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Elasticsearch Essentials

You're reading from   Elasticsearch Essentials Harness the power of ElasticSearch to build and manage scalable search and analytics solutions with this fast-paced guide

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Product type Paperback
Published in Jan 2016
Publisher
ISBN-13 9781784391010
Length 240 pages
Edition 1st Edition
Languages
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Toc

Table of Contents (12) Chapters Close

Preface 1. Getting Started with Elasticsearch FREE CHAPTER 2. Understanding Document Analysis and Creating Mappings 3. Putting Elasticsearch into Action 4. Aggregations for Analytics 5. Data Looks Better on Maps: Master Geo-Spatiality 6. Document Relationships in NoSQL World 7. Different Methods of Search and Bulk Operations 8. Controlling Relevancy 9. Cluster Scaling in Production Deployments 10. Backups and Security Index

Working with geo-point data


Geo-points are single location points defined by a latitude-longitude pair on the surface of the earth. Using geo-points you can do the following things:

  • Calculate the distance between two points

  • Find the document that falls in a specified rectangular area

  • Sort documents based on distance and score results based on it

  • Create clusters of geo-points using aggregations

Mapping geo-point fields

Unlike all the data types in Elasticsearch, geo-point fields can't be determined dynamically. So, you have to define the mapping in advance before indexing data. The mapping for a geo-point field can be defined in the following format:

"location": {
    "type": "geo_point"
}

A geo_point mapping indexes a single field (the location in our example) in the lat-lon format. You can optionally index .lat and .lon separately by setting the lat-lon parameter to true.

Indexing geo-point data

Elasticsearch supports the following three formats to index geo_point data with the same mapping that...

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