Support optimize_filters_for_memory for Ribbon filter (#7774)
Summary: Primarily this change refactors the optimize_filters_for_memory code for Bloom filters, based on malloc_usable_size, to also work for Ribbon filters. This change also replaces the somewhat slow but general BuiltinFilterBitsBuilder::ApproximateNumEntries with implementation-specific versions for Ribbon (new) and Legacy Bloom (based on a recently deleted version). The reason is to emphasize speed in ApproximateNumEntries rather than 100% accuracy. Justification: ApproximateNumEntries (formerly CalculateNumEntry) is only used by RocksDB for range-partitioned filters, called each time we start to construct one. (In theory, it should be possible to reuse the estimate, but the abstractions provided by FilterPolicy don't really make that workable.) But this is only used as a heuristic estimate for hitting a desired partitioned filter size because of alignment to data blocks, which have various numbers of unique keys or prefixes. The two factors lead us to prioritize reasonable speed over 100% accuracy. optimize_filters_for_memory adds extra complication, because precisely calculating num_entries for some allowed number of bytes depends on state with optimize_filters_for_memory enabled. And the allocator-agnostic implementation of optimize_filters_for_memory, using malloc_usable_size, means we would have to actually allocate memory, many times, just to precisely determine how many entries (keys) could be added and stay below some size budget, for the current state. (In a draft, I got this working, and then realized the balance of speed vs. accuracy was all wrong.) So related to that, I have made CalculateSpace, an internal-only API only used for testing, non-authoritative also if optimize_filters_for_memory is enabled. This simplifies some code. Pull Request resolved: https://github.com/facebook/rocksdb/pull/7774 Test Plan: unit test updated, and for FilterSize test, range of tested values is greatly expanded (still super fast) Also tested `db_bench -benchmarks=fillrandom,stats -bloom_bits=10 -num=1000000 -partition_index_and_filters -format_version=5 [-optimize_filters_for_memory] [-use_ribbon_filter]` with temporary debug output of generated filter sizes. Bloom+optimize_filters_for_memory: 1 Filter size: 197 (224 in memory) 134 Filter size: 3525 (3584 in memory) 107 Filter size: 4037 (4096 in memory) Total on disk: 904,506 Total in memory: 918,752 Ribbon+optimize_filters_for_memory: 1 Filter size: 3061 (3072 in memory) 110 Filter size: 3573 (3584 in memory) 58 Filter size: 4085 (4096 in memory) Total on disk: 633,021 (-30.0%) Total in memory: 634,880 (-30.9%) Bloom (no offm): 1 Filter size: 261 (320 in memory) 1 Filter size: 3333 (3584 in memory) 240 Filter size: 3717 (4096 in memory) Total on disk: 895,674 (-1% on disk vs. +offm; known tolerable overhead of offm) Total in memory: 986,944 (+7.4% vs. +offm) Ribbon (no offm): 1 Filter size: 2949 (3072 in memory) 1 Filter size: 3381 (3584 in memory) 167 Filter size: 3701 (4096 in memory) Total on disk: 624,397 (-30.3% vs. Bloom) Total in memory: 690,688 (-30.0% vs. Bloom) Note that optimize_filters_for_memory is even more effective for Ribbon filter than for cache-local Bloom, because it can close the unused memory gap even tighter than Bloom filter, because of 16 byte increments for Ribbon vs. 64 byte increments for Bloom. Reviewed By: jay-zhuang Differential Revision: D25592970 Pulled By: pdillinger fbshipit-source-id: 606fdaa025bb790d7e9c21601e8ea86e10541912
This commit is contained in:
Peter Dillinger
Facebook GitHub Bot
parent
04b3524ad0
commit
239d17a19c
@ -15,6 +15,7 @@
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### New Features
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* User defined timestamp feature supports `CompactRange` and `GetApproximateSizes`.
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* Experimental option BlockBasedTableOptions::optimize_filters_for_memory now works with experimental Ribbon filter (as well as Bloom filter).
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### Public API Change
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* Deprecated public but rarely-used FilterBitsBuilder::CalculateNumEntry, which is replaced with ApproximateNumEntries taking a size_t parameter and returning size_t.
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@ -11,6 +11,7 @@
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#include <array>
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#include <deque>
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#include <limits>
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#include "rocksdb/slice.h"
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#include "table/block_based/block_based_filter_block.h"
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@ -24,27 +25,17 @@
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namespace ROCKSDB_NAMESPACE {
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size_t BuiltinFilterBitsBuilder::ApproximateNumEntries(size_t bytes) {
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size_t cur = 1;
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// Find overestimate
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while (CalculateSpace(cur) <= bytes && cur * 2 > cur) {
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cur *= 2;
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}
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// Change to underestimate less than factor of two from answer
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cur /= 2;
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// Binary search
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size_t delta = cur / 2;
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while (delta > 0) {
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if (CalculateSpace(cur + delta) <= bytes) {
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cur += delta;
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}
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delta /= 2;
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}
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return cur;
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}
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namespace {
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// Metadata trailer size for built-in filters. (This is separate from
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// block-based table block trailer.)
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//
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// Originally this was 1 byte for num_probes and 4 bytes for number of
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// cache lines in the Bloom filter, but now the first trailer byte is
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// usually an implementation marker and remaining 4 bytes have various
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// meanings.
