60af964372
Summary: Added experimental public API for Ribbon filter: NewExperimentalRibbonFilterPolicy(). This experimental API will take a "Bloom equivalent" bits per key, and configure the Ribbon filter for the same FP rate as Bloom would have but ~30% space savings. (Note: optimize_filters_for_memory is not yet implemented for Ribbon filter. That can be added with no effect on schema.) Internally, the Ribbon filter is configured using a "one_in_fp_rate" value, which is 1 over desired FP rate. For example, use 100 for 1% FP rate. I'm expecting this will be used in the future for configuring Bloom-like filters, as I expect people to more commonly hold constant the filter accuracy and change the space vs. time trade-off, rather than hold constant the space (per key) and change the accuracy vs. time trade-off, though we might make that available. ### Benchmarking ``` $ ./filter_bench -impl=2 -quick -m_keys_total_max=200 -average_keys_per_filter=100000 -net_includes_hashing Building... Build avg ns/key: 34.1341 Number of filters: 1993 Total size (MB): 238.488 Reported total allocated memory (MB): 262.875 Reported internal fragmentation: 10.2255% Bits/key stored: 10.0029 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 18.7508 Random filter net ns/op: 258.246 Average FP rate %: 0.968672 ---------------------------- Done. (For more info, run with -legend or -help.) $ ./filter_bench -impl=3 -quick -m_keys_total_max=200 -average_keys_per_filter=100000 -net_includes_hashing Building... Build avg ns/key: 130.851 Number of filters: 1993 Total size (MB): 168.166 Reported total allocated memory (MB): 183.211 Reported internal fragmentation: 8.94626% Bits/key stored: 7.05341 ---------------------------- Mixed inside/outside queries... Single filter net ns/op: 58.4523 Random filter net ns/op: 363.717 Average FP rate %: 0.952978 ---------------------------- Done. (For more info, run with -legend or -help.) ``` 168.166 / 238.488 = 0.705 -> 29.5% space reduction 130.851 / 34.1341 = 3.83x construction time for this Ribbon filter vs. lastest Bloom filter (could make that as little as about 2.5x for less space reduction) ### Working around a hashing "flaw" bloom_test discovered a flaw in the simple hashing applied in StandardHasher when num_starts == 1 (num_slots == 128), showing an excessively high FP rate. The problem is that when many entries, on the order of number of hash bits or kCoeffBits, are associated with the same start location, the correlation between the CoeffRow and ResultRow (for efficiency) can lead to a solution that is "universal," or nearly so, for entries mapping to that start location. (Normally, variance in start location breaks the effective association between CoeffRow and ResultRow; the same value for CoeffRow is effectively different if start locations are different.) Without kUseSmash and with num_starts > 1 (thus num_starts ~= num_slots), this flaw should be completely irrelevant. Even with 10M slots, the chances of a single slot having just 16 (or more) entries map to it--not enough to cause an FP problem, which would be local to that slot if it happened--is 1 in millions. This spreadsheet formula shows that: =1/(10000000*(1 - POISSON(15, 1, TRUE))) As kUseSmash==false (the setting for Standard128RibbonBitsBuilder) is intended for CPU efficiency of filters with many more entries/slots than kCoeffBits, a very reasonable work-around is to disallow num_starts==1 when !kUseSmash, by making the minimum non-zero number of slots 2*kCoeffBits. This is the work-around I've applied. This also means that the new Ribbon filter schema (Standard128RibbonBitsBuilder) is not space-efficient for less than a few hundred entries. Because of this, I have made it fall back on constructing a Bloom filter, under existing schema, when that is more space efficient for small filters. (We can change this in the future if we want.) TODO: better unit tests for this case in ribbon_test, and probably update StandardHasher for kUseSmash case so that it can scale nicely to small filters. ### Other related changes * Add Ribbon filter to stress/crash test * Add Ribbon filter to filter_bench as -impl=3 * Add option string support, as in "filter_policy=experimental_ribbon:5.678;" where 5.678 is the Bloom equivalent bits per key. * Rename internal mode BloomFilterPolicy::kAuto to kAutoBloom * Add a general BuiltinFilterBitsBuilder::CalculateNumEntry based on binary searching CalculateSpace (inefficient), so that subclasses (especially experimental ones) don't have to provide an efficient implementation inverting CalculateSpace. * Minor refactor FastLocalBloomBitsBuilder for new base class XXH3pFilterBitsBuilder shared with new Standard128RibbonBitsBuilder, which allows the latter to fall back on Bloom construction in some extreme cases. * Mostly updated bloom_test for Ribbon filter, though a test like FullBloomTest::Schema is a next TODO to ensure schema stability (in case this becomes production-ready schema as it is). * Add some APIs to ribbon_impl.h for configuring Ribbon filters. Although these are reasonably covered by bloom_test, TODO more unit tests in ribbon_test * Added a "tool" FindOccupancyForSuccessRate to ribbon_test to get data for constructing the linear approximations in GetNumSlotsFor95PctSuccess. Pull Request resolved: https://github.com/facebook/rocksdb/pull/7658 Test Plan: Some unit tests updated but other testing is left TODO. This is considered experimental but laying down schema compatibility as early as possible in case it proves production-quality. Also tested in stress/crash test. Reviewed By: jay-zhuang Differential Revision: D24899349 Pulled By: pdillinger fbshipit-source-id: 9715f3e6371c959d923aea8077c9423c7a9f82b8
999 lines
31 KiB
C++
999 lines
31 KiB
C++
// 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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//
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// Copyright (c) 2012 The LevelDB Authors. All rights reserved.
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// Use of this source code is governed by a BSD-style license that can be
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// found in the LICENSE file. See the AUTHORS file for names of contributors.
