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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)

Relevance

A traditional database usually contains structured data. A query on a database limits the data depending on different conditions specified by the user. Each condition in the query is evaluated as true/false, and the rows that don't satisfy the conditions are eliminated. However, full-text search is much more complicated. The data is unstructured, or at least the queries are.

We often need to search for the same text across one or more fields. The documents can be quite large, and the query word might appear multiple times in the same document and across several documents. Displaying all the results of the search will not help as there could be hundreds, if not more, and most documents might not even be relevant to the search.

To solve this problem, all the documents that match the query are assigned a score. The score is assigned based on how relevant each document...

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