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Pure Python Bloom Filter module

Project description

A pure python bloom filter (low storage requirement, probabilistic set datastructure) is provided. It is known to work on CPython 2.x, CPython 3.x, Pypy and Jython.

Includes mmap, in-memory and disk-seek backends.

The user specifies the desired maximum number of elements and the desired maximum false positive probability, and the module calculates the rest.

Usage:

from bloom_filter import BloomFilter

# instantiate BloomFilter with custom settings,
# max_elements is how many elements you expect the filter to hold.
# error_rate defines accuracy; You can use defaults with
# `BloomFilter()` without any arguments. Following example
# is same as defaults:
bloom = BloomFilter(max_elements=10000, error_rate=0.1)

# Test whether the bloom-filter has seen a key:
assert "test-key" in bloom is False

# Mark the key as seen
bloom.add("test-key")

# Now check again
assert "test-key" in bloom is True

Project details


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Filename, size & hash SHA256 hash help File type Python version Upload date
bloom_filter-1.3-py3-none-any.whl (8.8 kB) Copy SHA256 hash SHA256 Wheel py3 Apr 30, 2017
bloom_filter-1.3.tar.gz (6.7 kB) Copy SHA256 hash SHA256 Source None Apr 30, 2017

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