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rocksdb/util/bloom_test.cc
Peter Dillinger da6b90ab48 Improve bloom_test bits_per_key flag (#8093) 
Summary:
Improved handling of -bits_per_key other than 10, but at least
the OptimizeForMemory test is simply not designed for generally handling
other settings. (ribbon_test does have a statistical framework for this
kind of testing, but it's not important to do that same for Bloom right
now.)

Closes https://github.com/facebook/rocksdb/issues/7019

Pull Request resolved: https://github.com/facebook/rocksdb/pull/8093

Test Plan: for I in `seq 1 20`; do ./bloom_test --gtest_filter=-*OptimizeForMemory* --bits_per_key=$I &> /dev/null || echo FAILED; done

Reviewed By: mrambacher

Differential Revision: D27275875

Pulled By: pdillinger

fbshipit-source-id: 7362e8ac2c41ea11f639412e4f30c8b375f04388
2021-03-23 21:42:40 -07:00

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// Copyright (c) 2011-present, Facebook, Inc. All rights reserved.
// This source code is licensed under both the GPLv2 (found in the
// COPYING file in the root directory) and Apache 2.0 License
// (found in the LICENSE.Apache file in the root directory).
//
// Copyright (c) 2012 The LevelDB Authors. All rights reserved.
// Use of this source code is governed by a BSD-style license that can be
// found in the LICENSE file. See the AUTHORS file for names of contributors.
#ifndef GFLAGS
#include <cstdio>
int main() {
fprintf(stderr, "Please install gflags to run this test... Skipping...\n");
return 0;
}
#else
#include <array>
#include <cmath>
#include <vector>
#include "memory/arena.h"
#include "port/jemalloc_helper.h"
#include "rocksdb/filter_policy.h"
#include "table/block_based/filter_policy_internal.h"
#include "test_util/testharness.h"
#include "test_util/testutil.h"
#include "util/gflags_compat.h"
#include "util/hash.h"
using GFLAGS_NAMESPACE::ParseCommandLineFlags;
// The test is not fully designed for bits_per_key other than 10, but with
// this parameter you can easily explore the behavior of other bits_per_key.
// See also filter_bench.
DEFINE_int32(bits_per_key, 10, "");
namespace ROCKSDB_NAMESPACE {
static const int kVerbose = 1;
static Slice Key(int i, char* buffer) {
std::string s;
PutFixed32(&s, static_cast<uint32_t>(i));
memcpy(buffer, s.c_str(), sizeof(i));
return Slice(buffer, sizeof(i));
}
static int NextLength(int length) {
if (length < 10) {
length += 1;
} else if (length < 100) {
length += 10;
} else if (length < 1000) {
length += 100;
} else {
length += 1000;
}
return length;
}
class BlockBasedBloomTest : public testing::Test {
private:
std::unique_ptr<const FilterPolicy> policy_;
std::string filter_;
std::vector<std::string> keys_;
public:
BlockBasedBloomTest() { ResetPolicy(); }
void Reset() {
keys_.clear();
filter_.clear();
}
void ResetPolicy(double bits_per_key) {
policy_.reset(new BloomFilterPolicy(bits_per_key,
BloomFilterPolicy::kDeprecatedBlock));
Reset();
}
void ResetPolicy() { ResetPolicy(FLAGS_bits_per_key); }
void Add(const Slice& s) {
