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)

Indexing your data

Document are indexed using the index API. We can index a new person document into the chapter4 index as shown here:

 PUT chapter4/person/1
{
"id": 1,
"name": "user1",
"age": "55",
"gender": "M",
"email": "[email protected]",
"last_modified_date": "2017-02-15"
}

The document we just indexed is uniquely identified by the index, type and identifier. You can either specify your identifier or let Elasticsearch pick one for you. If you want to specify an identifier, you have to use the PUT HTTP method. If you use the POST HTTP method, a unique identifier is automatically assigned to the document. The response to the preceding command is shown as follows:

 {
"_index": "chapter4",
"_type": "person",
&quot...