// Copyright (c) 2011-present, Facebook, Inc. All rights reserved. // This source code is licensed under both the GPLv2 (found in the // COPYING file in the root directory) and Apache 2.0 License // (found in the LICENSE.Apache file in the root directory). // // Copyright (c) 2012 The LevelDB Authors. All rights reserved. // Use of this source code is governed by a BSD-style license that can be // found in the LICENSE file. See the AUTHORS file for names of contributors. #ifndef GFLAGS #include int main() { fprintf(stderr, "Please install gflags to run this test... Skipping...\n"); return 0; } #else #include #include #include #include "cache/cache_entry_roles.h" #include "cache/cache_reservation_manager.h" #include "memory/arena.h" #include "port/jemalloc_helper.h" #include "rocksdb/filter_policy.h" #include "table/block_based/filter_policy_internal.h" #include "test_util/testharness.h" #include "test_util/testutil.h" #include "util/gflags_compat.h" #include "util/hash.h" using GFLAGS_NAMESPACE::ParseCommandLineFlags; // The test is not fully designed for bits_per_key other than 10, but with // this parameter you can easily explore the behavior of other bits_per_key. // See also filter_bench. DEFINE_int32(bits_per_key, 10, ""); namespace ROCKSDB_NAMESPACE { namespace { const std::string kLegacyBloom = test::LegacyBloomFilterPolicy::kClassName(); const std::string kFastLocalBloom = test::FastLocalBloomFilterPolicy::kClassName(); const std::string kStandard128Ribbon = test::Standard128RibbonFilterPolicy::kClassName(); } // namespace static const int kVerbose = 1; static Slice Key(int i, char* buffer) { std::string s; PutFixed32(&s, static_cast(i)); memcpy(buffer, s.c_str(), sizeof(i)); return Slice(buffer, sizeof(i)); } static int NextLength(int length) { if (length < 10) { length += 1; } else if (length < 100) { length += 10; } else if (length < 1000) { length += 100; } else { length += 1000; } return length; } class BlockBasedBloomTest : public testing::Test { private: std::unique_ptr policy_; std::string filter_; std::vector keys_; public: BlockBasedBloomTest() { ResetPolicy(); } void Reset() { keys_.clear(); filter_.clear(); } void ResetPolicy(double bits_per_key) { policy_.reset(new DeprecatedBlockBasedBloomFilterPolicy(bits_per_key)); Reset(); } void ResetPolicy() { ResetPolicy(FLAGS_bits_per_key); } void Add(const Slice& s) { keys_.push_back(s.ToString()); } void Build() { std::vector key_slices; for (size_t i = 0; i < keys_.size(); i++) { key_slices.push_back(Slice(keys_[i])); } filter_.clear(); DeprecatedBlockBasedBloomFilterPolicy::CreateFilter( &key_slices[0], static_cast(key_slices.size()), policy_->GetWholeBitsPerKey(), &filter_); keys_.clear(); if (kVerbose >= 2) DumpFilter(); } size_t FilterSize() const { return filter_.size(); } Slice FilterData() const { return Slice(filter_); } void DumpFilter() { fprintf(stderr, "F("); for (size_t i = 0; i+1 < filter_.size(); i++) { const unsigned int c = static_cast(filter_[i]); for (int j = 0; j < 8; j++) { fprintf(stderr, "%c", (c & (1 <= 1) { fprintf(stderr, "False positives: %5.2f%% @ length = %6d ; bytes = %6d\n", rate*100.0, length, static_cast(FilterSize())); } if (FLAGS_bits_per_key == 10) { ASSERT_LE(rate, 0.02); // Must not be over 2% if (rate > 0.0125) { mediocre_filters++; // Allowed, but not too often } else { good_filters++; } } } if (FLAGS_bits_per_key == 10 && kVerbose >= 1) { fprintf(stderr, "Filters: %d good, %d mediocre\n", good_filters, mediocre_filters); } ASSERT_LE(mediocre_filters, good_filters/5); } // Ensure the implementation doesn't accidentally change in an // incompatible way TEST_F(BlockBasedBloomTest, Schema) { char buffer[sizeof(int)]; ResetPolicy(8); // num_probes = 5 for (int key = 0; key < 87; key++) { Add(Key(key, buffer)); } Build(); ASSERT_EQ(BloomHash(FilterData()), 3589896109U); ResetPolicy(9); // num_probes = 6 for (int key = 0; key < 87; key++) { Add(Key(key, buffer)); } Build(); ASSERT_EQ(BloomHash(FilterData()), 969445585U); ResetPolicy(11); // num_probes = 7 for (int key = 0; key < 87; key++) { Add(Key(key, buffer)); } Build(); ASSERT_EQ(BloomHash(FilterData()), 1694458207U); ResetPolicy(10); // num_probes = 6 for (int key = 0; key < 87; key++) { Add(Key(key, buffer)); } Build(); ASSERT_EQ(BloomHash(FilterData()), 2373646410U); ResetPolicy(10); for (int key = /*CHANGED*/ 1; key < 87; key++) { Add(Key(key, buffer)); } Build(); ASSERT_EQ(BloomHash(FilterData()), 1908442116U); ResetPolicy(10); for (int key = 1; key < /*CHANGED*/ 88; key++) { Add(Key(key, buffer)); } Build(); ASSERT_EQ(BloomHash(FilterData()), 3057004015U); // With new fractional bits_per_key, check that we are rounding to // whole bits per key for old Bloom filters. ResetPolicy(9.5); // Treated as 10 for (int key = 1; key < 88; key++) { Add(Key(key, buffer)); } Build(); ASSERT_EQ(BloomHash(FilterData()), /*SAME*/ 