This document discusses Bloom filters, which are space-efficient probabilistic data structures used to test whether an element is present in a set. It explains how Bloom filters work by using hash functions to map elements to bit arrays and sets multiple bits to represent an element. The document also provides an example demonstrating how Bloom filters can be used to check if users with certain attributes are present in a dataset in a memory efficient manner with some probability of false positives.

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BloomFilter

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CM

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2.

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( )

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➞
➞
or

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(0|1) m
[0 <= n < m]
k

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m
0 1 1
14 (m = 14)

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k
3 (k = 3)
[0 <= n < 14]
14

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A
: 185cm -> 185 % 14 = 3
: 77kg -> 77 % 14 = 7
: 32 -> 32 % 14 = 4

20. 12 . 2
B
: 172cm -> 172 % 14 = 4
: 55kg -> 55 % 14 = 13
: 24 -> 24 % 14 = 10
4

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22. 13 . 2
A
: 185cm -> 185 % 14 = 3
: 77kg -> 77 % 14 = 7
: 32 -> 32 % 14 = 4

23. 13 . 3
C
: 174cm -> 174 % 14 = 6
: 63kg -> 63 % 14 = 7
: 28 -> 28 % 14 = 0

24. 13 . 4
D
: 161cm -> 161 % 14 = 7
: 52kg -> 52 % 14 = 10
: 45 -> 45 % 14 = 3
D

25. 13 . 5
A B
A : 32 -> 32 % 14 = 4
B : 172cm -> 172 % 14 = 4
B 3
A 1
A
BloomFilter
Counting BloomFilter

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k seed hash
Cassandra MurMur3 Hash
m

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m k
m k

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Have a nice
BloomFilter!

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