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rocksdb/util/math.h

127 lines
4.6 KiB

Basic MultiGet support for partitioned filters (#6757) Summary: In MultiGet, access each applicable filter partition only once per batch, rather than for each applicable key. Also, * Fix Bloom stats for MultiGet * Fix/refactor MultiGetContext::Range::KeysLeft, including * Add efficient BitsSetToOne implementation * Assert that MultiGetContext::Range does not go beyond shift range Performance test: Generate db: $ ./db_bench --benchmarks=fillrandom --num=15000000 --cache_index_and_filter_blocks -bloom_bits=10 -partition_index_and_filters=true ... Before (middle performing run of three; note some missing Bloom stats): $ ./db_bench --use-existing-db --benchmarks=multireadrandom --num=15000000 --cache_index_and_filter_blocks --bloom_bits=10 --threads=16 --cache_size=20000000 -partition_index_and_filters -batch_size=32 -multiread_batched -statistics --duration=20 2>&1 | egrep 'micros/op|block.cache.filter.hit|bloom.filter.(full|use)|number.multiget' multireadrandom : 26.403 micros/op 597517 ops/sec; (548427 of 671968 found) rocksdb.block.cache.filter.hit COUNT : 83443275 rocksdb.bloom.filter.useful COUNT : 0 rocksdb.bloom.filter.full.positive COUNT : 0 rocksdb.bloom.filter.full.true.positive COUNT : 7931450 rocksdb.number.multiget.get COUNT : 385984 rocksdb.number.multiget.keys.read COUNT : 12351488 rocksdb.number.multiget.bytes.read COUNT : 793145000 rocksdb.number.multiget.keys.found COUNT : 7931450 After (middle performing run of three): $ ./db_bench_new --use-existing-db --benchmarks=multireadrandom --num=15000000 --cache_index_and_filter_blocks --bloom_bits=10 --threads=16 --cache_size=20000000 -partition_index_and_filters -batch_size=32 -multiread_batched -statistics --duration=20 2>&1 | egrep 'micros/op|block.cache.filter.hit|bloom.filter.(full|use)|number.multiget' multireadrandom : 21.024 micros/op 752963 ops/sec; (705188 of 863968 found) rocksdb.block.cache.filter.hit COUNT : 49856682 rocksdb.bloom.filter.useful COUNT : 45684579 rocksdb.bloom.filter.full.positive COUNT : 10395458 rocksdb.bloom.filter.full.true.positive COUNT : 9908456 rocksdb.number.multiget.get COUNT : 481984 rocksdb.number.multiget.keys.read COUNT : 15423488 rocksdb.number.multiget.bytes.read COUNT : 990845600 rocksdb.number.multiget.keys.found COUNT : 9908456 So that's about 25% higher throughput even for random keys Pull Request resolved: https://github.com/facebook/rocksdb/pull/6757 Test Plan: unit test included Reviewed By: anand1976 Differential Revision: D21243256 Pulled By: pdillinger fbshipit-source-id: 5644a1468d9e8c8575be02f4e04bc5d62dbbb57f
4 years ago
// Copyright (c) Facebook, Inc. and its affiliates. 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).
#pragma once
#include <assert.h>
#include <stdint.h>
#ifdef _MSC_VER
#include <intrin.h>
#endif
namespace ROCKSDB_NAMESPACE {
// Fast implementation of floor(log2(v)). Undefined for 0 or negative
// numbers (in case of signed type).
