When working with thousands of documents, a question that emerges is how to find documents that are similar to a given document or a set of documents. There are often uses cases when one would like to show documents that are similar to the document that the user is viewing, or is interested in. Elasticsearch has a query feature called “More Like This Query”, also known as the MLT Query, that tackles these cases.

Following along from the previous article in this series, we are going to familiarize ourselves with more queries like the term query, multi-match query and the bool query. We will be seeing how each query works and how and when to apply them.

In previous articles, we saw how queries and filters operate in elasticsearch and their differences between both. In this series, we will concentrate on the most commonly used queries in elasticsearch, which includes match, phrase match, prefix, term, multi_much, and bool. This post provides an overview and deep dives into match and phrase match examples.

In the previous posts in this series we created a basic Django app and populated a database with automatically generated data. We also added data to the elasticsearch index in bulk, wrote a basic command, and added a mapping to the elasticsearch index. In this final article we will add functional frontend items, write queries, allow the index to update, and discuss a bonus tip.

Imagine we have a website where users can put any query in any number of formats. We should map them to standard format and use the words that we have in the fields of our data set.

The main problems we can meet are:

– Incorrect words

– Pluralsingular forms

– Too many stopwords

In this post we will show you how to solve these problems in order to improve your free query results.