Go

Welcome to this series of blog posts which covers Redis Streams with the help of a practical example. We will use a sample application to make Twitter data available for search and query in real-time. RediSearch and Redis Streams serve as the backbone of this solution that consists of several co-operating components, each of which will we covered in a dedicated blog post.

The code is available in this GitHub repo - https://github.com/abhirockzz/redis-streams-in-action

This is the first part which explores the use case, motivations and provides a high level overview of the Redis features used in the solution.

Redis has a versatile set of data structures ranging from simple Strings all the way to powerful abstractions such as Redis Streams. The native data types can take you a long way, but there are certain use cases that may require a workaround. One example is the requirement to use secondary indexes in Redis in order to go beyond the key-based search/lookup for richer query capabilities. Though you can use Sorted Sets, Lists, and so on to get the job done, you’ll need to factor in some trade-offs.

Azure Cosmos DB is a resource governed system that allows you to execute a certain number of operations per second based on the provisioned throughput you have configured. If clients exceed that limit and consume more request units than what was provisioned, it leads to rate limiting of subsequent requests and exceptions being thrown – they are also referred to as 429 errors.

With the help of a practical example, I’ll demonstrate how to incorporate fault-tolerance in your Go applications by handling and retrying operations affected by these rate limiting errors. To help you follow along, the sample application code for this blog is available on GitHub - it uses the gocql driver for Apache Cassandra.