fork of https://github.com/oxigraph/rocksdb and https://github.com/facebook/rocksdb for nextgraph and oxigraph
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667 lines
23 KiB
667 lines
23 KiB
// Copyright (c) 2011-present, Facebook, Inc. All rights reserved.
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// This source code is licensed under both the GPLv2 (found in the
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// COPYING file in the root directory) and Apache 2.0 License
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// (found in the LICENSE.Apache file in the root directory).
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#if !defined(GFLAGS) || defined(ROCKSDB_LITE)
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#include <cstdio>
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int main() {
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fprintf(stderr, "filter_bench requires gflags and !ROCKSDB_LITE\n");
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return 1;
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}
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#else
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#include <cinttypes>
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#include <iostream>
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#include <sstream>
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#include <vector>
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#include "memory/arena.h"
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#include "port/port.h"
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#include "port/stack_trace.h"
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#include "rocksdb/filter_policy.h"
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#include "table/block_based/full_filter_block.h"
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#include "table/block_based/mock_block_based_table.h"
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#include "table/plain/plain_table_bloom.h"
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#include "util/gflags_compat.h"
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#include "util/hash.h"
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#include "util/random.h"
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#include "util/stop_watch.h"
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using GFLAGS_NAMESPACE::ParseCommandLineFlags;
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using GFLAGS_NAMESPACE::RegisterFlagValidator;
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using GFLAGS_NAMESPACE::SetUsageMessage;
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DEFINE_uint32(seed, 0, "Seed for random number generators");
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DEFINE_double(working_mem_size_mb, 200,
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"MB of memory to get up to among all filters");
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DEFINE_uint32(average_keys_per_filter, 10000,
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"Average number of keys per filter");
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DEFINE_uint32(key_size, 24, "Average number of bytes for each key");
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DEFINE_bool(vary_key_alignment, true,
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"Whether to vary key alignment (default: at least 32-bit "
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"alignment)");
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DEFINE_uint32(vary_key_size_log2_interval, 5,
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"Use same key size 2^n times, then change. Key size varies from "
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"-2 to +2 bytes vs. average, unless n>=30 to fix key size.");
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DEFINE_uint32(batch_size, 8, "Number of keys to group in each batch");
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DEFINE_uint32(bits_per_key, 10, "Bits per key setting for filters");
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DEFINE_double(m_queries, 200, "Millions of queries for each test mode");
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DEFINE_bool(use_full_block_reader, false,
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"Use FullFilterBlockReader interface rather than FilterBitsReader");
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DEFINE_bool(use_plain_table_bloom, false,
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"Use PlainTableBloom structure and interface rather than "
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"FilterBitsReader/FullFilterBlockReader");
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DEFINE_uint32(impl, 0,
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"Select filter implementation. Without -use_plain_table_bloom:"
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"0 = full filter, 1 = block-based filter. With "
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"-use_plain_table_bloom: 0 = no locality, 1 = locality.");
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DEFINE_bool(net_includes_hashing, false,
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"Whether query net ns/op times should include hashing. "
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"(if not, dry run will include hashing) "
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"(build times always include hashing)");
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DEFINE_bool(quick, false, "Run more limited set of tests, fewer queries");
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DEFINE_bool(best_case, false, "Run limited tests only for best-case");
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DEFINE_bool(allow_bad_fp_rate, false, "Continue even if FP rate is bad");
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DEFINE_bool(legend, false,
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"Print more information about interpreting results instead of "
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"running tests");
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void _always_assert_fail(int line, const char *file, const char *expr) {
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fprintf(stderr, "%s: %d: Assertion %s failed\n", file, line, expr);
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abort();
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}
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#define ALWAYS_ASSERT(cond) \
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((cond) ? (void)0 : ::_always_assert_fail(__LINE__, __FILE__, #cond))
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using rocksdb::Arena;
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using rocksdb::BlockContents;
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using rocksdb::BloomHash;
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using rocksdb::CachableEntry;
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using rocksdb::EncodeFixed32;
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using rocksdb::fastrange32;
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using rocksdb::FilterBitsBuilder;
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using rocksdb::FilterBitsReader;
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using rocksdb::FullFilterBlockReader;
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using rocksdb::GetSliceHash;
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using rocksdb::GetSliceHash64;
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using rocksdb::Lower32of64;
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using rocksdb::ParsedFullFilterBlock;
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using rocksdb::PlainTableBloomV1;
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using rocksdb::Random32;
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using rocksdb::Slice;
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using rocksdb::mock::MockBlockBasedTableTester;
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struct KeyMaker {
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KeyMaker(size_t avg_size)
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: smallest_size_(avg_size -
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(FLAGS_vary_key_size_log2_interval >= 30 ? 2 : 0)),
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buf_size_(avg_size + 11), // pad to vary key size and alignment
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buf_(new char[buf_size_]) {
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memset(buf_.get(), 0, buf_size_);
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assert(smallest_size_ > 8);
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}
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size_t smallest_size_;
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size_t buf_size_;
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std::unique_ptr<char[]> buf_;
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// Returns a unique(-ish) key based on the given parameter values. Each
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// call returns a Slice from the same buffer so previously returned
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// Slices should be considered invalidated.
