mirror of https://github.com/facebook/rocksdb.git
Minimize memory internal fragmentation for Bloom filters (#6427)
Summary:
New experimental option BBTO::optimize_filters_for_memory builds
filters that maximize their use of "usable size" from malloc_usable_size,
which is also used to compute block cache charges.
Rather than always "rounding up," we track state in the
BloomFilterPolicy object to mix essentially "rounding down" and
"rounding up" so that the average FP rate of all generated filters is
the same as without the option. (YMMV as heavily accessed filters might
be unluckily lower accuracy.)
Thus, the option near-minimizes what the block cache considers as
"memory used" for a given target Bloom filter false positive rate and
Bloom filter implementation. There are no forward or backward
compatibility issues with this change, though it only works on the
format_version=5 Bloom filter.
With Jemalloc, we see about 10% reduction in memory footprint (and block
cache charge) for Bloom filters, but 1-2% increase in storage footprint,
due to encoding efficiency losses (FP rate is non-linear with bits/key).
Why not weighted random round up/down rather than state tracking? By
only requiring malloc_usable_size, we don't actually know what the next
larger and next smaller usable sizes for the allocator are. We pick a
requested size, accept and use whatever usable size it has, and use the
difference to inform our next choice. This allows us to narrow in on the
right balance without tracking/predicting usable sizes.
Why not weight history of generated filter false positive rates by
number of keys? This could lead to excess skew in small filters after
generating a large filter.
Results from filter_bench with jemalloc (irrelevant details omitted):
(normal keys/filter, but high variance)
$ ./filter_bench -quick -impl=2 -average_keys_per_filter=30000 -vary_key_count_ratio=0.9
Build avg ns/key: 29.6278
Number of filters: 5516
Total size (MB): 200.046
Reported total allocated memory (MB): 220.597
Reported internal fragmentation: 10.2732%
Bits/key stored: 10.0097
Average FP rate %: 0.965228
$ ./filter_bench -quick -impl=2 -average_keys_per_filter=30000 -vary_key_count_ratio=0.9 -optimize_filters_for_memory
Build avg ns/key: 30.5104
Number of filters: 5464
Total size (MB): 200.015
Reported total allocated memory (MB): 200.322
Reported internal fragmentation: 0.153709%
Bits/key stored: 10.1011
Average FP rate %: 0.966313
(very few keys / filter, optimization not as effective due to ~59 byte
internal fragmentation in blocked Bloom filter representation)
$ ./filter_bench -quick -impl=2 -average_keys_per_filter=1000 -vary_key_count_ratio=0.9
Build avg ns/key: 29.5649
Number of filters: 162950
Total size (MB): 200.001
Reported total allocated memory (MB): 224.624
Reported internal fragmentation: 12.3117%
Bits/key stored: 10.2951
Average FP rate %: 0.821534
$ ./filter_bench -quick -impl=2 -average_keys_per_filter=1000 -vary_key_count_ratio=0.9 -optimize_filters_for_memory
Build avg ns/key: 31.8057
Number of filters: 159849
Total size (MB): 200
Reported total allocated memory (MB): 208.846
Reported internal fragmentation: 4.42297%
Bits/key stored: 10.4948
Average FP rate %: 0.811006
(high keys/filter)
$ ./filter_bench -quick -impl=2 -average_keys_per_filter=1000000 -vary_key_count_ratio=0.9
Build avg ns/key: 29.7017
Number of filters: 164
Total size (MB): 200.352
Reported total allocated memory (MB): 221.5
Reported internal fragmentation: 10.5552%
Bits/key stored: 10.0003
Average FP rate %: 0.969358
$ ./filter_bench -quick -impl=2 -average_keys_per_filter=1000000 -vary_key_count_ratio=0.9 -optimize_filters_for_memory
Build avg ns/key: 30.7131
Number of filters: 160
Total size (MB): 200.928
Reported total allocated memory (MB): 200.938
Reported internal fragmentation: 0.00448054%
Bits/key stored: 10.1852
Average FP rate %: 0.963387
And from db_bench (block cache) with jemalloc:
$ ./db_bench -db=/dev/shm/dbbench.no_optimize -benchmarks=fillrandom -format_version=5 -value_size=90 -bloom_bits=10 -num=2000000 -threads=8 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=false
$ ./db_bench -db=/dev/shm/dbbench -benchmarks=fillrandom -format_version=5 -value_size=90 -bloom_bits=10 -num=2000000 -threads=8 -optimize_filters_for_memory -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=false
$ (for FILE in /dev/shm/dbbench.no_optimize/*.sst; do ./sst_dump --file=$FILE --show_properties | grep 'filter block' ; done) | awk '{ t += $4; } END { print t; }'
17063835
$ (for FILE in /dev/shm/dbbench/*.sst; do ./sst_dump --file=$FILE --show_properties | grep 'filter block' ; done) | awk '{ t += $4; } END { print t; }'
17430747
$ #^ 2.1% additional filter storage
$ ./db_bench -db=/dev/shm/dbbench.no_optimize -use_existing_db -benchmarks=readrandom,stats -statistics -bloom_bits=10 -num=2000000 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=false -duration=10 -cache_index_and_filter_blocks -cache_size=1000000000
rocksdb.block.cache.index.add COUNT : 33
rocksdb.block.cache.index.bytes.insert COUNT : 8440400
rocksdb.block.cache.filter.add COUNT : 33
rocksdb.block.cache.filter.bytes.insert COUNT : 21087528
rocksdb.bloom.filter.useful COUNT : 4963889
rocksdb.bloom.filter.full.positive COUNT : 1214081
rocksdb.bloom.filter.full.true.positive COUNT : 1161999
