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ElasticSearch Cookbook

ElasticSearch Cookbook

By : Alberto Paro
4.1 (7)
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ElasticSearch Cookbook

ElasticSearch Cookbook

4.1 (7)
By: Alberto Paro

Overview of this book

ElasticSearch is one of the most promising NoSQL technologies available and is built to provide a scalable search solution with built-in support for near real-time search and multi-tenancy. This practical guide is a complete reference for using ElasticSearch and covers 360 degrees of the ElasticSearch ecosystem. We will get started by showing you how to choose the correct transport layer, communicate with the server, and create custom internal actions for boosting tailored needs. Starting with the basics of the ElasticSearch architecture and how to efficiently index, search, and execute analytics on it, you will learn how to extend ElasticSearch by scripting and monitoring its behaviour. Step-by-step, this book will help you to improve your ability to manage data in indexing with more tailored mappings, along with searching and executing analytics with facets. The topics explored in the book also cover how to integrate ElasticSearch with Python and Java applications. This comprehensive guide will allow you to master storing, searching, and analyzing data with ElasticSearch.
Table of Contents (14 chapters)
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13
Index

Executing range facets


The previous recipe describes a facet type that can be very useful if facets are returned on terms or on a limited number of items. Otherwise, it's often required to return the facets aggregated in ranges: the range facets answers this requirement. Common scenarios are:

  • Price range (used in shops)

  • Size range

  • Alphabetical range

Getting ready

You need a working ElasticSearch cluster and an index populated with the script available in the online code.

How to do it...

For executing range facets, we will perform the steps given as follows:

  1. We want to provide three types of facet ranges:

    • Price facet, that aggregates the price of items in ranges

    • Age facet, that aggregates the age contained in document in four ranges of 25 years

    • Date facet, the ranges of 6 months of the previous year and all this year

  2. To obtain this result, we need to execute a similar query:

    curl -XGET 'http://127.0.0.1:9200/test-index/test-type/_search?pretty=true&size=0' -d '{
        "query": {
            "match_all"...
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