Add m-esr52 at 52.6.0

1 files changed, 256 insertions, 0 deletions

diff --git a/mfbt/BloomFilter.h b/mfbt/BloomFilter.h
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+++ b/mfbt/BloomFilter.h
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+/* -*- 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 */