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Diffstat (limited to 'mfbt/BloomFilter.h')
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diff --git a/mfbt/BloomFilter.h b/mfbt/BloomFilter.h new file mode 100644 index 0000000000..06c7143e03 --- /dev/null +++ b/mfbt/BloomFilter.h @@ -0,0 +1,230 @@ +/* -*- Mode: C++; tab-width: 8; indent-tabs-mode: nil; c-basic-offset: 2 -*- */ +/* vim: set ts=8 sts=2 et sw=2 tw=80: */ +/* This Source Code Form is subject to the terms of the Mozilla Public + * License, v. 2.0. If a copy of the MPL was not distributed with this + * file, You can obtain one at http://mozilla.org/MPL/2.0/. */ + +/* + * A counting Bloom filter implementation. This allows consumers to + * do fast probabilistic "is item X in set Y?" testing which will + * never answer "no" when the correct answer is "yes" (but might + * incorrectly answer "yes" when the correct answer is "no"). + */ + +#ifndef mozilla_BloomFilter_h +#define mozilla_BloomFilter_h + +#include "mozilla/Assertions.h" +#include "mozilla/Likely.h" + +#include <stdint.h> +#include <string.h> + +namespace mozilla { + +/* + * This class implements a counting Bloom filter as described at + * <http://en.wikipedia.org/wiki/Bloom_filter#Counting_filters>, with + * 8-bit counters. This allows quick probabilistic answers to the + * question "is object X in set Y?" where the contents of Y might not + * be time-invariant. The probabilistic nature of the test means that + * sometimes the answer will be "yes" when it should be "no". If the + * answer is "no", then X is guaranteed not to be in Y. + * + * The filter is parametrized on KeySize, which is the size of the key + * generated by each of hash functions used by the filter, in bits, + * and the type of object T being added and removed. T must implement + * a |uint32_t hash() const| method which returns a uint32_t hash key + * that will be used to generate the two separate hash functions for + * the Bloom filter. This hash key MUST be well-distributed for good + * results! KeySize is not allowed to be larger than 16. + * + * The filter uses exactly 2**KeySize bytes of memory. From now on we + * will refer to the memory used by the filter as M. + * + * The expected rate of incorrect "yes" answers depends on M and on + * the number N of objects in set Y. As long as N is small compared + * to M, the rate of such answers is expected to be approximately + * 4*(N/M)**2 for this filter. In practice, if Y has a few hundred + * elements then using a KeySize of 12 gives a reasonably low + * incorrect answer rate. A KeySize of 12 has the additional benefit + * of using exactly one page for the filter in typical hardware + * configurations. + */ + +template <unsigned KeySize, class T> +class BloomFilter { + /* + * A counting Bloom filter with 8-bit counters. For now we assume + * that having two hash functions is enough, but we may revisit that + * decision later. + * + * The filter uses an array with 2**KeySize entries. + * + * Assuming a well-distributed hash function, a Bloom filter with + * array size M containing N elements and + * using k hash function has expected false positive rate exactly + * + * $ (1 - (1 - 1/M)^{kN})^k $ + * + * because each array slot has a + * + * $ (1 - 1/M)^{kN} $ + * + * chance of being 0, and the expected false positive rate is the + * probability that all of the k hash functions will hit a nonzero + * slot. + * + * For reasonable assumptions (M large, kN large, which should both + * hold if we're worried about false positives) about M and kN this + * becomes approximately + * + * $$ (1 - \exp(-kN/M))^k $$ + * + * For our special case of k == 2, that's $(1 - \exp(-2N/M))^2$, + * or in other words + * + * $$ N/M = -0.5 * \ln(1 - \sqrt(r)) $$ + * + * where r is the false positive rate. This can be used to compute + * the desired KeySize for a given load N and false positive rate r. + * + * If N/M is assumed small, then the false