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Elasticsearch 8.x Cookbook

You're reading from   Elasticsearch 8.x Cookbook Over 180 recipes to perform fast, scalable, and reliable searches for your enterprise

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Product type Paperback
Published in May 2022
Publisher Packt
ISBN-13 9781801079815
Length 750 pages
Edition 5th Edition
Languages
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Author (1):
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Alberto Paro Alberto Paro
Author Profile Icon Alberto Paro
Alberto Paro
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Table of Contents (20) Chapters Close

Preface 1. Chapter 1: Getting Started 2. Chapter 2: Managing Mappings FREE CHAPTER 3. Chapter 3: Basic Operations 4. Chapter 4: Exploring Search Capabilities 5. Chapter 5: Text and Numeric Queries 6. Chapter 6: Relationships and Geo Queries 7. Chapter 7: Aggregations 8. Chapter 8: Scripting in Elasticsearch 9. Chapter 9: Managing Clusters 10. Chapter 10: Backups and Restoring Data 11. Chapter 11: User Interfaces 12. Chapter 12: Using the Ingest Module 13. Chapter 13: Java Integration 14. Chapter 14: Scala Integration 15. Chapter 15: Python Integration 16. Chapter 16: Plugin Development 17. Chapter 17: Big Data Integration 18. Chapter 18: X-Pack 19. Other Books You May Enjoy

Mapping a GeoShape field

An extension of the concept of a point is its shape. Elasticsearch provides a type that allows you to manage arbitrary polygons in GeoShape.

Getting ready

You will need an up-and-running Elasticsearch installation, as we described in the Downloading and installing Elasticsearch recipe of Chapter 1, Getting Started.

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

  • Spatial4J (v0.3)
  • JTS (v1.13)

How to do it…

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

  • tree (the default is geohash): This is the name of the PrefixTree implementation – GeohashPrefixTree and quadtree for QuadPrefixTree.
  • 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, 10 m, 10 km, 10 miles, and so on.
  • tree_levels: This is the maximum number of layers to be used in the prefix tree.
  • distance_error_pct: This sets the maximum errors that are allowed in a prefix tree (0,025% - max 0,5% by default).

The customer_location mapping, which we saw in the previous recipe using geo_shape, will be as follows:

"customer_location": {
  "type": "geo_shape",
  "tree": "quadtree",
  "precision": "1m" },

How it works…

When a shape is indexed or searched internally, a path tree is created and used.

A path tree is a list of terms that contain geographic information and are computed to improve performance in evaluating geo calculus.

The path tree also depends on the shape's type: point, linestring, polygon, multipoint, or multipolygon.

See also

To understand the logic behind the GeoShape, some good resources are the Elasticsearch page, which tells you about GeoShape, and the sites of the libraries that are used for geographic calculus (https://github.com/spatial4j/spatial4j and http://central.maven.org/maven2/com/vividsolutions/jts/1.13/, respectively).

You have been reading a chapter from
Elasticsearch 8.x Cookbook - Fifth Edition
Published in: May 2022
Publisher: Packt
ISBN-13: 9781801079815
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