SQL LIKE Pattern Matching Tutorial

The SQL LIKE Operator for Pattern Matching

Like it or not, the LIKE operator is essential in SQL. It gives data practitioners the power to filter data on specific string matches. This article provides a quick tutorial on LIKE for beginners and intermediates. If you learn better with hands-on practice, you can also follow along (and run the code) on this DataCamp Workspace.

Suppose you have an employees table and would like to find all names that start with ‘A’. You could spend time looking through the table manually. But why would you do that when you have the LIKE operator?

SELECT DISTINCT
	first_name
FROM employees
WHERE first_name LIKE 'A%'

The magic here is in the clause `WHERE first_name LIKE ‘A%’`, which means “find all first_name starting with A and ending with any number of characters.” The `A%` here is known as a pattern for matching. 

The `%` is not the only wildcard you can use in conjunction with the LIKE operator. You could use the underscore sign `_` too. 

• `%` matches any number of characters.
• `_` matches any single character.

The syntax is easy to remember. It is simply

column_name LIKE pattern

You can use LIKE to achieve a variety of pattern-matching. Here’s how.

Beginner SQL LIKE Examples

Below, we’ve outlined some practical examples of how you can use the LIKE statement and the results from our sample data set. 

1. Use LIKE for Exact String Match

If you’d like to perform an exact string match, use LIKE without ‘%’ or ‘_’

SELECT
    first_name, last_name
FROM employees
WHERE first_name LIKE 'Barry' -- the same as WHERE first_name = ‘Barry’

2. Use ‘%’ to match any number of characters 

‘%’ can be used to match any (even zero) number of characters – a number, an alphabet, or a symbol.

Suppose you want to find all employees whose name starts with ‘Adam’; you can use the pattern ‘Adam%’

SELECT DISTINCT
    first_name
FROM employees
WHERE first_name LIKE 'Adam%'

To find names that end with ’Z’, try the pattern ‘%z’. You can also use multiple ‘%’ in one pattern. For example, if you want to find names that contain z, use ‘%z%’.

3. Use ‘_’ to match one (and only one) character

Like the game Hangman, the underscore sign _ can only fit one character.

How many ways are there to spell Le_n? The pattern would match anything from ‘Lexn’, ‘LeAn’, Le3n’, or ‘Le-n’.

SELECT DISTINCT
    first_name
FROM employees
WHERE first_name LIKE 'Le_n'

What are the different names with only three characters? We can find out using three consecutive underscores ___ as the pattern.

SELECT DISTINCT
    first_name
FROM employees
WHERE first_name LIKE '___'

4. Use both ‘%’ and ‘_’ to match any pattern

Of course, you can use both ‘%’ and ‘_’ to create interesting patterns. 

SELECT DISTINCT
    first_name
FROM employees
WHERE first_name LIKE '%ann_'

The pattern ‘%ann_’ matches a string that starts with any number of characters and ends with ‘ann’ and one other character.

5. Use NOT to find strings that do not match a pattern

What if you want to find all rows that do not match a specific criterion? You can use the NOT LIKE operator. For example, to find all titles except for Staff, we can use the syntax

`WHERE title NOT LIKE ‘Staff’`

This is exactly equivalent to the syntax

`WHERE title != ‘Staff’`

SELECT DISTINCT
    title
FROM titles
WHERE title NOT LIKE 'Staff'

Of course, you can use `NOT LIKE` with any of the patterns we described.

SELECT DISTINCT
    title
FROM titles 
WHERE title NOT LIKE '%engineer'

6. Use LOWER (or UPPER) with LIKE for case-insensitive pattern matching

If you need to perform pattern matching but aren’t sure if the string is stored in lowercase, uppercase, or mixed case, you can use the following syntax.

`LOWER(column_name) LIKE pattern`

The function LOWER() returns all strings in lowercase, regardless of whether they are stored as upper-, lower- or mixed case. 

When using the syntax, make sure you spell the pattern in all lowercase! Else, you might not get any matches.

You could replace LOWER with UPPER in the syntax above too. Be sure to spell out the pattern in CAPITAL LETTERS.

`UPPER(column_name) LIKE PATTERN`

For example, to find out if an employee’s name is Joanne, JoAnne, Joanna, or JoAnna, try either of the following.

