Introducing Regular Expression (Regex) Support in Azure SQL DB

Abhiman Tiwari

We are pleased to announce the private preview of regular expressions (regex) support in Azure SQL Database. Regex is a powerful tool that allows you to search, manipulate, and validate text data in flexible ways. With regex support, you can enhance your SQL queries with pattern matching, extraction, replacement, and more. You can also combine them with other SQL functions and operators to create complex expressions and logic.

This feature can help you to:

• Enhance your data quality and accuracy by validating and correcting data formats, such as phone numbers, email addresses, zip codes, etc.
• Extract valuable insights and information from the data by extracting and grouping specific text patterns, such as keywords, hashtags, mentions, etc.
• Transform and standardize your data by replacing, splitting, or joining text patterns, such as abbreviations, acronyms, synonyms, etc.
• Clean and optimize your data by removing or filtering out unwanted text patterns, such as whitespace, punctuation, duplicates, etc.

The Regex feature in Azure SQL DB follows the POSIX standard and is compatible with the standard regex syntax and supports a variety of regex functions, such as REGEXP_LIKE, REGEXP_COUNT, REGEXP_INSTR, REGEXP_REPLACE, and REGEXP_SUBSTR. The feature also supports case sensitivity, character classes, quantifiers, anchors, and capturing groups. The feature is available for all Azure SQL DB service tiers and offerings.

Feature Exploration

Here is the list of Regex functions that are being introduced:

• REGEXP_LIKE: This function returns TRUE if a string matches a regular expression pattern, or FALSE otherwise.
• REGEXP_COUNT: This function returns the number of times a regular expression pattern matches in a string.
• REGEXP_INSTR: This function returns the starting or ending position, based on the specified option, of the given occurrence of a regular expression pattern in a string.
• REGEXP_REPLACE: This function returns a modified string replaced by a ‘replacement string’, where occurrence of the regular expression pattern found.
• REGEXP_SUBSTR: This function returns a substring that matches a regular expression pattern from a string.

Examples

We’ll use the following table as an example to demonstrate the usage of these functions. The table contains some information about employees, such as their names, email addresses, and phone numbers.

The following are some examples of using Regex functions in SQL queries.

REGEXP_LIKE

This function returns True if the input string matches the regex pattern, and False otherwise. You can use it to filter rows based on a regex condition and to apply check constraints to ensure that the data fulfils the specified criteria.

Let’s create Employee table with some records and Check constraints for Email and Phone_Number columns:

-- Create Employees table with some records and check constraints for Email and Phone_Number columns
DROP TABLE IF EXISTS Employees
CREATE TABLE Employees (
    ID INT IDENTITY(101,1),
    [Name] VARCHAR(150),
    Email VARCHAR(320)
    CHECK (REGEXP_LIKE(Email, '^[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}$')),
    Phone_Number VARCHAR(20)
    CHECK (REGEXP_LIKE (Phone_Number, '^(\d{3})-(\d{3})-(\d{4})$'))
);
-- Insert some sample data
INSERT INTO Employees ([Name], Email, Phone_Number) VALUES
    ('John Doe', 'john@contoso.com', '123-456-7890'),
    ('Alice Smith', 'alice@fabrikam.com', '234-567-8901'),
    ('Bob Johnson', 'bob@fabrikam.net','345-678-9012'),
    ('Eve Jones', 'eve@contoso.com', '456-789-0123'),
    ('Charlie Brown', 'charlie@contoso.co.in', '567-890-1234');

Insert a new row into the Employees table with the following values to validate the Check constraints:

--Failure:  
INSERT INTO Employees ([Name], Email, Phone_Number) VALUES 
    ('Demo Data', 'demo@contoso@com', '123-456_7890') 

--Success: 
INSERT INTO Employees ([Name], Email, Phone_Number) VALUES 
    ('Demo Data', 'demo@contoso.com', '123-456-7890') 

Filter rows based on regex condition:

For example, the following query returns all the rows from the Employees table where the email address domain ends with ‘.com’.

-- find all the employees whose email addresses end with .com
SELECT [Name], Email
FROM Employees
WHERE REGEXP_LIKE(Email, '\.com$');

Results:

REGEXP_COUNT

This function returns the number of times a regex pattern matches in the input string. You can use it to count the occurrences of a pattern within a string. For example, the following query returns the number of vowels in each Employee’s name.

-- for each employee, count the number of vowels in their name
SELECT [Name], REGEXP_COUNT([Name], '[AEIOU]',1,'i') AS Vowel_Count
FROM Employees;

Results:

REGEXP_INSTR

This function returns the starting or ending position, based on the specified option, of the given occurrence of a regular expression pattern in a string. You can use it to locate the index of a pattern within a string. For example, the following query returns the position of ‘@’ in each Employee’s email address.

-- for each employee, show their name, email, and the position of the @ sign in their email
SELECT [Name], Email, REGEXP_INSTR(email, '@') AS Position_of_@
FROM Employees;

Results:

REGEXP_REPLACE

This function returns a modified string replaced by a ‘replacement string’, where occurrence of the regular expression pattern found. You can use it to modify or transform text data based on a regex pattern. For example, the following query returns the phone number of each Employee in a standardized format.

-- format the phone numbers in the Employees table to the format (XXX) XXX-XXXX.
SELECT Phone_Number, REGEXP_REPLACE(Phone_Number, '(\d{3})-(\d{3})-(\d{4})', '(\1) \2-\3',1) AS Phone_Format
FROM Employees;

Results:

REGEXP_SUBSTR

This function returns the substring that matches the regular expression pattern from a string. You can use it to extract parts of a string based on a regex pattern. For example, the following query returns the domain name of each employee’s email address.

-- for each employee, show the domain of their email address
SELECT [Name], Email, REGEXP_SUBSTR(email, '@(.+)$', 1, 1,'c',1) AS Domain
FROM Employees;

Results:

Private Preview Sign-up

We are currently accepting requests from customers who would like to participate in the private preview and try out the regex feature. If you are interested, please fill out this form: https://aka.ms/regex-preview-signup

Wrapping up

We value your feedback and suggestions as we continue to improve and enhance SQL DB. Please let us know what you think of the regex feature and how it helps you with your data analysis and manipulation. You can submit your feedback using this Form or leave a comment on this blog.

Thank you for choosing Azure SQL Database as your data platform. We hope you enjoy the regex feature and find it beneficial for your needs.

Abhiman Tiwari Senior Program Manager, Azure SQL

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