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static constexpr uint32_t kMetadataLen = 5;
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Slice FinishAlwaysFalse(std::unique_ptr<const char[]>* /*buf*/) {
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// Missing metadata, treated as zero entries
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return Slice(nullptr, 0);
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@ -54,6 +45,10 @@ Slice FinishAlwaysFalse(std::unique_ptr<const char[]>* /*buf*/) {
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// also known as Hash64 or GetSliceHash64.
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class XXH3pFilterBitsBuilder : public BuiltinFilterBitsBuilder {
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public:
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explicit XXH3pFilterBitsBuilder(
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std::atomic<int64_t>* aggregate_rounding_balance)
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: aggregate_rounding_balance_(aggregate_rounding_balance) {}
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~XXH3pFilterBitsBuilder() override {}
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virtual void AddKey(const Slice& key) override {
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@ -73,9 +68,113 @@ class XXH3pFilterBitsBuilder : public BuiltinFilterBitsBuilder {
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std::swap(hash_entries_, other->hash_entries_);
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}
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virtual size_t RoundDownUsableSpace(size_t available_size) = 0;
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// To choose size using malloc_usable_size, we have to actually allocate.
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size_t AllocateMaybeRounding(size_t target_len_with_metadata,
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size_t num_entries,
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std::unique_ptr<char[]>* buf) {
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// Return value set to a default; overwritten in some cases
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size_t rv = target_len_with_metadata;
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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 be
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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 =
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EstimatedFpRate(num_entries, target_len_with_metadata);
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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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size_t target_len = target_len_with_metadata - kMetadataLen;
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assert(target_len < target_len_with_metadata); // check underflow
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for (uint64_t maybe_len_rough :
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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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size_t maybe_len_with_metadata =
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RoundDownUsableSpace(maybe_len_rough + kMetadataLen);
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double maybe_fp_rate =
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EstimatedFpRate(num_entries, maybe_len_with_metadata);
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if (maybe_fp_rate <= for_balance_fp_rate) {
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rv = maybe_len_with_metadata;
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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 size_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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buf->reset(new char[requested]);
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size_t usable = malloc_usable_size(buf->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(((*buf)[usable - 1] = 'x'));
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} else if (usable > requested) {
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rv = RoundDownUsableSpace(usable - kExtraPadding);
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assert(rv <= usable - kExtraPadding);
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rv_fp_rate = EstimatedFpRate(num_entries, rv);
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} else {
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// Too small means bad malloc_usable_size
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assert(usable == requested);
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}
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memset(buf->get(), 0, rv);
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// Update balance
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int64_t diff = static_cast<int64_t>((rv_fp_rate - target_fp_rate) *
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double{0x100000000});
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*aggregate_rounding_balance_ += diff;
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} else {
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buf->reset(new char[rv]());
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}
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#else
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(void)num_entries;
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buf->reset(new char[rv]());
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#endif // ROCKSDB_MALLOC_USABLE_SIZE
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return rv;
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}
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// A deque avoids unnecessary copying of already-saved values
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// and has near-minimal peak memory use.
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std::deque<uint64_t> hash_entries_;
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// See BloomFilterPolicy::aggregate_rounding_balance_. If nullptr,
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// always "round up" like historic behavior.
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std::atomic<int64_t>* aggregate_rounding_balance_;
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};
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// #################### FastLocalBloom implementation ################## //
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@ -88,8 +187,8 @@ class FastLocalBloomBitsBuilder : public XXH3pFilterBitsBuilder {
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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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aggregate_rounding_balance_(aggregate_rounding_balance) {
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: XXH3pFilterBitsBuilder(aggregate_rounding_balance),
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millibits_per_key_(millibits_per_key) {
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assert(millibits_per_key >= 1000);
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}
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@ -101,12 +200,14 @@ class FastLocalBloomBitsBuilder : public XXH3pFilterBitsBuilder {
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virtual Slice Finish(std::unique_ptr<const char[]>* buf) override {
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size_t num_entries = hash_entries_.size();
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size_t len_with_metadata = CalculateSpace(num_entries);
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std::unique_ptr<char[]> mutable_buf;
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size_t len_with_metadata = CalculateAndAllocate(num_entries, &mutable_buf,
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/*update_balance*/ true);
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len_with_metadata =
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AllocateMaybeRounding(len_with_metadata, num_entries, &mutable_buf);
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assert(mutable_buf);
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assert(len_with_metadata >= 5);
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assert(len_with_metadata >= kMetadataLen);
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// Max size supported by implementation
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assert(len_with_metadata <= 0xffffffffU);
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@ -114,7 +215,7 @@ class FastLocalBloomBitsBuilder : public XXH3pFilterBitsBuilder {
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// Compute num_probes after any rounding / adjustments
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int num_probes = GetNumProbes(num_entries, len_with_metadata);
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uint32_t len = static_cast<uint32_t>(len_with_metadata - 5);
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uint32_t len = static_cast<uint32_t>(len_with_metadata - kMetadataLen);
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if (len > 0) {
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AddAllEntries(mutable_buf.get(), len, num_probes);
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}
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@ -136,22 +237,13 @@ class FastLocalBloomBitsBuilder : public XXH3pFilterBitsBuilder {
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}
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size_t ApproximateNumEntries(size_t bytes) override {
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size_t bytes_no_meta = bytes >= 5u ? bytes - 5u : 0;
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size_t bytes_no_meta =
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bytes >= kMetadataLen ? RoundDownUsableSpace(bytes) - kMetadataLen : 0;
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return static_cast<size_t>(uint64_t{8000} * bytes_no_meta /
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millibits_per_key_);
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}
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size_t CalculateSpace(size_t num_entries) override {
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return CalculateAndAllocate(num_entries,
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/* buf */ nullptr,
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/*update_balance*/ false);
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}
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// To choose size using malloc_usable_size, we have to actually allocate.
