In the previous chapter, we discussed different ways to query documents based on the user search input. But the search input might contain typos and spelling mistakes. Automatically correcting the user's spelling mistakes and typos improves the overall search experience. The term or match query that we discussed in the previous chapter only looks for the exact term in the inverted index. In this section, we will discuss different types of queries Elasticsearch provides to correct the typos.

Learning Elasticsearch
By :

Learning Elasticsearch
By:
Overview of this book
Elasticsearch is a modern, fast, distributed, scalable, fault tolerant, and open source
search and analytics engine. You can use Elasticsearch for small or large applications with billions of documents. It is built to scale horizontally and can handle both structured and unstructured data. Packed with easy-to- follow examples, this book will ensure you will have a firm understanding of the basics of Elasticsearch and know how to utilize its capabilities efficiently.
You will install and set up Elasticsearch and Kibana, and handle documents using the Distributed Document Store. You will see how to query, search, and index your data, and perform aggregation-based analytics with ease. You will see how to use Kibana to explore and visualize your data.
Further on, you will learn to handle document relationships, work with geospatial data, and much more, with this easy-to-follow guide. Finally, you will see how you can set up and scale your Elasticsearch clusters in production environments.
Table of Contents (11 chapters)
Preface
Introduction to Elasticsearch
Setting Up Elasticsearch and Kibana
Modeling Your Data and Document Relations
Indexing and Updating Your Data
Organizing Your Data and Bulk Data Ingestion
All About Search
More Than a Search Engine (Geofilters, Autocomplete, and More)
How to Slice and Dice Your Data Using Aggregations
Production and Beyond
Exploring Elastic Stack (Elastic Cloud, Security, Graph, and Alerting)
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