How to Use fuzzystrmatch Extension in PostgreSQL 18

In real-world databases, data is rarely perfect. Users enter names with spelling mistakes, alternate spellings, phonetic variations, or typing errors. Traditional SQL comparisons using the equality operator (=) fail in these situations. To handle such imperfect data efficiently, PostgreSQL provides a powerful extension called fuzzystrmatch.

This extension enables approximate string matching and phonetic comparisons directly at the database level. It plays a major role in search optimization, data cleaning, and intelligent matching systems.

This blog explains the complete workflow of the fuzzystrmatch extension with installation steps, function descriptions, real SQL examples, and use cases from a database administrator’s perspective.

What is fuzzystrmatch in PostgreSQL?

The fuzzystrmatch extension provides a collection of functions that help in:

1. Identifying strings that sound similar.
2. Detecting human typing errors.
3. Measuring similarity between two text values.
4. Matching inconsistent or noisy data.

It is commonly used in applications such as search systems, CRM platforms, identity verification, record deduplication, and data quality management.

Why fuzzystrmatch Is Important for a Database Administrator

From a DBA perspective, this extension is extremely valuable because:

1. It helps detect and remove duplicate records generated due to spelling differences.
2. It improves search accuracy without modifying application-side logic.
3. It assists in data normalization and cleansing.
4. It reduces dependence on external text-processing systems.

By handling fuzzy logic directly inside the database, DBAs can improve both performance and consistency.

Step 1: Check Installed Extensions

\dx

If it is installed, you get a result like this:

fuzzystrmatch | 1.2 | public | determine similarities and distance between strings

This confirms that the extension is available in your PostgreSQL cluster.

Step 2: Create the Extension

CREATE EXTENSION fuzzystrmatch;

This command registers all fuzzy string matching functions inside your database so they can be used in SQL queries.

Step 3: List All Functions in the Extension

\dx+ fuzzystrmatch

You get a result like this:

postgres=# \dx+ fuzzystrmatch 
                    Objects in extension "fuzzystrmatch"
                             Object description                             
----------------------------------------------------------------------------
 function daitch_mokotoff(text)
 function difference(text,text)
 function dmetaphone_alt(text)
 function dmetaphone(text)
 function levenshtein_less_equal(text,text,integer)
 function levenshtein_less_equal(text,text,integer,integer,integer,integer)
 function levenshtein(text,text)
 function levenshtein(text,text,integer,integer,integer)
 function metaphone(text,integer)
 function soundex(text)
 function text_soundex(text)
(11 rows)

Each function serves a different type of fuzzy matching. All of them are explained below with purpose and output.

1. soundex() – Basic Phonetic Encoding

The soundex function converts a word into a phonetic code based on how it sounds when spoken.

Purpose and Use Cases

1. It allows matching words that sound the same but are spelled differently.
2. It is widely used for matching person names in search systems and citizen databases.
3. It helps reduce spelling dependency in query conditions.

Examples

SELECT soundex('Smith');

Output:

S530

Let’s try with a name similar to this

SELECT soundex('Smythe');

Output:

S530

Both values produce the same Soundex code, meaning they are phonetically equivalent.

This confirms that the function correctly matches similar pronunciations with different spellings.

2. text_soundex() – Extended Soundex for Longer Words

The text_soundex function behaves similarly to soundex, but it is designed to work efficiently with longer strings and more complex words.

Purpose and Use Cases

1. It improves phonetic matching accuracy for full names and large text inputs.
2. It is useful in CRM and government databases where long names are common.
3. It provides better normalization than the traditional Soundex algorithm.

Examples

SELECT text_soundex('Robert');

Output:

R163

Let’s try a similar name to this

SELECT text_soundex('Rupert');

Output:

R163

Even though the spelling differs, both names return the same phonetic code, proving that they sound alike.

3. difference() – Sound-Based Similarity Score

The difference function compares two words based on their Soundex values and returns a similarity score from 0 to 4.

Purpose and Use Cases

1. It quantifies how similar two words sound.
2. It is useful in fuzzy ranking systems.

Examples

SELECT difference('John', 'Jon');

Output:

4

Let’s try with different names

SELECT difference('Peter', 'Johnson');

Output:

0

Higher numbers represent stronger phonetic similarity.

