Book Image

Learning Elastic Stack 6.0

By : Pranav Shukla, Sharath Kumar M N
Book Image

Learning Elastic Stack 6.0

By: Pranav Shukla, Sharath Kumar M N

Overview of this book

The Elastic Stack is a powerful combination of tools for distributed search, analytics, logging, and visualization of data from medium to massive data sets. The newly released Elastic Stack 6.0 brings new features and capabilities that empower users to find unique, actionable insights through these techniques. This book will give you a fundamental understanding of what the stack is all about, and how to use it efficiently to build powerful real-time data processing applications. After a quick overview of the newly introduced features in Elastic Stack 6.0, you’ll learn how to set up the stack by installing the tools, and see their basic configurations. Then it shows you how to use Elasticsearch for distributed searching and analytics, along with Logstash for logging, and Kibana for data visualization. It also demonstrates the creation of custom plugins using Kibana and Beats. You’ll find out about Elastic X-Pack, a useful extension for effective security and monitoring. We also provide useful tips on how to use the Elastic Cloud and deploy the Elastic Stack in production environments. On completing this book, you’ll have a solid foundational knowledge of the basic Elastic Stack functionalities. You’ll also have a good understanding of the role of each component in the stack to solve different data processing problems.
Table of Contents (19 chapters)
Title Page
Credits
Disclaimer
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Metric aggregations


Metric aggregations work with numeric data, computing one or more aggregate metrics within the given context. The context could 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 the chapter, we will also learn how to use metric aggregations inside bucket aggregations.

We will learn about the following metric aggregations:

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

Let us learn about them one by one.

Sum, average, min, and max aggregations

Finding the sum of a field, the minimum value for a field, the maximum value for a field, or an average, are very common operations. For the people who are familiar with...