MySQL Window Functions

Window functions in MySQL are a powerful set of functions that allow you to perform calculations across a set of table rows that are somehow related to the current row, without collapsing the rows into a single output row (as aggregation does). These functions are used to calculate running totals, moving averages, rankings, and other operations that involve a "window" of rows.

Window functions provide an easy way to compute complex operations that require looking at multiple rows, without needing to join or aggregate data.

Key Components of Window Functions

A window function has two essential components:

The Function: This could be an aggregate function (SUM(), COUNT(), AVG()) or a ranking function (ROW_NUMBER(), RANK()) or others such as LEAD() and LAG().
The OVER() Clause: This defines the window for the function. The window tells MySQL which rows to consider for the function calculation.

Syntax of a Window Function

<window_function> OVER (
    [PARTITION BY <column(s)>]
    [ORDER BY <column(s)>]
    [ROWS BETWEEN <frame_specification>]
)

PARTITION BY: Divides the result set into partitions (subsets of rows). The window function is calculated separately for each partition.
ORDER BY: Determines the order of the rows within each partition.
ROWS BETWEEN: Defines the range of rows to be included in the window.

If you don't specify PARTITION BY, the function operates over all rows in the result set.

Types of Window Functions in MySQL

Ranking Functions:
- ROW_NUMBER(): Assigns a unique number to each row, starting from 1. The numbering is determined by the ORDER BY clause.
- RANK(): Similar to ROW_NUMBER(), but when there are ties, it assigns the same rank to rows with identical values and skips the next rank(s).
- DENSE_RANK(): Similar to RANK(), but without skipping ranks when there are ties.
- NTILE(n): Divides the result set into n groups and assigns each row a bucket number (starting from 1).
Aggregate Functions:
- SUM(): Calculates the sum of values over a window of rows.
- COUNT(): Counts the number of rows over a window.
- AVG(): Calculates the average value over a window of rows.
- MIN() and MAX(): Find the minimum or maximum values within a window of rows.
Value Functions:
- LEAD(): Returns the value from a row that follows the current row within the same window.
- LAG(): Returns the value from a row that precedes the current row within the same window.
- FIRST_VALUE(): Returns the first value of a specified column in the window.
- LAST_VALUE(): Returns the last value of a specified column in the window.

Examples of Using Window Functions

ROW_NUMBER() Example: This example assigns a unique row number to each row, ordered by salary:
```
SELECT name, salary, 
       ROW_NUMBER() OVER (ORDER BY salary DESC) AS row_num
FROM employees;
```
This will generate a list of employees ordered by salary in descending order, with each row getting a unique row number.
RANK() Example: In this example, employees with the same salary will get the same rank:
```
SELECT name, salary, 
       RANK() OVER (ORDER BY salary DESC) AS rank
FROM employees;
```
If two employees have the same salary, they will have the same rank, and the next rank will be skipped.

DENSE_RANK() Example: In this case, no ranks will be skipped even if two employees have the same salary:

SELECT name, salary, 
       DENSE_RANK() OVER (ORDER BY salary DESC) AS dense_rank
FROM employees;

LEAD() and LAG() Example: These functions are useful for comparing values in different rows within the same result set.
- LEAD(): Get the salary of the next employee.
- LAG(): Get the salary of the previous employee.
```
SELECT name, salary,
       LEAD(salary, 1) OVER (ORDER BY salary DESC) AS next_salary,
       LAG(salary, 1) OVER (ORDER BY salary DESC) AS prev_salary
FROM employees;
```
This example compares the current employee’s salary with the next and previous ones.
SUM() with a Window: Calculating the running total of salaries:
```
SELECT name, salary, 
       SUM(salary) OVER (ORDER BY salary) AS running_total
FROM employees;
```
This will give you a cumulative sum of salaries, ordered by salary.
NTILE(n) Example: This divides the employees into 4 groups based on their salary:
```
SELECT name, salary, 
       NTILE(4) OVER (ORDER BY salary) AS salary_quartile
FROM employees;
```
This will create 4 quartiles, where each quartile contains roughly the same number of employees.

Using PARTITION BY with Window Functions

Partitioning the data can help you calculate the window function separately for different subsets of rows. For instance, you can partition by department to calculate the salary rank for each department separately:

SELECT name, department, salary, 
       RANK() OVER (PARTITION BY department ORDER BY salary DESC) AS rank
FROM employees;

This query will rank employees within each department based on their salary, so employees in different departments will have independent rankings.

Frame Specifications with `ROWS BETWEEN`

You can specify the window frame (the range of rows to consider) using ROWS BETWEEN to define the window of data that should be considered when calculating the window function.

SELECT name, salary, 
       SUM(salary) OVER (ORDER BY salary 
                         ROWS BETWEEN 1 PRECEDING AND 1 FOLLOWING) AS window_sum
FROM employees;

In this example:

The window is defined to include the current row, the previous row (1 PRECEDING), and the next row (1 FOLLOWING).
The sum is calculated for this window of rows.

Limitations of Window Functions in MySQL

Version Availability: Window functions are available starting from MySQL 8.0. If you're using an earlier version, you won't be able to use window functions.
Performance: While window functions are powerful, they can impact query performance, especially on large datasets. Proper indexing and query optimization may be required for complex window functions.

Conclusion

MySQL window functions provide powerful capabilities to analyze and manipulate data across related rows. Whether you are calculating running totals, ranking rows, or comparing values within the same result set, window functions offer a cleaner and more efficient solution than complex subqueries or self-joins.

Souy Soeng

MySQL Window Functions