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size_t CalculateAndAllocate(size_t num_entries, 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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@ -165,119 +257,34 @@ class FastLocalBloomBitsBuilder : public XXH3pFilterBitsBuilder {
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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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size_t target_len = (raw_target_len + 63) & ~size_t{63};
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// Return value set to a default; overwritten in some cases
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size_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_entries, 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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size_t maybe_len = maybe_len64 & ~size_t{63};
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double maybe_fp_rate = EstimatedFpRate(num_entries, 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 size_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 = (usable_len & ~size_t{63}) +
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/* metadata */ 5;
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rv_fp_rate = EstimatedFpRate(num_entries, rv);
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} else {
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// Too small means bad malloc_usable_size
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assert(usable == requested);
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}
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memset(tmpbuf.get(), 0, rv);
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if (update_balance) {
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int64_t diff = static_cast<int64_t>((rv_fp_rate - target_fp_rate) *
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double{0x100000000});
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*aggregate_rounding_balance_ += diff;
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}
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}
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#else
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(void)update_balance;
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#endif // ROCKSDB_MALLOC_USABLE_SIZE
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if (buf) {
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if (tmpbuf) {
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*buf = std::move(tmpbuf);
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} else {
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buf->reset(new char[rv]());
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}
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}
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return rv;
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return ((raw_target_len + 63) & ~size_t{63}) + kMetadataLen;
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}
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double EstimatedFpRate(size_t keys, size_t len_with_metadata) override {
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int num_probes = GetNumProbes(keys, len_with_metadata);
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return FastLocalBloomImpl::EstimatedFpRate(
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keys, len_with_metadata - /*metadata*/ 5, num_probes, /*hash bits*/ 64);
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keys, len_with_metadata - kMetadataLen, num_probes, /*hash bits*/ 64);
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}
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protected:
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size_t RoundDownUsableSpace(size_t available_size) override {
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size_t rv = available_size - kMetadataLen;
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if (rv >= size_t{0xffffffc0}) {
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// Max supported for this data structure implementation
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rv = size_t{0xffffffc0};
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}
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// round down to multiple of 64 (block size)
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rv &= ~size_t{63};
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return rv + kMetadataLen;
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}
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private:
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// Compute num_probes after any rounding / adjustments
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int GetNumProbes(size_t keys, size_t len_with_metadata) {
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uint64_t millibits = uint64_t{len_with_metadata - 5} * 8000;
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uint64_t millibits = uint64_t{len_with_metadata - kMetadataLen} * 8000;
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int actual_millibits_per_key =
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static_cast<int>(millibits / std::max(keys, size_t{1}));
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// BEGIN XXX/TODO(peterd): preserving old/default behavior for now to
|
||||
@ -339,9 +346,6 @@ class FastLocalBloomBitsBuilder : public XXH3pFilterBitsBuilder {
|
||||
|
||||
// Target allocation per added key, in thousandths of a bit.
|
||||
int millibits_per_key_;
|
||||
// See BloomFilterPolicy::aggregate_rounding_balance_. If nullptr,
|
||||
// always "round up" like historic behavior.
|
||||
std::atomic<int64_t>* aggregate_rounding_balance_;
|
||||
};
|
||||
|
||||
// See description in FastLocalBloomImpl
|
||||
@ -411,12 +415,13 @@ using Standard128RibbonTypesAndSettings =
|
||||
|
||||
class Standard128RibbonBitsBuilder : public XXH3pFilterBitsBuilder {
|
||||
public:
|
||||
