Window Functions in SQL

Window Functions are designed to perform calculations across a set of table rows that are somehow related to the current row. Unlike aggregate functions, which return a single value for a group of rows, window functions return a value for each row in the group.

Why Use Window Functions?

• Enhanced Data Analysis: Perform calculations like cumulative sums and moving averages.
• Ranking and Row Numbering: Assign ranks to rows within a partition of a result set.
• Data Comparison: Compare values between current and previous or next rows.

The basic syntax of a window function involves using the OVER clause, which defines the window or the set of rows the function operates on.

window_function() OVER (
    [PARTITION BY partition_expression, ... ]
    [ORDER BY sort_expression [ASC | DESC], ... ]
    [frame_clause]
)

Here is an example:

SELECT
    employee_id,
    salary,
    RANK() OVER (ORDER BY salary DESC) as salary_rank
FROM
    employees;

This query ranks employees based on their salaries in descending order.

The ROW_NUMBER() function assigns a unique sequential integer to rows within a partition of a result set.

SELECT
    employee_id,
    department_id,
    salary,
    ROW_NUMBER() OVER (PARTITION BY department_id ORDER BY salary DESC) as row_num
FROM
    employees;

The RANK() function assigns a rank to each row within a partition of a result set. If there are ties, gaps are left in the ranking sequence.

SELECT
    employee_id,
    department_id,
    salary,
    RANK() OVER (PARTITION BY department_id ORDER BY salary DESC) as rank
FROM
    employees;

Similar to RANK(), but without gaps in the ranking sequence.

SELECT
    employee_id,
    department_id,
    salary,
    DENSE_RANK() OVER (PARTITION BY department_id ORDER BY salary DESC) as dense_rank
FROM
    employees;

The NTILE() function distributes rows of an ordered partition into a specified number of approximately equal groups.

SELECT
    employee_id,
    department_id,
    salary,
    NTILE(4) OVER (ORDER BY salary DESC) as quartile
FROM
    employees;

These functions access data from previous and subsequent rows in the same result set without the need for a self-join.

SELECT
    employee_id,
    salary,
    LAG(salary, 1) OVER (ORDER BY salary) as prev_salary,
    LEAD(salary, 1) OVER (ORDER BY salary) as next_salary
FROM
    employees;

The PARTITION BY clause divides the result set into partitions to which the window function is applied.

SELECT
    employee_id,
    department_id,
    salary,
    SUM(salary) OVER (PARTITION BY department_id ORDER BY employee_id) as department_running_total
FROM
    employees;

The ORDER BY clause specifies the logical order in which the window function calculation is performed.

SELECT
    employee_id,
    department_id,
    salary,
    SUM(salary) OVER (ORDER BY salary DESC) as running_total
FROM
    employees;

Defines a subset of rows within a partition for the window function to operate on.

SELECT
    employee_id,
    salary,
    SUM(salary) OVER (ORDER BY employee_id ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) as running_total
FROM
    employees;

Q: What are SQL Window Functions?
A: SQL Window Functions perform calculations across sets of table rows that are related to the current row, providing advanced data analysis capabilities.

Q: How do Window Functions differ from aggregate functions?
A: Unlike aggregate functions, which return a single value for a group of rows, window functions return a value for each row in the group.

Q: Can I use multiple Window Functions in a single query?
A: Yes, you can use multiple window functions in a single query, each with its own OVER clause.

Q: Do Window Functions improve query performance?
A: Window functions enhance analytical capabilities but can be resource-intensive. Proper indexing and query optimization are essential for performance.

Q: Are Window Functions supported in all SQL databases?
A: Most modern SQL databases support window functions, including PostgreSQL, MySQL, SQL Server, and Oracle.