Book Image

ElasticSearch Cookbook

By : Alberto Paro
Book Image

ElasticSearch Cookbook

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 (19 chapters)
ElasticSearch Cookbook
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Mapping arrays


Arrays or multi-value fields are very common in data models, but not natively supported in traditional SQL solutions.

In SQL, multi-value fields require the creation of accessory tables that must be joined to gather all the values, resulting in poor performance when the cardinality of records is huge.

Getting ready

You need a working ElasticSearch cluster.

How to do it...

Every field is automatically managed as an array. For example, to store tags for a document, the mapping will be as shown in the following code snippet:

{
  "document" : {
    "properties" : {
      "name" : {"type" : "string",  "index":"analyzed"},
      "tag" : {"type" : "string", "store" : "yes" , "index":"not_analyzed"},
    
    }
  }
}

This mapping is valid for indexing the following documents:

{"name": "document1", "tag": "awesome"}

and

{"name": "document2", "tag": ["cool", "awesome", "amazing"] }

How it works...

ElasticSearch transparently manages the array; there is no difference if you declare a single value...