# jaybaird/python-bloomfilter

Scalable Bloom Filter implemented in Python

Repository: https://github.com/jaybaird/python-bloomfilter
Canonical: https://ross.abutalabs.com/products/python-bloomfilter
Language: Python
License: MIT
License Family: permissive
Archived: true
Last push: 2021-07-01T08:40:04+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 6474, "days_push": 1889, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, archived
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1618, forks 321 (observed 2026-08-28T04:05:12.141785+00:00)

## What it is
pybloom is a Python module implementing Bloom Filters and Scalable Bloom Filters as a probabilistic set membership data structure. It grows filter capacity automatically based on false positive probability targets, following the Almeida et al. GLOBECOM 2007 paper.

## Use cases
- check if an element is in a large set without storing it all
- build a scalable bloom filter that grows with the dataset
- avoid expensive lookups with a probabilistic membership test
- deduplicate items with a small memory footprint
- implement a spell checker or cache filter with controlled false positive rate

## When to choose
- you need memory-efficient approximate set membership in pure Python
- you don't know the final set size in advance and want automatic scaling
- a small false positive rate is acceptable for your use case

## When to avoid
- you need exact membership answers with zero false positives
- you need deletion support, which bloom filters don't provide
- you need a actively maintained or high-performance implementation for production systems

## Facets
- artifact type: library
- maturity: maintenance
- function: caching, search-engine, data-science
- domain: databases, developer-tools
- platform: python, cross-platform
- tags: bloom-filter, probabilistic-data-structure, scalable-bloom-filter, algorithms

## Member repositories
- jaybaird/python-bloomfilter (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:12.141785+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:49:38.656370+00:00, confidence not recorded.
  - readme: https://github.com/jaybaird/python-bloomfilter (fetched 2026-08-28T04:05:12.141785+00:00, sha 4d8bd263e786)
- Data as of 2026-08-30T08:39:29.467469+00:00.