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Slice Get(uint32_t filter_num, uint32_t val_num) {
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size_t start = FLAGS_vary_key_alignment ? val_num % 4 : 0;
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size_t len = smallest_size_;
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if (FLAGS_vary_key_size_log2_interval < 30) {
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// To get range [avg_size - 2, avg_size + 2]
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// use range [smallest_size, smallest_size + 4]
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len += fastrange32(
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(val_num >> FLAGS_vary_key_size_log2_interval) * 1234567891, 5);
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}
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char * data = buf_.get() + start;
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// Populate key data such that all data makes it into a key of at
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// least 8 bytes. We also don't want all the within-filter key
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// variance confined to a contiguous 32 bits, because then a 32 bit
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// hash function can "cheat" the false positive rate by
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// approximating a perfect hash.
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EncodeFixed32(data, val_num);
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EncodeFixed32(data + 4, filter_num + val_num);
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// ensure clearing leftovers from different alignment
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EncodeFixed32(data + 8, 0);
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return Slice(data, len);
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}
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};
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void PrintWarnings() {
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#if defined(__GNUC__) && !defined(__OPTIMIZE__)
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fprintf(stdout,
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"WARNING: Optimization is disabled: benchmarks unnecessarily slow\n");
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#endif
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#ifndef NDEBUG
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fprintf(stdout,
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"WARNING: Assertions are enabled; benchmarks unnecessarily slow\n");
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#endif
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}
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struct FilterInfo {
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uint32_t filter_id_ = 0;
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std::unique_ptr<const char[]> owner_;
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Slice filter_;
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uint32_t keys_added_ = 0;
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std::unique_ptr<FilterBitsReader> reader_;
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std::unique_ptr<FullFilterBlockReader> full_block_reader_;
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std::unique_ptr<PlainTableBloomV1> plain_table_bloom_;
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uint64_t outside_queries_ = 0;
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uint64_t false_positives_ = 0;
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};
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enum TestMode {
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kSingleFilter,
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kBatchPrepared,
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kBatchUnprepared,
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kFiftyOneFilter,
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kEightyTwentyFilter,
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kRandomFilter,
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};
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static const std::vector<TestMode> allTestModes = {
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kSingleFilter, kBatchPrepared, kBatchUnprepared,
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kFiftyOneFilter, kEightyTwentyFilter, kRandomFilter,
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};
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static const std::vector<TestMode> quickTestModes = {
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kSingleFilter,
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kRandomFilter,
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};
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static const std::vector<TestMode> bestCaseTestModes = {
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kSingleFilter,
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};
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const char *TestModeToString(TestMode tm) {
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switch (tm) {
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case kSingleFilter:
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return "Single filter";
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case kBatchPrepared:
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return "Batched, prepared";
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case kBatchUnprepared:
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return "Batched, unprepared";
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case kFiftyOneFilter:
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return "Skewed 50% in 1%";
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case kEightyTwentyFilter:
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return "Skewed 80% in 20%";
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case kRandomFilter:
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return "Random filter";
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}
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return "Bad TestMode";
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}
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// Do just enough to keep some data dependence for the
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// compiler / CPU
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static uint32_t DryRunNoHash(Slice &s) {
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uint32_t sz = static_cast<uint32_t>(s.size());
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if (sz >= 4) {
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return sz + s.data()[3];
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} else {
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return sz;
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}
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}
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static uint32_t DryRunHash32(Slice &s) {
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// Same perf characteristics as GetSliceHash()
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return BloomHash(s);
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}
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static uint32_t DryRunHash64(Slice &s) {
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return Lower32of64(GetSliceHash64(s));
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}
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struct FilterBench : public MockBlockBasedTableTester {
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std::vector<KeyMaker> kms_;
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std::vector<FilterInfo> infos_;
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Random32 random_;
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std::ostringstream fp_rate_report_;
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Arena arena_;
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FilterBench()
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: MockBlockBasedTableTester(
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rocksdb::NewBloomFilterPolicy(FLAGS_bits_per_key)),
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random_(FLAGS_seed) {
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for (uint32_t i = 0; i < FLAGS_batch_size; ++i) {
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kms_.emplace_back(FLAGS_key_size < 8 ? 8 : FLAGS_key_size);
