Book Image

Elasticsearch Server - Third Edition

By : Rafal Kuc
Book Image

Elasticsearch Server - Third Edition

By: Rafal Kuc

Overview of this book

ElasticSearch is a very fast and scalable open source search engine, designed with distribution and cloud in mind, complete with all the goodies that Apache Lucene has to offer. ElasticSearch’s schema-free architecture allows developers to index and search unstructured content, making it perfectly suited for both small projects and large big data warehouses, even those with petabytes of unstructured data. This book will guide you through the world of the most commonly used ElasticSearch server functionalities. You’ll start off by getting an understanding of the basics of ElasticSearch and its data indexing functionality. Next, you will see the querying capabilities of ElasticSearch, followed by a through explanation of scoring and search relevance. After this, you will explore the aggregation and data analysis capabilities of ElasticSearch and will learn how cluster administration and scaling can be used to boost your application performance. You’ll find out how to use the friendly REST APIs and how to tune ElasticSearch to make the most of it. By the end of this book, you will have be able to create amazing search solutions as per your project’s specifications.
Table of Contents (18 chapters)
Elasticsearch Server Third Edition
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Preface
Index

Influencing scores with query boosts


In the beginning of this chapter, we learned what scoring is and how Elasticsearch uses the scoring formula. When an application grows, the need for improving the quality of search also increases - we call it search experience. We need to gain knowledge about what is more important to the user and we see how the users use the searches functionality. This leads to various conclusions; for example, we see that some parts of the documents are more important than others or that particular queries emphasize one field at the cost of others. We need to include such information in our data and queries so that both sides of the scoring equation are closer to our business needs. This is where boosting can be used.

The boost

Boost is an additional value used in the process of scoring. We already know it can be applied to:

  • Query: When used, we inform the search engine that the given query is a part of a complex query and is more significant than the other parts.

  • Document...