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

Searching content in different languages


Until now, when discussing language analysis, we've talked mostly about theory. We didn't see an example regarding language analysis, handling multiple languages that our data can consist of, and so on. Now this will change, as this section is dedicated to information about how we can handle data in multiple languages.

Handling languages differently

As you already know, Elasticsearch allows us to choose different analyzers for our data. We can have our data divided on the basis of whitespaces, or have them lowercased, and so on. This can usually be done regardless of the language –the same tokenization on the basis of whitespaces will work for English, German, and Polish, although it won't work for Chinese. However, what if you want to find documents that contain words such as cat and cats by only sending the word cat to Elasticsearch? This is where language analysis comes into play with stemming algorithms for different languages, which allow the...