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// Copyright (c) 2011-present, Facebook, Inc. All rights reserved.
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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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#include "table/cuckoo/cuckoo_table_builder.h"
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#include <assert.h>
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#include <algorithm>
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#include <limits>
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#include <string>
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#include <vector>
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#include "db/dbformat.h"
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#include "file/writable_file_writer.h"
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#include "rocksdb/env.h"
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#include "rocksdb/table.h"
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#include "table/block_based/block_builder.h"
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#include "table/cuckoo/cuckoo_table_factory.h"
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#include "table/format.h"
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#include "table/meta_blocks.h"
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#include "util/autovector.h"
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#include "util/random.h"
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#include "util/string_util.h"
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namespace ROCKSDB_NAMESPACE {
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const std::string CuckooTablePropertyNames::kEmptyKey =
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"rocksdb.cuckoo.bucket.empty.key";
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const std::string CuckooTablePropertyNames::kNumHashFunc =
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"rocksdb.cuckoo.hash.num";
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const std::string CuckooTablePropertyNames::kHashTableSize =
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"rocksdb.cuckoo.hash.size";
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const std::string CuckooTablePropertyNames::kValueLength =
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"rocksdb.cuckoo.value.length";
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const std::string CuckooTablePropertyNames::kIsLastLevel =
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"rocksdb.cuckoo.file.islastlevel";
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const std::string CuckooTablePropertyNames::kCuckooBlockSize =
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"rocksdb.cuckoo.hash.cuckooblocksize";
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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
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const std::string CuckooTablePropertyNames::kIdentityAsFirstHash =
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"rocksdb.cuckoo.hash.identityfirst";
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const std::string CuckooTablePropertyNames::kUseModuleHash =
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"rocksdb.cuckoo.hash.usemodule";
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const std::string CuckooTablePropertyNames::kUserKeyLength =
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"rocksdb.cuckoo.hash.userkeylength";
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// Obtained by running echo rocksdb.table.cuckoo | sha1sum
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extern const uint64_t kCuckooTableMagicNumber = 0x926789d0c5f17873ull;
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CuckooTableBuilder::CuckooTableBuilder(
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Move rate_limiter, write buffering, most perf context instrumentation and most random kill out of Env
Summary: We want to keep Env a think layer for better portability. Less platform dependent codes should be moved out of Env. In this patch, I create a wrapper of file readers and writers, and put rate limiting, write buffering, as well as most perf context instrumentation and random kill out of Env. It will make it easier to maintain multiple Env in the future.
Test Plan: Run all existing unit tests.
Reviewers: anthony, kradhakrishnan, IslamAbdelRahman, yhchiang, igor
Reviewed By: igor
Subscribers: leveldb, dhruba
Differential Revision: https://reviews.facebook.net/D42321
10 years ago
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WritableFileWriter* file, double max_hash_table_ratio,
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uint32_t max_num_hash_table, uint32_t max_search_depth,
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const Comparator* user_comparator, uint32_t cuckoo_block_size,
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bool use_module_hash, bool identity_as_first_hash,
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uint64_t (*get_slice_hash)(const Slice&, uint32_t, uint64_t),
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uint32_t column_family_id, const std::string& column_family_name,
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const std::string& db_id, const std::string& db_session_id,
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uint64_t file_number)
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: num_hash_func_(2),
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file_(file),
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Improve Cuckoo Table Reader performance. Inlined hash function and number of buckets a power of two.
Summary:
Use inlined hash functions instead of function pointer. Make number of buckets a power of two and use bitwise and instead of mod.
After these changes, we get almost 50% improvement in performance.
Results:
With 120000000 items, utilization is 89.41%, number of hash functions: 2.
Time taken per op is 0.231us (4.3 Mqps) with batch size of 0
Time taken per op is 0.229us (4.4 Mqps) with batch size of 0
Time taken per op is 0.185us (5.4 Mqps) with batch size of 0
With 120000000 items, utilization is 89.41%, number of hash functions: 2.
Time taken per op is 0.108us (9.3 Mqps) with batch size of 10
Time taken per op is 0.100us (10.0 Mqps) with batch size of 10
Time taken per op is 0.103us (9.7 Mqps) with batch size of 10
With 120000000 items, utilization is 89.41%, number of hash functions: 2.
Time taken per op is 0.101us (9.9 Mqps) with batch size of 25
Time taken per op is 0.098us (10.2 Mqps) with batch size of 25
Time taken per op is 0.097us (10.3 Mqps) with batch size of 25
With 120000000 items, utilization is 89.41%, number of hash functions: 2.
Time taken per op is 0.100us (10.0 Mqps) with batch size of 50
Time taken per op is 0.097us (10.3 Mqps) with batch size of 50
Time taken per op is 0.097us (10.3 Mqps) with batch size of 50
With 120000000 items, utilization is 89.41%, number of hash functions: 2.
Time taken per op is 0.102us (9.8 Mqps) with batch size of 100
Time taken per op is 0.098us (10.2 Mqps) with batch size of 100
Time taken per op is 0.115us (8.7 Mqps) with batch size of 100
With 100000000 items, utilization is 74.51%, number of hash functions: 2.
Time taken per op is 0.201us (5.0 Mqps) with batch size of 0
Time taken per op is 0.155us (6.5 Mqps) with batch size of 0
Time taken per op is 0.152us (6.6 Mqps) with batch size of 0
With 100000000 items, utilization is 74.51%, number of hash functions: 2.
Time taken per op is 0.089us (11.3 Mqps) with batch size of 10
Time taken per op is 0.084us (11.9 Mqps) with batch size of 10
Time taken per op is 0.086us (11.6 Mqps) with batch size of 10
With 100000000 items, utilization is 74.51%, number of hash functions: 2.
Time taken per op is 0.087us (11.5 Mqps) with batch size of 25
Time taken per op is 0.085us (11.7 Mqps) with batch size of 25
Time taken per op is 0.093us (10.8 Mqps) with batch size of 25
With 100000000 items, utilization is 74.51%, number of hash functions: 2.
Time taken per op is 0.094us (10.6 Mqps) with batch size of 50
Time taken per op is 0.094us (10.7 Mqps) with batch size of 50
Time taken per op is 0.093us (10.8 Mqps) with batch size of 50
With 100000000 items, utilization is 74.51%, number of hash functions: 2.
Time taken per op is 0.092us (10.9 Mqps) with batch size of 100
Time taken per op is 0.089us (11.2 Mqps) with batch size of 100
Time taken per op is 0.088us (11.3 Mqps) with batch size of 100
With 80000000 items, utilization is 59.60%, number of hash functions: 2.
Time taken per op is 0.154us (6.5 Mqps) with batch size of 0
Time taken per op is 0.168us (6.0 Mqps) with batch size of 0
Time taken per op is 0.190us (5.3 Mqps) with batch size of 0
With 80000000 items, utilization is 59.60%, number of hash functions: 2.
Time taken per op is 0.081us (12.4 Mqps) with batch size of 10
Time taken per op is 0.077us (13.0 Mqps) with batch size of 10
Time taken per op is 0.083us (12.1 Mqps) with batch size of 10
With 80000000 items, utilization is 59.60%, number of hash functions: 2.
Time taken per op is 0.077us (13.0 Mqps) with batch size of 25
Time taken per op is 0.073us (13.7 Mqps) with batch size of 25
Time taken per op is 0.073us (13.7 Mqps) with batch size of 25
With 80000000 items, utilization is 59.60%, number of hash functions: 2.
Time taken per op is 0.076us (13.1 Mqps) with batch size of 50
Time taken per op is 0.072us (13.8 Mqps) with batch size of 50
Time taken per op is 0.072us (13.8 Mqps) with batch size of 50
With 80000000 items, utilization is 59.60%, number of hash functions: 2.
Time taken per op is 0.077us (13.0 Mqps) with batch size of 100
Time taken per op is 0.074us (13.6 Mqps) with batch size of 100
Time taken per op is 0.073us (13.6 Mqps) with batch size of 100
With 70000000 items, utilization is 52.15%, number of hash functions: 2.
Time taken per op is 0.190us (5.3 Mqps) with batch size of 0
Time taken per op is 0.186us (5.4 Mqps) with batch size of 0
Time taken per op is 0.184us (5.4 Mqps) with batch size of 0
With 70000000 items, utilization is 52.15%, number of hash functions: 2.
Time taken per op is 0.079us (12.7 Mqps) with batch size of 10
Time taken per op is 0.070us (14.2 Mqps) with batch size of 10
Time taken per op is 0.072us (14.0 Mqps) with batch size of 10
With 70000000 items, utilization is 52.15%, number of hash functions: 2.
Time taken per op is 0.080us (12.5 Mqps) with batch size of 25
Time taken per op is 0.072us (14.0 Mqps) with batch size of 25
Time taken per op is 0.071us (14.1 Mqps) with batch size of 25
With 70000000 items, utilization is 52.15%, number of hash functions: 2.
Time taken per op is 0.082us (12.1 Mqps) with batch size of 50
Time taken per op is 0.071us (14.1 Mqps) with batch size of 50
Time taken per op is 0.073us (13.6 Mqps) with batch size of 50
With 70000000 items, utilization is 52.15%, number of hash functions: 2.
