How to Use Redis Streams in Your Apps

Data processing has been revolutionized in recent years, and these changes present tremendous possibilities. For example, if we consider a variety of use cases — from IoT and Artificial Intelligence to user activity monitoring, fraud detection and FinTech — what do all of these cases have in common? They all collect and process high volumes of data, which arrive at high velocities. After processing this data, these technologies then deliver them to all the appropriate consumers of data.

With the release of version 5.0, Redis launched an innovative new way to manage streams while collecting high volumes of data — Redis Streams. Redis Streams is a data structure that, among other functions, can effectively manage data consumption, persist data when consumers are offline with a data fail-safe, and create a data channel between many producers and consumers. It allows users to scale the number of consumers using an app, enables asynchronous communications between producers and consumers and efficiently uses main memory. Ultimately, Redis Streams is designed to meet consumers' diverse needs, from real-time data processing to historical data access, while remaining easy to manage.

Redis Streams offers several possibilities for users, including the ability to integrate this new data structure into various apps. In order to make it easier for users to start using Redis Streams, we have written up a few tutorials to help get you started:

1. How to use Redis Streams: In this article, we walk you through the basics of using Redis Streams. We'll look at how we can add data to a stream, and how we can read that data (all at once, asynchronously, as it arrives, etc.) to satisfy different consumer use cases. We hope that this tutorial will help you understand data flow in Redis Streams, as well as how to consume or partition data from a stream.
2. How to use consumer groups in Redis Streams: In this article, we explain how to use consumer groups in Redis Streams. A consumer group is a way to split a stream of messages among multiple clients to speed up processing or lighten the load for slower consumers; its aim is to scale out your data consumption process. This tutorial can help you not only understand the usage of consumer groups, but also how to read, manage, and consume Redis Streams data, recover from app failures, and remove processed messages from pending entries lists.
3. How to build a Redis Streams application: In this article, we demonstrate how to develop a data stream processing application using Redis Streams. This tutorial will walk you through the recommended technological and design components of a Redis Streams application, outline how to run and test such an application, and explain how to verify the data in your streams.

We hope that these three articles will help you get a better grasp on how Redis Streams can be used — and how you can optimize data processing with Redis Streams as a tool. Data is currently being produced at faster rates than ever, which in turn produces new challenges. Over at Redis Labs, we hope that innovations like Redis Streams can help our users tackle these challenges head-on.