RedisBloom/RedisBloom

Health v2 · maintenance only

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Adoption not part of the score

1784 stars · 268 forks observed · 2026-08-28

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

RedisBloom is a Redis module written in C that adds probabilistic data structures—Bloom filters, Cuckoo filters, Count-min sketch, Top-K, and t-digest—to Redis. It enables memory-efficient approximate queries over streaming data with a tunable accuracy-versus-memory trade-off. As of Redis 8, its functionality is merged into core Redis and standalone releases have stopped.

Use cases

• check if a value has already appeared in a stream without storing all elements
• count approximate frequencies of events with a count-min sketch
• find the top-k most frequent items in a data stream
• compute percentiles and quantiles over streaming data with t-digest
• reduce memory usage for set membership checks at scale
• deduplicate URLs or user IDs in a high-throughput pipeline

When to choose

• you need memory-efficient approximate membership or frequency queries over large streams
• you are running Redis 7 or earlier and need probabilistic data structures as a module
• you can tolerate a small false-positive rate in exchange for large memory savings

When to avoid

• you run Redis 8 or later, where these data structures are built in and no module install is needed
• you require exact counts or exact set membership with zero false positives
• you need a 32-bit system, which is not supported

Facets

library · maturity maintenance

caching database search-engine data-science databases big-data analytics developer-tools self-hosted cross-platform bloom-filter cuckoo-filter count-min-sketch top-k t-digest probabilistic-data-structures redis-module streaming-data linux macos docker

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