Low latency and compact probabilistic data structures for Redis

RedisBloom provides Redis with support for additional probabilistic data structures. These structures allow for constant memory space and extremely fast processing while still maintaining a low error rate. It supports scalable Bloom and Cuckoo filters to determine whether an item is present or absent from a collection with a given degree of certainty, Count-min sketch to count the frequency of the different items in sub-linear space, and Top-K to count top k events in a near deterministic manner.

Watch Cristian Castiblanco from Scopely describe its use
of RedisBloom.


Highly optimized for performance

State-of-the-art and academically proven probabilistic data structures and algorithms optimized for Redis.

Significant savings with a small compute and memory footprint

Advanced probabilistic algorithms reduce the need for compute and memory for massive data sets while still delivering an acceptable level of accuracy.

Reliable and scalable architecture

Any number of probabilistic filters and counters can be managed in a fully reliable and durable manner without requiring advance knowledge of the number of elements examined.

Use cases

Fraud mitigation

Identify unusual behaviors by comparing to previous activity without storing massive volumes of information.


Build fast and small leaderboards for very large data sets and user bases.


Ensure users see specific ads only a limited amount of times in a given period.

Available probabilistic data structures

Bloom filter

A data structure designed to rapidly determine if an element is present in a set in a highly memory-efficient manner.

Cuckoo filter

An alternative to Bloom filters with additional support for deletion of elements from a set.

Count-Min Sketch

Calculates frequency of events in data samples.


A deterministic algorithm that approximates frequencies for the top k items.

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