Summary: Adds an improved, replacement Bloom filter implementation (FastLocalBloom) for full and partitioned filters in the block-based table. This replacement is faster and more accurate, especially for high bits per key or millions of keys in a single filter. Speed The improved speed, at least on recent x86_64, comes from * Using fastrange instead of modulo (%) * Using our new hash function (XXH3 preview, added in a previous commit), which is much faster for large keys and only *slightly* slower on keys around 12 bytes if hashing the same size many thousands of times in a row. * Optimizing the Bloom filter queries with AVX2 SIMD operations. (Added AVX2 to the USE_SSE=1 build.) Careful design was required to support (a) SIMD-optimized queries, (b) compatible non-SIMD code that's simple and efficient, (c) flexible choice of number of probes, and (d) essentially maximized accuracy for a cache-local Bloom filter. Probes are made eight at a time, so any number of probes up to 8 is the same speed, then up to 16, etc. * Prefetching cache lines when building the filter. Although this optimization could be applied to the old structure as well, it seems to balance out the small added cost of accumulating 64 bit hashes for adding to the filter rather than 32 bit hashes. Here's nominal speed data from filter_bench (200MB in filters, about 10k keys each, 10 bits filter data / key, 6 probes, avg key size 24 bytes, includes hashing time) on Skylake DE (relatively low clock speed): $ ./filter_bench -quick -impl=2 -net_includes_hashing # New Bloom filter Build avg ns/key: 47.7135 Mixed inside/outside queries... Single filter net ns/op: 26.2825 Random filter net ns/op: 150.459 Average FP rate %: 0.954651 $ ./filter_bench -quick -impl=0 -net_includes_hashing # Old Bloom filter Build avg ns/key: 47.2245 Mixed inside/outside queries... Single filter net ns/op: 63.2978 Random filter net ns/op: 188.038 Average FP rate %: 1.13823 Similar build time but dramatically faster query times on hot data (63 ns to 26 ns), and somewhat faster on stale data (188 ns to 150 ns). Performance differences on batched and skewed query loads are between these extremes as expected. The only other interesting thing about speed is "inside" (query key was added to filter) vs. "outside" (query key was not added to filter) query times. The non-SIMD implementations are substantially slower when most queries are "outside" vs. "inside". This goes against what one might expect or would have observed years ago, as "outside" queries only need about two probes on average, due to short-circuiting, while "inside" always have num_probes (say 6). The problem is probably the nastily unpredictable branch. The SIMD implementation has few branches (very predictable) and has pretty consistent running time regardless of query outcome. Accuracy The generally improved accuracy (re: Issue https://github.com/facebook/rocksdb/issues/5857) comes from a better design for probing indices within a cache line (re: Issue https://github.com/facebook/rocksdb/issues/4120) and improved accuracy for millions of keys in a single filter from using a 64-bit hash function (XXH3p). Design details in code comments. Accuracy data (generalizes, except old impl gets worse with millions of keys): Memory bits per key: FP rate percent old impl -> FP rate percent new impl 6: 5.70953 -> 5.69888 8: 2.45766 -> 2.29709 10: 1.13977 -> 0.959254 12: 0.662498 -> 0.411593 16: 0.353023 -> 0.0873754 24: 0.261552 -> 0.0060971 50: 0.225453 -> ~0.00003 (less than 1 in a million queries are FP) Fixes https://github.com/facebook/rocksdb/issues/5857 Fixes https://github.com/facebook/rocksdb/issues/4120 Unlike the old implementation, this implementation has a fixed cache line size (64 bytes). At 10 bits per key, the accuracy of this new implementation is very close to the old implementation with 128-byte cache line size. If there's sufficient demand, this implementation could be generalized. Compatibility Although old releases would see the new structure as corrupt filter data and read the table as if there's no filter, we've decided only to enable the new Bloom filter with new format_version=5. This provides a smooth path for automatic adoption over time, with an option for early opt-in. Pull Request resolved: https://github.com/facebook/rocksdb/pull/6007 Test Plan: filter_bench has been used thoroughly to validate speed, accuracy, and correctness. Unit tests have been carefully updated to exercise new and old implementations, as well as the logic to select an implementation based on context (format_version). Differential Revision: D18294749 Pulled By: pdillinger fbshipit-source-id: d44c9db3696e4d0a17caaec47075b7755c262c5f
680 lines
24 KiB
C++
680 lines
24 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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#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 "table/block_based/filter_policy_internal.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::BloomFilterPolicy;
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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::FilterBuildingContext;
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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(new BloomFilterPolicy(
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FLAGS_bits_per_key,
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static_cast<BloomFilterPolicy::Mode>(FLAGS_impl))),
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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_use_plain_table_bloom) {
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if (FLAGS_impl > 1) {
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throw std::runtime_error(
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"-impl must currently be >= 0 and <= 1 for Plain table");
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}
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} else {
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if (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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if (FLAGS_impl > 2) {
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throw std::runtime_error(
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"-impl must currently be 0 or 2 for Block-based table");
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}
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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(FilterBuildingContext(table_options_).GetBuilder());
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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 =
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static_cast<uint32_t>(FLAGS_m_queries * 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);
|
|
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>(FLAGS_m_queries * 1000000 + 0.50);
|
|
// Some filters may be considered secondary in order to implement skewed
|
|
// queries. num_primary_filters is the number that are to be treated as
|
|
// equal, and any remainder will be treated as secondary.
|
|
uint32_t num_primary_filters = num_infos;
|
|
// The proportion (when divided by 2^32 - 1) of filter queries going to
|
|
// the primary filters (default = all). The remainder of queries are
|
|
// against secondary filters.
|
|
uint32_t primary_filter_threshold = 0xffffffff;
|
|
if (mode == kSingleFilter) {
|
|
// 100% of queries to 1 filter
|
|
num_primary_filters = 1;
|
|
} else if (mode == kFiftyOneFilter) {
|
|
// 50% of queries
|
|
primary_filter_threshold /= 2;
|
|
// to 1% of filters
|
|
num_primary_filters = (num_primary_filters + 99) / 100;
|
|
} else if (mode == kEightyTwentyFilter) {
|
|
// 80% of queries
|
|
primary_filter_threshold = primary_filter_threshold / 5 * 4;
|
|
// to 20% of filters
|
|
num_primary_filters = (num_primary_filters + 4) / 5;
|
|
}
|
|
uint32_t batch_size = 1;
|
|
std::unique_ptr<Slice[]> batch_slices;
|
|
std::unique_ptr<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)
|