fdf882ded2
Summary: When dynamically linking two binaries together, different builds of RocksDB from two sources might cause errors. To provide a tool for user to solve the problem, the RocksDB namespace is changed to a flag which can be overridden in build time. Pull Request resolved: https://github.com/facebook/rocksdb/pull/6433 Test Plan: Build release, all and jtest. Try to build with ROCKSDB_NAMESPACE with another flag. Differential Revision: D19977691 fbshipit-source-id: aa7f2d0972e1c31d75339ac48478f34f6cfcfb3e
484 lines
21 KiB
C++
484 lines
21 KiB
C++
// Copyright (c) 2019-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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//
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// Implementation details of various Bloom filter implementations used in
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// RocksDB. (DynamicBloom is in a separate file for now because it
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// supports concurrent write.)
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#pragma once
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#include <stddef.h>
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#include <stdint.h>
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#include <cmath>
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#include "rocksdb/slice.h"
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#include "util/hash.h"
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#ifdef HAVE_AVX2
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#include <immintrin.h>
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#endif
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namespace ROCKSDB_NAMESPACE {
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class BloomMath {
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public:
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// False positive rate of a standard Bloom filter, for given ratio of
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// filter memory bits to added keys, and number of probes per operation.
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// (The false positive rate is effectively independent of scale, assuming
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// the implementation scales OK.)
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static double StandardFpRate(double bits_per_key, int num_probes) {
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// Standard very-good-estimate formula. See
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// https://en.wikipedia.org/wiki/Bloom_filter#Probability_of_false_positives
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return std::pow(1.0 - std::exp(-num_probes / bits_per_key), num_probes);
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}
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// False positive rate of a "blocked"/"shareded"/"cache-local" Bloom filter,
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// for given ratio of filter memory bits to added keys, number of probes per
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// operation (all within the given block or cache line size), and block or
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// cache line size.
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static double CacheLocalFpRate(double bits_per_key, int num_probes,
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int cache_line_bits) {
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double keys_per_cache_line = cache_line_bits / bits_per_key;
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// A reasonable estimate is the average of the FP rates for one standard
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// deviation above and below the mean bucket occupancy. See
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// https://github.com/facebook/rocksdb/wiki/RocksDB-Bloom-Filter#the-math
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double keys_stddev = std::sqrt(keys_per_cache_line);
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double crowded_fp = StandardFpRate(
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cache_line_bits / (keys_per_cache_line + keys_stddev), num_probes);
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double uncrowded_fp = StandardFpRate(
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cache_line_bits / (keys_per_cache_line - keys_stddev), num_probes);
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return (crowded_fp + uncrowded_fp) / 2;
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}
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// False positive rate of querying a new item against `num_keys` items, all
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// hashed to `fingerprint_bits` bits. (This assumes the fingerprint hashes
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// themselves are stored losslessly. See Section 4 of
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// http://www.ccs.neu.edu/home/pete/pub/bloom-filters-verification.pdf)
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static double FingerprintFpRate(size_t num_keys, int fingerprint_bits) {
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double inv_fingerprint_space = std::pow(0.5, fingerprint_bits);
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// Base estimate assumes each key maps to a unique fingerprint.
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// Could be > 1 in extreme cases.
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double base_estimate = num_keys * inv_fingerprint_space;
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// To account for potential overlap, we choose between two formulas
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if (base_estimate > 0.0001) {
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// A very good formula assuming we don't construct a floating point
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// number extremely close to 1. Always produces a probability < 1.
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return 1.0 - std::exp(-base_estimate);
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} else {
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// A very good formula when base_estimate is far below 1. (Subtract
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// away the integral-approximated sum that some key has same hash as
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// one coming before it in a list.)
