rocksdb/util/filter_bench.cc

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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).
#ifndef GFLAGS
#include <cstdio>
int main() {
fprintf(stderr, "Please install gflags to run rocksdb tools\n");
return 1;
}
#else
#include <cinttypes>
#include <iostream>
#include <vector>
#include "port/port.h"
#include "port/stack_trace.h"
#include "rocksdb/filter_policy.h"
#include "table/block_based/full_filter_block.h"
#include "table/block_based/mock_block_based_table.h"
#include "util/gflags_compat.h"
#include "util/hash.h"
#include "util/random.h"
#include "util/stop_watch.h"
using GFLAGS_NAMESPACE::ParseCommandLineFlags;
using GFLAGS_NAMESPACE::RegisterFlagValidator;
using GFLAGS_NAMESPACE::SetUsageMessage;
DEFINE_uint32(seed, 0, "Seed for random number generators");
DEFINE_double(working_mem_size_mb, 200,
"MB of memory to get up to among all filters");
DEFINE_uint32(average_keys_per_filter, 10000,
"Average number of keys per filter");
DEFINE_uint32(key_size, 16, "Number of bytes each key should be");
DEFINE_uint32(batch_size, 8, "Number of keys to group in each batch");
DEFINE_uint32(bits_per_key, 10, "Bits per key setting for filters");
DEFINE_double(m_queries, 200, "Millions of queries for each test mode");
DEFINE_bool(use_full_block_reader, false,
"Use FullFilterBlockReader interface rather than FilterBitsReader");
DEFINE_bool(quick, false, "Run more limited set of tests, fewer queries");
DEFINE_bool(allow_bad_fp_rate, false, "Continue even if FP rate is bad");
DEFINE_bool(legend, false,
"Print more information about interpreting results instead of "
"running tests");
void _always_assert_fail(int line, const char *file, const char *expr) {
fprintf(stderr, "%s: %d: Assertion %s failed\n", file, line, expr);
abort();
}
#define ALWAYS_ASSERT(cond) \
((cond) ? (void)0 : ::_always_assert_fail(__LINE__, __FILE__, #cond))
using rocksdb::BlockContents;
using rocksdb::CachableEntry;
using rocksdb::fastrange32;
using rocksdb::FilterBitsBuilder;
using rocksdb::FilterBitsReader;
using rocksdb::FullFilterBlockReader;
Store the filter bits reader alongside the filter block contents (#5936) Summary: Amongst other things, PR https://github.com/facebook/rocksdb/issues/5504 refactored the filter block readers so that only the filter block contents are stored in the block cache (as opposed to the earlier design where the cache stored the filter block reader itself, leading to potentially dangling pointers and concurrency bugs). However, this change introduced a performance hit since with the new code, the metadata fields are re-parsed upon every access. This patch reunites the block contents with the filter bits reader to eliminate this overhead; since this is still a self-contained pure data object, it is safe to store it in the cache. (Note: this is similar to how the zstd digest is handled.) Pull Request resolved: https://github.com/facebook/rocksdb/pull/5936 Test Plan: make asan_check filter_bench results for the old code: ``` $ ./filter_bench -quick WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 26.7153 Number of filters: 16669 Total memory (MB): 200.009 Bits/key actual: 10.0647 ---------------------------- Inside queries... Dry run (46b) ns/op: 33.4258 Single filter ns/op: 42.5974 Random filter ns/op: 217.861 ---------------------------- Outside queries... Dry run (25d) ns/op: 32.4217 Single filter ns/op: 50.9855 