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

What's text normalization?

Text normalization is the process of transforming text into a common form. That is necessary in order to remove insignificant differences among identical words.

Let's look at déjà-vu word to handle.

The word deja-vu is not equal to déjà-vu for string comparison. Even Déjà-vu is not equal to déjà-vu. Similarly, Michè'le is not equal to Michèle. All these words (that is, tokens) are not equal because the comparison is made at the byte-level by Elasticsearch. This means, for two tokens to be considered the same, they need to consist of exactly the same bytes when these tokens are compared.

However, these words have similar meanings. In other words, the same thing is being sought when a user is searching for the word déjà-vu and another user, deja-vu or deja vu. It should also be noted that the Unicode standard allows you to create equivalent text in multiple ways.

For example, take letters é (Latin Capital letter e with grave) and é (Latin Capital letter e with acute...