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return base_estimate - (base_estimate * base_estimate * 0.5);
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}
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}
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// Returns the probably of either of two independent(-ish) events
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// happening, given their probabilities. (This is useful for combining
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// results from StandardFpRate or CacheLocalFpRate with FingerprintFpRate
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// for a hash-efficient Bloom filter's FP rate. See Section 4 of
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// http://www.ccs.neu.edu/home/pete/pub/bloom-filters-verification.pdf)
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static double IndependentProbabilitySum(double rate1, double rate2) {
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// Use formula that avoids floating point extremely close to 1 if
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// rates are extremely small.
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return rate1 + rate2 - (rate1 * rate2);
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}
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};
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// A fast, flexible, and accurate cache-local Bloom implementation with
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// SIMD-optimized query performance (currently using AVX2 on Intel). Write
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// performance and non-SIMD read are very good, benefiting from fastrange32
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// used in place of % and single-cycle multiplication on recent processors.
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//
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// Most other SIMD Bloom implementations sacrifice flexibility and/or
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// accuracy by requiring num_probes to be a power of two and restricting
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// where each probe can occur in a cache line. This implementation sacrifices
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// SIMD-optimization for add (might still be possible, especially with AVX512)
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// in favor of allowing any num_probes, not crossing cache line boundary,
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// and accuracy close to theoretical best accuracy for a cache-local Bloom.
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// E.g. theoretical best for 10 bits/key, num_probes=6, and 512-bit bucket
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// (Intel cache line size) is 0.9535% FP rate. This implementation yields
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// about 0.957%. (Compare to LegacyLocalityBloomImpl<false> at 1.138%, or
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// about 0.951% for 1024-bit buckets, cache line size for some ARM CPUs.)
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//
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// This implementation can use a 32-bit hash (let h2 be h1 * 0x9e3779b9) or
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// a 64-bit hash (split into two uint32s). With many millions of keys, the
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// false positive rate associated with using a 32-bit hash can dominate the
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// false positive rate of the underlying filter. At 10 bits/key setting, the
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// inflection point is about 40 million keys, so 32-bit hash is a bad idea
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// with 10s of millions of keys or more.
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//
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// Despite accepting a 64-bit hash, this implementation uses 32-bit fastrange
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// to pick a cache line, which can be faster than 64-bit in some cases.
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// This only hurts accuracy as you get into 10s of GB for a single filter,
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// and accuracy abruptly breaks down at 256GB (2^32 cache lines). Switch to
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// 64-bit fastrange if you need filters so big. ;)
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//
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// Using only a 32-bit input hash within each cache line has negligible
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// impact for any reasonable cache line / bucket size, for arbitrary filter
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// size, and potentially saves intermediate data size in some cases vs.
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// tracking full 64 bits. (Even in an implementation using 64-bit arithmetic
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// to generate indices, I might do the same, as a single multiplication
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// suffices to generate a sufficiently mixed 64 bits from 32 bits.)
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//
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// This implementation is currently tied to Intel cache line size, 64 bytes ==
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// 512 bits. If there's sufficient demand for other cache line sizes, this is
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// a pretty good implementation to extend, but slight performance enhancements
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// are possible with an alternate implementation (probably not very compatible
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// with SIMD):
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// (1) Use rotation in addition to multiplication for remixing
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// (like murmur hash). (Using multiplication alone *slightly* hurts accuracy
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// because lower bits never depend on original upper bits.)
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// (2) Extract more than one bit index from each re-mix. (Only if rotation
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// or similar is part of remix, because otherwise you're making the
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// multiplication-only problem worse.)
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// (3) Re-mix full 64 bit hash, to get maximum number of bit indices per
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// re-mix.
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//
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class FastLocalBloomImpl {
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public:
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// NOTE: this has only been validated to enough accuracy for producing
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// reasonable warnings / user feedback, not for making functional decisions.