Time taken per op is 0.080us (12.5 Mqps) with batch size of 100
Time taken per op is 0.077us (13.0 Mqps) with batch size of 100
Time taken per op is 0.078us (12.8 Mqps) with batch size of 100
Test Plan:
make check all
make valgrind_check
make asan_check
Reviewers: sdong, ljin
Reviewed By: ljin
Subscribers: leveldb
Differential Revision: https://reviews.facebook.net/D22539
10 years ago
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max_hash_table_ratio_(max_hash_table_ratio),
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max_num_hash_func_(max_num_hash_table),
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max_search_depth_(max_search_depth),
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cuckoo_block_size_(std::max(1U, cuckoo_block_size)),
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hash_table_size_(use_module_hash ? 0 : 2),
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is_last_level_file_(false),
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has_seen_first_key_(false),
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has_seen_first_value_(false),
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key_size_(0),
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value_size_(0),
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num_entries_(0),
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num_values_(0),
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ucomp_(user_comparator),
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use_module_hash_(use_module_hash),
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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
|
|
|
identity_as_first_hash_(identity_as_first_hash),
|
|
|
|
get_slice_hash_(get_slice_hash),
|
|
|
|
closed_(false) {
|
|
|
|
// Data is in a huge block.
|
|
|
|
properties_.num_data_blocks = 1;
|
|
|
|
properties_.index_size = 0;
|
|
|
|
properties_.filter_size = 0;
|
|
|
|
properties_.column_family_id = column_family_id;
|
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|
|
properties_.column_family_name = column_family_name;
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properties_.db_id = db_id;
|
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|
|
properties_.db_session_id = db_session_id;
|
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|
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properties_.orig_file_number = file_number;
|
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|
|
status_.PermitUncheckedError();
|
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|
|
io_status_.PermitUncheckedError();
|
|
|
|
}
|
|
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|
|
|
|
|
void CuckooTableBuilder::Add(const Slice& key, const Slice& value) {
|
|
|
|
if (num_entries_ >= kMaxVectorIdx - 1) {
|
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|
|
status_ = Status::NotSupported("Number of keys in a file must be < 2^32-1");
|
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|
|
return;
|
|
|
|
}
|
|
|
|
ParsedInternalKey ikey;
|
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|
|
Status pik_status =
|
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|
|
ParseInternalKey(key, &ikey, false /* log_err_key */); // TODO
|
|
|
|
if (!pik_status.ok()) {
|
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|
|
status_ = Status::Corruption("Unable to parse key into internal key. ",
|
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|
|
pik_status.getState());
|
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|
|
return;
|
|
|
|
}
|
|
|
|
if (ikey.type != kTypeDeletion && ikey.type != kTypeValue) {
|
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|
|
status_ = Status::NotSupported("Unsupported key type " +
|
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|
|
std::to_string(ikey.type));
|
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|
|
return;
|
|
|
|
}
|
|
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|
|
|
// Determine if we can ignore the sequence number and value type from
|
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|
|
// internal keys by looking at sequence number from first key. We assume
|
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|
// that if first key has a zero sequence number, then all the remaining
|
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|
|
// keys will have zero seq. no.
|
|
|
|
if (!has_seen_first_key_) {
|
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|
is_last_level_file_ = ikey.sequence == 0;
|
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|
has_seen_first_key_ = true;
|
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|
|
smallest_user_key_.assign(ikey.user_key.data(), ikey.user_key.size());
|
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|
|
largest_user_key_.assign(ikey.user_key.data(), ikey.user_key.size());
|
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|
|
key_size_ = is_last_level_file_ ? ikey.user_key.size() : key.size();
|
|
|
|
}
|
|
|
|
if (key_size_ != (is_last_level_file_ ? ikey.user_key.size() : key.size())) {
|
|
|
|
status_ = Status::NotSupported("all keys have to be the same size");
|
|
|
|
return;
|
|
|
|
}
|
|
|
|
|
|
|
|
if (ikey.type == kTypeValue) {
|
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|
|
if (!has_seen_first_value_) {
|
|
|
|
has_seen_first_value_ = true;
|
|
|
|
value_size_ = value.size();
|
|
|
|
}
|
|
|
|
if (value_size_ != value.size()) {
|
|
|
|
status_ = Status::NotSupported("all values have to be the same size");
|
|
|
|
return;
|
|
|
|
}
|
|
|
|
|
|
|
|
if (is_last_level_file_) {
|
|
|
|
kvs_.append(ikey.user_key.data(), ikey.user_key.size());
|
|
|
|
} else {
|
|
|
|
kvs_.append(key.data(), key.size());
|
|
|
|
}
|
|
|
|
kvs_.append(value.data(), value.size());
|
|
|
|
++num_values_;
|
|
|
|
} else {
|
|
|
|
if (is_last_level_file_) {
|
|
|
|
deleted_keys_.append(ikey.user_key.data(), ikey.user_key.size());
|
|
|
|
} else {
|
|
|
|
deleted_keys_.append(key.data(), key.size());
|
|
|
|
}
|
|
|
|
}
|
|
|
|
++num_entries_;
|
|
|
|
|
|
|
|
// In order to fill the empty buckets in the hash table, we identify a
|
|
|
|
// key which is not used so far (unused_user_key). We determine this by
|
|
|
|
// maintaining smallest and largest keys inserted so far in bytewise order
|
|
|
|
// and use them to find a key outside this range in Finish() operation.
|
|
|
|
// Note that this strategy is independent of user comparator used here.
|
|
|
|
if (ikey.user_key.compare(smallest_user_key_) < 0) {
|
|
|
|
smallest_user_key_.assign(ikey.user_key.data(), ikey.user_key.size());
|
|
|
|
} else if (ikey.user_key.compare(largest_user_key_) > 0) {
|
|
|
|
largest_user_key_.assign(ikey.user_key.data(), ikey.user_key.size());
|
|
|
|
}
|
|
|
|
if (!use_module_hash_) {
|
|
|
|
if (hash_table_size_ < num_entries_ / max_hash_table_ratio_) {
|
|
|
|
hash_table_size_ *= 2;
|
|
|
|
}
|
Improve Cuckoo Table Reader performance. Inlined hash function and number of buckets a power of two.
Summary:
Use inlined hash functions instead of function pointer. Make number of buckets a power of two and use bitwise and instead of mod.
After these changes, we get almost 50% improvement in performance.
Results:
With 120000000 items, utilization is 89.41%, number of hash functions: 2.
Time taken per op is 0.231us (4.3 Mqps) with batch size of 0
Time taken per op is 0.229us (4.4 Mqps) with batch size of 0
Time taken per op is 0.185us (5.4 Mqps) with batch size of 0
With 120000000 items, utilization is 89.41%, number of hash functions: 2.
Time taken per op is 0.108us (9.3 Mqps) with batch size of 10
Time taken per op is 0.100us (10.0 Mqps) with batch size of 10
Time taken per op is 0.103us (9.7 Mqps) with batch size of 10
With 120000000 items, utilization is 89.41%, number of hash functions: 2.
Time taken per op is 0.101us (9.9 Mqps) with batch size of 25
Time taken per op is 0.098us (10.2 Mqps) with batch size of 25
Time taken per op is 0.097us (10.3 Mqps) with batch size of 25
With 120000000 items, utilization is 89.41%, number of hash functions: 2.
Time taken per op is 0.100us (10.0 Mqps) with batch size of 50
Time taken per op is 0.097us (10.3 Mqps) with batch size of 50
Time taken per op is 0.097us (10.3 Mqps) with batch size of 50
With 120000000 items, utilization is 89.41%, number of hash functions: 2.
Time taken per op is 0.102us (9.8 Mqps) with batch size of 100
Time taken per op is 0.098us (10.2 Mqps) with batch size of 100
Time taken per op is 0.115us (8.7 Mqps) with batch size of 100
With 100000000 items, utilization is 74.51%, number of hash functions: 2.
Time taken per op is 0.201us (5.0 Mqps) with batch size of 0
Time taken per op is 0.155us (6.5 Mqps) with batch size of 0
Time taken per op is 0.152us (6.6 Mqps) with batch size of 0
With 100000000 items, utilization is 74.51%, number of hash functions: 2.
Time taken per op is 0.089us (11.3 Mqps) with batch size of 10
Time taken per op is 0.084us (11.9 Mqps) with batch size of 10
Time taken per op is 0.086us (11.6 Mqps) with batch size of 10
With 100000000 items, utilization is 74.51%, number of hash functions: 2.
Time taken per op is 0.087us (11.5 Mqps) with batch size of 25
Time taken per op is 0.085us (11.7 Mqps) with batch size of 25
Time taken per op is 0.093us (10.8 Mqps) with batch size of 25
With 100000000 items, utilization is 74.51%, number of hash functions: 2.
Time taken per op is 0.094us (10.6 Mqps) with batch size of 50
Time taken per op is 0.094us (10.7 Mqps) with batch size of 50
Time taken per op is 0.093us (10.8 Mqps) with batch size of 50
With 100000000 items, utilization is 74.51%, number of hash functions: 2.
Time taken per op is 0.092us (10.9 Mqps) with batch size of 100
Time taken per op is 0.089us (11.2 Mqps) with batch size of 100
Time taken per op is 0.088us (11.3 Mqps) with batch size of 100
With 80000000 items, utilization is 59.60%, number of hash functions: 2.
Time taken per op is 0.154us (6.5 Mqps) with batch size of 0
Time taken per op is 0.168us (6.0 Mqps) with batch size of 0
Time taken per op is 0.190us (5.3 Mqps) with batch size of 0
With 80000000 items, utilization is 59.60%, number of hash functions: 2.
Time taken per op is 0.081us (12.4 Mqps) with batch size of 10
Time taken per op is 0.077us (13.0 Mqps) with batch size of 10
Time taken per op is 0.083us (12.1 Mqps) with batch size of 10
With 80000000 items, utilization is 59.60%, number of hash functions: 2.
Time taken per op is 0.077us (13.0 Mqps) with batch size of 25
Time taken per op is 0.073us (13.7 Mqps) with batch size of 25
Time taken per op is 0.073us (13.7 Mqps) with batch size of 25
With 80000000 items, utilization is 59.60%, number of hash functions: 2.
Time taken per op is 0.076us (13.1 Mqps) with batch size of 50
Time taken per op is 0.072us (13.8 Mqps) with batch size of 50
Time taken per op is 0.072us (13.8 Mqps) with batch size of 50
With 80000000 items, utilization is 59.60%, number of hash functions: 2.
Time taken per op is 0.077us (13.0 Mqps) with batch size of 100
Time taken per op is 0.074us (13.6 Mqps) with batch size of 100
Time taken per op is 0.073us (13.6 Mqps) with batch size of 100
With 70000000 items, utilization is 52.15%, number of hash functions: 2.
Time taken per op is 0.190us (5.3 Mqps) with batch size of 0
Time taken per op is 0.186us (5.4 Mqps) with batch size of 0
Time taken per op is 0.184us (5.4 Mqps) with batch size of 0
With 70000000 items, utilization is 52.15%, number of hash functions: 2.
Time taken per op is 0.079us (12.7 Mqps) with batch size of 10
Time taken per op is 0.070us (14.2 Mqps) with batch size of 10
Time taken per op is 0.072us (14.0 Mqps) with batch size of 10
With 70000000 items, utilization is 52.15%, number of hash functions: 2.
Time taken per op is 0.080us (12.5 Mqps) with batch size of 25
Time taken per op is 0.072us (14.0 Mqps) with batch size of 25
Time taken per op is 0.071us (14.1 Mqps) with batch size of 25
With 70000000 items, utilization is 52.15%, number of hash functions: 2.