$ #^ 1.04 % observed FP rate
$ ./db_bench -db=/dev/shm/dbbench -use_existing_db -benchmarks=readrandom,stats -statistics -bloom_bits=10 -num=2000000 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=false -optimize_filters_for_memory -duration=10 -cache_index_and_filter_blocks -cache_size=1000000000
rocksdb.block.cache.index.add COUNT : 33
rocksdb.block.cache.index.bytes.insert COUNT : 8448592
rocksdb.block.cache.filter.add COUNT : 33
rocksdb.block.cache.filter.bytes.insert COUNT : 18220328
rocksdb.bloom.filter.useful COUNT : 5360933
rocksdb.bloom.filter.full.positive COUNT : 1321315
rocksdb.bloom.filter.full.true.positive COUNT : 1262999
$ #^ 1.08 % observed FP rate, 13.6% less memory usage for filters
(Due to specific key density, this example tends to generate filters that are "worse than average" for internal fragmentation. "Better than average" cases can show little or no improvement.)
Pull Request resolved: https://github.com/facebook/rocksdb/pull/6427
Test Plan: unit test added, 'make check' with gcc, clang and valgrind
Reviewed By: siying
Differential Revision: D22124374
Pulled By: pdillinger
fbshipit-source-id: f3e3aa152f9043ddf4fae25799e76341d0d8714e
This commit is contained in:
Peter Dillinger
Facebook GitHub Bot
parent
1092f19d95
commit
5b2bbacb6f
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@ -14,6 +14,7 @@
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### New Features
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* DB identity (`db_id`) and DB session identity (`db_session_id`) are added to table properties and stored in SST files. SST files generated from SstFileWriter and Repairer have DB identity “SST Writer” and “DB Repairer”, respectively. Their DB session IDs are generated in the same way as `DB::GetDbSessionId`. The session ID for SstFileWriter (resp., Repairer) resets every time `SstFileWriter::Open` (resp., `Repairer::Run`) is called.
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* Added experimental option BlockBasedTableOptions::optimize_filters_for_memory for reducing allocated memory size of Bloom filters (~10% savings with Jemalloc) while preserving the same general accuracy. To have an effect, the option requires format_version=5 and malloc_usable_size. Enabling this option is forward and backward compatible with existing format_version=5.
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### Bug Fixes
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* Fail recovery and report once hitting a physical log record checksum mismatch, while reading MANIFEST. RocksDB should not continue processing the MANIFEST any further.
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@ -146,6 +146,7 @@ DECLARE_int32(reopen);
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DECLARE_double(bloom_bits);
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DECLARE_bool(use_block_based_filter);
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DECLARE_bool(partition_filters);
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DECLARE_bool(optimize_filters_for_memory);
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DECLARE_int32(index_type);
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DECLARE_string(db);
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DECLARE_string(secondaries_base);
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@ -361,6 +361,11 @@ DEFINE_bool(partition_filters, false,
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"use partitioned filters "
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"for block-based table");
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DEFINE_bool(
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optimize_filters_for_memory,
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ROCKSDB_NAMESPACE::BlockBasedTableOptions().optimize_filters_for_memory,
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"Minimize memory footprint of filters");
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DEFINE_int32(
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index_type,
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static_cast<int32_t>(
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@ -1762,6 +1762,8 @@ void StressTest::Open() {
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static_cast<int32_t>(FLAGS_index_block_restart_interval);
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block_based_options.filter_policy = filter_policy_;
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block_based_options.partition_filters = FLAGS_partition_filters;
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block_based_options.optimize_filters_for_memory =
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FLAGS_optimize_filters_for_memory;
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block_based_options.index_type =
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static_cast<BlockBasedTableOptions::IndexType>(FLAGS_index_type);
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options_.table_factory.reset(
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@ -200,6 +200,40 @@ struct BlockBasedTableOptions {
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// incompatible with block-based filters.