3057004015U); ResetPolicy(10.499); // Treated as 10 for (int key = 1; key < 88; key++) { Add(Key(key, buffer)); } Build(); ASSERT_EQ(BloomHash(FilterData()), /*SAME*/ 3057004015U); ResetPolicy(); } // Different bits-per-byte class FullBloomTest : public testing::TestWithParam { protected: BlockBasedTableOptions table_options_; private: std::shared_ptr& policy_; std::unique_ptr bits_builder_; std::unique_ptr bits_reader_; std::unique_ptr buf_; size_t filter_size_; public: FullBloomTest() : policy_(table_options_.filter_policy), filter_size_(0) { ResetPolicy(); } BuiltinFilterBitsBuilder* GetBuiltinFilterBitsBuilder() { // Throws on bad cast return dynamic_cast(bits_builder_.get()); } const BloomLikeFilterPolicy* GetBloomLikeFilterPolicy() { // Throws on bad cast return &dynamic_cast(*policy_); } void Reset() { bits_builder_.reset(BloomFilterPolicy::GetBuilderFromContext( FilterBuildingContext(table_options_))); bits_reader_.reset(nullptr); buf_.reset(nullptr); filter_size_ = 0; } void ResetPolicy(double bits_per_key) { policy_ = BloomLikeFilterPolicy::Create(GetParam(), bits_per_key); Reset(); } void ResetPolicy() { ResetPolicy(FLAGS_bits_per_key); } void Add(const Slice& s) { bits_builder_->AddKey(s); } void OpenRaw(const Slice& s) { bits_reader_.reset(policy_->GetFilterBitsReader(s)); } void Build() { Slice filter = bits_builder_->Finish(&buf_); bits_reader_.reset(policy_->GetFilterBitsReader(filter)); filter_size_ = filter.size(); } size_t FilterSize() const { return filter_size_; } Slice FilterData() { return Slice(buf_.get(), filter_size_); } int GetNumProbesFromFilterData() { assert(filter_size_ >= 5); int8_t raw_num_probes = static_cast(buf_.get()[filter_size_ - 5]); if (raw_num_probes == -1) { // New bloom filter marker return static_cast(buf_.get()[filter_size_ - 3]); } else { return raw_num_probes; } } int GetRibbonSeedFromFilterData() { assert(filter_size_ >= 5); // Check for ribbon marker assert(-2 == static_cast(buf_.get()[filter_size_ - 5])); return static_cast(buf_.get()[filter_size_ - 4]); } bool Matches(const Slice& s) { if (bits_reader_ == nullptr) { Build(); } return bits_reader_->MayMatch(s); } // Provides a kind of fingerprint on the Bloom filter's // behavior, for reasonbly high FP rates. uint64_t PackedMatches() { char buffer[sizeof(int)]; uint64_t result = 0; for (int i = 0; i < 64; i++) { if (Matches(Key(i + 12345, buffer))) { result |= uint64_t{1} << i; } } return result; } // Provides a kind of fingerprint on the Bloom filter's // behavior, for lower FP rates. std::string FirstFPs(int count) { char buffer[sizeof(int)]; std::string rv; int fp_count = 0; for (int i = 0; i < 1000000; i++) { // Pack four match booleans into each hexadecimal digit if (Matches(Key(i + 1000000, buffer))) { ++fp_count; rv += std::to_string(i); if (fp_count == count) { break; } rv += ','; } } return rv; } double FalsePositiveRate() { char buffer[sizeof(int)]; int result = 0; for (int i = 0; i < 10000; i++) { if (Matches(Key(i + 1000000000, buffer))) { result++; } } return result / 10000.0; } }; TEST_P(FullBloomTest, FilterSize) { // In addition to checking the consistency of space computation, we are // checking that denoted and computed doubles are interpreted as expected // as bits_per_key values. bool some_computed_less_than_denoted = false; // Note: to avoid unproductive configurations, bits_per_key < 0.5 is rounded // down to 0 (no filter), and 0.5 <= bits_per_key < 1.0 is rounded up to 1 // bit per key (1000 millibits). Also, enforced maximum is 100 bits per key // (100000 millibits). for (auto bpk : std::vector >{{-HUGE_VAL, 0}, {-INFINITY, 0}, {0.0, 0}, {0.499, 0}, {0.5, 1000}, {1.234, 1234}, {3.456, 3456}, {9.5, 9500}, {10.0, 10000}, {10.499, 10499}, {21.345, 21345}, {99.999, 99999}, {1234.0, 100000}, {HUGE_VAL, 100000}, {INFINITY, 100000}, {NAN, 100000}}) { ResetPolicy(bpk.first); auto bfp = GetBloomLikeFilterPolicy(); EXPECT_EQ(bpk.second, bfp->GetMillibitsPerKey()); EXPECT_EQ((bpk.second + 500) / 1000, bfp->GetWholeBitsPerKey()); double computed = bpk.first; // This transforms e.g. 9.5 -> 9.499999999999998, which we still // round to 10 for whole bits per key. computed += 0.5; computed /= 1234567.0; computed *= 1234567.0; computed -= 0.5; some_computed_less_than_denoted |= (computed < bpk.first); ResetPolicy(computed); bfp = GetBloomLikeFilterPolicy(); EXPECT_EQ(bpk.second, bfp->GetMillibitsPerKey()); EXPECT_EQ((bpk.second + 500) / 1000, bfp->GetWholeBitsPerKey()); auto bits_builder = GetBuiltinFilterBitsBuilder(); if (bpk.second == 0) { ASSERT_EQ(bits_builder, nullptr); continue; } size_t n = 1; size_t space = 0; for (; n < 1000000; n += 1 + n / 1000) { // Ensure consistency between CalculateSpace and ApproximateNumEntries space = bits_builder->CalculateSpace(n); size_t n2 = bits_builder->ApproximateNumEntries(space); EXPECT_GE(n2, n); size_t