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static double EstimatedFpRate(size_t keys, size_t bytes, int num_probes,
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int hash_bits) {
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return BloomMath::IndependentProbabilitySum(
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BloomMath::CacheLocalFpRate(8.0 * bytes / keys, num_probes,
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/*cache line bits*/ 512),
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BloomMath::FingerprintFpRate(keys, hash_bits));
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}
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static inline int ChooseNumProbes(int millibits_per_key) {
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// Since this implementation can (with AVX2) make up to 8 probes
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// for the same cost, we pick the most accurate num_probes, based
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// on actual tests of the implementation. Note that for higher
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// bits/key, the best choice for cache-local Bloom can be notably
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// smaller than standard bloom, e.g. 9 instead of 11 @ 16 b/k.
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if (millibits_per_key <= 2080) {
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return 1;
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} else if (millibits_per_key <= 3580) {
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return 2;
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} else if (millibits_per_key <= 5100) {
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return 3;
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} else if (millibits_per_key <= 6640) {
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return 4;
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} else if (millibits_per_key <= 8300) {
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return 5;
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} else if (millibits_per_key <= 10070) {
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return 6;
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} else if (millibits_per_key <= 11720) {
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return 7;
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} else if (millibits_per_key <= 14001) {
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// Would be something like <= 13800 but sacrificing *slightly* for
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// more settings using <= 8 probes.
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return 8;
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} else if (millibits_per_key <= 16050) {
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return 9;
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} else if (millibits_per_key <= 18300) {
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return 10;
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} else if (millibits_per_key <= 22001) {
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return 11;
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} else if (millibits_per_key <= 25501) {
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return 12;
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} else if (millibits_per_key > 50000) {
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// Top out at 24 probes (three sets of 8)
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return 24;
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} else {
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// Roughly optimal choices for remaining range
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// e.g.
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// 28000 -> 12, 28001 -> 13
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// 50000 -> 23, 50001 -> 24
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return (millibits_per_key - 1) / 2000 - 1;
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}
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}
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static inline void AddHash(uint32_t h1, uint32_t h2, uint32_t len_bytes,
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int num_probes, char *data) {
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uint32_t bytes_to_cache_line = fastrange32(len_bytes >> 6, h1) << 6;
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AddHashPrepared(h2, num_probes, data + bytes_to_cache_line);
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}
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static inline void AddHashPrepared(uint32_t h2, int num_probes,
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char *data_at_cache_line) {
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uint32_t h = h2;
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for (int i = 0; i < num_probes; ++i, h *= uint32_t{0x9e3779b9}) {
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// 9-bit address within 512 bit cache line
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int bitpos = h >> (32 - 9);
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data_at_cache_line[bitpos >> 3] |= (uint8_t{1} << (bitpos & 7));
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}
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}
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static inline void PrepareHash(uint32_t h1, uint32_t len_bytes,
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const char *data,
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uint32_t /*out*/ *byte_offset) {
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uint32_t bytes_to_cache_line = fastrange32(len_bytes >> 6, h1) << 6;
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PREFETCH(data + bytes_to_cache_line, 0 /* rw */, 1 /* locality */);
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PREFETCH(data + bytes_to_cache_line + 63, 0 /* rw */, 1 /* locality */);
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*byte_offset = bytes_to_cache_line;
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}
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static inline bool HashMayMatch(uint32_t h1, uint32_t h2, uint32_t len_bytes,
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int num_probes, const char *data) {
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uint32_t bytes_to_cache_line = fastrange32(len_bytes >> 6, h1) << 6;
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return HashMayMatchPrepared(h2, num_probes, data + bytes_to_cache_line);
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}
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static inline bool HashMayMatchPrepared(uint32_t h2, int num_probes,
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const char *data_at_cache_line) {
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uint32_t h = h2;
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#ifdef HAVE_AVX2
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int rem_probes = num_probes;
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// NOTE: For better performance for num_probes in {1, 2, 9, 10, 17, 18,
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// etc.} one can insert specialized code for rem_probes <= 2, bypassing
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// the SIMD code in those cases. There is a detectable but minor overhead
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// applied to other values of num_probes (when not statically determined),
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// but smoother performance curve vs. num_probes. But for now, when
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// in doubt, don't add unnecessary code.