SELECT DISTINCT
    first_name
FROM employees
WHERE lower(first_name) LIKE 'joann_'

SELECT DISTINCT
    first_name
FROM employees
WHERE UPPER(first_name) LIKE 'JOANN_'

7. SQL LIKE with Multiple Values Using OR/AND

You can combine multiple conditions using the LIKE syntax too. 

For example, use the OR condition to find results that satisfy at least one out of multiple LIKE patterns.

SELECT DISTINCT
    first_name
FROM employees
WHERE
first_name LIKE 'Ad_l' OR
first_name LIKE 'Ad_m'

On the other hand, to find a string that matches multiple LIKE conditions, use the AND keyword.

SELECT DISTINCT
    first_name
FROM employees
WHERE
first_name LIKE '%am%' AND
first_name LIKE '%me%'

The LIKE syntax can be applied to multiple columns, as long as their variable type is a variable-length character (varchar). Knowing that we can find out the names of employees whose initials are ‘Z. Z.’

SELECT DISTINCT
    first_name, last_name
FROM employees
WHERE
first_name LIKE 'Z%' AND
last_name LIKE 'Z%'

8. Use LIKE in the SELECT CASE WHEN clause 

Thus far, we have focused on using LIKE as a condition for selecting records in the WHERE clause. We also use LIKE in the SELECT clause too. For example, can we find out how many employees with the name ‘Adam’ are in our database?

SELECT
    COUNT(CASE WHEN first_name LIKE 'Adam' THEN 1 END) num_employees_adam
FROM employees

On the other hand, how many employees have the initials ‘A.Z.’?

SELECT
    COUNT(CASE WHEN first_name LIKE 'A%' AND last_name LIKE 'Z%' THEN 1 END) num_employees
FROM employees

Intermediate Examples of SQL LIKE

The LIKE function is largely similar across different flavors of SQL (e.g. PostgreSQL, MySQL, Redshift, etc.). Some have additional variations of the LIKE function that are worth mentioning. 

1. The ILIKE operator

Available in Redshift and PostgreSQL, ILIKE is the case-insensitive version of LIKE. As such, all of the following are equivalent.

SELECT
  datacamp ILIKE ‘datacamp’, -- returns TRUE
  DATACAMP ILIKE ‘datacamp’, -- returns TRUE
  Datacamp ILIKE ‘datacamp’, -- returns TRUE
  datacamp ILIKE ‘DataCamp’, -- returns TRUE

2. Using square brackets [] and [^] as wildcard characters

Users of T-SQL or SQL Server have additional wildcard characters for more complex pattern matching.

The square bracket syntax [] matches any single character specific within the range or set. For example, the following will all return TRUE.

SELECT
  ‘Carson’ LIKE ‘[C-K]arson’, -- returns TRUE because C is in the range C-K
  ‘Karson’ LIKE ‘[C-K]arson’, -- returns TRUE because K is in range
  ‘Larson’ LIKE ‘[CKL]arson’, -- returns TRUE because L is in the set [CKL]
  ‘Parson’ LIKE ‘[C-K]arson’ -- returns FALSE because P is out of range

The caret-in-square-bracket [^] wildcard matches any single character that is not within the specified range or set. Can you see why the following results are such? 

SELECT
  ‘Carson’ LIKE ‘[^C-K]arson’ -- returns FALSE 
  ‘Parson’ LIKE ‘[^C-K]arson’ -- returns TRUE

Here, since C is within the range of [C-K], the pattern ‘C’ will not match [^C-K]. Thus, ‘Carson’ will not match ‘[^C-K]arson.

3. The RLIKE operator

Available in MySQL, the RLIKE operator recognizes regular expressions (RegEx) in the pattern. RegEx is a powerful and versatile tool for advanced pattern matching. 

It does not hurt to have a basic understanding of RegEx, especially if your flavor of SQL supports RLIKE. You can learn more about RegEx with our Regular Expressions in Python course.

SELECT DISTINCT
    first_name
FROM employees
WHERE first_name RLIKE 'Susann[a-e]'

4. The ‘~~’ operator

In PostgreSQL, ‘~~’ is completely synonymous with LIKE. There are also equivalents of ILIKE, NOT LIKE, and NOT ILIKE.

Use SQL LIKE Confidently

Mastering SQL functions is key to succeeding in data science, and SQL’s LIKE command is no exception. Good command over SQL will supercharge your analytics progress, so be sure to learn it well!

For more resources on SQL, be sure to check out the following:

• Introduction to SQL Course
• Learn SQL hub
• SQL Basics Cheat Sheet