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.
State-of-the-art and academically proven probabilistic data structures and algorithms optimized for Redis.
Advanced probabilistic algorithms reduce the need for compute and memory for massive data sets while still delivering an acceptable level of accuracy.
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.
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.
A data structure designed to rapidly determine if an element is present in a set in a highly memory-efficient manner.
An alternative to Bloom filters 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.
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