space2 = bits_builder->CalculateSpace(n2); if (n > 12000 && GetParam() == kStandard128Ribbon) { // TODO(peterd): better approximation? EXPECT_GE(space2, space); EXPECT_LE(space2 * 0.998, space * 1.0); } else { EXPECT_EQ(space2, space); } } // Until size_t overflow for (; n < (n + n / 3); n += n / 3) { // Ensure space computation is not overflowing; capped is OK size_t space2 = bits_builder->CalculateSpace(n); EXPECT_GE(space2, space); space = space2; } } // Check that the compiler hasn't optimized our computation into nothing EXPECT_TRUE(some_computed_less_than_denoted); ResetPolicy(); } TEST_P(FullBloomTest, FullEmptyFilter) { // Empty filter is not match, at this level ASSERT_TRUE(!Matches("hello")); ASSERT_TRUE(!Matches("world")); } TEST_P(FullBloomTest, FullSmall) { Add("hello"); Add("world"); ASSERT_TRUE(Matches("hello")); ASSERT_TRUE(Matches("world")); ASSERT_TRUE(!Matches("x")); ASSERT_TRUE(!Matches("foo")); } TEST_P(FullBloomTest, FullVaryingLengths) { char buffer[sizeof(int)]; // Count number of filters that significantly exceed the false positive rate int mediocre_filters = 0; int good_filters = 0; for (int length = 1; length <= 10000; length = NextLength(length)) { Reset(); for (int i = 0; i < length; i++) { Add(Key(i, buffer)); } Build(); EXPECT_LE(FilterSize(), (size_t)((length * FLAGS_bits_per_key / 8) + CACHE_LINE_SIZE * 2 + 5)); // All added keys must match for (int i = 0; i < length; i++) { ASSERT_TRUE(Matches(Key(i, buffer))) << "Length " << length << "; key " << i; } // Check false positive rate double rate = FalsePositiveRate(); if (kVerbose >= 1) { fprintf(stderr, "False positives: %5.2f%% @ length = %6d ; bytes = %6d\n", rate*100.0, length, static_cast(FilterSize())); } if (FLAGS_bits_per_key == 10) { EXPECT_LE(rate, 0.02); // Must not be over 2% if (rate > 0.0125) { mediocre_filters++; // Allowed, but not too often } else { good_filters++; } } } if (kVerbose >= 1) { fprintf(stderr, "Filters: %d good, %d mediocre\n", good_filters, mediocre_filters); } EXPECT_LE(mediocre_filters, good_filters / 5); } TEST_P(FullBloomTest, OptimizeForMemory) { char buffer[sizeof(int)]; for (bool offm : {true, false}) { table_options_.optimize_filters_for_memory = offm; ResetPolicy(); Random32 rnd(12345); uint64_t total_size = 0; uint64_t total_mem = 0; int64_t total_keys = 0; double total_fp_rate = 0; constexpr int nfilters = 100; for (int i = 0; i < nfilters; ++i) { int nkeys = static_cast(rnd.Uniformish(10000)) + 100; Reset(); for (int j = 0; j < nkeys; ++j) { Add(Key(j, buffer)); } Build(); size_t size = FilterData().size(); total_size += size; // optimize_filters_for_memory currently depends on malloc_usable_size // but we run the rest of the test to ensure no bad behavior without it. #ifdef ROCKSDB_MALLOC_USABLE_SIZE size = malloc_usable_size(const_cast(FilterData().data())); #endif // ROCKSDB_MALLOC_USABLE_SIZE total_mem += size; total_keys += nkeys; total_fp_rate += FalsePositiveRate(); } if (FLAGS_bits_per_key == 10) { EXPECT_LE(total_fp_rate / double{nfilters}, 0.011); EXPECT_GE(total_fp_rate / double{nfilters}, CACHE_LINE_SIZE >= 256 ? 0.007 : 0.008); } int64_t ex_min_total_size = int64_t{FLAGS_bits_per_key} * total_keys / 8; if (GetParam() == kStandard128Ribbon) { // ~ 30% savings vs. Bloom filter ex_min_total_size = 7 * ex_min_total_size / 10; } EXPECT_GE(static_cast(total_size), ex_min_total_size); int64_t blocked_bloom_overhead = nfilters * (CACHE_LINE_SIZE + 5); if (GetParam() == kLegacyBloom) { // this config can add extra cache line to make odd number blocked_bloom_overhead += nfilters * CACHE_LINE_SIZE; } EXPECT_GE(total_mem, total_size); // optimize_filters_for_memory not implemented with legacy Bloom if (offm && GetParam() != kLegacyBloom) { // This value can include a small extra penalty for kExtraPadding fprintf(stderr, "Internal fragmentation (optimized): %g%%\n", (total_mem - total_size) * 100.0 / total_size); // Less than 1% internal fragmentation EXPECT_LE(total_mem, total_size * 101 / 100); // Up to 2% storage penalty EXPECT_LE(static_cast(total_size), ex_min_total_size * 102 / 100 + blocked_bloom_overhead); } else { fprintf(stderr, "Internal fragmentation (not optimized): %g%%\n", (total_mem - total_size) * 100.0 / total_size); // TODO: add control checks for more allocators? #ifdef ROCKSDB_JEMALLOC fprintf(stderr, "Jemalloc detected? %d\n", HasJemalloc()); if (HasJemalloc()) { #ifdef ROCKSDB_MALLOC_USABLE_SIZE // More than 5% internal fragmentation EXPECT_GE(total_mem, total_size * 105 / 100); #endif // ROCKSDB_MALLOC_USABLE_SIZE } #endif // ROCKSDB_JEMALLOC // No storage penalty, just usual overhead EXPECT_LE(static_cast(total_size), ex_min_total_size + blocked_bloom_overhead); } } } TEST(FullBloomFilterConstructionReserveMemTest, RibbonFilterFallBackOnLargeBanding) { constexpr std::size_t kCacheCapacity = 8 * CacheReservationManagerImpl< CacheEntryRole::kFilterConstruction>::GetDummyEntrySize(); constexpr std::size_t num_entries_for_cache_full = kCacheCapacity / 8; for (bool reserve_builder_mem : {true, false}) { bool will_fall_back = reserve_builder_mem; BlockBasedTableOptions table_options; table_options.reserve_table_builder_memory = reserve_builder_mem; LRUCacheOptions lo; lo.capacity = kCacheCapacity; lo.num_shard_bits = 0; // 2^0 shard lo.strict_capacity_limit = true; std::shared_ptr cache(NewLRUCache(lo)); table_options.block_cache = cache; table_options.filter_policy = BloomLikeFilterPolicy::Create(kStandard128Ribbon, FLAGS_bits_per_key); FilterBuildingContext ctx(table_options); std::unique_ptr filter_bits_builder( table_options.filter_policy->GetBuilderWithContext(ctx)); char key_buffer[sizeof(int)]; for (std::size_t i = 0; i < num_entries_for_cache_full; ++i) { filter_bits_builder->AddKey(Key(static_cast(i), key_buffer)); } std::unique_ptr buf; Slice filter = filter_bits_builder->Finish(&buf); // To verify Ribbon Filter fallbacks to Bloom Filter properly // based on cache reservation result // See BloomFilterPolicy::GetBloomBitsReader re: metadata // -1 = Marker for newer Bloom implementations // -2 = Marker for Standard128 Ribbon if (will_fall_back) { EXPECT_EQ(filter.data()[filter.size() - 5], static_cast(-1)); } else { EXPECT_EQ(filter.data()[filter.size() - 5], static_cast(-2)); } if (reserve_builder_mem) { const size_t dummy_entry_num = static_cast(std::ceil( filter.size() * 1.0 / CacheReservationManagerImpl< CacheEntryRole::kFilterConstruction>::GetDummyEntrySize())); EXPECT_GE( cache->GetPinnedUsage(), dummy_entry_num * CacheReservationManagerImpl< CacheEntryRole::kFilterConstruction>::GetDummyEntrySize()); EXPECT_LT( cache->GetPinnedUsage(), (dummy_entry_num + 1) * CacheReservationManagerImpl< CacheEntryRole::kFilterConstruction>::GetDummyEntrySize()); } else { EXPECT_EQ(cache->GetPinnedUsage(), 0); } } } namespace { inline uint32_t SelectByCacheLineSize(uint32_t for64, uint32_t for128, uint32_t for256) { (void)for64; (void)for128; (void)for256; #if CACHE_LINE_SIZE == 64 return for64; #elif CACHE_LINE_SIZE == 128 return for128; #elif CACHE_LINE_SIZE == 256 return for256; #else #error "CACHE_LINE_SIZE unknown or unrecognized" #endif } } // namespace // Ensure the implementation doesn't accidentally change in an // incompatible way. This test doesn't check the reading side // (FirstFPs/PackedMatches) for LegacyBloom because it requires the // ability to read filters generated using other cache line sizes. // See RawSchema. TEST_P(FullBloomTest, Schema) { #define EXPECT_EQ_Bloom(a, b) \ { \ if (GetParam() != kStandard128Ribbon) { \ EXPECT_EQ(a, b); \ } \ } #define EXPECT_EQ_Ribbon(a, b) \ { \ if (GetParam() == kStandard128Ribbon) { \ EXPECT_EQ(a, b); \ } \ } #define EXPECT_EQ_FastBloom(a, b) \ { \ if (GetParam() == kFastLocalBloom) { \ EXPECT_EQ(a, b); \ } \ } #define EXPECT_EQ_LegacyBloom(a, b) \ { \ if (GetParam() == kLegacyBloom) { \ EXPECT_EQ(a, b); \ } \ } #define EXPECT_EQ_NotLegacy(a, b) \ { \ if (GetParam() != kLegacyBloom) { \ EXPECT_EQ(a, b); \ } \ } char buffer[sizeof(int)]; // First do a small number of keys, where Ribbon config will fall back on // fast Bloom filter and generate the same data ResetPolicy(5); // num_probes = 3 for (int key = 0; key < 87; key++) { Add(Key(key, buffer)); } Build(); EXPECT_EQ(GetNumProbesFromFilterData(), 3); EXPECT_EQ_NotLegacy(BloomHash(FilterData()), 4130687756U); EXPECT_EQ_NotLegacy("31,38,40,43,61,83,86,112,125,131", FirstFPs(10)); // Now use enough keys so that changing bits / key by 1 is guaranteed to // change number of allocated cache lines. So keys > max cache line bits. // Note that the first attempted Ribbon seed is determined by the hash // of the first key added (for pseudorandomness in practice, determinism in // testing) ResetPolicy(2); // num_probes = 1 for (int key = 0; key < 2087; key++) { Add(Key(key, buffer)); } Build(); EXPECT_EQ_Bloom(GetNumProbesFromFilterData(), 1); EXPECT_EQ_Ribbon(GetRibbonSeedFromFilterData(), 61); EXPECT_EQ_LegacyBloom( BloomHash(FilterData()), SelectByCacheLineSize(1567096579, 1964771444, 2659542661U)); EXPECT_EQ_FastBloom(BloomHash(FilterData()), 3817481309U); EXPECT_EQ_Ribbon(BloomHash(FilterData()), 1705851228U); EXPECT_EQ_FastBloom("11,13,17,25,29,30,35,37,45,53", FirstFPs(10)); EXPECT_EQ_Ribbon("3,8,10,17,19,20,23,28,31,32", FirstFPs(10)); ResetPolicy(3); // num_probes = 2 for (int key = 0; key < 2087; key++) { Add(Key(key, buffer)); } Build(); EXPECT_EQ_Bloom(GetNumProbesFromFilterData(), 2); EXPECT_EQ_Ribbon(GetRibbonSeedFromFilterData(), 61); EXPECT_EQ_LegacyBloom( BloomHash(FilterData()), SelectByCacheLineSize(2707206547U, 2571983456U, 218344685)); EXPECT_EQ_FastBloom(BloomHash(FilterData()), 2807269961U); EXPECT_EQ_Ribbon(BloomHash(FilterData()), 