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Redis Stack for Application Modernization

Redis Stack for Application Modernization

By : Luigi Fugaro, Ortensi
5 (2)
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Redis Stack for Application Modernization

Redis Stack for Application Modernization

5 (2)
By: Luigi Fugaro, 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)
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1
Part 1: Introduction to Redis Stack
6
Part 2: Data Modeling
11
Part 3: From Development to Production

Going beyond the real-time cache with Redis Stack

With the addition of new capabilities, Redis Stack pushes Redis forward and meets the expectations of software architects and developers who are having to deal with myriad new requirements. However, it also pushes the old traditional ones to the next step, which means finding new ways to resolve traditional problems with real-time performance, scalability, and high availability easily and inexpensively. Since its inception, Redis’ objective has been to provide suitable data structures and algorithms to ensure speed and minimal footprint. As an example, think of the Bitmap data structure, which grants access down to the bit level. With such flexibility, we can flag the days in a given month when a user has authenticated to a certain service:

127.0.0.1:6379> SETBIT user:032023 0 1
(integer) 0
127.0.0.1:6379> SETBIT user:032023 5 1
(integer) 0
127.0.0.1:6379> SETBIT user:032023 10 1
(integer) 0
127.0.0.1:6379> SETBIT...

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