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}
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}
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void Go();
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double RandomQueryTest(uint32_t inside_threshold, bool dry_run,
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TestMode mode);
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};
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void FilterBench::Go() {
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if (FLAGS_use_plain_table_bloom && FLAGS_use_full_block_reader) {
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throw std::runtime_error(
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"Can't combine -use_plain_table_bloom and -use_full_block_reader");
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}
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if (FLAGS_impl > 1) {
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throw std::runtime_error("-impl must currently be >= 0 and <= 1");
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}
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if (!FLAGS_use_plain_table_bloom && FLAGS_impl == 1) {
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throw std::runtime_error(
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"Block-based filter not currently supported by filter_bench");
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}
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std::unique_ptr<FilterBitsBuilder> builder;
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if (!FLAGS_use_plain_table_bloom && FLAGS_impl != 1) {
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builder.reset(table_options_.filter_policy->GetFilterBitsBuilder());
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}
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uint32_t variance_mask = 1;
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while (variance_mask * variance_mask * 4 < FLAGS_average_keys_per_filter) {
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variance_mask = variance_mask * 2 + 1;
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}
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const std::vector<TestMode> &testModes =
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FLAGS_best_case ? bestCaseTestModes
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: FLAGS_quick ? quickTestModes : allTestModes;
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if (FLAGS_quick) {
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FLAGS_m_queries /= 7.0;
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} else if (FLAGS_best_case) {
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FLAGS_m_queries /= 3.0;
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FLAGS_working_mem_size_mb /= 10.0;
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}
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std::cout << "Building..." << std::endl;
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size_t total_memory_used = 0;
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size_t total_keys_added = 0;
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rocksdb::StopWatchNano timer(rocksdb::Env::Default(), true);
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while (total_memory_used < 1024 * 1024 * FLAGS_working_mem_size_mb) {
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uint32_t filter_id = random_.Next();
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uint32_t keys_to_add = FLAGS_average_keys_per_filter +
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(random_.Next() & variance_mask) -
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(variance_mask / 2);
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infos_.emplace_back();
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FilterInfo &info = infos_.back();
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info.filter_id_ = filter_id;
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info.keys_added_ = keys_to_add;
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if (FLAGS_use_plain_table_bloom) {
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info.plain_table_bloom_.reset(new PlainTableBloomV1());
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info.plain_table_bloom_->SetTotalBits(
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&arena_, keys_to_add * FLAGS_bits_per_key, FLAGS_impl,
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0 /*huge_page*/, nullptr /*logger*/);
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for (uint32_t i = 0; i < keys_to_add; ++i) {
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uint32_t hash = GetSliceHash(kms_[0].Get(filter_id, i));
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info.plain_table_bloom_->AddHash(hash);
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}
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info.filter_ = info.plain_table_bloom_->GetRawData();
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} else {
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for (uint32_t i = 0; i < keys_to_add; ++i) {
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builder->AddKey(kms_[0].Get(filter_id, i));
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}
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info.filter_ = builder->Finish(&info.owner_);
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info.reader_.reset(
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table_options_.filter_policy->GetFilterBitsReader(info.filter_));
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CachableEntry<ParsedFullFilterBlock> block(
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new ParsedFullFilterBlock(table_options_.filter_policy.get(),
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BlockContents(info.filter_)),
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nullptr /* cache */, nullptr /* cache_handle */,
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true /* own_value */);
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info.full_block_reader_.reset(
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new FullFilterBlockReader(table_.get(), std::move(block)));
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}
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total_memory_used += info.filter_.size();
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total_keys_added += keys_to_add;
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}
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uint64_t elapsed_nanos = timer.ElapsedNanos();
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double ns = double(elapsed_nanos) / total_keys_added;
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std::cout << "Build avg ns/key: " << ns << std::endl;
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std::cout << "Number of filters: " << infos_.size() << std::endl;
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std::cout << "Total memory (MB): " << total_memory_used / 1024.0 / 1024.0
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<< std::endl;
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double bpk = total_memory_used * 8.0 / total_keys_added;
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std::cout << "Bits/key actual: " << bpk << std::endl;
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if (!FLAGS_quick && !FLAGS_best_case) {
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double tolerable_rate = std::pow(2.0, -(bpk - 1.0) / (1.4 + bpk / 50.0));
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std::cout << "Best possible FP rate %: " << 100.0 * std::pow(2.0, -bpk)
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<< std::endl;
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std::cout << "Tolerable FP rate %: " << 100.0 * tolerable_rate << std::endl;
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std::cout << "----------------------------" << std::endl;
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std::cout << "Verifying..." << std::endl;
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uint32_t outside_q_per_f = 1000000 / infos_.size();
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uint64_t fps = 0;
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for (uint32_t i = 0; i < infos_.size(); ++i) {
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FilterInfo &info = infos_[i];
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for (uint32_t j = 0; j < info.keys_added_; ++j) {
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if (FLAGS_use_plain_table_bloom) {