Time taken per op is 0.082us (12.1 Mqps) with batch size of 50
Time taken per op is 0.071us (14.1 Mqps) with batch size of 50
Time taken per op is 0.073us (13.6 Mqps) with batch size of 50
With 70000000 items, utilization is 52.15%, number of hash functions: 2.
Time taken per op is 0.080us (12.5 Mqps) with batch size of 100
Time taken per op is 0.077us (13.0 Mqps) with batch size of 100
Time taken per op is 0.078us (12.8 Mqps) with batch size of 100
Test Plan:
make check all
make valgrind_check
make asan_check
Reviewers: sdong, ljin
Reviewed By: ljin
Subscribers: leveldb
Differential Revision: https://reviews.facebook.net/D22539
10 years ago
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
bool CuckooTableBuilder::IsDeletedKey(uint64_t idx) const {
|
|
|
|
assert(closed_);
|
|
|
|
return idx >= num_values_;
|
|
|
|
}
|
|
|
|
|
|
|
|
Slice CuckooTableBuilder::GetKey(uint64_t idx) const {
|
|
|
|
assert(closed_);
|
|
|
|
if (IsDeletedKey(idx)) {
|
|
|
|
return Slice(
|
|
|
|
&deleted_keys_[static_cast<size_t>((idx - num_values_) * key_size_)],
|
|
|
|
static_cast<size_t>(key_size_));
|
|
|
|
}
|
|
|
|
return Slice(&kvs_[static_cast<size_t>(idx * (key_size_ + value_size_))],
|
|
|
|
static_cast<size_t>(key_size_));
|
|
|
|
}
|
|
|
|
|
|
|
|
Slice CuckooTableBuilder::GetUserKey(uint64_t idx) const {
|
|
|
|
assert(closed_);
|
|
|
|
return is_last_level_file_ ? GetKey(idx) : ExtractUserKey(GetKey(idx));
|
|
|
|
}
|
|
|
|
|
|
|
|
Slice CuckooTableBuilder::GetValue(uint64_t idx) const {
|
|
|
|
assert(closed_);
|
|
|
|
if (IsDeletedKey(idx)) {
|
|
|
|
static std::string empty_value(static_cast<unsigned int>(value_size_), 'a');
|
|
|
|
return Slice(empty_value);
|
|
|
|
}
|
|
|
|
return Slice(
|
|
|
|
&kvs_[static_cast<size_t>(idx * (key_size_ + value_size_) + key_size_)],
|
|
|
|
static_cast<size_t>(value_size_));
|
|
|
|
}
|
|
|
|
|
|
|
|
Status CuckooTableBuilder::MakeHashTable(std::vector<CuckooBucket>* buckets) {
|
|
|
|
buckets->resize(
|
|
|
|
static_cast<size_t>(hash_table_size_ + cuckoo_block_size_ - 1));
|
|
|
|
uint32_t make_space_for_key_call_id = 0;
|
|
|
|
for (uint32_t vector_idx = 0; vector_idx < num_entries_; vector_idx++) {
|
|
|
|
uint64_t bucket_id = 0;
|
|
|
|
bool bucket_found = false;
|
|
|
|
autovector<uint64_t> hash_vals;
|
|
|
|
Slice user_key = GetUserKey(vector_idx);
|
|
|
|
for (uint32_t hash_cnt = 0; hash_cnt < num_hash_func_ && !bucket_found;
|
|
|
|
++hash_cnt) {
|
|
|
|
uint64_t hash_val =
|
|
|
|
CuckooHash(user_key, hash_cnt, use_module_hash_, hash_table_size_,
|
|
|
|
identity_as_first_hash_, get_slice_hash_);
|
|
|
|
// If there is a collision, check next cuckoo_block_size_ locations for
|
|
|
|
// empty locations. While checking, if we reach end of the hash table,
|
|
|
|
// stop searching and proceed for next hash function.
|
|
|
|
for (uint32_t block_idx = 0; block_idx < cuckoo_block_size_;
|
|
|
|
++block_idx, ++hash_val) {
|
|
|
|
if ((*buckets)[static_cast<size_t>(hash_val)].vector_idx ==
|
|
|
|
kMaxVectorIdx) {
|
|
|
|
bucket_id = hash_val;
|
|
|
|
bucket_found = true;
|
|
|
|
break;
|
|
|
|
} else {
|
|
|
|
if (ucomp_->Compare(
|
|
|
|
user_key, GetUserKey((*buckets)[static_cast<size_t>(hash_val)]
|
|
|
|
.vector_idx)) == 0) {
|
|
|
|
return Status::NotSupported("Same key is being inserted again.");
|
|
|
|
}
|
|
|
|
hash_vals.push_back(hash_val);
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
while (!bucket_found &&
|
|
|
|
!MakeSpaceForKey(hash_vals, ++make_space_for_key_call_id, buckets,
|
|
|
|
&bucket_id)) {
|
|
|
|
// Rehash by increashing number of hash tables.
|
|
|
|
if (num_hash_func_ >= max_num_hash_func_) {
|
|
|
|
return Status::NotSupported("Too many collisions. Unable to hash.");
|
|
|
|
}
|
|
|
|
// We don't really need to rehash the entire table because old hashes are
|
|
|
|
// still valid and we only increased the number of hash functions.
|
|
|
|
uint64_t hash_val = CuckooHash(user_key, num_hash_func_, use_module_hash_,
|
|
|
|
hash_table_size_, identity_as_first_hash_,
|
|
|
|
get_slice_hash_);
|
|
|
|
++num_hash_func_;
|
|
|
|
for (uint32_t block_idx = 0; block_idx < cuckoo_block_size_;
|
|
|
|
++block_idx, ++hash_val) {
|
|
|
|
if ((*buckets)[static_cast<size_t>(hash_val)].vector_idx ==
|
|
|
|
kMaxVectorIdx) {
|
|
|
|
bucket_found = true;
|
|
|
|
bucket_id = hash_val;
|
|
|
|
break;
|
|
|
|
} else {
|
|
|
|
hash_vals.push_back(hash_val);
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
(*buckets)[static_cast<size_t>(bucket_id)].vector_idx = vector_idx;
|
|
|
|
}
|
|
|
|
return Status::OK();
|
|
|
|
}
|
|
|
|
|
|
|
|
Status CuckooTableBuilder::Finish() {
|
|
|
|
assert(!closed_);
|
|
|
|
closed_ = true;
|
|
|
|
std::vector<CuckooBucket> buckets;
|
|
|
|
std::string unused_bucket;
|
|
|
|
if (num_entries_ > 0) {
|
|
|
|
// Calculate the real hash size if module hash is enabled.
|
|
|
|
if (use_module_hash_) {
|
|
|
|
hash_table_size_ =
|
|
|
|
static_cast<uint64_t>(num_entries_ / max_hash_table_ratio_);
|
|
|
|
}
|
Pass IOStatus to write path and set retryable IO Error as hard error in BG jobs (#6487)
Summary:
In the current code base, we use Status to get and store the returned status from the call. Specifically, for IO related functions, the current Status cannot reflect the IO Error details such as error scope, error retryable attribute, and others. With the implementation of https://github.com/facebook/rocksdb/issues/5761, we have the new Wrapper for IO, which returns IOStatus instead of Status. However, the IOStatus is purged at the lower level of write path and transferred to Status.
The first job of this PR is to pass the IOStatus to the write path (flush, WAL write, and Compaction). The second job is to identify the Retryable IO Error as HardError, and set the bg_error_ as HardError. In this case, the DB Instance becomes read only. User is informed of the Status and need to take actions to deal with it (e.g., call db->Resume()).
Pull Request resolved: https://github.com/facebook/rocksdb/pull/6487
Test Plan: Added the testing case to error_handler_fs_test. Pass make asan_check
Reviewed By: anand1976
Differential Revision: D20685017
Pulled By: zhichao-cao
fbshipit-source-id: ff85f042896243abcd6ef37877834e26f36b6eb0
5 years ago
|
|
|
status_ = MakeHashTable(&buckets);
|
|
|
|
if (!status_.ok()) {
|
|
|
|
return status_;
|
Improve Cuckoo Table Reader performance. Inlined hash function and number of buckets a power of two.
Summary:
Use inlined hash functions instead of function pointer. Make number of buckets a power of two and use bitwise and instead of mod.
After these changes, we get almost 50% improvement in performance.
Results:
With 120000000 items, utilization is 89.41%, number of hash functions: 2.
Time taken per op is 0.231us (4.3 Mqps) with batch size of 0
Time taken per op is 0.229us (4.4 Mqps) with batch size of 0
Time taken per op is 0.185us (5.4 Mqps) with batch size of 0
With 120000000 items, utilization is 89.41%, number of hash functions: 2.
Time taken per op is 0.108us (9.3 Mqps) with batch size of 10
Time taken per op is 0.100us (10.0 Mqps) with batch size of 10
Time taken per op is 0.103us (9.7 Mqps) with batch size of 10
With 120000000 items, utilization is 89.41%, number of hash functions: 2.
Time taken per op is 0.101us (9.9 Mqps) with batch size of 25
Time taken per op is 0.098us (10.2 Mqps) with batch size of 25
Time taken per op is 0.097us (10.3 Mqps) with batch size of 25
With 120000000 items, utilization is 89.41%, number of hash functions: 2.
Time taken per op is 0.100us (10.0 Mqps) with batch size of 50
Time taken per op is 0.097us (10.3 Mqps) with batch size of 50
Time taken per op is 0.097us (10.3 Mqps) with batch size of 50
With 120000000 items, utilization is 89.41%, number of hash functions: 2.
Time taken per op is 0.102us (9.8 Mqps) with batch size of 100
Time taken per op is 0.098us (10.2 Mqps) with batch size of 100
Time taken per op is 0.115us (8.7 Mqps) with batch size of 100
With 100000000 items, utilization is 74.51%, number of hash functions: 2.
Time taken per op is 0.201us (5.0 Mqps) with batch size of 0
Time taken per op is 0.155us (6.5 Mqps) with batch size of 0
Time taken per op is 0.152us (6.6 Mqps) with batch size of 0
With 100000000 items, utilization is 74.51%, number of hash functions: 2.
Time taken per op is 0.089us (11.3 Mqps) with batch size of 10
Time taken per op is 0.084us (11.9 Mqps) with batch size of 10
Time taken per op is 0.086us (11.6 Mqps) with batch size of 10
With 100000000 items, utilization is 74.51%, number of hash functions: 2.