explicit Standard128RibbonBitsBuilder(double desired_one_in_fp_rate,
|
||||
int bloom_millibits_per_key,
|
||||
Logger* info_log)
|
||||
: desired_one_in_fp_rate_(desired_one_in_fp_rate),
|
||||
explicit Standard128RibbonBitsBuilder(
|
||||
double desired_one_in_fp_rate, int bloom_millibits_per_key,
|
||||
std::atomic<int64_t>* aggregate_rounding_balance, Logger* info_log)
|
||||
: XXH3pFilterBitsBuilder(aggregate_rounding_balance),
|
||||
desired_one_in_fp_rate_(desired_one_in_fp_rate),
|
||||
info_log_(info_log),
|
||||
bloom_fallback_(bloom_millibits_per_key, nullptr) {
|
||||
bloom_fallback_(bloom_millibits_per_key, aggregate_rounding_balance) {
|
||||
assert(desired_one_in_fp_rate >= 1.0);
|
||||
}
|
||||
|
||||
@ -440,25 +445,23 @@ class Standard128RibbonBitsBuilder : public XXH3pFilterBitsBuilder {
|
||||
return FinishAlwaysFalse(buf);
|
||||
}
|
||||
uint32_t num_entries = static_cast<uint32_t>(hash_entries_.size());
|
||||
uint32_t num_slots = BandingType::GetNumSlotsFor95PctSuccess(num_entries);
|
||||
num_slots = SolnType::RoundUpNumSlots(num_slots);
|
||||
uint32_t num_slots;
|
||||
size_t len_with_metadata;
|
||||
|
||||
uint32_t entropy = 0;
|
||||
if (num_entries > 0) {
|
||||
entropy = Lower32of64(hash_entries_.front());
|
||||
}
|
||||
size_t len = SolnType::GetBytesForOneInFpRate(
|
||||
num_slots, desired_one_in_fp_rate_, /*rounding*/ entropy);
|
||||
size_t len_with_metadata = len + 5;
|
||||
CalculateSpaceAndSlots(num_entries, &len_with_metadata, &num_slots);
|
||||
|
||||
// Use Bloom filter when it's better for small filters
|
||||
if (num_slots < 1024 && bloom_fallback_.CalculateSpace(static_cast<int>(
|
||||
num_entries)) < len_with_metadata) {
|
||||
// Bloom fall-back indicator
|
||||
if (num_slots == 0) {
|
||||
SwapEntriesWith(&bloom_fallback_);
|
||||
assert(hash_entries_.empty());
|
||||
return bloom_fallback_.Finish(buf);
|
||||
}
|
||||
|
||||
uint32_t entropy = 0;
|
||||
if (!hash_entries_.empty()) {
|
||||
entropy = Lower32of64(hash_entries_.front());
|
||||
}
|
||||
|
||||
BandingType banding;
|
||||
bool success = banding.ResetAndFindSeedToSolve(
|
||||
num_slots, hash_entries_.begin(), hash_entries_.end(),
|
||||
@ -477,7 +480,9 @@ class Standard128RibbonBitsBuilder : public XXH3pFilterBitsBuilder {
|
||||
uint32_t seed = banding.GetOrdinalSeed();
|
||||
assert(seed < 256);
|
||||
|
||||
std::unique_ptr<char[]> mutable_buf(new char[len_with_metadata]);
|
||||
std::unique_ptr<char[]> mutable_buf;
|
||||
len_with_metadata =
|
||||
AllocateMaybeRounding(len_with_metadata, num_entries, &mutable_buf);
|
||||
|
||||
SolnType soln(mutable_buf.get(), len_with_metadata);
|
||||
soln.BackSubstFrom(banding);
|
||||
@ -492,37 +497,152 @@ class Standard128RibbonBitsBuilder : public XXH3pFilterBitsBuilder {
|
||||
|
||||
// See BloomFilterPolicy::GetBloomBitsReader re: metadata
|
||||
// -2 = Marker for Standard128 Ribbon
|
||||
mutable_buf[len] = static_cast<char>(-2);
|
||||
mutable_buf[len_with_metadata - 5] = static_cast<char>(-2);
|
||||
// Hash seed
|
||||
mutable_buf[len + 1] = static_cast<char>(seed);
|
||||
mutable_buf[len_with_metadata - 4] = static_cast<char>(seed);
|
||||
// Number of blocks, in 24 bits
|
||||
// (Along with bytes, we can derive other settings)
|
||||
mutable_buf[len + 2] = static_cast<char>(num_blocks & 255);
|
||||
mutable_buf[len + 3] = static_cast<char>((num_blocks >> 8) & 255);
|
||||
mutable_buf[len + 4] = static_cast<char>((num_blocks >> 16) & 255);
|
||||
mutable_buf[len_with_metadata - 3] = static_cast<char>(num_blocks & 255);
|
||||
mutable_buf[len_with_metadata - 2] =
|
||||
static_cast<char>((num_blocks >> 8) & 255);
|
||||
mutable_buf[len_with_metadata - 1] =
|
||||
static_cast<char>((num_blocks >> 16) & 255);
|
||||
|
||||
Slice rv(mutable_buf.get(), len_with_metadata);
|
||||
*buf = std::move(mutable_buf);
|
||||
return rv;
|
||||
}
|
||||
|
||||
size_t CalculateSpace(size_t num_entries) override {
|
||||
// Setting num_slots to 0 means "fall back on Bloom filter."
|
||||
// And note this implementation does not support num_entries or num_slots
|
||||
// beyond uint32_t; see kMaxRibbonEntries.
|
||||
void CalculateSpaceAndSlots(size_t num_entries,
|
||||
size_t* target_len_with_metadata,
|
||||
uint32_t* num_slots) {
|
||||
if (num_entries > kMaxRibbonEntries) {
|
||||
// More entries than supported by this Ribbon
|
||||
return bloom_fallback_.CalculateSpace(num_entries);
|
||||
*num_slots = 0; // use Bloom
|
||||
*target_len_with_metadata = bloom_fallback_.CalculateSpace(num_entries);
|
||||
return;
|
||||
}
|
||||
uint32_t num_slots =
|
||||
NumEntriesToNumSlots(static_cast<uint32_t>(num_entries));
|
||||
size_t ribbon =
|
||||
SolnType::GetBytesForOneInFpRate(num_slots, desired_one_in_fp_rate_,
|
||||
/*rounding*/ 0) +
|
||||
/*metadata*/ 5;
|
||||
uint32_t entropy = 0;
|
||||
if (!hash_entries_.empty()) {
|
||||
entropy = Upper32of64(hash_entries_.front());
|
||||
}
|
||||
|
||||
*num_slots = NumEntriesToNumSlots(static_cast<uint32_t>(num_entries));
|
||||
*target_len_with_metadata =
|
||||
SolnType::GetBytesForOneInFpRate(*num_slots, desired_one_in_fp_rate_,
|
||||
/*rounding*/ entropy) +
|
||||
kMetadataLen;
|
||||
|
||||
// Consider possible Bloom fallback for small filters
|
||||
if (num_slots < 1024) {
|
||||
if (*num_slots < 1024) {
|
||||
size_t bloom = bloom_fallback_.CalculateSpace(num_entries);
|
||||
return std::min(bloom, ribbon);
|
||||
if (bloom < *target_len_with_metadata) {
|
||||
*num_slots = 0; // use Bloom
|
||||
*target_len_with_metadata = bloom;
|
||||
return;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
size_t CalculateSpace(size_t num_entries) override {
|
||||
if (num_entries == 0) {
|
||||
// See FinishAlwaysFalse
|
||||
return 0;
|
||||
}
|
||||
size_t target_len_with_metadata;
|
||||
uint32_t num_slots;
|
||||
CalculateSpaceAndSlots(num_entries, &target_len_with_metadata, &num_slots);
|
||||
(void)num_slots;
|
||||
return target_len_with_metadata;
|
||||
}
|
||||
|
||||
// This is a somewhat ugly but reasonably fast and reasonably accurate
|
||||
// reversal of CalculateSpace.
