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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)

Difference between full-text search and exact match

In this section, we will describe analyzers and why they are necessary for text search. Let's say we have a document containing the following information:

{
"date": "2017/02/01",
"desc": "It will be raining in yosemite this weekend"
}

If we want to search for the documents that contain word yosemite, we could run an SQL query as shown here:

select * from news where desc like '%yosemite%'

This functionality is very limited and is never sufficient for real-world text-search queries. For example, if a user is looking for the weather forecast in Yosemite, he/she would query for the same in human language using something such as rain in yosemite. Since SQL can only match the exact words, and the document doesn't contain the word rain, the query will not come back with...

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