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!