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Elasticsearch 8.x Cookbook

You're reading from   Elasticsearch 8.x Cookbook Over 180 recipes to perform fast, scalable, and reliable searches for your enterprise

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Product type Paperback
Published in May 2022
Publisher Packt
ISBN-13 9781801079815
Length 750 pages
Edition 5th Edition
Languages
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Author (1):
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Alberto Paro Alberto Paro
Author Profile Icon Alberto Paro
Alberto Paro
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Table of Contents (20) Chapters Close

Preface 1. Chapter 1: Getting Started 2. Chapter 2: Managing Mappings FREE CHAPTER 3. Chapter 3: Basic Operations 4. Chapter 4: Exploring Search Capabilities 5. Chapter 5: Text and Numeric Queries 6. Chapter 6: Relationships and Geo Queries 7. Chapter 7: Aggregations 8. Chapter 8: Scripting in Elasticsearch 9. Chapter 9: Managing Clusters 10. Chapter 10: Backups and Restoring Data 11. Chapter 11: User Interfaces 12. Chapter 12: Using the Ingest Module 13. Chapter 13: Java Integration 14. Chapter 14: Scala Integration 15. Chapter 15: Python Integration 16. Chapter 16: Plugin Development 17. Chapter 17: Big Data Integration 18. Chapter 18: X-Pack 19. Other Books You May Enjoy

Specifying different analyzers

In the previous recipes, we learned how to map different fields and objects in Elasticsearch, and we described how easy it is to change the standard analyzer with the analyzer and search_analyzer properties.

In this recipe, we will look at several analyzers and learn how to use them to improve indexing and searching quality.

Getting ready

You will need an up-and-running Elasticsearch installation, as we described in the Downloading and installing Elasticsearch recipe of Chapter 1Getting Started.

How to do it…

Every core type field allows you to specify a custom analyzer for indexing and for searching as field parameters.

For example, if we want the name field to use a standard analyzer for indexing and a simple analyzer for searching, the mapping will be as follows:

{ "name": {
    "type": "string",
    "index_analyzer": "standard",
    "search_analyzer": "simple"
  } }

How it works…

The concept of the analyzer comes from Lucene (the core of Elasticsearch). An analyzer is a Lucene element that is composed of a tokenizer that splits text into tokens, as well as one or more token filters. These filters carry out token manipulation such as lowercasing, normalization, removing stop words, stemming, and so on.

During the indexing phase, when Elasticsearch processes a field that must be indexed, an analyzer is chosen. First, it checks whether it is defined in the index_analyzer field, then in the document, and finally, in the index.

Choosing the correct analyzer is essential to getting good results during the query phase.

Elasticsearch provides several analyzers in its standard installation. The following table shows the most common ones:

Figure 2.4 – List of the most common general-purpose analyzers

Figure 2.4 – List of the most common general-purpose analyzers

For special language purposes, Elasticsearch supports a set of analyzers aimed at analyzing text in a specific language, such as Arabic, Armenian, Basque, Brazilian, Bulgarian, Catalan, Chinese, CJK, Czech, Danish, Dutch, English, Finnish, French, Galician, German, Greek, Hindi, Hungarian, Indonesian, Italian, Norwegian, Persian, Portuguese, Romanian, Russian, Spanish, Swedish, Turkish, and Thai.

See also

Several Elasticsearch plugins extend the list of available analyzers. The most famous ones are as follows:

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Elasticsearch 8.x Cookbook - Fifth Edition
Published in: May 2022
Publisher: Packt
ISBN-13: 9781801079815
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