Book Image

Redis Essentials

Book Image

Redis Essentials

Overview of this book

Redis is the most popular in-memory key-value data store. It's very lightweight and its data types give it an edge over the other competitors. If you need an in-memory database or a high-performance cache system that is simple to use and highly scalable, Redis is what you need. Redis Essentials is a fast-paced guide that teaches the fundamentals on data types, explains how to manage data through commands, and shares experiences from big players in the industry. We start off by explaining the basics of Redis followed by the various data types such as Strings, hashes, lists, and more. Next, Common pitfalls for various scenarios are described, followed by solutions to ensure you do not fall into common traps. After this, major differences between client implementations in PHP, Python, and Ruby are presented. Next, you will learn how to extend Redis with Lua, get to know security techniques such as basic authorization, firewall rules, and SSL encryption, and discover how to use Twemproxy, Redis Sentinel, and Redis Cluster to scale infrastructures horizontally. At the end of this book, you will be able to utilize all the essential features of Redis to optimize your project's performance.
Table of Contents (17 chapters)
Redis Essentials
Credits
About the Authors
Acknowledgments
About the Reviewers
www.PacktPub.com
Preface
5
Clients for Your Favorite Language (Become a Redis Polyglot)
Index

The wrong data type for the job


When we learn about a new feature of a tool, we often unconsciously try to apply it to our current set of problems. Many times, there is nothing wrong with this, but that's not always the case with Redis.

At Yipit, we used to store all deals that were going to be sent to users in a Redis Set. Although the solution worked, developers thought it was memory-inefficient because the Yipit user base was large. To rectify this issue, some of the developers thought that changing the Set implementation to a Bitmap implementation would make the solution memory-efficient. In other contexts, Bitmaps performed so well that developers thought they were the answer to everything—this turned out to be untrue.

No benchmark tests were performed based on the wrong assumption that Bitmaps would always be more memory-efficient than Sets.

The Bitmap implementation sounded logical and was deployed to production. The DevOps engineers received alerts and noticed that the Redis memory...