|
||||
size_t ApproximateNumEntries(size_t bytes) override {
|
||||
size_t len_no_metadata =
|
||||
RoundDownUsableSpace(std::max(bytes, size_t{kMetadataLen})) -
|
||||
kMetadataLen;
|
||||
|
||||
if (!(desired_one_in_fp_rate_ > 1.0)) {
|
||||
// Effectively asking for 100% FP rate, or NaN etc.
|
||||
// Note that NaN is neither < 1.0 nor > 1.0
|
||||
return kMaxRibbonEntries;
|
||||
}
|
||||
|
||||
// Find a slight under-estimate for actual average bits per slot
|
||||
double min_real_bits_per_slot;
|
||||
if (desired_one_in_fp_rate_ >= 1.0 + std::numeric_limits<uint32_t>::max()) {
|
||||
// Max of 32 solution columns (result bits)
|
||||
min_real_bits_per_slot = 32.0;
|
||||
} else {
|
||||
return ribbon;
|
||||
// Account for mix of b and b+1 solution columns being slightly
|
||||
// suboptimal vs. ideal log2(1/fp_rate) bits.
|
||||
uint32_t rounded = static_cast<uint32_t>(desired_one_in_fp_rate_);
|
||||
int upper_bits_per_key = 1 + FloorLog2(rounded);
|
||||
double fp_rate_for_upper = std::pow(2.0, -upper_bits_per_key);
|
||||
double portion_lower =
|
||||
(1.0 / desired_one_in_fp_rate_ - fp_rate_for_upper) /
|
||||
fp_rate_for_upper;
|
||||
min_real_bits_per_slot = upper_bits_per_key - portion_lower;
|
||||
assert(min_real_bits_per_slot > 0.0);
|
||||
assert(min_real_bits_per_slot <= 32.0);
|
||||
}
|
||||
|
||||
// An overestimate, but this should only be O(1) slots away from truth.
|
||||
double max_slots = len_no_metadata * 8.0 / min_real_bits_per_slot;
|
||||
|
||||
// Let's not bother accounting for overflow to Bloom filter
|
||||
// (Includes NaN case)
|
||||
if (!(max_slots <
|
||||
BandingType::GetNumSlotsFor95PctSuccess(kMaxRibbonEntries))) {
|
||||
return kMaxRibbonEntries;
|
||||
}
|
||||
|
||||
// Set up for short iteration
|
||||
uint32_t slots = static_cast<uint32_t>(max_slots);
|
||||
slots = SolnType::RoundUpNumSlots(slots);
|
||||
|
||||
// Assert that we have a valid upper bound on slots
|
||||
assert(SolnType::GetBytesForOneInFpRate(
|
||||
SolnType::RoundUpNumSlots(slots + 1), desired_one_in_fp_rate_,
|
||||
/*rounding*/ 0) > len_no_metadata);
|
||||
|
||||
// Iterate up to a few times to rather precisely account for small effects
|
||||
for (int i = 0; slots > 0; ++i) {
|
||||
size_t reqd_bytes =
|
||||
SolnType::GetBytesForOneInFpRate(slots, desired_one_in_fp_rate_,
|
||||
/*rounding*/ 0);
|
||||
if (reqd_bytes <= len_no_metadata) {
|
||||
break; // done
|
||||
}
|
||||
if (i >= 2) {
|
||||
// should have been enough iterations
|
||||
assert(false);
|
||||
break;
|
||||
}
|
||||
slots = SolnType::RoundDownNumSlots(slots - 1);
|
||||
}
|
||||
|
||||
// Using slots instead of entries to get overhead factor estimate
|
||||
double f = BandingType::GetFactorFor95PctSuccess(slots);
|
||||
uint32_t num_entries = static_cast<uint32_t>(slots / f);
|
||||
// Improve precision with another round
|
||||
f = BandingType::GetFactorFor95PctSuccess(num_entries);
|
||||
num_entries = static_cast<uint32_t>(slots / f + 0.999999999);
|
||||
|
||||
// Consider possible Bloom fallback for small filters
|
||||
if (slots < 1024) {
|
||||
size_t bloom = bloom_fallback_.ApproximateNumEntries(bytes);
|
||||
if (bloom > num_entries) {
|
||||
return bloom;
|
||||
} else {
|
||||
return num_entries;
|
||||
}
|
||||
} else {
|
||||
return std::min(num_entries, kMaxRibbonEntries);
|
||||
}
|
||||
}
|
||||
|
||||
@ -539,6 +659,16 @@ class Standard128RibbonBitsBuilder : public XXH3pFilterBitsBuilder {
|
||||
return fake_soln.ExpectedFpRate();
|
||||
}
|
||||
|
||||
protected:
|
||||
size_t RoundDownUsableSpace(size_t available_size) override {
|
||||
size_t rv = available_size - kMetadataLen;
|
||||
|
||||
// round down to multiple of 16 (segment size)
|
||||
rv &= ~size_t{15};
|
||||
|
||||
return rv + kMetadataLen;
|
||||
}
|
||||
|
||||
private:
|
||||
using TS = Standard128RibbonTypesAndSettings;
|
||||
using SolnType = ribbon::SerializableInterleavedSolution<TS>;
|
||||
@ -556,7 +686,7 @@ class Standard128RibbonBitsBuilder : public XXH3pFilterBitsBuilder {
|
||||
// within an order of magnitude of implementation limit on num_slots
|
||||
// fitting in 32 bits, and even closer for num_blocks fitting in 24 bits
|
||||
// (for filter metadata).
