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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

Understanding Probabilistic Data Structures

The probabilistic data structures of Redis Stack are packed into a set of capabilities also known as Bloom filters. Such structures owe their name to Burton Howard Bloom, a computer scientist who introduced the concept of a probabilistic data structure in 1970 to resolve the problem of verifying whether an item belongs to a set. By using hash data representations, it is possible to achieve a sufficient approximation to the problem under analysis, allowing false positives (the item may belong to the set), but without false negatives (the item definitely does not belong to the set).

The Bloom filter has since become a widely used data structure in computer science. It is used in various applications, such as spell-checking, network routing, content filtering, and DNA sequence analysis.

Probabilistic data structures process large volumes of data in real time with minimal memory requirements. This chapter covers several types of probabilistic...

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