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

Adding labels to data points

Labels are metadata attached to time series data points to provide additional context or information about the data. They are key-value pairs that help group, query, filter, or aggregate data. This makes it easier to manage and analyze large volumes of time-series data. For example, you might use labels to indicate the data source, measurement units, or the device or location from which the data was collected. By using labels, you can perform more granular and focused queries on your time-series data, making it easier to understand trends, relationships, and patterns.

Let’s apply a few labels to the mortensi.com site. Since its time series already exists, we can add labels by modifying the current time series as follows:

TS.ALTER mortensi.com LABELS dev python database redis

After applying the labels, the TS.INFO command for the mortensi.com time series will display them as shown here:

TS.INFO mortensi.com
1) "totalSamples"
2...