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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

Introduction


Elasticsearch is very commonly used as a datastore for logs and other kind of data, so if you store valuable data you also need tools to back up and restore this data to support disaster recovery.

In the first versions of Elasticsearch the only viable solution was to dump your data with a complete scan and then reindex it. As Elasticsearch matured as a complete product, it supported native functionalities to back up the data and to restore it.

In this chapter, we'll see how to configure a shared storage via NFS for storing your backups, and how to execute and restore a backup.

In the last recipe of the chapter we will see how to use the reindex functionality to clone data between different Elasticsearch clusters. This approach is very useful if you are not able to use standard backup/restore functionalities due to moving from an old Elasticsearch version to the new one.

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