Book Image

Elasticsearch 5.x Cookbook - Third Edition

By : Alberto Paro
Book Image

Elasticsearch 5.x Cookbook - Third Edition

By: Alberto Paro

Overview of this book

Elasticsearch is a Lucene-based distributed search server that allows users to index and search unstructured content with petabytes of data. This book is your one-stop guide to master the complete Elasticsearch ecosystem. We’ll guide you through comprehensive recipes on what’s new in Elasticsearch 5.x, showing you how to create complex queries and analytics, and perform index mapping, aggregation, and scripting. Further on, you will explore the modules of Cluster and Node monitoring and see ways to back up and restore a snapshot of an index. You will understand how to install Kibana to monitor a cluster and also to extend Kibana for plugins. Finally, you will also see how you can integrate your Java, Scala, Python, and Big Data applications such as Apache Spark and Pig with Elasticsearch, and add enhanced functionalities with custom plugins. By the end of this book, you will have an in-depth knowledge of the implementation of the Elasticsearch architecture and will be able to manage data efficiently and effectively with Elasticsearch.
Table of Contents (25 chapters)
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Dedication
Preface

Managing mappings include the mapping


After creating an index, the next step is to add some type mappings to it. We have already seen how to include a mapping via the REST API in Chapter 4, Basic Operations.

Getting ready

You need an up-and-running Elasticsearch installation, as we described in the Downloading and installing Elasticsearch recipe in Chapter 2, Downloading and Setup.

You also need the Python installed packages of Creating a client recipe of this chapter.

The code for this recipe is in the chapter_16/mapping_management.py file.

How to do it…

After having initialized a client and created an index, the steps for managing the indices are as follows:

  1. Create a mapping.

  2. Retrieve a mapping.

These steps are easily managed with the following code:

  1. We initialize the client:

            import elasticsearch
            es = elasticsearch.Elasticsearch()
    
  2. We create an index:

            index_name = "my_index"
            type_name = "my_type"
            if es.indices.exists(index_name):
       ...