Basic MultiGet support for partitioned filters (#6757) Summary: In MultiGet, access each applicable filter partition only once per batch, rather than for each applicable key. Also, * Fix Bloom stats for MultiGet * Fix/refactor MultiGetContext::Range::KeysLeft, including * Add efficient BitsSetToOne implementation * Assert that MultiGetContext::Range does not go beyond shift range Performance test: Generate db: $ ./db_bench --benchmarks=fillrandom --num=15000000 --cache_index_and_filter_blocks -bloom_bits=10 -partition_index_and_filters=true ... Before (middle performing run of three; note some missing Bloom stats): $ ./db_bench --use-existing-db --benchmarks=multireadrandom --num=15000000 --cache_index_and_filter_blocks --bloom_bits=10 --threads=16 --cache_size=20000000 -partition_index_and_filters -batch_size=32 -multiread_batched -statistics --duration=20 2>&1 | egrep 'micros/op|block.cache.filter.hit|bloom.filter.(full|use)|number.multiget' multireadrandom : 26.403 micros/op 597517 ops/sec; (548427 of 671968 found) rocksdb.block.cache.filter.hit COUNT : 83443275 rocksdb.bloom.filter.useful COUNT : 0 rocksdb.bloom.filter.full.positive COUNT : 0 rocksdb.bloom.filter.full.true.positive COUNT : 7931450 rocksdb.number.multiget.get COUNT : 385984 rocksdb.number.multiget.keys.read COUNT : 12351488 rocksdb.number.multiget.bytes.read COUNT : 793145000 rocksdb.number.multiget.keys.found COUNT : 7931450 After (middle performing run of three): $ ./db_bench_new --use-existing-db --benchmarks=multireadrandom --num=15000000 --cache_index_and_filter_blocks --bloom_bits=10 --threads=16 --cache_size=20000000 -partition_index_and_filters -batch_size=32 -multiread_batched -statistics --duration=20 2>&1 | egrep 'micros/op|block.cache.filter.hit|bloom.filter.(full|use)|number.multiget' multireadrandom : 21.024 micros/op 752963 ops/sec; (705188 of 863968 found) rocksdb.block.cache.filter.hit COUNT : 49856682 rocksdb.bloom.filter.useful COUNT : 45684579 rocksdb.bloom.filter.full.positive COUNT : 10395458 rocksdb.bloom.filter.full.true.positive COUNT : 9908456 rocksdb.number.multiget.get COUNT : 481984 rocksdb.number.multiget.keys.read COUNT : 15423488 rocksdb.number.multiget.bytes.read COUNT : 990845600 rocksdb.number.multiget.keys.found COUNT : 9908456 So that's about 25% higher throughput even for random keys Pull Request resolved: https://github.com/facebook/rocksdb/pull/6757 Test Plan: unit test included Reviewed By: anand1976 Differential Revision: D21243256 Pulled By: pdillinger fbshipit-source-id: 5644a1468d9e8c8575be02f4e04bc5d62dbbb57f
4 years ago
template <typename T>
inline int FloorLog2(T v) {
Basic MultiGet support for partitioned filters (#6757) Summary: In MultiGet, access each applicable filter partition only once per batch, rather than for each applicable key. Also, * Fix Bloom stats for MultiGet * Fix/refactor MultiGetContext::Range::KeysLeft, including * Add efficient BitsSetToOne implementation * Assert that MultiGetContext::Range does not go beyond shift range Performance test: Generate db: $ ./db_bench --benchmarks=fillrandom --num=15000000 --cache_index_and_filter_blocks -bloom_bits=10 -partition_index_and_filters=true ... Before (middle performing run of three; note some missing Bloom stats): $ ./db_bench --use-existing-db --benchmarks=multireadrandom --num=15000000 --cache_index_and_filter_blocks --bloom_bits=10 --threads=16 --cache_size=20000000 -partition_index_and_filters -batch_size=32 -multiread_batched -statistics --duration=20 2>&1 | egrep 'micros/op|block.cache.filter.hit|bloom.filter.