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#ifndef GFLAGS
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#include <cstdio>
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int main() {
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fprintf(stderr, "Please install gflags to run this test... Skipping...\n");
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return 0;
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}
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#else
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#include <array>
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#include <cmath>
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#include <vector>
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#include "memory/arena.h"
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#include "port/jemalloc_helper.h"
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#include "rocksdb/filter_policy.h"
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#include "table/block_based/filter_policy_internal.h"
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#include "test_util/testharness.h"
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#include "test_util/testutil.h"
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#include "util/gflags_compat.h"
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#include "util/hash.h"
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using GFLAGS_NAMESPACE::ParseCommandLineFlags;
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DEFINE_int32(bits_per_key, 10, "");
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namespace ROCKSDB_NAMESPACE {
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static const int kVerbose = 1;
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static Slice Key(int i, char* buffer) {
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std::string s;
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PutFixed32(&s, static_cast<uint32_t>(i));
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memcpy(buffer, s.c_str(), sizeof(i));
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return Slice(buffer, sizeof(i));
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}
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static int NextLength(int length) {
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if (length < 10) {
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length += 1;
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} else if (length < 100) {
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length += 10;
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} else if (length < 1000) {
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length += 100;
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} else {
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length += 1000;
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}
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return length;
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}
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class BlockBasedBloomTest : public testing::Test {
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private:
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std::unique_ptr<const FilterPolicy> policy_;
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std::string filter_;
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std::vector<std::string> keys_;
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public:
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BlockBasedBloomTest() { ResetPolicy(); }
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void Reset() {
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keys_.clear();
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filter_.clear();
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}
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void ResetPolicy(double bits_per_key) {
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policy_.reset(new BloomFilterPolicy(bits_per_key,
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BloomFilterPolicy::kDeprecatedBlock));
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Reset();
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}
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void ResetPolicy() { ResetPolicy(FLAGS_bits_per_key); }
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void Add(const Slice& s) {
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keys_.push_back(s.ToString());
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}
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void Build() {
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std::vector<Slice> key_slices;
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for (size_t i = 0; i < keys_.size(); i++) {
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key_slices.push_back(Slice(keys_[i]));
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}
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filter_.clear();
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policy_->CreateFilter(&key_slices[0], static_cast<int>(key_slices.size()),
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&filter_);
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keys_.clear();
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if (kVerbose >= 2) DumpFilter();
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}
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size_t FilterSize() const {
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return filter_.size();
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}
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Slice FilterData() const { return Slice(filter_); }
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void DumpFilter() {
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fprintf(stderr, "F(");
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for (size_t i = 0; i+1 < filter_.size(); i++) {
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const unsigned int c = static_cast<unsigned int>(filter_[i]);
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for (int j = 0; j < 8; j++) {
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fprintf(stderr, "%c", (c & (1 <<j)) ? '1' : '.');
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}
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}
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fprintf(stderr, ")\n");
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}
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bool Matches(const Slice& s) {
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if (!keys_.empty()) {
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Build();
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}
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return policy_->KeyMayMatch(s, filter_);
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}
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double FalsePositiveRate() {
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char buffer[sizeof(int)];
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int result = 0;
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for (int i = 0; i < 10000; i++) {
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if (Matches(Key(i + 1000000000, buffer))) {
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result++;
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}
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}
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return result / 10000.0;
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}
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};
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TEST_F(BlockBasedBloomTest, EmptyFilter) {
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ASSERT_TRUE(! Matches("hello"));
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ASSERT_TRUE(! Matches("world"));
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}
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TEST_F(BlockBasedBloomTest, Small) {
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Add("hello");
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Add("world");
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ASSERT_TRUE(Matches("hello"));
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ASSERT_TRUE(Matches("world"));
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ASSERT_TRUE(! Matches("x"));
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ASSERT_TRUE(! Matches("foo"));
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}
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TEST_F(BlockBasedBloomTest, VaryingLengths) {
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char buffer[sizeof(int)];
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// Count number of filters that significantly exceed the false positive rate
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int mediocre_filters = 0;
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int good_filters = 0;
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for (int length = 1; length <= 10000; length = NextLength(length)) {
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Reset();
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for (int i = 0; i < length; i++) {
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Add(Key(i, buffer));
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}
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Build();
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ASSERT_LE(FilterSize(), (size_t)((length * 10 / 8) + 40)) << length;
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// All added keys must match
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for (int i = 0; i < length; i++) {
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ASSERT_TRUE(Matches(Key(i, buffer)))
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<< "Length " << length << "; key " << i;
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}
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// Check false positive rate
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double rate = FalsePositiveRate();
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if (kVerbose >= 1) {
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fprintf(stderr, "False positives: %5.2f%% @ length = %6d ; bytes = %6d\n",
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rate*100.0, length, static_cast<int>(FilterSize()));
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}
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ASSERT_LE(rate, 0.02); // Must not be over 2%
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if (rate > 0.0125) mediocre_filters++; // Allowed, but not too often
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else good_filters++;
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}
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if (kVerbose >= 1) {
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fprintf(stderr, "Filters: %d good, %d mediocre\n",
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good_filters, mediocre_filters);
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}
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ASSERT_LE(mediocre_filters, good_filters/5);
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}
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// Ensure the implementation doesn't accidentally change in an
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// incompatible way
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TEST_F(BlockBasedBloomTest, Schema) {
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char buffer[sizeof(int)];
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ResetPolicy(8); // num_probes = 5
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for (int key = 0; key < 87; key++) {
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Add(Key(key, buffer));
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}
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Build();
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ASSERT_EQ(BloomHash(FilterData()), 3589896109U);
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ResetPolicy(9); // num_probes = 6
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for (int key = 0; key < 87; key++) {
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Add(Key(key, buffer));
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}
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Build();
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ASSERT_EQ(BloomHash(FilterData()), 969445585U);
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ResetPolicy(11); // num_probes = 7
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for (int key = 0; key < 87; key++) {
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Add(Key(key, buffer));
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}
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Build();
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ASSERT_EQ(BloomHash(FilterData()), 1694458207U);
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ResetPolicy(10); // num_probes = 6
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for (int key = 0; key < 87; key++) {
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Add(Key(key, buffer));
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}
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Build();
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ASSERT_EQ(BloomHash(FilterData()), 2373646410U);
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ResetPolicy(10);
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for (int key = /*CHANGED*/ 1; key < 87; key++) {
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Add(Key(key, buffer));
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}
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Build();
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ASSERT_EQ(BloomHash(FilterData()), 1908442116U);
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ResetPolicy(10);
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for (int key = 1; key < /*CHANGED*/ 88; key++) {
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Add(Key(key, buffer));
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}
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Build();
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ASSERT_EQ(BloomHash(FilterData()), 3057004015U);
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// With new fractional bits_per_key, check that we are rounding to
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// whole bits per key for old Bloom filters.
