OkBloomer 0.0.4

An implementation of the OkBloomer algorithm, an autoscaling Bloom filter with ultra-low memory footprint for Python.

Ok Bloomer

An implementation of the OkBloomer algorithm, an autoscaling Bloom filter with ultra-low memory footprint for Python. Ok Bloomer employs a novel layered filtering strategy that allows it to expand while maintaining an upper bound on the false positive rate. As such, Ok Bloomer is suitable for streaming data where the size is not known a priori.

• Ultra-low memory footprint
• Autoscaling works on streaming data
• Bounded maximum false positive rate
• Open-source and free to use commercially

Installation

Install DNA Hash using a Python package manager, example pip:

pip install okbloomer

Parameters

#	Name	Default	Type	Description
1	max_false_positive_rate	0.01	float	The upper bound on the false positivity rate.
2	num_hashes	4	int	The number of hash functions used, i.e. the number of slices per layer.
3	layer_size	32000000	int	The size of each layer of the filter in bits. Ideal sizes can be divided evenly by num_hashes.

Example Usage

from okbloomer import BloomFilter

filter = BloomFilter(
    max_false_positive_rate=0.01,
    num_hashes=4,
    layer_size=32000000,
)

filter.insert('foo')

print(filter.exists('foo'))

print(filter.existsOrInsert('bar'))

print(filter.exists('bar'))

print(filter.false_positive_rate())

True 

False

True

3.906249999999999e-27

References

• [1] A. DalPino. (2021). OkBloomer, a novel autoscaling Bloom Filter [link].
• [2] K. Christensen, et al. A New Analysis of the False-Positive Rate of a Bloom Filter.
• [3] A. Kirsch, et al. Less Hashing, Same Performance: Building a Better Bloom Filter, 2006.