Time taken per op is 0.087us (11.5 Mqps) with batch size of 25
Time taken per op is 0.085us (11.7 Mqps) with batch size of 25
Time taken per op is 0.093us (10.8 Mqps) with batch size of 25
With 100000000 items, utilization is 74.51%, number of hash functions: 2.
Time taken per op is 0.094us (10.6 Mqps) with batch size of 50
Time taken per op is 0.094us (10.7 Mqps) with batch size of 50
Time taken per op is 0.093us (10.8 Mqps) with batch size of 50
With 100000000 items, utilization is 74.51%, number of hash functions: 2.
Time taken per op is 0.092us (10.9 Mqps) with batch size of 100
Time taken per op is 0.089us (11.2 Mqps) with batch size of 100
Time taken per op is 0.088us (11.3 Mqps) with batch size of 100
With 80000000 items, utilization is 59.60%, number of hash functions: 2.
Time taken per op is 0.154us (6.5 Mqps) with batch size of 0
Time taken per op is 0.168us (6.0 Mqps) with batch size of 0
Time taken per op is 0.190us (5.3 Mqps) with batch size of 0
With 80000000 items, utilization is 59.60%, number of hash functions: 2.
Time taken per op is 0.081us (12.4 Mqps) with batch size of 10
Time taken per op is 0.077us (13.0 Mqps) with batch size of 10
Time taken per op is 0.083us (12.1 Mqps) with batch size of 10
With 80000000 items, utilization is 59.60%, number of hash functions: 2.
Time taken per op is 0.077us (13.0 Mqps) with batch size of 25
Time taken per op is 0.073us (13.7 Mqps) with batch size of 25
Time taken per op is 0.073us (13.7 Mqps) with batch size of 25
With 80000000 items, utilization is 59.60%, number of hash functions: 2.
Time taken per op is 0.076us (13.1 Mqps) with batch size of 50
Time taken per op is 0.072us (13.8 Mqps) with batch size of 50
Time taken per op is 0.072us (13.8 Mqps) with batch size of 50
With 80000000 items, utilization is 59.60%, number of hash functions: 2.
Time taken per op is 0.077us (13.0 Mqps) with batch size of 100
Time taken per op is 0.074us (13.6 Mqps) with batch size of 100
Time taken per op is 0.073us (13.6 Mqps) with batch size of 100
With 70000000 items, utilization is 52.15%, number of hash functions: 2.
Time taken per op is 0.190us (5.3 Mqps) with batch size of 0
Time taken per op is 0.186us (5.4 Mqps) with batch size of 0
Time taken per op is 0.184us (5.4 Mqps) with batch size of 0
With 70000000 items, utilization is 52.15%, number of hash functions: 2.
Time taken per op is 0.079us (12.7 Mqps) with batch size of 10
Time taken per op is 0.070us (14.2 Mqps) with batch size of 10
Time taken per op is 0.072us (14.0 Mqps) with batch size of 10
With 70000000 items, utilization is 52.15%, number of hash functions: 2.
Time taken per op is 0.080us (12.5 Mqps) with batch size of 25
Time taken per op is 0.072us (14.0 Mqps) with batch size of 25
Time taken per op is 0.071us (14.1 Mqps) with batch size of 25
With 70000000 items, utilization is 52.15%, number of hash functions: 2.
Time taken per op is 0.082us (12.1 Mqps) with batch size of 50
Time taken per op is 0.071us (14.1 Mqps) with batch size of 50
Time taken per op is 0.073us (13.6 Mqps) with batch size of 50
With 70000000 items, utilization is 52.15%, number of hash functions: 2.
Time taken per op is 0.080us (12.5 Mqps) with batch size of 100
Time taken per op is 0.077us (13.0 Mqps) with batch size of 100
Time taken per op is 0.078us (12.8 Mqps) with batch size of 100
Test Plan:
make check all
make valgrind_check
make asan_check
Reviewers: sdong, ljin
Reviewed By: ljin
Subscribers: leveldb
Differential Revision: https://reviews.facebook.net/D22539
10 years ago
|
|
|
}
|
|
|
|
// Determine unused_user_key to fill empty buckets.
|
|
|
|
std::string unused_user_key = smallest_user_key_;
|
|
|
|
int curr_pos = static_cast<int>(unused_user_key.size()) - 1;
|
|
|
|
while (curr_pos >= 0) {
|
|
|
|
--unused_user_key[curr_pos];
|
|
|
|
if (Slice(unused_user_key).compare(smallest_user_key_) < 0) {
|
|
|
|
break;
|
|
|
|
}
|
|
|
|
--curr_pos;
|
|
|
|
}
|
|
|
|
if (curr_pos < 0) {
|
|
|
|
// Try using the largest key to identify an unused key.
|
|
|
|
unused_user_key = largest_user_key_;
|
|
|
|
curr_pos = static_cast<int>(unused_user_key.size()) - 1;
|
|
|
|
while (curr_pos >= 0) {
|
|
|
|
++unused_user_key[curr_pos];
|
|
|
|
if (Slice(unused_user_key).compare(largest_user_key_) > 0) {
|
|
|
|
break;
|
|
|
|
}
|
|
|
|
--curr_pos;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
if (curr_pos < 0) {
|
|
|
|
return Status::Corruption("Unable to find unused key");
|
|
|
|
}
|
|
|
|
if (is_last_level_file_) {
|
|
|
|
unused_bucket = unused_user_key;
|
|
|
|
} else {
|
|
|
|
ParsedInternalKey ikey(unused_user_key, 0, kTypeValue);
|
|
|
|
AppendInternalKey(&unused_bucket, ikey);
|
|
|
|
}
|
|
|
|
}
|
|
|
|
properties_.num_entries = num_entries_;
|
|
|
|
properties_.num_deletions = num_entries_ - num_values_;
|
|
|
|
properties_.fixed_key_len = key_size_;
|
|
|
|
properties_.user_collected_properties[CuckooTablePropertyNames::kValueLength]
|
|
|
|
.assign(reinterpret_cast<const char*>(&value_size_), sizeof(value_size_));
|
|
|
|
|
|
|
|
uint64_t bucket_size = key_size_ + value_size_;
|
|
|
|
unused_bucket.resize(static_cast<size_t>(bucket_size), 'a');
|
|
|
|
// Write the table.
|
|
|
|
uint32_t num_added = 0;
|
|
|
|
for (auto& bucket : buckets) {
|
|
|
|
if (bucket.vector_idx == kMaxVectorIdx) {
|
Pass IOStatus to write path and set retryable IO Error as hard error in BG jobs (#6487)
Summary:
In the current code base, we use Status to get and store the returned status from the call. Specifically, for IO related functions, the current Status cannot reflect the IO Error details such as error scope, error retryable attribute, and others. With the implementation of https://github.com/facebook/rocksdb/issues/5761, we have the new Wrapper for IO, which returns IOStatus instead of Status. However, the IOStatus is purged at the lower level of write path and transferred to Status.
The first job of this PR is to pass the IOStatus to the write path (flush, WAL write, and Compaction). The second job is to identify the Retryable IO Error as HardError, and set the bg_error_ as HardError. In this case, the DB Instance becomes read only. User is informed of the Status and need to take actions to deal with it (e.g., call db->Resume()).
Pull Request resolved: https://github.com/facebook/rocksdb/pull/6487
Test Plan: Added the testing case to error_handler_fs_test. Pass make asan_check
Reviewed By: anand1976
Differential Revision: D20685017
Pulled By: zhichao-cao
fbshipit-source-id: ff85f042896243abcd6ef37877834e26f36b6eb0
5 years ago
|
|
|
io_status_ = file_->Append(Slice(unused_bucket));
|
|
|
|
} else {
|
|
|
|
++num_added;
|
Pass IOStatus to write path and set retryable IO Error as hard error in BG jobs (#6487)
Summary:
In the current code base, we use Status to get and store the returned status from the call. Specifically, for IO related functions, the current Status cannot reflect the IO Error details such as error scope, error retryable attribute, and others. With the implementation of https://github.com/facebook/rocksdb/issues/5761, we have the new Wrapper for IO, which returns IOStatus instead of Status. However, the IOStatus is purged at the lower level of write path and transferred to Status.
The first job of this PR is to pass the IOStatus to the write path (flush, WAL write, and Compaction). The second job is to identify the Retryable IO Error as HardError, and set the bg_error_ as HardError. In this case, the DB Instance becomes read only. User is informed of the Status and need to take actions to deal with it (e.g., call db->Resume()).
Pull Request resolved: https://github.com/facebook/rocksdb/pull/6487
Test Plan: Added the testing case to error_handler_fs_test. Pass make asan_check
Reviewed By: anand1976
Differential Revision: D20685017
Pulled By: zhichao-cao
fbshipit-source-id: ff85f042896243abcd6ef37877834e26f36b6eb0
5 years ago
|
|
|
io_status_ = file_->Append(GetKey(bucket.vector_idx));
|
|
|
|
if (io_status_.ok()) {
|
|
|
|
if (value_size_ > 0) {
|
Pass IOStatus to write path and set retryable IO Error as hard error in BG jobs (#6487)
Summary:
In the current code base, we use Status to get and store the returned status from the call. Specifically, for IO related functions, the current Status cannot reflect the IO Error details such as error scope, error retryable attribute, and others. With the implementation of https://github.com/facebook/rocksdb/issues/5761, we have the new Wrapper for IO, which returns IOStatus instead of Status. However, the IOStatus is purged at the lower level of write path and transferred to Status.
The first job of this PR is to pass the IOStatus to the write path (flush, WAL write, and Compaction). The second job is to identify the Retryable IO Error as HardError, and set the bg_error_ as HardError. In this case, the DB Instance becomes read only. User is informed of the Status and need to take actions to deal with it (e.g., call db->Resume()).
Pull Request resolved: https://github.com/facebook/rocksdb/pull/6487
Test Plan: Added the testing case to error_handler_fs_test. Pass make asan_check
Reviewed By: anand1976
Differential Revision: D20685017
Pulled By: zhichao-cao
fbshipit-source-id: ff85f042896243abcd6ef37877834e26f36b6eb0
5 years ago
|
|
|
io_status_ = file_->Append(GetValue(bucket.vector_idx));
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
Pass IOStatus to write path and set retryable IO Error as hard error in BG jobs (#6487)
Summary:
In the current code base, we use Status to get and store the returned status from the call. Specifically, for IO related functions, the current Status cannot reflect the IO Error details such as error scope, error retryable attribute, and others. With the implementation of https://github.com/facebook/rocksdb/issues/5761, we have the new Wrapper for IO, which returns IOStatus instead of Status. However, the IOStatus is purged at the lower level of write path and transferred to Status.
