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Elasticsearch 5.x Cookbook

Elasticsearch 5.x Cookbook

By : Alberto Paro
2.5 (4)
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Elasticsearch 5.x Cookbook

Elasticsearch 5.x Cookbook

2.5 (4)
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)
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Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Dedication
Preface

Adding metadata to a mapping


Sometimes when we are working with our mapping, it is required to store some additional data to be used for display purpose, ORM facilities, permissions, or simply to track them in the mapping.

Elasticsearch allows storing every kind of JSON data we want in the mapping with the special field _meta.

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.

How to do it...

  1. The _meta mapping field can be populated with any data we want. Consider the following example:

            { 
                 "order": { 
                     "_meta": { 
                        "attr1": ["value1", "value2"], 
                        "attr2": { 
                             "attr3": "value3" 
                        } 
                    } 
                } 
             } 
    

How it works...

When Elasticsearch processes a new mapping and finds a _meta field...

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