keys_.push_back(s.ToString());
}
void Build() {
std::vector<Slice> key_slices;
for (size_t i = 0; i < keys_.size(); i++) {
key_slices.push_back(Slice(keys_[i]));
}
filter_.clear();
policy_->CreateFilter(&key_slices[0], static_cast<int>(key_slices.size()),
&filter_);
keys_.clear();
if (kVerbose >= 2) DumpFilter();
}
size_t FilterSize() const {
return filter_.size();
}
Slice FilterData() const { return Slice(filter_); }
void DumpFilter() {
fprintf(stderr, "F(");
for (size_t i = 0; i+1 < filter_.size(); i++) {
const unsigned int c = static_cast<unsigned int>(filter_[i]);
for (int j = 0; j < 8; j++) {
fprintf(stderr, "%c", (c & (1 <<j)) ? '1' : '.');
}
}
fprintf(stderr, ")\n");
}
bool Matches(const Slice& s) {
if (!keys_.empty()) {
Build();
}
return policy_->KeyMayMatch(s, filter_);
}
double FalsePositiveRate() {
char buffer[sizeof(int)];
int result = 0;
for (int i = 0; i < 10000; i++) {
if (Matches(Key(i + 1000000000, buffer))) {
result++;
}
}
return result / 10000.0;
}
};
TEST_F(BlockBasedBloomTest, EmptyFilter) {
ASSERT_TRUE(! Matches("hello"));
ASSERT_TRUE(! Matches("world"));
}
TEST_F(BlockBasedBloomTest, Small) {
Add("hello");
Add("world");
ASSERT_TRUE(Matches("hello"));
ASSERT_TRUE(Matches("world"));
ASSERT_TRUE(! Matches("x"));
ASSERT_TRUE(! Matches("foo"));
}
TEST_F(BlockBasedBloomTest, VaryingLengths) {
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();
ASSERT_LE(FilterSize(), (size_t)((length * FLAGS_bits_per_key / 8) + 40))
<< length;
// 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()));
}
if (FLAGS_bits_per_key == 10) {
ASSERT_LE(rate, 0.02); // Must not be over 2%
if (rate > 0.0125) {
mediocre_filters++; // Allowed, but not too often
} else {
good_filters++;
}
}
}
if (FLAGS_bits_per_key == 10 && kVerbose >= 1) {
fprintf(stderr, "Filters: %d good, %d mediocre\n",
good_filters, mediocre_filters);
}
ASSERT_LE(mediocre_filters, good_filters/5);
}
// Ensure the implementation doesn't accidentally change in an
// incompatible way
TEST_F(BlockBasedBloomTest, Schema) {
char buffer[sizeof(int)];
ResetPolicy(8); // num_probes = 5
for (int key = 0; key < 87; key++) {
Add(Key(key, buffer));
}
Build();
ASSERT_EQ(BloomHash(FilterData()), 3589896109U);
ResetPolicy(9); // num_probes = 6
for (int key = 0; key < 87; key++) {
Add(Key(key, buffer));
}
Build();
ASSERT_EQ(BloomHash(FilterData()), 969445585U);
ResetPolicy(11); // num_probes = 7
for (int key = 0; key < 87; key++) {
Add(Key(key, buffer));
}
Build();
ASSERT_EQ(BloomHash(FilterData()), 1694458207U);
ResetPolicy(10); // num_probes = 6
for (int key = 0; key < 87; key++) {
Add(Key(key, buffer));
}
Build();
ASSERT_EQ(BloomHash(FilterData()), 2373646410U);
ResetPolicy(10);
for (int key = /*CHANGED*/ 1; key < 87; key++) {
Add(Key(key, buffer));
}
Build();
ASSERT_EQ(BloomHash(FilterData()), 1908442116U);
ResetPolicy(10);
for (int key = 1; key < /*CHANGED*/ 88; key++) {
Add(Key(key, buffer));
}
Build();
ASSERT_EQ(BloomHash(FilterData()), 3057004015U);
// With new fractional bits_per_key, check that we are rounding to
// whole bits per key for old Bloom filters.