(full|use)|number.multiget' multireadrandom : 26.403 micros/op 597517 ops/sec; (548427 of 671968 found) rocksdb.block.cache.filter.hit COUNT : 83443275 rocksdb.bloom.filter.useful COUNT : 0 rocksdb.bloom.filter.full.positive COUNT : 0 rocksdb.bloom.filter.full.true.positive COUNT : 7931450 rocksdb.number.multiget.get COUNT : 385984 rocksdb.number.multiget.keys.read COUNT : 12351488 rocksdb.number.multiget.bytes.read COUNT : 793145000 rocksdb.number.multiget.keys.found COUNT : 7931450 After (middle performing run of three): $ ./db_bench_new --use-existing-db --benchmarks=multireadrandom --num=15000000 --cache_index_and_filter_blocks --bloom_bits=10 --threads=16 --cache_size=20000000 -partition_index_and_filters -batch_size=32 -multiread_batched -statistics --duration=20 2>&1 | egrep 'micros/op|block.cache.filter.hit|bloom.filter.(full|use)|number.multiget' multireadrandom : 21.024 micros/op 752963 ops/sec; (705188 of 863968 found) rocksdb.block.cache.filter.hit COUNT : 49856682 rocksdb.bloom.filter.useful COUNT : 45684579 rocksdb.bloom.filter.full.positive COUNT : 10395458 rocksdb.bloom.filter.full.true.positive COUNT : 9908456 rocksdb.number.multiget.get COUNT : 481984 rocksdb.number.multiget.keys.read COUNT : 15423488 rocksdb.number.multiget.bytes.read COUNT : 990845600 rocksdb.number.multiget.keys.found COUNT : 9908456 So that's about 25% higher throughput even for random keys Pull Request resolved: https://github.com/facebook/rocksdb/pull/6757 Test Plan: unit test included Reviewed By: anand1976 Differential Revision: D21243256 Pulled By: pdillinger fbshipit-source-id: 5644a1468d9e8c8575be02f4e04bc5d62dbbb57f
4 years ago
static_assert(std::is_integral<T>::value, "non-integral type");
assert(v > 0);
Basic MultiGet support for partitioned filters (#6757) Summary: In MultiGet, access each applicable filter partition only once per batch, rather than for each applicable key. Also, * Fix Bloom stats for MultiGet * Fix/refactor MultiGetContext::Range::KeysLeft, including * Add efficient BitsSetToOne implementation * Assert that MultiGetContext::Range does not go beyond shift range Performance test: Generate db: $ ./db_bench --benchmarks=fillrandom --num=15000000 --cache_index_and_filter_blocks -bloom_bits=10 -partition_index_and_filters=true ... Before (middle performing run of three; note some missing Bloom stats): $ ./db_bench --use-existing-db --benchmarks=multireadrandom --num=15000000 --cache_index_and_filter_blocks --bloom_bits=10 --threads=16 --cache_size=20000000 -partition_index_and_filters -batch_size=32 -multiread_batched -statistics --duration=20 2>&1 | egrep 'micros/op|block.cache.filter.hit|bloom.filter.(full|use)|number.multiget' multireadrandom : 26.403 micros/op 597517 ops/sec; (548427 of 671968 found) rocksdb.block.cache.filter.hit COUNT : 83443275 rocksdb.bloom.filter.useful COUNT : 0 rocksdb.bloom.filter.full.positive COUNT : 0 rocksdb.bloom.filter.full.true.positive COUNT : 7931450 rocksdb.number.multiget.get COUNT : 385984 rocksdb.number.multiget.keys.read COUNT : 12351488 rocksdb.number.multiget.bytes.read COUNT : 793145000 rocksdb.number.multiget.keys.found COUNT : 7931450 After (middle performing run of three): $ ./db_bench_new --use-existing-db --benchmarks=multireadrandom --num=15000000 --cache_index_and_filter_blocks --bloom_bits=10 --threads=16 --cache_size=20000000 -partition_index_and_filters -batch_size=32 -multiread_batched -statistics --duration=20 2>&1 | egrep 'micros/op|block.cache.filter.hit|bloom.filter.