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
Note: You can also build and load the module yourself.
1. Launch RedisBloom with Docker
docker run -p 6379:6379 --name redis-redisbloom redislabs/rebloom:latest
2. Use RedisBloom with
docker exec -it redis-redisbloom bash # redis-cli # 127.0.0.1:6379>
Create a new bloom filter by adding a new item:
# 127.0.0.1:6379> BF.ADD newFilter foo (integer) 1
Find out whether an item exists in the filter:
# 127.0.0.1:6379> BF.EXISTS newFilter foo (integer) 1
In this case,
1 means that the
foo is most likely in the set represented by
newFilter. But recall that false positives are possible with Bloom filters.
# 127.0.0.1:6379> BF.EXISTS newFilter bar (integer) 0
0 means that
bar is definitely not in the set. Bloom filters do not allow for false negatives.
Building and Loading RedisBloom
To use RedisBloom, first build its shared library by running
make. If the build is successful, you'll have a shared library called
To load the library, pass its path to the
loadmodule directive when starting
$ redis-server --loadmodule /path/to/redisbloom.so
Documentation and full command reference at redisbloom.io.
Mailing List / Forum
Got questions? Feel free to ask at the RedisBloom mailing list.
RedisBloom is licensed under the Redis Source Available License Agreement