rocksdb/util/bloom_test.cc
Peter Dillinger 5b2bbacb6f Minimize memory internal fragmentation for Bloom filters (#6427)
Summary:
New experimental option BBTO::optimize_filters_for_memory builds
filters that maximize their use of "usable size" from malloc_usable_size,
which is also used to compute block cache charges.

Rather than always "rounding up," we track state in the
BloomFilterPolicy object to mix essentially "rounding down" and
"rounding up" so that the average FP rate of all generated filters is
the same as without the option. (YMMV as heavily accessed filters might
be unluckily lower accuracy.)

Thus, the option near-minimizes what the block cache considers as
"memory used" for a given target Bloom filter false positive rate and
Bloom filter implementation. There are no forward or backward
compatibility issues with this change, though it only works on the
format_version=5 Bloom filter.

With Jemalloc, we see about 10% reduction in memory footprint (and block
cache charge) for Bloom filters, but 1-2% increase in storage footprint,
due to encoding efficiency losses (FP rate is non-linear with bits/key).

Why not weighted random round up/down rather than state tracking? By
only requiring malloc_usable_size, we don't actually know what the next
larger and next smaller usable sizes for the allocator are. We pick a
requested size, accept and use whatever usable size it has, and use the
difference to inform our next choice. This allows us to narrow in on the
right balance without tracking/predicting usable sizes.

Why not weight history of generated filter false positive rates by
number of keys? This could lead to excess skew in small filters after
generating a large filter.

Results from filter_bench with jemalloc (irrelevant details omitted):

    (normal keys/filter, but high variance)
    $ ./filter_bench -quick -impl=2 -average_keys_per_filter=30000 -vary_key_count_ratio=0.9
    Build avg ns/key: 29.6278
    Number of filters: 5516
    Total size (MB): 200.046
    Reported total allocated memory (MB): 220.597
    Reported internal fragmentation: 10.2732%
    Bits/key stored: 10.0097
    Average FP rate %: 0.965228
    $ ./filter_bench -quick -impl=2 -average_keys_per_filter=30000 -vary_key_count_ratio=0.9 -optimize_filters_for_memory
    Build avg ns/key: 30.5104
    Number of filters: 5464
    Total size (MB): 200.015
    Reported total allocated memory (MB): 200.322
    Reported internal fragmentation: 0.153709%
    Bits/key stored: 10.1011
    Average FP rate %: 0.966313

    (very few keys / filter, optimization not as effective due to ~59 byte
     internal fragmentation in blocked Bloom filter representation)
    $ ./filter_bench -quick -impl=2 -average_keys_per_filter=1000 -vary_key_count_ratio=0.9
    Build avg ns/key: 29.5649
    Number of filters: 162950
    Total size (MB): 200.001
    Reported total allocated memory (MB): 224.624
    Reported internal fragmentation: 12.3117%
    Bits/key stored: 10.2951
    Average FP rate %: 0.821534
    $ ./filter_bench -quick -impl=2 -average_keys_per_filter=1000 -vary_key_count_ratio=0.9 -optimize_filters_for_memory
    Build avg ns/key: 31.8057
    Number of filters: 159849
    Total size (MB): 200
    Reported total allocated memory (MB): 208.846
    Reported internal fragmentation: 4.42297%
    Bits/key stored: 10.4948
    Average FP rate %: 0.811006

    (high keys/filter)
    $ ./filter_bench -quick -impl=2 -average_keys_per_filter=1000000 -vary_key_count_ratio=0.9
    Build avg ns/key: 29.7017
    Number of filters: 164
    Total size (MB): 200.352
    Reported total allocated memory (MB): 221.5
    Reported internal fragmentation: 10.5552%
    Bits/key stored: 10.0003
    Average FP rate %: 0.969358
    $ ./filter_bench -quick -impl=2 -average_keys_per_filter=1000000 -vary_key_count_ratio=0.9 -optimize_filters_for_memory
    Build avg ns/key: 30.7131
    Number of filters: 160
    Total size (MB): 200.928
    Reported total allocated memory (MB): 200.938
    Reported internal fragmentation: 0.00448054%
    Bits/key stored: 10.1852
    Average FP rate %: 0.963387

And from db_bench (block cache) with jemalloc:

