Fuzzy Search in Oracle
In this article, we'll talk about "fuzzy search in Oracle." At the end, you'll see a practical example of how to perform this type of search on a product table.
4/8/20253 min read
Fuzzy Search in Oracle
An Approach to Handling Inaccurate Data
In this article, we'll discuss fuzzy search in Oracle. At the end of the article, you'll find a practical example of performing this type of search in a product table. This search is based on a 4,000-character text field called "specifications."
What is fuzzy search in Oracle?
Fuzzy search is a powerful technique within Oracle that allows you to find approximate matches in text data. This is especially useful when dealing with data that may contain spelling errors, spelling variations, or inconsistently entered data.
Why is fuzzy search important?
In many real-world applications, data is never perfect. Users can make typos, databases can contain inconsistencies, and data can come from diverse sources with different writing conventions. Fuzzy search addresses these problems by allowing users to find the information they need, even if they don't spell it exactly the same.
In Oracle, this type of search is implemented using specific techniques and tools to handle queries where strict matching is not required.
Fuzzy search is based on algorithms that identify similarities between text strings. For example, it allows a system to recognize that "John Doe" and "J. Doe" could represent the same person, even if they do not match exactly. This has practical applications in areas such as correcting duplicate records, database searching, and analyzing unstructured data.
Fuzzy Search Features in Oracle:
Oracle offers several tools and functions for performing fuzzy searches:
LIKE operator and wildcard characters:
The LIKE operator is used to search for patterns in character strings.
The wildcard characters "%" (any sequence of characters) and "_" (any single character) allow for approximate searches.
Example:
SELECT *
FROM clients
WHERE name LIKE 'Smit%'; /*found "Smith", "Smither" and similar*/
Edit distance functions:
Oracle supports functions that calculate the distance between two strings, measuring how many changes are required to transform one string into another.
These functions, such as the Levenshtein distance, are useful for finding approximate matches based on string similarity.
Specifically, the UTL_MATCH package includes functions such as EDIT_DISTANCE and JARO_WINKLER, which calculate the similarity between two text strings based on edit distance algorithms.
Example:
SELECT name, UTL_MATCH.EDIT_DISTANCE('Jhon', name) AS similarity
FROM employees
WHERE UTL_MATCH.EDIT_DISTANCE('Jhon', name) < 3;
Here, the EDIT_DISTANCE function calculates the number of operations (insertions, deletions, substitutions) required to transform one string into another. The result filters out strings with a distance less than 3.
Oracle Text:
Oracle Text is a powerful tool for indexing and searching text in Oracle databases.
It offers advanced fuzzy search capabilities, including phonetic search, stemming (reducing words to their root), and proximity search.
Oracle Text is especially useful for searching large amounts of text, such as documents or articles.
You can configure indexes and use queries with similarity options to perform non-exact searches. By creating contextual indexes, Oracle Text optimizes fuzzy searches by preprocessing the data and performing linguistic analysis.
Example:
SELECT *
FROM employees
WHERE CONTAINS(name, 'FUZZY(Jhon)', 1) > 0;
This example finds approximate matches for the term "John" in the name column of the employees table.
Regular Expressions
Regular expressions (REGEXP) are not specifically fuzzy, but they can be used as a complement to search for flexible patterns in text. (See article: Validating email format with regular expressions in PL/SQL).
Matching Features:
Oracle also provides functions that help find matches and calculate the degree of similarity between strings, which is very useful for fuzzy search.
Common use cases:
User Experience: Improves the experience in applications that require text searches, such as web portals or CRM systems.
Customer Name Search: Allows you to find customers even if their names are misspelled or have variations.
Product Search: Helps users find products even if they don't remember the exact name.
Data Quality Management: Fuzzy search identifies and helps correct duplicate or inconsistent records.
Text Analysis: Allows you to search for patterns and trends in large amounts of text.
Important considerations:
Fuzzy search can be computationally expensive, especially for large data sets.
Optimizing queries, indexes, and configurations is critical to maintaining performance.
The choice of the appropriate fuzzy search technique depends on the specific requirements of the application.
In short, fuzzy search is an essential tool for any application that works with inconsistent data, allowing you to obtain relevant results without relying on exact matches. Oracle provides a variety of tools and functions to perform fuzzy searches effectively, allowing users to find the information they need quickly and accurately.
Practical example: given a table of products, perform a fuzzy search in the text of the specifications of said products.