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ResetPolicy(9.5); // Treated as 10
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for (int key = 1; key < 88; key++) {
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Add(Key(key, buffer));
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}
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Build();
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ASSERT_EQ(BloomHash(FilterData()), /*SAME*/ 3057004015U);
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ResetPolicy(10.499); // Treated as 10
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for (int key = 1; key < 88; key++) {
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Add(Key(key, buffer));
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}
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Build();
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ASSERT_EQ(BloomHash(FilterData()), /*SAME*/ 3057004015U);
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ResetPolicy();
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}
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// Different bits-per-byte
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class FullBloomTest : public testing::TestWithParam<BloomFilterPolicy::Mode> {
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protected:
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BlockBasedTableOptions table_options_;
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private:
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std::shared_ptr<const FilterPolicy>& policy_;
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std::unique_ptr<FilterBitsBuilder> bits_builder_;
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std::unique_ptr<FilterBitsReader> bits_reader_;
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std::unique_ptr<const char[]> buf_;
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size_t filter_size_;
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public:
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FullBloomTest() : policy_(table_options_.filter_policy), filter_size_(0) {
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ResetPolicy();
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}
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BuiltinFilterBitsBuilder* GetBuiltinFilterBitsBuilder() {
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// Throws on bad cast
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return &dynamic_cast<BuiltinFilterBitsBuilder&>(*bits_builder_);
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}
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const BloomFilterPolicy* GetBloomFilterPolicy() {
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// Throws on bad cast
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return &dynamic_cast<const BloomFilterPolicy&>(*policy_);
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}
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void Reset() {
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bits_builder_.reset(BloomFilterPolicy::GetBuilderFromContext(
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FilterBuildingContext(table_options_)));
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bits_reader_.reset(nullptr);
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buf_.reset(nullptr);
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filter_size_ = 0;
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}
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void ResetPolicy(double bits_per_key) {
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policy_.reset(new BloomFilterPolicy(bits_per_key, GetParam()));
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Reset();
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}
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void ResetPolicy() { ResetPolicy(FLAGS_bits_per_key); }
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void Add(const Slice& s) {
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bits_builder_->AddKey(s);
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}
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void OpenRaw(const Slice& s) {
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bits_reader_.reset(policy_->GetFilterBitsReader(s));
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}
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void Build() {
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Slice filter = bits_builder_->Finish(&buf_);
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bits_reader_.reset(policy_->GetFilterBitsReader(filter));
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filter_size_ = filter.size();
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}
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size_t FilterSize() const {
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return filter_size_;
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}
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Slice FilterData() { return Slice(buf_.get(), filter_size_); }
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int GetNumProbesFromFilterData() {
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assert(filter_size_ >= 5);
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int8_t raw_num_probes = static_cast<int8_t>(buf_.get()[filter_size_ - 5]);
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if (raw_num_probes == -1) { // New bloom filter marker
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return static_cast<uint8_t>(buf_.get()[filter_size_ - 3]);
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} else {
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return raw_num_probes;
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}
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}
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bool Matches(const Slice& s) {
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if (bits_reader_ == nullptr) {
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Build();
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}
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return bits_reader_->MayMatch(s);
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}
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// Provides a kind of fingerprint on the Bloom filter's
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// behavior, for reasonbly high FP rates.
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uint64_t PackedMatches() {
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char buffer[sizeof(int)];
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uint64_t result = 0;
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for (int i = 0; i < 64; i++) {
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if (Matches(Key(i + 12345, buffer))) {
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result |= uint64_t{1} << i;
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}
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}
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return result;
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}
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// Provides a kind of fingerprint on the Bloom filter's
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// behavior, for lower FP rates.
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std::string FirstFPs(int count) {
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char buffer[sizeof(int)];
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std::string rv;
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int fp_count = 0;
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for (int i = 0; i < 1000000; i++) {
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// Pack four match booleans into each hexadecimal digit
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if (Matches(Key(i + 1000000, buffer))) {
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++fp_count;
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rv += std::to_string(i);
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if (fp_count == count) {
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break;
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}
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rv += ',';
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}
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}
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return rv;
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}
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double FalsePositiveRate() {
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char buffer[sizeof(int)];
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int result = 0;
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for (int i = 0; i < 10000; i++) {
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if (Matches(Key(i + 1000000000, buffer))) {
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result++;
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}
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}
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return result / 10000.0;
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}
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uint32_t SelectByImpl(uint32_t for_legacy_bloom,
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uint32_t for_fast_local_bloom) {
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switch (GetParam()) {
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case BloomFilterPolicy::kLegacyBloom:
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return for_legacy_bloom;
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case BloomFilterPolicy::kFastLocalBloom:
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return for_fast_local_bloom;
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case BloomFilterPolicy::kDeprecatedBlock:
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case BloomFilterPolicy::kAutoBloom:
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case BloomFilterPolicy::kStandard128Ribbon:
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/* N/A */;
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}
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// otherwise
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assert(false);
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return 0;
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}
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};
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TEST_P(FullBloomTest, FilterSize) {
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// In addition to checking the consistency of space computation, we are
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// checking that denoted and computed doubles are interpreted as expected
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// as bits_per_key values.
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bool some_computed_less_than_denoted = false;
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// Note: enforced minimum is 1 bit per key (1000 millibits), and enforced
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// maximum is 100 bits per key (100000 millibits).