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// Powers of 32-bit golden ratio, mod 2**32.
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const __m256i multipliers =
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_mm256_setr_epi32(0x00000001, 0x9e3779b9, 0xe35e67b1, 0x734297e9,
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0x35fbe861, 0xdeb7c719, 0x448b211, 0x3459b749);
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for (;;) {
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// Eight copies of hash
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__m256i hash_vector = _mm256_set1_epi32(h);
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// Same effect as repeated multiplication by 0x9e3779b9 thanks to
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// associativity of multiplication.
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hash_vector = _mm256_mullo_epi32(hash_vector, multipliers);
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// Now the top 9 bits of each of the eight 32-bit values in
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// hash_vector are bit addresses for probes within the cache line.
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// While the platform-independent code uses byte addressing (6 bits
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// to pick a byte + 3 bits to pick a bit within a byte), here we work
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// with 32-bit words (4 bits to pick a word + 5 bits to pick a bit
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// within a word) because that works well with AVX2 and is equivalent
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// under little-endian.
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// Shift each right by 28 bits to get 4-bit word addresses.
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const __m256i word_addresses = _mm256_srli_epi32(hash_vector, 28);
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// Gather 32-bit values spread over 512 bits by 4-bit address. In
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// essence, we are dereferencing eight pointers within the cache
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// line.
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//
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// Option 1: AVX2 gather (seems to be a little slow - understandable)
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// const __m256i value_vector =
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// _mm256_i32gather_epi32(static_cast<const int
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// *>(data_at_cache_line),
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// word_addresses,
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// /*bytes / i32*/ 4);
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// END Option 1
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// Potentially unaligned as we're not *always* cache-aligned -> loadu
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const __m256i *mm_data =
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reinterpret_cast<const __m256i *>(data_at_cache_line);
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__m256i lower = _mm256_loadu_si256(mm_data);
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__m256i upper = _mm256_loadu_si256(mm_data + 1);
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// Option 2: AVX512VL permute hack
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// Only negligibly faster than Option 3, so not yet worth supporting
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// const __m256i value_vector =
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// _mm256_permutex2var_epi32(lower, word_addresses, upper);
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// END Option 2
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// Option 3: AVX2 permute+blend hack
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// Use lowest three bits to order probing values, as if all from same
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// 256 bit piece.
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lower = _mm256_permutevar8x32_epi32(lower, word_addresses);
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upper = _mm256_permutevar8x32_epi32(upper, word_addresses);
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// Just top 1 bit of address, to select between lower and upper.
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const __m256i upper_lower_selector = _mm256_srai_epi32(hash_vector, 31);
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// Finally: the next 8 probed 32-bit values, in probing sequence order.
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const __m256i value_vector =
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_mm256_blendv_epi8(lower, upper, upper_lower_selector);
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// END Option 3
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// We might not need to probe all 8, so build a mask for selecting only
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// what we need. (The k_selector(s) could be pre-computed but that
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// doesn't seem to make a noticeable performance difference.)
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const __m256i zero_to_seven = _mm256_setr_epi32(0, 1, 2, 3, 4, 5, 6, 7);
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// Subtract rem_probes from each of those constants
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__m256i k_selector =
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_mm256_sub_epi32(zero_to_seven, _mm256_set1_epi32(rem_probes));
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// Negative after subtract -> use/select
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// Keep only high bit (logical shift right each by 31).
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k_selector = _mm256_srli_epi32(k_selector, 31);
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// Strip off the 4 bit word address (shift left)
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__m256i bit_addresses = _mm256_slli_epi32(hash_vector, 4);
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// And keep only 5-bit (32 - 27) bit-within-32-bit-word addresses.
