Data quality issues plague even the most meticulously maintained databases. Typos, misspellings, and phonetic variations can create duplicate records, hindering analysis and decision-making. Fortunately, Oracle Database 23c delivers two powerful tools for fuzzy string matching: FUZZY_MATCH
and PHONIC_ENCODE
.
This article delves into these operators, exploring their potential and providing practical code examples to unlock their power.
Fuzzy Matching in Action: FUZZY_MATCH
Imagine searching for customers named “Michael” but encountering variations like “Michal” or “Micheal.” Here’s where FUZZY_MATCH
shines. It calculates the similarity between two strings using various algorithms, returning a score indicating their closeness. Higher scores represent greater similarity. Here’s an example:
SELECT customer_id, name, FUZZY_MATCH('SOUNDEX', name, 'Michael') AS match_score
FROM customers;
OUTPUT:
customer_id | name | match_score
----------- | ------------- | -----------
1 | Michael | 100
2 | Michal | 80
3 | Micheal | 90
```
We used the SOUNDEX
algorithm, which encodes names based on pronunciation. Other algorithms available include LEVENSHTEIN
(edit distance) and JARO_Winkler
(similarity measure).
Phoning it In: PHONIC_ENCODE
Sometimes, variations arise due to pronunciation differences, not spelling errors. In these cases, PHONIC_ENCODE
is your ally. It converts strings into a phonetic representation, focusing on sound, not character sequence.
For instance, “Chris” and “Kris” might have different spellings but share the same phonetic code, allowing you to identify potential duplicates:
SELECT customer_id, name, PHONIC_ENCODE(name) AS phonetic_code
FROM customers;
OUTPUT:
customer_id | name | phonetic_code
----------- | ------------- | -------------
1 | Michael | MKL
2 | Michal | MKL
3 | Micheal | MKL
4 | Chris | KRS
5 | Kris | KRS
```
By comparing phonetic codes, you can efficiently uncover near-duplicate records based on pronunciation similarity.
PL/SQL Support
While FUZZY_MATCH
and PHONIC_ENCODE
are powerful data quality operators, direct assignment within PL/SQL blocks isn’t currently possible.
DECLARE
my_name VARCHAR2(50);
BEGIN
-- Attempting direct assignment (doesn't work)
my_name := FUZZY_MATCH('SOUNDEX', 'Michael', 'Michal');
END;
/
...
PLS-00201: identifier 'FUZZY_MATCH' must be declared
...
Fortunately, we can leverage the SELECT … INTO construct to retrieve the desired output from the operator and store it in a PL/SQL variable:
DECLARE
my_name VARCHAR2(50);
BEGIN
-- Select the match score and store it in the variable
SELECT FUZZY_MATCH('SOUNDEX', 'Michael', 'Michal') INTO my_name
FROM DUAL;
END;
/
Practical Applications
- Deduplication: Identify and merge near-duplicate customer records, product entries, or any other textual data.
- Data cleansing: Correct typos and misspellings, improving data accuracy and consistency.
- Fuzzy search: Enable flexible search functionalities, accommodating spelling variations in queries.
Conclusion
By embracing FUZZY_MATCH
and PHONIC_ENCODE
, you empower your Oracle Database 23c to handle imperfect data with agility and precision. Explore these tools to enhance data quality, streamline data management, and gain valuable insights from your information assets.
For more information:
- Documentation: Data Quality Operators