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

Mapping a GeoShape field


An extension to the concept of point is the shape. Elasticsearch provides a type that facilitates the management of arbitrary polygons: the GeoShape.

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.

To be able to use advanced shape management, Elasticsearch requires two JAR libraries in its classpath (usually the lib directory):

  • Spatial4J (v0.3)

  • JTS (v1.13)

How to do it

To map a geo_shape type, a user must explicitly provide some parameters:

  • tree: This is the name of the PrefixTree implementation: geohash for GeohashPrefixTree and quadtree for QuadPrefixTree (default geohash)

  • precision: This is used instead of tree_levels to provide a more human value to be used in the tree level. The precision number can be followed by the unit, that is, 10m, 10km, 10miles, and so on

  • tree_levels: This is the maximum number of layers to be used in the prefix tree

  • distance_error_pct...