1 /* -*- Mode: C++; tab-width: 8; indent-tabs-mode: nil; c-basic-offset: 2 -*- */
2 /* vim: set ts=8 sts=2 et sw=2 tw=80: */
3 /* This Source Code Form is subject to the terms of the Mozilla Public
4 * License, v. 2.0. If a copy of the MPL was not distributed with this
5 * file, You can obtain one at http://mozilla.org/MPL/2.0/. */
8 * A counting Bloom filter implementation. This allows consumers to
9 * do fast probabilistic "is item X in set Y?" testing which will
10 * never answer "no" when the correct answer is "yes" (but might
11 * incorrectly answer "yes" when the correct answer is "no").
14 #ifndef mozilla_BloomFilter_h
15 #define mozilla_BloomFilter_h
17 #include "mozilla/Assertions.h"
18 #include "mozilla/Likely.h"
26 * This class implements a counting Bloom filter as described at
27 * <http://en.wikipedia.org/wiki/Bloom_filter#Counting_filters>, with
28 * 8-bit counters. This allows quick probabilistic answers to the
29 * question "is object X in set Y?" where the contents of Y might not
30 * be time-invariant. The probabilistic nature of the test means that
31 * sometimes the answer will be "yes" when it should be "no". If the
32 * answer is "no", then X is guaranteed not to be in Y.
34 * The filter is parametrized on KeySize, which is the size of the key
35 * generated by each of hash functions used by the filter, in bits,
36 * and the type of object T being added and removed. T must implement
37 * a |uint32_t hash() const| method which returns a uint32_t hash key
38 * that will be used to generate the two separate hash functions for
39 * the Bloom filter. This hash key MUST be well-distributed for good
40 * results! KeySize is not allowed to be larger than 16.
42 * The filter uses exactly 2**KeySize bytes of memory. From now on we
43 * will refer to the memory used by the filter as M.
45 * The expected rate of incorrect "yes" answers depends on M and on
46 * the number N of objects in set Y. As long as N is small compared
47 * to M, the rate of such answers is expected to be approximately
48 * 4*(N/M)**2 for this filter. In practice, if Y has a few hundred
49 * elements then using a KeySize of 12 gives a reasonably low
50 * incorrect answer rate. A KeySize of 12 has the additional benefit
51 * of using exactly one page for the filter in typical hardware
55 template<unsigned KeySize
, class T
>
59 * A counting Bloom filter with 8-bit counters. For now we assume
60 * that having two hash functions is enough, but we may revisit that
63 * The filter uses an array with 2**KeySize entries.
65 * Assuming a well-distributed hash function, a Bloom filter with
66 * array size M containing N elements and
67 * using k hash function has expected false positive rate exactly
69 * $ (1 - (1 - 1/M)^{kN})^k $
71 * because each array slot has a
75 * chance of being 0, and the expected false positive rate is the
76 * probability that all of the k hash functions will hit a nonzero
79 * For reasonable assumptions (M large, kN large, which should both
80 * hold if we're worried about false positives) about M and kN this
81 * becomes approximately
83 * $$ (1 - \exp(-kN/M))^k $$
85 * For our special case of k == 2, that's $(1 - \exp(-2N/M))^2$,
88 * $$ N/M = -0.5 * \ln(1 - \sqrt(r)) $$
90 * where r is the false positive rate. This can be used to compute
91 * the desired KeySize for a given load N and false positive rate r.
93 * If N/M is assumed small, then the false positive rate can
94 * further be approximated as 4*N^2/M^2. So increasing KeySize by
95 * 1, which doubles M, reduces the false positive rate by about a
96 * factor of 4, and a false positive rate of 1% corresponds to
99 * What this means in practice is that for a few hundred keys using a
100 * KeySize of 12 gives false positive rates on the order of 0.25-4%.
102 * Similarly, using a KeySize of 10 would lead to a 4% false
103 * positive rate for N == 100 and to quite bad false positive
104 * rates for larger N.
