Book Image

Learning Elastic Stack 7.0 - Second Edition

By : Pranav Shukla, Sharath Kumar M N
Book Image

Learning Elastic Stack 7.0 - Second Edition

By: Pranav Shukla, Sharath Kumar M N

Overview of this book

The Elastic Stack is a powerful combination of tools that help in performing distributed search, analytics, logging, and visualization of data. Elastic Stack 7.0 encompasses new features and capabilities that will enable you to find unique insights into analytics using these techniques. This book will give you a fundamental understanding of what the stack is all about, and guide you in using it efficiently to build powerful real-time data processing applications. The first few sections of the book will help you understand how to set up the stack by installing tools and exploring their basic configurations. You’ll then get up to speed with using Elasticsearch for distributed search and analytics, Logstash for logging, and Kibana for data visualization. As you work through the book, you will discover the technique of creating custom plugins using Kibana and Beats. This is followed by coverage of the Elastic X-Pack, a useful extension for effective security and monitoring. You’ll also find helpful tips on how to use Elastic Cloud and deploy Elastic Stack in production environments. By the end of this book, you’ll be well-versed with fundamental Elastic Stack functionalities and the role of each component in the stack to solve different data processing problems.
Table of Contents (17 chapters)
Free Chapter
Section 1: Introduction to Elastic Stack and Elasticsearch
Section 2: Analytics and Visualizing Data
Section 3: Elastic Stack Extensions
Section 4: Production and Server Infrastructure

Metric aggregations

Metric aggregations work with numerical data, computing one or more aggregate metrics within the given context. The context can be a query, filter, or no query, to include the whole index/type. Metric aggregations can also be nested inside other bucket aggregations. In this case, these metrics will be computed for each bucket in the bucket aggregations.

We will start with simple metric aggregations, without nesting them inside bucket aggregations. When we learn about bucket aggregations later in this chapter, we will also learn how to use metric aggregations inside bucket aggregations.

In this section, we will go over the following metric aggregations:

  • Sum, average, min, and max aggregations
  • Stats and extended stats aggregations
  • Cardinality aggregations

Let's learn about them, one by one.