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for (auto bpk :
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std::vector<std::pair<double, int> >{{-HUGE_VAL, 1000},
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{-INFINITY, 1000},
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{0.0, 1000},
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{1.234, 1234},
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{3.456, 3456},
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{9.5, 9500},
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{10.0, 10000},
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{10.499, 10499},
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{21.345, 21345},
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{99.999, 99999},
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{1234.0, 100000},
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{HUGE_VAL, 100000},
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{INFINITY, 100000},
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{NAN, 100000}}) {
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ResetPolicy(bpk.first);
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auto bfp = GetBloomFilterPolicy();
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EXPECT_EQ(bpk.second, bfp->GetMillibitsPerKey());
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EXPECT_EQ((bpk.second + 500) / 1000, bfp->GetWholeBitsPerKey());
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double computed = bpk.first;
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// This transforms e.g. 9.5 -> 9.499999999999998, which we still
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// round to 10 for whole bits per key.
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computed += 0.5;
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computed /= 1234567.0;
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computed *= 1234567.0;
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computed -= 0.5;
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some_computed_less_than_denoted |= (computed < bpk.first);
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ResetPolicy(computed);
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bfp = GetBloomFilterPolicy();
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EXPECT_EQ(bpk.second, bfp->GetMillibitsPerKey());
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EXPECT_EQ((bpk.second + 500) / 1000, bfp->GetWholeBitsPerKey());
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auto bits_builder = GetBuiltinFilterBitsBuilder();
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for (int n = 1; n < 100; n++) {
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auto space = bits_builder->CalculateSpace(n);
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auto n2 = bits_builder->CalculateNumEntry(space);
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EXPECT_GE(n2, n);
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auto space2 = bits_builder->CalculateSpace(n2);
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EXPECT_EQ(space, space2);
|
|
}
|
|
}
|
|
// Check that the compiler hasn't optimized our computation into nothing
|
|
EXPECT_TRUE(some_computed_less_than_denoted);
|
|
ResetPolicy();
|
|
}
|
|
|
|
TEST_P(FullBloomTest, FullEmptyFilter) {
|
|
// Empty filter is not match, at this level
|
|
ASSERT_TRUE(!Matches("hello"));
|
|
ASSERT_TRUE(!Matches("world"));
|
|
}
|
|
|
|
TEST_P(FullBloomTest, FullSmall) {
|
|
Add("hello");
|
|
Add("world");
|
|
ASSERT_TRUE(Matches("hello"));
|
|
ASSERT_TRUE(Matches("world"));
|
|
ASSERT_TRUE(!Matches("x"));
|
|
ASSERT_TRUE(!Matches("foo"));
|
|
}
|
|
|
|
TEST_P(FullBloomTest, FullVaryingLengths) {
|
|
char buffer[sizeof(int)];
|
|
|
|
// Count number of filters that significantly exceed the false positive rate
|
|
int mediocre_filters = 0;
|
|
int good_filters = 0;
|
|
|
|
for (int length = 1; length <= 10000; length = NextLength(length)) {
|
|
Reset();
|
|
for (int i = 0; i < length; i++) {
|
|
Add(Key(i, buffer));
|
|
}
|
|
Build();
|
|
|
|
EXPECT_LE(FilterSize(),
|
|
(size_t)((length * 10 / 8) + CACHE_LINE_SIZE * 2 + 5));
|
|
|
|
// All added keys must match
|
|
for (int i = 0; i < length; i++) {
|
|
ASSERT_TRUE(Matches(Key(i, buffer)))
|
|
<< "Length " << length << "; key " << i;
|
|
}
|
|
|
|
// Check false positive rate
|
|
double rate = FalsePositiveRate();
|
|
if (kVerbose >= 1) {
|
|
fprintf(stderr, "False positives: %5.2f%% @ length = %6d ; bytes = %6d\n",
|
|
rate*100.0, length, static_cast<int>(FilterSize()));
|
|
}
|
|
EXPECT_LE(rate, 0.02); // Must not be over 2%
|
|
if (rate > 0.0125)
|
|
mediocre_filters++; // Allowed, but not too often
|
|
else
|
|
good_filters++;
|
|
}
|
|
if (kVerbose >= 1) {
|
|
fprintf(stderr, "Filters: %d good, %d mediocre\n",
|
|
good_filters, mediocre_filters);
|
|
}
|
|
EXPECT_LE(mediocre_filters, good_filters / 5);
|
|
}
|
|
|
|
TEST_P(FullBloomTest, OptimizeForMemory) {
|
|
if (GetParam() == BloomFilterPolicy::kStandard128Ribbon) {
|
|
// TODO Not yet implemented
|
|
return;
|
|
}
|
|
char buffer[sizeof(int)];
|
|
for (bool offm : {true, false}) {
|
|
table_options_.optimize_filters_for_memory = offm;
|
|
ResetPolicy();
|
|
Random32 rnd(12345);
|
|
uint64_t total_size = 0;
|
|
uint64_t total_mem = 0;
|
|
int64_t total_keys = 0;
|
|
double total_fp_rate = 0;
|
|
constexpr int nfilters = 100;
|
|
for (int i = 0; i < nfilters; ++i) {
|
|
int nkeys = static_cast<int>(rnd.Uniformish(10000)) + 100;
|
|
Reset();
|
|
for (int j = 0; j < nkeys; ++j) {
|
|
Add(Key(j, buffer));
|
|
}
|
|
Build();
|
|
size_t size = FilterData().size();
|
|
total_size += size;
|
|
// optimize_filters_for_memory currently depends on malloc_usable_size
|
|
// but we run the rest of the test to ensure no bad behavior without it.