ResetPolicy(9.5); // Treated as 10
for (int key = 1; key < 88; key++) {
Add(Key(key, buffer));
}
Build();
ASSERT_EQ(BloomHash(FilterData()), /*SAME*/ 3057004015U);
ResetPolicy(10.499); // Treated as 10
for (int key = 1; key < 88; key++) {
Add(Key(key, buffer));
}
Build();
ASSERT_EQ(BloomHash(FilterData()), /*SAME*/ 3057004015U);
ResetPolicy();
}
// Different bits-per-byte
class FullBloomTest : public testing::TestWithParam<BloomFilterPolicy::Mode> {
protected:
BlockBasedTableOptions table_options_;
private:
std::shared_ptr<const FilterPolicy>& policy_;
std::unique_ptr<FilterBitsBuilder> bits_builder_;
std::unique_ptr<FilterBitsReader> bits_reader_;
std::unique_ptr<const char[]> buf_;
size_t filter_size_;
public:
FullBloomTest() : policy_(table_options_.filter_policy), filter_size_(0) {
ResetPolicy();
}
BuiltinFilterBitsBuilder* GetBuiltinFilterBitsBuilder() {
// Throws on bad cast
return &dynamic_cast<BuiltinFilterBitsBuilder&>(*bits_builder_);
}
const BloomFilterPolicy* GetBloomFilterPolicy() {
// Throws on bad cast
return &dynamic_cast<const BloomFilterPolicy&>(*policy_);
}
void Reset() {
bits_builder_.reset(BloomFilterPolicy::GetBuilderFromContext(
FilterBuildingContext(table_options_)));
bits_reader_.reset(nullptr);
buf_.reset(nullptr);
filter_size_ = 0;
}
void ResetPolicy(double bits_per_key) {
policy_.reset(new BloomFilterPolicy(bits_per_key, GetParam()));
Reset();
}
void ResetPolicy() { ResetPolicy(FLAGS_bits_per_key); }
void Add(const Slice& s) {
bits_builder_->AddKey(s);
}
void OpenRaw(const Slice& s) {
bits_reader_.reset(policy_->GetFilterBitsReader(s));
}
void Build() {
Slice filter = bits_builder_->Finish(&buf_);
bits_reader_.reset(policy_->GetFilterBitsReader(filter));
filter_size_ = filter.size();
}
size_t FilterSize() const {
return filter_size_;
}
Slice FilterData() { return Slice(buf_.get(), filter_size_); }
int GetNumProbesFromFilterData() {
assert(filter_size_ >= 5);
int8_t raw_num_probes = static_cast<int8_t>(buf_.get()[filter_size_ - 5]);
if (raw_num_probes == -1) { // New bloom filter marker
return static_cast<uint8_t>(buf_.get()[filter_size_ - 3]);
} else {
return raw_num_probes;
}
}
int GetRibbonSeedFromFilterData() {
assert(filter_size_ >= 5);
// Check for ribbon marker
assert(-2 == static_cast<int8_t>(buf_.get()[filter_size_ - 5]));
return static_cast<uint8_t>(buf_.get()[filter_size_ - 4]);
}
bool Matches(const Slice& s) {
if (bits_reader_ == nullptr) {
Build();
}
return bits_reader_->MayMatch(s);
}
// Provides a kind of fingerprint on the Bloom filter's
// behavior, for reasonbly high FP rates.
uint64_t PackedMatches() {
char buffer[sizeof(int)];
uint64_t result = 0;
for (int i = 0; i < 64; i++) {
if (Matches(Key(i + 12345, buffer))) {
result |= uint64_t{1} << i;
}
}
return result;
}
// Provides a kind of fingerprint on the Bloom filter's
// behavior, for lower FP rates.
std::string FirstFPs(int count) {
char buffer[sizeof(int)];
std::string rv;
int fp_count = 0;
for (int i = 0; i < 1000000; i++) {
// Pack four match booleans into each hexadecimal digit
if (Matches(Key(i + 1000000, buffer))) {
++fp_count;
rv += std::to_string(i);
if (fp_count == count) {
break;
}
rv += ',';
}
}
return rv;
}
double FalsePositiveRate() {
char buffer[sizeof(int)];
int result = 0;
for (int i = 0; i < 10000; i++) {
if (Matches(Key(i + 1000000000, buffer))) {
result++;
}
}
return result / 10000.0;
}
};
TEST_P(FullBloomTest, FilterSize) {
// In addition to checking the consistency of space computation, we are
// checking that denoted and computed doubles are interpreted as expected
// as bits_per_key values.
bool some_computed_less_than_denoted = false;
// Note: enforced minimum is 1 bit per key (1000 millibits), and enforced
// maximum is 100 bits per key (100000 millibits).
