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

When does index-time boosting make sense?


In the previous section, we discussed boosting queries. This kind of approach to handling differences in the weight of documents is very handy, powerful, and easy to use. It is also sufficient in most situations. However, there are cases when a more convenient way of documents boosting is index-time boosting. One of such use case is the situation when we know which documents are important during the indexing phase. In such a case, we can prepare the document boost and include it as part of the document. We gain a boost that is independent from a query at the cost of reindexing the documents when the boost value is changed (because we need to apply the changed boost). In addition to that, the performance gets slightly better because some parts needed in the boosting process are already calculated at index time, which can matter when your indices have a large number of documents. Information about the boost is stored as a part of the normalization...