|
|
#ifdef ROCKSDB_MALLOC_USABLE_SIZE
|
|
size = malloc_usable_size(const_cast<char*>(FilterData().data()));
|
|
#endif // ROCKSDB_MALLOC_USABLE_SIZE
|
|
total_mem += size;
|
|
total_keys += nkeys;
|
|
total_fp_rate += FalsePositiveRate();
|
|
}
|
|
EXPECT_LE(total_fp_rate / double{nfilters}, 0.011);
|
|
EXPECT_GE(total_fp_rate / double{nfilters}, 0.008);
|
|
|
|
int64_t ex_min_total_size = int64_t{FLAGS_bits_per_key} * total_keys / 8;
|
|
EXPECT_GE(static_cast<int64_t>(total_size), ex_min_total_size);
|
|
|
|
int64_t blocked_bloom_overhead = nfilters * (CACHE_LINE_SIZE + 5);
|
|
if (GetParam() == BloomFilterPolicy::kLegacyBloom) {
|
|
// this config can add extra cache line to make odd number
|
|
blocked_bloom_overhead += nfilters * CACHE_LINE_SIZE;
|
|
}
|
|
|
|
EXPECT_GE(total_mem, total_size);
|
|
|
|
// optimize_filters_for_memory not implemented with legacy Bloom
|
|
if (offm && GetParam() != BloomFilterPolicy::kLegacyBloom) {
|
|
// This value can include a small extra penalty for kExtraPadding
|
|
fprintf(stderr, "Internal fragmentation (optimized): %g%%\n",
|
|
(total_mem - total_size) * 100.0 / total_size);
|
|
// Less than 1% internal fragmentation
|
|
EXPECT_LE(total_mem, total_size * 101 / 100);
|
|
// Up to 2% storage penalty
|
|
EXPECT_LE(static_cast<int64_t>(total_size),
|
|
ex_min_total_size * 102 / 100 + blocked_bloom_overhead);
|
|
} else {
|
|
fprintf(stderr, "Internal fragmentation (not optimized): %g%%\n",
|
|
(total_mem - total_size) * 100.0 / total_size);
|
|
// TODO: add control checks for more allocators?
|
|
#ifdef ROCKSDB_JEMALLOC
|
|
fprintf(stderr, "Jemalloc detected? %d\n", HasJemalloc());
|
|
if (HasJemalloc()) {
|
|
// More than 5% internal fragmentation
|
|
EXPECT_GE(total_mem, total_size * 105 / 100);
|
|
}
|
|
#endif // ROCKSDB_JEMALLOC
|
|
// No storage penalty, just usual overhead
|
|
EXPECT_LE(static_cast<int64_t>(total_size),
|
|
ex_min_total_size + blocked_bloom_overhead);
|
|
}
|
|
}
|
|
}
|
|
|
|
namespace {
|
|
inline uint32_t SelectByCacheLineSize(uint32_t for64, uint32_t for128,
|
|
uint32_t for256) {
|
|
(void)for64;
|
|
(void)for128;
|
|
(void)for256;
|
|
#if CACHE_LINE_SIZE == 64
|
|
return for64;
|
|
#elif CACHE_LINE_SIZE == 128
|
|
return for128;
|
|
#elif CACHE_LINE_SIZE == 256
|
|
return for256;
|
|
#else
|
|
#error "CACHE_LINE_SIZE unknown or unrecognized"
|
|
#endif
|
|
}
|
|
} // namespace
|
|
|
|
// Ensure the implementation doesn't accidentally change in an
|
|
// incompatible way. This test doesn't check the reading side
|
|
// (FirstFPs/PackedMatches) for LegacyBloom because it requires the
|
|
// ability to read filters generated using other cache line sizes.
|
|
// See RawSchema.
|
|
TEST_P(FullBloomTest, Schema) {
|
|
if (GetParam() == BloomFilterPolicy::kStandard128Ribbon) {
|
|
// TODO ASAP to ensure schema stability
|
|
return;
|
|
}
|
|
char buffer[sizeof(int)];
|
|
|
|
// Use enough keys so that changing bits / key by 1 is guaranteed to
|
|
// change number of allocated cache lines. So keys > max cache line bits.
|
|
|
|
ResetPolicy(2); // num_probes = 1
|
|
for (int key = 0; key < 2087; key++) {
|
|
Add(Key(key, buffer));
|
|
}
|
|
Build();
|
|
EXPECT_EQ(GetNumProbesFromFilterData(), 1);
|
|
EXPECT_EQ(
|
|
BloomHash(FilterData()),
|
|
SelectByImpl(SelectByCacheLineSize(1567096579, 1964771444, 2659542661U),
|
|
3817481309U));
|
|
if (GetParam() == BloomFilterPolicy::kFastLocalBloom) {
|
|
EXPECT_EQ("11,13,17,25,29,30,35,37,45,53", FirstFPs(10));
|
|
}
|
|
|
|
ResetPolicy(3); // num_probes = 2
|
|
for (int key = 0; key < 2087; key++) {
|
|
Add(Key(key, buffer));
|
|
}
|
|
Build();
|
|
EXPECT_EQ(GetNumProbesFromFilterData(), 2);
|
|
EXPECT_EQ(
|
|
BloomHash(FilterData()),
|
|
SelectByImpl(SelectByCacheLineSize(2707206547U, 2571983456U, 218344685),
|
|
2807269961U));
|
|
if (GetParam() == BloomFilterPolicy::kFastLocalBloom) {
|
|
EXPECT_EQ("4,15,17,24,27,28,29,53,63,70", FirstFPs(10));
|
|
}
|
|
|
|
ResetPolicy(5); // num_probes = 3
|
|
for (int key = 0; key < 2087; key++) {
|
|
Add(Key(key, buffer));
|
|
}
|
|
Build();
|
|
EXPECT_EQ(GetNumProbesFromFilterData(), 3);
|
|
EXPECT_EQ(
|
|
BloomHash(FilterData()),
|
|
SelectByImpl(SelectByCacheLineSize(515748486, 94611728, 2436112214U),
|
|
204628445));
|
|
if (GetParam() == BloomFilterPolicy::kFastLocalBloom) {
|
|
EXPECT_EQ("15,24,29,39,53,87,89,100,103,104", FirstFPs(10));
|
|
}
|
|
|
|
ResetPolicy(8); // num_probes = 5
|
|
for (int key = 0; key < 2087; key++) {
