Bloom filters and other probabilistic data structures for Redis

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RedisBloom adds four probabilistic data structures to Redis: a scalable Bloom filter, a cuckoo filter, a count-min sketch, and a top-k. These data structures trade perfect accuracy for extreme memory efficiency, so they're especially useful for big data and streaming applications.

Bloom and cuckoo filters are used to determine, with a high degree of certainty, whether an element is a member of a set.

A count-min sketch is generally used to determine the frequency of events in a stream. You can query the count-min sketch get an estimate of the frequency of any given event.

A top-k maintains a list of k most frequently seen items.

Bloom vs. Cuckoo filters

Bloom filters typically exhibit better performance and scalability when inserting items (so if you're often adding items to your dataset, then a Bloom filter may be ideal). Cuckoo filters are quicker on check operations and also allow deletions.

Academic sources

Bloom Filter

Cuckoo Filter

Count-Min Sketch




  1. Probabilistic Data Structures - The most useful thing in Redis you probably aren't using

Blog posts

  1. RedisBloom Quick Start Tutorial
  2. Developing with Bloom Filters
  3. RedisBloom on Redis Enterprise
  4. Probably and No: Redis, RedisBloom, and Bloom Filters
  5. RedisBloom – Bloom Filter Datatype for Redis

Mailing List / Forum

Got questions? Feel free to ask at the RedisBloom forum.


RedisBloom is licensed under the Redis Source Available License Agreement


Commands Overview

Quick start

"Quick start guide"

Client Libraries

RedisBloom has several client libraries, written by the module authors and community members - abstracting the API in different programming languages. While it is possible and simple to use the raw Redis commands API, in most cases it's easier to just use a client library abstracting it.

Configuration Parameters

RedisBloom supports multiple module configuration parameters. All of these parameters can only be set at load-time.