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 recommendation engine

Typically, we would retrieve documents based on their data, which means that we would resort to different indexing methods to perform a search, such as TEXT, TAG, or NUMERIC. However, to provide realistic recommendations, we can’t just rely on the content or taxonomy of the information stored in a database – we must also rely on other methods that take into account the popularity and feedback from users who may have rated that content. This leads to the introduction of another variable: the relevance of the results. As an example, if a certain item is rated to be top-quality and affordable, our database should return this item rather than other items that are inferior or more expensive and sort the results by relevance.

In addition to searches based on the relevance of the documents, another type of recommendation can be based on the appearance of an item, or other properties that are also intrinsic, such as a textual description...