Book Image

Learning Elasticsearch

By : Abhishek Andhavarapu
Book Image

Learning Elasticsearch

By: Abhishek Andhavarapu

Overview of this book

Elasticsearch is a modern, fast, distributed, scalable, fault tolerant, and open source search and analytics engine. You can use Elasticsearch for small or large applications with billions of documents. It is built to scale horizontally and can handle both structured and unstructured data. Packed with easy-to- follow examples, this book will ensure you will have a firm understanding of the basics of Elasticsearch and know how to utilize its capabilities efficiently. You will install and set up Elasticsearch and Kibana, and handle documents using the Distributed Document Store. You will see how to query, search, and index your data, and perform aggregation-based analytics with ease. You will see how to use Kibana to explore and visualize your data. Further on, you will learn to handle document relationships, work with geospatial data, and much more, with this easy-to-follow guide. Finally, you will see how you can set up and scale your Elasticsearch clusters in production environments.
Table of Contents (11 chapters)
10
Exploring Elastic Stack (Elastic Cloud, Security, Graph, and Alerting)

Types of aggregations

At a higher level, Elasticsearch supports four types of aggregation:

  • Bucket aggregation: This can be used to group or create buckets. Buckets can be created based on an existing field, custom filters, ranges, and so on
  • Metric aggregation: This can be used to calculate a metric, such as a count, sum, average, and so on
  • Pipeline aggregation: This can be used to chain aggregations. The output of other aggregations can be the input for a pipeline aggregation
  • Matrix aggregation: This can be used to calculate statistics over a set of fields

In this section, we will discuss bucket and metric aggregations. Pipeline and matrix aggregations are still experimental and out of the scope of this book.

Terms aggregations (group by)

...