This chapter covers
In the first chapter, I introduced you to what Redis is about and what it’s capable of. In this chapter, I’ll continue on that path, starting to dig into several examples that come up in the context of some types of web applications. Though I’ve simplified the problems quite a bit compared to what happens in the real world, each of these pieces can actually be used with little modification directly in your applications. This chapter is primarily meant as a practical guide to what you can do with Redis, and chapter 3 is more of a command reference.
To start out, let’s look at what we mean by a web application from the high level. Generally, we mean a server or service that responds over the HTTP protocol to requests made by web browsers. Here are the typical steps that a web server goes through to respond to a request:
This list is a high-level overview of what happens in a typical web server. Web requests in this type of situation are considered to be stateless in that the web servers themselves don’t hold information about past requests, in an attempt to allow for easy replacement of failed servers. Books have been written about how to optimize every step in that process, and this book does similarly. How this book differs is that it explains how to replace some queries against a typical relational database with faster queries against Redis, and how to use Redis with access patterns that would’ve been too costly using a relational database.
Through this chapter, we’ll look at and solve problems that come up in the context of Fake Web Retailer, a fairly large (fake) web store that gets about 100 million hits per day from roughly 5 million unique users who buy more than 100,000 items per day. These numbers are big, but if we can solve big problems easily, then small and medium problems should be even easier. And though these solutions target a large web retailer, all but one of them can be handled by a Redis server with no more than a few gigabytes of memory, and are intended to improve performance of a system responding to requests in real time.
Each of the solutions presented (or some variant of them) has been used to solve real problems in production environments. More specifically, by reducing traditional database load by offloading some processing and storage to Redis, web pages were loaded faster with fewer resources.
Our first problem is to use Redis to help with managing user login sessions.
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