How to Measure String Similarity in MySQL Using Overlapping Words and Levenshtein Distance?

How to Calculate String Similarity in MySQL

To compute the similarity between two strings in MySQL, we can leverage string manipulation functions and mathematical expressions. Consider the following example where we have two strings:

SET @a = "Welcome to Stack Overflow";
SET @b = "Hello to stack overflow";

Similarity Calculation Using Overlapping Words

We can count the number of words that appear in both strings and use that as a measure of similarity. In this case, the following words overlap:

• Welcome
• to
• stack
• overflow

Calculating the Similarity Index

The similarity index is calculated as follows:

similarity = count(similar words between @a and @b) / (count(@a) + count(@b) - count(intersection))

Using the Levenshtein Function

MySQL does not natively support functions for string similarity. However, we can use a user-defined function (UDF) called levenshtein to compute the Levenshtein distance, which measures the number of edits (insertions, deletions, or substitutions) required to transform one string into another.

Creating the Levenshtein UDF

CREATE FUNCTION `levenshtein`(s1 text, s2 text) RETURNS int(11)
DETERMINISTIC
...

For more details on the Levenshtein UDF, please refer to the provided code snippet.

Computing the Similarity Ratio

Finally, we can compute the similarity ratio by normalizing the Levenshtein distance against the maximum length of the two strings:

CREATE FUNCTION `levenshtein_ratio`(s1 text, s2 text) RETURNS int(11)
DETERMINISTIC
...

For instance, the similarity ratio between @a and @b using the Levenshtein ratio function can be calculated as:

SELECT levenshtein_ratio(@a, @b);

This will return the similarity ratio as a percentage value.

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