for (auto bpk :
std::vector<std::pair<double, int> >{{-HUGE_VAL, 1000},
{-INFINITY, 1000},
{0.0, 1000},
{1.234, 1234},
{3.456, 3456},
{9.5, 9500},
{10.0, 10000},
{10.499, 10499},
{21.345, 21345},
{99.999, 99999},
{1234.0, 100000},
{HUGE_VAL, 100000},
{INFINITY, 100000},
{NAN, 100000}}) {
ResetPolicy(bpk.first);
auto bfp = GetBloomFilterPolicy();
EXPECT_EQ(bpk.second, bfp->GetMillibitsPerKey());
EXPECT_EQ((bpk.second + 500) / 1000, bfp->GetWholeBitsPerKey());
double computed = bpk.first;
// This transforms e.g. 9.5 -> 9.499999999999998, which we still
// round to 10 for whole bits per key.
computed += 0.5;
computed /= 1234567.0;
computed *= 1234567.0;
computed -= 0.5;
some_computed_less_than_denoted |= (computed < bpk.first);
ResetPolicy(computed);
bfp = GetBloomFilterPolicy();
EXPECT_EQ(bpk.second, bfp->GetMillibitsPerKey());
EXPECT_EQ((bpk.second + 500) / 1000, bfp->GetWholeBitsPerKey());
auto bits_builder = GetBuiltinFilterBitsBuilder();
size_t n = 1;
size_t space = 0;
for (; n < 1000000; n += 1 + n / 1000) {
// Ensure consistency between CalculateSpace and ApproximateNumEntries
space = bits_builder->CalculateSpace(n);
size_t n2 = bits_builder->ApproximateNumEntries(space);
EXPECT_GE(n2, n);
size_t space2 = bits_builder->CalculateSpace(n2);
if (n > 12000 && GetParam() == BloomFilterPolicy::kStandard128Ribbon) {
// TODO(peterd): better approximation?
EXPECT_GE(space2, space);
EXPECT_LE(space2 * 0.998, space * 1.0);
} else {
EXPECT_EQ(space2, space);
}
}
// Until size_t overflow
for (; n < (n + n / 3); n += n / 3) {
// Ensure space computation is not overflowing; capped is OK
size_t space2 = bits_builder->CalculateSpace(n);
EXPECT_GE(space2, space);
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 * FLAGS_bits_per_key / 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()));
}
if (FLAGS_bits_per_key == 10) {
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) {
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();
}
if (FLAGS_bits_per_key == 10) {
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;
if (GetParam() == BloomFilterPolicy::kStandard128Ribbon) {
// ~ 30% savings vs. Bloom filter
ex_min_total_size = 7 * ex_min_total_size / 10;
}
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()) {
#ifdef ROCKSDB_MALLOC_USABLE_SIZE
// More than 5% internal fragmentation
EXPECT_GE(total_mem, total_size * 105 / 100);
#endif // ROCKSDB_MALLOC_USABLE_SIZE
}
#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) {
#define EXPECT_EQ_Bloom(a, b) \
{ \
if (GetParam() != BloomFilterPolicy::kStandard128Ribbon) { \
EXPECT_EQ(a, b); \
} \
}
#define EXPECT_EQ_Ribbon(a, b) \
{ \
if (GetParam() == BloomFilterPolicy::kStandard128Ribbon) { \
EXPECT_EQ(a, b); \
} \
}
#define EXPECT_EQ_FastBloom(a, b) \
{ \
if (GetParam() == BloomFilterPolicy::kFastLocalBloom) { \
EXPECT_EQ(a, b); \
} \
}
#define EXPECT_EQ_LegacyBloom(a, b) \
{ \
if (GetParam() == BloomFilterPolicy::kLegacyBloom) { \
EXPECT_EQ(a, b); \
} \
}
#define EXPECT_EQ_NotLegacy(a, b) \
{ \
if (GetParam() != BloomFilterPolicy::kLegacyBloom) { \
EXPECT_EQ(a, b); \
} \
}
char buffer[sizeof(int)];
// First do a small number of keys, where Ribbon config will fall back on
// fast Bloom filter and generate the same data
ResetPolicy(5); // num_probes = 3
for (int key = 0; key < 87; key++) {
Add(Key(key, buffer));
}
Build();
EXPECT_EQ(GetNumProbesFromFilterData(), 3);
EXPECT_EQ_NotLegacy(BloomHash(FilterData()), 4130687756U);
EXPECT_EQ_NotLegacy("31,38,40,43,61,83,86,112,125,131", FirstFPs(10));
// Now 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.
