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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

Using the function score query


This kind of query is one of the most powerful queries available, because it allows extensive customization of scoring algorithm. The function score query allows defining a function that controls the score of the documents that are returned by a query.

Generally, these functions are CPU intensive and executing them on a large dataset requires a lot of memory, but computing them on a small subset can significantly improve the search quality.

The common scenarios used for this query are:

  • Creating a custom score function (with decay function, for example)

  • Creating a custom boost factor, for example, based on another field (that is, boosting a document by distance from a point)

  • Creating a custom filter score function, for example, based on scripting Elasticsearch capabilities

  • Ordering the documents randomly

Getting ready

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

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