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RedisBloom - Probablistic Datatypes Module for Redis¶

This module provides three datatypes, a Scalable Bloom Filter, Cuckoo Filter and a Count-Mins-Sketch. The first two datatypes are used to determine (with a given degree of certainty) whether an item is present or absent from a collection while the last is used to count the frequency of the different items in sub-linear space.

Note: You can also build and load the module yourself.

You can find a command reference in Bloom Commands.md and Cuckoo Commands

Launch RedisBloom with Docker¶

docker run -p 6379:6379 --name redis-redisbloom redislabs/rebloom:latest

Use RedisBloom with `redis-cli`¶

docker exec -it redis-redisbloom bash

# redis-cli
# 127.0.0.1:6379>

Start a new bloom filter by adding a new item

# 127.0.0.1:6379> BF.ADD newFilter foo
(integer) 1

Checking if an item exists in the filter

# 127.0.0.1:6379> BF.EXISTS newFilter foo
(integer) 1

Building and Loading RedisBloom¶

In order to use this module, build it using make and load it into Redis.

Loading¶

Invoking redis with the module loaded

$ redis-server --loadmodule /path/to/rebloom.so

You can find a command reference in Bloom_Commands.md and Cuckoo_Commands.md

Building¶

In order to use this module, build it using make and load it into Redis.

Module Options¶

You can adjust the default error ratio and the initial filter size (for bloom filters) using the ERROR_RATE and INITIAL_SIZE options respectively when loading the module, e.g.

$ redis-server --loadmodule /path/to/rebloom.so INITIAL_SIZE 400 ERROR_RATE 0.004

The default error rate is 0.01 and the default initial capacity is 100.

Bloom vs. Cuckoo¶

Bloom Filters typically exhibit better performance and scalability when inserting items (so if you're often adding items to your dataset then Bloom may be ideal), whereas Cuckoo Filters are quicker on check operations and also allow deletions.