In this lesson, you will learn how to identify records that represent the same entity despite minor spelling errors or inconsistencies.
When working with large datasets, you often encounter "dirty" data where the same customer or product is stored under slightly different names, such as "John Smith" and "Jhon Smith." Because standard SQL equality operators (=) look for an exact bit-by-bit match, these two records would be treated as completely different people. Fuzzy matching allows us to bridge this gap by calculating the "distance" between strings.
The Levenshtein Distance
The most common algorithm used for this purpose is the Levenshtein Distance. It measures the minimum number of single-character edits (insertions, deletions, or substitutions) required to change one word into another.
- "John" vs. "Jhon" = 2 edits (a 'h' and 'o' are swapped).
- "John" vs. "John" = 0 edits.
In SQL environments, we don't always have a built-in LEVENSHTEIN() function, but we can implement the logic using conditional mappings or by leveraging specialized extensions (like fuzzystrmatch in PostgreSQL). Conceptually, you are looking for records where the character distance is below a specific threshold.
Practical Implementation
Imagine you have a table Customers and you want to find duplicates caused by typos:
SELECT a.name AS name1, b.name AS name2
FROM Customers a
JOIN Customers b
ON a.id < b.id
WHERE levenshtein(a.name, b.name) <= 2;
By joining the table to itself and filtering for a low edit distance, you reveal rows that are likely the same person. This technique is vital when cleaning data before performing your regular joins or aggregations. By identifying these "fuzzy" matches, you can standardize the data, merge the records, or assign a unique master ID to them, ensuring that your subsequent reporting—like grouping or set operations—is based on clean, consistent information.
Key Takeaway: Fuzzy matching identifies records as "the same" by measuring how many character edits it takes to transform one string into another, effectively overcoming minor typos that break standard SQL equality checks.