|
|
Add(Key(key, buffer));
|
|
}
|
|
Build();
|
|
EXPECT_EQ(GetNumProbesFromFilterData(), 5);
|
|
EXPECT_EQ(
|
|
BloomHash(FilterData()),
|
|
SelectByImpl(SelectByCacheLineSize(1302145999, 2811644657U, 756553699),
|
|
355564975));
|
|
if (GetParam() == BloomFilterPolicy::kFastLocalBloom) {
|
|
EXPECT_EQ("16,60,66,126,220,238,244,256,265,287", FirstFPs(10));
|
|
}
|
|
|
|
ResetPolicy(9); // num_probes = 6
|
|
for (int key = 0; key < 2087; key++) {
|
|
Add(Key(key, buffer));
|
|
}
|
|
Build();
|
|
EXPECT_EQ(GetNumProbesFromFilterData(), 6);
|
|
EXPECT_EQ(
|
|
BloomHash(FilterData()),
|
|
SelectByImpl(SelectByCacheLineSize(2092755149, 661139132, 1182970461),
|
|
2137566013U));
|
|
if (GetParam() == BloomFilterPolicy::kFastLocalBloom) {
|
|
EXPECT_EQ("156,367,791,872,945,1015,1139,1159,1265,1435", FirstFPs(10));
|
|
}
|
|
|
|
ResetPolicy(11); // num_probes = 7
|
|
for (int key = 0; key < 2087; key++) {
|
|
Add(Key(key, buffer));
|
|
}
|
|
Build();
|
|
EXPECT_EQ(GetNumProbesFromFilterData(), 7);
|
|
EXPECT_EQ(
|
|
BloomHash(FilterData()),
|
|
SelectByImpl(SelectByCacheLineSize(3755609649U, 1812694762, 1449142939),
|
|
2561502687U));
|
|
if (GetParam() == BloomFilterPolicy::kFastLocalBloom) {
|
|
EXPECT_EQ("34,74,130,236,643,882,962,1015,1035,1110", FirstFPs(10));
|
|
}
|
|
|
|
// This used to be 9 probes, but 8 is a better choice for speed,
|
|
// especially with SIMD groups of 8 probes, with essentially no
|
|
// change in FP rate.
|
|
// FP rate @ 9 probes, old Bloom: 0.4321%
|
|
// FP rate @ 9 probes, new Bloom: 0.1846%
|
|
// FP rate @ 8 probes, new Bloom: 0.1843%
|
|
ResetPolicy(14); // num_probes = 8 (new), 9 (old)
|
|
for (int key = 0; key < 2087; key++) {
|
|
Add(Key(key, buffer));
|
|
}
|
|
Build();
|
|
EXPECT_EQ(static_cast<uint32_t>(GetNumProbesFromFilterData()),
|
|
SelectByImpl(9, 8));
|
|
EXPECT_EQ(
|
|
BloomHash(FilterData()),
|
|
SelectByImpl(SelectByCacheLineSize(178861123, 379087593, 2574136516U),
|
|
3709876890U));
|
|
if (GetParam() == BloomFilterPolicy::kFastLocalBloom) {
|
|
EXPECT_EQ("130,240,522,565,989,2002,2526,3147,3543", FirstFPs(9));
|
|
}
|
|
|
|
// This used to be 11 probes, but 9 is a better choice for speed
|
|
// AND accuracy.
|
|
// FP rate @ 11 probes, old Bloom: 0.3571%
|
|
// FP rate @ 11 probes, new Bloom: 0.0884%
|
|
// FP rate @ 9 probes, new Bloom: 0.0843%
|
|
ResetPolicy(16); // num_probes = 9 (new), 11 (old)
|
|
for (int key = 0; key < 2087; key++) {
|
|
Add(Key(key, buffer));
|
|
}
|
|
Build();
|
|
EXPECT_EQ(static_cast<uint32_t>(GetNumProbesFromFilterData()),
|
|
SelectByImpl(11, 9));
|
|
EXPECT_EQ(
|
|
BloomHash(FilterData()),
|
|
SelectByImpl(SelectByCacheLineSize(1129406313, 3049154394U, 1727750964),
|
|
1087138490));
|
|
if (GetParam() == BloomFilterPolicy::kFastLocalBloom) {
|
|
EXPECT_EQ("3299,3611,3916,6620,7822,8079,8482,8942,10167", FirstFPs(9));
|
|
}
|
|
|
|
ResetPolicy(10); // num_probes = 6, but different memory ratio vs. 9
|
|
for (int key = 0; key < 2087; key++) {
|
|
Add(Key(key, buffer));
|
|
}
|
|
Build();
|
|
EXPECT_EQ(GetNumProbesFromFilterData(), 6);
|
|
EXPECT_EQ(
|
|
BloomHash(FilterData()),
|
|
SelectByImpl(SelectByCacheLineSize(1478976371, 2910591341U, 1182970461),
|
|
2498541272U));
|
|
if (GetParam() == BloomFilterPolicy::kFastLocalBloom) {
|
|
EXPECT_EQ("16,126,133,422,466,472,813,1002,1035,1159", FirstFPs(10));
|
|
}
|
|
|
|
ResetPolicy(10);
|
|
for (int key = /*CHANGED*/ 1; key < 2087; key++) {
|
|
Add(Key(key, buffer));
|
|
}
|
|
Build();
|
|
EXPECT_EQ(GetNumProbesFromFilterData(), 6);
|
|
EXPECT_EQ(
|
|
BloomHash(FilterData()),
|
|
SelectByImpl(SelectByCacheLineSize(4205696321U, 1132081253U, 2385981855U),
|
|
2058382345U));
|
|
if (GetParam() == BloomFilterPolicy::kFastLocalBloom) {
|
|
EXPECT_EQ("16,126,133,422,466,472,813,1002,1035,1159", FirstFPs(10));
|
|
}
|
|
|
|
ResetPolicy(10);
|
|
for (int key = 1; key < /*CHANGED*/ 2088; key++) {
|
|
Add(Key(key, buffer));
|
|
}
|
|
Build();
|
|
EXPECT_EQ(GetNumProbesFromFilterData(), 6);
|
|
EXPECT_EQ(
|
|
BloomHash(FilterData()),
|
|
SelectByImpl(SelectByCacheLineSize(2885052954U, 769447944, 4175124908U),
|
|
23699164));
|
|
if (GetParam() == BloomFilterPolicy::kFastLocalBloom) {
|
|
EXPECT_EQ("16,126,133,422,466,472,813,1002,1035,1159", FirstFPs(10));
|
|
}
|
|
|
|
// With new fractional bits_per_key, check that we are rounding to
|
|
// whole bits per key for old Bloom filters but fractional for
|
|
// new Bloom filter.