|
||||
static constexpr size_t kMaxRibbonEntries = 950000000; // ~ 1 billion
|
||||
static constexpr uint32_t kMaxRibbonEntries = 950000000; // ~ 1 billion
|
||||
|
||||
// A desired value for 1/fp_rate. For example, 100 -> 1% fp rate.
|
||||
double desired_one_in_fp_rate_;
|
||||
@ -569,6 +699,9 @@ class Standard128RibbonBitsBuilder : public XXH3pFilterBitsBuilder {
|
||||
FastLocalBloomBitsBuilder bloom_fallback_;
|
||||
};
|
||||
|
||||
// for the linker, at least with DEBUG_LEVEL=2
|
||||
constexpr uint32_t Standard128RibbonBitsBuilder::kMaxRibbonEntries;
|
||||
|
||||
class Standard128RibbonBitsReader : public FilterBitsReader {
|
||||
public:
|
||||
Standard128RibbonBitsReader(const char* data, size_t len_bytes,
|
||||
@ -631,10 +764,12 @@ class LegacyBloomBitsBuilder : public BuiltinFilterBitsBuilder {
|
||||
}
|
||||
|
||||
double EstimatedFpRate(size_t keys, size_t bytes) override {
|
||||
return LegacyBloomImpl::EstimatedFpRate(keys, bytes - /*metadata*/ 5,
|
||||
return LegacyBloomImpl::EstimatedFpRate(keys, bytes - kMetadataLen,
|
||||
num_probes_);
|
||||
}
|
||||
|
||||
size_t ApproximateNumEntries(size_t bytes) override;
|
||||
|
||||
private:
|
||||
int bits_per_key_;
|
||||
int num_probes_;
|
||||
@ -717,7 +852,29 @@ Slice LegacyBloomBitsBuilder::Finish(std::unique_ptr<const char[]>* buf) {
|
||||
buf->reset(const_data);
|
||||
hash_entries_.clear();
|
||||
|
||||
return Slice(data, total_bits / 8 + 5);
|
||||
return Slice(data, total_bits / 8 + kMetadataLen);
|
||||
}
|
||||
|
||||
size_t LegacyBloomBitsBuilder::ApproximateNumEntries(size_t bytes) {
|
||||
assert(bits_per_key_);
|
||||
assert(bytes > 0);
|
||||
|
||||
uint64_t total_bits_tmp = bytes * 8;
|
||||
// total bits, including temporary computations, cannot exceed 2^32
|
||||
// for compatibility
|
||||
total_bits_tmp = std::min(total_bits_tmp, uint64_t{0xffff0000});
|
||||
|
||||
uint32_t high = static_cast<uint32_t>(total_bits_tmp) /
|
||||
static_cast<uint32_t>(bits_per_key_) +
|
||||
1;
|
||||
uint32_t low = 1;
|
||||
uint32_t n = high;
|
||||
for (; n >= low; n--) {
|
||||
if (CalculateSpace(n) <= bytes) {
|
||||
break;
|
||||
}
|
||||
}
|
||||
return n;
|
||||
}
|
||||
|
||||
uint32_t LegacyBloomBitsBuilder::GetTotalBitsForLocality(uint32_t total_bits) {
|
||||
@ -754,7 +911,7 @@ uint32_t LegacyBloomBitsBuilder::CalculateSpace(size_t num_entries,
|
||||
|
||||
// Reserve space for Filter
|
||||
uint32_t sz = *total_bits / 8;
|
||||
sz += 5; // 4 bytes for num_lines, 1 byte for num_probes
|
||||
sz += kMetadataLen; // 4 bytes for num_lines, 1 byte for num_probes
|
||||
return sz;
|
||||
}
|
||||
|
||||
@ -1000,7 +1157,8 @@ FilterBitsBuilder* BloomFilterPolicy::GetBuilderWithContext(
|
||||
context.info_log);
|
||||
case kStandard128Ribbon:
|
||||
return new Standard128RibbonBitsBuilder(
|
||||
desired_one_in_fp_rate_, millibits_per_key_, context.info_log);
|
||||
desired_one_in_fp_rate_, millibits_per_key_,
|
||||
offm ? &aggregate_rounding_balance_ : nullptr, context.info_log);
|
||||
}
|
||||
}
|
||||
assert(false);
|
||||
@ -1021,7 +1179,7 @@ FilterBitsBuilder* BloomFilterPolicy::GetBuilderFromContext(
|
||||
FilterBitsReader* BloomFilterPolicy::GetFilterBitsReader(
|
||||
const Slice& contents) const {
|
||||
uint32_t len_with_meta = static_cast<uint32_t>(contents.size());
|
||||
if (len_with_meta <= 5) {
|
||||
if (len_with_meta <= kMetadataLen) {
|
||||
// filter is empty or broken. Treat like zero keys added.
