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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
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6
Part 2: Data Modeling
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Chapter 8: Understanding Probabilistic Data Structures
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Part 3: From Development to Production
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Chapter 10: RedisInsight – the Data Management GUI
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Index
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Aggregation framework

The Redis Stack for Time Series aggregation framework provides functions that enable users to perform operations such as calculating the average, sum, minimum, maximum, count, or standard deviation of data points, within a specific time bucket or range. By using these functions, you can derive insights, detect trends, and analyze patterns in your time-series data more effectively.

The following is a list of aggregation functions:

  • avg: Calculates the average (mean) value of data points within a specified time bucket or range. It is useful for analyzing and summarizing time-series data to understand trends and patterns over time.
  • sum: Calculates the total (sum) of data points within a specified time bucket or range. It is useful for aggregating time-series data to understand the cumulative effect or total value of the data points over time.
  • min: Calculates the minimum value of data points within a specified time bucket or range. It is useful for...

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