Book Image

Elasticsearch Indexing

By : Huseyin Akdogan
Book Image

Elasticsearch Indexing

By: Huseyin Akdogan

Overview of this book

Beginning with an overview of the way ElasticSearch stores data, you’ll begin to extend your knowledge to tackle indexing and mapping, and learn how to configure ElasticSearch to meet your users’ needs. You’ll then find out how to use analysis and analyzers for greater intelligence in how you organize and pull up search results – to guarantee that every search query is met with the relevant results! You’ll explore the anatomy of an ElasticSearch cluster, and learn how to set up configurations that give you optimum availability as well as scalability. Once you’ve learned how these elements work, you’ll find real-world solutions to help you improve indexing performance, as well as tips and guidance on safety so you can back up and restore data. Once you’ve learned each component outlined throughout, you will be confident that you can help to deliver an improved search experience – exactly what modern users demand and expect.
Table of Contents (15 chapters)
Elasticsearch Indexing
About the Author
About the Reviewer

Chapter 4. Analysis and Analyzers

In the previous chapter, we looked at the basic concepts and definitions of mapping. We talked about fields of metadata and data types. Then, we discussed the relationship between mapping and relevant search results. Finally, we tried to have a good grasp of understanding what the schema-less is in Elasticsearch.

In this chapter, we will review the process of analysis and analyzers. We will examine the tokenizers and we will look closely at the character and token filters. In addition, we will review how to add analyzers to an Elasticsearch configuration. By the end of this chapter, we would have covered the following topics:

  • What is analysis process?

  • What is built-in analyzers?

  • What are doing tokenizers, character, and token filters?

  • What is text normalization?

  • How to create custom analyzers?