|
|
ResetPolicy(9.5);
|
|
for (int key = 1; key < 2088; key++) {
|
|
Add(Key(key, buffer));
|
|
}
|
|
Build();
|
|
EXPECT_EQ(GetNumProbesFromFilterData(), 6);
|
|
EXPECT_EQ(BloomHash(FilterData()),
|
|
SelectByImpl(/*SAME*/ SelectByCacheLineSize(2885052954U, 769447944,
|
|
4175124908U),
|
|
/*CHANGED*/ 3166884174U));
|
|
if (GetParam() == BloomFilterPolicy::kFastLocalBloom) {
|
|
EXPECT_EQ(/*CHANGED*/ "126,156,367,444,458,791,813,976,1015,1035",
|
|
FirstFPs(10));
|
|
}
|
|
|
|
ResetPolicy(10.499);
|
|
for (int key = 1; key < 2088; key++) {
|
|
Add(Key(key, buffer));
|
|
}
|
|
Build();
|
|
EXPECT_EQ(static_cast<uint32_t>(GetNumProbesFromFilterData()),
|
|
SelectByImpl(6, 7));
|
|
EXPECT_EQ(BloomHash(FilterData()),
|
|
SelectByImpl(/*SAME*/ SelectByCacheLineSize(2885052954U, 769447944,
|
|
4175124908U),
|
|
/*CHANGED*/ 4098502778U));
|
|
if (GetParam() == BloomFilterPolicy::kFastLocalBloom) {
|
|
EXPECT_EQ(/*CHANGED*/ "16,236,240,472,1015,1045,1111,1409,1465,1612",
|
|
FirstFPs(10));
|
|
}
|
|
|
|
ResetPolicy();
|
|
}
|
|
|
|
// A helper class for testing custom or corrupt filter bits as read by
|
|
// built-in FilterBitsReaders.
|
|
struct RawFilterTester {
|
|
// Buffer, from which we always return a tail Slice, so the
|
|
// last five bytes are always the metadata bytes.
|
|
std::array<char, 3000> data_;
|
|
// Points five bytes from the end
|
|
char* metadata_ptr_;
|
|
|
|
RawFilterTester() : metadata_ptr_(&*(data_.end() - 5)) {}
|
|
|
|
Slice ResetNoFill(uint32_t len_without_metadata, uint32_t num_lines,
|
|
uint32_t num_probes) {
|
|
metadata_ptr_[0] = static_cast<char>(num_probes);
|
|
EncodeFixed32(metadata_ptr_ + 1, num_lines);
|
|
uint32_t len = len_without_metadata + /*metadata*/ 5;
|
|
assert(len <= data_.size());
|
|
return Slice(metadata_ptr_ - len_without_metadata, len);
|
|
}
|
|
|
|
Slice Reset(uint32_t len_without_metadata, uint32_t num_lines,
|
|
uint32_t num_probes, bool fill_ones) {
|
|
data_.fill(fill_ones ? 0xff : 0);
|
|
return ResetNoFill(len_without_metadata, num_lines, num_probes);
|
|
}
|
|
|
|
Slice ResetWeirdFill(uint32_t len_without_metadata, uint32_t num_lines,
|
|
uint32_t num_probes) {
|
|
for (uint32_t i = 0; i < data_.size(); ++i) {
|
|
data_[i] = static_cast<char>(0x7b7b >> (i % 7));
|
|
}
|
|
return ResetNoFill(len_without_metadata, num_lines, num_probes);
|
|
}
|
|
};
|
|
|
|
TEST_P(FullBloomTest, RawSchema) {
|
|
RawFilterTester cft;
|
|
// Two probes, about 3/4 bits set: ~50% "FP" rate
|
|
// One 256-byte cache line.
|
|
OpenRaw(cft.ResetWeirdFill(256, 1, 2));
|
|
EXPECT_EQ(uint64_t{11384799501900898790U}, PackedMatches());
|
|
|
|
// Two 128-byte cache lines.
|
|
OpenRaw(cft.ResetWeirdFill(256, 2, 2));
|
|
EXPECT_EQ(uint64_t{10157853359773492589U}, PackedMatches());
|
|
|
|
// Four 64-byte cache lines.
