Window Functions
A window function performs a calculation across a set of table rows that are somehow related to the current row. This is comparable to the type of calculation that can be done with an aggregate function. However, window functions do not cause rows to become grouped into a single output row like non-window aggregate calls would. Instead, the rows retain their separate identities. Behind the scenes, the window function is able to access more than just the current row of the query result
Here is an example that shows how to compare each employee's salary with the average salary in his or her department:
SELECT depname, empno, salary, avg(salary) OVER (PARTITION BY depname) FROM empsalary;
+-----------+-------+--------+-------------------+
| depname | empno | salary | avg |
+-----------+-------+--------+-------------------+
| personnel | 2 | 3900 | 3700.0 |
| personnel | 5 | 3500 | 3700.0 |
| develop | 8 | 6000 | 5020.0 |
| develop | 10 | 5200 | 5020.0 |
| develop | 11 | 5200 | 5020.0 |
| develop | 9 | 4500 | 5020.0 |
| develop | 7 | 4200 | 5020.0 |
| sales | 1 | 5000 | 4866.666666666667 |
| sales | 4 | 4800 | 4866.666666666667 |
| sales | 3 | 4800 | 4866.666666666667 |
+-----------+-------+--------+-------------------+
A window function call always contains an OVER clause directly following the window function's name and argument(s). This is what syntactically distinguishes it from a normal function or non-window aggregate. The OVER clause determines exactly how the rows of the query are split up for processing by the window function. The PARTITION BY clause within OVER divides the rows into groups, or partitions, that share the same values of the PARTITION BY expression(s). For each row, the window function is computed across the rows that fall into the same partition as the current row. The previous example showed how to count the average of a column per partition.
You can also control the order in which rows are processed by window functions using ORDER BY within OVER. (The window ORDER BY does not even have to match the order in which the rows are output.) Here is an example:
SELECT depname, empno, salary,
rank() OVER (PARTITION BY depname ORDER BY salary DESC)
FROM empsalary;
+-----------+-------+--------+--------+
| depname | empno | salary | rank |
+-----------+-------+--------+--------+
| personnel | 2 | 3900 | 1 |
| develop | 8 | 6000 | 1 |
| develop | 10 | 5200 | 2 |
| develop | 11 | 5200 | 2 |
| develop | 9 | 4500 | 4 |
| develop | 7 | 4200 | 5 |
| sales | 1 | 5000 | 1 |
| sales | 4 | 4800 | 2 |
| personnel | 5 | 3500 | 2 |
| sales | 3 | 4800 | 2 |
+-----------+-------+--------+--------+
There is another important concept associated with window functions: for each row, there is a set of rows within its partition called its window frame. Some window functions act only on the rows of the window frame, rather than of the whole partition. Here is an example of using window frames in queries:
SELECT depname, empno, salary,
avg(salary) OVER(ORDER BY salary ASC ROWS BETWEEN 1 PRECEDING AND 1 FOLLOWING) AS avg,
min(salary) OVER(ORDER BY empno ASC ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS cum_min
FROM empsalary
ORDER BY empno ASC;
+-----------+-------+--------+--------------------+---------+
| depname | empno | salary | avg | cum_min |
+-----------+-------+--------+--------------------+---------+
| sales | 1 | 5000 | 5000.0 | 5000 |
| personnel | 2 | 3900 | 3866.6666666666665 | 3900 |
| sales | 3 | 4800 | 4700.0 | 3900 |
| sales | 4 | 4800 | 4866.666666666667 | 3900 |
| personnel | 5 | 3500 | 3700.0 | 3500 |
| develop | 7 | 4200 | 4200.0 | 3500 |
| develop | 8 | 6000 | 5600.0 | 3500 |
| develop | 9 | 4500 | 4500.0 | 3500 |
| develop | 10 | 5200 | 5133.333333333333 | 3500 |
| develop | 11 | 5200 | 5466.666666666667 | 3500 |
+-----------+-------+--------+--------------------+---------+
When a query involves multiple window functions, it is possible to write out each one with a separate OVER clause, but this is duplicative and error-prone if the same windowing behavior is wanted for several functions. Instead, each windowing behavior can be named in a WINDOW clause and then referenced in OVER. For example:
SELECT sum(salary) OVER w, avg(salary) OVER w
FROM empsalary
WINDOW w AS (PARTITION BY depname ORDER BY salary DESC);
Syntax
The syntax for the OVER-clause is
function([expr])
OVER(
[PARTITION BY expr[, …]]
[ORDER BY expr [ ASC | DESC ][, …]]
[ frame_clause ]
)
where frame_clause is one of:
{ RANGE | ROWS | GROUPS } frame_start
{ RANGE | ROWS | GROUPS } BETWEEN frame_start AND frame_end
and frame_start and frame_end can be one of
UNBOUNDED PRECEDING
offset PRECEDING
CURRENT ROW
offset FOLLOWING
UNBOUNDED FOLLOWING
where offset is an non-negative integer.
RANGE and GROUPS modes require an ORDER BY clause (with RANGE the ORDER BY must specify exactly one column).
Aggregate functions
All aggregate functions can be used as window functions.
Ranking functions
row_number
Number of the current row within its partition, counting from 1.
row_number()
rank
Rank of the current row with gaps; same as row_number of its first peer.
rank()
dense_rank
Rank of the current row without gaps; this function counts peer groups.
dense_rank()
ntile
Integer ranging from 1 to the argument value, dividing the partition as equally as possible.
ntile(expression)
Arguments
- expression: An integer describing the number groups the partition should be split into
Analytical functions
cume_dist
Relative rank of the current row: (number of rows preceding or peer with current row) / (total rows).
cume_dist()
percent_rank
Relative rank of the current row: (rank - 1) / (total rows - 1).
percent_rank()
lag
Returns value evaluated at the row that is offset rows before the current row within the partition; if there is no such row, instead return default (which must be of the same type as value). Both offset and default are evaluated with respect to the current row. If omitted, offset defaults to 1 and default to null.
lag(expression, offset, default)
Arguments
- expression: Expression to operate on
- offset: Integer. Specifies how many rows back the value of expression should be retrieved. Defaults to 1.
- default: The default value if the offset is not within the partition. Must be of the same type as expression.
lead
Returns value evaluated at the row that is offset rows after the current row within the partition; if there is no such row, instead return default (which must be of the same type as value). Both offset and default are evaluated with respect to the current row. If omitted, offset defaults to 1 and default to null.
lead(expression, offset, default)
Arguments
- expression: Expression to operate on
- offset: Integer. Specifies how many rows forward the value of expression should be retrieved. Defaults to 1.
- default: The default value if the offset is not within the partition. Must be of the same type as expression.
first_value
Returns value evaluated at the row that is the first row of the window frame.
first_value(expression)
Arguments
- expression: Expression to operate on
last_value
Returns value evaluated at the row that is the last row of the window frame.
last_value(expression)
Arguments
- expression: Expression to operate on
nth_value
Returns value evaluated at the row that is the nth row of the window frame (counting from 1); null if no such row.
nth_value(expression, n)
Arguments
- expression: The name the column of which nth value to retrieve
- n: Integer. Specifies the n in nth
Large portions of this page is copied from the Apache Datafusion documentation on January 26th 2024 - where there have been customisations to match Hypi's deployment this has been noted. Apache Datafusion and the Apache name are the property of the Apache Foundation and licensed under the Apache V2 license .