(full|use)|number.multiget' multireadrandom : 21.024 micros/op 752963 ops/sec; (705188 of 863968 found) rocksdb.block.cache.filter.hit COUNT : 49856682 rocksdb.bloom.filter.useful COUNT : 45684579 rocksdb.bloom.filter.full.positive COUNT : 10395458 rocksdb.bloom.filter.full.true.positive COUNT : 9908456 rocksdb.number.multiget.get COUNT : 481984 rocksdb.number.multiget.keys.read COUNT : 15423488 rocksdb.number.multiget.bytes.read COUNT : 990845600 rocksdb.number.multiget.keys.found COUNT : 9908456 So that's about 25% higher throughput even for random keys Pull Request resolved: https://github.com/facebook/rocksdb/pull/6757 Test Plan: unit test included Reviewed By: anand1976 Differential Revision: D21243256 Pulled By: pdillinger fbshipit-source-id: 5644a1468d9e8c8575be02f4e04bc5d62dbbb57f
4 years ago
#ifdef _MSC_VER
static_assert(sizeof(T) <= sizeof(uint64_t), "type too big");
unsigned long lz = 0;
if (sizeof(T) <= sizeof(uint32_t)) {
_BitScanReverse(&lz, static_cast<uint32_t>(v));
} else {
_BitScanReverse64(&lz, static_cast<uint64_t>(v));
}
return 63 - static_cast<int>(lz);
#else
static_assert(sizeof(T) <= sizeof(unsigned long long), "type too big");
if (sizeof(T) <= sizeof(unsigned int)) {
int lz = __builtin_clz(static_cast<unsigned int>(v));
return int{sizeof(unsigned int)} * 8 - 1 - lz;
} else if (sizeof(T) <= sizeof(unsigned long)) {
int lz = __builtin_clzl(static_cast<unsigned long>(v));
return int{sizeof(unsigned long)} * 8 - 1 - lz;
} else {
int lz = __builtin_clzll(static_cast<unsigned long long>(v));
return int{sizeof(unsigned long long)} * 8 - 1 - lz;
}
#endif
}
// Number of low-order zero bits before the first 1 bit. Undefined for 0.
template <typename T>
inline int CountTrailingZeroBits(T v) {
static_assert(std::is_integral<T>::value, "non-integral type");
assert(v != 0);
#ifdef _MSC_VER
static_assert(sizeof(T) <= sizeof(uint64_t), "type too big");
unsigned long tz = 0;
if (sizeof(T) <= sizeof(uint32_t)) {
_BitScanForward(&tz, static_cast<uint32_t>(v));
Basic MultiGet support for partitioned filters (#6757) Summary: In MultiGet, access each applicable filter partition only once per batch, rather than for each applicable key. Also, * Fix Bloom stats for MultiGet * Fix/refactor MultiGetContext::Range::KeysLeft, including * Add efficient BitsSetToOne implementation * Assert that MultiGetContext::Range does not go beyond shift range Performance test: Generate db: $ ./db_bench --benchmarks=fillrandom --num=15000000 --cache_index_and_filter_blocks -bloom_bits=10 -partition_index_and_filters=true ... Before (middle performing run of three; note some missing Bloom stats): $ ./db_bench --use-existing-db --benchmarks=multireadrandom --num=15000000 --cache_index_and_filter_blocks --bloom_bits=10 --threads=16 --cache_size=20000000 -partition_index_and_filters -batch_size=32 -multiread_batched -statistics --duration=20 2>&1 | egrep 'micros/op|block.cache.filter.hit|bloom.filter.(full|use)|number.multiget' multireadrandom : 26.403 micros/op 597517 ops/sec; (548427 of 671968 found) rocksdb.block.cache.filter.hit COUNT : 83443275 rocksdb.bloom.filter.useful COUNT : 0 rocksdb.bloom.filter.full.positive COUNT : 0 rocksdb.bloom.filter.full.true.positive COUNT : 7931450 rocksdb.number.multiget.get COUNT : 385984 rocksdb.number.multiget.keys.read COUNT : 12351488 rocksdb.number.multiget.bytes.read COUNT : 793145000 rocksdb.number.multiget.keys.found COUNT : 7931450 After (middle performing run of three): $ ./db_bench_new --use-existing-db --benchmarks=multireadrandom --num=15000000 --cache_index_and_filter_blocks --bloom_bits=10 --threads=16 --cache_size=20000000 -partition_index_and_filters -batch_size=32 -multiread_batched -statistics --duration=20 2>&1 | egrep 'micros/op|block.cache.filter.hit|bloom.filter.