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Learning Elasticsearch

You're reading from   Learning Elasticsearch Structured and unstructured data using distributed real-time search and analytics

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Product type Paperback
Published in Jun 2017
Publisher Packt
ISBN-13 9781787128453
Length 404 pages
Edition 1st Edition
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Author (1):
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Abhishek Andhavarapu Abhishek Andhavarapu
Author Profile Icon Abhishek Andhavarapu
Abhishek Andhavarapu
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Toc

Table of Contents (11) Chapters Close

Preface 1. Introduction to Elasticsearch FREE CHAPTER 2. Setting Up Elasticsearch and Kibana 3. Modeling Your Data and Document Relations 4. Indexing and Updating Your Data 5. Organizing Your Data and Bulk Data Ingestion 6. All About Search 7. More Than a Search Engine (Geofilters, Autocomplete, and More) 8. How to Slice and Dice Your Data Using Aggregations 9. Production and Beyond 10. Exploring Elastic Stack (Elastic Cloud, Security, Graph, and Alerting)

Mapping

Mapping is the process of defining the schema or the structure of the documents. It describes the properties of the fields in the document. The properties of the field include the data type (for example, string, integer, and so on) and the metadata. In the previous chapters, we discussed how the documents are converted into the inverted index when we index them. During indexing, the mappings of the fields define how the fields are indexed and stored in the inverted index.

Just like you would define table schema in SQL, it is important to set the mappings of the index before you index any data. As we discussed before, a type in Elasticsearch is like an SQL table, which groups documents of similar nature (you would define one type for users, one for orders). Each type has its mapping defined. Having different mappings could also be a motivation to define a new type.

Apache...
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