1095342358U); EXPECT_EQ_FastBloom("4,15,17,24,27,28,29,53,63,70", FirstFPs(10)); EXPECT_EQ_Ribbon("3,17,20,28,32,33,36,43,49,54", FirstFPs(10)); ResetPolicy(5); // num_probes = 3 for (int key = 0; key < 2087; key++) { Add(Key(key, buffer)); } Build(); EXPECT_EQ_Bloom(GetNumProbesFromFilterData(), 3); EXPECT_EQ_Ribbon(GetRibbonSeedFromFilterData(), 61); EXPECT_EQ_LegacyBloom( BloomHash(FilterData()), SelectByCacheLineSize(515748486, 94611728, 2436112214U)); EXPECT_EQ_FastBloom(BloomHash(FilterData()), 204628445U); EXPECT_EQ_Ribbon(BloomHash(FilterData()), 3971337699U); EXPECT_EQ_FastBloom("15,24,29,39,53,87,89,100,103,104", FirstFPs(10)); EXPECT_EQ_Ribbon("3,33,36,43,67,70,76,78,84,102", FirstFPs(10)); ResetPolicy(8); // num_probes = 5 for (int key = 0; key < 2087; key++) { Add(Key(key, buffer)); } Build(); EXPECT_EQ_Bloom(GetNumProbesFromFilterData(), 5); EXPECT_EQ_Ribbon(GetRibbonSeedFromFilterData(), 61); EXPECT_EQ_LegacyBloom( BloomHash(FilterData()), SelectByCacheLineSize(1302145999, 2811644657U, 756553699)); EXPECT_EQ_FastBloom(BloomHash(FilterData()), 355564975U); EXPECT_EQ_Ribbon(BloomHash(FilterData()), 3651449053U); EXPECT_EQ_FastBloom("16,60,66,126,220,238,244,256,265,287", FirstFPs(10)); EXPECT_EQ_Ribbon("33,187,203,296,300,322,411,419,547,582", FirstFPs(10)); ResetPolicy(9); // num_probes = 6 for (int key = 0; key < 2087; key++) { Add(Key(key, buffer)); } Build(); EXPECT_EQ_Bloom(GetNumProbesFromFilterData(), 6); EXPECT_EQ_Ribbon(GetRibbonSeedFromFilterData(), 61); EXPECT_EQ_LegacyBloom( BloomHash(FilterData()), SelectByCacheLineSize(2092755149, 661139132, 1182970461)); EXPECT_EQ_FastBloom(BloomHash(FilterData()), 2137566013U); EXPECT_EQ_Ribbon(BloomHash(FilterData()), 1005676675U); EXPECT_EQ_FastBloom("156,367,791,872,945,1015,1139,1159,1265", FirstFPs(9)); EXPECT_EQ_Ribbon("33,187,203,296,411,419,604,612,615,619", FirstFPs(10)); ResetPolicy(11); // num_probes = 7 for (int key = 0; key < 2087; key++) { Add(Key(key, buffer)); } Build(); EXPECT_EQ_Bloom(GetNumProbesFromFilterData(), 7); EXPECT_EQ_Ribbon(GetRibbonSeedFromFilterData(), 61); EXPECT_EQ_LegacyBloom( BloomHash(FilterData()), SelectByCacheLineSize(3755609649U, 1812694762, 1449142939)); EXPECT_EQ_FastBloom(BloomHash(FilterData()), 2561502687U); EXPECT_EQ_Ribbon(BloomHash(FilterData()), 3129900846U); EXPECT_EQ_FastBloom("34,74,130,236,643,882,962,1015,1035,1110", FirstFPs(10)); EXPECT_EQ_Ribbon("411,419,623,665,727,794,955,1052,1323,1330", FirstFPs(10)); // This used to be 9 probes, but 8 is a better choice for speed, // especially with SIMD groups of 8 probes, with essentially no // change in FP rate. // FP rate @ 9 probes, old Bloom: 0.4321% // FP rate @ 9 probes, new Bloom: 0.1846% // FP rate @ 8 probes, new Bloom: 0.1843% ResetPolicy(14); // num_probes = 8 (new), 9 (old) for (int key = 0; key < 2087; key++) { Add(Key(key, buffer)); } Build(); EXPECT_EQ_LegacyBloom(GetNumProbesFromFilterData(), 9); EXPECT_EQ_FastBloom(GetNumProbesFromFilterData(), 8); EXPECT_EQ_Ribbon(GetRibbonSeedFromFilterData(), 61); EXPECT_EQ_LegacyBloom( BloomHash(FilterData()), SelectByCacheLineSize(178861123, 379087593, 2574136516U)); EXPECT_EQ_FastBloom(BloomHash(FilterData()), 3709876890U); EXPECT_EQ_Ribbon(BloomHash(FilterData()), 1855638875U); EXPECT_EQ_FastBloom("130,240,522,565,989,2002,2526,3147,3543", FirstFPs(9)); EXPECT_EQ_Ribbon("665,727,1323,1755,3866,4232,4442,4492,4736", FirstFPs(9)); // This used to be 11 probes, but 9 is a better choice for speed // AND accuracy. // FP rate @ 11 probes, old Bloom: 0.3571% // FP rate @ 11 probes, new Bloom: 0.0884% // FP rate @ 9 probes, new Bloom: 0.0843% ResetPolicy(16); // num_probes = 9 (new), 11 (old) for (int key = 0; key < 2087; key++) { Add(Key(key, buffer)); } Build(); EXPECT_EQ_LegacyBloom(GetNumProbesFromFilterData(), 11); EXPECT_EQ_FastBloom(GetNumProbesFromFilterData(), 9); EXPECT_EQ_Ribbon(GetRibbonSeedFromFilterData(), 61); EXPECT_EQ_LegacyBloom( BloomHash(FilterData()), SelectByCacheLineSize(1129406313, 3049154394U, 1727750964)); EXPECT_EQ_FastBloom(BloomHash(FilterData()), 1087138490U); EXPECT_EQ_Ribbon(BloomHash(FilterData()), 459379967U); EXPECT_EQ_FastBloom("3299,3611,3916,6620,7822,8079,8482,8942", FirstFPs(8)); EXPECT_EQ_Ribbon("727,1323,1755,4442,4736,5386,6974,7154,8222", FirstFPs(9)); ResetPolicy(10); // num_probes = 6, but different memory ratio vs. 9 for (int key = 0; key < 2087; key++) { Add(Key(key, buffer)); } Build(); EXPECT_EQ_Bloom(GetNumProbesFromFilterData(), 6); EXPECT_EQ_Ribbon(GetRibbonSeedFromFilterData(), 61); EXPECT_EQ_LegacyBloom( BloomHash(FilterData()), SelectByCacheLineSize(1478976371, 2910591341U, 1182970461)); EXPECT_EQ_FastBloom(BloomHash(FilterData()), 2498541272U); EXPECT_EQ_Ribbon(BloomHash(FilterData()), 1273231667U); EXPECT_EQ_FastBloom("16,126,133,422,466,472,813,1002,1035", FirstFPs(9)); EXPECT_EQ_Ribbon("296,411,419,612,619,623,630,665,686,727", FirstFPs(10)); ResetPolicy(10); for (int key = /*CHANGED*/ 1; key < 2087; key++) { Add(Key(key, buffer)); } Build(); EXPECT_EQ_Bloom(GetNumProbesFromFilterData(), 6); EXPECT_EQ_Ribbon(GetRibbonSeedFromFilterData(), /*CHANGED*/ 184); EXPECT_EQ_LegacyBloom( BloomHash(FilterData()), SelectByCacheLineSize(4205696321U, 1132081253U, 2385981855U)); EXPECT_EQ_FastBloom(BloomHash(FilterData()), 2058382345U); EXPECT_EQ_Ribbon(BloomHash(FilterData()), 3007790572U); EXPECT_EQ_FastBloom("16,126,133,422,466,472,813,1002,1035", FirstFPs(9)); EXPECT_EQ_Ribbon("33,152,383,497,589,633,737,781,911,990", FirstFPs(10)); ResetPolicy(10); for (int key = 1; key < /*CHANGED*/ 2088; key++) { Add(Key(key, buffer)); } Build(); EXPECT_EQ_Bloom(GetNumProbesFromFilterData(), 6); EXPECT_EQ_Ribbon(GetRibbonSeedFromFilterData(), 184); EXPECT_EQ_LegacyBloom( BloomHash(FilterData()), SelectByCacheLineSize(2885052954U, 769447944, 4175124908U)); EXPECT_EQ_FastBloom(BloomHash(FilterData()), 23699164U); EXPECT_EQ_Ribbon(BloomHash(FilterData()), 1942323379U); EXPECT_EQ_FastBloom("16,126,133,422,466,472,813,1002,1035", FirstFPs(9)); EXPECT_EQ_Ribbon("33,95,360,589,737,911,990,1048,1081,1414", FirstFPs(10)); // With new fractional bits_per_key, check that we are rounding to // whole bits per key for old Bloom filters but fractional for // new Bloom filter. ResetPolicy(9.5); for (int key = 1; key < 2088; key++) { Add(Key(key, buffer)); } Build(); EXPECT_EQ_Bloom(GetNumProbesFromFilterData(), 6); EXPECT_EQ_Ribbon(GetRibbonSeedFromFilterData(), 184); EXPECT_EQ_LegacyBloom( BloomHash(FilterData()), /*SAME*/ SelectByCacheLineSize(2885052954U, 769447944, 4175124908U)); EXPECT_EQ_FastBloom(BloomHash(FilterData()), 3166884174U); EXPECT_EQ_Ribbon(BloomHash(FilterData()), 1148258663U); EXPECT_EQ_FastBloom("126,156,367,444,458,791,813,976,1015", FirstFPs(9)); EXPECT_EQ_Ribbon("33,54,95,360,589,693,737,911,990,1048", FirstFPs(10)); ResetPolicy(10.499); for (int key = 1; key < 2088; key++) { Add(Key(key, buffer)); } Build(); EXPECT_EQ_LegacyBloom(GetNumProbesFromFilterData(), 6); EXPECT_EQ_FastBloom(GetNumProbesFromFilterData(), 7); EXPECT_EQ_Ribbon(GetRibbonSeedFromFilterData(), 184); EXPECT_EQ_LegacyBloom( BloomHash(FilterData()), /*SAME*/ SelectByCacheLineSize(2885052954U, 769447944, 4175124908U)); EXPECT_EQ_FastBloom(BloomHash(FilterData()), 4098502778U); EXPECT_EQ_Ribbon(BloomHash(FilterData()), 792138188U); EXPECT_EQ_FastBloom("16,236,240,472,1015,1045,1111,1409,1465", FirstFPs(9)); EXPECT_EQ_Ribbon("33,95,360,589,737,990,1048,1081,1414,1643", FirstFPs(10)); ResetPolicy(); } // A helper class for testing custom or corrupt filter bits as read by // built-in FilterBitsReaders. struct RawFilterTester { // Buffer, from which we always return a tail Slice, so the // last five bytes are always the metadata bytes. std::array data_; // Points five bytes from the end char* metadata_ptr_; RawFilterTester() : metadata_ptr_(&*(data_.end() - 5)) {} Slice ResetNoFill(uint32_t len_without_metadata, uint32_t num_lines, uint32_t num_probes) { metadata_ptr_[0] = static_cast(num_probes); EncodeFixed32(metadata_ptr_ + 1, num_lines); uint32_t len = len_without_metadata + /*metadata*/ 5; assert(len <= data_.size()); return Slice(metadata_ptr_ - len_without_metadata, len); } Slice Reset(uint32_t len_without_metadata, uint32_t num_lines, uint32_t num_probes, bool fill_ones) { data_.fill(fill_ones ? 0xff : 0); return ResetNoFill(len_without_metadata, num_lines, num_probes); } Slice ResetWeirdFill(uint32_t len_without_metadata, uint32_t num_lines, uint32_t num_probes) { for (uint32_t i = 0; i < data_.size(); ++i) { data_[i] = static_cast(0x7b7b >> (i % 7)); } return ResetNoFill(len_without_metadata, num_lines, num_probes); } }; TEST_P(FullBloomTest, RawSchema) { RawFilterTester cft; // Legacy Bloom configurations // Two probes, about 3/4 bits set: ~50% "FP" rate // One 256-byte cache line. OpenRaw(cft.ResetWeirdFill(256, 1, 2)); EXPECT_EQ(uint64_t{11384799501900898790U}, PackedMatches()); // Two 128-byte cache lines. OpenRaw(cft.ResetWeirdFill(256, 2, 2)); EXPECT_EQ(uint64_t{10157853359773492589U}, PackedMatches()); // Four 64-byte cache lines. OpenRaw(cft.ResetWeirdFill(256, 4, 2)); EXPECT_EQ(uint64_t{7123594913907464682U}, PackedMatches()); // Fast local Bloom configurations (marker 255 -> -1) // Two probes, about 3/4 bits set: ~50% "FP" rate // Four 64-byte cache lines. OpenRaw(cft.ResetWeirdFill(256, 2U << 8, 255)); EXPECT_EQ(uint64_t{9957045189927952471U}, PackedMatches()); // Ribbon configurations (marker 254 -> -2) // Even though the builder never builds configurations this // small (preferring Bloom), we can test that the configuration // can be read, for possible future-proofing. // 256 slots, one result column = 32 bytes (2 blocks, seed 0) // ~50% FP rate: // 0b0101010111110101010000110000011011011111100100001110010011101010 