(full|use)|number.multiget' multireadrandom : 21.024 micros/op 752963 ops/sec; (705188 of 863968 found) rocksdb.block.cache.filter.hit COUNT : 49856682 rocksdb.bloom.filter.useful COUNT : 45684579 rocksdb.bloom.filter.full.positive COUNT : 10395458 rocksdb.bloom.filter.full.true.positive COUNT : 9908456 rocksdb.number.multiget.get COUNT : 481984 rocksdb.number.multiget.keys.read COUNT : 15423488 rocksdb.number.multiget.bytes.read COUNT : 990845600 rocksdb.number.multiget.keys.found COUNT : 9908456 So that's about 25% higher throughput even for random keys Pull Request resolved: https://github.com/facebook/rocksdb/pull/6757 Test Plan: unit test included Reviewed By: anand1976 Differential Revision: D21243256 Pulled By: pdillinger fbshipit-source-id: 5644a1468d9e8c8575be02f4e04bc5d62dbbb57f
4 years ago
} else {
_BitScanForward64(&tz, static_cast<uint64_t>(v));
}
return static_cast<int>(tz);
#else
static_assert(sizeof(T) <= sizeof(unsigned long long), "type too big");
if (sizeof(T) <= sizeof(unsigned int)) {
return __builtin_ctz(static_cast<unsigned int>(v));
} else if (sizeof(T) <= sizeof(unsigned long)) {
return __builtin_ctzl(static_cast<unsigned long>(v));
} else {
return __builtin_ctzll(static_cast<unsigned long long>(v));
}
#endif
}
// Number of bits set to 1. Also known as "population count".
template <typename T>
inline int BitsSetToOne(T v) {
static_assert(std::is_integral<T>::value, "non-integral type");
#ifdef _MSC_VER
static_assert(sizeof(T) <= sizeof(uint64_t), "type too big");
if (sizeof(T) < sizeof(uint32_t)) {
// This bit mask is to avoid a compiler warning on unused path
constexpr auto mm = 8 * sizeof(uint32_t) - 1;
// The bit mask is to neutralize sign extension on small signed types
constexpr uint32_t m = (uint32_t{1} << ((8 * sizeof(T)) & mm)) - 1;
return static_cast<int>(__popcnt(static_cast<uint32_t>(v) & m));
} else if (sizeof(T) == sizeof(uint32_t)) {
Basic MultiGet support for partitioned filters (#6757) Summary: In MultiGet, access each applicable filter partition only once per batch, rather than for each applicable key. Also, * Fix Bloom stats for MultiGet * Fix/refactor MultiGetContext::Range::KeysLeft, including * Add efficient BitsSetToOne implementation * Assert that MultiGetContext::Range does not go beyond shift range Performance test: Generate db: $ ./db_bench --benchmarks=fillrandom --num=15000000 --cache_index_and_filter_blocks -bloom_bits=10 -partition_index_and_filters=true ... Before (middle performing run of three; note some missing Bloom stats): $ ./db_bench --use-existing-db --benchmarks=multireadrandom --num=15000000 --cache_index_and_filter_blocks --bloom_bits=10 --threads=16 --cache_size=20000000 -partition_index_and_filters -batch_size=32 -multiread_batched -statistics --duration=20 2>&1 | egrep 'micros/op|block.cache.filter.hit|bloom.filter.