positive rate can + * further be approximated as 4*N^2/M^2. So increasing KeySize by + * 1, which doubles M, reduces the false positive rate by about a + * factor of 4, and a false positive rate of 1% corresponds to + * about M/N == 20. + * + * What this means in practice is that for a few hundred keys using a + * KeySize of 12 gives false positive rates on the order of 0.25-4%. + * + * Similarly, using a KeySize of 10 would lead to a 4% false + * positive rate for N == 100 and to quite bad false positive + * rates for larger N. + */ + public: + BloomFilter() { + static_assert(KeySize <= kKeyShift, "KeySize too big"); + + // Should we have a custom operator new using calloc instead and + // require that we're allocated via the operator? + clear(); + } + + /* + * Clear the filter. This should be done before reusing it, because + * just removing all items doesn't clear counters that hit the upper + * bound. + */ + void clear(); + + /* + * Add an item to the filter. + */ + void add(const T* aValue); + + /* + * Remove an item from the filter. + */ + void remove(const T* aValue); + + /* + * Check whether the filter might contain an item. This can + * sometimes return true even if the item is not in the filter, + * but will never return false for items that are actually in the + * filter. + */ + bool mightContain(const T* aValue) const; + + /* + * Methods for add/remove/contain when we already have a hash computed + */ + void add(uint32_t aHash); + void remove(uint32_t aHash); + bool mightContain(uint32_t aHash) const; + + private: + static const size_t kArraySize = (1 << KeySize); + static const uint32_t kKeyMask = (1 << KeySize) - 1; + static const uint32_t kKeyShift = 16; + + static uint32_t hash1(uint32_t aHash) { return aHash & kKeyMask; } + static uint32_t hash2(uint32_t aHash) { + return (aHash >> kKeyShift) & kKeyMask; + } + + uint8_t& firstSlot(uint32_t aHash) { return mCounters[hash1(aHash)]; } + uint8_t& secondSlot(uint32_t aHash) { return mCounters[hash2(aHash)]; } + + const uint8_t& firstSlot(uint32_t aHash) const { + return mCounters[hash1(aHash)]; + } + const uint8_t& secondSlot(uint32_t aHash) const { + return mCounters[hash2(aHash)]; + } + + static bool full(const uint8_t& aSlot) { return aSlot == UINT8_MAX; } + + uint8_t mCounters[kArraySize]; +}; + +template <unsigned KeySize, class T> +inline void BloomFilter<KeySize, T>::clear() { + memset(mCounters, 0, kArraySize); +} + +template <unsigned KeySize, class T> +inline void BloomFilter<KeySize, T>::add(uint32_t aHash) { + uint8_t& slot1 = firstSlot(aHash); + if (MOZ_LIKELY(!full(slot1))) { + ++slot1; + } + uint8_t& slot2 = secondSlot(aHash); + if (MOZ_LIKELY(!full(slot2))) { + ++slot2; + } +} + +template <unsigned KeySize, class T> +MOZ_ALWAYS_INLINE void BloomFilter<KeySize, T>::add(const T* aValue) { + uint32_t hash = aValue->hash(); + return add(hash); +} + +template <unsigned KeySize, class T> +inline void BloomFilter<KeySize, T>::remove(uint32_t aHash) { + // If the slots are full, we don't know whether we bumped them to be + // there when we added or not, so just leave them full. + uint8_t& slot1 = firstSlot(aHash); + if (MOZ_LIKELY(!full(slot1))) { + --slot1; + } + uint8_t& slot2 = secondSlot(aHash); + if (MOZ_LIKELY(!full(slot2))) { + --slot2; + } +} + +template <unsigned KeySize, class T> +MOZ_ALWAYS_INLINE void BloomFilter<KeySize, T>::remove(const T* aValue) { + uint32_t hash = aValue->hash(); + remove(hash); +} + +template <unsigned KeySize, class T> +MOZ_ALWAYS_INLINE bool BloomFilter<KeySize, T>::mightContain( + uint32_t aHash) const { + // Check that all the slots for this hash contain something + return firstSlot(aHash) && secondSlot(aHash); +} + +template <unsigned KeySize, class T> +MOZ_ALWAYS_INLINE bool BloomFilter<KeySize, T>::mightContain( + const T* aValue) const { + uint32_t hash = aValue->hash(); + return mightContain(hash); +} + +} // namespace mozilla + +#endif /* mozilla_BloomFilter_h */ |