rocksdb/util/hash.h

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// Copyright (c) 2011-present, Facebook, Inc. All rights reserved.
// This source code is licensed under both the GPLv2 (found in the
// COPYING file in the root directory) and Apache 2.0 License
// (found in the LICENSE.Apache file in the root directory).
//
// Copyright (c) 2011 The LevelDB Authors. All rights reserved.
// Use of this source code is governed by a BSD-style license that can be
// found in the LICENSE file. See the AUTHORS file for names of contributors.
//
// Simple hash function used for internal data structures
#pragma once
#include <stddef.h>
#include <stdint.h>
#include "rocksdb/slice.h"
Consolidate hash function used for non-persistent data in a new function (#5155) Summary: Create new function NPHash64() and GetSliceNPHash64(), which are currently implemented using murmurhash. Replace the current direct call of murmurhash() to use the new functions if the hash results are not used in on-disk format. This will make it easier to try out or switch to alternative functions in the uses where data format compatibility doesn't need to be considered. This part shouldn't have any performance impact. Also, the sharded cache hash function is changed to the new format, because it falls into this categoery. It doesn't show visible performance impact in db_bench results. CPU showed by perf is increased from about 0.2% to 0.4% in an extreme benchmark setting (4KB blocks, no-compression, everything cached in block cache). We've known that the current hash function used, our own Hash() has serious hash quality problem. It can generate a lots of conflicts with similar input. In this use case, it means extra lock contention for reads from the same file. This slight CPU regression is worthy to me to counter the potential bad performance with hot keys. And hopefully this will get further improved in the future with a better hash function. cache_test's condition is relaxed a little bit to. The new hash is slightly more skewed in this use case, but I manually checked the data and see the hash results are still in a reasonable range. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5155 Differential Revision: D14834821 Pulled By: siying fbshipit-source-id: ec9a2c0a2f8ae4b54d08b13a5c2e9cc97aa80cb5
2019-04-08 22:24:29 +02:00
#include "util/murmurhash.h"
namespace rocksdb {
Consolidate hash function used for non-persistent data in a new function (#5155) Summary: Create new function NPHash64() and GetSliceNPHash64(), which are currently implemented using murmurhash. Replace the current direct call of murmurhash() to use the new functions if the hash results are not used in on-disk format. This will make it easier to try out or switch to alternative functions in the uses where data format compatibility doesn't need to be considered. This part shouldn't have any performance impact. Also, the sharded cache hash function is changed to the new format, because it falls into this categoery. It doesn't show visible performance impact in db_bench results. CPU showed by perf is increased from about 0.2% to 0.4% in an extreme benchmark setting (4KB blocks, no-compression, everything cached in block cache). We've known that the current hash function used, our own Hash() has serious hash quality problem. It can generate a lots of conflicts with similar input. In this use case, it means extra lock contention for reads from the same file. This slight CPU regression is worthy to me to counter the potential bad performance with hot keys. And hopefully this will get further improved in the future with a better hash function. cache_test's condition is relaxed a little bit to. The new hash is slightly more skewed in this use case, but I manually checked the data and see the hash results are still in a reasonable range. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5155 Differential Revision: D14834821 Pulled By: siying fbshipit-source-id: ec9a2c0a2f8ae4b54d08b13a5c2e9cc97aa80cb5
2019-04-08 22:24:29 +02:00
// Non-persistent hash. Only used for in-memory data structure
// The hash results are applicable to change.
