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

Why Redis Stack for Time Series?

Redis Stack, with its high-performance capabilities, can ingest and process large volumes of data efficiently. As you delve deeper into this chapter, you will learn about various built-in features that Redis Stack provides for working with time-series data:

  • High-performance: Redis Stack for Time Series is designed to provide high-speed data ingestion, querying, and processing, leveraging the in-memory nature of Redis.
  • Data retention policies: Redis Stack for Time Series allows you to set data retention policies to automatically expire older data points based on time. This helps to manage storage efficiently, especially when dealing with large volumes of time-series data.
  • Downsampling and aggregation: Redis Stack for Time Series provides built-in support for data aggregation and downsampling, allowing you to reduce the granularity of data and store aggregated values over time. The available aggregation functions include average, minimum...