Random filter ns/op: 219.167 Average FP rate %: 1.13993 ---------------------------- Done. (For more info, run with -legend or -help.) $ ./filter_bench -quick -use_full_block_reader WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 26.5172 Number of filters: 16669 Total memory (MB): 200.009 Bits/key actual: 10.0647 ---------------------------- Inside queries... Dry run (46b) ns/op: 32.3556 Single filter ns/op: 83.2239 Random filter ns/op: 370.676 ---------------------------- Outside queries... Dry run (25d) ns/op: 32.2265 Single filter ns/op: 93.5651 Random filter ns/op: 408.393 Average FP rate %: 1.13993 ---------------------------- Done. (For more info, run with -legend or -help.) ``` With the new code: ``` $ ./filter_bench -quick WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 25.4285 Number of filters: 16669 Total memory (MB): 200.009 Bits/key actual: 10.0647 ---------------------------- Inside queries... Dry run (46b) ns/op: 31.0594 Single filter ns/op: 43.8974 Random filter ns/op: 226.075 ---------------------------- Outside queries... Dry run (25d) ns/op: 31.0295 Single filter ns/op: 50.3824 Random filter ns/op: 226.805 Average FP rate %: 1.13993 ---------------------------- Done. (For more info, run with -legend or -help.) $ ./filter_bench -quick -use_full_block_reader WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 26.5308 Number of filters: 16669 Total memory (MB): 200.009 Bits/key actual: 10.0647 ---------------------------- Inside queries... Dry run (46b) ns/op: 33.2968 Single filter ns/op: 58.6163 Random filter ns/op: 291.434 ---------------------------- Outside queries... Dry run (25d) ns/op: 32.1839 Single filter ns/op: 66.9039 Random filter ns/op: 292.828 Average FP rate %: 1.13993 ---------------------------- Done. (For more info, run with -legend or -help.) ``` Differential Revision: D17991712 Pulled By: ltamasi fbshipit-source-id: 7ea205550217bfaaa1d5158ebd658e5832e60f29
2019-10-19 04:30:47 +02:00
using rocksdb::ParsedFullFilterBlock;
using rocksdb::Random32;
using rocksdb::Slice;
using rocksdb::mock::MockBlockBasedTableTester;
struct KeyMaker {
KeyMaker(size_t size)
: data_(new char[size]),
slice_(data_.get(), size),
vals_(reinterpret_cast<uint32_t *>(data_.get())) {
assert(size >= 8);
memset(data_.get(), 0, size);
}
std::unique_ptr<char[]> data_;
Slice slice_;
uint32_t *vals_;
Slice Get(uint32_t filter_num, uint32_t val_num) {
vals_[0] = filter_num + val_num;
vals_[1] = val_num;
return slice_;
}
};
void PrintWarnings() {
#if defined(__GNUC__) && !defined(__OPTIMIZE__)
fprintf(stdout,
"WARNING: Optimization is disabled: benchmarks unnecessarily slow\n");
#endif
#ifndef NDEBUG
fprintf(stdout,
"WARNING: Assertions are enabled; benchmarks unnecessarily slow\n");
#endif
}
struct FilterInfo {
uint32_t filter_id_ = 0;
std::unique_ptr<const char[]> owner_;
Slice filter_;
uint32_t keys_added_ = 0;
std::unique_ptr<FilterBitsReader> reader_;
std::unique_ptr<FullFilterBlockReader> full_block_reader_;
uint64_t outside_queries_ = 0;
uint64_t false_positives_ = 0;
};
enum TestMode {
kSingleFilter,
kBatchPrepared,
kBatchUnprepared,
kFiftyOneFilter,
kEightyTwentyFilter,
kRandomFilter,
};
static const std::vector<TestMode> allTestModes = {
kSingleFilter, kBatchPrepared, kBatchUnprepared,
kFiftyOneFilter, kEightyTwentyFilter, kRandomFilter,
};
static const std::vector<TestMode> quickTestModes = {
kSingleFilter,
kRandomFilter,
};