The first job of this PR is to pass the IOStatus to the write path (flush, WAL write, and Compaction). The second job is to identify the Retryable IO Error as HardError, and set the bg_error_ as HardError. In this case, the DB Instance becomes read only. User is informed of the Status and need to take actions to deal with it (e.g., call db->Resume()).
Pull Request resolved: https://github.com/facebook/rocksdb/pull/6487
Test Plan: Added the testing case to error_handler_fs_test. Pass make asan_check
Reviewed By: anand1976
Differential Revision: D20685017
Pulled By: zhichao-cao
fbshipit-source-id: ff85f042896243abcd6ef37877834e26f36b6eb0
5 years ago
|
|
|
if (!io_status_.ok()) {
|
|
|
|
status_ = io_status_;
|
|
|
|
return status_;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
assert(num_added == NumEntries());
|
|
|
|
properties_.raw_key_size = num_added * properties_.fixed_key_len;
|
|
|
|
properties_.raw_value_size = num_added * value_size_;
|
|
|
|
|
|
|
|
uint64_t offset = buckets.size() * bucket_size;
|
|
|
|
properties_.data_size = offset;
|
|
|
|
unused_bucket.resize(static_cast<size_t>(properties_.fixed_key_len));
|
|
|
|
properties_.user_collected_properties[CuckooTablePropertyNames::kEmptyKey] =
|
|
|
|
unused_bucket;
|
|
|
|
properties_.user_collected_properties[CuckooTablePropertyNames::kNumHashFunc]
|
|
|
|
.assign(reinterpret_cast<char*>(&num_hash_func_), sizeof(num_hash_func_));
|
|
|
|
|
|
|
|
properties_
|
|
|
|
.user_collected_properties[CuckooTablePropertyNames::kHashTableSize]
|
|
|
|
.assign(reinterpret_cast<const char*>(&hash_table_size_),
|
|
|
|
sizeof(hash_table_size_));
|
|
|
|
properties_.user_collected_properties[CuckooTablePropertyNames::kIsLastLevel]
|
|
|
|
.assign(reinterpret_cast<const char*>(&is_last_level_file_),
|
|
|
|
sizeof(is_last_level_file_));
|
|
|
|
properties_
|
|
|
|
.user_collected_properties[CuckooTablePropertyNames::kCuckooBlockSize]
|
|
|
|
.assign(reinterpret_cast<const char*>(&cuckoo_block_size_),
|
|
|
|
sizeof(cuckoo_block_size_));
|
|
|
|
properties_
|
|
|
|
.user_collected_properties[CuckooTablePropertyNames::kIdentityAsFirstHash]
|
|
|
|
.assign(reinterpret_cast<const char*>(&identity_as_first_hash_),
|
|
|
|
sizeof(identity_as_first_hash_));
|
|
|
|
properties_
|
|
|
|
.user_collected_properties[CuckooTablePropertyNames::kUseModuleHash]
|
|
|
|
.assign(reinterpret_cast<const char*>(&use_module_hash_),
|
|
|
|
sizeof(use_module_hash_));
|
|
|
|
uint32_t user_key_len = static_cast<uint32_t>(smallest_user_key_.size());
|
|
|
|
properties_
|
|
|
|
.user_collected_properties[CuckooTablePropertyNames::kUserKeyLength]
|
|
|
|
.assign(reinterpret_cast<const char*>(&user_key_len),
|
|
|
|
sizeof(user_key_len));
|
|
|
|
|
|
|
|
// Write meta blocks.
|
|
|
|
MetaIndexBuilder meta_index_builder;
|
|
|
|
PropertyBlockBuilder property_block_builder;
|
|
|
|
|
|
|
|
property_block_builder.AddTableProperty(properties_);
|
|
|
|
property_block_builder.Add(properties_.user_collected_properties);
|
|
|
|
Slice property_block = property_block_builder.Finish();
|
|
|
|
BlockHandle property_block_handle;
|
|
|
|
property_block_handle.set_offset(offset);
|
|
|
|
property_block_handle.set_size(property_block.size());
|
Pass IOStatus to write path and set retryable IO Error as hard error in BG jobs (#6487)
Summary:
In the current code base, we use Status to get and store the returned status from the call. Specifically, for IO related functions, the current Status cannot reflect the IO Error details such as error scope, error retryable attribute, and others. With the implementation of https://github.com/facebook/rocksdb/issues/5761, we have the new Wrapper for IO, which returns IOStatus instead of Status. However, the IOStatus is purged at the lower level of write path and transferred to Status.
The first job of this PR is to pass the IOStatus to the write path (flush, WAL write, and Compaction). The second job is to identify the Retryable IO Error as HardError, and set the bg_error_ as HardError. In this case, the DB Instance becomes read only. User is informed of the Status and need to take actions to deal with it (e.g., call db->Resume()).
Pull Request resolved: https://github.com/facebook/rocksdb/pull/6487
Test Plan: Added the testing case to error_handler_fs_test. Pass make asan_check
Reviewed By: anand1976
Differential Revision: D20685017
Pulled By: zhichao-cao
fbshipit-source-id: ff85f042896243abcd6ef37877834e26f36b6eb0
5 years ago
|
|
|
io_status_ = file_->Append(property_block);
|
|
|
|
offset += property_block.size();
|
Pass IOStatus to write path and set retryable IO Error as hard error in BG jobs (#6487)
Summary:
In the current code base, we use Status to get and store the returned status from the call. Specifically, for IO related functions, the current Status cannot reflect the IO Error details such as error scope, error retryable attribute, and others. With the implementation of https://github.com/facebook/rocksdb/issues/5761, we have the new Wrapper for IO, which returns IOStatus instead of Status. However, the IOStatus is purged at the lower level of write path and transferred to Status.
The first job of this PR is to pass the IOStatus to the write path (flush, WAL write, and Compaction). The second job is to identify the Retryable IO Error as HardError, and set the bg_error_ as HardError. In this case, the DB Instance becomes read only. User is informed of the Status and need to take actions to deal with it (e.g., call db->Resume()).
Pull Request resolved: https://github.com/facebook/rocksdb/pull/6487
Test Plan: Added the testing case to error_handler_fs_test. Pass make asan_check
Reviewed By: anand1976
Differential Revision: D20685017
Pulled By: zhichao-cao
fbshipit-source-id: ff85f042896243abcd6ef37877834e26f36b6eb0
5 years ago
|
|
|
if (!io_status_.ok()) {
|
|
|
|
status_ = io_status_;
|
|
|
|
return status_;
|
|
|
|
}
|
|
|
|
|
|
|
|
meta_index_builder.Add(kPropertiesBlockName, property_block_handle);
|
|
|
|
Slice meta_index_block = meta_index_builder.Finish();
|
|
|
|
|
|
|
|
BlockHandle meta_index_block_handle;
|
|
|
|
meta_index_block_handle.set_offset(offset);
|
|
|
|
meta_index_block_handle.set_size(meta_index_block.size());
|
Pass IOStatus to write path and set retryable IO Error as hard error in BG jobs (#6487)
Summary:
In the current code base, we use Status to get and store the returned status from the call. Specifically, for IO related functions, the current Status cannot reflect the IO Error details such as error scope, error retryable attribute, and others. With the implementation of https://github.com/facebook/rocksdb/issues/5761, we have the new Wrapper for IO, which returns IOStatus instead of Status. However, the IOStatus is purged at the lower level of write path and transferred to Status.
The first job of this PR is to pass the IOStatus to the write path (flush, WAL write, and Compaction). The second job is to identify the Retryable IO Error as HardError, and set the bg_error_ as HardError. In this case, the DB Instance becomes read only. User is informed of the Status and need to take actions to deal with it (e.g., call db->Resume()).
Pull Request resolved: https://github.com/facebook/rocksdb/pull/6487
Test Plan: Added the testing case to error_handler_fs_test. Pass make asan_check
Reviewed By: anand1976
Differential Revision: D20685017
Pulled By: zhichao-cao
fbshipit-source-id: ff85f042896243abcd6ef37877834e26f36b6eb0
5 years ago
|
|
|
io_status_ = file_->Append(meta_index_block);
|
|
|
|
if (!io_status_.ok()) {
|
|
|
|
status_ = io_status_;
|
|
|
|
return status_;
|
|
|
|
}
|
|
|
|
|
|
|
|
FooterBuilder footer;
|
format_version=6 and context-aware block checksums (#9058)
Summary:
## Context checksum
All RocksDB checksums currently use 32 bits of checking
power, which should be 1 in 4 billion false negative (FN) probability (failing to
detect corruption). This is true for random corruptions, and in some cases
small corruptions are guaranteed to be detected. But some possible
corruptions, such as in storage metadata rather than storage payload data,
would have a much higher FN rate. For example:
* Data larger than one SST block is replaced by data from elsewhere in
the same or another SST file. Especially with block_align=true, the
probability of exact block size match is probably around 1 in 100, making
the FN probability around that same. Without `block_align=true` the
probability of same block start location is probably around 1 in 10,000,
for FN probability around 1 in a million.
To solve this problem in new format_version=6, we add "context awareness"
to block checksum checks. The stored and expected checksum value is
modified based on the block's position in the file and which file it is in. The
modifications are cleverly chosen so that, for example
* blocks within about 4GB of each other are guaranteed to use different context
* blocks that are offset by exactly some multiple of 4GiB are guaranteed to use
different context
* files generated by the same process are guaranteed to use different context
for the same offsets, until wrap-around after 2^32 - 1 files
Thus, with format_version=6, if a valid SST block and checksum is misplaced,
its checksum FN probability should be essentially ideal, 1 in 4B.
## Footer checksum
This change also adds checksum protection to the SST footer (with
format_version=6), for the first time without relying on whole file checksum.
To prevent a corruption of the format_version in the footer (e.g. 6 -> 5) to
defeat the footer checksum, we change much of the footer data format
including an "extended magic number" in format_version 6 that would be
interpreted as empty index and metaindex block handles in older footer
versions. We also change the encoding of handles to free up space for
other new data in footer.