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bool partition_filters = false;
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// EXPERIMENTAL Option to generate Bloom filters that minimize memory
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// internal fragmentation.
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//
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// When false, malloc_usable_size is not available, or format_version < 5,
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// filters are generated without regard to internal fragmentation when
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// loaded into memory (historical behavior). When true (and
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// malloc_usable_size is available and format_version >= 5), then Bloom
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// filters are generated to "round up" and "round down" their sizes to
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// minimize internal fragmentation when loaded into memory, assuming the
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// reading DB has the same memory allocation characteristics as the
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// generating DB. This option does not break forward or backward
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// compatibility.
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//
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// While individual filters will vary in bits/key and false positive rate
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// when setting is true, the implementation attempts to maintain a weighted
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// average FP rate for filters consistent with this option set to false.
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//
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// With Jemalloc for example, this setting is expected to save about 10% of
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// the memory footprint and block cache charge of filters, while increasing
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// disk usage of filters by about 1-2% due to encoding efficiency losses
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// with variance in bits/key.
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//
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// NOTE: Because some memory counted by block cache might be unmapped pages
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// within internal fragmentation, this option can increase observed RSS
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// memory usage. With cache_index_and_filter_blocks=true, this option makes
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// the block cache better at using space it is allowed.
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//
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// NOTE: Do not set to true if you do not trust malloc_usable_size. With
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// this option, RocksDB might access an allocated memory object beyond its
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// original size if malloc_usable_size says it is safe to do so. While this
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// can be considered bad practice, it should not produce undefined behavior
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// unless malloc_usable_size is buggy or broken.
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bool optimize_filters_for_memory = false;
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// Use delta encoding to compress keys in blocks.
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// ReadOptions::pin_data requires this option to be disabled.
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//
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@ -167,6 +167,7 @@ TEST_F(OptionsSettableTest, BlockBasedTableOptionsAllFieldsSettable) {
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"block_size_deviation=8;block_restart_interval=4; "
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"metadata_block_size=1024;"
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"partition_filters=false;"
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"optimize_filters_for_memory=true;"
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"index_block_restart_interval=4;"
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"filter_policy=bloomfilter:4:true;whole_key_filtering=1;"
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"format_version=1;"
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@ -268,6 +268,10 @@ static std::unordered_map<std::string, OptionTypeInfo>
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{offsetof(struct BlockBasedTableOptions, partition_filters),
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OptionType::kBoolean, OptionVerificationType::kNormal,
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OptionTypeFlags::kNone, 0}},
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{"optimize_filters_for_memory",
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{offsetof(struct BlockBasedTableOptions, optimize_filters_for_memory),
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OptionType::kBoolean, OptionVerificationType::kNormal,
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OptionTypeFlags::kNone, 0}},
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{"filter_policy",
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{offsetof(struct BlockBasedTableOptions, filter_policy),
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OptionType::kUnknown, OptionVerificationType::kByNameAllowFromNull,
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@ -28,9 +28,12 @@ namespace {
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// See description in FastLocalBloomImpl
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class FastLocalBloomBitsBuilder : public BuiltinFilterBitsBuilder {
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public:
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explicit FastLocalBloomBitsBuilder(const int millibits_per_key)
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// Non-null aggregate_rounding_balance implies optimize_filters_for_memory
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explicit FastLocalBloomBitsBuilder(
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const int millibits_per_key,
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std::atomic<int64_t>* aggregate_rounding_balance)
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: millibits_per_key_(millibits_per_key),
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num_probes_(FastLocalBloomImpl::ChooseNumProbes(millibits_per_key_)) {
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aggregate_rounding_balance_(aggregate_rounding_balance) {
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assert(millibits_per_key >= 1000);
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}
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@ -48,33 +51,36 @@ class FastLocalBloomBitsBuilder : public BuiltinFilterBitsBuilder {
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}
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virtual Slice Finish(std::unique_ptr<const char[]>* buf) override {
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size_t num_entry = hash_entries_.size();
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std::unique_ptr<char[]> mutable_buf;
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uint32_t len_with_metadata =
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CalculateSpace(static_cast<uint32_t>(hash_entries_.size()));
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char* data = new char[len_with_metadata];
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memset(data, 0, len_with_metadata);
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CalculateAndAllocate(num_entry, &mutable_buf, /*update_balance*/ true);
