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rocksdb/util/filter_bench.cc
Peter Dillinger ab65278b1f Misc filter_bench improvements (#6444) 
Summary:
Useful in validating/testing internal fragmentation changes (https://github.com/facebook/rocksdb/issues/6427)
Pull Request resolved: https://github.com/facebook/rocksdb/pull/6444

Test Plan: manual (no changes to production code)

Differential Revision: D20040076

Pulled By: pdillinger

fbshipit-source-id: 32d26f363d2a9ab9f5bebd281dcebd9915ae340e
2020-02-21 13:31:57 -08:00

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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).
#if !defined(GFLAGS) || defined(ROCKSDB_LITE)
#include <cstdio>
int main() {
fprintf(stderr, "filter_bench requires gflags and !ROCKSDB_LITE\n");
return 1;
}
#else
#include <cinttypes>
#include <iostream>
#include <sstream>
#include <vector>
#include "memory/arena.h"
#include "port/port.h"
#include "port/stack_trace.h"
#include "table/block_based/filter_policy_internal.h"
#include "table/block_based/full_filter_block.h"
#include "table/block_based/mock_block_based_table.h"
#include "table/plain/plain_table_bloom.h"
#include "util/gflags_compat.h"
#include "util/hash.h"
#include "util/random.h"
#include "util/stderr_logger.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, unless "
"m_keys_total_max is specified.");
DEFINE_uint32(average_keys_per_filter, 10000,
"Average number of keys per filter");
DEFINE_double(vary_key_count_ratio, 0.4,
"Vary number of keys by up to +/- vary_key_count_ratio * "
"average_keys_per_filter.");
DEFINE_uint32(key_size, 24, "Average number of bytes for each key");
DEFINE_bool(vary_key_alignment, true,
"Whether to vary key alignment (default: at least 32-bit "
"alignment)");
DEFINE_uint32(vary_key_size_log2_interval, 5,
"Use same key size 2^n times, then change. Key size varies from "
"-2 to +2 bytes vs. average, unless n>=30 to fix key size.");
DEFINE_uint32(batch_size, 8, "Number of keys to group in each batch");
DEFINE_double(bits_per_key, 10.0, "Bits per key setting for filters");
DEFINE_double(m_queries, 200, "Millions of queries for each test mode");
DEFINE_double(m_keys_total_max, 0,
"Maximum total keys added to filters, in millions. "
"0 (default) disables. Non-zero overrides working_mem_size_mb "
"option.");
DEFINE_bool(use_full_block_reader, false,
"Use FullFilterBlockReader interface rather than FilterBitsReader");
DEFINE_bool(use_plain_table_bloom, false,
"Use PlainTableBloom structure and interface rather than "
"FilterBitsReader/FullFilterBlockReader");
DEFINE_bool(new_builder, false,
"Whether to create a new builder for each new filter");
DEFINE_uint32(impl, 0,
"Select filter implementation. Without -use_plain_table_bloom:"
"0 = full filter, 1 = block-based filter. With "
"-use_plain_table_bloom: 0 = no locality, 1 = locality.");
DEFINE_bool(net_includes_hashing, false,
"Whether query net ns/op times should include hashing. "
"(if not, dry run will include hashing) "
"(build times always include hashing)");
DEFINE_bool(quick, false, "Run more limited set of tests, fewer queries");
DEFINE_bool(best_case, false, "Run limited tests only for best-case");
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");
DEFINE_uint32(runs, 1, "Number of times to rebuild and run benchmark 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))
#ifndef NDEBUG
// This could affect build times enough that we should not include it for
// accurate speed tests
#define PREDICT_FP_RATE
#endif
using ROCKSDB_NAMESPACE::Arena;
using ROCKSDB_NAMESPACE::BlockContents;
using ROCKSDB_NAMESPACE::BloomFilterPolicy;
using ROCKSDB_NAMESPACE::BloomHash;
using ROCKSDB_NAMESPACE::BuiltinFilterBitsBuilder;
using ROCKSDB_NAMESPACE::CachableEntry;
using ROCKSDB_NAMESPACE::EncodeFixed32;
using ROCKSDB_NAMESPACE::fastrange32;
using ROCKSDB_NAMESPACE::FilterBitsReader;
using ROCKSDB_NAMESPACE::FilterBuildingContext;
using ROCKSDB_NAMESPACE::FullFilterBlockReader;