## More detail: making space in footer
In order to keep footer the same size in format_version=6 (avoid change to IO
patterns), we have to free up some space for new data. We do this two ways:
* Metaindex block handle is encoded down to 4 bytes (from 10) by assuming
it immediately precedes the footer, and by assuming it is < 4GB.
* Index block handle is moved into metaindex. (I don't know why it was
in footer to begin with.)
## Performance
In case of small performance penalty, I've made a "pay as you go" optimization
to compensate: replace `MutableCFOptions` in BlockBasedTableBuilder::Rep
with the only field used in that structure after construction: `prefix_extractor`.
This makes the PR an overall performance improvement (results below).
Nevertheless I'm seeing essentially no difference going from fv=5 to fv=6,
even including that improvement for both. That's based on extreme case table
write performance testing, many files with many blocks. This is relatively
checksum intensive (small blocks) and salt generation intensive (small files).
```
(for I in `seq 1 100`; do TEST_TMPDIR=/dev/shm/dbbench2 ./db_bench -benchmarks=fillseq -memtablerep=vector -disable_wal=1 -allow_concurrent_memtable_write=false -num=3000000 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=0 -write_buffer_size=100000 -compression_type=none -block_size=1000; done) 2>&1 | grep micros/op | tee out
awk '{ tot += $5; n += 1; } END { print int(1.0 * tot / n) }' < out
```
Each value below is ops/s averaged over 100 runs, run simultaneously with competing
configuration for load fairness
Before -> after (both fv=5): 483530 -> 483673 (negligible)
Re-run 1: 480733 -> 485427 (1.0% faster)
Re-run 2: 483821 -> 484541 (0.1% faster)
Before (fv=5) -> after (fv=6): 482006 -> 485100 (0.6% faster)
Re-run 1: 482212 -> 485075 (0.6% faster)
Re-run 2: 483590 -> 484073 (0.1% faster)
After fv=5 -> after fv=6: 483878 -> 485542 (0.3% faster)
Re-run 1: 485331 -> 483385 (0.4% slower)
Re-run 2: 485283 -> 483435 (0.4% slower)
Re-run 3: 483647 -> 486109 (0.5% faster)
Pull Request resolved: https://github.com/facebook/rocksdb/pull/9058
Test Plan:
unit tests included (table_test, db_properties_test, salt in env_test). General DB tests
and crash test updated to test new format_version.
Also temporarily updated the default format version to 6 and saw some test failures. Almost all
were due to an inadvertent additional read in VerifyChecksum to verify the index block checksum,
though it's arguably a bug that VerifyChecksum does not appear to (re-)verify the index block
checksum, just assuming it was verified in opening the index reader (probably *usually* true but
probably not always true). Some other concerns about VerifyChecksum are left in FIXME
comments. The only remaining test failure on change of default (in block_fetcher_test) now
has a comment about how to upgrade the test.
The format compatibility test does not need updating because we have not updated the default
format_version.
Reviewed By: ajkr, mrambacher
Differential Revision: D33100915
Pulled By: pdillinger
fbshipit-source-id: 8679e3e572fa580181a737fd6d113ed53c5422ee
1 year ago
|
|
|
Status s = footer.Build(kCuckooTableMagicNumber, /* format_version */ 1,
|
|
|
|
offset, kNoChecksum, meta_index_block_handle);
|
|
|
|
if (!s.ok()) {
|
|
|
|
status_ = s;
|
|
|
|
return status_;
|
|
|
|
}
|
|
|
|
io_status_ = file_->Append(footer.GetSlice());
|
Pass IOStatus to write path and set retryable IO Error as hard error in BG jobs (#6487)
Summary:
In the current code base, we use Status to get and store the returned status from the call. Specifically, for IO related functions, the current Status cannot reflect the IO Error details such as error scope, error retryable attribute, and others. With the implementation of https://github.com/facebook/rocksdb/issues/5761, we have the new Wrapper for IO, which returns IOStatus instead of Status. However, the IOStatus is purged at the lower level of write path and transferred to Status.
The first job of this PR is to pass the IOStatus to the write path (flush, WAL write, and Compaction). The second job is to identify the Retryable IO Error as HardError, and set the bg_error_ as HardError. In this case, the DB Instance becomes read only. User is informed of the Status and need to take actions to deal with it (e.g., call db->Resume()).
Pull Request resolved: https://github.com/facebook/rocksdb/pull/6487
Test Plan: Added the testing case to error_handler_fs_test. Pass make asan_check
Reviewed By: anand1976
Differential Revision: D20685017
Pulled By: zhichao-cao
fbshipit-source-id: ff85f042896243abcd6ef37877834e26f36b6eb0
5 years ago
|
|
|
status_ = io_status_;
|
|
|
|
return status_;
|
|
|
|
}
|
|
|
|
|
|
|
|
void CuckooTableBuilder::Abandon() {
|
|
|
|
assert(!closed_);
|
|
|
|
closed_ = true;
|
|
|
|
}
|
|
|
|
|
|
|
|
uint64_t CuckooTableBuilder::NumEntries() const { return num_entries_; }
|
|
|
|
|
|
|
|
uint64_t CuckooTableBuilder::FileSize() const {
|
|
|
|
if (closed_) {
|
|
|
|
return file_->GetFileSize();
|
|
|
|
} else if (num_entries_ == 0) {
|
|
|
|
return 0;
|
|
|
|
}
|
Improve Cuckoo Table Reader performance. Inlined hash function and number of buckets a power of two.
Summary:
Use inlined hash functions instead of function pointer. Make number of buckets a power of two and use bitwise and instead of mod.
After these changes, we get almost 50% improvement in performance.
Results:
With 120000000 items, utilization is 89.41%, number of hash functions: 2.
Time taken per op is 0.231us (4.3 Mqps) with batch size of 0
Time taken per op is 0.229us (4.4 Mqps) with batch size of 0
Time taken per op is 0.185us (5.4 Mqps) with batch size of 0
With 120000000 items, utilization is 89.41%, number of hash functions: 2.
Time taken per op is 0.108us (9.3 Mqps) with batch size of 10
Time taken per op is 0.100us (10.0 Mqps) with batch size of 10
Time taken per op is 0.103us (9.7 Mqps) with batch size of 10
With 120000000 items, utilization is 89.41%, number of hash functions: 2.
Time taken per op is 0.101us (9.9 Mqps) with batch size of 25
Time taken per op is 0.098us (10.2 Mqps) with batch size of 25
Time taken per op is 0.097us (10.3 Mqps) with batch size of 25
With 120000000 items, utilization is 89.41%, number of hash functions: 2.
Time taken per op is 0.100us (10.0 Mqps) with batch size of 50
Time taken per op is 0.097us (10.3 Mqps) with batch size of 50
Time taken per op is 0.097us (10.3 Mqps) with batch size of 50
With 120000000 items, utilization is 89.41%, number of hash functions: 2.
Time taken per op is 0.102us (9.8 Mqps) with batch size of 100
Time taken per op is 0.098us (10.2 Mqps) with batch size of 100
Time taken per op is 0.115us (8.7 Mqps) with batch size of 100
With 100000000 items, utilization is 74.51%, number of hash functions: 2.
Time taken per op is 0.201us (5.0 Mqps) with batch size of 0
Time taken per op is 0.155us (6.5 Mqps) with batch size of 0
Time taken per op is 0.152us (6.6 Mqps) with batch size of 0
With 100000000 items, utilization is 74.51%, number of hash functions: 2.
Time taken per op is 0.089us (11.3 Mqps) with batch size of 10
Time taken per op is 0.084us (11.9 Mqps) with batch size of 10
Time taken per op is 0.086us (11.6 Mqps) with batch size of 10
With 100000000 items, utilization is 74.51%, number of hash functions: 2.
Time taken per op is 0.087us (11.5 Mqps) with batch size of 25
Time taken per op is 0.085us (11.7 Mqps) with batch size of 25
Time taken per op is 0.093us (10.8 Mqps) with batch size of 25
With 100000000 items, utilization is 74.51%, number of hash functions: 2.
Time taken per op is 0.094us (10.6 Mqps) with batch size of 50
Time taken per op is 0.094us (10.7 Mqps) with batch size of 50
Time taken per op is 0.093us (10.8 Mqps) with batch size of 50
With 100000000 items, utilization is 74.51%, number of hash functions: 2.
Time taken per op is 0.092us (10.9 Mqps) with batch size of 100
Time taken per op is 0.089us (11.2 Mqps) with batch size of 100
Time taken per op is 0.088us (11.3 Mqps) with batch size of 100
With 80000000 items, utilization is 59.60%, number of hash functions: 2.
Time taken per op is 0.154us (6.5 Mqps) with batch size of 0
Time taken per op is 0.168us (6.0 Mqps) with batch size of 0
Time taken per op is 0.190us (5.3 Mqps) with batch size of 0
With 80000000 items, utilization is 59.60%, number of hash functions: 2.
Time taken per op is 0.081us (12.4 Mqps) with batch size of 10
Time taken per op is 0.077us (13.0 Mqps) with batch size of 10
Time taken per op is 0.083us (12.1 Mqps) with batch size of 10
With 80000000 items, utilization is 59.60%, number of hash functions: 2.
Time taken per op is 0.077us (13.0 Mqps) with batch size of 25
Time taken per op is 0.073us (13.7 Mqps) with batch size of 25
Time taken per op is 0.073us (13.7 Mqps) with batch size of 25
With 80000000 items, utilization is 59.60%, number of hash functions: 2.
Time taken per op is 0.076us (13.1 Mqps) with batch size of 50
Time taken per op is 0.072us (13.8 Mqps) with batch size of 50
Time taken per op is 0.072us (13.8 Mqps) with batch size of 50
With 80000000 items, utilization is 59.60%, number of hash functions: 2.
Time taken per op is 0.077us (13.0 Mqps) with batch size of 100
Time taken per op is 0.074us (13.6 Mqps) with batch size of 100
Time taken per op is 0.073us (13.6 Mqps) with batch size of 100
With 70000000 items, utilization is 52.15%, number of hash functions: 2.
Time taken per op is 0.190us (5.3 Mqps) with batch size of 0
Time taken per op is 0.186us (5.4 Mqps) with batch size of 0
Time taken per op is 0.184us (5.4 Mqps) with batch size of 0
With 70000000 items, utilization is 52.15%, number of hash functions: 2.