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assert(data);
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assert(mutable_buf);
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assert(len_with_metadata >= 5);
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// Compute num_probes after any rounding / adjustments
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int num_probes = GetNumProbes(num_entry, len_with_metadata);
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uint32_t len = len_with_metadata - 5;
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if (len > 0) {
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AddAllEntries(data, len);
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AddAllEntries(mutable_buf.get(), len, num_probes);
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}
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assert(hash_entries_.empty());
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// See BloomFilterPolicy::GetBloomBitsReader re: metadata
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// -1 = Marker for newer Bloom implementations
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data[len] = static_cast<char>(-1);
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mutable_buf[len] = static_cast<char>(-1);
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// 0 = Marker for this sub-implementation
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data[len + 1] = static_cast<char>(0);
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mutable_buf[len + 1] = static_cast<char>(0);
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// num_probes (and 0 in upper bits for 64-byte block size)
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data[len + 2] = static_cast<char>(num_probes_);
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mutable_buf[len + 2] = static_cast<char>(num_probes);
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// rest of metadata stays zero
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const char* const_data = data;
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buf->reset(const_data);
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assert(hash_entries_.empty());
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return Slice(data, len_with_metadata);
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Slice rv(mutable_buf.get(), len_with_metadata);
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*buf = std::move(mutable_buf);
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return rv;
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}
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int CalculateNumEntry(const uint32_t bytes) override {
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@ -84,26 +90,163 @@ class FastLocalBloomBitsBuilder : public BuiltinFilterBitsBuilder {
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}
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uint32_t CalculateSpace(const int num_entry) override {
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uint32_t num_cache_lines = 0;
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if (millibits_per_key_ > 0 && num_entry > 0) {
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num_cache_lines = static_cast<uint32_t>(
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(int64_t{num_entry} * millibits_per_key_ + 511999) / 512000);
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}
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return num_cache_lines * 64 + /*metadata*/ 5;
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// NB: the BuiltinFilterBitsBuilder API presumes len fits in uint32_t.
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return static_cast<uint32_t>(
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CalculateAndAllocate(static_cast<size_t>(num_entry),
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/* buf */ nullptr,
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/*update_balance*/ false));
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}
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double EstimatedFpRate(size_t keys, size_t bytes) override {
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return FastLocalBloomImpl::EstimatedFpRate(keys, bytes - /*metadata*/ 5,
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num_probes_, /*hash bits*/ 64);
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// To choose size using malloc_usable_size, we have to actually allocate.
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uint32_t CalculateAndAllocate(size_t num_entry, std::unique_ptr<char[]>* buf,
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bool update_balance) {
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std::unique_ptr<char[]> tmpbuf;
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// If not for cache line blocks in the filter, what would the target
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// length in bytes be?
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size_t raw_target_len = static_cast<size_t>(
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(uint64_t{num_entry} * millibits_per_key_ + 7999) / 8000);
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if (raw_target_len >= size_t{0xffffffc0}) {
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// Max supported for this data structure implementation
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raw_target_len = size_t{0xffffffc0};
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}
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// Round up to nearest multiple of 64 (block size). This adjustment is
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// used for target FP rate only so that we don't receive complaints about
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// lower FP rate vs. historic Bloom filter behavior.
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uint32_t target_len =
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static_cast<uint32_t>(raw_target_len + 63) & ~uint32_t{63};
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// Return value set to a default; overwritten in some cases
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uint32_t rv = target_len + /* metadata */ 5;
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#ifdef ROCKSDB_MALLOC_USABLE_SIZE
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if (aggregate_rounding_balance_ != nullptr) {
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// Do optimize_filters_for_memory, using malloc_usable_size.
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// Approach: try to keep FP rate balance better than or on
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// target (negative aggregate_rounding_balance_). We can then select a
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// lower bound filter size (within reasonable limits) that gets us as
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// close to on target as possible. We request allocation for that filter
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// size and use malloc_usable_size to "round up" to the actual
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// allocation size.
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// Although it can be considered bad practice to use malloc_usable_size
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// to access an object beyond its original size, this approach should
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// quite general: working for all allocators that properly support
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// malloc_usable_size.
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// Race condition on balance is OK because it can only cause temporary
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// skew in rounding up vs. rounding down, as long as updates are atomic
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// and relative.
