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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

The basics of aggregations


In contrast to search, analytics deals with the bigger picture. Searching addresses the need for zooming in to a few records; analytics addresses the need for zooming out and slicing the data in different ways. While learning about searching, we used the API of the following form:

POST /<index_name>/<type_name>/_search
{
  "query": 
  {
    ... type of query ...
  }
}

All aggregation queries take a common form. Let us understand the structure.

The aggregations or aggs element allows us to aggregate data. All aggregation requests take the following form:

POST /<index_name>/<type_name>/_search
{  
  "aggs": {                                 
    ... type of aggregation ...
          },
  "query": {  ... type of query ... },              //optional query part
  "size": 0                                         //size typically set to 0
}

The aggs element should contain the actual aggregation query. The body depends on the type of aggregation that...

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