Window Functions (NEW)¶
Note: this documentation is in the process of being migrated to be automatically created from the codebase. Please see the Window Functions (Old) page for the rest of the documentation.
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¶
cume_dist¶
Relative rank of the current row: (number of rows preceding or peer with current row) / (total rows).
cume_dist()
dense_rank¶
Returns the rank of the current row without gaps. This function ranks rows in a dense manner, meaning consecutive ranks are assigned even for identical values.
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
percent_rank¶
Returns the percentage rank of the current row within its partition. The value ranges from 0 to 1 and is computed as (rank - 1) / (total_rows - 1).
percent_rank()
rank¶
Returns the rank of the current row within its partition, allowing gaps between ranks. This function provides a ranking similar to row_number, but skips ranks for identical values.
rank()
row_number¶
Number of the current row within its partition, counting from 1.
row_number()
Analytical Functions¶
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).
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).
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.