const char *TestModeToString(TestMode tm) {
switch (tm) {
case kSingleFilter:
return "Single filter";
case kBatchPrepared:
return "Batched, prepared";
case kBatchUnprepared:
return "Batched, unprepared";
case kFiftyOneFilter:
return "Skewed 50% in 1%";
case kEightyTwentyFilter:
return "Skewed 80% in 20%";
case kRandomFilter:
return "Random filter";
}
return "Bad TestMode";
}
struct FilterBench : public MockBlockBasedTableTester {
std::vector<KeyMaker> kms_;
std::vector<FilterInfo> infos_;
Random32 random_;
FilterBench()
: MockBlockBasedTableTester(
rocksdb::NewBloomFilterPolicy(FLAGS_bits_per_key)),
random_(FLAGS_seed) {
for (uint32_t i = 0; i < FLAGS_batch_size; ++i) {
kms_.emplace_back(FLAGS_key_size < 8 ? 8 : FLAGS_key_size);
}
}
void Go();
void RandomQueryTest(bool inside, bool dry_run, TestMode mode);
};
void FilterBench::Go() {
std::unique_ptr<FilterBitsBuilder> builder(
table_options_.filter_policy->GetFilterBitsBuilder());
uint32_t variance_mask = 1;
while (variance_mask * variance_mask * 4 < FLAGS_average_keys_per_filter) {
variance_mask = variance_mask * 2 + 1;
}
const std::vector<TestMode> &testModes =
FLAGS_quick ? quickTestModes : allTestModes;
if (FLAGS_quick) {
FLAGS_m_queries /= 10.0;
}
std::cout << "Building..." << std::endl;
size_t total_memory_used = 0;
size_t total_keys_added = 0;
rocksdb::StopWatchNano timer(rocksdb::Env::Default(), true);
while (total_memory_used < 1024 * 1024 * FLAGS_working_mem_size_mb) {
uint32_t filter_id = random_.Next();
uint32_t keys_to_add = FLAGS_average_keys_per_filter +
(random_.Next() & variance_mask) -
(variance_mask / 2);
for (uint32_t i = 0; i < keys_to_add; ++i) {
builder->AddKey(kms_[0].Get(filter_id, i));
}
infos_.emplace_back();
FilterInfo &info = infos_.back();
info.filter_id_ = filter_id;
info.filter_ = builder->Finish(&info.owner_);
info.keys_added_ = keys_to_add;
info.reader_.reset(
table_options_.filter_policy->GetFilterBitsReader(info.filter_));
Store the filter bits reader alongside the filter block contents (#5936) Summary: Amongst other things, PR https://github.com/facebook/rocksdb/issues/5504 refactored the filter block readers so that only the filter block contents are stored in the block cache (as opposed to the earlier design where the cache stored the filter block reader itself, leading to potentially dangling pointers and concurrency bugs). However, this change introduced a performance hit since with the new code, the metadata fields are re-parsed upon every access. This patch reunites the block contents with the filter bits reader to eliminate this overhead; since this is still a self-contained pure data object, it is safe to store it in the cache. (Note: this is similar to how the zstd digest is handled.) Pull Request resolved: https://github.com/facebook/rocksdb/pull/5936 Test Plan: make asan_check filter_bench results for the old code: ``` $ ./filter_bench -quick WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 26.7153 Number of filters: 16669 Total memory (MB): 200.009 Bits/key actual: 10.0647 ---------------------------- Inside queries... Dry run (46b) ns/op: 33.4258 Single filter ns/op: 42.5974 Random filter ns/op: 217.861 ---------------------------- Outside queries... Dry run (25d) ns/op: 32.4217 Single filter ns/op: 50.9855 Random filter ns/op: 219.167 Average FP rate %: 1.13993 ---------------------------- Done. (For more info, run with -legend or -help.) $ ./filter_bench -quick -use_full_block_reader WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 26.5172 Number of filters: 16669 Total memory (MB): 200.009 Bits/key actual: 10.0647 ---------------------------- Inside queries... Dry run (46b) ns/op: 32.3556 Single filter ns/op: 83.2239 Random filter ns/op: 370.676 ---------------------------- Outside queries... Dry run (25d) ns/op: 32.2265 Single filter ns/op: 93.5651 Random filter ns/op: 408.393 Average FP rate %: 1.13993 ---------------------------- Done. (For more info, run with -legend or -help.) ``` With the new code: ``` $ ./filter_bench -quick WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 25.4285 Number of filters: 16669 Total memory (MB): 200.009 Bits/key actual: 10.0647 ---------------------------- Inside queries... Dry run (46b) ns/op: 31.0594 Single filter ns/op: 43.8974 Random filter ns/op: 226.075 ---------------------------- Outside queries... Dry run (25d) ns/op: 31.0295 Single filter ns/op: 50.3824 Random filter ns/op: 226.805 Average FP rate %: 1.13993 ---------------------------- Done. (For more info, run with -legend or -help.) $ ./filter_bench -quick -use_full_block_reader WARNING: Assertions are enabled; benchmarks unnecessarily slow Building... Build avg ns/key: 26.5308 Number of filters: 16669 Total memory (MB): 200.009 Bits/key actual: 10.0647 ---------------------------- Inside queries... Dry run (46b) ns/op: 33.2968 Single filter ns/op: 58.6163 Random filter ns/op: 291.434 ---------------------------- Outside queries... Dry run (25d) ns/op: 32.1839 Single filter ns/op: 66.9039 Random filter ns/op: 292.828 Average FP rate %: 1.13993 ---------------------------- Done. (For more info, run with -legend or -help.) ``` Differential Revision: D17991712 Pulled By: ltamasi fbshipit-source-id: 7ea205550217bfaaa1d5158ebd658e5832e60f29
2019-10-19 04:30:47 +02:00
CachableEntry<ParsedFullFilterBlock> block(
new ParsedFullFilterBlock(table_options_.filter_policy.get(),
BlockContents(info.filter_)),
nullptr /* cache */, nullptr /* cache_handle */, true /* own_value */);
info.full_block_reader_.reset(
new FullFilterBlockReader(table_.get(), std::move(block)));
total_memory_used += info.filter_.size();
total_keys_added += keys_to_add;
}
uint64_t elapsed_nanos = timer.ElapsedNanos();
double ns = double(elapsed_nanos) / total_keys_added;
std::cout << "Build avg ns/key: " << ns << std::endl;
std::cout << "Number of filters: " << infos_.size() << std::endl;
std::cout << "Total memory (MB): " << total_memory_used / 1024.0 / 1024.0
<< std::endl;
double bpk = total_memory_used * 8.0 / total_keys_added;
std::cout << "Bits/key actual: " << bpk << std::endl;
if (!FLAGS_quick) {
double tolerable_rate = std::pow(2.0, -(bpk - 1.0) / (1.4 + bpk / 50.0));
std::cout << "Best possible FP rate %: " << 100.0 * std::pow(2.0, -bpk)
<< std::endl;
std::cout << "Tolerable FP rate %: " << 100.0 * tolerable_rate << std::endl;
std::cout << "----------------------------" << std::endl;
std::cout << "Verifying..." << std::endl;
uint32_t outside_q_per_f = 1000000 / infos_.size();
uint64_t fps = 0;
for (uint32_t i = 0; i < infos_.size(); ++i) {
FilterInfo &info = infos_[i];
for (uint32_t j = 0; j < info.keys_added_; ++j) {
ALWAYS_ASSERT(info.reader_->MayMatch(kms_[0].Get(info.filter_id_, j)));
}
for (uint32_t j = 0; j < outside_q_per_f; ++j) {
fps += info.reader_->MayMatch(
kms_[0].Get(info.filter_id_, j | 0x80000000));
}
}
std::cout << " No FNs :)" << std::endl;
