Book Image

Redis Stack for Application Modernization

By : Luigi Fugaro, Mirko Ortensi
1 (1)
Book Image

Redis Stack for Application Modernization

1 (1)
By: Luigi Fugaro, Mirko Ortensi

Overview of this book

In modern applications, efficiency in both operational and analytical aspects is paramount, demanding predictable performance across varied workloads. This book introduces you to Redis Stack, an extension of Redis and guides you through its broad data modeling capabilities. With practical examples of real-time queries and searches, you’ll explore Redis Stack’s new approach to providing a rich data modeling experience all within the same database server. You’ll learn how to model and search your data in the JSON and hash data types and work with features such as vector similarity search, which adds semantic search capabilities to your applications to search for similar texts, images, or audio files. The book also shows you how to use the probabilistic Bloom filters to efficiently resolve recurrent big data problems. As you uncover the strengths of Redis Stack as a data platform, you’ll explore use cases for managing database events and leveraging introduce stream processing features. Finally, you’ll see how Redis Stack seamlessly integrates into microservices architectures, completing the picture. By the end of this book, you’ll be equipped with best practices for administering and managing the server, ensuring scalability, high availability, data integrity, stored functions, and more.
Table of Contents (18 chapters)
1
Part 1: Introduction to Redis Stack
6
Part 2: Data Modeling
11
Part 3: From Development to Production

Count-Min sketch

The Count-Min sketch probabilistic data structure, like HyperLogLog, counts the items that have been added, with the difference that the Count-Min sketch counts the number of times specific items have been added – that is, their frequency.

When using a Count-Min sketch data structure, any frequency counts below a predetermined threshold (established by the error rate) should be disregarded. The Count-Min sketch serves as a valuable tool for counting element frequencies in a data stream, especially when dealing with higher counts. Nevertheless, very low counts are often perceived as noise and are typically discarded in this context. To start using the data structure, we have the option to initialize it either based on the probabilities to be maintained or on the desired dimensions. It is important to note that the dimensions of the Count-Min sketch play a significant role because to merge two Count-Min sketches, they must have identical dimensions.

We can...