using ROCKSDB_NAMESPACE::GetSliceHash;
using ROCKSDB_NAMESPACE::GetSliceHash64;
using ROCKSDB_NAMESPACE::Lower32of64;
using ROCKSDB_NAMESPACE::ParsedFullFilterBlock;
using ROCKSDB_NAMESPACE::PlainTableBloomV1;
using ROCKSDB_NAMESPACE::Random32;
using ROCKSDB_NAMESPACE::Slice;
using ROCKSDB_NAMESPACE::StderrLogger;
using ROCKSDB_NAMESPACE::mock::MockBlockBasedTableTester;
struct KeyMaker {
KeyMaker(size_t avg_size)
: smallest_size_(avg_size -
(FLAGS_vary_key_size_log2_interval >= 30 ? 2 : 0)),
buf_size_(avg_size + 11), // pad to vary key size and alignment
buf_(new char[buf_size_]) {
memset(buf_.get(), 0, buf_size_);
assert(smallest_size_ > 8);
}
size_t smallest_size_;
size_t buf_size_;
std::unique_ptr<char[]> buf_;
// Returns a unique(-ish) key based on the given parameter values. Each
// call returns a Slice from the same buffer so previously returned
// Slices should be considered invalidated.
Slice Get(uint32_t filter_num, uint32_t val_num) {
size_t start = FLAGS_vary_key_alignment ? val_num % 4 : 0;
size_t len = smallest_size_;
if (FLAGS_vary_key_size_log2_interval < 30) {
// To get range [avg_size - 2, avg_size + 2]
// use range [smallest_size, smallest_size + 4]
len += fastrange32(
(val_num >> FLAGS_vary_key_size_log2_interval) * 1234567891, 5);
}
char * data = buf_.get() + start;
// Populate key data such that all data makes it into a key of at
// least 8 bytes. We also don't want all the within-filter key
// variance confined to a contiguous 32 bits, because then a 32 bit
// hash function can "cheat" the false positive rate by
// approximating a perfect hash.
EncodeFixed32(data, val_num);
EncodeFixed32(data + 4, filter_num + val_num);
// ensure clearing leftovers from different alignment
EncodeFixed32(data + 8, 0);
return Slice(data, len);
}
};
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_;
std::unique_ptr<PlainTableBloomV1> plain_table_bloom_;
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,
};
static const std::vector<TestMode> bestCaseTestModes = {
kSingleFilter,
};
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";
}
// Do just enough to keep some data dependence for the
// compiler / CPU
static uint32_t DryRunNoHash(Slice &s) {
uint32_t sz = static_cast<uint32_t>(s.size());
if (sz >= 4) {
return sz + s.data()[3];
} else {
return sz;
}
}
static uint32_t DryRunHash32(Slice &s) {
// Same perf characteristics as GetSliceHash()
return BloomHash(s);
}
static uint32_t DryRunHash64(Slice &s) {
return Lower32of64(GetSliceHash64(s));
}
struct FilterBench : public MockBlockBasedTableTester {
std::vector<KeyMaker> kms_;
std::vector<FilterInfo> infos_;
Random32 random_;
std::ostringstream fp_rate_report_;
Arena arena_;
StderrLogger stderr_logger_;
double m_queries_;
FilterBench()
: MockBlockBasedTableTester(new BloomFilterPolicy(
FLAGS_bits_per_key,
static_cast<BloomFilterPolicy::Mode>(FLAGS_impl))),
random_(FLAGS_seed),
m_queries_(0) {
for (uint32_t i = 0; i < FLAGS_batch_size; ++i) {
kms_.emplace_back(FLAGS_key_size < 8 ? 8 : FLAGS_key_size);
}
ioptions_.info_log = &stderr_logger_;
}
void Go();
double RandomQueryTest(uint32_t inside_threshold, bool dry_run,
TestMode mode);
};
void FilterBench::Go() {
if (FLAGS_use_plain_table_bloom && FLAGS_use_full_block_reader) {
throw std::runtime_error(
"Can't combine -use_plain_table_bloom and -use_full_block_reader");
}
if (FLAGS_use_plain_table_bloom) {
if (FLAGS_impl > 1) {
throw std::runtime_error(
"-impl must currently be >= 0 and <= 1 for Plain table");
}
} else {
if (FLAGS_impl == 1) {
throw std::runtime_error(
"Block-based filter not currently supported by filter_bench");
}
if (FLAGS_impl > 2) {
throw std::runtime_error(
"-impl must currently be 0 or 2 for Block-based table");
}
}
if (FLAGS_vary_key_count_ratio < 0.0 || FLAGS_vary_key_count_ratio > 1.0) {
throw std::runtime_error("-vary_key_count_ratio must be >= 0.0 and <= 1.0");
}
// For example, average_keys_per_filter = 100, vary_key_count_ratio = 0.1.