Time taken per op is 0.079us (12.7 Mqps) with batch size of 10
Time taken per op is 0.070us (14.2 Mqps) with batch size of 10
Time taken per op is 0.072us (14.0 Mqps) with batch size of 10
With 70000000 items, utilization is 52.15%, number of hash functions: 2.
Time taken per op is 0.080us (12.5 Mqps) with batch size of 25
Time taken per op is 0.072us (14.0 Mqps) with batch size of 25
Time taken per op is 0.071us (14.1 Mqps) with batch size of 25
With 70000000 items, utilization is 52.15%, number of hash functions: 2.
Time taken per op is 0.082us (12.1 Mqps) with batch size of 50
Time taken per op is 0.071us (14.1 Mqps) with batch size of 50
Time taken per op is 0.073us (13.6 Mqps) with batch size of 50
With 70000000 items, utilization is 52.15%, number of hash functions: 2.
Time taken per op is 0.080us (12.5 Mqps) with batch size of 100
Time taken per op is 0.077us (13.0 Mqps) with batch size of 100
Time taken per op is 0.078us (12.8 Mqps) with batch size of 100
Test Plan:
make check all
make valgrind_check
make asan_check
Reviewers: sdong, ljin
Reviewed By: ljin
Subscribers: leveldb
Differential Revision: https://reviews.facebook.net/D22539
10 years ago
|
|
|
|
|
|
|
if (use_module_hash_) {
|
|
|
|
return static_cast<uint64_t>((key_size_ + value_size_) * num_entries_ /
|
|
|
|
max_hash_table_ratio_);
|
|
|
|
} else {
|
|
|
|
// Account for buckets being a power of two.
|
|
|
|
// As elements are added, file size remains constant for a while and
|
|
|
|
// doubles its size. Since compaction algorithm stops adding elements
|
|
|
|
// only after it exceeds the file limit, we account for the extra element
|
|
|
|
// being added here.
|
|
|
|
uint64_t expected_hash_table_size = hash_table_size_;
|
|
|
|
if (expected_hash_table_size < (num_entries_ + 1) / max_hash_table_ratio_) {
|
|
|
|
expected_hash_table_size *= 2;
|
|
|
|
}
|
|
|
|
return (key_size_ + value_size_) * expected_hash_table_size - 1;
|
Improve Cuckoo Table Reader performance. Inlined hash function and number of buckets a power of two.
Summary:
Use inlined hash functions instead of function pointer. Make number of buckets a power of two and use bitwise and instead of mod.
After these changes, we get almost 50% improvement in performance.
Results:
With 120000000 items, utilization is 89.41%, number of hash functions: 2.
Time taken per op is 0.231us (4.3 Mqps) with batch size of 0
Time taken per op is 0.229us (4.4 Mqps) with batch size of 0
Time taken per op is 0.185us (5.4 Mqps) with batch size of 0
With 120000000 items, utilization is 89.41%, number of hash functions: 2.
Time taken per op is 0.108us (9.3 Mqps) with batch size of 10
Time taken per op is 0.100us (10.0 Mqps) with batch size of 10
Time taken per op is 0.103us (9.7 Mqps) with batch size of 10
With 120000000 items, utilization is 89.41%, number of hash functions: 2.
Time taken per op is 0.101us (9.9 Mqps) with batch size of 25
Time taken per op is 0.098us (10.2 Mqps) with batch size of 25
Time taken per op is 0.097us (10.3 Mqps) with batch size of 25
With 120000000 items, utilization is 89.41%, number of hash functions: 2.
Time taken per op is 0.100us (10.0 Mqps) with batch size of 50
Time taken per op is 0.097us (10.3 Mqps) with batch size of 50
Time taken per op is 0.097us (10.3 Mqps) with batch size of 50
With 120000000 items, utilization is 89.41%, number of hash functions: 2.
Time taken per op is 0.102us (9.8 Mqps) with batch size of 100
Time taken per op is 0.098us (10.2 Mqps) with batch size of 100
Time taken per op is 0.115us (8.7 Mqps) with batch size of 100
With 100000000 items, utilization is 74.51%, number of hash functions: 2.
Time taken per op is 0.201us (5.0 Mqps) with batch size of 0
Time taken per op is 0.155us (6.5 Mqps) with batch size of 0
Time taken per op is 0.152us (6.6 Mqps) with batch size of 0
With 100000000 items, utilization is 74.51%, number of hash functions: 2.
Time taken per op is 0.089us (11.3 Mqps) with batch size of 10
Time taken per op is 0.084us (11.9 Mqps) with batch size of 10
Time taken per op is 0.086us (11.6 Mqps) with batch size of 10
With 100000000 items, utilization is 74.51%, number of hash functions: 2.
Time taken per op is 0.087us (11.5 Mqps) with batch size of 25
Time taken per op is 0.085us (11.7 Mqps) with batch size of 25
Time taken per op is 0.093us (10.8 Mqps) with batch size of 25
With 100000000 items, utilization is 74.51%, number of hash functions: 2.
Time taken per op is 0.094us (10.6 Mqps) with batch size of 50
Time taken per op is 0.094us (10.7 Mqps) with batch size of 50
Time taken per op is 0.093us (10.8 Mqps) with batch size of 50
With 100000000 items, utilization is 74.51%, number of hash functions: 2.
Time taken per op is 0.092us (10.9 Mqps) with batch size of 100
Time taken per op is 0.089us (11.2 Mqps) with batch size of 100
Time taken per op is 0.088us (11.3 Mqps) with batch size of 100
With 80000000 items, utilization is 59.60%, number of hash functions: 2.
Time taken per op is 0.154us (6.5 Mqps) with batch size of 0
Time taken per op is 0.168us (6.0 Mqps) with batch size of 0
Time taken per op is 0.190us (5.3 Mqps) with batch size of 0
With 80000000 items, utilization is 59.60%, number of hash functions: 2.
Time taken per op is 0.081us (12.4 Mqps) with batch size of 10
Time taken per op is 0.077us (13.0 Mqps) with batch size of 10
Time taken per op is 0.083us (12.1 Mqps) with batch size of 10
With 80000000 items, utilization is 59.60%, number of hash functions: 2.
Time taken per op is 0.077us (13.0 Mqps) with batch size of 25
Time taken per op is 0.073us (13.7 Mqps) with batch size of 25
Time taken per op is 0.073us (13.7 Mqps) with batch size of 25
With 80000000 items, utilization is 59.60%, number of hash functions: 2.
Time taken per op is 0.076us (13.1 Mqps) with batch size of 50
Time taken per op is 0.072us (13.8 Mqps) with batch size of 50
Time taken per op is 0.072us (13.8 Mqps) with batch size of 50
With 80000000 items, utilization is 59.60%, number of hash functions: 2.
Time taken per op is 0.077us (13.0 Mqps) with batch size of 100
Time taken per op is 0.074us (13.6 Mqps) with batch size of 100
Time taken per op is 0.073us (13.6 Mqps) with batch size of 100
With 70000000 items, utilization is 52.15%, number of hash functions: 2.
Time taken per op is 0.190us (5.3 Mqps) with batch size of 0
Time taken per op is 0.186us (5.4 Mqps) with batch size of 0
Time taken per op is 0.184us (5.4 Mqps) with batch size of 0
With 70000000 items, utilization is 52.15%, number of hash functions: 2.
Time taken per op is 0.079us (12.7 Mqps) with batch size of 10
Time taken per op is 0.070us (14.2 Mqps) with batch size of 10
Time taken per op is 0.072us (14.0 Mqps) with batch size of 10
With 70000000 items, utilization is 52.15%, number of hash functions: 2.
Time taken per op is 0.080us (12.5 Mqps) with batch size of 25
Time taken per op is 0.072us (14.0 Mqps) with batch size of 25
Time taken per op is 0.071us (14.1 Mqps) with batch size of 25
With 70000000 items, utilization is 52.15%, number of hash functions: 2.
Time taken per op is 0.082us (12.1 Mqps) with batch size of 50
Time taken per op is 0.071us (14.1 Mqps) with batch size of 50
Time taken per op is 0.073us (13.6 Mqps) with batch size of 50
With 70000000 items, utilization is 52.15%, number of hash functions: 2.
Time taken per op is 0.080us (12.5 Mqps) with batch size of 100
Time taken per op is 0.077us (13.0 Mqps) with batch size of 100
Time taken per op is 0.078us (12.8 Mqps) with batch size of 100
Test Plan:
make check all
make valgrind_check
make asan_check
Reviewers: sdong, ljin
Reviewed By: ljin
Subscribers: leveldb
Differential Revision: https://reviews.facebook.net/D22539
10 years ago
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
// This method is invoked when there is no place to insert the target key.
|
|
|
|
// It searches for a set of elements that can be moved to accommodate target
|
|
|
|
// key. The search is a BFS graph traversal with first level (hash_vals)
|
|
|
|
// being all the buckets target key could go to.
|
|
|
|
// Then, from each node (curr_node), we find all the buckets that curr_node
|
|
|
|
// could go to. They form the children of curr_node in the tree.
|
|
|
|
// We continue the traversal until we find an empty bucket, in which case, we
|
|
|
|
// move all elements along the path from first level to this empty bucket, to
|
|
|
|
// make space for target key which is inserted at first level (*bucket_id).
|
|
|
|
// If tree depth exceedes max depth, we return false indicating failure.
|
|
|
|
bool CuckooTableBuilder::MakeSpaceForKey(
|
|
|
|
const autovector<uint64_t>& hash_vals,
|
|
|
|
const uint32_t make_space_for_key_call_id,
|
|
|
|
std::vector<CuckooBucket>* buckets, uint64_t* bucket_id) {
|
|
|
|
struct CuckooNode {
|
|
|
|
uint64_t bucket_id;
|
|
|
|
uint32_t depth;
|
|
|
|
uint32_t parent_pos;
|
|
|
|
CuckooNode(uint64_t _bucket_id, uint32_t _depth, int _parent_pos)
|
|
|
|
: bucket_id(_bucket_id), depth(_depth), parent_pos(_parent_pos) {}
|
|
|
|
};
|
|
|
|
// This is BFS search tree that is stored simply as a vector.
|
|
|
|
// Each node stores the index of parent node in the vector.
|
|
|
|
std::vector<CuckooNode> tree;
|
|
|
|
// We want to identify already visited buckets in the current method call so
|
|
|
|
// that we don't add same buckets again for exploration in the tree.
