Book Image

Mastering Kibana 6.x

Book Image

Mastering Kibana 6.x

Overview of this book

Kibana is one of the popular tools among data enthusiasts for slicing and dicing large datasets and uncovering Business Intelligence (BI) with the help of its rich and powerful visualizations. To begin with, Mastering Kibana 6.x quickly introduces you to the features of Kibana 6.x, before teaching you how to create smart dashboards in no time. You will explore metric analytics and graph exploration, followed by understanding how to quickly customize Kibana dashboards. In addition to this, you will learn advanced analytics such as maps, hits, and list analytics. All this will help you enhance your skills in running and comparing multiple queries and filters, influencing your data visualization skills at scale. With Kibana’s Timelion feature, you can analyze time series data with histograms and stats analytics. By the end of this book, you will have created a speedy machine learning job using X-Pack capabilities.
Table of Contents (21 chapters)
Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
Index

Pie charts


Before creating a pie chart, we need to understand the different terminology that's used in Kibana for creating visualizations. So, for chart creation, in which we will discuss the pie chart for now, we need to perform Elasticsearch aggregation. There are mainly two categories of aggregation that we need to configure for creating pie charts: metric aggregation and bucket aggregation.

Metric aggregation

The total slice size of a pie chart is calculated by the metrics aggregation. In the case of a pie chart, we have the following aggregations:

  • Count: This aggregation is the count of documents in the selected index pattern.
  • Sum: This aggregation returns the total sum of a numeric field. After choosing this option, we can only select numeric fields.
  • Unique count: This aggregation returns the number of unique values for a given field.

Bucket aggregation

Bucket aggregation is used to determine the type of information we are trying to get from the dataset. These are the following types of bucket...