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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

t-digest

t-digest is a data structure for estimating quantiles from a data stream or a large dataset using a compact sketch.

The t-digest data structure enables the resolution of various inquiries, such as “What proportion of values in the data stream is less than a specific value?” and “How many values in the data stream are below a given threshold?” To better understand t-digest, we need to define quantiles and percentiles.

A quantile is a value or cut point that divides a dataset into intervals with equal proportions or frequencies of observations. As an example, the median is an example of a quantile as it divides the dataset in half (that is, 50% of observations below and 50% above).

A percentile represents a specific position within a dataset, where a certain percentage of the data falls below that position. For example, if a value is at the 75th percentile of a dataset, it means that 75% of the data falls below that value. Percentiles are...

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