# bits-and-blooms/bloom

Go package implementing Bloom filters, used by many important systems

Repository: https://github.com/bits-and-blooms/bloom
Canonical: https://ross.abutalabs.com/products/bloom
Homepage: http://bits-and-blooms.github.io/bloom/
Language: Go
License: BSD-2-Clause
License Family: permissive
Topics: bloom, bloom-filters, go
Last push: 2026-07-10T13:18:56+00:00

## Health v2 (maintenance only)
Score: 69/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 91, release rhythm 22, longevity 100
- inputs: {"age_days": 5583, "days_push": 54, "days_rel": 311, "gap_med": null, "n_releases_24m": 1}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2807, forks 260 (observed 2026-08-28T04:07:22.802856+00:00)

## What it is
A Go library implementing fast, space-efficient Bloom filters backed by a compact bitset and murmur3 hashing. It supports membership queries with no false negatives, a tunable false-positive rate, set algebra, cardinality estimation, and serialization.

## Use cases
- check whether an element might be in a large set without storing the set
- avoid expensive database lookups for definitely-absent keys
- build a cache-aside layer that skips misses cheaply
- estimate the number of items added to a filter
- merge filters from multiple shards into one
- serialize a filter to disk or over the network

## When to choose
- you need probabilistic set membership in Go with tunable false positives and zero false negatives
- memory is constrained and an occasional false positive is acceptable
- you need a mature, widely used Bloom filter implementation with serialization support

## When to avoid
- you need exact membership answers with no false positives
- you need a dynamic structure with unknown capacity ahead of time
- you need deletion support (standard Bloom filters cannot remove items)

## Facets
- artifact type: library
- maturity: stable
- function: search-engine, caching, developer-tools
- domain: databases, performance, developer-tools
- platform: go, cross-platform
- tags: bloom-filter, probabilistic-data-structures, membership-query, bitset, murmur3, algorithms

## Member repositories
- bits-and-blooms/bloom (main) score 69

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:22.802856+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 2026-08-30T08:14:27.246568+00:00, confidence not recorded.
  - readme: https://github.com/bits-and-blooms/bloom (fetched 2026-08-28T04:07:22.802856+00:00, sha 154273b825e5)
  - homepage: http://bits-and-blooms.github.io/bloom/ (fetched 2026-08-29T09:54:15.715864+00:00, sha 34a302ef70f7)
- Data as of 2026-08-30T08:39:29.467469+00:00.