Time series with Redis stack

Features

• 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, twa) for any time bucket
   
• Configurable maximum retention period
   
• Downsampling/compaction for automatically updated aggregate timeseries
   
• Secondary indexing for time series entries. Each time series has labels (field value pairs) which will allow querying by labels.

Client libraries

RedisTimeSeries contains a number of client libraries that abstract the API in several programming languages and were built by the module authors and community members.

While using the raw Redis commands API is possible and straightforward, it is usually more convenient to use a client library that abstracts it.

Using 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.

Prometheus

Redis TimeSeries Adapter gets Prometheus metrics through remote write and uses the TimeSeries module to write them to Redis.

To build the project:

make build
cd bin

To send metrics to Redis, provide the address in host:port format.

redis-ts-adapter --redis-address localhost:6379

To receive metrics from Prometheus, Add remote write section to prometheus configuration:

remote_write:
  - url: 'http://127.0.0.1:9201/write'

Makefile commands

run tests:

make test

go linting:

make lint

Redis Sentinel

Use the redis-sentinel flag if you're using Redis Sentinel for high availability redis:

redis-ts-adapter --redis-sentinel-address localhost:26379 --redis-sentinel-master mydb

Grafana

Grafana's Redis Data Source is a plugin that allows users to connect to any Redis database, both on-premises and in the cloud. It includes pre-built dashboards and allows you to create custom dashboards to monitor Redis and application data.

Redis Data Source can be downloaded through the Grafana Marketplace or installed using the grafana-cli command line tool:

grafana-cli plugins install redis-datasource

Source

For Docker instructions and installation without Internet access, follow the Quickstart page.

Configuration

Data Source allows connection to Redis using TCP port, Unix socket, Cluster, Sentinel and supports SSL/TLS authentication.

Source

Telegraph

Telegraph is a collection, processing, aggregation, and writing agent for metrics. Based on a plugin framework that allows community developers to simply add support for new metric collecting. There are four distinct types of plugins:

1. Input Plugins gather data from the system, services, and third-party APIs.
    
2. Processor Plugins decorate, transform, and filter metrics
    
3. Aggregator Plugins generate total metrics (e.g., min, max, mean, quantiles, etc.)
    
4. Output Plugins write metrics to various destinations

Build From Source

Telegraph requires the Go version 1.18 or newer, and the Makefile requires GNU make.

1. Install Go >=1.18 (1.18.0 recommended)
    
2. Clone the Telegraf repository:
   git clone https://github.com/influxdata/telegraf.git
    
3. Run make from the source directory
   cd telegraph

make

Memory model

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

Frequently Asked Questions

Is Redis a timeseries database?

The RedisTimeSeries module makes using Redis for time-series applications like IoT data, market prices, and telemetry much easier. You may consume and query millions of samples and events at the speed of Redis with RedisTimeSeries.
 

What is the Redis time series?

A time series is a collection of memory bits connected together. Each chunk has a predetermined sample size. Each sample is a 128-bit tuple, consisting of 64 bits for the timestamp and 64 bits for the value.
 

Is Redis good for real-time?

With streaming technologies, Redis can be used as an in-memory data storage system like Apache Kafka and Amazon Kinesis to ingest, process, and analyze real-time data with sub-millisecond latency. For real-time analytics use cases, including social media analytics, ad targeting, personalization, and IoT, Redis is an excellent solution.

Conclusion

In this article, we have extensively discussed the way of adding Time Series API with Redis stack.

We hope that this blog has helped you enhance your knowledge regarding Time Series API with Redis stack. 

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