A Quick Start Guide to RedisTimeSeries ¶
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.
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/redistimeseries.so
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 https://github.com/RedisTimeSeries/RedisTimeSeries.git
Then, to install required build artifacts, invoke the following:
cd RedisTimeSeries make setup
Or you can install required dependencies manually listed in system-setup.py .
is not yet available, the following commands are equivalent:
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 system-setup.py and install packages manually,
- Utilize a Python virtual environment,
Use Docker with the
--volumeoption to create an isolated build environment.
Binary artifacts are placed under the
In your redis-server run:
For more information about modules, go to the redis official documentation .
Give it a try with
After you setup RedisTimeSeries, you can interact with it using redis-cli.
$ redis-cli 127.0.0.1:6379> TS.CREATE sensor1 OK
Creating a timeseries ¶
A new timeseries can be created with the
command; for example, to create a timeseries named
run the following:
You can prevent your timeseries growing indefinitely by setting a maximum age for samples compared to the last event time (in milliseconds) with the
option. The default value for retention is
, which means the series will not be trimmed.
TS.CREATE sensor1 RETENTION 2678400000
This will create a timeseries called
and trim it to values of up to one month.
Adding data points ¶
For adding new data points to a timeseries we use the
TS.ADD key timestamp value
argument is the UNIX timestamp of the sample in milliseconds and
is the numeric data value of the sample.
TS.ADD sensor1 1626434637914 26
add a datapoint with the current timestamp
you can use a
instead of a specific timestamp:
TS.ADD sensor1 * 26
append data points to multiple timeseries
at the same time with the
TS.MADD key timestamp value [key timestamp value ...]
Deleting data points ¶
Data points between two timestamps (inclusive) can be deleted with the
TS.DEL key fromTimestamp toTimestamp
TS.DEL sensor1 1000 2000
To delete a single timestamp, use it as both the "from" and "to" timestamp:
TS.DEL sensor1 1000 1000
Note: When a sample is deleted, the data in all downsampled timeseries will be recalculated for the specific bucket. If part of the bucket has already been removed though, because it's outside of the retention period, we won't be able to recalculate the full bucket, so in those cases we will refuse the delete operation.
Labels are key-value metadata we attach to data points, allowing us to group and filter. They can be either string or numeric values and are added to a timeseries on creation:
TS.CREATE sensor1 LABELS region east
Another useful feature of RedisTimeSeries is compacting data by creating a rule for downsampling (
). For example, if you have collected more than one billion data points in a day, you could aggregate the data by every minute in order to downsample it, thereby reducing the dataset size to 24 * 60 = 1,440 data points. You can choose one of the many available aggregation types in order to aggregate multiple data points from a certain minute into a single one. The currently supported aggregation types are:
avg, sum, min, max, range, count, first, last, std.p, std.s, var.p and var.s
It's important to point out that there is no data rewriting on the original timeseries; the compaction happens in a new series, while the original one stays the same. In order to prevent the original timeseries from growing indefinitely, you can use the retention option, which will trim it down to a certain period of time.
NOTE: You need to create the destination (the compacted) timeseries before creating the rule.
TS.CREATERULE sourceKey destKey AGGREGATION aggregationType timeBucket
TS.CREATE sensor1_compacted # Create the destination timeseries first TS.CREATERULE sensor1 sensor1_compacted AGGREGATION avg 60000 # Create the rule
With this creation rule, datapoints added to the
timeseries will be grouped into buckets of 60 seconds (60000ms), averaged, and saved in the
RedisTimeSeries allows to filter by value, timestamp and by labels:
Filtering by label ¶
You can retrieve datapoints from multiple timeseries in the same query, and the way to do this is by using label filters. For example:
TS.MRANGE - + FILTER area_id=32
This query will show data from all sensors (timeseries) that have a label of
with a value of
. The results will be grouped by timeseries.
Or we can also use the
command to get the last sample that matches the specific filter:
TS.MGET FILTER area_id=32
Filtering by value ¶
We can filter by value across a single or multiple timeseries:
TS.RANGE sensor1 - + FILTER_BY_VALUE 25 30
This command will return all data points whose value sits between 25 and 30, inclusive.
To achieve the same filtering on multiple series we have to combine the filtering by value with filtering by label:
TS.MRANGE - + FILTER_BY_VALUE 20 30 FILTER region=east
Filtering by timestamp ¶
To retrieve the datapoints for specific timestamps on one or multiple timeseries we can use the
Filter on one timeseries:
TS.RANGE sensor1 - + FILTER_BY_TS 1626435230501 1626443276598
Filter on multiple timeseries:
TS.MRANGE - + FILTER_BY_TS 1626435230501 1626443276598 FILTER region=east
It's possible to combine values of one or more timeseries by leveraging aggregation functions:
TS.RANGE ... AGGREGATION aggType timeBucket...
For example, to find the average temperature per hour in our
series we could run:
TS.RANGE sensor1 - + + AGGREGATION avg 3600000
To achieve the same across multiple sensors from the area with id of 32 we would run:
TS.MRANGE - + AGGREGATION avg 3600000 FILTER area_id=32
Aggregation bucket alignment ¶
When doing aggregations, the aggregation buckets will be aligned to 0 as so:
TS.RANGE sensor3 10 70 + AGGREGATION min 25
Value: | (1000) (2000) (3000) (4000) (5000) (6000) (7000) Timestamp: |-------|10|-------|20|-------|30|-------|40|-------|50|-------|60|-------|70|---> Bucket(25ms): |_________________________||_________________________||___________________________| V V V min(1000, 2000)=1000 min(3000, 4000)=3000 min(5000, 6000, 7000)=5000
And we will get the following datapoints: 1000, 3000, 5000.
You can choose to align the buckets to the start or end of the queried interval as so:
TS.RANGE sensor3 10 70 + AGGREGATION min 25 ALIGN start
Value: | (1000) (2000) (3000) (4000) (5000) (6000) (7000) Timestamp: |-------|10|-------|20|-------|30|-------|40|-------|50|-------|60|-------|70|---> Bucket(25ms): |__________________________||_________________________||___________________________| V V V min(1000, 2000, 3000)=1000 min(4000, 5000)=4000 min(6000, 7000)=6000
The result array will contain the following datapoints: 1000, 4000 and 6000
Aggregation across timeseries ¶
By default, results of multiple timeseries will be grouped by timeseries, but (since v1.6) you can use the
options to group them by label and apply an additional aggregation.
To find minimum temperature per region, for example, we can run:
TS.MRANGE - + FILTER region=(east,west) GROUPBY region REDUCE min