Filtering and Pattern Matching

Learn advanced filtering techniques including pattern matching, ranges, and NULL handling.

IN Operator

The IN operator allows you to specify multiple values in a WHERE clause.

Syntax

SELECT column1, column2
FROM table_name
WHERE column_name IN (value1, value2, ...);

Example

Instead of writing:

SELECT * FROM students
WHERE city = 'NYC' OR city = 'LA' OR city = 'Chicago';

You can write:

SELECT * FROM students
WHERE city IN ('NYC', 'LA', 'Chicago');

Using NOT IN:

SELECT * FROM students
WHERE grade NOT IN ('C', 'F');

BETWEEN Operator

The BETWEEN operator selects values within a given range (inclusive).

Syntax

SELECT column1, column2
FROM table_name
WHERE column_name BETWEEN value1 AND value2;

Examples

Numeric ranges:

SELECT name, age FROM students
WHERE age BETWEEN 20 AND 22;

Result:

+-----------+-----+
| name      | age |
+-----------+-----+
| Alice     | 20  |
| Bob       | 22  |
| Charlie   | 21  |
| Eva       | 20  |
+-----------+-----+

Date ranges:

SELECT * FROM orders
WHERE order_date BETWEEN '2024-01-01' AND '2024-12-31';

NOT BETWEEN:

SELECT name, age FROM students
WHERE age NOT BETWEEN 20 AND 21;

LIKE Operator

The LIKE operator searches for a specified pattern in a column.

Wildcards

Wildcard	Description	Example
%	Represents zero or more characters	'a%' finds values starting with "a"
_	Represents a single character	'a_' finds two-character values starting with "a"

Examples

Starts with 'A':

SELECT name FROM students
WHERE name LIKE 'A%';

Result:

+-----------+
| name      |
+-----------+
| Alice     |
+-----------+

Ends with 'e':

SELECT name FROM students
WHERE name LIKE '%e';

Result:

+-----------+
| name      |
+-----------+
| Alice     |
| Charlie   |
+-----------+

Contains 'a' anywhere:

SELECT name FROM students
WHERE name LIKE '%a%';

Second letter is 'i':

SELECT name FROM students
WHERE name LIKE '_i%';

Result:

+-----------+
| name      |
+-----------+
| Diana     |
+-----------+

Exactly 3 characters:

SELECT name FROM students
WHERE name LIKE '___';

Result:

+-----------+
| name      |
+-----------+
| Bob       |
| Eva       |
+-----------+

Case-Insensitive Searching

Different databases handle case sensitivity differently:

MySQL (case-insensitive by default):

SELECT * FROM students WHERE name LIKE 'alice';

PostgreSQL (case-sensitive, use ILIKE):

SELECT * FROM students WHERE name ILIKE 'alice';

SQL Server (use UPPER or LOWER):

SELECT * FROM students WHERE UPPER(name) LIKE 'ALICE';

NULL Values

NULL represents missing or unknown data. It's different from zero or empty string.

IS NULL

Find records with NULL values:

SELECT * FROM students
WHERE email IS NULL;

IS NOT NULL

Find records without NULL values:

SELECT * FROM students
WHERE email IS NOT NULL;

!!! warning "NULL Comparison" You cannot use = or != with NULL. Always use IS NULL or IS NOT NULL.

```sql
-- WRONG
WHERE email = NULL

-- CORRECT
WHERE email IS NULL
```

Combining Multiple Conditions

You can combine different filtering techniques:

SELECT name, age, city FROM students
WHERE (grade IN ('A', 'B'))
  AND (age BETWEEN 20 AND 22)
  AND (city LIKE '%C%')
  AND (email IS NOT NULL);

Practical Examples

Example 1: Find students whose name starts with 'C' or 'D' and are older than 20

SELECT * FROM students
WHERE name LIKE 'C%' OR name LIKE 'D%'
  AND age > 20;

Example 2: Find students not from NYC or LA with grades A or B

SELECT name, city, grade FROM students
WHERE city NOT IN ('NYC', 'LA')
  AND grade IN ('A', 'B');

Example 3: Find students with names containing 'ar' and age between 18-25

SELECT * FROM students
WHERE name LIKE '%ar%'
  AND age BETWEEN 18 AND 25;

Pattern Matching Best Practices

1. Use specific patterns - name LIKE 'A%' is faster than name LIKE '%A%'
2. Avoid leading wildcards - LIKE '%text' can be slow on large tables
3. Consider indexes - Columns frequently used in LIKE should be indexed
4. Use exact matches when possible - = is faster than LIKE

Practice Exercises

Using a hypothetical employees table:

+----+-----------+-----------+--------+------------+
| id | name      | department| salary | hire_date  |
+----+-----------+-----------+--------+------------+
| 1  | John Doe  | Sales     | 50000  | 2020-01-15 |
| 2  | Jane Smith| IT        | 75000  | 2019-06-20 |
| 3  | Bob Wilson| Sales     | 55000  | 2021-03-10 |
| 4  | Alice Lee | HR        | NULL   | 2022-01-05 |
+----+-----------+-----------+--------+------------+

Try these queries:

1. Find employees in Sales or IT departments
2. Find employees hired between 2020 and 2022
3. Find employees whose name contains 'son'
4. Find employees with no salary information
5. Find employees in Sales with salary above 52000

??? success "Solutions" ```sql -- 1. Employees in Sales or IT SELECT * FROM employees WHERE department IN ('Sales', 'IT');

-- 2. Hired between 2020 and 2022
SELECT * FROM employees
WHERE hire_date BETWEEN '2020-01-01' AND '2022-12-31';

-- 3. Name contains 'son'
SELECT * FROM employees
WHERE name LIKE '%son%';

-- 4. No salary information
SELECT * FROM employees
WHERE salary IS NULL;

-- 5. Sales employees with salary > 52000
SELECT * FROM employees
WHERE department = 'Sales' AND salary > 52000;
```

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