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Advanced Elasticsearch 7.0

You're reading from   Advanced Elasticsearch 7.0 A practical guide to designing, indexing, and querying advanced distributed search engines

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Product type Paperback
Published in Aug 2019
Publisher Packt
ISBN-13 9781789957754
Length 560 pages
Edition 1st Edition
Languages
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Author (1):
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Wai Tak Wong Wai Tak Wong
Author Profile Icon Wai Tak Wong
Wai Tak Wong
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Toc

Table of Contents (25) Chapters Close

Preface 1. Section 1: Fundamentals and Core APIs FREE CHAPTER
2. Overview of Elasticsearch 7 3. Index APIs 4. Document APIs 5. Mapping APIs 6. Anatomy of an Analyzer 7. Search APIs 8. Section 2: Data Modeling, Aggregations Framework, Pipeline, and Data Analytics
9. Modeling Your Data in the Real World 10. Aggregation Frameworks 11. Preprocessing Documents in Ingest Pipelines 12. Using Elasticsearch for Exploratory Data Analysis 13. Section 3: Programming with the Elasticsearch Client
14. Elasticsearch from Java Programming 15. Elasticsearch from Python Programming 16. Section 4: Elastic Stack
17. Using Kibana, Logstash, and Beats 18. Working with Elasticsearch SQL 19. Working with Elasticsearch Analysis Plugins 20. Section 5: Advanced Features
21. Machine Learning with Elasticsearch 22. Spark and Elasticsearch for Real-Time Analytics 23. Building Analytics RESTful Services 24. Other Books You May Enjoy

Anatomy of an Analyzer

In Chapter 4, Mapping APIs, we learned about the mapping API; we also mentioned that the analyzer is one of the mapping parameters. In Chapter 1, Overview of Elasticsearch 7, we introduced analyzers and gave an example of a standard analyzer. The building blocks of an analyzer are character filters, tokenizers, and token filters. They efficiently and accurately search for targets and relevant scores, and you must understand the true meaning of the data and how a well-suited analyzer must be used. In this chapter, we will drill down to the anatomy of the analyzer and demonstrate the use of different analyzers in depth. During an index operation, the contents of a document are processed by an analyzer and the generated tokens are used to build the inverted index. During a search operation, the query content is processed by a search analyzer to generate tokens...

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