(full|use)|number.multiget' multireadrandom : 26.403 micros/op 597517 ops/sec; (548427 of 671968 found) rocksdb.block.cache.filter.hit COUNT : 83443275 rocksdb.bloom.filter.useful COUNT : 0 rocksdb.bloom.filter.full.positive COUNT : 0 rocksdb.bloom.filter.full.true.positive COUNT : 7931450 rocksdb.number.multiget.get COUNT : 385984 rocksdb.number.multiget.keys.read COUNT : 12351488 rocksdb.number.multiget.bytes.read COUNT : 793145000 rocksdb.number.multiget.keys.found COUNT : 7931450 After (middle performing run of three): $ ./db_bench_new --use-existing-db --benchmarks=multireadrandom --num=15000000 --cache_index_and_filter_blocks --bloom_bits=10 --threads=16 --cache_size=20000000 -partition_index_and_filters -batch_size=32 -multiread_batched -statistics --duration=20 2>&1 | egrep 'micros/op|block.cache.filter.hit|bloom.filter.(full|use)|number.multiget' multireadrandom : 21.024 micros/op 752963 ops/sec; (705188 of 863968 found) rocksdb.block.cache.filter.hit COUNT : 49856682 rocksdb.bloom.filter.useful COUNT : 45684579 rocksdb.bloom.filter.full.positive COUNT : 10395458 rocksdb.bloom.filter.full.true.positive COUNT : 9908456 rocksdb.number.multiget.get COUNT : 481984 rocksdb.number.multiget.keys.read COUNT : 15423488 rocksdb.number.multiget.bytes.read COUNT : 990845600 rocksdb.number.multiget.keys.found COUNT : 9908456 So that's about 25% higher throughput even for random keys Pull Request resolved: https://github.com/facebook/rocksdb/pull/6757 Test Plan: unit test included Reviewed By: anand1976 Differential Revision: D21243256 Pulled By: pdillinger fbshipit-source-id: 5644a1468d9e8c8575be02f4e04bc5d62dbbb57f
4 years ago
return static_cast<int>(__popcnt(static_cast<uint32_t>(v)));
} else {
return static_cast<int>(__popcnt64(static_cast<uint64_t>(v)));
Basic MultiGet support for partitioned filters (#6757) Summary: In MultiGet, access each applicable filter partition only once per batch, rather than for each applicable key. Also, * Fix Bloom stats for MultiGet * Fix/refactor MultiGetContext::Range::KeysLeft, including * Add efficient BitsSetToOne implementation * Assert that MultiGetContext::Range does not go beyond shift range Performance test: Generate db: $ ./db_bench --benchmarks=fillrandom --num=15000000 --cache_index_and_filter_blocks -bloom_bits=10 -partition_index_and_filters=true ... Before (middle performing run of three; note some missing Bloom stats): $ ./db_bench --use-existing-db --benchmarks=multireadrandom --num=15000000 --cache_index_and_filter_blocks --bloom_bits=10 --threads=16 --cache_size=20000000 -partition_index_and_filters -batch_size=32 -multiread_batched -statistics --duration=20 2>&1 | egrep 'micros/op|block.cache.filter.hit|bloom.filter.(full|use)|number.multiget' multireadrandom : 26.403 micros/op 597517 ops/sec; (548427 of 671968 found) rocksdb.block.cache.filter.hit COUNT : 83443275 rocksdb.bloom.filter.useful COUNT : 0 rocksdb.bloom.filter.full.positive COUNT : 0 rocksdb.bloom.filter.full.true.positive COUNT : 7931450 rocksdb.number.multiget.get COUNT : 385984 rocksdb.number.multiget.keys.read COUNT : 12351488 rocksdb.number.multiget.bytes.read COUNT : 793145000 rocksdb.number.multiget.keys.found COUNT : 7931450 After (middle performing run of three): $ ./db_bench_new --use-existing-db --benchmarks=multireadrandom --num=15000000 --cache_index_and_filter_blocks --bloom_bits=10 --threads=16 --cache_size=20000000 -partition_index_and_filters -batch_size=32 -multiread_batched -statistics --duration=20 2>&1 | egrep 'micros/op|block.cache.filter.hit|bloom.filter.(full|use)|number.multiget' multireadrandom : 21.024 micros/op 752963 ops/sec; (705188 of 863968 found) rocksdb.block.cache.filter.hit COUNT : 49856682 rocksdb.bloom.filter.useful COUNT : 45684579 rocksdb.bloom.filter.full.positive COUNT : 10395458 rocksdb.bloom.filter.full.true.positive COUNT : 9908456 rocksdb.number.multiget.get COUNT : 481984 rocksdb.number.multiget.keys.read COUNT : 15423488 rocksdb.number.multiget.bytes.read COUNT : 990845600 rocksdb.number.multiget.keys.found COUNT : 9908456 So that's about 25% higher throughput even for random keys Pull Request resolved: https://github.com/facebook/rocksdb/pull/6757 Test Plan: unit test included Reviewed By: anand1976 Differential Revision: D21243256 Pulled By: pdillinger fbshipit-source-id: 5644a1468d9e8c8575be02f4e04bc5d62dbbb57f