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int64_t balance = aggregate_rounding_balance_->load();
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double target_fp_rate = EstimatedFpRate(num_entry, target_len + 5);
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double rv_fp_rate = target_fp_rate;
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if (balance < 0) {
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// See formula for BloomFilterPolicy::aggregate_rounding_balance_
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double for_balance_fp_rate =
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-balance / double{0x100000000} + target_fp_rate;
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// To simplify, we just try a few modified smaller sizes. This also
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// caps how much we vary filter size vs. target, to avoid outlier
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// behavior from excessive variance.
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for (uint64_t maybe_len64 :
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{uint64_t{3} * target_len / 4, uint64_t{13} * target_len / 16,
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uint64_t{7} * target_len / 8, uint64_t{15} * target_len / 16}) {
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uint32_t maybe_len =
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static_cast<uint32_t>(maybe_len64) & ~uint32_t{63};
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double maybe_fp_rate = EstimatedFpRate(num_entry, maybe_len + 5);
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if (maybe_fp_rate <= for_balance_fp_rate) {
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rv = maybe_len + /* metadata */ 5;
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rv_fp_rate = maybe_fp_rate;
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break;
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}
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}
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}
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// Filter blocks are loaded into block cache with their block trailer.
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// We need to make sure that's accounted for in choosing a
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// fragmentation-friendly size.
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const uint32_t kExtraPadding = kBlockTrailerSize;
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size_t requested = rv + kExtraPadding;
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// Allocate and get usable size
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tmpbuf.reset(new char[requested]);
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size_t usable = malloc_usable_size(tmpbuf.get());
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if (usable - usable / 4 > requested) {
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// Ratio greater than 4/3 is too much for utilizing, if it's
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// not a buggy or mislinked malloc_usable_size implementation.
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// Non-linearity of FP rates with bits/key means rapidly
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// diminishing returns in overall accuracy for additional
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// storage on disk.
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// Nothing to do, except assert that the result is accurate about
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// the usable size. (Assignment never used.)
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assert(tmpbuf[usable - 1] = 'x');
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} else if (usable > requested) {
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// Adjust for reasonably larger usable size
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size_t usable_len = (usable - kExtraPadding - /* metadata */ 5);
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if (usable_len >= size_t{0xffffffc0}) {
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// Max supported for this data structure implementation
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usable_len = size_t{0xffffffc0};
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}
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rv = (static_cast<uint32_t>(usable_len) & ~uint32_t{63}) +
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/* metadata */ 5;
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rv_fp_rate = EstimatedFpRate(num_entry, rv);
|
||||
} else {
|
||||
// Too small means bad malloc_usable_size
|
||||
assert(usable == requested);
|
||||
}
|
||||
memset(tmpbuf.get(), 0, rv);
|
||||
|
||||
if (update_balance) {
|
||||
int64_t diff = static_cast<int64_t>((rv_fp_rate - target_fp_rate) *
|
||||
double{0x100000000});
|
||||
*aggregate_rounding_balance_ += diff;
|
||||
}
|
||||
}
|
||||
#else
|
||||
(void)update_balance;
|
||||
#endif // ROCKSDB_MALLOC_USABLE_SIZE
|
||||
if (buf) {
|
||||
if (tmpbuf) {
|
||||
*buf = std::move(tmpbuf);
|
||||
} else {
|
||||
buf->reset(new char[rv]());
|
||||
}
|
||||
}
|
||||
return rv;
|
||||
}
|
||||
|
||||
double EstimatedFpRate(size_t keys, size_t len_with_metadata) override {
|
||||
int num_probes = GetNumProbes(keys, len_with_metadata);
|
||||
return FastLocalBloomImpl::EstimatedFpRate(
|
||||
keys, len_with_metadata - /*metadata*/ 5, num_probes, /*hash bits*/ 64);
|
||||
}
|
||||
|
||||
private:
|
||||
void AddAllEntries(char* data, uint32_t len) {
|
||||
// Compute num_probes after any rounding / adjustments
|
||||
int GetNumProbes(size_t keys, size_t len_with_metadata) {
|
||||
uint64_t millibits = uint64_t{len_with_metadata - 5} * 8000;
|
||||
int actual_millibits_per_key =
|
||||
static_cast<int>(millibits / std::max(keys, size_t{1}));
|
||||
// BEGIN XXX/TODO(peterd): preserving old/default behavior for now to
|
||||
// minimize unit test churn. Remove this some time.