// Varys up to +/- 10 keys. variance_range = 21 (generating value 0..20).
// variance_offset = 10, so value - offset average value is always 0.
const uint32_t variance_range =
1 + 2 * static_cast<uint32_t>(FLAGS_vary_key_count_ratio *
FLAGS_average_keys_per_filter);
const uint32_t variance_offset = variance_range / 2;
const std::vector<TestMode> &testModes =
FLAGS_best_case ? bestCaseTestModes
: FLAGS_quick ? quickTestModes : allTestModes;
m_queries_ = FLAGS_m_queries;
double working_mem_size_mb = FLAGS_working_mem_size_mb;
if (FLAGS_quick) {
m_queries_ /= 7.0;
} else if (FLAGS_best_case) {
m_queries_ /= 3.0;
working_mem_size_mb /= 10.0;
}
std::cout << "Building..." << std::endl;
std::unique_ptr<BuiltinFilterBitsBuilder> builder;
size_t total_memory_used = 0;
size_t total_keys_added = 0;
#ifdef PREDICT_FP_RATE
double weighted_predicted_fp_rate = 0.0;
#endif
size_t max_total_keys;
size_t max_mem;
if (FLAGS_m_keys_total_max > 0) {
max_total_keys = static_cast<size_t>(1000000 * FLAGS_m_keys_total_max);
max_mem = SIZE_MAX;
} else {
max_total_keys = SIZE_MAX;
max_mem = static_cast<size_t>(1024 * 1024 * working_mem_size_mb);
}
ROCKSDB_NAMESPACE::StopWatchNano timer(ROCKSDB_NAMESPACE::Env::Default(),
true);
infos_.clear();
while ((working_mem_size_mb == 0 || total_memory_used < max_mem) &&
total_keys_added < max_total_keys) {
uint32_t filter_id = random_.Next();
uint32_t keys_to_add = FLAGS_average_keys_per_filter +
fastrange32(random_.Next(), variance_range) -
variance_offset;
if (max_total_keys - total_keys_added < keys_to_add) {
keys_to_add = static_cast<uint32_t>(max_total_keys - total_keys_added);
}
infos_.emplace_back();
FilterInfo &info = infos_.back();
info.filter_id_ = filter_id;
info.keys_added_ = keys_to_add;
if (FLAGS_use_plain_table_bloom) {
info.plain_table_bloom_.reset(new PlainTableBloomV1());
info.plain_table_bloom_->SetTotalBits(
&arena_, static_cast<uint32_t>(keys_to_add * FLAGS_bits_per_key),
FLAGS_impl, 0 /*huge_page*/, nullptr /*logger*/);
for (uint32_t i = 0; i < keys_to_add; ++i) {
uint32_t hash = GetSliceHash(kms_[0].Get(filter_id, i));
info.plain_table_bloom_->AddHash(hash);
}
info.filter_ = info.plain_table_bloom_->GetRawData();
} else {
if (!builder) {
builder.reset(&dynamic_cast<BuiltinFilterBitsBuilder &>(*GetBuilder()));
}
for (uint32_t i = 0; i < keys_to_add; ++i) {
builder->AddKey(kms_[0].Get(filter_id, i));
}
info.filter_ = builder->Finish(&info.owner_);
#ifdef PREDICT_FP_RATE
weighted_predicted_fp_rate +=
keys_to_add *
builder->EstimatedFpRate(keys_to_add, info.filter_.size());
#endif
if (FLAGS_new_builder) {
builder.reset();
}
info.reader_.reset(
table_options_.filter_policy->GetFilterBitsReader(info.filter_));
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;
#ifdef PREDICT_FP_RATE
std::cout << "Predicted FP rate %: "
<< 100.0 * (weighted_predicted_fp_rate / total_keys_added)
<< std::endl;
#endif
if (!FLAGS_quick && !FLAGS_best_case) {
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 =
static_cast<uint32_t>(m_queries_ * 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) {
if (FLAGS_use_plain_table_bloom) {
uint32_t hash = GetSliceHash(kms_[0].Get(info.filter_id_, j));
ALWAYS_ASSERT(info.plain_table_bloom_->MayContainHash(hash));
} else {
ALWAYS_ASSERT(
info.reader_->MayMatch(kms_[0].Get(info.filter_id_, j)));
