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Pattern Matching in mysql - GeeksforGeeks

Last Updated : 23 Jul, 2025

Pattern matching is a key component of database management, allowing users to find and retrieve data within their domain of interest quickly. In this article, we'll understand how to perform Pattern Matching in MySQL with the help of various examples and their output and explanation of output with the Best Practices.

Pattern matching in MySQL primarily revolves around two key operators: LIKE and REGEXP. Such operators allow text sorting within text field types like strings and characters.

This LIKE operator may be the one most often used in MySQL queries to run pattern matching. It finds an exact match to the given pattern inside the string. Pattern structure will comprise wildcards to represent any number of symbols or a single symbol.

• '%' Wildcard: The percent sign '%' denotes zero or more characters. As an example, '%apple' obtains any string that begins with '%apple', followed by zero or more characters.
  
• '' Wildcard: Underscore '' is used to show a character's presence. For instance, 'c_t' corresponds to 'cat, cot, cut', etc.
  
• Combination: Adding '%' and '_' together allows for a more complicated form. For example, 'a%e' matches any string that began with 'a' and ended with 'e'.

The REGEXP operator gives a more complex matching option by utilizing regular expressions for matching patterns. Regular expressions are powerful pattern-matching tools that allow for sophisticated searches involving complicated and specific patterns within text data.

• Character Classes: Regular expressions will include character classes such as [a-z] for any lowercase letter and [0-9] for any digit.
  
• Quantifiers: Quantifiers specify the number of occurrences of a character or group, such as '*' for zero or more occurrences and '+' for one or more occurrences.
  
• Anchors: Anchors specify the position of a pattern within a string, such as '^' for the start of a string and '$' for the end of a string.

Let's consider a table named Products that stores information about various products, including their names. We'll perform a few examples of pattern-matching queries using this table.

CREATE TABLE products (
    product_id INT AUTO_INCREMENT PRIMARY KEY,
    product_name VARCHAR(100)
);
INSERT INTO products (product_name) VALUES

('Apple iPhone 12'),
('Samsung Galaxy S20'),
('Google Pixel 5'),
('Huawei P40 Pro'),
('OnePlus 8T');

Output:

| product_id | product_name      |
|------------|-------------------|
| 1          | Apple iPhone 12   |
| 2          | Samsung Galaxy S20|
| 3          | Google Pixel 5    |
| 4          | Huawei P40 Pro    |
| 5          | OnePlus 8T        |

Suppose our products table looks like this:

SELECT * FROM products WHERE product_name LIKE 'S%';

Output:

| product_id | product_name      |
|------------|-------------------|
| 2          | Samsung Galaxy S20|

Explanation: This query selects products where the product name starts with 'S'. The '%' wildcard allows for zero or more characters after 'S', so it matches 'Samsung Galaxy S20'.

Suppose we want to find all products with names that contain a number in them.

SELECT * FROM products WHERE product_name REGEXP '[0-9]';

Output:

| product_id | product_name      |
|------------|-------------------|
| 1          | Apple iPhone 12   |
| 2          | Samsung Galaxy S20|
| 5          | OnePlus 8T        |

Explanation: This query selects products where the product name contains any digit (0-9). The regular expression '[0-9]' matches any single digit within the product name.

Suppose we want to find products with names that start with 'A' and end with 'e'.

SELECT * FROM products WHERE product_name LIKE 'A%e';

Output:

| product_id | product_name      |
|------------|-------------------|
| 1          | Apple iPhone 12   |
| 5          | OnePlus 8T        |

Explanation: This query selects products where the product name starts with 'A' and ends with 'e'. The '%' wildcard allows for zero or more characters in between, and 'e' at the end ensures the name ends with 'e'.

To maximize the effectiveness of pattern matching in MySQL, consider the following best practices:

• Index Optimization: Index optimization is the key when the queries involve pattern matching that has to be performed on large datasets. Index optimization is a very strong factor for improving query performance. Column indexes on `columns` that are part of pattern-matching operations can elevate the speed of data retrieval.
  
• Use of Collations: Take notice of the case-insensitive mode, when performing pattern matching. MySQL operates on different collations that reveal, how different characters are being compared and sorted influencing the pattern-matching operations in the databases.
  
• Regular Expression Optimization: Implementing regular expressions is quite resource-heavy as it may deal with complex-specified patterns or large sets of data. Stay away from the use of the too-complicated regex in most cases. That and evaluating the performance of your queries also apply.

Pattern matching provides MySQL with the opportunity to have a tool that is multi-functional in searching and analyzing text data. Whether you are looking for specific patterns, to extract information, or to analyze data, being conversant with the capabilities of pattern matching operators like LIKE and REGEXP is imperative in that it helps to improve your query performance and enhance the efficiency of your MySQL queries.

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