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
- Stats
- Entropy analysis
- Internal and external hashing of data.
- raw filter merging
Installing:
sudo pip install fastbloomfilter
External creating of the bloom filter file:
python mkbloom.py /tmp/filter.blf
Importing:
from fastBloomFilter import bloom
bf = bloom.BloomFilter(filename='/tmp/filter.blf')
Adding data to it:
bf.add('30000')
bf.add('1230213')
bf.add('1')
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:
bf.stat()
Or:
bf.info()
Querying data:
print(bf.query('1')) # True
print(bf.query('1230213')) # True
print(bf.query('12')) # False
Contributons:
Are welcome!
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