// Note that the first attempted Ribbon seed is determined by the hash
// of the first key added (for pseudorandomness in practice, determinism in
// testing)
ResetPolicy(2); // num_probes = 1
for (int key = 0; key < 2087; key++) {
Add(Key(key, buffer));
}
Build();
EXPECT_EQ_Bloom(GetNumProbesFromFilterData(), 1);
EXPECT_EQ_Ribbon(GetRibbonSeedFromFilterData(), 61);
EXPECT_EQ_LegacyBloom(
BloomHash(FilterData()),
SelectByCacheLineSize(1567096579, 1964771444, 2659542661U));
EXPECT_EQ_FastBloom(BloomHash(FilterData()), 3817481309U);
EXPECT_EQ_Ribbon(BloomHash(FilterData()), 1705851228);
EXPECT_EQ_FastBloom("11,13,17,25,29,30,35,37,45,53", FirstFPs(10));
EXPECT_EQ_Ribbon("3,8,10,17,19,20,23,28,31,32", FirstFPs(10));
ResetPolicy(3); // num_probes = 2
for (int key = 0; key < 2087; key++) {
Add(Key(key, buffer));
}
Build();
EXPECT_EQ_Bloom(GetNumProbesFromFilterData(), 2);
EXPECT_EQ_Ribbon(GetRibbonSeedFromFilterData(), 61);
EXPECT_EQ_LegacyBloom(
BloomHash(FilterData()),
SelectByCacheLineSize(2707206547U, 2571983456U, 218344685));
EXPECT_EQ_FastBloom(BloomHash(FilterData()), 2807269961U);
EXPECT_EQ_Ribbon(BloomHash(FilterData()), 1095342358);
EXPECT_EQ_FastBloom("4,15,17,24,27,28,29,53,63,70", FirstFPs(10));
EXPECT_EQ_Ribbon("3,17,20,28,32,33,36,43,49,54", FirstFPs(10));
ResetPolicy(5); // num_probes = 3
for (int key = 0; key < 2087; key++) {
Add(Key(key, buffer));
}
Build();
EXPECT_EQ_Bloom(GetNumProbesFromFilterData(), 3);
EXPECT_EQ_Ribbon(GetRibbonSeedFromFilterData(), 61);
EXPECT_EQ_LegacyBloom(
BloomHash(FilterData()),
SelectByCacheLineSize(515748486, 94611728, 2436112214U));
EXPECT_EQ_FastBloom(BloomHash(FilterData()), 204628445);
EXPECT_EQ_Ribbon(BloomHash(FilterData()), 3971337699U);
EXPECT_EQ_FastBloom("15,24,29,39,53,87,89,100,103,104", FirstFPs(10));
EXPECT_EQ_Ribbon("3,33,36,43,67,70,76,78,84,102", FirstFPs(10));
ResetPolicy(8); // num_probes = 5
for (int key = 0; key < 2087; key++) {
Add(Key(key, buffer));
}
Build();
EXPECT_EQ_Bloom(GetNumProbesFromFilterData(), 5);
EXPECT_EQ_Ribbon(GetRibbonSeedFromFilterData(), 61);
EXPECT_EQ_LegacyBloom(
BloomHash(FilterData()),
SelectByCacheLineSize(1302145999, 2811644657U, 756553699));
EXPECT_EQ_FastBloom(BloomHash(FilterData()), 355564975);
EXPECT_EQ_Ribbon(BloomHash(FilterData()), 3651449053U);
EXPECT_EQ_FastBloom("16,60,66,126,220,238,244,256,265,287", FirstFPs(10));
EXPECT_EQ_Ribbon("33,187,203,296,300,322,411,419,547,582", FirstFPs(10));
ResetPolicy(9); // num_probes = 6
for (int key = 0; key < 2087; key++) {
Add(Key(key, buffer));
}
Build();
EXPECT_EQ_Bloom(GetNumProbesFromFilterData(), 6);
EXPECT_EQ_Ribbon(GetRibbonSeedFromFilterData(), 61);
EXPECT_EQ_LegacyBloom(
BloomHash(FilterData()),