double prelim_rate = double(fps) / outside_q_per_f / infos_.size();
std::cout << " Prelim FP rate %: " << (100.0 * prelim_rate) << std::endl;
if (!FLAGS_allow_bad_fp_rate) {
ALWAYS_ASSERT(prelim_rate < tolerable_rate);
}
}
std::cout << "----------------------------" << std::endl;
std::cout << "Inside queries..." << std::endl;
random_.Seed(FLAGS_seed + 1);
RandomQueryTest(/*inside*/ true, /*dry_run*/ true, kRandomFilter);
for (TestMode tm : testModes) {
random_.Seed(FLAGS_seed + 1);
RandomQueryTest(/*inside*/ true, /*dry_run*/ false, tm);
}
std::cout << "----------------------------" << std::endl;
std::cout << "Outside queries..." << std::endl;
random_.Seed(FLAGS_seed + 2);
RandomQueryTest(/*inside*/ false, /*dry_run*/ true, kRandomFilter);
for (TestMode tm : testModes) {
random_.Seed(FLAGS_seed + 2);
RandomQueryTest(/*inside*/ false, /*dry_run*/ false, tm);
}
std::cout << "----------------------------" << std::endl;
std::cout << "Done. (For more info, run with -legend or -help.)" << std::endl;
}
void FilterBench::RandomQueryTest(bool inside, bool dry_run, TestMode mode) {
for (auto &info : infos_) {
info.outside_queries_ = 0;
info.false_positives_ = 0;
}
uint32_t num_infos = static_cast<uint32_t>(infos_.size());
uint32_t dry_run_hash = 0;
uint64_t max_queries =
static_cast<uint64_t>(FLAGS_m_queries * 1000000 + 0.50);
// Some filters may be considered secondary in order to implement skewed
// queries. num_primary_filters is the number that are to be treated as
// equal, and any remainder will be treated as secondary.
uint32_t num_primary_filters = num_infos;
// The proportion (when divided by 2^32 - 1) of filter queries going to
// the primary filters (default = all). The remainder of queries are
// against secondary filters.
uint32_t primary_filter_threshold = 0xffffffff;
if (mode == kSingleFilter) {
// 100% of queries to 1 filter
num_primary_filters = 1;
} else if (mode == kFiftyOneFilter) {
// 50% of queries
primary_filter_threshold /= 2;
// to 1% of filters
num_primary_filters = (num_primary_filters + 99) / 100;
} else if (mode == kEightyTwentyFilter) {
// 80% of queries
primary_filter_threshold = primary_filter_threshold / 5 * 4;
// to 20% of filters
num_primary_filters = (num_primary_filters + 4) / 5;
}
uint32_t batch_size = 1;
std::unique_ptr<Slice *[]> batch_slices;
std::unique_ptr<bool[]> batch_results;
if (mode == kBatchPrepared || mode == kBatchUnprepared) {
batch_size = static_cast<uint32_t>(kms_.size());
batch_slices.reset(new Slice *[batch_size]);
batch_results.reset(new bool[batch_size]);
for (uint32_t i = 0; i < batch_size; ++i) {
batch_slices[i] = &kms_[i].slice_;
batch_results[i] = false;
}
}
rocksdb::StopWatchNano timer(rocksdb::Env::Default(), true);
for (uint64_t q = 0; q < max_queries; q += batch_size) {
uint32_t filter_index;
if (random_.Next() <= primary_filter_threshold) {
filter_index = random_.Uniformish(num_primary_filters);
} else {
// secondary
filter_index = num_primary_filters +
random_.Uniformish(num_infos - num_primary_filters);
}
FilterInfo &info = infos_[filter_index];
for (uint32_t i = 0; i < batch_size; ++i) {
if (inside) {
kms_[i].Get(info.filter_id_, random_.Uniformish(info.keys_added_));
} else {
kms_[i].Get(info.filter_id_, random_.Next() | uint32_t{0x80000000});
info.outside_queries_++;
}
}
// TODO: implement batched interface to full block reader
if (mode == kBatchPrepared && !dry_run && !FLAGS_use_full_block_reader) {