109 static_assert(KeySize
<= kKeyShift
, "KeySize too big");
111 // Should we have a custom operator new using calloc instead and
112 // require that we're allocated via the operator?
117 * Clear the filter. This should be done before reusing it, because
118 * just removing all items doesn't clear counters that hit the upper
124 * Add an item to the filter.
126 void add(const T
* aValue
);
129 * Remove an item from the filter.
131 void remove(const T
* aValue
);
134 * Check whether the filter might contain an item. This can
135 * sometimes return true even if the item is not in the filter,
136 * but will never return false for items that are actually in the
139 bool mightContain(const T
* aValue
) const;
142 * Methods for add/remove/contain when we already have a hash computed
144 void add(uint32_t aHash
);
145 void remove(uint32_t aHash
);
146 bool mightContain(uint32_t aHash
) const;
149 static const size_t kArraySize
= (1 << KeySize
);
150 static const uint32_t kKeyMask
= (1 << KeySize
) - 1;
151 static const uint32_t kKeyShift
= 16;
153 static uint32_t hash1(uint32_t aHash
)
155 return aHash
& kKeyMask
;
157 static uint32_t hash2(uint32_t aHash
)
159 return (aHash
>> kKeyShift
) & kKeyMask
;
162 uint8_t& firstSlot(uint32_t aHash
)
164 return mCounters
[hash1(aHash
)];
166 uint8_t& secondSlot(uint32_t aHash
)
168 return mCounters
[hash2(aHash
)];
171 const uint8_t& firstSlot(uint32_t aHash
) const
173 return mCounters
[hash1(aHash
)];
175 const uint8_t& secondSlot(uint32_t aHash
) const
177 return mCounters
[hash2(aHash
)];
180 static bool full(const uint8_t& aSlot
) { return aSlot
== UINT8_MAX
; }
182 uint8_t mCounters
[kArraySize
];
185 template<unsigned KeySize
, class T
>
187 BloomFilter
<KeySize
, T
>::clear()
189 memset(mCounters
, 0, kArraySize
);
192 template<unsigned KeySize
, class T
>
194 BloomFilter
<KeySize
, T
>::add(uint32_t aHash
)
196 uint8_t& slot1
= firstSlot(aHash
);
197 if (MOZ_LIKELY(!full(slot1
))) {
200 uint8_t& slot2
= secondSlot(aHash
);
201 if (MOZ_LIKELY(!full(slot2
))) {
206 template<unsigned KeySize
, class T
>
207 MOZ_ALWAYS_INLINE
void
208 BloomFilter
<KeySize
, T
>::add(const T
* aValue
)
210 uint32_t hash
= aValue
->hash();
214 template<unsigned KeySize
, class T
>
216 BloomFilter
<KeySize
, T
>::remove(uint32_t aHash
)
218 // If the slots are full, we don't know whether we bumped them to be
219 // there when we added or not, so just leave them full.
220 uint8_t& slot1
= firstSlot(aHash
);
221 if (MOZ_LIKELY(!full(slot1
))) {
224 uint8_t& slot2
= secondSlot(aHash
);
225 if (MOZ_LIKELY(!full(slot2
))) {
230 template<unsigned KeySize
, class T
>
231 MOZ_ALWAYS_INLINE
void
232 BloomFilter
<KeySize
, T
>::remove(const T
* aValue
)
234 uint32_t hash
= aValue
->hash();
238 template<unsigned KeySize
, class T
>
239 MOZ_ALWAYS_INLINE
bool
240 BloomFilter
<KeySize
, T
>::mightContain(uint32_t aHash
) const
242 // Check that all the slots for this hash contain something
243 return firstSlot(aHash
) && secondSlot(aHash
);
246 template<unsigned KeySize
, class T
>
247 MOZ_ALWAYS_INLINE
bool
248 BloomFilter
<KeySize
, T
>::mightContain(const T
* aValue
) const
250 uint32_t hash
= aValue
->hash();
251 return mightContain(hash
);
254 } // namespace mozilla
256 #endif /* mozilla_BloomFilter_h */