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A fast and simple probabilistic bloom filter that supports compression

Project description

# Simple and fast pythonic bloomfilter

From wikipedia: "A Bloom filter is a space-efficient probabilistic data structure, conceived by Burton Howard Bloom in 1970, that is used to test whether an element is a member of a set. False positive matches are possible, but false negatives are not – in other words, a query returns either "possibly in set" or "definitely not in set". Elements can be added to the set, but not removed (though this can be addressed with a "counting" filter); the more elements that are added to the set, the larger the probability of false positives."

This filter supports:

Saving, reloading, compressed bloomfilter file lrzip like
for compression: lz4>lzo>zlib>bz2>lzma
for decompression: lzma>bz2>zlib>lzo>lz4
Entropy analysis
Internal and external hashing of data.
raw filter merging


sudo pip install fastbloomfilter

External creating of the bloom filter file:

python /tmp/filter.blf


from fastBloomFilter import bloom

bf = bloom.BloomFilter(filename='/tmp/filter.blf')

Adding data to it:




Adding data and at the same time querying it:

print bf.update('1') # True

print bf.update('1') # True

print bf.update('2') # False

print bf.update('2') # True

Printing stats:



Querying data:

print bf.query('1') # True

print bf.query('1230213') # True

print bf.query('12') # False

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Files for fastBloomFilter, version 0.0.4
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