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Mastering Elasticsearch 5.x

You're reading from   Mastering Elasticsearch 5.x Master the intricacies of Elasticsearch 5 and use it to create flexible and scalable search solutions

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Product type Paperback
Published in Feb 2017
Publisher Packt
ISBN-13 9781786460189
Length 428 pages
Edition 3rd Edition
Languages
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Author (1):
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Bharvi Dixit Bharvi Dixit
Author Profile Icon Bharvi Dixit
Bharvi Dixit
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Table of Contents (13) Chapters Close

Preface 1. Revisiting Elasticsearch and the Changes FREE CHAPTER 2. The Improved Query DSL 3. Beyond Full Text Search 4. Data Modeling and Analytics 5. Improving the User Search Experience 6. The Index Distribution Architecture 7. Low-Level Index Control 8. Elasticsearch Administration 9. Data Transformation and Federated Search 10. Improving Performance 11. Developing Elasticsearch Plugins 12. Introducing Elastic Stack 5.0

Suggesters


Before we continue with querying and analyzing the responses, we would like to write a few words about the available suggester types—the functionality responsible for finding suggestions when using the Elasticsearch suggest API. Elasticsearch allows us to use four suggesters currently: the term one, the phrase one, the completion one, and the context one. The first two allow us to correct spelling mistakes, while the third and fourth ones allow us to develop a very fast autocomplete functionality. However, for now, let's not focus on any particular suggester type, but let's look at the query possibilities and the responses returned by Elasticsearch. We will try to show you the general principles, and then we will get into more detail about each of the available suggesters.

Using a suggester under the _search endpoint

Before Elasticsearch 5.0, there was a possibility to get suggestions for a given text by using a dedicated _suggest REST endpoint. But in Elasticsearch 5.0, this dedicated...

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