4. metaphone() – Advanced Phonetic Encoding

The metaphone function produces a more accurate phonetic key than Soundex. It is optimized for English pronunciation rules.

Purpose and Use Cases

1. It delivers more precise phonetic encoding than Soundex.
2. It improves matching of complex English pronunciations.
3. It is useful in search engines and recommendation systems.
4. It supports configurable output length.

Examples

SELECT metaphone('Catherine', 10);
K0RN
SELECT metaphone('Katherine', 10);
K0RN

Let’s try a different word

SELECT metaphone('Philosophy', 10);
FLSF
SELECT metaphone('Filosophy', 10);
FLSF

The number parameter represents the length limit for t

These examples show how metaphone normalizes different spellings into the same phonetic representation.

5. dmetaphone() and dmetaphone_alt() – Double Metaphone

Double Metaphone provides two phonetic representations: a primary and an alternate. This improves matching across different languages and accents.

Purpose and Use Cases

1. It supports multilingual name matching.
2. It handles silent letters and foreign pronunciations.
3. It increases recall in identity matching systems.

Examples

SELECT dmetaphone('Smith');

Output:

SM0

Let’s try the double metaphone function

SELECT dmetaphone_alt('Smith');

Output:

XMT

6. levenshtein() – Edit Distance for Typo Detection

The levenshtein function calculates the minimum number of single-character edits required to convert one string into another.

Purpose and Use Cases

1. It detects spelling mistakes and typing errors.
2. It measures how far two words are from each other.

Examples

SELECT levenshtein('kitten', 'sitting');

Output:

3

SELECT levenshtein('Saturday', 'Sunday');

Output:

3

The levenshtein function calculates the minimum number of single-character operations required to transform one string into another. The allowed operations are:

• Insertion of a character
• Deletion of a character
• Substitution (replacement) of a character

Each operation has a default cost of 1 unless custom costs are provided.

A distance of zero means the strings are identical.

Custom Cost Example

The syntax is this

levenshtein(source, target, insert_cost, delete_cost, substitute_cost)

Where:

• insert_cost is the cost of inserting one character
• delete_cost is the cost of deleting one character
• substitute_cost is the cost of replacing one character

By changing these values, you can control how strictly differences between two strings are measured.

SELECT levenshtein('hello', 'hallo', 1, 1, 2);
2
SELECT levenshtein('hello', 'hallo', 1, 1, 1);
1

This shows how substitution, insertion, and deletion costs can be customized.

7. levenshtein_less_equal() – Fast Threshold Matching

This function computes the Levenshtein distance only up to a given limit and stops early if the distance exceeds that limit.

Purpose and Use Cases

• It improves performance for large datasets.
• It supports fast filtering in real-time search.
• It avoids unnecessary full-distance calculations.

Examples

SELECT levenshtein_less_equal('PostgreSQL', 'Postgres', 2);

Output:

3

SELECT levenshtein_less_equal('cat', 'cats', 2);

Output:

1

8. daitch_mokotoff() – Genealogical Name Matching

The Daitch–Mokotoff Soundex algorithm is specially designed for matching Jewish and Slavic surnames.

Purpose and Use Cases

1. It is used in genealogy and ancestry databases.
2. It supports historical surname variations.

Examples

SELECT daitch_mokotoff('Rosenberg');

Output:

{946795}

Each result returns an array of phonetic codes.

Conclusion

The fuzzystrmatch extension significantly enhances PostgreSQL’s capability to handle imperfect and inconsistent text data. Enabling phonetic matching and approximate string comparison at the database level, it allows systems to accurately process spelling variations, pronunciation differences, and transcription errors that are common in real-world datasets.

From a database administration perspective, this extension plays a vital role in improving data consistency, minimizing duplicate records, optimizing fuzzy search performance, and strengthening overall data quality. It eliminates the need for complex application-side string processing and centralizes intelligent matching logic within the database.

For any production system that manages human-generated text such as names, addresses, product catalogs, or free-form user input, fuzzystrmatch is not just a convenience feature but a practical and essential tool for building reliable and scalable data-driven applications.