|
||||
if (!aggregate_rounding_balance_) {
|
||||
actual_millibits_per_key = millibits_per_key_;
|
||||
}
|
||||
// END XXX/TODO
|
||||
return FastLocalBloomImpl::ChooseNumProbes(actual_millibits_per_key);
|
||||
}
|
||||
|
||||
void AddAllEntries(char* data, uint32_t len, int num_probes) {
|
||||
// Simple version without prefetching:
|
||||
//
|
||||
// for (auto h : hash_entries_) {
|
||||
// FastLocalBloomImpl::AddHash(Lower32of64(h), Upper32of64(h), len,
|
||||
// num_probes_, data);
|
||||
// num_probes, data);
|
||||
// }
|
||||
|
||||
const size_t num_entries = hash_entries_.size();
|
||||
|
|
@ -129,7 +272,7 @@ class FastLocalBloomBitsBuilder : public BuiltinFilterBitsBuilder {
|
|||
uint32_t& hash_ref = hashes[i & kBufferMask];
|
||||
uint32_t& byte_offset_ref = byte_offsets[i & kBufferMask];
|
||||
// Process (add)
|
||||
FastLocalBloomImpl::AddHashPrepared(hash_ref, num_probes_,
|
||||
FastLocalBloomImpl::AddHashPrepared(hash_ref, num_probes,
|
||||
data + byte_offset_ref);
|
||||
// And buffer
|
||||
uint64_t h = hash_entries_.front();
|
||||
|
|
@ -141,13 +284,16 @@ class FastLocalBloomBitsBuilder : public BuiltinFilterBitsBuilder {
|
|||
|
||||
// Finish processing
|
||||
for (i = 0; i <= kBufferMask && i < num_entries; ++i) {
|
||||
FastLocalBloomImpl::AddHashPrepared(hashes[i], num_probes_,
|
||||
FastLocalBloomImpl::AddHashPrepared(hashes[i], num_probes,
|
||||
data + byte_offsets[i]);
|
||||
}
|
||||
}
|
||||
|
||||
// Target allocation per added key, in thousandths of a bit.
|
||||
int millibits_per_key_;
|
||||
int num_probes_;
|
||||
// See BloomFilterPolicy::aggregate_rounding_balance_. If nullptr,
|
||||
// always "round up" like historic behavior.
|
||||
std::atomic<int64_t>* aggregate_rounding_balance_;
|
||||
// A deque avoids unnecessary copying of already-saved values
|
||||
// and has near-minimal peak memory use.
|
||||
std::deque<uint64_t> hash_entries_;
|
||||
|
|
@ -457,7 +603,7 @@ const std::vector<BloomFilterPolicy::Mode> BloomFilterPolicy::kAllUserModes = {
|
|||
};
|
||||
|
||||
BloomFilterPolicy::BloomFilterPolicy(double bits_per_key, Mode mode)
|
||||
: mode_(mode), warned_(false) {
|
||||
: mode_(mode), warned_(false), aggregate_rounding_balance_(0) {
|
||||
// Sanitize bits_per_key
|
||||
if (bits_per_key < 1.0) {
|
||||
bits_per_key = 1.0;
|
||||
|
|
@ -549,6 +695,7 @@ FilterBitsBuilder* BloomFilterPolicy::GetFilterBitsBuilder() const {
|
|||
FilterBitsBuilder* BloomFilterPolicy::GetBuilderWithContext(
|
||||
const FilterBuildingContext& context) const {
|
||||
Mode cur = mode_;
|
||||
bool offm = context.table_options.optimize_filters_for_memory;
|
||||
// Unusual code construction so that we can have just
|
||||
// one exhaustive switch without (risky) recursion
|
||||
for (int i = 0; i < 2; ++i) {
|
||||
|
|
@ -563,7 +710,8 @@ FilterBitsBuilder* BloomFilterPolicy::GetBuilderWithContext(
|
|||
case kDeprecatedBlock:
|
||||
return nullptr;
|
||||
case kFastLocalBloom:
|
||||
return new FastLocalBloomBitsBuilder(millibits_per_key_);
|
||||
return new FastLocalBloomBitsBuilder(
|
||||
millibits_per_key_, offm ? &aggregate_rounding_balance_ : nullptr);
|
||||
case kLegacyBloom:
|
||||
if (whole_bits_per_key_ >= 14 && context.info_log &&
|
||||
!warned_.load(std::memory_order_relaxed)) {
|
||||
|
|
|
|||
|
|
@ -135,6 +135,16 @@ class BloomFilterPolicy : public FilterPolicy {
|
|||
// only report once per BloomFilterPolicy instance, to keep the noise down.)
