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)

How to Slice and Dice Your Data Using Aggregations

In this chapter, you’ll learn how to unleash the analytics power of Elasticsearch. In Elasticsearch 5.0, the aggregation framework has been completely revamped. The query syntax is very simple and easy to understand. The distributed nature of Elasticsearch makes the queries very performant and can easily scale to large datasets. We will go through the different types of aggregations Elasticsearch supports and how easy it is to run these queries. We will discuss how to use Kibana to visualize the data. You will also learn doc values and field data, the internal data structures used to power aggregations.

By the end of this chapter, we will cover the following:

  • Different types of aggregations
  • Child aggregations
  • Aggregation on nested documents
  • Aggregation on geolocations
  • Doc values
  • Memory considerations
  • Data...