Window Functions - Data 101 Course Notes

Last updated: April 7, 2025

Window functions allow us to perform calculations across a window of rows related to the current row, without collapsing the result into a single value like GROUP BY does. Think of them as a way to get aggregate-like insights while still preserving the individual rows. In contrast to GROUP BY aggregate functions, which return one row per group, window functions return one row per input row.

Examples of when you might want to use a window function:

Compute a cumulative sum
Determine the rank within a group
Compare current row values to previous/next ones

Basic Syntax¶

<window or agg_func> OVER (
  [PARTITION BY <...>]
  [ORDER BY <...>]
  [ROWS/RANGE BETWEEN <...> AND <...>]
)

The syntax can consist of 3 ingredients:

PARTITION BY (optional): Defines how to divide the dataset into partitions (similar to groups in GROUP BY). If omitted, the entire table is treated as one partition.
ORDER BY (optional): Orders the rows within each partition. Required if using ROWS or RANGE.
ROWS/RANGE BETWEEN (optional): Specifies the window frame or the subset of rows relative to the current row. Requires ORDER BY to be present.
- If omitted entirely, the default frame is:
  - ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING (if no ORDER BY is specified).
  - ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW (if ORDER BY is specified).

Example¶

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

This query ranks employees within each department by salary. The higher the salary, the better the rank (1 = highest).

ROWS vs. RANGE¶

These two keywords define the frame of rows to include around the current row.

ROWS: Refers to a specific number of physical rows based on position (e.g., 2 before and 2 after)

SUM(sales) OVER (
  ORDER BY sale_date
  ROWS BETWEEN 2 PRECEDING AND CURRENT ROW
)

This would include the current row and the 2 before it, based strictly on row positions.

RANGE: Includes all rows with values within the same range as the current row based on the ORDER BY field. It’s more value-sensitive than row-sensitve.

SUM(sales) OVER (
  ORDER BY sale_amount
  RANGE BETWEEN CURRENT ROW AND UNBOUNDED FOLLOWING
  )

This includes all rows that have the same or greater sale_amount as the current row.

Common Window Functions¶

Function	Description
`RANK()`	Assigns a rank to each row within the partition. If multiple rows have the same value, they receive the same rank, and the next rank(s) are skipped
`DENSE_RANK()`	Like `RANK()`, but no gaps in rank values. Tied rows share the same rank, and the next rank continues consecutively
`ROW_NUMBER()`	Assigns a unique row number within each partition
`LEAD(expr, n)`	Returns the value of `expr` n rows after the current row. `expr` can be any column or expression
`LAG(expr, n)`	Returns the value of `expr` n rows before the current row
`PERCENT_RANK()`	Relative rank as a fraction between 0 and 1
`NTH_VALUE(expr, n)`	Returns the value of expr at position n in the window

The WINDOW clause¶

If you’re applying multiple window functions using the same partition/order structure, don’t repeat yourself! Use the WINDOW clause to define the logic once:

SELECT name,
       SUM(salary) OVER w AS total_salary,
       AVG(salary) OVER w AS avg_salary
FROM employees
WINDOW w AS (PARTITION BY department ORDER BY salary DESC);

This makes your SQL cleaner and avoids errors when copy-pasting window definitions.

1.7 Window Functions

Basic Syntax¶

Example¶

ROWS vs. RANGE¶

Common Window Functions¶

The WINDOW clause¶