Quickstart
Quick Start Guide to Time Series
Setup
You can get RedisTimeSeries setup in the cloud, in a Docker container or on your own machine.
Redis Cloud
Redis Time Series are available on all Redis Cloud managed services, including a completely free managed database up to 30MB.
Docker
To quickly try out Redis Time Series, launch an instance of Redis Stack using docker:
docker run -p 6379:6379 -it --rm redis/redis-stack-server
Download and running binaries
First download the pre-compiled version from the Redis 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.
Requirements
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.
If make is not yet available, the following commands are equivalent:
./deps/readies/bin/getpy3
./system-setup.py
Note that system-setup.py 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.
Build
make build
Binary artifacts are placed under the bin directory.
Run
In your redis-server run: loadmodule bin/redistimeseries.so
For more information about modules, go to the redis official documentation.
Give it a try with redis-cli
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 TS.CREATE command; for example, to create a timeseries named sensor1 run the following:
TS.CREATE sensor1
You can prevent your timeseries growing indefinitely by setting a maximum age for samples compared to the last event time (in milliseconds) with the RETENTION option. The default value for retention is 0, which means the series will not be trimmed.
TS.CREATE sensor1 RETENTION 2678400000
This will create a timeseries called sensor1 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 command:
TS.ADD key timestamp value
The timestamp argument is the UNIX timestamp of the sample in milliseconds and value is the numeric data value of the sample.
Example:
TS.ADD sensor1 1626434637914 26
To add a datapoint with the current timestamp you can use a * instead of a specific timestamp:
TS.ADD sensor1 * 26
You can append data points to multiple timeseries at the same time with the TS.MADD command:
TS.MADD key timestamp value [key timestamp value ...]
Deleting data points
Data points between two timestamps (inclusive) can be deleted with the TS.DEL command:
TS.DEL key fromTimestamp toTimestamp
Example:
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
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
Compaction
Another useful feature of Redis Time Series is compacting data by creating a rule for compaction (TS.CREATERULE). 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, var.s and twa.
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 bucketDuration
Example:
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 sensor1 timeseries will be grouped into buckets of 60 seconds (60000ms), averaged, and saved in the sensor1_compacted timeseries.
Filtering
You can filter your time series by value, timestamp and 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 area_id with a value of 32. The results will be grouped by timeseries.
Or we can also use the TS.MGET 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_BY_TS argument:
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
Aggregation
It's possible to combine values of one or more timeseries by leveraging aggregation functions:
TS.RANGE ... AGGREGATION aggType bucketDuration...
For example, to find the average temperature per hour in our sensor1 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 GROUPBY and REDUCE 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