bits-and-blooms/bloom

Health v2 · maintenance only

Adoption not part of the score

2816 stars · 260 forks observed · 2026-10-09

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

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

library · maturity stable

search-engine caching developer-tools databases performance developer-tools go cross-platform bloom-filter probabilistic-data-structures membership-query bitset murmur3 algorithms

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