefetching. Test Plan: ./cuckoo_table_reader_test --enable_perf Results (We get better results if we used int64 comparator instead of string comparator (TBD in future diffs)): With 100000000 items and hash table ratio 0.500000, number of hash functions used: 2. Time taken per op is 0.208us (4.8 Mqps) with batch size of 0 With 100000000 items and hash table ratio 0.500000, number of hash functions used: 2. Time taken per op is 0.182us (5.5 Mqps) with batch size of 10 With 100000000 items and hash table ratio 0.500000, number of hash functions used: 2. Time taken per op is 0.161us (6.2 Mqps) with batch size of 25 With 100000000 items and hash table ratio 0.500000, number of hash functions used: 2. Time taken per op is 0.161us (6.2 Mqps) with batch size of 50 With 100000000 items and hash table ratio 0.500000, number of hash functions used: 2. Time taken per op is 0.163us (6.1 Mqps) with batch size of 100 With 100000000 items and hash table ratio 0.600000, number of hash functions used: 3. Time taken per op is 0.252us (4.0 Mqps) with batch size of 0 With 100000000 items and hash table ratio 0.600000, number of hash functions used: 3. Time taken per op is 0.192us (5.2 Mqps) with batch size of 10 With 100000000 items and hash table ratio 0.600000, number of hash functions used: 3. Time taken per op is 0.195us (5.1 Mqps) with batch size of 25 With 100000000 items and hash table ratio 0.600000, number of hash functions used: 3. Time taken per op is 0.191us (5.2 Mqps) with batch size of 50 With 100000000 items and hash table ratio 0.600000, number of hash functions used: 3. Time taken per op is 0.194us (5.1 Mqps) with batch size of 100 With 100000000 items and hash table ratio 0.750000, number of hash functions used: 3. Time taken per op is 0.228us (4.4 Mqps) with batch size of 0 With 100000000 items and hash table ratio 0.750000, number of hash functions used: 3. Time taken per op is 0.185us (5.4 Mqps) with batch size of 10 With 100000000 items and hash table ratio 0.750000, number of hash functions used: 3. Time taken per op is 0.186us (5.4 Mqps) with batch size of 25 With 100000000 items and hash table ratio 0.750000, number of hash functions used: 3. Time taken per op is 0.189us (5.3 Mqps) with batch size of 50 With 100000000 items and hash table ratio 0.750000, number of hash functions used: 3. Time taken per op is 0.188us (5.3 Mqps) with batch size of 100 With 100000000 items and hash table ratio 0.900000, number of hash functions used: 3. Time taken per op is 0.325us (3.1 Mqps) with batch size of 0 With 100000000 items and hash table ratio 0.900000, number of hash functions used: 3. Time taken per op is 0.196us (5.1 Mqps) with batch size of 10 With 100000000 items and hash table ratio 0.900000, number of hash functions used: 3. Time taken per op is 0.199us (5.0 Mqps) with batch size of 25 With 100000000 items and hash table ratio 0.900000, number of hash functions used: 3. Time taken per op is 0.196us (5.1 Mqps) with batch size of 50 With 100000000 items and hash table ratio 0.900000, number of hash functions used: 3. Time taken per op is 0.209us (4.8 Mqps) with batch size of 100 Reviewers: sdong, yhchiang, igor, ljin Reviewed By: ljin Subscribers: leveldb Differential Revision: https://reviews.facebook.net/D22167
10 years ago
void Prepare(const Slice& target) override;
// Report an approximation of how much memory has been used.
size_t ApproximateMemoryUsage() const override;
// Following methods are not implemented for Cuckoo Table Reader
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
5 years ago
uint64_t ApproximateOffsetOf(const Slice& /*key*/,
TableReaderCaller /*caller*/) override {
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
5 years ago
return 0;
}
uint64_t ApproximateSize(const Slice& /*start*/, const Slice& /*end*/,
TableReaderCaller /*caller*/) override {
return 0;
}
void SetupForCompaction() override {}
// End of methods not implemented.
