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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

Working with time series

As mentioned at the beginning of the chapter, a time series is a sequence of data points collected and recorded over time at regular intervals. Redis Stack provides a rich API to manage data points collected into a time series.

To start with an example, you will need to create a time series. The simplest method to accomplish this is by utilizing the TS.CREATE command followed by a key, representing the time series name, as shown here:

TS.CREATE key

However, there are additional parameters that can be employed when creating the time series, as outlined here:

TS.CREATE key [RETENTION retentionTime] [ENCODING [UNCOMPRESSED|COMPRESSED]] [CHUNK_SIZE size] [DUPLICATE_POLICY policy] [LABELS label value..]

Each parameter can be adjusted for performance optimization, reducing memory footprint, or enhancing querying and aggregation capabilities, as detailed here:

  • key: This is the key that identifies the time series.
  • RETENTION: This is the retention...

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