|
||||
return new AlwaysFalseFilter();
|
||||
}
|
||||
@ -1039,7 +1197,7 @@ FilterBitsReader* BloomFilterPolicy::GetFilterBitsReader(
|
||||
// len_with_meta +-----------------------------------+
|
||||
|
||||
int8_t raw_num_probes =
|
||||
static_cast<int8_t>(contents.data()[len_with_meta - 5]);
|
||||
static_cast<int8_t>(contents.data()[len_with_meta - kMetadataLen]);
|
||||
// NB: *num_probes > 30 and < 128 probably have not been used, because of
|
||||
// BloomFilterPolicy::initialize, unless directly calling
|
||||
// LegacyBloomBitsBuilder as an API, but we are leaving those cases in
|
||||
@ -1069,7 +1227,7 @@ FilterBitsReader* BloomFilterPolicy::GetFilterBitsReader(
|
||||
assert(num_probes >= 1);
|
||||
assert(num_probes <= 127);
|
||||
|
||||
uint32_t len = len_with_meta - 5;
|
||||
uint32_t len = len_with_meta - kMetadataLen;
|
||||
assert(len > 0);
|
||||
|
||||
uint32_t num_lines = DecodeFixed32(contents.data() + len_with_meta - 4);
|
||||
@ -1102,7 +1260,7 @@ FilterBitsReader* BloomFilterPolicy::GetFilterBitsReader(
|
||||
FilterBitsReader* BloomFilterPolicy::GetRibbonBitsReader(
|
||||
const Slice& contents) const {
|
||||
uint32_t len_with_meta = static_cast<uint32_t>(contents.size());
|
||||
uint32_t len = len_with_meta - 5;
|
||||
uint32_t len = len_with_meta - kMetadataLen;
|
||||
|
||||
assert(len > 0); // precondition
|
||||
|
||||
@ -1126,7 +1284,7 @@ FilterBitsReader* BloomFilterPolicy::GetRibbonBitsReader(
|
||||
FilterBitsReader* BloomFilterPolicy::GetBloomBitsReader(
|
||||
const Slice& contents) const {
|
||||
uint32_t len_with_meta = static_cast<uint32_t>(contents.size());
|
||||
uint32_t len = len_with_meta - 5;
|
||||
uint32_t len = len_with_meta - kMetadataLen;
|
||||
|
||||
assert(len > 0); // precondition
|
||||
|
||||
|
||||
@ -26,13 +26,12 @@ class BuiltinFilterBitsBuilder : public FilterBitsBuilder {
|
||||
public:
|
||||
// Calculate number of bytes needed for a new filter, including
|
||||
// metadata. Passing the result to ApproximateNumEntries should
|
||||
// return >= the num_entry passed in.
|
||||
// (ideally, usually) return >= the num_entry passed in.
|
||||
// When optimize_filters_for_memory is enabled, this function
|
||||
// is not authoritative but represents a target size that should
|
||||
// be close to the average size.
|
||||
virtual size_t CalculateSpace(size_t num_entries) = 0;
|
||||
|
||||
// A somewhat expensive but workable default implementation
|
||||
// using binary search on CalculateSpace
|
||||
size_t ApproximateNumEntries(size_t bytes) override;
|
||||
|
||||
// Returns an estimate of the FP rate of the returned filter if
|
||||
// `num_entries` keys are added and the filter returned by Finish
|
||||
// is `bytes` bytes.