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uint32_t hash = GetSliceHash(kms_[0].Get(info.filter_id_, j));
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ALWAYS_ASSERT(info.plain_table_bloom_->MayContainHash(hash));
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} else {
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ALWAYS_ASSERT(
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info.reader_->MayMatch(kms_[0].Get(info.filter_id_, j)));
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}
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}
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for (uint32_t j = 0; j < outside_q_per_f; ++j) {
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if (FLAGS_use_plain_table_bloom) {
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uint32_t hash =
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GetSliceHash(kms_[0].Get(info.filter_id_, j | 0x80000000));
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fps += info.plain_table_bloom_->MayContainHash(hash);
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} else {
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fps += info.reader_->MayMatch(
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kms_[0].Get(info.filter_id_, j | 0x80000000));
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}
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}
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}
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std::cout << " No FNs :)" << std::endl;
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double prelim_rate = double(fps) / outside_q_per_f / infos_.size();
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std::cout << " Prelim FP rate %: " << (100.0 * prelim_rate) << std::endl;
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if (!FLAGS_allow_bad_fp_rate) {
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ALWAYS_ASSERT(prelim_rate < tolerable_rate);
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}
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}
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std::cout << "----------------------------" << std::endl;
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std::cout << "Mixed inside/outside queries..." << std::endl;
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// 50% each inside and outside
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uint32_t inside_threshold = UINT32_MAX / 2;
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for (TestMode tm : testModes) {
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random_.Seed(FLAGS_seed + 1);
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double f = RandomQueryTest(inside_threshold, /*dry_run*/ false, tm);
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random_.Seed(FLAGS_seed + 1);
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double d = RandomQueryTest(inside_threshold, /*dry_run*/ true, tm);
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std::cout << " " << TestModeToString(tm) << " net ns/op: " << (f - d)
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<< std::endl;
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}
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if (!FLAGS_quick) {
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std::cout << "----------------------------" << std::endl;
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std::cout << "Inside queries (mostly)..." << std::endl;
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// Do about 95% inside queries rather than 100% so that branch predictor
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// can't give itself an artifically crazy advantage.
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inside_threshold = UINT32_MAX / 20 * 19;
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for (TestMode tm : testModes) {
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random_.Seed(FLAGS_seed + 1);
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double f = RandomQueryTest(inside_threshold, /*dry_run*/ false, tm);
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random_.Seed(FLAGS_seed + 1);
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double d = RandomQueryTest(inside_threshold, /*dry_run*/ true, tm);
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std::cout << " " << TestModeToString(tm) << " net ns/op: " << (f - d)
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<< std::endl;
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}
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std::cout << "----------------------------" << std::endl;
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std::cout << "Outside queries (mostly)..." << std::endl;
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// Do about 95% outside queries rather than 100% so that branch predictor
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// can't give itself an artifically crazy advantage.
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inside_threshold = UINT32_MAX / 20;
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for (TestMode tm : testModes) {
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random_.Seed(FLAGS_seed + 2);
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double f = RandomQueryTest(inside_threshold, /*dry_run*/ false, tm);
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random_.Seed(FLAGS_seed + 2);
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double d = RandomQueryTest(inside_threshold, /*dry_run*/ true, tm);
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std::cout << " " << TestModeToString(tm) << " net ns/op: " << (f - d)
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<< std::endl;
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}
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}
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std::cout << fp_rate_report_.str();
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std::cout << "----------------------------" << std::endl;
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std::cout << "Done. (For more info, run with -legend or -help.)" << std::endl;
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}
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double FilterBench::RandomQueryTest(uint32_t inside_threshold, bool dry_run,
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TestMode mode) {
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for (auto &info : infos_) {
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info.outside_queries_ = 0;
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info.false_positives_ = 0;
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}
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auto dry_run_hash_fn = DryRunNoHash;
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if (!FLAGS_net_includes_hashing) {
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if (FLAGS_impl < 2 || FLAGS_use_plain_table_bloom) {
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dry_run_hash_fn = DryRunHash32;
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} else {
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dry_run_hash_fn = DryRunHash64;
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}
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}
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uint32_t num_infos = static_cast<uint32_t>(infos_.size());
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uint32_t dry_run_hash = 0;
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uint64_t max_queries =
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static_cast<uint64_t>(FLAGS_m_queries * 1000000 + 0.50);
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// Some filters may be considered secondary in order to implement skewed
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// queries. num_primary_filters is the number that are to be treated as
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// equal, and any remainder will be treated as secondary.
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uint32_t num_primary_filters = num_infos;
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// 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::StopWatchNano timer(rocksdb::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::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_ = std::ostringstream();
|
|
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::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;
|
|
b.Go();
|
|
}
|
|
|
|
return 0;
|
|
}
|
|
|
|
#endif // !defined(GFLAGS) || defined(ROCKSDB_LITE)
|
|
|