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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

Product type Paperback

Published in Feb 2017

Publisher Packt

ISBN-13 9781786460189

Length 428 pages

Edition 3rd Edition

Languages

Java

Tools

Elasticsearch

Concepts

Enterprise Search

Author (1):

Bharvi Dixit

View More author details

Table of Contents (13) Chapters

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

Correcting user spelling mistakes

One of the simplest ways to improve the user search experience is to correct spelling mistakes either automatically, or by just showing the correct query phrase and allowing the user to use it. For example, this is what Google shows us when we type in elasticsaerch instead of Elasticsearch:

Starting from version 0.90.0 Beta1, Elasticsearch allows us to use the suggest API to correct user spelling mistakes. With the newer versions of Elasticsearch, the API was changed, bringing new features and becoming more and more powerful. In this section, we will try to bring you a comprehensive guide on how to use the suggest API provided by Elasticsearch, both in simple use cases and in ones that require more configuration.

Testing data

For the purposes of this section, we need a bit more data than a few documents. In order to get the data, we have decided to index some news documents from Wikipedia dump.

Let's follow the given steps for downloading and importing data...

The rest of the chapter is locked

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Authors (1)

Bharvi Dixit

Bharvi Dixit

Bharvi Dixit is an IT professional with extensive experience of working on search servers, NoSQL databases, and cloud services. He holds a master's degree in computer science and is currently working with Sentieo, a USA-based financial data and equity research platform, where he leads the overall platform and architecture of the company spanning across hundreds of servers. At Sentieo, he also plays a key role in the search and data team. He is also the organizer of Delhi's Elasticsearch Meetup Group, where he speaks about Elasticsearch and Lucene and is continuously building the community around these technologies. Bharvi also works as a freelance Elasticsearch consultant and has helped more than half a dozen organizations adapt Elasticsearch to solve their complex search problems around different use cases, such as creating search solutions for big data-automated intelligence platforms in the area of counter-terrorism and risk management, as well as in other domains, such as recruitment, e-commerce, finance, social search, and log monitoring. He has a keen interest in creating scalable backend platforms. His other areas of interests are search engineering, data analytics, and distributed computing. Java and Python are the primary languages in which he loves to write code. He has also built a proprietary software for consultancy firms. In 2013, he started working on Lucene and Elasticsearch, and in 2016, he authored his first book, Elasticsearch Essentials, which was published by Packt. He has also worked as a technical reviewer for the book Learning Kibana 5.0 by Packt. You can connect with him on LinkedIn at https://in.linkedin.com/in/bharvidixit or can follow him on Twitter @d_bharvi.

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