|
||||
mutable std::atomic<bool> warned_;
|
||||
|
||||
// State for implementing optimize_filters_for_memory. Essentially, this
|
||||
// tracks a surplus or deficit in total FP rate of filters generated by
|
||||
// builders under this policy vs. what would have been generated without
|
||||
// optimize_filters_for_memory.
|
||||
//
|
||||
// To avoid floating point weirdness, the actual value is
|
||||
// Sum over all generated filters f:
|
||||
// (predicted_fp_rate(f) - predicted_fp_rate(f|o_f_f_m=false)) * 2^32
|
||||
mutable std::atomic<int64_t> aggregate_rounding_balance_;
|
||||
|
||||
// For newer Bloom filter implementation(s)
|
||||
FilterBitsReader* GetBloomBitsReader(const Slice& contents) const;
|
||||
};
|
||||
|
|
|
|||
|
|
@ -467,6 +467,11 @@ DEFINE_bool(partition_index, false, "Partition index blocks");
|
|||
|
||||
DEFINE_bool(index_with_first_key, false, "Include first key in the index");
|
||||
|
||||
DEFINE_bool(
|
||||
optimize_filters_for_memory,
|
||||
ROCKSDB_NAMESPACE::BlockBasedTableOptions().optimize_filters_for_memory,
|
||||
"Minimize memory footprint of filters");
|
||||
|
||||
DEFINE_int64(
|
||||
index_shortening_mode, 2,
|
||||
"mode to shorten index: 0 for no shortening; 1 for only shortening "
|
||||
|
|
@ -3821,6 +3826,8 @@ class Benchmark {
|
|||
default:
|
||||
fprintf(stderr, "Unknown key shortening mode\n");
|
||||
}
|
||||
block_based_options.optimize_filters_for_memory =
|
||||
FLAGS_optimize_filters_for_memory;
|
||||
block_based_options.index_shortening = index_shortening;
|
||||
if (cache_ == nullptr) {
|
||||
block_based_options.no_block_cache = true;
|
||||
|
|
|
|||
|
|
@ -73,6 +73,7 @@ default_params = {
|
|||
"mmap_read": lambda: random.randint(0, 1),
|
||||
"nooverwritepercent": 1,
|
||||
"open_files": lambda : random.choice([-1, -1, 100, 500000]),
|
||||
"optimize_filters_for_memory": lambda: random.randint(0, 1),
|
||||
"partition_filters": lambda: random.randint(0, 1),
|
||||
"pause_background_one_in": 1000000,
|
||||
"prefixpercent": 5,
|
||||
|
|
|
|||
|
|
@ -21,6 +21,7 @@ int main() {
|
|||
|
||||
#include "logging/logging.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"
|
||||
|
|
@ -252,8 +253,10 @@ TEST_F(BlockBasedBloomTest, Schema) {
|
|||
// Different bits-per-byte
|
||||
|
||||
class FullBloomTest : public testing::TestWithParam<BloomFilterPolicy::Mode> {
|
||||
private:
|
||||
protected:
|
||||
BlockBasedTableOptions table_options_;
|
||||
|
||||
private:
|
||||
std::shared_ptr<const FilterPolicy>& policy_;
|
||||
std::unique_ptr<FilterBitsBuilder> bits_builder_;
|
||||
std::unique_ptr<FilterBitsReader> bits_reader_;
|
||||
|
|
@ -499,6 +502,77 @@ TEST_P(FullBloomTest, FullVaryingLengths) {
|
|||
ASSERT_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<int>(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<char*>(FilterData().data()));
|
||||
#endif // ROCKSDB_MALLOC_USABLE_SIZE
|
||||
total_mem += size;
|
||||
total_keys += nkeys;
|
||||
total_fp_rate += FalsePositiveRate();
|
||||
}
|
||||
EXPECT_LE(total_fp_rate / double{nfilters}, 0.011);
|
||||
EXPECT_GE(total_fp_rate / double{nfilters}, 0.008);
|
||||
|
||||
int64_t ex_min_total_size = int64_t{FLAGS_bits_per_key} * total_keys / 8;
|
||||
EXPECT_GE(static_cast<int64_t>(total_size), ex_min_total_size);
|
||||
|
||||
int64_t blocked_bloom_overhead = nfilters * (CACHE_LINE_SIZE + 5);
|
||||
if (GetParam() == BloomFilterPolicy::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() != BloomFilterPolicy::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<int64_t>(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()) {
|
||||
