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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

Summary

In this chapter, we learned how Redis Stack can store and index vectors to perform similarity searches and recommend similar documents. The power of VSS is that it can be used to find similar matches from incomplete or uncorrelated data. It can also leverage innovative AI/ML models, different search algorithms such as FLAT or HNSW, which focus on the precision or speed of the search, and different distances so that the most suitable option can be configured concerning the entity described by the vector embedding. VSS has been used for recommendation engines, but there are additional and relevant use cases that are trending right now, such as question answering, where we take advantage of generative models and their ability to take a set of prompts and perform text completion from the results of VSS so that we can provide complete answers out of them.

Data classification is another use case: by pre-training the database with a set of vectors modeling known objects (labeled...

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