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

Redis Stack as a Time Series Database

Redis Stack, as we learned in Chapter 1, Introducing Redis Stack, is also capable of handling a particular data type called time-series data points.

Time series are a sequence of data points collected and recorded over time at regular intervals. In a time series, the data points are typically measured by intervals ranging from hourly to daily, weekly, monthly, or yearly. Time-series data can represent various types of observations, such as stock prices, weather data, sales figures, or economic indicators, among others.

Time-series analysis is the process of using statistical techniques to study and forecast trends, patterns, and correlations within the data. This can help in understanding the underlying structure and behavior of the data, as well as making predictions about future values.

In this chapter, you will explore various aspects of Redis Stack for time series, including storing and managing data, understanding the use cases it...