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

Summary

In this chapter, you learned about the basics of working with time-series data in Redis. You learned how to insert data points into Redis Stack for Time Series and how to add labels to those data points to make them more easily identifiable and searchable.

You also learned about the aggregation framework in Redis for Time Series, which allows you to analyze and aggregate data points within a specified time range or time bucket using various statistical functions such as mean, sum, min, max, and standard deviation. By utilizing the aggregation framework, you can gain deeper insights into your time-series data and identify trends, patterns, and anomalies more easily.

Additionally, you discovered how to shrink your dataset while keeping important information. This is done by choosing a method for combining data (aggregation method) and setting a time period (time bucket) for organizing the data.

To help visualize these aggregations, you learned how to use RedisInsight...