OpenRaw(cft.ResetWeirdFill(32, 2U << 8, 254)); EXPECT_EQ(uint64_t{6193930559317665002U}, PackedMatches()); // 256 slots, three-to-four result columns = 112 bytes // ~ 1 in 10 FP rate: // 0b0000000000100000000000000000000001000001000000010000101000000000 OpenRaw(cft.ResetWeirdFill(112, 2U << 8, 254)); EXPECT_EQ(uint64_t{9007200345328128U}, PackedMatches()); } TEST_P(FullBloomTest, CorruptFilters) { RawFilterTester cft; for (bool fill : {false, true}) { // Legacy Bloom configurations // Good filter bits - returns same as fill OpenRaw(cft.Reset(CACHE_LINE_SIZE, 1, 6, fill)); ASSERT_EQ(fill, Matches("hello")); ASSERT_EQ(fill, Matches("world")); // Good filter bits - returns same as fill OpenRaw(cft.Reset(CACHE_LINE_SIZE * 3, 3, 6, fill)); ASSERT_EQ(fill, Matches("hello")); ASSERT_EQ(fill, Matches("world")); // Good filter bits - returns same as fill // 256 is unusual but legal cache line size OpenRaw(cft.Reset(256 * 3, 3, 6, fill)); ASSERT_EQ(fill, Matches("hello")); ASSERT_EQ(fill, Matches("world")); // Good filter bits - returns same as fill // 30 should be max num_probes OpenRaw(cft.Reset(CACHE_LINE_SIZE, 1, 30, fill)); ASSERT_EQ(fill, Matches("hello")); ASSERT_EQ(fill, Matches("world")); // Good filter bits - returns same as fill // 1 should be min num_probes OpenRaw(cft.Reset(CACHE_LINE_SIZE, 1, 1, fill)); ASSERT_EQ(fill, Matches("hello")); ASSERT_EQ(fill, Matches("world")); // Type 1 trivial filter bits - returns true as if FP by zero probes OpenRaw(cft.Reset(CACHE_LINE_SIZE, 1, 0, fill)); ASSERT_TRUE(Matches("hello")); ASSERT_TRUE(Matches("world")); // Type 2 trivial filter bits - returns false as if built from zero keys OpenRaw(cft.Reset(0, 0, 6, fill)); ASSERT_FALSE(Matches("hello")); ASSERT_FALSE(Matches("world")); // Type 2 trivial filter bits - returns false as if built from zero keys OpenRaw(cft.Reset(0, 37, 6, fill)); ASSERT_FALSE(Matches("hello")); ASSERT_FALSE(Matches("world")); // Type 2 trivial filter bits - returns false as 0 size trumps 0 probes OpenRaw(cft.Reset(0, 0, 0, fill)); ASSERT_FALSE(Matches("hello")); ASSERT_FALSE(Matches("world")); // Bad filter bits - returns true for safety // No solution to 0 * x == CACHE_LINE_SIZE OpenRaw(cft.Reset(CACHE_LINE_SIZE, 0, 6, fill)); ASSERT_TRUE(Matches("hello")); ASSERT_TRUE(Matches("world")); // Bad filter bits - returns true for safety // Can't have 3 * x == 4 for integer x OpenRaw(cft.Reset(4, 3, 6, fill)); ASSERT_TRUE(Matches("hello")); ASSERT_TRUE(Matches("world")); // Bad filter bits - returns true for safety // 97 bytes is not a power of two, so not a legal cache line size OpenRaw(cft.Reset(97 * 3, 3, 6, fill)); ASSERT_TRUE(Matches("hello")); ASSERT_TRUE(Matches("world")); // Bad filter bits - returns true for safety // 65 bytes is not a power of two, so not a legal cache line size OpenRaw(cft.Reset(65 * 3, 3, 6, fill)); ASSERT_TRUE(Matches("hello")); ASSERT_TRUE(Matches("world")); // Bad filter bits - returns false as if built from zero keys // < 5 bytes overall means missing even metadata OpenRaw(cft.Reset(static_cast(-1), 3, 6, fill)); ASSERT_FALSE(Matches("hello")); ASSERT_FALSE(Matches("world")); OpenRaw(cft.Reset(static_cast(-5), 3, 6, fill)); ASSERT_FALSE(Matches("hello")); ASSERT_FALSE(Matches("world")); // Dubious filter bits - returns same as fill (for now) // 31 is not a useful num_probes, nor generated by RocksDB unless directly // using filter bits API without BloomFilterPolicy. OpenRaw(cft.Reset(CACHE_LINE_SIZE, 1, 31, fill)); ASSERT_EQ(fill, Matches("hello")); ASSERT_EQ(fill, Matches("world")); // Dubious filter bits - returns same as fill (for now) // Similar, with 127, largest positive char OpenRaw(cft.Reset(CACHE_LINE_SIZE, 1, 127, fill)); ASSERT_EQ(fill, Matches("hello")); ASSERT_EQ(fill, Matches("world")); // Dubious filter bits - returns true (for now) // num_probes set to 128 / -128, lowest negative char // NB: Bug in implementation interprets this as negative and has same // effect as zero probes, but effectively reserves negative char values // for future use. OpenRaw(cft.Reset(CACHE_LINE_SIZE, 1, 128, fill)); ASSERT_TRUE(Matches("hello")); ASSERT_TRUE(Matches("world")); // Dubious filter bits - returns true (for now) // Similar, with 253 / -3 OpenRaw(cft.Reset(CACHE_LINE_SIZE, 1, 253, fill)); ASSERT_TRUE(Matches("hello")); ASSERT_TRUE(Matches("world")); // ######################################################### // Fast local Bloom configurations (marker 255 -> -1) // Good config with six probes OpenRaw(cft.Reset(CACHE_LINE_SIZE, 6U << 8, 255, fill)); ASSERT_EQ(fill, Matches("hello")); ASSERT_EQ(fill, Matches("world")); // Becomes bad/reserved config (always true) if any other byte set OpenRaw(cft.Reset(CACHE_LINE_SIZE, (6U << 8) | 1U, 255, fill)); ASSERT_TRUE(Matches("hello")); ASSERT_TRUE(Matches("world")); OpenRaw(cft.Reset(CACHE_LINE_SIZE, (6U << 8) | (1U << 16), 