    $ ./db_bench -db=/dev/shm/dbbench.no_optimize -benchmarks=fillrandom -format_version=5 -value_size=90 -bloom_bits=10 -num=2000000 -threads=8 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=false
    $ ./db_bench -db=/dev/shm/dbbench -benchmarks=fillrandom -format_version=5 -value_size=90 -bloom_bits=10 -num=2000000 -threads=8 -optimize_filters_for_memory -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=false
    $ (for FILE in /dev/shm/dbbench.no_optimize/*.sst; do ./sst_dump --file=$FILE --show_properties | grep 'filter block' ; done) | awk '{ t += $4; } END { print t; }'
    17063835
    $ (for FILE in /dev/shm/dbbench/*.sst; do ./sst_dump --file=$FILE --show_properties | grep 'filter block' ; done) | awk '{ t += $4; } END { print t; }'
    17430747
    $ #^ 2.1% additional filter storage
    $ ./db_bench -db=/dev/shm/dbbench.no_optimize -use_existing_db -benchmarks=readrandom,stats -statistics -bloom_bits=10 -num=2000000 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=false -duration=10 -cache_index_and_filter_blocks -cache_size=1000000000
    rocksdb.block.cache.index.add COUNT : 33
    rocksdb.block.cache.index.bytes.insert COUNT : 8440400
    rocksdb.block.cache.filter.add COUNT : 33
    rocksdb.block.cache.filter.bytes.insert COUNT : 21087528
    rocksdb.bloom.filter.useful COUNT : 4963889
    rocksdb.bloom.filter.full.positive COUNT : 1214081
    rocksdb.bloom.filter.full.true.positive COUNT : 1161999
    $ #^ 1.04 % observed FP rate
    $ ./db_bench -db=/dev/shm/dbbench -use_existing_db -benchmarks=readrandom,stats -statistics -bloom_bits=10 -num=2000000 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=false -optimize_filters_for_memory -duration=10 -cache_index_and_filter_blocks -cache_size=1000000000
    rocksdb.block.cache.index.add COUNT : 33
    rocksdb.block.cache.index.bytes.insert COUNT : 8448592
    rocksdb.block.cache.filter.add COUNT : 33
    rocksdb.block.cache.filter.bytes.insert COUNT : 18220328
    rocksdb.bloom.filter.useful COUNT : 5360933
    rocksdb.bloom.filter.full.positive COUNT : 1321315
    rocksdb.bloom.filter.full.true.positive COUNT : 1262999
    $ #^ 1.08 % observed FP rate, 13.6% less memory usage for filters

(Due to specific key density, this example tends to generate filters that are "worse than average" for internal fragmentation. "Better than average" cases can show little or no improvement.)
Pull Request resolved: https://github.com/facebook/rocksdb/pull/6427

Test Plan: unit test added, 'make check' with gcc, clang and valgrind

Reviewed By: siying

Differential Revision: D22124374

Pulled By: pdillinger

fbshipit-source-id: f3e3aa152f9043ddf4fae25799e76341d0d8714e
2020-06-22 13:32:07 -07:00

990 lines
31 KiB
C++

// 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 "logging/logging.h"
#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;
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 * 10 / 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()));
}
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 (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;
}
}
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;
}
uint32_t SelectByImpl(uint32_t for_legacy_bloom,
uint32_t for_fast_local_bloom) {
switch (GetParam()) {
case BloomFilterPolicy::kLegacyBloom:
return for_legacy_bloom;
case BloomFilterPolicy::kFastLocalBloom:
return for_fast_local_bloom;
case BloomFilterPolicy::kDeprecatedBlock:
case BloomFilterPolicy::kAuto:
/* N/A */;
}
// otherwise
assert(false);
return 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();
for (int n = 1; n < 100; n++) {
auto space = bits_builder->CalculateSpace(n);
auto n2 = bits_builder->CalculateNumEntry(space);
EXPECT_GE(n2, n);
auto space2 = bits_builder->CalculateSpace(n2);
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();
ASSERT_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()));
}
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 (kVerbose >= 1) {
fprintf(stderr, "Filters: %d good, %d mediocre\n",
good_filters, mediocre_filters);
}
ASSERT_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();
}
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) {
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));
} // namespace ROCKSDB_NAMESPACE
int main(int argc, char** argv) {
::testing::InitGoogleTest(&argc, argv);
ParseCommandLineFlags(&argc, &argv, true);
return RUN_ALL_TESTS();
}
#endif // GFLAGS