4 years ago
}
#else
static_assert(sizeof(T) <= sizeof(unsigned long long), "type too big");
if (sizeof(T) < sizeof(unsigned int)) {
// This bit mask is to avoid a compiler warning on unused path
constexpr auto mm = 8 * sizeof(unsigned int) - 1;
// This bit mask is to neutralize sign extension on small signed types
constexpr unsigned int m = (1U << ((8 * sizeof(T)) & mm)) - 1;
return __builtin_popcount(static_cast<unsigned int>(v) & m);
} else if (sizeof(T) == sizeof(unsigned int)) {
return __builtin_popcount(static_cast<unsigned int>(v));
} else if (sizeof(T) <= sizeof(unsigned long)) {
Basic MultiGet support for partitioned filters (#6757) Summary: In MultiGet, access each applicable filter partition only once per batch, rather than for each applicable key. Also, * Fix Bloom stats for MultiGet * Fix/refactor MultiGetContext::Range::KeysLeft, including * Add efficient BitsSetToOne implementation * Assert that MultiGetContext::Range does not go beyond shift range Performance test: Generate db: $ ./db_bench --benchmarks=fillrandom --num=15000000 --cache_index_and_filter_blocks -bloom_bits=10 -partition_index_and_filters=true ... Before (middle performing run of three; note some missing Bloom stats): $ ./db_bench --use-existing-db --benchmarks=multireadrandom --num=15000000 --cache_index_and_filter_blocks --bloom_bits=10 --threads=16 --cache_size=20000000 -partition_index_and_filters -batch_size=32 -multiread_batched -statistics --duration=20 2>&1 | egrep 'micros/op|block.cache.filter.hit|bloom.filter.(full|use)|number.multiget' multireadrandom : 26.403 micros/op 597517 ops/sec; (548427 of 671968 found) rocksdb.block.cache.filter.hit COUNT : 83443275 rocksdb.bloom.filter.useful COUNT : 0 rocksdb.bloom.filter.full.positive COUNT : 0 rocksdb.bloom.filter.full.true.positive COUNT : 7931450 rocksdb.number.multiget.get COUNT : 385984 rocksdb.number.multiget.keys.read COUNT : 12351488 rocksdb.number.multiget.bytes.read COUNT : 793145000 rocksdb.number.multiget.keys.found COUNT : 7931450 After (middle performing run of three): $ ./db_bench_new --use-existing-db --benchmarks=multireadrandom --num=15000000 --cache_index_and_filter_blocks --bloom_bits=10 --threads=16 --cache_size=20000000 -partition_index_and_filters -batch_size=32 -multiread_batched -statistics --duration=20 2>&1 | egrep 'micros/op|block.cache.filter.hit|bloom.filter.(full|use)|number.multiget' multireadrandom : 21.024 micros/op 752963 ops/sec; (705188 of 863968 found) rocksdb.block.cache.filter.hit COUNT : 49856682 rocksdb.bloom.filter.useful COUNT : 45684579 rocksdb.bloom.filter.full.positive COUNT : 10395458 rocksdb.bloom.filter.full.true.positive COUNT : 9908456 rocksdb.number.multiget.get COUNT : 481984 rocksdb.number.multiget.keys.read COUNT : 15423488 rocksdb.number.multiget.bytes.read COUNT : 990845600 rocksdb.number.multiget.keys.found COUNT : 9908456 So that's about 25% higher throughput even for random keys Pull Request resolved: https://github.com/facebook/rocksdb/pull/6757 Test Plan: unit test included Reviewed By: anand1976 Differential Revision: D21243256 Pulled By: pdillinger fbshipit-source-id: 5644a1468d9e8c8575be02f4e04bc5d62dbbb57f
