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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

Storing and querying documents in Redis Stack

The usual approach to organizing information that helps to describe a system is to identify the entities in the specific business domain and the relationships interconnecting them. Examples of entities could be companies and employees, and the relationship interconnecting them would describe the employee as part of the headcount. Other examples include universities and students, cars and their components, and so on. This high-level description is referred to as the conceptual data model, where we describe the things that are interesting for the domain we are considering.

Once this synthetic description has been completed, we refine it into a logical data model by describing all the elements in detail. Here, the entities and relationships are defined more specifically, with attributes, keys, and data types (for example, strings or integers). Finally, when the domain description is completed and we need a concrete implementation to manage...

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