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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

Nested types to the rescue


Using the nested datatype is one alternative for the inner objects and it solves the issue with the flattening of documents in Elasticsearch. Let's see how we can use the nested datatype. Let's consider the following example:

curl -XPOST localhost:9200/authors/author/1 -d'{
  "name": "Multi G. Enre",
  "books": [
    {
      "name": "Guns and lasers",
      "genre": "scifi",
      "publisher": "orbit"
    },
    {
      "name": "Dead in the night",
      "genre": "thriller",
      "publisher": "penguin"
    }
  ]
}'
curl -XPOST localhost:9200/authors/author/2 -d'{
  "name": "Alastair Reynolds",
  "books": [
    {
      "name": "Revelation Space",
      "genre": "scifi",
      "publisher": "penguin"
    }
  ]
}'

If you examine carefully, you would ideally notice no difference as to how the document looks like when compared to the inner objects. This is true as well. To make your Elasticsearch instance understand your intention of using the nested datatype, you must...

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