// More than 5% internal fragmentation
|
||||
EXPECT_GE(total_mem, total_size * 105 / 100);
|
||||
}
|
||||
#endif // ROCKSDB_JEMALLOC
|
||||
// No storage penalty, just usual overhead
|
||||
EXPECT_LE(static_cast<int64_t>(total_size),
|
||||
ex_min_total_size + blocked_bloom_overhead);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
namespace {
|
||||
inline uint32_t SelectByCacheLineSize(uint32_t for64, uint32_t for128,
|
||||
uint32_t for256) {
|
||||
|
|
|
|||
|
|
@ -88,6 +88,9 @@ DEFINE_bool(net_includes_hashing, false,
|
|||
"(if not, dry run will include hashing) "
|
||||
"(build times always include hashing)");
|
||||
|
||||
DEFINE_bool(optimize_filters_for_memory, false,
|
||||
"Setting for BlockBasedTableOptions::optimize_filters_for_memory");
|
||||
|
||||
DEFINE_bool(quick, false, "Run more limited set of tests, fewer queries");
|
||||
|
||||
DEFINE_bool(best_case, false, "Run limited tests only for best-case");
|
||||
|
|
@ -278,6 +281,8 @@ struct FilterBench : public MockBlockBasedTableTester {
|
|||
kms_.emplace_back(FLAGS_key_size < 8 ? 8 : FLAGS_key_size);
|
||||
}
|
||||
ioptions_.info_log = &stderr_logger_;
|
||||
table_options_.optimize_filters_for_memory =
|
||||
FLAGS_optimize_filters_for_memory;
|
||||
}
|
||||
|
||||
void Go();
|
||||
|
|
@ -337,6 +342,7 @@ void FilterBench::Go() {
|
|||
std::unique_ptr<BuiltinFilterBitsBuilder> builder;
|
||||
|
||||
size_t total_memory_used = 0;
|
||||
size_t total_size = 0;
|
||||
size_t total_keys_added = 0;
|
||||
#ifdef PREDICT_FP_RATE
|
||||
double weighted_predicted_fp_rate = 0.0;
|
||||
|
|
@ -355,7 +361,7 @@ void FilterBench::Go() {
|
|||
true);
|
||||
|
||||
infos_.clear();
|
||||
while ((working_mem_size_mb == 0 || total_memory_used < max_mem) &&
|
||||
while ((working_mem_size_mb == 0 || total_size < max_mem) &&
|
||||
total_keys_added < max_total_keys) {
|
||||
uint32_t filter_id = random_.Next();
|
||||
uint32_t keys_to_add = FLAGS_average_keys_per_filter +
|
||||
|
|
@ -405,7 +411,11 @@ void FilterBench::Go() {
|
|||
info.full_block_reader_.reset(
|
||||
new FullFilterBlockReader(table_.get(), std::move(block)));
|
||||
}
|
||||
total_memory_used += info.filter_.size();
|
||||
total_size += info.filter_.size();
|
||||
#ifdef ROCKSDB_MALLOC_USABLE_SIZE
|
||||
total_memory_used +=
|
||||
malloc_usable_size(const_cast<char *>(info.filter_.data()));
|
||||
#endif // ROCKSDB_MALLOC_USABLE_SIZE
|
||||
total_keys_added += keys_to_add;
|
||||
}
|
||||
|
||||
|
|
@ -413,11 +423,17 @@ void FilterBench::Go() {
|
|||
double ns = double(elapsed_nanos) / total_keys_added;
|
||||
std::cout << "Build avg ns/key: " << ns << std::endl;
|
||||
std::cout << "Number of filters: " << infos_.size() << std::endl;
|
||||
std::cout << "Total memory (MB): " << total_memory_used / 1024.0 / 1024.0
|
||||
std::cout << "Total size (MB): " << total_size / 1024.0 / 1024.0 << std::endl;
|
||||
if (total_memory_used > 0) {
|
||||
std::cout << "Reported total allocated memory (MB): "
|
||||
<< total_memory_used / 1024.0 / 1024.0 << std::endl;
|
||||
std::cout << "Reported internal fragmentation: "
|
||||
<< (total_memory_used - total_size) * 100.0 / total_size << "%"
|
||||
<< std::endl;
|
||||
}
|
||||
|
||||
double bpk = total_memory_used * 8.0 / total_keys_added;
|
||||
std::cout << "Bits/key actual: " << bpk << std::endl;
|
||||
double bpk = total_size * 8.0 / total_keys_added;
|
||||
std::cout << "Bits/key stored: " << bpk << std::endl;
|
||||
#ifdef PREDICT_FP_RATE
|
||||
std::cout << "Predicted FP rate %: "
|
||||
<< 100.0 * (weighted_predicted_fp_rate / total_keys_added)
|
||||
|
|
|
|||
Loading…
Reference in New Issue