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

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...