# RedisBloom/RedisBloom

Probabilistic Datatypes Module for Redis

Repository: https://github.com/RedisBloom/RedisBloom
Canonical: https://ross.abutalabs.com/products/redisbloom
Homepage: https://redis.io/docs/stack/bloom/
Language: C
License: NOASSERTION
License Family: other
Topics: bloom-filter, redis, redis-module, redisbloom
Last push: 2026-08-25T08:36:19+00:00

## Health v2 (maintenance only)
Score: 99/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 98, longevity 100
- inputs: {"age_days": 3359, "days_push": 8, "days_rel": 16, "gap_med": 0, "n_releases_24m": 24}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1784, forks 268 (observed 2026-08-28T04:05:35.958230+00:00)

## What it is
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
- artifact type: library
- maturity: maintenance
- function: caching, database, search-engine, data-science
- domain: databases, big-data, analytics, developer-tools
- platform: self-hosted, cross-platform
- tags: bloom-filter, cuckoo-filter, count-min-sketch, top-k, t-digest, probabilistic-data-structures, redis-module, streaming-data, linux, macos, docker

## Member repositories
- RedisBloom/RedisBloom (main) score 99

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:35.958230+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 2026-08-30T03:24:14.713915+00:00, confidence not recorded.
  - readme: https://github.com/RedisBloom/RedisBloom (fetched 2026-08-28T04:05:35.958230+00:00, sha 24770a3b97bd)
  - homepage: https://redis.io/docs/stack/bloom/ (fetched 2026-08-29T11:02:38.876732+00:00, sha f4109d5785c0)
- Data as of 2026-08-30T08:39:29.467469+00:00.