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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

Data modeling in Elasticsearch

Data is modeled in documents in Elasticsearch. This means a single item irrespective of whether it is an entity like a company, country, or product has to be modeled as a single document. A document can contain any number of fields and values associated with it. This information is modeled around the JSON format that helps us to express the behavior of our data using arrays, objects, or simple values.

Note

Elasticsearch is schemaless, which means that you can index a document to Elasticsearch before you create its index or mapping. Elasticsearch guesses the best type for each field value.

Inherently, JSON supports formats such as string, numbers, object, array, and even Boolean. Elasticsearch adds various other formats on top of it, such as the date, IP and so on. You will find the list of such supported types in the following table. The use of date types to store date values will help us do effective date-specific operations, such as date range and aggregation...

You have been reading a chapter from
ElasticSearch Blueprints
Published in: Jul 2015
Publisher: Packt
ISBN-13: 9781783984923
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