Book Image

Mastering Redis

By : Jeremy Nelson
Book Image

Mastering Redis

By: Jeremy Nelson

Overview of this book

Redis is the most popular, open-source, key value data structure server that provides a wide range of capabilities on which multiple platforms can be be built. Its fast and flexible data structures give your existing applications an edge in the development environment. This book is a practical guide which aims to help you deep dive into the world of Redis data structure to exploit its excellent features. We start our journey by understanding the need of Redis in brief, followed by an explanation of Advanced key management. Next, you will learn about design patterns, best practices for using Redis in DevOps environment and Docker containerization paradigm in detail. After this, you will understand the concept of scaling with Redis cluster and Redis Sentinel , followed by a through explanation of incorporating Redis with NoSQL technologies such as Elasticsearch and MongoDB. At the end of this section, you will be able to develop competent applications using these technologies. You will then explore the message queuing and task management features of Redis and will be able to implement them in your applications. Finally, you will learn how Redis can be used to build real-time data analytic dashboards, for different disparate data streams.
Table of Contents (18 chapters)
Mastering Redis
About the Author
About the Reviewers


The newest Redis data type is a probabilistic data structure that provides an estimated count of unique items in a collection. Under typical or normal situations, to get a unique count of a collection's items requires an amount of memory that is equal to the number of items or at least a time complexity of O(n). Why? To ensure that no items are double-counted if they are duplicated in the collection, the algorithm must keep a record of each item for comparison with any new items. This amount of overhead becomes quite large and expensive to calculate as the size of the collections increases in the order of millions of items. In contrast, storing unique elements in a HyperLogLog structure computes and stores an estimate of the size of the set as a probability instead of the actual value with a relatively small error rate of less than 1%. Adding one or more elements to a HyperLogLog with the PFADD command is an O(1) operation, while retrieving the count of unique items with a PFCOUNT...