Window functions
Applies to: 
Databricks SQL Databricks Runtime
Functions that operate on a group of rows, referred to as a window, and calculate a return value for each row based on the group of rows. Window functions are useful for processing tasks such as calculating a moving average, computing a cumulative statistic, or accessing the value of rows given the relative position of the current row.
Syntax
function OVER { window_name | ( window_name ) | window_spec }
function
{ ranking_function | analytic_function | aggregate_function }
over_clause
OVER { window_name | ( window_name ) | window_spec }
window_spec
( [ PARTITION BY partition [ , ... ] ] [ order_by ] [ window_frame ] )
Parameters
function
The function operating on the window. Different classes of functions support different configurations of window specifications.
ranking_function
Any of the Ranking window functions.
If specified the window_spec must include an ORDER BY clause, but not a window_frame clause.
analytic_function
Any of the Analytic window functions.
aggregate_function
Any of the Aggregate functions.
If specified the function must not include a FILTER clause.
window_name
Identifies a named window specification defined by the query.
window_spec
This clause defines how the rows will be grouped, sorted within the group, and which rows within a partition a function operates on.
partition
One or more expression used to specify a group of rows defining the scope on which the function operates. If no PARTITION clause is specified the partition is comprised of all rows.
order_by
The ORDER BY clause specifies the order of rows within a partition.
window_frame
The window frame clause specifies a sliding subset of rows within the partition on which the aggregate or analytics function operates.
You can specify SORT BY as an alias for ORDER BY.
You can also specify DISTRIBUTE BY as an alias for PARTITION BY. You can use CLUSTER BY as an alias for PARTITION BY in the absence of ORDER BY.
Examples
> CREATE TABLE employees
(name STRING, dept STRING, salary INT, age INT);
> INSERT INTO employees
VALUES ('Lisa', 'Sales', 10000, 35),
('Evan', 'Sales', 32000, 38),
('Fred', 'Engineering', 21000, 28),
('Alex', 'Sales', 30000, 33),
('Tom', 'Engineering', 23000, 33),
('Jane', 'Marketing', 29000, 28),
('Jeff', 'Marketing', 35000, 38),
('Paul', 'Engineering', 29000, 23),
('Chloe', 'Engineering', 23000, 25);
> SELECT name, dept, salary, age FROM employees;
Chloe Engineering 23000 25
Fred Engineering 21000 28
Paul Engineering 29000 23
Helen Marketing 29000 40
Tom Engineering 23000 33
Jane Marketing 29000 28
Jeff Marketing 35000 38
Evan Sales 32000 38
Lisa Sales 10000 35
Alex Sales 30000 33
> SELECT name,
dept,
RANK() OVER (PARTITION BY dept ORDER BY salary) AS rank
FROM employees;
Lisa Sales 10000 1
Alex Sales 30000 2
Evan Sales 32000 3
Fred Engineering 21000 1
Tom Engineering 23000 2
Chloe Engineering 23000 2
Paul Engineering 29000 4
Helen Marketing 29000 1
Jane Marketing 29000 1
Jeff Marketing 35000 3
> SELECT name,
dept,
DENSE_RANK() OVER (PARTITION BY dept ORDER BY salary
ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS dense_rank
FROM employees;
Lisa Sales 10000 1
Alex Sales 30000 2
Evan Sales 32000 3
Fred Engineering 21000 1
Tom Engineering 23000 2
Chloe Engineering 23000 2
Paul Engineering 29000 3
Helen Marketing 29000 1
Jane Marketing 29000 1
Jeff Marketing 35000 2
> SELECT name,
dept,
age,
CUME_DIST() OVER (PARTITION BY dept ORDER BY age
RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS cume_dist
FROM employees;
Alex Sales 33 0.3333333333333333
Lisa Sales 35 0.6666666666666666
Evan Sales 38 1.0
Paul Engineering 23 0.25
Chloe Engineering 25 0.50
Fred Engineering 28 0.75
Tom Engineering 33 1.0
Jane Marketing 28 0.3333333333333333
Jeff Marketing 38 0.6666666666666666
Helen Marketing 40 1.0
> SELECT name,
dept,
salary,
MIN(salary) OVER (PARTITION BY dept ORDER BY salary) AS min
FROM employees;
Lisa Sales 10000 10000
Alex Sales 30000 10000
Evan Sales 32000 10000
Helen Marketing 29000 29000
Jane Marketing 29000 29000
Jeff Marketing 35000 29000
Fred Engineering 21000 21000
Tom Engineering 23000 21000
Chloe Engineering 23000 21000
Paul Engineering 29000 21000
> SELECT name,
salary,
LAG(salary) OVER (PARTITION BY dept ORDER BY salary) AS lag,
LEAD(salary, 1, 0) OVER (PARTITION BY dept ORDER BY salary) AS lead
FROM employees;
Lisa Sales 10000 NULL 30000
Alex Sales 30000 10000 32000
Evan Sales 32000 30000 0
Fred Engineering 21000 NULL 23000
Chloe Engineering 23000 21000 23000
Tom Engineering 23000 23000 29000
Paul Engineering 29000 23000 0
Helen Marketing 29000 NULL 29000
Jane Marketing 29000 29000 35000
Jeff Marketing 35000 29000 0