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

Bloom filter

A Bloom filter is one of the probabilistic data structures supported by Redis Stack and is used to test whether an item is a member of a set. It is crucial as a data deduplication solution – that is, for removing duplicated data from a set. It is a memory-efficient and fast data structure that uses a bit array and a set of hash functions to determine whether an item is in the set or not. Testing for membership to the filter can return “possibly in the set” or “definitely not in the set,” which means that false positives are possible, but false negatives are not. Imprecisions (or approximations) are around the corner in every aspect of life, and digital computing does not make any difference. Think of the lossy compression algorithms for images (JPEG) or audio files (MP3): we can still enjoy media files and not even realize there is a loss of quality. A Bloom filter simplifies the management and speed of solutions that require the existence...