Fuzzy Matching a String in SQL: A Comprehensive Guide

Introduction

When working with data, it’s not uncommon to encounter duplicate records or similar values that can be matched using fuzzy matching. In this article, we’ll explore how to perform fuzzy matching on strings in SQL, specifically focusing on PostgreSQL and Databricks.

Background

Fuzzy matching is a technique used to find similar values in a dataset. It’s commonly used in applications such as spell checking, autocomplete suggestions, and duplicate detection. The goal of fuzzy matching is to identify records that have a certain level of similarity, often measured by a distance metric such as Levenshtein distance.

Levenshtein Distance

Levenshtein distance is a measure of the minimum number of single-character edits (insertions, deletions, or substitutions) required to change one word into another. It’s commonly used in fuzzy matching applications because it provides a quantitative measure of similarity between two strings.

In PostgreSQL, the fuzzystrmatch package provides an implementation of Levenshtein distance and other distance metrics, making it easy to perform fuzzy matching on strings.

Fuzzy Matching with Levenshtein Distance

To perform fuzzy matching using Levenshtein distance in PostgreSQL, you can use the following exemplary predicate:

WHERE levenshtein(street_address, '123 Main Avex') <= 1;

This will match all records where the Levenshtein distance between street_address and '123 Main Avex' is less than or equal to 1. The value 1 here represents a strict match, meaning only exact matches are returned.

However, you can adjust the threshold value to achieve a looser or stricter match. A larger value will return more records, while a smaller value will return fewer records.

Relative Distance

As @IVO GELOV suggests, using relative distance (distance divided by the length) instead of an absolute distance metric like Levenshtein distance can provide more meaningful results.

WHERE levenshtein(street_address, '123 Main Avex') / LENGTH('123 Main Avex') <= 0.1;

In this example, a value less than or equal to 0.1 is considered acceptable. This approach can help to balance the trade-off between matching records with similar but not identical values.

Concatenating Fields for Fuzzy Matching

One common approach to fuzzy matching is to concatenate multiple fields into a single string and then apply Levenshtein distance or other distance metrics.

For example, you can concatenate first_name, last_name, and street_address as follows:

SELECT * FROM users WHERE levenshtein(CONCAT(first_name, last_name, street_address), 'Mary Doe 123 Main Ave') <= 1;

This will match records where the concatenated string has a Levenshtein distance of less than or equal to 1 with the target string 'Mary Doe 123 Main Ave'.

Fuzzy Matching in Databricks

Databricks, being built on top of Apache Spark, provides similar functionality for fuzzy matching. You can use the fuzzywuzzy library, which is a Java-based implementation of Levenshtein distance and other distance metrics.

Here’s an example code snippet in Python:

from fuzzywuzzy import fuzz

users = [
    {'id': 11, 'first_name': 'Mary', 'last_name': 'Doe', 'street_address': '123 Main Ave'},
    {'id': 21, 'first_name': 'Mary', 'last_name': 'Doe', 'street_address': '123 Main Ave'},
    {'id': 13, 'first_name': 'Mary Esq', 'last_name': 'Doe', 'street_address': '123 Main Ave'}
]

for user in users:
    score = fuzz.token_sort_ratio(user['first_name'] + ' ' + user['last_name'], 'Mary Doe 123 Main Ave')
    if score > 80:  # Adjust the threshold value as needed
        print(f"Match found for user {user['id']} with score {score}")

This code snippet uses the fuzz.token_sort_ratio function from the fuzzywuzzy library to calculate a similarity score between the concatenated string and the target string.

Conclusion

Fuzzy matching is a powerful technique for finding similar values in datasets. By using Levenshtein distance or other distance metrics, you can perform fuzzy matching on strings in PostgreSQL and Databricks. Remember to adjust threshold values and concatenation fields as needed to achieve the desired level of similarity between matched records.

Additional Tips and Variations

• For more advanced fuzzy matching scenarios, consider using machine learning algorithms like cosine similarity or text embeddings.
• When working with large datasets, consider using indexing strategies like full-text indexing to improve query performance.
• For more fine-grained control over Levenshtein distance calculations, explore alternative libraries like python-Levenshtein or PyLevenshtein.
• Don’t forget to optimize database queries and indexing for efficient fuzzy matching performance.

Last modified on 2024-06-13