extern uint64_t NPHash64(const char* data, size_t n, uint32_t seed);
extern uint32_t Hash(const char* data, size_t n, uint32_t seed);
inline uint32_t BloomHash(const Slice& key) {
return Hash(key.data(), key.size(), 0xbc9f1d34);
}
Consolidate hash function used for non-persistent data in a new function (#5155) Summary: Create new function NPHash64() and GetSliceNPHash64(), which are currently implemented using murmurhash. Replace the current direct call of murmurhash() to use the new functions if the hash results are not used in on-disk format. This will make it easier to try out or switch to alternative functions in the uses where data format compatibility doesn't need to be considered. This part shouldn't have any performance impact. Also, the sharded cache hash function is changed to the new format, because it falls into this categoery. It doesn't show visible performance impact in db_bench results. CPU showed by perf is increased from about 0.2% to 0.4% in an extreme benchmark setting (4KB blocks, no-compression, everything cached in block cache). We've known that the current hash function used, our own Hash() has serious hash quality problem. It can generate a lots of conflicts with similar input. In this use case, it means extra lock contention for reads from the same file. This slight CPU regression is worthy to me to counter the potential bad performance with hot keys. And hopefully this will get further improved in the future with a better hash function. cache_test's condition is relaxed a little bit to. The new hash is slightly more skewed in this use case, but I manually checked the data and see the hash results are still in a reasonable range. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5155 Differential Revision: D14834821 Pulled By: siying fbshipit-source-id: ec9a2c0a2f8ae4b54d08b13a5c2e9cc97aa80cb5
2019-04-08 22:24:29 +02:00
inline uint64_t GetSliceNPHash64(const Slice& s) {
return NPHash64(s.data(), s.size(), 0);
}
inline uint32_t GetSliceHash(const Slice& s) {
return Hash(s.data(), s.size(), 397);
}
Consolidate hash function used for non-persistent data in a new function (#5155) Summary: Create new function NPHash64() and GetSliceNPHash64(), which are currently implemented using murmurhash. Replace the current direct call of murmurhash() to use the new functions if the hash results are not used in on-disk format. This will make it easier to try out or switch to alternative functions in the uses where data format compatibility doesn't need to be considered. This part shouldn't have any performance impact. Also, the sharded cache hash function is changed to the new format, because it falls into this categoery. It doesn't show visible performance impact in db_bench results. CPU showed by perf is increased from about 0.2% to 0.4% in an extreme benchmark setting (4KB blocks, no-compression, everything cached in block cache). We've known that the current hash function used, our own Hash() has serious hash quality problem. It can generate a lots of conflicts with similar input. In this use case, it means extra lock contention for reads from the same file. This slight CPU regression is worthy to me to counter the potential bad performance with hot keys. And hopefully this will get further improved in the future with a better hash function. cache_test's condition is relaxed a little bit to. The new hash is slightly more skewed in this use case, but I manually checked the data and see the hash results are still in a reasonable range. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5155 Differential Revision: D14834821 Pulled By: siying fbshipit-source-id: ec9a2c0a2f8ae4b54d08b13a5c2e9cc97aa80cb5
2019-04-08 22:24:29 +02:00
inline uint64_t NPHash64(const char* data, size_t n, uint32_t seed) {
// Right now murmurhash2B is used. It should able to be freely
// changed to a better hash, without worrying about backward
// compatibility issue.
return MURMUR_HASH(data, static_cast<int>(n),
static_cast<unsigned int>(seed));
}
// std::hash compatible interface.
struct SliceHasher {
uint32_t operator()(const Slice& s) const { return GetSliceHash(s); }
};
Faster new DynamicBloom implementation (for memtable) (#5762) Summary: Since DynamicBloom is now only used in-memory, we're free to change it without schema compatibility issues. The new implementation is drawn from (with manifest permission) https://github.com/pdillinger/wormhashing/blob/303542a767437f56d8b66cea6ebecaac0e6a61e9/bloom_simulation_tests/foo.cc#L613 This has several speed advantages over the prior implementation: * Uses fastrange instead of % * Minimum logic to determine first (and all) probed memory addresses * (Major) Two probes per 64-bit memory fetch/write. * Very fast and effective (murmur-like) hash expansion/re-mixing. (At least on recent CPUs, integer multiplication is very cheap.) While a Bloom filter with 512-bit cache locality has about a 1.15x FP rate penalty (e.g. 0.84% to 0.97%), further restricting to two probes per 64 bits incurs an additional 