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

Cuckoo filters

Cuckoo filters are an evolution of Bloom filters that were published in 2014 and address the limitations of Bloom filters, especially around collision handling. This filter inherits its name from the cuckoo bird, famous for laying its eggs in the nests of other bird species and leaving them to be raised by the host bird. In doing so, the cuckoo pushes the other eggs or chicks out of the nest. This behavior describes the implementation of Cuckoo filters. Differently from Bloom filters, Cuckoo filters use a fingerprint-based technique that allows for the fast and efficient handling of collisions and reduces the rate of false positives while maintaining the same space requirements as Bloom filters.

Instead of storing the hashed version of an item as Bloom filters do, Cuckoo filters use multiple locations, or buckets, to store the fingerprint representation of the item. When a new item is added to the filter and a collision occurs at a candidate bucket, the existing item...