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bit_addresses = _mm256_srli_epi32(bit_addresses, 27);
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// Build a bit mask
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const __m256i bit_mask = _mm256_sllv_epi32(k_selector, bit_addresses);
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// Like ((~value_vector) & bit_mask) == 0)
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bool match = _mm256_testc_si256(value_vector, bit_mask) != 0;
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// This check first so that it's easy for branch predictor to optimize
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// num_probes <= 8 case, making it free of unpredictable branches.
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if (rem_probes <= 8) {
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return match;
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} else if (!match) {
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return false;
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}
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// otherwise
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// Need another iteration. 0xab25f4c1 == golden ratio to the 8th power
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h *= 0xab25f4c1;
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rem_probes -= 8;
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}
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#else
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for (int i = 0; i < num_probes; ++i, h *= uint32_t{0x9e3779b9}) {
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// 9-bit address within 512 bit cache line
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int bitpos = h >> (32 - 9);
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if ((data_at_cache_line[bitpos >> 3] & (char(1) << (bitpos & 7))) == 0) {
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return false;
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}
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}
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return true;
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#endif
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}
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};
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// A legacy Bloom filter implementation with no locality of probes (slow).
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// It uses double hashing to generate a sequence of hash values.
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// Asymptotic analysis is in [Kirsch,Mitzenmacher 2006], but known to have
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// subtle accuracy flaws for practical sizes [Dillinger,Manolios 2004].
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//
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// DO NOT REUSE
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//
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class LegacyNoLocalityBloomImpl {
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public:
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static inline int ChooseNumProbes(int bits_per_key) {
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// We intentionally round down to reduce probing cost a little bit
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int num_probes = static_cast<int>(bits_per_key * 0.69); // 0.69 =~ ln(2)
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if (num_probes < 1) num_probes = 1;
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if (num_probes > 30) num_probes = 30;
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return num_probes;
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}
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static inline void AddHash(uint32_t h, uint32_t total_bits, int num_probes,
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char *data) {
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const uint32_t delta = (h >> 17) | (h << 15); // Rotate right 17 bits
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for (int i = 0; i < num_probes; i++) {
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const uint32_t bitpos = h % total_bits;
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data[bitpos / 8] |= (1 << (bitpos % 8));
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h += delta;
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}
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}
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static inline bool HashMayMatch(uint32_t h, uint32_t total_bits,
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int num_probes, const char *data) {
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const uint32_t delta = (h >> 17) | (h << 15); // Rotate right 17 bits
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for (int i = 0; i < num_probes; i++) {
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const uint32_t bitpos = h % total_bits;
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if ((data[bitpos / 8] & (1 << (bitpos % 8))) == 0) {
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return false;
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}
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h += delta;
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}
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return true;
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}
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};
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// A legacy Bloom filter implementation with probes local to a single
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// cache line (fast). Because SST files might be transported between
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// platforms, the cache line size is a parameter rather than hard coded.
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// (But if specified as a constant parameter, an optimizing compiler
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// should take advantage of that.)
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//
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// When ExtraRotates is false, this implementation is notably deficient in
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// accuracy. Specifically, it uses double hashing with a 1/512 chance of the
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// increment being zero (when cache line size is 512 bits). Thus, there's a
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// 1/512 chance of probing only one index, which we'd expect to incur about
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// a 1/2 * 1/512 or absolute 0.1% FP rate penalty. More detail at
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// https://github.com/facebook/rocksdb/issues/4120
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//
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// DO NOT REUSE
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//
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template <bool ExtraRotates>
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class LegacyLocalityBloomImpl {
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private:
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static inline uint32_t GetLine(uint32_t h, uint32_t num_lines) {
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uint32_t offset_h = ExtraRotates ? (h >> 11) | (h << 21) : h;
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return offset_h % num_lines;
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}
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public:
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// NOTE: this has only been validated to enough accuracy for producing
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// reasonable warnings / user feedback, not for making functional decisions.
