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ElasticSearch Blueprints

You're reading from   ElasticSearch Blueprints A practical project-based guide to generating compelling search solutions using the dynamic and powerful features of Elasticsearch

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Product type Paperback
Published in Jul 2015
Publisher Packt
ISBN-13 9781783984923
Length 192 pages
Edition 1st Edition
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Toc

Table of Contents (10) Chapters Close

Preface 1. Google-like Web Search 2. Building Your Own E-Commerce Solution FREE CHAPTER 3. Relevancy and Scoring 4. Managing Relational Content 5. Analytics Using Elasticsearch 6. Improving the Search Experience 7. Spicing Up a Search Using Geo 8. Handling Time-based Data Index

Using a parent-child approach


A parent-child datatype is yet another alternative to establish relational data. In the parent-child approach, we establish the parent-child relation during index time. In a map reduce or sharded architecture, it's not possible for a map or shard to communicate with each other. The work has to be done independently by each shard. Keeping that in mind, relational data can't be maintained as a cross shard, which means I can't have the school information document in one shard and student information in another.

Hence, to establish a parent-child relation, it's required that the parent and child documents be in the same shard. Let's first see how to index parent and child documents and then how this rule is imposed.

Let's index some documents first. The index and type names are posts and post, respectively and the IDs corresponding them are manually given as 1, 2, and 3, as shown in the following code:

curl -XPOST localhost:9200/posts/post/1 -d '{
   "question":" elasticsearch...
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