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Applies to: 
SQL Server 2025 (17.x) Preview
Azure SQL Database
Azure SQL Managed Instance
SQL database in Microsoft Fabric Preview
Use fuzzy, or approximate, string matching to check if two strings are similar, and calculate the difference between two strings. Use this capability to identify strings that may be different because of character corruption. Corruption causes may include spelling errors, transposed characters, missing characters, or abbreviations. Fuzzy string matching uses algorithms to detect similar sounding strings.
Note
- Fuzzy string matching is currently in preview.
- SQL Server support for fuzzy string matching introduced in SQL Server 2025 (17.x) Preview.
- Fuzzy string matching is available in Azure SQL Managed Instance configured with the Always-up-to-date update policy.
Fuzzy functions
| Function | Description |
|---|---|
| EDIT_DISTANCE | Calculates the number of insertions, deletions, substitutions, and transpositions needed to transform one string to another. |
| EDIT_DISTANCE_SIMILARITY | Calculates a similarity value ranging from 0 (indicating no match) to 100 (indicating full match). |
| JARO_WINKLER_DISTANCE | Calculates the edit distance between two strings giving preference to strings that match from the beginning for a set prefix length. |
| JARO_WINKLER_SIMILARITY | Calculates a similarity value ranging from 0 (indicating no match) to 1 (indicating full match). |
Note
Currently, the functions do not adhere to the comparison semantics defined by collation settings, such as case-insensitivity and other collation-specific rules. Once support for collation rules is implemented, the functions' output will reflect these semantics and may change accordingly.
Examples
The following examples demonstrate the fuzzy string matching functions.
Example table
Before you can run example queries, create and populate an example table.
To create and populate the example table, connect to a non-production user database, and run the following script:
-- Step 1: Create the table
CREATE TABLE WordPairs (
WordID INT IDENTITY(1,1) PRIMARY KEY, -- Auto-incrementing ID
WordUK NVARCHAR(50), -- UK English word
WordUS NVARCHAR(50) -- US English word
);
-- Step 2: Insert the data
INSERT INTO WordPairs (WordUK, WordUS) VALUES
('Colour', 'Color'),
('Flavour', 'Flavor'),
('Centre', 'Center'),
('Theatre', 'Theater'),
('Organise', 'Organize'),
('Analyse', 'Analyze'),
('Catalogue', 'Catalog'),
('Programme', 'Program'),
('Metre', 'Meter'),
('Honour', 'Honor'),
('Neighbour', 'Neighbor'),
('Travelling', 'Traveling'),
('Grey', 'Gray'),
('Defence', 'Defense'),
('Practise', 'Practice'), -- Verb form in UK
('Practice', 'Practice'), -- Noun form in both
('Aluminium', 'Aluminum'),
('Cheque', 'Check'); -- Bank cheque vs. check
Example EDIT_DISTANCE
SELECT WordUK, WordUS, EDIT_DISTANCE(WordUK, WordUS) AS Distance
FROM WordPairs
WHERE EDIT_DISTANCE(WordUK, WordUS) <= 2
ORDER BY Distance ASC;
Returns:
WordUK WordUS Distance
------------------------------ ------------------------------ -----------
Practice Practice 0
Aluminium Aluminum 1
Honour Honor 1
Neighbour Neighbor 1
Travelling Traveling 1
Grey Gray 1
Defence Defense 1
Practise Practice 1
Colour Color 1
Flavour Flavor 1
Organise Organize 1
Analyse Analyze 1
Catalogue Catalog 2
Programme Program 2
Metre Meter 2
Centre Center 2
Theatre Theater 2
Example EDIT_DISTANCE_SIMILARITY
SELECT WordUK, WordUS, EDIT_DISTANCE_SIMILARITY(WordUK, WordUS) AS Similarity
FROM WordPairs
WHERE EDIT_DISTANCE_SIMILARITY(WordUK, WordUS) >=75
ORDER BY Similarity DESC;
Returns:
WordUK WordUS Similarity
------------------------------ ------------------------------ -----------
Practice Practice 100
Travelling Traveling 90
Aluminium Aluminum 89
Neighbour Neighbor 89
Organise Organize 88
Practise Practice 88
Defence Defense 86
Analyse Analyze 86
Flavour Flavor 86
Colour Color 83
Honour Honor 83
Catalogue Catalog 78
Programme Program 78
Grey Gray 75
Example JARO_WINKLER_DISTANCE
SELECT WordUK, WordUS, JARO_WINKLER_DISTANCE(WordUK, WordUS) AS Distance
FROM WordPairs
WHERE JARO_WINKLER_DISTANCE(WordUK, WordUS) <= .05
ORDER BY Distance ASC;
Returns:
WordUK WordUS Distance
------------------------------ ------------------------------ -----------
Practice Practice 0
Travelling Traveling 0.02
Neighbour Neighbor 0.0222222222222223
Aluminium Aluminum 0.0222222222222223
Theatre Theater 0.0285714285714286
Flavour Flavor 0.0285714285714286
Centre Center 0.0333333333333333
Colour Color 0.0333333333333333
Honour Honor 0.0333333333333333
Catalogue Catalog 0.0444444444444444
Programme Program 0.0444444444444444
Metre Meter 0.0466666666666667
Example JARO_WINKLER_SIMILARITY
SELECT WordUK, WordUS, JARO_WINKLER_SIMILARITY(WordUK, WordUS) AS Similarity
FROM WordPairs
WHERE JARO_WINKLER_SIMILARITY(WordUK, WordUS) > 90
ORDER BY Similarity DESC;
Returns:
WordUK WordUS Similarity
------------------------------ ------------------------------ -----------
Practice Practice 100
Aluminium Aluminum 98
Neighbour Neighbor 98
Travelling Traveling 98
Colour Color 97
Flavour Flavor 97
Centre Center 97
Theatre Theater 97
Honour Honor 97
Catalogue Catalog 96
Programme Program 96
Metre Meter 95
Organise Organize 95
Practise Practice 95
Analyse Analyze 94
Defence Defense 94
Example query with all functions
The following query demonstrates all of the regular expression functions currently available.
SELECT T.source_string,
T.target_string,
EDIT_DISTANCE(T.source_string, T.target_string) as ED_Distance,
JARO_WINKLER_DISTANCE(T.source_string, T.target_string) as JW_Distance,
EDIT_DISTANCE_SIMILARITY(T.source_string, T.target_string) as ED_Similarity,
JARO_WINKLER_SIMILARITY(T.source_string, T.target_string) as JW_Similarity
FROM (VALUES('Black', 'Red'),
('Colour', 'Yellow'),
('Colour', 'Color'),
('Microsoft', 'Msft'),
('Regex', 'Regex')) as T(source_string, target_string);
Returns:
source_string target_string ED_Distance JW_Distance ED_Similarity JW_Similarity
-------------- -------------- -------------- --------------------- -------------- --------------
Black Red 5 1 0 0
Colour Yellow 5 0.444444444444445 17 55
Colour Color 1 0.0333333333333333 83 96
Microsoft Msft 5 0.491666666666667 44 50
Regex Regex 0 0 100 100
Clean up
After you are done using the example data, delete the example table.
IF OBJECT_ID('dbo.WordPairs', 'U') IS NOT NULL
BEGIN
DROP TABLE dbo.WordPairs;
END