Querying Similar Strings in a Database: A PostgreSQL Approach

In this article, we’ll explore how to query similar strings in a database using PostgreSQL’s built-in pg_trgm extension. This extension provides functions for comparing strings and finding the most similar matches.

Introduction

When working with text data in a database, it’s often necessary to find similar values or patterns. In this case, we’re interested in finding rows that have the most similarity from the starting character of a given string.

PostgreSQL offers an efficient solution using its pg_trgm extension, which provides functions for comparing strings and finding similarities. We’ll delve into how to use these functions to query similar strings in our database.

Prerequisites

Before we begin, ensure you have PostgreSQL installed on your system. If not, you can download the latest version from the official PostgreSQL website: https://www.postgresql.org/download/

Additionally, create a new PostgreSQL database and schema for this example:

CREATE DATABASE similarity_query;
\c similarity_query

CREATE TABLE x (
    id SERIAL PRIMARY KEY,
    value VARCHAR(50)
);

INSERT INTO x (value) VALUES ('a'), ('abc'), ('absde'), ('abcdef'), ('abcdefg');

This creates a new table x with an id column and a value column. The value column contains sample strings.

Enabling the pg_trgm Extension

The first step in using the pg_trgm extension is to enable it in your PostgreSQL database:

CREATE EXTENSION IF NOT EXISTS pg_trgm;

This command creates the pg_trgm extension if it doesn’t already exist. If you’ve already created an extension with this name, this command will do nothing.

Querying Similar Strings

There are two main functions provided by the pg_trgm extension: similarity() and similarity_ratio(). The similarity() function returns a value between 0 (no similarity) and 1 (identical strings), while the similarity_ratio() function returns the ratio of matching characters.

We’ll use the similarity() function to find rows that have more than 35% similarity with the given string. You can adjust this threshold value as needed.

SELECT x.value FROM x WHERE similarity('abcdefghijklmnop', x.value) > 0.35;

This query selects all rows from the x table where the similarity between the value column and the given string is greater than 35%.

Finding the Most Similar Row

If you want to find the most similar row, you can use the similarity() function with an additional ORDER BY clause:

SELECT * FROM x ORDER BY similarity('abcdefghijklmnop', x.value) DESC LIMIT 1;

This query selects all rows from the x table, orders them by their similarity to the given string in descending order (most similar first), and limits the result to just one row.

Tips and Variations

Use the similarity_ratio() function if you want to compare the ratio of matching characters instead of using the similarity() function directly.
Adjust the threshold value in the WHERE clause to change the minimum similarity required for a match.
Use the ilike operator instead of the similarity() function, but be aware that this will not provide an exact similarity score.

Example Fiddle and Documentation

For more information on the pg_trgm extension, check out the official PostgreSQL documentation: https://www.postgresql.org/docs/current/pgtrgm.html

You can also try our example fiddle at http://sqlfiddle.com/#!17/c901e/2 to see the results in action.

Best Practices

When working with text data and similarity queries, consider the following best practices:

Normalize your text data before comparing it. This can include techniques like stemming or lemmatization.
Use a suitable data type for your text column. For example, PostgreSQL’s varchar data type is not ideal for large strings due to its overhead.
Optimize your queries by indexing relevant columns and using efficient joining strategies.

By following these guidelines and leveraging the powerful functions provided by the pg_trgm extension, you can efficiently query similar strings in your database and unlock valuable insights from your text data.

Last modified on 2023-09-06