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static double EstimatedFpRate(size_t keys, size_t bytes, int num_probes) {
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double bits_per_key = 8.0 * bytes / keys;
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double filter_rate = BloomMath::CacheLocalFpRate(bits_per_key, num_probes,
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/*cache line bits*/ 512);
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if (!ExtraRotates) {
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// Good estimate of impact of flaw in index computation.
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// Adds roughly 0.002 around 50 bits/key and 0.001 around 100 bits/key.
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// The + 22 shifts it nicely to fit for lower bits/key.
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|
filter_rate += 0.1 / (bits_per_key * 0.75 + 22);
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} else {
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|
// Not yet validated
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|
assert(false);
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|
}
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|
// Always uses 32-bit hash
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|
double fingerprint_rate = BloomMath::FingerprintFpRate(keys, 32);
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|
return BloomMath::IndependentProbabilitySum(filter_rate, fingerprint_rate);
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|
}
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|
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|
static inline void AddHash(uint32_t h, uint32_t num_lines, int num_probes,
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char *data, int log2_cache_line_bytes) {
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|
const int log2_cache_line_bits = log2_cache_line_bytes + 3;
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|
|
|
char *data_at_offset =
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|
data + (GetLine(h, num_lines) << log2_cache_line_bytes);
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|
const uint32_t delta = (h >> 17) | (h << 15);
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|
for (int i = 0; i < num_probes; ++i) {
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|
// Mask to bit-within-cache-line address
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|
const uint32_t bitpos = h & ((1 << log2_cache_line_bits) - 1);
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|
data_at_offset[bitpos / 8] |= (1 << (bitpos % 8));
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|
if (ExtraRotates) {
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|
h = (h >> log2_cache_line_bits) | (h << (32 - log2_cache_line_bits));
|
|
}
|
|
h += delta;
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|
}
|
|
}
|
|
|
|
static inline void PrepareHashMayMatch(uint32_t h, uint32_t num_lines,
|
|
const char *data,
|
|
uint32_t /*out*/ *byte_offset,
|
|
int log2_cache_line_bytes) {
|
|
uint32_t b = GetLine(h, num_lines) << log2_cache_line_bytes;
|
|
PREFETCH(data + b, 0 /* rw */, 1 /* locality */);
|
|
PREFETCH(data + b + ((1 << log2_cache_line_bytes) - 1), 0 /* rw */,
|
|
1 /* locality */);
|
|
*byte_offset = b;
|
|
}
|
|
|
|
static inline bool HashMayMatch(uint32_t h, uint32_t num_lines,
|
|
int num_probes, const char *data,
|
|
int log2_cache_line_bytes) {
|
|
uint32_t b = GetLine(h, num_lines) << log2_cache_line_bytes;
|
|
return HashMayMatchPrepared(h, num_probes, data + b, log2_cache_line_bytes);
|
|
}
|
|
|
|
static inline bool HashMayMatchPrepared(uint32_t h, int num_probes,
|
|
const char *data_at_offset,
|
|
int log2_cache_line_bytes) {
|
|
const int log2_cache_line_bits = log2_cache_line_bytes + 3;
|
|
|
|
const uint32_t delta = (h >> 17) | (h << 15);
|
|
for (int i = 0; i < num_probes; ++i) {
|
|
// Mask to bit-within-cache-line address
|
|
const uint32_t bitpos = h & ((1 << log2_cache_line_bits) - 1);
|
|
if (((data_at_offset[bitpos / 8]) & (1 << (bitpos % 8))) == 0) {
|
|
return false;
|
|
}
|
|
if (ExtraRotates) {
|
|
h = (h >> log2_cache_line_bits) | (h << (32 - log2_cache_line_bits));
|
|
}
|
|
h += delta;
|
|
}
|
|
return true;
|
|
}
|
|
};
|
|
|
|
} // namespace ROCKSDB_NAMESPACE
|