4 years ago
return __builtin_popcountl(static_cast<unsigned long>(v));
} else {
return __builtin_popcountll(static_cast<unsigned long long>(v));
}
#endif
}
template <typename T>
inline int BitParity(T v) {
static_assert(std::is_integral<T>::value, "non-integral type");
#ifdef _MSC_VER
// bit parity == oddness of popcount
return BitsSetToOne(v) & 1;
#else
static_assert(sizeof(T) <= sizeof(unsigned long long), "type too big");
if (sizeof(T) <= sizeof(unsigned int)) {
// On any sane systen, potential sign extension here won't change parity
return __builtin_parity(static_cast<unsigned int>(v));
} else if (sizeof(T) <= sizeof(unsigned long)) {
return __builtin_parityl(static_cast<unsigned long>(v));
} else {
return __builtin_parityll(static_cast<unsigned long long>(v));
Basic MultiGet support for partitioned filters (#6757) Summary: In MultiGet, access each applicable filter partition only once per batch, rather than for each applicable key. Also, * Fix Bloom stats for MultiGet * Fix/refactor MultiGetContext::Range::KeysLeft, including * Add efficient BitsSetToOne implementation * Assert that MultiGetContext::Range does not go beyond shift range Performance test: Generate db: $ ./db_bench --benchmarks=fillrandom --num=15000000 --cache_index_and_filter_blocks -bloom_bits=10 -partition_index_and_filters=true ... Before (middle performing run of three; note some missing Bloom stats): $ ./db_bench --use-existing-db --benchmarks=multireadrandom --num=15000000 --cache_index_and_filter_blocks --bloom_bits=10 --threads=16 --cache_size=20000000 -partition_index_and_filters -batch_size=32 -multiread_batched -statistics --duration=20 2>&1 | egrep 'micros/op|block.cache.filter.hit|bloom.filter.(full|use)|number.multiget' multireadrandom : 26.403 micros/op 597517 ops/sec; (548427 of 671968 found) rocksdb.block.cache.filter.hit COUNT : 83443275 rocksdb.bloom.filter.useful COUNT : 0 rocksdb.bloom.filter.full.positive COUNT : 0 rocksdb.bloom.filter.full.true.positive COUNT : 7931450 rocksdb.number.multiget.get COUNT : 385984 rocksdb.number.multiget.keys.read COUNT : 12351488 rocksdb.number.multiget.bytes.read COUNT : 793145000 rocksdb.number.multiget.keys.found COUNT : 7931450 After (middle performing run of three): $ ./db_bench_new --use-existing-db --benchmarks=multireadrandom --num=15000000 --cache_index_and_filter_blocks --bloom_bits=10 --threads=16 --cache_size=20000000 -partition_index_and_filters -batch_size=32 -multiread_batched -statistics --duration=20 2>&1 | egrep 'micros/op|block.cache.filter.hit|bloom.filter.(full|use)|number.multiget' multireadrandom : 21.024 micros/op 752963 ops/sec; (705188 of 863968 found) rocksdb.block.cache.filter.hit COUNT : 49856682 rocksdb.bloom.filter.useful COUNT : 45684579 rocksdb.bloom.filter.full.positive COUNT : 10395458 rocksdb.bloom.filter.full.true.positive COUNT : 9908456 rocksdb.number.multiget.get COUNT : 481984 rocksdb.number.multiget.keys.read COUNT : 15423488 rocksdb.number.multiget.bytes.read COUNT : 990845600 rocksdb.number.multiget.keys.found COUNT : 9908456 So that's about 25% higher throughput even for random keys Pull Request resolved: https://github.com/facebook/rocksdb/pull/6757 Test Plan: unit test included Reviewed By: anand1976 Differential Revision: D21243256 Pulled By: pdillinger fbshipit-source-id: 5644a1468d9e8c8575be02f4e04bc5d62dbbb57f
4 years ago
}
#endif
}
} // namespace ROCKSDB_NAMESPACE