Bloom filter

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.

A good use case for a Bloom filter is to check for an already used username. On a small scale, this is no problem, but as a service grows, this can be very taxing on a database. It is very simple to implement this with a ReBloom.

> BF.ADD usernames funnyfred
(integer) 1
> BF.ADD usernames fredisfunny
(integer) 1
> BF.ADD usernames fred
(integer) 1
> BF.ADD usernames funfred
(integer) 1

> BF.EXISTS usernames fred
(integer) 1
> BF.EXISTS usernames fred_is_funny
(integer) 0