SelectByCacheLineSize(2092755149, 661139132, 1182970461));
EXPECT_EQ_FastBloom(BloomHash(FilterData()), 2137566013U);
EXPECT_EQ_Ribbon(BloomHash(FilterData()), 1005676675);
EXPECT_EQ_FastBloom("156,367,791,872,945,1015,1139,1159,1265", FirstFPs(9));
EXPECT_EQ_Ribbon("33,187,203,296,411,419,604,612,615,619", FirstFPs(10));
ResetPolicy(11); // num_probes = 7
for (int key = 0; key < 2087; key++) {
Add(Key(key, buffer));
}
Build();
EXPECT_EQ_Bloom(GetNumProbesFromFilterData(), 7);
EXPECT_EQ_Ribbon(GetRibbonSeedFromFilterData(), 61);
EXPECT_EQ_LegacyBloom(
BloomHash(FilterData()),
SelectByCacheLineSize(3755609649U, 1812694762, 1449142939));
EXPECT_EQ_FastBloom(BloomHash(FilterData()), 2561502687U);
EXPECT_EQ_Ribbon(BloomHash(FilterData()), 3129900846U);
EXPECT_EQ_FastBloom("34,74,130,236,643,882,962,1015,1035,1110", FirstFPs(10));
EXPECT_EQ_Ribbon("411,419,623,665,727,794,955,1052,1323,1330", 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_LegacyBloom(GetNumProbesFromFilterData(), 9);
EXPECT_EQ_FastBloom(GetNumProbesFromFilterData(), 8);
EXPECT_EQ_Ribbon(GetRibbonSeedFromFilterData(), 61);
EXPECT_EQ_LegacyBloom(
BloomHash(FilterData()),
SelectByCacheLineSize(178861123, 379087593, 2574136516U));
EXPECT_EQ_FastBloom(BloomHash(FilterData()), 3709876890U);
EXPECT_EQ_Ribbon(BloomHash(FilterData()), 1855638875);
EXPECT_EQ_FastBloom("130,240,522,565,989,2002,2526,3147,3543", FirstFPs(9));
EXPECT_EQ_Ribbon("665,727,1323,1755,3866,4232,4442,4492,4736", 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_LegacyBloom(GetNumProbesFromFilterData(), 11);
EXPECT_EQ_FastBloom(GetNumProbesFromFilterData(), 9);
EXPECT_EQ_Ribbon(GetRibbonSeedFromFilterData(), 61);
EXPECT_EQ_LegacyBloom(
BloomHash(FilterData()),
SelectByCacheLineSize(1129406313, 3049154394U, 1727750964));
EXPECT_EQ_FastBloom(BloomHash(FilterData()), 1087138490);
EXPECT_EQ_Ribbon(BloomHash(FilterData()), 459379967);
EXPECT_EQ_FastBloom("3299,3611,3916,6620,7822,8079,8482,8942", FirstFPs(8));
EXPECT_EQ_Ribbon("727,1323,1755,4442,4736,5386,6974,7154,8222", 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_Bloom(GetNumProbesFromFilterData(), 6);
EXPECT_EQ_Ribbon(GetRibbonSeedFromFilterData(), 61);
EXPECT_EQ_LegacyBloom(
BloomHash(FilterData()),
SelectByCacheLineSize(1478976371, 2910591341U, 1182970461));
EXPECT_EQ_FastBloom(BloomHash(FilterData()), 2498541272U);
EXPECT_EQ_Ribbon(BloomHash(FilterData()), 1273231667);
EXPECT_EQ_FastBloom("16,126,133,422,466,472,813,1002,1035", FirstFPs(9));
EXPECT_EQ_Ribbon("296,411,419,612,619,623,630,665,686,727", FirstFPs(10));
ResetPolicy(10);
for (int key = /*CHANGED*/ 1; key < 2087; key++) {
Add(Key(key, buffer));
}
Build();
EXPECT_EQ_Bloom(GetNumProbesFromFilterData(), 6);
EXPECT_EQ_Ribbon(GetRibbonSeedFromFilterData(), /*CHANGED*/ 184);
