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  • Book Overview & Buying Elasticsearch 5.x Cookbook
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Elasticsearch 5.x Cookbook

Elasticsearch 5.x Cookbook - Third Edition

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

Introduction


Mapping is a very important concept in Elasticsearch, as it defines how the search engine should process a document.

Search engines perform two main operations:

  • Indexing: This is the action to receive a document and store/index/process in an index

  • Searching: This is the action to retrieve the data from the index

These two parts are strictly connected. An error in the indexing step leads to unwanted or missing search results.

Elasticsearch has explicit mapping on an index/type level. When indexing, if a mapping is not provided, a default one is created, guessing the structure from the data fields that compose the document; then, this new mapping is automatically propagated to all cluster nodes.

The default type mapping has sensible default values, but when you want to change their behavior or you want to customize several other aspects of indexing (storing, ignoring, completion, and so on), you need to provide a new mapping definition.

In this chapter, we'll see all the possible types...

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