tylertreat/BoomFilters

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1645 stars · 118 forks observed · 2026-08-28

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

A Go library of probabilistic data structures for processing continuous, unbounded data streams, including Stable, Scalable, Counting, and Inverse Bloom filters, Cuckoo filters, HyperLogLog, Count-Min Sketch, Top-K, and MinHash. It enables approximate set membership, cardinality estimation, frequency estimation, and set-similarity comparison with bounded memory.

Use cases

• deduplicate events from an unbounded stream
• estimate cardinality of a large dataset with low memory
• track top-k most frequent elements in a stream
• approximate similarity between two sets or documents
• count element frequencies with a count-min sketch
• membership testing when dataset size is unknown ahead of time

When to choose

• you're processing unbounded or streaming data in Go and can tolerate small false-positive rates
• you need memory-efficient approximate counting, cardinality, or deduplication
• you need filters that support insertion and deletion (Counting/Cuckoo filters)

When to avoid

• you need exact set membership, counts, or cardinality
• you're not working in Go
• your dataset is small enough that exact structures fit in memory

Facets

library · maturity stable

search-engine data-science developer-tools data-science big-data analytics go cross-platform bloom-filter cuckoo-filter hyperloglog count-min-sketch minhash stream-processing probabilistic-data-structures sketching algorithms

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