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

Recommendations based on visual search

We have seen a basic example of semantic similarity search, but there are other ways of generating recommendations from the item under consideration. One is by looking at its appearance. Using the pre-trained models in the PyTorch library, we can extract embeddings from images and associate them with their Hash or JSON representation in the database. The sample Python excerpt we’ll be looking at makes use of the Img2Vec wrapper library, which can be installed as follows:

pip install img2vec_pytorch

This script opens a file and produces an embedding of 1,024 numbers using the densenet model. Let’s prototype a simple application with three items in the database – a glass, a spoon, and a cup:

Figure 6.1 – Training images for VSS

This Python snippet of...