}
}
for (uint32_t j = 0; j < outside_q_per_f; ++j) {
if (FLAGS_use_plain_table_bloom) {
uint32_t hash =
GetSliceHash(kms_[0].Get(info.filter_id_, j | 0x80000000));
fps += info.plain_table_bloom_->MayContainHash(hash);
} else {
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 << "Mixed inside/outside queries..." << std::endl;
// 50% each inside and outside
uint32_t inside_threshold = UINT32_MAX / 2;
for (TestMode tm : testModes) {
random_.Seed(FLAGS_seed + 1);
double f = RandomQueryTest(inside_threshold, /*dry_run*/ false, tm);
random_.Seed(FLAGS_seed + 1);
double d = RandomQueryTest(inside_threshold, /*dry_run*/ true, tm);
std::cout << " " << TestModeToString(tm) << " net ns/op: " << (f - d)
<< std::endl;
}
if (!FLAGS_quick) {
std::cout << "----------------------------" << std::endl;
std::cout << "Inside queries (mostly)..." << std::endl;
// Do about 95% inside queries rather than 100% so that branch predictor
// can't give itself an artifically crazy advantage.
inside_threshold = UINT32_MAX / 20 * 19;
for (TestMode tm : testModes) {
random_.Seed(FLAGS_seed + 1);
double f = RandomQueryTest(inside_threshold, /*dry_run*/ false, tm);
random_.Seed(FLAGS_seed + 1);
double d = RandomQueryTest(inside_threshold, /*dry_run*/ true, tm);
std::cout << " " << TestModeToString(tm) << " net ns/op: " << (f - d)
<< std::endl;
}
std::cout << "----------------------------" << std::endl;
std::cout << "Outside queries (mostly)..." << std::endl;
// Do about 95% outside queries rather than 100% so that branch predictor
// can't give itself an artifically crazy advantage.
inside_threshold = UINT32_MAX / 20;
for (TestMode tm : testModes) {
random_.Seed(FLAGS_seed + 2);
double f = RandomQueryTest(inside_threshold, /*dry_run*/ false, tm);
random_.Seed(FLAGS_seed + 2);
double d = RandomQueryTest(inside_threshold, /*dry_run*/ true, tm);
std::cout << " " << TestModeToString(tm) << " net ns/op: " << (f - d)
<< std::endl;
}
}
std::cout << fp_rate_report_.str();
std::cout << "----------------------------" << std::endl;
std::cout << "Done. (For more info, run with -legend or -help.)" << std::endl;
}
double FilterBench::RandomQueryTest(uint32_t inside_threshold, bool dry_run,
TestMode mode) {
for (auto &info : infos_) {
info.outside_queries_ = 0;
info.false_positives_ = 0;
}
auto dry_run_hash_fn = DryRunNoHash;
if (!FLAGS_net_includes_hashing) {
if (FLAGS_impl < 2 || FLAGS_use_plain_table_bloom) {
dry_run_hash_fn = DryRunHash32;
} else {
dry_run_hash_fn = DryRunHash64;
}
}
uint32_t num_infos = static_cast<uint32_t>(infos_.size());
uint32_t dry_run_hash = 0;
uint64_t max_queries = static_cast<uint64_t>(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<Slice *[]> batch_slice_ptrs;
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_slice_ptrs.reset(new Slice *[batch_size]);
batch_results.reset(new bool[batch_size]);
for (uint32_t i = 0; i < batch_size; ++i) {
batch_results[i] = false;
batch_slice_ptrs[i] = &batch_slices[i];
}
ROCKSDB_NAMESPACE::StopWatchNano timer(ROCKSDB_NAMESPACE::Env::Default(),
true);
for (uint64_t q = 0; q < max_queries; q += batch_size) {
bool inside_this_time = random_.Next() <= inside_threshold;
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_this_time) {
batch_slices[i] =
