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Redis Stack for Application Modernization

You're reading from   Redis Stack for Application Modernization Build real-time multi-model applications at any scale with Redis

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Product type Paperback
Published in Dec 2023
Publisher Packt
ISBN-13 9781837638185
Length 336 pages
Edition 1st Edition
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Authors (2):
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Mirko Ortensi Mirko Ortensi
Author Profile Icon Mirko Ortensi
Mirko Ortensi
Luigi Fugaro Luigi Fugaro
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Luigi Fugaro
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Toc

Table of Contents (18) Chapters Close

Preface 1. Part 1: Introduction to Redis Stack
2. Chapter 1: Introducing Redis Stack FREE CHAPTER 3. Chapter 2: Developing Modern Use Cases with Redis Stack 4. Chapter 3: Getting Started with Redis Stack 5. Chapter 4: Setting Up Client Libraries 6. Part 2: Data Modeling
7. Chapter 5: Redis Stack as a Document Store 8. Chapter 6: Redis Stack as a Vector Database 9. Chapter 7: Redis Stack as a Time Series Database 10. Chapter 8: Understanding Probabilistic Data Structures 11. Part 3: From Development to Production
12. Chapter 9: The Programmability of Redis Stack 13. Chapter 10: RedisInsight – the Data Management GUI 14. Chapter 11: Using Redis Stack as a Primary Database 15. Chapter 12: Managing Development and Production Environments 16. Index 17. Other Books You May Enjoy

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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