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RedisTimeSeries is a Redis Module adding a Time Series data structure to Redis.


Read more about the v1.0 GA features here .

  • High volume inserts, low latency reads
  • Query by start time and end-time
  • Aggregated queries (Min, Max, Avg, Sum, Range, Count, First, Last, STD.P, STD.S, Var.P, Var.S) for any time bucket
  • Configurable maximum retention period
  • Downsampling/Compaction - automatically updated aggregated timeseries
  • Secondary index - each time series has labels (field value pairs) which will allows to query by labels

Client Libraries

Official and community client libraries in Python, Java, JavaScript, Ruby, Go, C#, Rust, and PHP.

See the Clients page for the full list.

Using with other tools metrics tools

In the RedisTimeSeries organization you can find projects that help you integrate RedisTimeSeries with other tools, including:

  1. Prometheus - read/write adapter to use RedisTimeSeries as backend db.
  2. Grafana 7.1+ - using the Redis Data Source .
  3. Telegraph
  4. StatsD, Graphite exports using graphite protocol.

Memory model

A time series is a linked list of memory chunks. Each chunk has a predefined size of samples. Each sample is a tuple of the time and the value of 128 bits, 64 bits for the timestamp and 64 bits for the value.


You can either get RedisTimeSeries setup in the cloud, in a Docker container or on your own machine.

Redis Cloud

RedisTimeSeries is available on all Redis Cloud managed services, including a completely free managed database up to 30MB.

Get started here


To quickly try out RedisTimeSeries, launch an instance using docker:

docker run -p 6379:6379 -it --rm redislabs/redistimeseries

Download and running binaries

First download the pre-compiled version from RedisLabs download center .

Next, run Redis with RedisTimeSeries:

$ redis-server --loadmodule /path/to/module/

Build and Run it yourself

You can also build and run RedisTimeSeries on your own machine.

Major Linux distributions as well as macOS are supported.


First, clone the RedisTimeSeries repository from git:

git clone --recursive

Then, to install required build artifacts, invoke the following:

cd RedisTimeSeries
make setup

Or you can install required dependencies manually listed in .

If make is not yet available, the following commands are equivalent:


Note that will install various packages on your system using the native package manager and pip. This requires root permissions (i.e. sudo) on Linux.

If you prefer to avoid that, you can:

  • Review and install packages manually,
  • Utilize a Python virtual environment,
  • Use Docker with the --volume option to create an isolated build environment.


make build

Binary artifacts are placed under the bin directory.


In your redis-server run: loadmodule bin/

For more information about modules, go to the redis official documentation .

Give it a try

After you setup RedisTimeSeries, you can interact with it using redis-cli.

Here we'll create a time series representing sensor temperature measurements. After you create the time series, you can send temperature measurements. Then you can query the data for a time range on some aggregation rule.

With redis-cli

$ redis-cli> TS.CREATE temperature:3:11 RETENTION 6000 LABELS sensor_id 2 area_id 32
OK> TS.ADD temperature:3:11 1548149181 30
OK> TS.ADD temperature:3:11 1548149191 42
OK>  TS.RANGE temperature:3:11 1548149180 1548149210 AGGREGATION avg 5
1) 1) (integer) 1548149180
   2) "30"
2) 1) (integer) 1548149190
   2) "42"

Mailing List / Forum

Got questions? Feel free to ask at the RedisTimeSeries mailing list .


Redis Source Available License Agreement - see LICENSE