EXPECT_EQ_LegacyBloom(
BloomHash(FilterData()),
SelectByCacheLineSize(4205696321U, 1132081253U, 2385981855U));
EXPECT_EQ_FastBloom(BloomHash(FilterData()), 2058382345U);
EXPECT_EQ_Ribbon(BloomHash(FilterData()), 3007790572U);
EXPECT_EQ_FastBloom("16,126,133,422,466,472,813,1002,1035", FirstFPs(9));
EXPECT_EQ_Ribbon("33,152,383,497,589,633,737,781,911,990", FirstFPs(10));
ResetPolicy(10);
for (int key = 1; key < /*CHANGED*/ 2088; key++) {
Add(Key(key, buffer));
}
Build();
EXPECT_EQ_Bloom(GetNumProbesFromFilterData(), 6);
EXPECT_EQ_Ribbon(GetRibbonSeedFromFilterData(), 184);
EXPECT_EQ_LegacyBloom(
BloomHash(FilterData()),
SelectByCacheLineSize(2885052954U, 769447944, 4175124908U));
EXPECT_EQ_FastBloom(BloomHash(FilterData()), 23699164);
EXPECT_EQ_Ribbon(BloomHash(FilterData()), 1942323379);
EXPECT_EQ_FastBloom("16,126,133,422,466,472,813,1002,1035", FirstFPs(9));
EXPECT_EQ_Ribbon("33,95,360,589,737,911,990,1048,1081,1414", 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_Bloom(GetNumProbesFromFilterData(), 6);
EXPECT_EQ_Ribbon(GetRibbonSeedFromFilterData(), 184);
EXPECT_EQ_LegacyBloom(
BloomHash(FilterData()),
/*SAME*/ SelectByCacheLineSize(2885052954U, 769447944, 4175124908U));
EXPECT_EQ_FastBloom(BloomHash(FilterData()), 3166884174U);
EXPECT_EQ_Ribbon(BloomHash(FilterData()), 1148258663);
EXPECT_EQ_FastBloom("126,156,367,444,458,791,813,976,1015", FirstFPs(9));
EXPECT_EQ_Ribbon("33,54,95,360,589,693,737,911,990,1048", FirstFPs(10));
ResetPolicy(10.499);
for (int key = 1; key < 2088; key++) {
Add(Key(key, buffer));
}
Build();
EXPECT_EQ_LegacyBloom(GetNumProbesFromFilterData(), 6);
EXPECT_EQ_FastBloom(GetNumProbesFromFilterData(), 7);
EXPECT_EQ_Ribbon(GetRibbonSeedFromFilterData(), 184);
EXPECT_EQ_LegacyBloom(
BloomHash(FilterData()),
/*SAME*/ SelectByCacheLineSize(2885052954U, 769447944, 4175124908U));
EXPECT_EQ_FastBloom(BloomHash(FilterData()), 4098502778U);
EXPECT_EQ_Ribbon(BloomHash(FilterData()), 792138188);
EXPECT_EQ_FastBloom("16,236,240,472,1015,1045,1111,1409,1465", FirstFPs(9));
EXPECT_EQ_Ribbon("33,95,360,589,737,990,1048,1081,1414,1643", 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;
// Legacy Bloom configurations
// 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());
// Fast local Bloom configurations (marker 255 -> -1)
// Two probes, about 3/4 bits set: ~50% "FP" rate
// Four 64-byte cache lines.
OpenRaw(cft.ResetWeirdFill(256, 2U << 8, 255));
EXPECT_EQ(uint64_t{9957045189927952471U}, PackedMatches());
// Ribbon configurations (marker 254 -> -2)
// Even though the builder never builds configurations this
// small (preferring Bloom), we can test that the configuration
// can be read, for possible future-proofing.
