Extending Redis with Bloom Filter, Cuckoo Filter, Count-Mins-Sketch, and TopK functionality

RedisBloom extends Redis core to support additional probabilistic data structures. It allows for solving computer science problems at a constant memory space with extremely fast processing and a low error rate. It supports scalable Bloom and Cuckoo Filters to determine (with a given degree of certainty) whether an item is present or absent from a collection. Count-Mins-Sketch is used to count the frequency of the different items in sub-linear space and TopK allows Redis to count top k events in a (close to) deterministic manner.

Watch Cristian Castiblanco from Scopely describe its use of RedisBloom.


Highly optimized for performance

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

Significant savings with small compute and memory footprint

Using advanced probabilistic algorithms optimized for Redis

Reliable and scalable architecture

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

Main capabilities

Bloom filter

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

Cuckoo filter

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


Calculates frequency of events in data samples.


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