The Tor Browser: mfbt/BloomFilter.h@6474c204b198 (annotated)

mfbt/BloomFilter.h@6474c204b198 (annotated)

mfbt/BloomFilter.h

Wed, 31 Dec 2014 06:09:35 +0100

author: Michael Schloh von Bennewitz <michael@schloh.com>
date: Wed, 31 Dec 2014 06:09:35 +0100
changeset 0: 6474c204b198
permissions: -rw-r--r--

Cloned upstream origin tor-browser at tor-browser-31.3.0esr-4.5-1-build1
revision ID fc1c9ff7c1b2defdbc039f12214767608f46423f for hacking purpose.

 /* -*- 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 <= keyShift, "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* t);
     /*
      * Remove an item from the filter.
      */
     void remove(const T* t);
     /*
      * 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* t) const;
     /*
      * Methods for add/remove/contain when we already have a hash computed
      */
     void add(uint32_t hash);
     void remove(uint32_t hash);
     bool mightContain(uint32_t hash) const;
   private:
     static const size_t arraySize = (1 << KeySize);
     static const uint32_t keyMask = (1 << KeySize) - 1;
     static const uint32_t keyShift = 16;
     static uint32_t hash1(uint32_t hash) { return hash & keyMask; }
     static uint32_t hash2(uint32_t hash) { return (hash >> keyShift) & keyMask; }
     uint8_t& firstSlot(uint32_t hash) { return counters[hash1(hash)]; }
     uint8_t& secondSlot(uint32_t hash) { return counters[hash2(hash)]; }
     const uint8_t& firstSlot(uint32_t hash) const { return counters[hash1(hash)]; }
     const uint8_t& secondSlot(uint32_t hash) const { return counters[hash2(hash)]; }
     static bool full(const uint8_t& slot) { return slot == UINT8_MAX; }
     uint8_t counters[arraySize];
 };
 template<unsigned KeySize, class T>
 inline void
 BloomFilter<KeySize, T>::clear()
 {
   memset(counters, 0, arraySize);
 }
 template<unsigned KeySize, class T>
 inline void
 BloomFilter<KeySize, T>::add(uint32_t hash)
 {
   uint8_t& slot1 = firstSlot(hash);
   if (MOZ_LIKELY(!full(slot1)))
     ++slot1;
   uint8_t& slot2 = secondSlot(hash);
   if (MOZ_LIKELY(!full(slot2)))
     ++slot2;
 }
 template<unsigned KeySize, class T>
 MOZ_ALWAYS_INLINE void
 BloomFilter<KeySize, T>::add(const T* t)
 {
   uint32_t hash = t->hash();
   return add(hash);
 }
 template<unsigned KeySize, class T>
 inline void
 BloomFilter<KeySize, T>::remove(uint32_t hash)
 {
   // 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(hash);
   if (MOZ_LIKELY(!full(slot1)))
     --slot1;
   uint8_t& slot2 = secondSlot(hash);
   if (MOZ_LIKELY(!full(slot2)))
     --slot2;
 }
 template<unsigned KeySize, class T>
 MOZ_ALWAYS_INLINE void
 BloomFilter<KeySize, T>::remove(const T* t)
 {
   uint32_t hash = t->hash();
   remove(hash);
 }
 template<unsigned KeySize, class T>
 MOZ_ALWAYS_INLINE bool
 BloomFilter<KeySize, T>::mightContain(uint32_t hash) const
 {
   // Check that all the slots for this hash contain something
   return firstSlot(hash) && secondSlot(hash);
 }
 template<unsigned KeySize, class T>
 MOZ_ALWAYS_INLINE bool
 BloomFilter<KeySize, T>::mightContain(const T* t) const
 {
   uint32_t hash = t->hash();
   return mightContain(hash);
 }
 } // namespace mozilla
 #endif /* mozilla_BloomFilter_h */

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mfbt/BloomFilter.h@6474c204b198 (annotated)

mfbt/BloomFilter.h