|
|
OpenRaw(cft.ResetWeirdFill(256, 4, 2));
|
|
EXPECT_EQ(uint64_t{7123594913907464682U}, PackedMatches());
|
|
}
|
|
|
|
TEST_P(FullBloomTest, CorruptFilters) {
|
|
RawFilterTester cft;
|
|
|
|
for (bool fill : {false, true}) {
|
|
// Good filter bits - returns same as fill
|
|
OpenRaw(cft.Reset(CACHE_LINE_SIZE, 1, 6, fill));
|
|
ASSERT_EQ(fill, Matches("hello"));
|
|
ASSERT_EQ(fill, Matches("world"));
|
|
|
|
// Good filter bits - returns same as fill
|
|
OpenRaw(cft.Reset(CACHE_LINE_SIZE * 3, 3, 6, fill));
|
|
ASSERT_EQ(fill, Matches("hello"));
|
|
ASSERT_EQ(fill, Matches("world"));
|
|
|
|
// Good filter bits - returns same as fill
|
|
// 256 is unusual but legal cache line size
|
|
OpenRaw(cft.Reset(256 * 3, 3, 6, fill));
|
|
ASSERT_EQ(fill, Matches("hello"));
|
|
ASSERT_EQ(fill, Matches("world"));
|
|
|
|
// Good filter bits - returns same as fill
|
|
// 30 should be max num_probes
|
|
OpenRaw(cft.Reset(CACHE_LINE_SIZE, 1, 30, fill));
|
|
ASSERT_EQ(fill, Matches("hello"));
|
|
ASSERT_EQ(fill, Matches("world"));
|
|
|
|
// Good filter bits - returns same as fill
|
|
// 1 should be min num_probes
|
|
OpenRaw(cft.Reset(CACHE_LINE_SIZE, 1, 1, fill));
|
|
ASSERT_EQ(fill, Matches("hello"));
|
|
ASSERT_EQ(fill, Matches("world"));
|
|
|
|
// Type 1 trivial filter bits - returns true as if FP by zero probes
|
|
OpenRaw(cft.Reset(CACHE_LINE_SIZE, 1, 0, fill));
|
|
ASSERT_TRUE(Matches("hello"));
|
|
ASSERT_TRUE(Matches("world"));
|
|
|
|
// Type 2 trivial filter bits - returns false as if built from zero keys
|
|
OpenRaw(cft.Reset(0, 0, 6, fill));
|
|
ASSERT_FALSE(Matches("hello"));
|
|
ASSERT_FALSE(Matches("world"));
|
|
|
|
// Type 2 trivial filter bits - returns false as if built from zero keys
|
|
OpenRaw(cft.Reset(0, 37, 6, fill));
|
|
ASSERT_FALSE(Matches("hello"));
|
|
ASSERT_FALSE(Matches("world"));
|
|
|
|
// Type 2 trivial filter bits - returns false as 0 size trumps 0 probes
|
|
OpenRaw(cft.Reset(0, 0, 0, fill));
|
|
ASSERT_FALSE(Matches("hello"));
|
|
ASSERT_FALSE(Matches("world"));
|
|
|
|
// Bad filter bits - returns true for safety
|
|
// No solution to 0 * x == CACHE_LINE_SIZE
|
|
OpenRaw(cft.Reset(CACHE_LINE_SIZE, 0, 6, fill));
|
|
ASSERT_TRUE(Matches("hello"));
|
|
ASSERT_TRUE(Matches("world"));
|
|
|
|
// Bad filter bits - returns true for safety
|
|
// Can't have 3 * x == 4 for integer x
|
|
OpenRaw(cft.Reset(4, 3, 6, fill));
|
|
ASSERT_TRUE(Matches("hello"));
|
|
ASSERT_TRUE(Matches("world"));
|
|
|
|
// Bad filter bits - returns true for safety
|
|
// 97 bytes is not a power of two, so not a legal cache line size
|
|
OpenRaw(cft.Reset(97 * 3, 3, 6, fill));
|
|
ASSERT_TRUE(Matches("hello"));
|
|
ASSERT_TRUE(Matches("world"));
|
|
|
|
// Bad filter bits - returns true for safety
|
|
// 65 bytes is not a power of two, so not a legal cache line size
|
|
OpenRaw(cft.Reset(65 * 3, 3, 6, fill));
|
|
ASSERT_TRUE(Matches("hello"));
|
|
ASSERT_TRUE(Matches("world"));
|
|
|
|
// Bad filter bits - returns false as if built from zero keys
|
|
// < 5 bytes overall means missing even metadata
|
|
OpenRaw(cft.Reset(static_cast<uint32_t>(-1), 3, 6, fill));
|
|
ASSERT_FALSE(Matches("hello"));
|
|
ASSERT_FALSE(Matches("world"));
|
|
|
|
OpenRaw(cft.Reset(static_cast<uint32_t>(-5), 3, 6, fill));
|
|
ASSERT_FALSE(Matches("hello"));
|
|
ASSERT_FALSE(Matches("world"));
|
|
|
|
// Dubious filter bits - returns same as fill (for now)
|
|
// 31 is not a useful num_probes, nor generated by RocksDB unless directly
|
|
// using filter bits API without BloomFilterPolicy.
|
|
OpenRaw(cft.Reset(CACHE_LINE_SIZE, 1, 31, fill));
|
|
ASSERT_EQ(fill, Matches("hello"));
|
|
ASSERT_EQ(fill, Matches("world"));
|
|
|
|
// Dubious filter bits - returns same as fill (for now)
|
|
// Similar, with 127, largest positive char
|
|
OpenRaw(cft.Reset(CACHE_LINE_SIZE, 1, 127, fill));
|
|
ASSERT_EQ(fill, Matches("hello"));
|
|
ASSERT_EQ(fill, Matches("world"));
|
|
|
|
// Dubious filter bits - returns true (for now)
|
|
// num_probes set to 128 / -128, lowest negative char
|
|
// NB: Bug in implementation interprets this as negative and has same
|
|
// effect as zero probes, but effectively reserves negative char values
|
|
// for future use.
|
|
OpenRaw(cft.Reset(CACHE_LINE_SIZE, 1, 128, fill));
|
|
ASSERT_TRUE(Matches("hello"));
|
|
ASSERT_TRUE(Matches("world"));
|
|
|
|
// Dubious filter bits - returns true (for now)
|
|
// Similar, with 255 / -1
|
|
OpenRaw(cft.Reset(CACHE_LINE_SIZE, 1, 255, fill));
|
|
ASSERT_TRUE(Matches("hello"));
|
|
ASSERT_TRUE(Matches("world"));
|
|
}
|
|
}
|
|
|
|
INSTANTIATE_TEST_CASE_P(Full, FullBloomTest,
|
|
testing::Values(BloomFilterPolicy::kLegacyBloom,
|
|
BloomFilterPolicy::kFastLocalBloom,
|
|
BloomFilterPolicy::kStandard128Ribbon));
|
|
|
|
} // namespace ROCKSDB_NAMESPACE
|
|
|
|
int main(int argc, char** argv) {
|
|
::testing::InitGoogleTest(&argc, argv);
|
|
ParseCommandLineFlags(&argc, &argv, true);
|
|
|
|
return RUN_ALL_TESTS();
|
|
}
|
|
|
|
#endif // GFLAGS
|