RedisBloom: Probabilistic Data Structures for Redis
RedisBloom adds a set of probabilistic data structures to Redis, including Bloom filter, Cuckoo filter, Count-min sketch, Top-K, and t-digest. Using this capability, you can query streaming data without needing to store all the elements of the stream. Probabilistic data structures each answer the following questions:
- Bloom filter and Cuckoo filter:
- Did value v already appear in the data stream?
- Count-min sketch:
- How many times did value v appear in the data stream?
- What are the k most frequent values in the data stream?
- Which fraction of the values in the data stream are smaller than a given value?
- How many values in the data stream are smaller than a given value?
- Which value is smaller than p percent of the values in the data stream? (What is the p-percentile value?)
- What is the mean value between the p1-percentile value and the p2-percentile value?
- What is the value of the nᵗʰ smallest/largest value in the data stream? (What is the value with [reverse] rank n?)
Answering each of these questions accurately can require a huge amount of memory, but you can lower the memory requirements drastically at the cost of reduced accuracy. Each of these data structures allows you to set a controllable trade-off between accuracy and memory consumption. In addition to having a smaller memory footprint, probabilistic data structures are generally much faster than accurate algorithms.
RedisBloom is part of Redis Stack.
You can either get RedisBloom setup in a Docker container or on your own machine.
To quickly try out RedisBloom, launch an instance using docker:
docker run -p 6379:6379 -it --rm redis/redis-stack-server:latest
Build it yourself
You can also build RedisBloom on your own machine. Major Linux distributions as well as macOS are supported.
First step is to have Redis installed, of course. The following, for example, builds Redis on a clean Ubuntu docker image (
docker pull ubuntu):
mkdir ~/Redis cd ~/Redis apt-get update -y && apt-get upgrade -y apt-get install -y wget make pkg-config build-essential wget https://download.redis.io/redis-stable.tar.gz tar -xzvf redis-stable.tar.gz cd redis-stable make distclean make make install
Next, you should get the RedisBloom repository from git and build it:
apt-get install -y git cd ~/Redis git clone --recursive https://github.com/RedisBloom/RedisBloom.git cd RedisBloom ./sbin/setup bash -l make
exit to exit bash.
Note: to get a specific version of RedisBloom, e.g. 2.4.5, add
-b v2.4.5 to the
git clone command above.
make run -n and copy the full path of the RedisBloom executable (e.g.,
Next, add RedisBloom module to
redis.conf, so Redis will load when started:
apt-get install -y vim cd ~/Redis/redis-stable vim redis.conf
loadmodule /root/Redis/RedisBloom/bin/linux-x64-release/redisbloom.so under the MODULES section (use the full path copied above).
Save and exit vim (ESC :wq ENTER)
For more information about modules, go to the Redis official documentation.
Run redis-server in the background and then redis-cli:
cd ~/Redis/redis-stable redis-server redis.conf & redis-cli
Give it a try
After you setup RedisBloom, you can interact with it using redis-cli.
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.
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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 2.0 (RSALv2) or the Server Side Public License v1 (SSPLv1).