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

Chapter 9. Redis Cluster and Redis Sentinel (Collective Intelligence)

Redis was initially designed to be very lightweight and fast. Previously, the only topology available for anyone using Redis was master/slave, in which the master receives all the writes and replicates the changes to the slave (or slaves). This happens without any sort of automatic failover or data sharding. This topology works well in many scenarios, such as when:

  • The master has enough memory to store all of the data that you need

  • More slaves can be added to scale reads better or when network bandwidth is a problem (the total read volume is higher than the hardware capability)

  • It is acceptable to stop your application when maintenance is required on the master machine

  • Data redundancy through slaves is enough

But it does not work well in other scenarios, such as when:

  • The dataset is bigger than the available memory in the master Redis instance

  • A given application cannot be stopped when there are issues with the master instance...