private:
friend class CuckooTableIterator;
void LoadAllKeys(std::vector<std::pair<Slice, uint32_t>>* key_to_bucket_id);
std::unique_ptr<RandomAccessFileReader> file_;
Slice file_data_;
bool is_last_level_;
CuckooTable: add one option to allow identity function for the first hash function Summary: MurmurHash becomes expensive when we do millions Get() a second in one thread. Add this option to allow the first hash function to use identity function as hash function. It results in QPS increase from 3.7M/s to ~4.3M/s. I did not observe improvement for end to end RocksDB performance. This may be caused by other bottlenecks that I will address in a separate diff. Test Plan: ``` [ljin@dev1964 rocksdb] ./cuckoo_table_reader_test --enable_perf --file_dir=/dev/shm --write --identity_as_first_hash=0 ==== Test CuckooReaderTest.WhenKeyExists ==== Test CuckooReaderTest.WhenKeyExistsWithUint64Comparator ==== Test CuckooReaderTest.CheckIterator ==== Test CuckooReaderTest.CheckIteratorUint64 ==== Test CuckooReaderTest.WhenKeyNotFound ==== Test CuckooReaderTest.TestReadPerformance With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.272us (3.7 Mqps) with batch size of 0, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.138us (7.2 Mqps) with batch size of 10, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.142us (7.1 Mqps) with batch size of 25, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.142us (7.0 Mqps) with batch size of 50, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.144us (6.9 Mqps) with batch size of 100, # of found keys 125829120 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.201us (5.0 Mqps) with batch size of 0, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.121us (8.3 Mqps) with batch size of 10, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.123us (8.1 Mqps) with batch size of 25, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.121us (8.3 Mqps) with batch size of 50, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.112us (8.9 Mqps) with batch size of 100, # of found keys 104857600 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.251us (4.0 Mqps) with batch size of 0, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.107us (9.4 Mqps) with batch size of 10, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.099us (10.1 Mqps) with batch size of 25, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.100us (10.0 Mqps) with batch size of 50, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.116us (8.6 Mqps) with batch size of 100, # of found keys 83886080 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.189us (5.3 Mqps) with batch size of 0, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.095us (10.5 Mqps) with batch size of 10, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.096us (10.4 Mqps) with batch size of 25, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.098us (10.2 Mqps) with batch size of 50, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.105us (9.5 Mqps) with batch size of 100, # of found keys 73400320 [ljin@dev1964 rocksdb] ./cuckoo_table_reader_test --enable_perf --file_dir=/dev/shm --write --identity_as_first_hash=1 ==== Test CuckooReaderTest.WhenKeyExists ==== Test CuckooReaderTest.WhenKeyExistsWithUint64Comparator ==== Test CuckooReaderTest.CheckIterator ==== Test CuckooReaderTest.CheckIteratorUint64 ==== Test CuckooReaderTest.WhenKeyNotFound ==== Test CuckooReaderTest.TestReadPerformance With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.230us (4.3 Mqps) with batch size of 0, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.086us (11.7 Mqps) with batch size of 10, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.088us (11.3 Mqps) with batch size of 25, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.083us (12.1 Mqps) with batch size of 50, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.083us (12.1 Mqps) with batch size of 100, # of found keys 125829120 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.159us (6.3 Mqps) with batch size of 0, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.078us (12.8 Mqps) with batch size of 10, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.080us (12.6 Mqps) with batch size of 25, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.080us (12.5 Mqps) with batch size of 50, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.082us (12.2 Mqps) with batch size of 100, # of found keys 104857600 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.154us (6.5 Mqps) with batch size of 0, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.077us (13.0 Mqps) with batch size of 10, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.077us (12.9 Mqps) with batch size of 25, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.078us (12.8 Mqps) with batch size of 50, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.079us (12.6 Mqps) with batch size of 100, # of found keys 83886080 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.218us (4.6 Mqps) with batch size of 0, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.083us (12.0 Mqps) with batch size of 10, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.085us (11.7 Mqps) with batch size of 25, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.086us (11.6 Mqps) with batch size of 50, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.078us (12.8 Mqps) with batch size of 100, # of found keys 73400320 ``` Reviewers: sdong, igor, yhchiang Reviewed By: igor Subscribers: leveldb Differential Revision: https://reviews.facebook.net/D23451
10 years ago
bool identity_as_first_hash_;
bool use_module_hash_;
std::shared_ptr<const TableProperties> table_props_;
Status status_;
uint32_t num_hash_func_;
std::string unused_key_;
uint32_t key_length_;
uint32_t user_key_length_;
uint32_t value_length_;
uint32_t bucket_length_;
uint32_t cuckoo_block_size_;
uint32_t cuckoo_block_bytes_minus_one_;
uint64_t table_size_;
const Comparator* ucomp_;
uint64_t (*get_slice_hash_)(const Slice& s, uint32_t index,
uint64_t max_num_buckets);
};
} // namespace ROCKSDB_NAMESPACE
#endif // ROCKSDB_LITE