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
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Index

Improving the relevancy of search results


In general, Elasticsearch is used for searching while it is a data analysis tool. In this respect, improving query relevance is an important issue. Of course, searching also means querying and scoring, thus it is a very important part of querying in Apache Lucene as well. We can use the re-scoring mechanism to improve the query's relevance. In addition to the capabilities of document scoring in the Apache Lucene library, Elasticsearch provides different query types to manipulate the score of the results returned by our queries. In this section, you will find several tips on this issue.

Boosting the query

Boosting queries allows us to effectively demote results that match a given query. This feature is very useful in that we can send some irrelevant records of the result set to the back. For example, we have an index that stores the skills of developers and we're looking for developers who know the Java language. We use a query such as the following...