1.12x FP rate penalty (e.g. 0.97% to 1.09%). Nevertheless, the unit tests show no "mediocre" FP rate samples, unlike the old implementation with more erratic FP rates. Especially for the memtable, we expect speed to outweigh somewhat higher FP rates. For example, a negative table query would have to be 1000x slower than a BF query to justify doubling BF query time to shave 10% off FP rate (working assumption around 1% FP rate). While that seems likely for SSTs, my data suggests a speed factor of roughly 50x for the memtable (vs. BF; ~1.5% lower write throughput when enabling memtable Bloom filter, after this change). Thus, it's probably not worth even 5% more time in the Bloom filter to shave off 1/10th of the Bloom FP rate, or 0.1% in absolute terms, and it's probably at least 20% slower to recoup that much FP rate from this new implementation. Because of this, we do not see a need for a 'locality' option that affects the MemTable Bloom filter and have decoupled the MemTable Bloom filter from Options::bloom_locality. Note that just 3% more memory to the Bloom filter (10.3 bits per key vs. just 10) is able to make up for the ~12% FP rate drop in the new implementation: [] # Nearly "ideal" FP-wise but reasonably fast cache-local implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out time: 3.29372 sampled_fp_rate: 0.00985956 ... [] # Close match to this new implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out 10000000 6 10.3 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.10072 sampled_fp_rate: 0.00985655 ... [] # Old locality=1 implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out time: 3.95472 sampled_fp_rate: 0.00988943 ... Also note the dramatic speed improvement vs. alternatives. -- Performance unit test: DynamicBloomTest.concurrent_with_perf is updated to report more precise timing data. (Measure running time of each thread, not just longest running thread, etc.) Results averaged over various sizes enabled with --enable_perf and 20 runs each; old dynamic bloom refers to locality=1, the faster of the old: old dynamic bloom, avg add latency = 65.6468 new dynamic bloom, avg add latency = 44.3809 old dynamic bloom, avg query latency = 50.6485 new dynamic bloom, avg query latency = 43.2186 old avg parallel add latency = 41.678 new avg parallel add latency = 24.5238 old avg parallel hit latency = 14.6322 new avg parallel hit latency = 12.3939 old avg parallel miss latency = 16.7289 new avg parallel miss latency = 12.2134 Tested on a dedicated 64-bit production machine at Facebook. Significant improvement all around. Despite now using std::atomic<uint64_t>, quick before-and-after test on a 32-bit machine (Intel Atom N270, released 2008) shows no regression in performance, in some cases modest improvement. -- Performance integration test (synthetic): with DEBUG_LEVEL=0, used TEST_TMPDIR=/dev/shm ./db_bench --benchmarks=fillrandom,readmissing,readrandom,stats --num=2000000 and optionally with -memtable_whole_key_filtering -memtable_bloom_size_ratio=0.01 300 runs each configuration. Write throughput change by enabling memtable bloom: Old locality=0: -3.06% Old locality=1: -2.37% New: -1.50% conclusion -> seems to substantially close the gap Readmissing throughput change by enabling memtable bloom: Old locality=0: +34.47% Old locality=1: +34.80% New: +33.25% conclusion -> maybe a small new penalty from FP rate Readrandom throughput change by enabling memtable bloom: Old locality=0: +31.54% Old locality=1: +31.13% New: +30.60% conclusion -> maybe also from FP rate (after memtable flush) -- Another conclusion we can draw from this new implementation is that the existing 32-bit hash function is not inherently crippling the Bloom filter speed or accuracy, below about 5 million keys. For speed, the implementation is essentially the same whether starting with 32-bits or 64-bits of hash; it just determines whether the first multiplication after fastrange is a pseudorandom expansion or needed re-mix. Note that this multiplication can occur while memory is fetching. For accuracy, in a standard configuration, you need about 5 million keys before you have about a 1.1x FP penalty due to using a 32-bit hash vs. 64-bit: [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.52069 sampled_fp_rate: 0.0118267 ... [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out time: 2.43871 sampled_fp_rate: 0.0109059 Pull Request resolved: https://github.com/facebook/rocksdb/pull/5762 Differential Revision: D17214194 Pulled By: pdillinger fbshipit-source-id: ad9da031772e985fd6b62a0e1db8e81892520595
2019-09-05 23:57:39 +02:00
// An alternative to % for mapping a hash value to an arbitrary range. See
// https://github.com/lemire/fastrange and
// https://github.com/pdillinger/wormhashing/blob/2c4035a4462194bf15f3e9fc180c27c513335225/bloom_simulation_tests/foo.cc#L57
inline uint32_t fastrange32(uint32_t a, uint32_t h) {
uint64_t product = static_cast<uint64_t>(a) * h;
return static_cast<uint32_t>(product >> 32);
}
} // namespace rocksdb