SELECT to_tsvector('english', 'github');
select word_similarity ( 'git', 'github' ), 'git' % 'github';
select word_similarity ('https://github.com/woylie/let_me', 'git'), 'https://github.com/woylie/let_me' %> 'git' ;

https://www.freecodecamp.org/news/fuzzy-string-matching-with-postgresql/

Full Text Search with Postgres

PostgreSQL is a powerful relational database management system that offers a wide range of features for full-text search. One of the most useful features is fuzzy string matching, which allows you to search for similar strings even if they are not exact matches. In this article, we will explore how to use fuzzy string matching with PostgreSQL.

What is Fuzzy String Matching?

Fuzzy string matching is a technique used to search for strings that are similar but not identical. It is often used in full-text search applications where you want to find documents that contain words or phrases that are similar to the search query. Fuzzy string matching algorithms work by comparing the similarity between two strings and returning a score that indicates how similar they are.

Using Fuzzy String Matching with Postgresql

PostgreSQL offers a built-in function called similarity() that allows you to calculate the similarity between two strings. The similarity() function uses a fuzzy string matching algorithm called Levenshtein distance to calculate the similarity between two strings.

To use the similarity() function, you need to install the pg_trgm extension. You can do this by running the following SQL command:

CREATE EXTENSION IF NOT EXISTS pg_trgm;

Once the extension is installed, you can use the similarity() function to calculate the similarity between two strings. Here is an example:

SELECT similarity('hello'::text, 'hallo'::text);

This will return a score that indicates how similar the two strings are.

Example Use Case

Let's say you are building a search engine for a website and you want to search for documents that contain the word 'hello'. However, you also want to return documents that contain words that are similar to 'hello', such as 'hallo' or 'hi'. You can use the similarity() function to calculate the similarity between the search query and the words in the documents.

Here is an example SQL query:

SELECT *
FROM documents
WHERE similarity(title, 'hello'::text) > 0.5;

This will return all documents that have a title that is similar to 'hello' with a score greater than 0.5.

Conclusion

Fuzzy string matching is a powerful technique for full-text search that allows you to search for similar strings even if they are not exact matches. PostgreSQL offers a built-in function called similarity() that allows you to calculate the similarity between two strings. By using the similarity() function, you can build powerful search engines that return relevant results even when the search query is not an exact match.

For more information on fuzzy string matching with PostgreSQL, you can refer to the official PostgreSQL documentation.

External Resources

• Fuzzy String Matching with PostgreSQL
• PostgreSQL Documentation: Similarity

Action Points

• Install the pg_trgm extension to use the similarity() function.
• Use the similarity() function to calculate the similarity between two strings.
• Experiment with different similarity scores to find the optimal score for your search engine.
• Use the similarity() function to build a powerful search engine that returns relevant results even when the search query is not an exact match.