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)

Core data types

In this section, we will discuss the core data types supported by Elasticsearch. You can set the mapping using the Mapping API.

Text

Starting Elasticsearch 5.0, the string data type is deprecated and replaced by the text and keyword data types. If you want to perform a full-text search as we discussed in the previous section, you should use text data type. If you only want an exact match, you should use keyword data type. We will discuss keyword data type in the next section.

Let's take the same example we used in Chapter 1, Introduction to Elasticsearch. We have a document containing the following fields:

{
"date": "2017-01-01",
"description": "Yosemite national...