|
||||
|
||||
@ -573,6 +573,9 @@ DEFINE_int32(writable_file_max_buffer_size, 1024 * 1024,
|
||||
|
||||
DEFINE_int32(bloom_bits, -1, "Bloom filter bits per key. Negative means"
|
||||
" use default settings.");
|
||||
|
||||
DEFINE_bool(use_ribbon_filter, false, "Use Ribbon instead of Bloom filter");
|
||||
|
||||
DEFINE_double(memtable_bloom_size_ratio, 0,
|
||||
"Ratio of memtable size used for bloom filter. 0 means no bloom "
|
||||
"filter.");
|
||||
@ -2688,10 +2691,13 @@ class Benchmark {
|
||||
Benchmark()
|
||||
: cache_(NewCache(FLAGS_cache_size)),
|
||||
compressed_cache_(NewCache(FLAGS_compressed_cache_size)),
|
||||
filter_policy_(FLAGS_bloom_bits >= 0
|
||||
? NewBloomFilterPolicy(FLAGS_bloom_bits,
|
||||
FLAGS_use_block_based_filter)
|
||||
: nullptr),
|
||||
filter_policy_(
|
||||
FLAGS_use_ribbon_filter
|
||||
? NewExperimentalRibbonFilterPolicy(FLAGS_bloom_bits)
|
||||
: FLAGS_bloom_bits >= 0
|
||||
? NewBloomFilterPolicy(FLAGS_bloom_bits,
|
||||
FLAGS_use_block_based_filter)
|
||||
: nullptr),
|
||||
prefix_extractor_(NewFixedPrefixTransform(FLAGS_prefix_size)),
|
||||
num_(FLAGS_num),
|
||||
key_size_(FLAGS_key_size),
|
||||
@ -4055,8 +4061,11 @@ class Benchmark {
|
||||
table_options->block_cache = cache_;
|
||||
}
|
||||
if (FLAGS_bloom_bits >= 0) {
|
||||
table_options->filter_policy.reset(NewBloomFilterPolicy(
|
||||
FLAGS_bloom_bits, FLAGS_use_block_based_filter));
|
||||
table_options->filter_policy.reset(
|
||||
FLAGS_use_ribbon_filter
|
||||
? NewExperimentalRibbonFilterPolicy(FLAGS_bloom_bits)
|
||||
: NewBloomFilterPolicy(FLAGS_bloom_bits,
|
||||
FLAGS_use_block_based_filter));
|
||||
}
|
||||
}
|
||||
if (FLAGS_row_cache_size) {
|
||||
|
||||
@ -425,13 +425,19 @@ TEST_P(FullBloomTest, FilterSize) {
|
||||
|
||||
size_t n = 1;
|
||||
size_t space = 0;
|
||||
for (; n < 100; n++) {
|
||||
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);
|
||||
EXPECT_EQ(space, space2);
|
||||
if (n > 6000 && GetParam() == BloomFilterPolicy::kStandard128Ribbon) {
|
||||
// TODO(peterd): better approximation?
|
||||
EXPECT_GE(space2, space);
|
||||
EXPECT_LE(space2 * 0.98 - 16.0, space * 1.0);
|
||||
} else {
|
||||
EXPECT_EQ(space2, space);
|
||||
}
|
||||
}
|
||||
// Until size_t overflow
|
||||
for (; n < (n + n / 3); n += n / 3) {
|
||||
@ -504,10 +510,6 @@ TEST_P(FullBloomTest, FullVaryingLengths) {
|
||||
}
|
||||
|
||||
TEST_P(FullBloomTest, OptimizeForMemory) {
|
||||
if (GetParam() == BloomFilterPolicy::kStandard128Ribbon) {
|
||||
// TODO Not yet implemented
|
||||
return;
|
||||
}
|
||||
char buffer[sizeof(int)];
|
||||
for (bool offm : {true, false}) {
|
||||
table_options_.optimize_filters_for_memory = offm;
|
||||
@ -540,6 +542,10 @@ TEST_P(FullBloomTest, OptimizeForMemory) {
|
||||
EXPECT_GE(total_fp_rate / double{nfilters}, 0.008);
|
||||
|
||||
int64_t ex_min_total_size = int64_t{FLAGS_bits_per_key} * total_keys / 8;
|
||||
if (GetParam() == BloomFilterPolicy::kStandard128Ribbon) {
|
||||
// ~ 30% savings vs. Bloom filter
|
||||
ex_min_total_size = 7 * ex_min_total_size / 10;
|
||||
}
|
||||
EXPECT_GE(static_cast<int64_t>(total_size), ex_min_total_size);
|
||||
|
||||
int64_t blocked_bloom_overhead = nfilters * (CACHE_LINE_SIZE + 5);
|
||||
|
||||
@ -900,6 +900,22 @@ class SerializableInterleavedSolution {
|
||||
return corrected;
|
||||
}
|
||||
|
||||
// Round down to a number of slots supported by this structure. Note that
|
||||
// this needs to be must be taken into account for the banding if this
|
||||
// solution layout/storage is to be used.
|
||||
static Index RoundDownNumSlots(Index num_slots) {
|
||||
// Must be multiple of kCoeffBits
|
||||
Index corrected = num_slots / kCoeffBits * kCoeffBits;
|
||||
|
||||
// Do not use num_starts==1 unless kUseSmash, because the hashing
|
||||
// might not be equipped for stacking up so many entries on a
|
||||
// single start location.
|
||||
if (!TypesAndSettings::kUseSmash && corrected == kCoeffBits) {
|
||||
corrected = 0;
|
||||
}
|
||||
return corrected;
|
||||
}
|
||||
|
||||
// Compute the number of bytes for a given number of slots and desired
|
||||
// FP rate. Since desired FP rate might not be exactly achievable,
|
||||
// rounding_bias32==0 means to always round toward lower FP rate
|
||||
@ -927,9 +943,13 @@ class SerializableInterleavedSolution {
|
||||
double desired_one_in_fp_rate,
|
||||
uint32_t rounding_bias32) {
|
||||
assert(TypesAndSettings::kIsFilter);
|
||||
if (TypesAndSettings::kAllowZeroStarts && num_slots == 0) {
|
||||
// Unusual. Zero starts presumes no keys added -> always false (no FPs)
|
||||
return 0U;
|
||||
if (TypesAndSettings::kAllowZeroStarts) {
|
||||
if (num_slots == 0) {
|
||||
// Unusual. Zero starts presumes no keys added -> always false (no FPs)
|
||||
return 0U;
|
||||
}
|
||||
} else {
|
||||
assert(num_slots > 0);
|
||||
}
|
||||
// Must be rounded up already.
|
||||
assert(RoundUpNumSlots(num_slots) == num_slots);
|
||||
|
||||
Loading…
Reference in New Issue
Block a user