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

An inappropriate persistence strategy


Once at Yipit, a Redis instance (read-intensive) was experiencing some slowdowns, but nobody could understand why. At first, the DevOps team thought that the application's code was making Redis slow, but after some investigation, they found that the issue was due to a periodic backup strategy. Chapter 8, Scaling Redis (Beyond a Single Instance), will cover persistence in depth.

When Redis starts the procedure to create an RDB snapshot or rewrite the AOF file, it creates a child process (using the fork() system call), and the new process handles the procedure.

During the fork() execution, the process is blocked and Redis will stop serving clients. This is when the perceived latency by clients increases.

The Yipit problem was due to a long fork() time on AWS. The instance type family used was M2, which is a family of ParaVirtual (PV) machines, as opposed to Hardware-assisted Virtual Machines (HVM). It is known that the fork() system call in a PV machine is...