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A Bloom filter is a data structure optimized for fast, space-efficient set membership tests. Bloom filters have the unusual property of requiring constant time to add an element to the set or test for membership, regardless of the size
of the elements or the number of elements already in the set. No other constant-space set data structure has this property.

It works by storing a bit vector representing the set S' = {h[i](x) | x in S, i = 1, …, k}, where h[1], …, h[k] := {0, 1} -> [n lg(1/ε) lg e] are hash functions. Additions are simply setting k bits to 1, specifically those at h[1](x), …, h[k](x). Checks are implemented by performing those same hash functions and returning true if all of the resulting positions are 1.

Because the set stored is a proper superset of the set of items added, false positives may occur, though false negatives cannot. The false positive rate can be specified.

Bloom filters offer the following advantages:

This implementation uses Dillinger & Manolios double hashing to provide all but the first two hash functions. By default the first hash function is the type's GetHashCode() method. This implementation also includes default secondary hash functions for strings (Jenkin's "One at a time" method) and integers (Wang's method).

Bloom filters are due to Burton H. Bloom, as described in the Communications of the ACM in July 1970. The full paper is available here.

It works by storing a bit vector representing the set S' = {h[i](x) | x in S, i = 1, …, k}, where h[1], …, h[k] := {0, 1} -> [n lg(1/ε) lg e] are hash functions. Additions are simply setting k bits to 1, specifically those at h[1](x), …, h[k](x). Checks are implemented by performing those same hash functions and returning true if all of the resulting positions are 1.

Because the set stored is a proper superset of the set of items added, false positives may occur, though false negatives cannot. The false positive rate can be specified.

Bloom filters offer the following advantages:

- Space: Approximately n * lg(1/ε), where ε is the false positive rate and n is the number of elements in the set.
- Example: There are approximately 170k words in the English language. If we consider that to be our set (therefore n = 1.7E5), and we wish to search a corpus for them with a 1% false positive rate, the filter would require about (1.7E5 * lg(1 / 0.01)) ≈ 162 KB. Contrast this with a hashtable, which would require (1.7E5 elements * 32 bits per element) ≈ 664 KB. Obviously explicit string storage would be significantly more.

- Precision: Arbitrary precision, where increasing precision requires more space (following the above size equation) but not more time.
- Example: If we wanted to reduce our false positive rate in the above example from one percent to one permille the space requirement would go from about 162 KB to about 207 KB.

- Time: O(k) where k is the number of hash functions. The optimal number of hash functions (though a different number can be supplied by the user if desired) is ceiling(lg(1/ε))
- Example: In keeping with our above example, if the accepted false positive rate is 0.001, k = 10.

This implementation uses Dillinger & Manolios double hashing to provide all but the first two hash functions. By default the first hash function is the type's GetHashCode() method. This implementation also includes default secondary hash functions for strings (Jenkin's "One at a time" method) and integers (Wang's method).

Bloom filters are due to Burton H. Bloom, as described in the Communications of the ACM in July 1970. The full paper is available here.

Last edited Nov 11, 2014 at 8:38 PM by JustinRussell, version 6