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

Redis Stack as a Vector Database

Vector similarity search (VSS) is a core functionality of Redis Stack, the foundation of a vast variety of use cases. In this chapter, we will go through the concepts you need to understand to make the most out of this capability. The main idea behind VSS is that Redis Stack can store, index, and search vectors, and vectors are an optimal representation of unstructured data (data without a data model and a structure, which is harder to organize) because vectors can be easily processed by machines and take advantage of optimized hardware such as GPUs. New techniques have emerged in the areas of AI and ML to help with the task of modeling, classifying, and understanding unstructured data such as images, audio files, text, and more. By leveraging this capability, it is possible to build systems that, while making sense of such data, resolve problems otherwise impossible to solve with traditional databases. Unstructured data is a relevant portion of the...