|
|
|
|
// We do this by maintaining a count of current method call in
|
|
|
|
// make_space_for_key_call_id, which acts as a unique id for this invocation
|
|
|
|
// of the method. We store this number into the nodes that we explore in
|
|
|
|
// current method call.
|
|
|
|
// It is unlikely for the increment operation to overflow because the maximum
|
|
|
|
// no. of times this will be called is <= max_num_hash_func_ + num_entries_.
|
|
|
|
for (uint32_t hash_cnt = 0; hash_cnt < num_hash_func_; ++hash_cnt) {
|
|
|
|
uint64_t bid = hash_vals[hash_cnt];
|
|
|
|
(*buckets)[static_cast<size_t>(bid)].make_space_for_key_call_id =
|
|
|
|
make_space_for_key_call_id;
|
|
|
|
tree.push_back(CuckooNode(bid, 0, 0));
|
|
|
|
}
|
|
|
|
bool null_found = false;
|
|
|
|
uint32_t curr_pos = 0;
|
|
|
|
while (!null_found && curr_pos < tree.size()) {
|
|
|
|
CuckooNode& curr_node = tree[curr_pos];
|
|
|
|
uint32_t curr_depth = curr_node.depth;
|
|
|
|
if (curr_depth >= max_search_depth_) {
|
|
|
|
break;
|
|
|
|
}
|
|
|
|
CuckooBucket& curr_bucket =
|
|
|
|
(*buckets)[static_cast<size_t>(curr_node.bucket_id)];
|
|
|
|
for (uint32_t hash_cnt = 0; hash_cnt < num_hash_func_ && !null_found;
|
|
|
|
++hash_cnt) {
|
|
|
|
uint64_t child_bucket_id = CuckooHash(
|
|
|
|
GetUserKey(curr_bucket.vector_idx), hash_cnt, use_module_hash_,
|
|
|
|
hash_table_size_, identity_as_first_hash_, get_slice_hash_);
|
Improve Cuckoo Table Reader performance. Inlined hash function and number of buckets a power of two.
Summary:
Use inlined hash functions instead of function pointer. Make number of buckets a power of two and use bitwise and instead of mod.
After these changes, we get almost 50% improvement in performance.
Results:
With 120000000 items, utilization is 89.41%, number of hash functions: 2.
Time taken per op is 0.231us (4.3 Mqps) with batch size of 0
Time taken per op is 0.229us (4.4 Mqps) with batch size of 0
Time taken per op is 0.185us (5.4 Mqps) with batch size of 0
With 120000000 items, utilization is 89.41%, number of hash functions: 2.
Time taken per op is 0.108us (9.3 Mqps) with batch size of 10
Time taken per op is 0.100us (10.0 Mqps) with batch size of 10
Time taken per op is 0.103us (9.7 Mqps) with batch size of 10
With 120000000 items, utilization is 89.41%, number of hash functions: 2.
Time taken per op is 0.101us (9.9 Mqps) with batch size of 25
Time taken per op is 0.098us (10.2 Mqps) with batch size of 25
Time taken per op is 0.097us (10.3 Mqps) with batch size of 25
With 120000000 items, utilization is 89.41%, number of hash functions: 2.
Time taken per op is 0.100us (10.0 Mqps) with batch size of 50
Time taken per op is 0.097us (10.3 Mqps) with batch size of 50
Time taken per op is 0.097us (10.3 Mqps) with batch size of 50
With 120000000 items, utilization is 89.41%, number of hash functions: 2.
Time taken per op is 0.102us (9.8 Mqps) with batch size of 100
Time taken per op is 0.098us (10.2 Mqps) with batch size of 100
Time taken per op is 0.115us (8.7 Mqps) with batch size of 100
With 100000000 items, utilization is 74.51%, number of hash functions: 2.
Time taken per op is 0.201us (5.0 Mqps) with batch size of 0
Time taken per op is 0.155us (6.5 Mqps) with batch size of 0
Time taken per op is 0.152us (6.6 Mqps) with batch size of 0
With 100000000 items, utilization is 74.51%, number of hash functions: 2.
Time taken per op is 0.089us (11.3 Mqps) with batch size of 10
Time taken per op is 0.084us (11.9 Mqps) with batch size of 10
Time taken per op is 0.086us (11.6 Mqps) with batch size of 10
With 100000000 items, utilization is 74.51%, number of hash functions: 2.
Time taken per op is 0.087us (11.5 Mqps) with batch size of 25
Time taken per op is 0.085us (11.7 Mqps) with batch size of 25
Time taken per op is 0.093us (10.8 Mqps) with batch size of 25
With 100000000 items, utilization is 74.51%, number of hash functions: 2.
Time taken per op is 0.094us (10.6 Mqps) with batch size of 50
Time taken per op is 0.094us (10.7 Mqps) with batch size of 50
Time taken per op is 0.093us (10.8 Mqps) with batch size of 50
With 100000000 items, utilization is 74.51%, number of hash functions: 2.
Time taken per op is 0.092us (10.9 Mqps) with batch size of 100
Time taken per op is 0.089us (11.2 Mqps) with batch size of 100
Time taken per op is 0.088us (11.3 Mqps) with batch size of 100
With 80000000 items, utilization is 59.60%, number of hash functions: 2.
Time taken per op is 0.154us (6.5 Mqps) with batch size of 0
Time taken per op is 0.168us (6.0 Mqps) with batch size of 0
Time taken per op is 0.190us (5.3 Mqps) with batch size of 0
With 80000000 items, utilization is 59.60%, number of hash functions: 2.
Time taken per op is 0.081us (12.4 Mqps) with batch size of 10
Time taken per op is 0.077us (13.0 Mqps) with batch size of 10
Time taken per op is 0.083us (12.1 Mqps) with batch size of 10
With 80000000 items, utilization is 59.60%, number of hash functions: 2.
Time taken per op is 0.077us (13.0 Mqps) with batch size of 25
Time taken per op is 0.073us (13.7 Mqps) with batch size of 25
Time taken per op is 0.073us (13.7 Mqps) with batch size of 25
With 80000000 items, utilization is 59.60%, number of hash functions: 2.
Time taken per op is 0.076us (13.1 Mqps) with batch size of 50
Time taken per op is 0.072us (13.8 Mqps) with batch size of 50
Time taken per op is 0.072us (13.8 Mqps) with batch size of 50
With 80000000 items, utilization is 59.60%, number of hash functions: 2.
Time taken per op is 0.077us (13.0 Mqps) with batch size of 100
Time taken per op is 0.074us (13.6 Mqps) with batch size of 100
Time taken per op is 0.073us (13.6 Mqps) with batch size of 100
With 70000000 items, utilization is 52.15%, number of hash functions: 2.
Time taken per op is 0.190us (5.3 Mqps) with batch size of 0
Time taken per op is 0.186us (5.4 Mqps) with batch size of 0
Time taken per op is 0.184us (5.4 Mqps) with batch size of 0
With 70000000 items, utilization is 52.15%, number of hash functions: 2.
Time taken per op is 0.079us (12.7 Mqps) with batch size of 10
Time taken per op is 0.070us (14.2 Mqps) with batch size of 10
Time taken per op is 0.072us (14.0 Mqps) with batch size of 10
With 70000000 items, utilization is 52.15%, number of hash functions: 2.
Time taken per op is 0.080us (12.5 Mqps) with batch size of 25
Time taken per op is 0.072us (14.0 Mqps) with batch size of 25
Time taken per op is 0.071us (14.1 Mqps) with batch size of 25
With 70000000 items, utilization is 52.15%, number of hash functions: 2.
Time taken per op is 0.082us (12.1 Mqps) with batch size of 50
Time taken per op is 0.071us (14.1 Mqps) with batch size of 50
Time taken per op is 0.073us (13.6 Mqps) with batch size of 50
With 70000000 items, utilization is 52.15%, number of hash functions: 2.
Time taken per op is 0.080us (12.5 Mqps) with batch size of 100
Time taken per op is 0.077us (13.0 Mqps) with batch size of 100
Time taken per op is 0.078us (12.8 Mqps) with batch size of 100
Test Plan:
make check all
make valgrind_check
make asan_check
Reviewers: sdong, ljin
Reviewed By: ljin
Subscribers: leveldb
Differential Revision: https://reviews.facebook.net/D22539
10 years ago
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// Iterate inside Cuckoo Block.
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for (uint32_t block_idx = 0; block_idx < cuckoo_block_size_;
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++block_idx, ++child_bucket_id) {
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if ((*buckets)[static_cast<size_t>(child_bucket_id)]
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.make_space_for_key_call_id == make_space_for_key_call_id) {
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continue;
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}
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(*buckets)[static_cast<size_t>(child_bucket_id)]
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.make_space_for_key_call_id = make_space_for_key_call_id;
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tree.push_back(CuckooNode(child_bucket_id, curr_depth + 1, curr_pos));
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if ((*buckets)[static_cast<size_t>(child_bucket_id)].vector_idx ==
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kMaxVectorIdx) {
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null_found = true;
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break;
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}
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}
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}
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++curr_pos;
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}
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if (null_found) {
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// There is an empty node in tree.back(). Now, traverse the path from this
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// empty node to top of the tree and at every node in the path, replace
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// child with the parent. Stop when first level is reached in the tree
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// (happens when 0 <= bucket_to_replace_pos < num_hash_func_) and return
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// this location in first level for target key to be inserted.
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uint32_t bucket_to_replace_pos = static_cast<uint32_t>(tree.size()) - 1;
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while (bucket_to_replace_pos >= num_hash_func_) {
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CuckooNode& curr_node = tree[bucket_to_replace_pos];
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(*buckets)[static_cast<size_t>(curr_node.bucket_id)] =
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(*buckets)[static_cast<size_t>(tree[curr_node.parent_pos].bucket_id)];
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bucket_to_replace_pos = curr_node.parent_pos;
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}
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*bucket_id = tree[bucket_to_replace_pos].bucket_id;
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}
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return null_found;
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}
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std::string CuckooTableBuilder::GetFileChecksum() const {
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if (file_ != nullptr) {
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return file_->GetFileChecksum();
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} else {
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return kUnknownFileChecksum;
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}
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}
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const char* CuckooTableBuilder::GetFileChecksumFuncName() const {
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if (file_ != nullptr) {
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return file_->GetFileChecksumFuncName();
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} else {
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return kUnknownFileChecksumFuncName;
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}
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}
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} // namespace ROCKSDB_NAMESPACE
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