85584d497e
In particular, we add a new FilterPolicy class. An instance of this class can be supplied in Options when opening a database. If supplied, the instance is used to generate summaries of keys (e.g., a bloom filter) which are placed in sstables. These summaries are consulted by DB::Get() so we can avoid reading sstable blocks that are guaranteed to not contain the key we are looking for. This change provides one implementation of FilterPolicy based on bloom filters. Other changes: - Updated version number to 1.4. - Some build tweaks. - C binding for CompactRange. - A few more benchmarks: deleteseq, deleterandom, readmissing, seekrandom. - Minor .gitignore update.
96 lines
2.8 KiB
C++
96 lines
2.8 KiB
C++
// Copyright (c) 2012 The LevelDB Authors. All rights reserved.
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// Use of this source code is governed by a BSD-style license that can be
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// found in the LICENSE file. See the AUTHORS file for names of contributors.
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#include "leveldb/filter_policy.h"
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#include "leveldb/slice.h"
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#include "util/hash.h"
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namespace leveldb {
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namespace {
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static uint32_t BloomHash(const Slice& key) {
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return Hash(key.data(), key.size(), 0xbc9f1d34);
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}
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class BloomFilterPolicy : public FilterPolicy {
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private:
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size_t bits_per_key_;
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size_t k_;
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public:
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explicit BloomFilterPolicy(int bits_per_key)
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: bits_per_key_(bits_per_key) {
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// We intentionally round down to reduce probing cost a little bit
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k_ = static_cast<size_t>(bits_per_key * 0.69); // 0.69 =~ ln(2)
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if (k_ < 1) k_ = 1;
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if (k_ > 30) k_ = 30;
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}
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virtual const char* Name() const {
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return "leveldb.BuiltinBloomFilter";
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}
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virtual void CreateFilter(const Slice* keys, int n, std::string* dst) const {
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// Compute bloom filter size (in both bits and bytes)
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size_t bits = n * bits_per_key_;
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// For small n, we can see a very high false positive rate. Fix it
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// by enforcing a minimum bloom filter length.
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if (bits < 64) bits = 64;
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size_t bytes = (bits + 7) / 8;
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bits = bytes * 8;
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const size_t init_size = dst->size();
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dst->resize(init_size + bytes, 0);
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dst->push_back(static_cast<char>(k_)); // Remember # of probes in filter
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char* array = &(*dst)[init_size];
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for (size_t i = 0; i < n; i++) {
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// Use double-hashing to generate a sequence of hash values.
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// See analysis in [Kirsch,Mitzenmacher 2006].
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uint32_t h = BloomHash(keys[i]);
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const uint32_t delta = (h >> 17) | (h << 15); // Rotate right 17 bits
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for (size_t j = 0; j < k_; j++) {
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const uint32_t bitpos = h % bits;
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array[bitpos/8] |= (1 << (bitpos % 8));
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h += delta;
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}
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}
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}
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virtual bool KeyMayMatch(const Slice& key, const Slice& bloom_filter) const {
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const size_t len = bloom_filter.size();
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if (len < 2) return false;
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const char* array = bloom_filter.data();
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const size_t bits = (len - 1) * 8;
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// Use the encoded k so that we can read filters generated by
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// bloom filters created using different parameters.
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const size_t k = array[len-1];
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if (k > 30) {
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// Reserved for potentially new encodings for short bloom filters.
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// Consider it a match.
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return true;
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}
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uint32_t h = BloomHash(key);
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const uint32_t delta = (h >> 17) | (h << 15); // Rotate right 17 bits
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for (size_t j = 0; j < k; j++) {
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const uint32_t bitpos = h % bits;
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if ((array[bitpos/8] & (1 << (bitpos % 8))) == 0) return false;
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h += delta;
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}
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return true;
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}
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};
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}
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const FilterPolicy* NewBloomFilterPolicy(int bits_per_key) {
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return new BloomFilterPolicy(bits_per_key);
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}
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} // namespace leveldb
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