255, fill)); ASSERT_TRUE(Matches("hello")); ASSERT_TRUE(Matches("world")); OpenRaw(cft.Reset(CACHE_LINE_SIZE, (6U << 8) | (1U << 24), 255, fill)); ASSERT_TRUE(Matches("hello")); ASSERT_TRUE(Matches("world")); // Good config, max 30 probes OpenRaw(cft.Reset(CACHE_LINE_SIZE, 30U << 8, 255, fill)); ASSERT_EQ(fill, Matches("hello")); ASSERT_EQ(fill, Matches("world")); // Bad/reserved config (always true) if more than 30 OpenRaw(cft.Reset(CACHE_LINE_SIZE, 31U << 8, 255, fill)); ASSERT_TRUE(Matches("hello")); ASSERT_TRUE(Matches("world")); OpenRaw(cft.Reset(CACHE_LINE_SIZE, 33U << 8, 255, fill)); ASSERT_TRUE(Matches("hello")); ASSERT_TRUE(Matches("world")); OpenRaw(cft.Reset(CACHE_LINE_SIZE, 66U << 8, 255, fill)); ASSERT_TRUE(Matches("hello")); ASSERT_TRUE(Matches("world")); OpenRaw(cft.Reset(CACHE_LINE_SIZE, 130U << 8, 255, fill)); ASSERT_TRUE(Matches("hello")); ASSERT_TRUE(Matches("world")); } // ######################################################### // Ribbon configurations (marker 254 -> -2) // ("fill" doesn't work to detect good configurations, we just // have to rely on TN probability) // Good: 2 blocks * 16 bytes / segment * 4 columns = 128 bytes // seed = 123 OpenRaw(cft.Reset(128, (2U << 8) + 123U, 254, false)); ASSERT_FALSE(Matches("hello")); ASSERT_FALSE(Matches("world")); // Good: 2 blocks * 16 bytes / segment * 8 columns = 256 bytes OpenRaw(cft.Reset(256, (2U << 8) + 123U, 254, false)); ASSERT_FALSE(Matches("hello")); ASSERT_FALSE(Matches("world")); // Surprisingly OK: 5000 blocks (640,000 slots) in only 1024 bits // -> average close to 0 columns OpenRaw(cft.Reset(128, (5000U << 8) + 123U, 254, false)); // *Almost* all FPs ASSERT_TRUE(Matches("hello")); ASSERT_TRUE(Matches("world")); // Need many queries to find a "true negative" for (int i = 0; Matches(std::to_string(i)); ++i) { ASSERT_LT(i, 1000); } // Bad: 1 block not allowed (for implementation detail reasons) OpenRaw(cft.Reset(128, (1U << 8) + 123U, 254, false)); ASSERT_TRUE(Matches("hello")); ASSERT_TRUE(Matches("world")); // Bad: 0 blocks not allowed OpenRaw(cft.Reset(128, (0U << 8) + 123U, 254, false)); ASSERT_TRUE(Matches("hello")); ASSERT_TRUE(Matches("world")); } INSTANTIATE_TEST_CASE_P(Full, FullBloomTest, testing::Values(kLegacyBloom, kFastLocalBloom, kStandard128Ribbon)); static double GetEffectiveBitsPerKey(FilterBitsBuilder* builder) { union { uint64_t key_value = 0; char key_bytes[8]; }; const unsigned kNumKeys = 1000; Slice key_slice{key_bytes, 8}; for (key_value = 0; key_value < kNumKeys; ++key_value) { builder->AddKey(key_slice); } std::unique_ptr buf; auto filter = builder->Finish(&buf); return filter.size() * /*bits per byte*/ 8 / (1.0 * kNumKeys); } static void SetTestingLevel(int levelish, FilterBuildingContext* ctx) { if (levelish == -1) { // Flush is treated as level -1 for this option but actually level 0 ctx->level_at_creation = 0; ctx->reason = TableFileCreationReason::kFlush; } else { ctx->level_at_creation = levelish; ctx->reason = TableFileCreationReason::kCompaction; } } TEST(RibbonTest, RibbonTestLevelThreshold) { BlockBasedTableOptions opts; FilterBuildingContext ctx(opts); // A few settings for (CompactionStyle cs : {kCompactionStyleLevel, kCompactionStyleUniversal, kCompactionStyleFIFO, kCompactionStyleNone}) { ctx.compaction_style = cs; for (int bloom_before_level : {-1, 0, 1, 10}) { std::vector > policies; policies.emplace_back(NewRibbonFilterPolicy(10, bloom_before_level)); if (bloom_before_level == 0) { // Also test new API default policies.emplace_back(NewRibbonFilterPolicy(10)); } for (std::unique_ptr& policy : policies) { // Claim to be generating filter for this level SetTestingLevel(bloom_before_level, &ctx); std::unique_ptr builder{ policy->GetBuilderWithContext(ctx)}; // Must be Ribbon (more space efficient than 10 bits per key) ASSERT_LT(GetEffectiveBitsPerKey(builder.get()), 8); if (bloom_before_level >= 0) { // Claim to be generating filter for previous level SetTestingLevel(bloom_before_level - 1, &ctx); builder.reset(policy->GetBuilderWithContext(ctx)); if (cs == kCompactionStyleLevel || cs == kCompactionStyleUniversal) { // Level is considered. // Must be Bloom (~ 10 bits per key) ASSERT_GT(GetEffectiveBitsPerKey(builder.get()), 9); } else { // Level is ignored under non-traditional compaction styles. // Must be Ribbon (more space efficient than 10 bits per key) ASSERT_LT(GetEffectiveBitsPerKey(builder.get()), 8); } } // Like SST file writer ctx.level_at_creation = -1; ctx.reason = TableFileCreationReason::kMisc; builder.reset(policy->GetBuilderWithContext(ctx)); // Must be Ribbon (more space efficient than 10 bits per key) ASSERT_LT(GetEffectiveBitsPerKey(builder.get()), 8); } } } } } // namespace ROCKSDB_NAMESPACE int main(int argc, char** argv) { ::testing::InitGoogleTest(&argc, argv); ParseCommandLineFlags(&argc, &argv, true); return RUN_ALL_TESTS(); } #endif // GFLAGS