for (uint32_t i = 0; i < batch_size; ++i) {
batch_results[i] = false;
}
info.reader_->MayMatch(batch_size, batch_slices.get(),
batch_results.get());
for (uint32_t i = 0; i < batch_size; ++i) {
if (inside) {
ALWAYS_ASSERT(batch_results[i]);
} else {
info.false_positives_ += batch_results[i];
}
}
} else {
for (uint32_t i = 0; i < batch_size; ++i) {
if (dry_run) {
dry_run_hash ^= rocksdb::BloomHash(kms_[i].slice_);
} else {
bool may_match;
if (FLAGS_use_full_block_reader) {
may_match = info.full_block_reader_->KeyMayMatch(
kms_[i].slice_,
/*prefix_extractor=*/nullptr,
/*block_offset=*/rocksdb::kNotValid,
/*no_io=*/false, /*const_ikey_ptr=*/nullptr,
/*get_context=*/nullptr,
/*lookup_context=*/nullptr);
} else {
may_match = info.reader_->MayMatch(kms_[i].slice_);
}
if (inside) {
ALWAYS_ASSERT(may_match);
} else {
info.false_positives_ += may_match;
}
}
}
}
}
uint64_t elapsed_nanos = timer.ElapsedNanos();
double ns = double(elapsed_nanos) / max_queries;
if (dry_run) {
// Printing part of hash prevents dry run components from being optimized
// away by compiler
std::cout << " Dry run (" << std::hex << (dry_run_hash & 0xfff) << std::dec
<< ") ";
} else {
std::cout << " " << TestModeToString(mode) << " ";
}
std::cout << "ns/op: " << ns << std::endl;
if (!inside && !dry_run && mode == kRandomFilter) {
uint64_t q = 0;
uint64_t fp = 0;
double worst_fp_rate = 0.0;
double best_fp_rate = 1.0;
for (auto &info : infos_) {
q += info.outside_queries_;
fp += info.false_positives_;
if (info.outside_queries_ > 0) {
double fp_rate = double(info.false_positives_) / info.outside_queries_;
worst_fp_rate = std::max(worst_fp_rate, fp_rate);
best_fp_rate = std::min(best_fp_rate, fp_rate);
}
}
std::cout << " Average FP rate %: " << 100.0 * fp / q << std::endl;
if (!FLAGS_quick) {
std::cout << " Worst FP rate %: " << 100.0 * worst_fp_rate
<< std::endl;
std::cout << " Best FP rate %: " << 100.0 * best_fp_rate
<< std::endl;
std::cout << " Best possible bits/key: "
<< -std::log(double(fp) / q) / std::log(2.0) << std::endl;
}
}
}
int main(int argc, char **argv) {
rocksdb::port::InstallStackTraceHandler();
SetUsageMessage(std::string("\nUSAGE:\n") + std::string(argv[0]) +
" [-quick] [OTHER OPTIONS]...");
ParseCommandLineFlags(&argc, &argv, true);
PrintWarnings();
if (FLAGS_legend) {
std::cout
<< "Legend:" << std::endl
<< " \"Inside\" - key that was added to filter" << std::endl
<< " \"Outside\" - key that was not added to filter" << std::endl
<< " \"FN\" - false negative query (must not happen)" << std::endl
<< " \"FP\" - false positive query (OK at low rate)" << std::endl
<< " \"Dry run\" - cost of testing and hashing overhead. Consider"
<< "\n subtracting this cost from the others." << std::endl
<< " \"Single filter\" - essentially minimum cost, assuming filter"
<< "\n fits easily in L1 CPU cache." << std::endl
<< " \"Batched, prepared\" - several queries at once against a"
<< "\n randomly chosen filter, using multi-query interface."
<< std::endl
<< " \"Batched, unprepared\" - similar, but using serial calls"
<< "\n to single query interface." << std::endl
<< " \"Random filter\" - a filter is chosen at random as target"
<< "\n of each query." << std::endl
<< " \"Skewed X% in Y%\" - like \"Random filter\" except Y% of"
<< "\n the filters are designated as \"hot\" and receive X%"
<< "\n of queries." << std::endl;
} else {
FilterBench b;
b.Go();
}
return 0;
}
#endif // GFLAGS