RedisBloom: Probabilistic Data Structures for Redis ¶

The RedisBloom module provides four data structures: 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.

Quick start guide ¶

Quick Start
Command references
Client libraries
References
License

Command references ¶

Detailed command references for each data structure:

Bloom vs. Cuckoo ¶

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.

Client libraries ¶

See each driver's README for details and documentation.

Project	Language	License	Author	URL
redisbloom-py	Python	BSD	Redis Labs	GitHub
JReBloom	Java	BSD	Redis Labs	GitHub
redisbloom-go	Python	BSD	Redis Labs	GitHub
rebloom	JavaScript	MIT	Albert Team	GitHub
phpredis-bloom	PHP	MIT	Rafa Campoy	GitHub
phpRebloom	PHP	MIT	Alessandro Balasco	GitHub

References ¶

Webinars ¶

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