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Elasticsearch Essentials

Elasticsearch Essentials

By : Bharvi Dixit
4.3 (6)
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Elasticsearch Essentials

Elasticsearch Essentials

4.3 (6)
By: Bharvi Dixit

Overview of this book

With constantly evolving and growing datasets, organizations have the need to find actionable insights for their business. ElasticSearch, which is the world's most advanced search and analytics engine, brings the ability to make massive amounts of data usable in a matter of milliseconds. It not only gives you the power to build blazing fast search solutions over a massive amount of data, but can also serve as a NoSQL data store. This guide will take you on a tour to become a competent developer quickly with a solid knowledge level and understanding of the ElasticSearch core concepts. Starting from the beginning, this book will cover these core concepts, setting up ElasticSearch and various plugins, working with analyzers, and creating mappings. This book provides complete coverage of working with ElasticSearch using Python and performing CRUD operations and aggregation-based analytics, handling document relationships in the NoSQL world, working with geospatial data, and taking data backups. Finally, we’ll show you how to set up and scale ElasticSearch clusters in production environments as well as providing some best practices.
Table of Contents (12 chapters)
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11
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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