// 256 slots, one result column = 32 bytes (2 blocks, seed 0)
// ~50% FP rate:
// 0b0101010111110101010000110000011011011111100100001110010011101010
OpenRaw(cft.ResetWeirdFill(32, 2U << 8, 254));
EXPECT_EQ(uint64_t{6193930559317665002U}, PackedMatches());
// 256 slots, three-to-four result columns = 112 bytes
// ~ 1 in 10 FP rate:
// 0b0000000000100000000000000000000001000001000000010000101000000000
OpenRaw(cft.ResetWeirdFill(112, 2U << 8, 254));
EXPECT_EQ(uint64_t{9007200345328128U}, PackedMatches());
}
TEST_P(FullBloomTest, CorruptFilters) {
RawFilterTester cft;
for (bool fill : {false, true}) {
// Legacy Bloom configurations
// 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 253 / -3
OpenRaw(cft.Reset(CACHE_LINE_SIZE, 1, 253, fill));
ASSERT_TRUE(Matches("hello"));
ASSERT_TRUE(Matches("world"));
// #########################################################
// Fast local Bloom configurations (marker 255 -> -1)
// Good config with six probes
OpenRaw(cft.Reset(CACHE_LINE_SIZE, 6U << 8, 255, fill));
ASSERT_EQ(fill, Matches("hello"));
ASSERT_EQ(fill, Matches("world"));
// Becomes bad/reserved config (always true) if any other byte set
OpenRaw(cft.Reset(CACHE_LINE_SIZE, (6U << 8) | 1U, 255, fill));
ASSERT_TRUE(Matches("hello"));
ASSERT_TRUE(Matches("world"));
OpenRaw(cft.Reset(CACHE_LINE_SIZE, (6U << 8) | (1U << 16), 255, fill));
ASSERT_TRUE(Matches("hello"));
ASSERT_TRUE(Matches("world"));
OpenRaw(cft.Reset(CACHE_LINE_SIZE, (6U << 8) | (1U << 24), 255, fill));
ASSERT_TRUE(Matches("hello"));
ASSERT_TRUE(Matches("world"));
// Good config, max 30 probes
OpenRaw(cft.Reset(CACHE_LINE_SIZE, 30U << 8, 255, fill));
ASSERT_EQ(fill, Matches("hello"));
ASSERT_EQ(fill, Matches("world"));
// Bad/reserved config (always true) if more than 30
OpenRaw(cft.Reset(CACHE_LINE_SIZE, 31U << 8, 255, fill));
ASSERT_TRUE(Matches("hello"));
ASSERT_TRUE(Matches("world"));
OpenRaw(cft.Reset(CACHE_LINE_SIZE, 33U << 8, 255, fill));
ASSERT_TRUE(Matches("hello"));
ASSERT_TRUE(Matches("world"));
OpenRaw(cft.Reset(CACHE_LINE_SIZE, 66U << 8, 255, fill));
ASSERT_TRUE(Matches("hello"));
ASSERT_TRUE(Matches("world"));
OpenRaw(cft.Reset(CACHE_LINE_SIZE, 130U << 8, 255, fill));
ASSERT_TRUE(Matches("hello"));
ASSERT_TRUE(Matches("world"));
}
// #########################################################
// Ribbon configurations (marker 254 -> -2)
// ("fill" doesn't work to detect good configurations, we just
// have to rely on TN probability)
// Good: 2 blocks * 16 bytes / segment * 4 columns = 128 bytes
// seed = 123
OpenRaw(cft.Reset(128, (2U << 8) + 123U, 254, false));
ASSERT_FALSE(Matches("hello"));
ASSERT_FALSE(Matches("world"));
// Good: 2 blocks * 16 bytes / segment * 8 columns = 256 bytes
OpenRaw(cft.Reset(256, (2U << 8) + 123U, 254, false));
ASSERT_FALSE(Matches("hello"));
ASSERT_FALSE(Matches("world"));
// Surprisingly OK: 5000 blocks (640,000 slots) in only 1024 bits
// -> average close to 0 columns
OpenRaw(cft.Reset(128, (5000U << 8) + 123U, 254, false));
// *Almost* all FPs
ASSERT_TRUE(Matches("hello"));
ASSERT_TRUE(Matches("world"));
// Need many queries to find a "true negative"
for (int i = 0; Matches(ToString(i)); ++i) {
ASSERT_LT(i, 1000);
}
// Bad: 1 block not allowed (for implementation detail reasons)
OpenRaw(cft.Reset(128, (1U << 8) + 123U, 254, false));
ASSERT_TRUE(Matches("hello"));
ASSERT_TRUE(Matches("world"));
// Bad: 0 blocks not allowed
OpenRaw(cft.Reset(128, (0U << 8) + 123U, 254, false));
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
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