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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

Geo-aggregations


Sometimes searches may return too many results but you might be just interested in finding out how many documents exist in a particular range of a location. A simple example can be to see how many news events related to crime occurred in an area by plotting them on a map or by generating a heatmap cluster of the events on the map, as shown in the following image:

Elasticsearch offers both metric and bucket aggregations for geo_point fields.

Geo distance aggregation

Geo distance aggregation is an extension of range aggregation. It allows you to create buckets of documents based on specified ranges. Let's see how this can be done using an example.

Python example

query = {
  "aggs": {
    "news_hotspots": {
      "geo_distance": {
        "field": "location",
        "origin": "28.61, 77.23",
     "unit": "km",
     "distance_type": "plane",
        "ranges": [
          {
            "to": 50
          },
          {
            "from": 50, "to": 200
          },
          {
 ...
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