2837008525
Summary: The first version of filter_bench has selectable key size but that size does not vary throughout a test run. This artificially favors "branchy" hash functions like the existing BloomHash, MurmurHash1, probably because of optimal return for branch prediction. This change primarily varies those key sizes from -2 to +2 bytes vs. the average selected size. We also set the default key size at 24 to better reflect our best guess of typical key size. But steadily random key sizes may not be realistic either. So this change introduces a new filter_bench option: -vary_key_size_log2_interval=n where the same key size is used 2^n times and then changes to another size. I've set the default at 5 (32 times same size) as a compromise between deployments with rather consistent vs. rather variable key sizes. On my Skylake system, the performance boost to MurmurHash1 largely lies between n=10 and n=15. Also added -vary_key_alignment (bool, now default=true), though this doesn't currently seem to matter in hash functions under consideration. This change also does a "dry run" for each testing scenario, to improve the accuracy of those numbers, as there was more difference between scenarios than expected. Subtracting gross test run times from dry run times is now also embedded in the output, because these "net" times are generally the most useful. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5933 Differential Revision: D18121683 Pulled By: pdillinger fbshipit-source-id: 3c7efee1c5661a5fe43de555e786754ddf80dc1e
520 lines
18 KiB
C++
520 lines
18 KiB
C++
// 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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#ifndef GFLAGS
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#include <cstdio>
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int main() {
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fprintf(stderr, "Please install gflags to run rocksdb tools\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 "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 "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(quick, false, "Run more limited set of tests, fewer queries");
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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::BlockContents;
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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::ParsedFullFilterBlock;
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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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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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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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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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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(bool inside, bool dry_run, TestMode mode);
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};
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void FilterBench::Go() {
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std::unique_ptr<FilterBitsBuilder> builder(
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table_options_.filter_policy->GetFilterBitsBuilder());
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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_quick ? quickTestModes : allTestModes;
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if (FLAGS_quick) {
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FLAGS_m_queries /= 7.0;
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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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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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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.filter_ = builder->Finish(&info.owner_);
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info.keys_added_ = keys_to_add;
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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 */, 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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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) {
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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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ALWAYS_ASSERT(info.reader_->MayMatch(kms_[0].Get(info.filter_id_, j)));
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}
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for (uint32_t j = 0; j < outside_q_per_f; ++j) {
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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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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 << "Inside queries..." << std::endl;
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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*/ true, /*dry_run*/ false, tm);
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random_.Seed(FLAGS_seed + 1);
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double d = RandomQueryTest(/*inside*/ true, /*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 << fp_rate_report_.str();
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std::cout << "----------------------------" << std::endl;
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std::cout << "Outside queries..." << std::endl;
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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*/ false, /*dry_run*/ false, tm);
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random_.Seed(FLAGS_seed + 2);
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double d = RandomQueryTest(/*inside*/ false, /*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 << 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(bool inside, bool dry_run, 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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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
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// the primary filters (default = all). The remainder of queries are
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// against secondary filters.
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uint32_t primary_filter_threshold = 0xffffffff;
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if (mode == kSingleFilter) {
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// 100% of queries to 1 filter
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num_primary_filters = 1;
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} else if (mode == kFiftyOneFilter) {
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// 50% of queries
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primary_filter_threshold /= 2;
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// to 1% of filters
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num_primary_filters = (num_primary_filters + 99) / 100;
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} else if (mode == kEightyTwentyFilter) {
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// 80% of queries
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primary_filter_threshold = primary_filter_threshold / 5 * 4;
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// to 20% of filters
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num_primary_filters = (num_primary_filters + 4) / 5;
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}
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uint32_t batch_size = 1;
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std::unique_ptr<Slice[]> batch_slices;
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std::unique_ptr<Slice *[]> batch_slice_ptrs;
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std::unique_ptr<bool[]> batch_results;
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if (mode == kBatchPrepared || mode == kBatchUnprepared) {
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batch_size = static_cast<uint32_t>(kms_.size());
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}
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batch_slices.reset(new Slice[batch_size]);
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batch_slice_ptrs.reset(new Slice *[batch_size]);
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batch_results.reset(new bool[batch_size]);
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for (uint32_t i = 0; i < batch_size; ++i) {
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batch_results[i] = false;
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batch_slice_ptrs[i] = &batch_slices[i];
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}
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rocksdb::StopWatchNano timer(rocksdb::Env::Default(), true);
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for (uint64_t q = 0; q < max_queries; q += batch_size) {
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uint32_t filter_index;
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if (random_.Next() <= primary_filter_threshold) {
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filter_index = random_.Uniformish(num_primary_filters);
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} else {
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// secondary
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filter_index = num_primary_filters +
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random_.Uniformish(num_infos - num_primary_filters);
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}
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FilterInfo &info = infos_[filter_index];
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for (uint32_t i = 0; i < batch_size; ++i) {
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if (inside) {
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batch_slices[i] =
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kms_[i].Get(info.filter_id_, random_.Uniformish(info.keys_added_));
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} else {
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batch_slices[i] =
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kms_[i].Get(info.filter_id_, random_.Uniformish(info.keys_added_) |
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uint32_t{0x80000000});
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info.outside_queries_++;
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}
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}
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// TODO: implement batched interface to full block reader
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if (mode == kBatchPrepared && !dry_run && !FLAGS_use_full_block_reader) {
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for (uint32_t i = 0; i < batch_size; ++i) {
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batch_results[i] = false;
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}
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info.reader_->MayMatch(batch_size, batch_slice_ptrs.get(),
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batch_results.get());
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for (uint32_t i = 0; i < batch_size; ++i) {
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if (inside) {
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ALWAYS_ASSERT(batch_results[i]);
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} else {
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info.false_positives_ += batch_results[i];
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}
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}
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} else {
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for (uint32_t i = 0; i < batch_size; ++i) {
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if (dry_run) {
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dry_run_hash ^= rocksdb::BloomHash(batch_slices[i]);
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} else {
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bool may_match;
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if (FLAGS_use_full_block_reader) {
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may_match = info.full_block_reader_->KeyMayMatch(
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batch_slices[i],
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/*prefix_extractor=*/nullptr,
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/*block_offset=*/rocksdb::kNotValid,
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/*no_io=*/false, /*const_ikey_ptr=*/nullptr,
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/*get_context=*/nullptr,
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/*lookup_context=*/nullptr);
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} else {
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may_match = info.reader_->MayMatch(batch_slices[i]);
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}
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if (inside) {
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ALWAYS_ASSERT(may_match);
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} else {
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info.false_positives_ += may_match;
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}
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}
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}
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}
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}
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uint64_t elapsed_nanos = timer.ElapsedNanos();
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double ns = double(elapsed_nanos) / max_queries;
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if (!FLAGS_quick) {
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if (dry_run) {
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// Printing part of hash prevents dry run components from being optimized
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// away by compiler
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std::cout << " Dry run (" << std::hex << (dry_run_hash & 0xfffff)
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<< std::dec << ") ";
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} else {
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std::cout << " Gross filter ";
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}
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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);
|
|
}
|
|
}
|
|
fp_rate_report_ << " Average FP rate %: " << 100.0 * fp / q << std::endl;
|
|
if (!FLAGS_quick) {
|
|
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;
|
|
}
|
|
} else {
|
|
fp_rate_report_.clear();
|
|
}
|
|
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 // GFLAGS
|