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Learning Elastic Stack 7.0

You're reading from   Learning Elastic Stack 7.0 Distributed search, analytics, and visualization using Elasticsearch, Logstash, Beats, and Kibana

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Product type Paperback
Published in May 2019
Publisher Packt
ISBN-13 9781789954395
Length 474 pages
Edition 2nd 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 (17) Chapters Close

Preface 1. Section 1: Introduction to Elastic Stack and Elasticsearch
2. Introducing Elastic Stack FREE CHAPTER 3. Getting Started with Elasticsearch 4. Section 2: Analytics and Visualizing Data
5. Searching - What is Relevant 6. Analytics with Elasticsearch 7. Analyzing Log Data 8. Building Data Pipelines with Logstash 9. Visualizing Data with Kibana 10. Section 3: Elastic Stack Extensions
11. Elastic X-Pack 12. Section 4: Production and Server Infrastructure
13. Running Elastic Stack in Production 14. Building a Sensor Data Analytics Application 15. Monitoring Server Infrastructure 16. Other Books You May Enjoy

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.

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