kms_[i].Get(info.filter_id_, random_.Uniformish(info.keys_added_));
} else {
batch_slices[i] =
kms_[i].Get(info.filter_id_, random_.Uniformish(info.keys_added_) |
uint32_t{0x80000000});
info.outside_queries_++;
}
}
// TODO: implement batched interface to full block reader
// TODO: implement batched interface to plain table bloom
if (mode == kBatchPrepared && !FLAGS_use_full_block_reader &&
!FLAGS_use_plain_table_bloom) {
for (uint32_t i = 0; i < batch_size; ++i) {
batch_results[i] = false;
}
if (dry_run) {
for (uint32_t i = 0; i < batch_size; ++i) {
batch_results[i] = true;
dry_run_hash += dry_run_hash_fn(batch_slices[i]);
}
} else {
info.reader_->MayMatch(batch_size, batch_slice_ptrs.get(),
batch_results.get());
}
for (uint32_t i = 0; i < batch_size; ++i) {
if (inside_this_time) {
ALWAYS_ASSERT(batch_results[i]);
} else {
info.false_positives_ += batch_results[i];
}
}
} else {
for (uint32_t i = 0; i < batch_size; ++i) {
bool may_match;
if (FLAGS_use_plain_table_bloom) {
if (dry_run) {
dry_run_hash += dry_run_hash_fn(batch_slices[i]);
may_match = true;
} else {
uint32_t hash = GetSliceHash(batch_slices[i]);
may_match = info.plain_table_bloom_->MayContainHash(hash);
}
} else if (FLAGS_use_full_block_reader) {
if (dry_run) {
dry_run_hash += dry_run_hash_fn(batch_slices[i]);
may_match = true;
} else {
may_match = info.full_block_reader_->KeyMayMatch(
batch_slices[i],
/*prefix_extractor=*/nullptr,
/*block_offset=*/ROCKSDB_NAMESPACE::kNotValid,
/*no_io=*/false, /*const_ikey_ptr=*/nullptr,
/*get_context=*/nullptr,
/*lookup_context=*/nullptr);
}
} else {
if (dry_run) {
dry_run_hash += dry_run_hash_fn(batch_slices[i]);
may_match = true;
} else {
may_match = info.reader_->MayMatch(batch_slices[i]);
}
}
if (inside_this_time) {
ALWAYS_ASSERT(may_match);
} else {
info.false_positives_ += may_match;
}
}
}
}
uint64_t elapsed_nanos = timer.ElapsedNanos();
double ns = double(elapsed_nanos) / max_queries;
if (!FLAGS_quick) {
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 & 0xfffff)
<< std::dec << ") ";
} else {
std::cout << " Gross filter ";
}
std::cout << "ns/op: " << ns << std::endl;
}
if (!dry_run) {
fp_rate_report_.str("");
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);
}
}
fp_rate_report_ << " Average FP rate %: " << 100.0 * fp / q << std::endl;
if (!FLAGS_quick && !FLAGS_best_case) {
fp_rate_report_ << " Worst FP rate %: " << 100.0 * worst_fp_rate
<< std::endl;
fp_rate_report_ << " Best FP rate %: " << 100.0 * best_fp_rate
<< std::endl;
fp_rate_report_ << " Best possible bits/key: "
<< -std::log(double(fp) / q) / std::log(2.0) << std::endl;
}
}
return ns;
}
int main(int argc, char **argv) {
ROCKSDB_NAMESPACE::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." << std::endl
<< " \"Gross filter\" - cost of filter queries including testing "
<< "\n and hashing overhead." << std::endl
<< " \"net\" - best estimate of time in filter operation, without "
<< "\n testing and hashing overhead (gross filter - dry run)"
<< std::endl
<< " \"ns/op\" - nanoseconds per operation (key query or add)"
<< 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;
for (uint32_t i = 0; i < FLAGS_runs; ++i) {
b.Go();
FLAGS_seed += 100;
b.random_.Seed(FLAGS_seed);
}
}
return 0;
}
#endif // !defined(GFLAGS) || defined(ROCKSDB_LITE)
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