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Elasticsearch Essentials

You're reading from   Elasticsearch Essentials Harness the power of ElasticSearch to build and manage scalable search and analytics solutions with this fast-paced guide

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Product type Paperback
Published in Jan 2016
Publisher
ISBN-13 9781784391010
Length 240 pages
Edition 1st Edition
Languages
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Toc

Table of Contents (12) Chapters Close

Preface 1. Getting Started with Elasticsearch FREE CHAPTER 2. Understanding Document Analysis and Creating Mappings 3. Putting Elasticsearch into Action 4. Aggregations for Analytics 5. Data Looks Better on Maps: Master Geo-Spatiality 6. Document Relationships in NoSQL World 7. Different Methods of Search and Bulk Operations 8. Controlling Relevancy 9. Cluster Scaling in Production Deployments 10. Backups and Security Index

Elasticsearch mapping

We have seen in the previous chapter how an index can have one or more types and each type has its own mapping.

Mappings are like database schemas that describe the fields or properties that the documents of that type may have. For example, the data type of each field, such as a string, integer, or date, and how these fields should be indexed and stored by Lucene.

One more thing to consider is that unlike a database, you cannot have a field with the same name with different types in the same index; otherwise, you will break doc_values, and the sorting/searching is also broken. For example, create myIndex and also index a document with a valid field that contains an integer value inside the type1 document type:

curl –XPOST localhost:9200/myIndex/type1/1 –d '{"valid":5}'

Now, index another document inside type2 in the same index with the valid field. This time the valid field contains a string value:

curl –XPOST localhost/myIndex...
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