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Fast Bloom Filter

Project description

Less Hash Bloom Filter

Build Status

Less Hash Bloom Filter is fast bloom filter suitable for Big Data.

The computation of hash functions and checking the existence of an element is a major computation overhead. Also, bloom filter requires multiple independent hash functions, and well-designed hash functions are computation-intensive like MD5, SHA-1 [1].

In this implementation, we use a different technique to generate the k hash functions from only two. Therefore, the bloom filter is fast.

Install

Install Less Hash Bloom Filter with pip as follows:

$ pip install LessHash-BloomFilter

Usage

LHBF needs to know the size of bloom filter m and number of hash functions k.

Note: You should use high m to avoid the collision of hash functions. The probability of two random strings colliding is ~ 1/m

from lhbf import BloomFilter

# Create a bloom filter 
bf = BloomFilter(m=200, k=2)

# Add an element
bf.add("a")

# Check if element exists
bf.might_contain("a")

# Estimate flase positive probability 
bf.estimate_fpp()

# Combine two bloom filters
bf2 = BloomFilter(m=200, k=2)
bf.combine(bf2)

Details

  • Hash functions used:

    • For integer, we use Knuth multiplicative hash [2]
    • For string, we use polynomial rolling hash function [3]
  • k hash functions:

    Using two hash functions, we calculate the k hash functions as follows:

    gi(x) = h1(x) + i x h2(x) mod m, where 0 ≤ i ≤ k-1

    It has been proved that using this method does not increase the asymptotic false positive probability [4].

Contributing

You're welcome to submit pull requests with any changes for this repository at any time. I'll be very glad to see any contributions.

References

  • [1] Luo, Lailong, et al. Optimizing bloom filter: challenges, solutions, and comparisons. IEEE Communications Surveys & Tutorials (2018).
  • [2] Knuth, Donald Ervin. The art of computer programming: sorting and searching. Vol. 3. Pearson Education, 1997.
  • [3] Karp, Richard M., and Michael O. Rabin. Efficient randomized pattern-matching algorithms. IBM journal of research and development 31.2 (1987): 249-260.
  • [4] Kirsch, Adam, and Michael Mitzenmacher. Less hashing, same performance: building a better bloom filter. European Symposium on Algorithms. Springer, Berlin, Heidelberg, 2006.

Project details


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