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

Filtering your results


In the previous chapter, we talked about various types of queries. The common part was that we always wanted to get the best results first. This is the main difference from the standard database approach where every document matches the query or not. In the database world, we do not ask how good the document is; our only interest lies in the results returned. When talking about full text search engines this is different – we are interested not only in the results, we are also interested in their quality. The reason is obvious, we are searching in unstructured data, using text fields that use language analysis, stemming, and so on. Because of that, the initial results of our queries, in most cases, give results that are far from optimal. This is why when we talk about searching, we talk about precision and document recall.

On the other hand, sometimes we want to limit the whole subset of documents to a chosen part. For example, in a library, we may want to search only...