3.5. 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 -----------+-------+--------+----------------------- develop | 11 | 5200 | 5020.0000000000000000 develop | 7 | 4200 | 5020.0000000000000000 develop | 9 | 4500 | 5020.0000000000000000 develop | 8 | 6000 | 5020.0000000000000000 develop | 10 | 5200 | 5020.0000000000000000 personnel | 5 | 3500 | 3700.0000000000000000 personnel | 2 | 3900 | 3700.0000000000000000 sales | 3 | 4800 | 4866.6666666666666667 sales | 1 | 5000 | 4866.6666666666666667 sales | 4 | 4800 | 4866.6666666666666667 (10 rows)
The first three output columns come directly from the table
, and there is one output row for each row in the
table. The fourth column represents an average taken across all the table
rows that have the same
value as the current row.
(This actually is the same function as the non-window
aggregate, but the
clause causes it to be
treated as a window function and computed across the window frame.)
A window function call always contains an
directly following the window function's name and argument(s). This is what
syntactically distinguishes it from a normal function or non-window
clause determines exactly how the
rows of the query are split up for processing by the window function.
divides the rows into groups, or partitions, that share the same
values of the
expression(s). For each row,
the window function is computed across the rows that fall into the
same partition as the current row.
You can also control the order in which rows are processed by
window functions using
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 -----------+-------+--------+------ develop | 8 | 6000 | 1 develop | 10 | 5200 | 2 develop | 11 | 5200 | 2 develop | 9 | 4500 | 4 develop | 7 | 4200 | 5 personnel | 2 | 3900 | 1 personnel | 5 | 3500 | 2 sales | 1 | 5000 | 1 sales | 4 | 4800 | 2 sales | 3 | 4800 | 2 (10 rows)
As shown here, the
function produces a numerical rank
for each distinct
value in the current row's
partition, using the order defined by the
needs no explicit parameter, because its behavior
is entirely determined by the
The rows considered by a window function are those of the
produced by the query's
clause as filtered by its
if any. For example, a row removed because it does not meet the
condition is not seen by any window function.
A query can contain multiple window functions that slice up the data
in different ways using different
they all act on the same collection of rows defined by this virtual table.
We already saw that
can be omitted if the ordering
of rows is not important. It is also possible to omit
, in which case there is a single partition containing all rows.
There is another important concept associated with window functions:
for each row, there is a set of rows within its partition called its
. Some window functions act only
on the rows of the window frame, rather than of the whole partition.
By default, if
is supplied then the frame consists of
all rows from the start of the partition up through the current row, plus
any following rows that are equal to the current row according to the
is omitted the
default frame consists of all rows in the partition.
Here is an example using
SELECT salary, sum(salary) OVER () FROM empsalary;
salary | sum --------+------- 5200 | 47100 5000 | 47100 3500 | 47100 4800 | 47100 3900 | 47100 4200 | 47100 4500 | 47100 4800 | 47100 6000 | 47100 5200 | 47100 (10 rows)
Above, since there is no
clause, the window frame is the same as the partition, which for lack of
is the whole table; in other words each sum is
taken over the whole table and so we get the same result for each output
row. But if we add an
clause, we get very different
SELECT salary, sum(salary) OVER (ORDER BY salary) FROM empsalary;
salary | sum --------+------- 3500 | 3500 3900 | 7400 4200 | 11600 4500 | 16100 4800 | 25700 4800 | 25700 5000 | 30700 5200 | 41100 5200 | 41100 6000 | 47100 (10 rows)
Here the sum is taken from the first (lowest) salary up through the current one, including any duplicates of the current one (notice the results for the duplicated salaries).
Window functions are permitted only in the
clause of the query. They are forbidden
elsewhere, such as in
clauses. This is because they logically
execute after the processing of those clauses. Also, window functions
execute after non-window aggregate functions. This means it is valid to
include an aggregate function call in the arguments of a window function,
but not vice versa.
If there is a need to filter or group rows after the window calculations are performed, you can use a sub-select. For example:
SELECT depname, empno, salary, enroll_date FROM (SELECT depname, empno, salary, enroll_date, rank() OVER (PARTITION BY depname ORDER BY salary DESC, empno) AS pos FROM empsalary ) AS ss WHERE pos < 3;
The above query only shows the rows from the inner query having
less than 3.
When a query involves multiple window functions, it is possible to write
out each one with a separate
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
clause and then referenced in
SELECT sum(salary) OVER w, avg(salary) OVER w FROM empsalary WINDOW w AS (PARTITION BY depname ORDER BY salary DESC);