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Learning Elastic Stack 6.0

You're reading from   Learning Elastic Stack 6.0 A beginner's guide to distributed search, analytics, and visualization using Elasticsearch, Logstash and Kibana

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Product type Paperback
Published in Dec 2017
Publisher Packt
ISBN-13 9781787281868
Length 434 pages
Edition 1st Edition
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Authors (2):
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Sharath Kumar Sharath Kumar
Author Profile Icon Sharath Kumar
Sharath Kumar
Pranav Shukla Pranav Shukla
Author Profile Icon Pranav Shukla
Pranav Shukla
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Toc

Table of Contents (12) Chapters Close

Preface 1. Introducing Elastic Stack FREE CHAPTER 2. Getting Started with Elasticsearch 3. Searching-What is Relevant 4. Analytics with Elasticsearch 5. Analyzing Log Data 6. Building Data Pipelines with Logstash 7. Visualizing data with Kibana 8. Elastic X-Pack 9. Running Elastic Stack in Production 10. Building a Sensor Data Analytics Application 11. Monitoring Server Infrastructure

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...

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