Amazon Redshift will no longer support the creation of new Python UDFs starting Patch 198. Existing Python UDFs will continue to function until June 30, 2026. For more information, see the [ blog post ](https://amazonaws-china.com/blogs/big-data/amazon-redshift-python-user-defined-functions-will-reach-end-of-support-after-june-30-2026/).
# SUM window function
The SUM window function returns the sum of the input column or expression values. The SUM function works with numeric values and ignores NULL values.
## Syntax
```
SUM ( [ ALL ] expression ) OVER
(
[ PARTITION BY expr_list ]
[ ORDER BY order_list
frame_clause ]
)
```
## Arguments
*expression *
The target column or expression that the function operates on.
ALL
With the argument ALL, the function retains all duplicate values from the expression. ALL is the default. DISTINCT is not supported.
OVER
Specifies the window clauses for the aggregation functions. The OVER clause distinguishes window aggregation functions from normal set aggregation functions.
PARTITION BY *expr\$1list*
Defines the window for the SUM function in terms of one or more expressions.
ORDER BY *order\$1list*
Sorts the rows within each partition. If no PARTITION BY is specified, ORDER BY uses the entire table.
*frame\$1clause*
If an ORDER BY clause is used for an aggregate function, an explicit frame clause is required. The frame clause refines the set of rows in a function's window, including or excluding sets of rows within the ordered result. The frame clause consists of the ROWS keyword and associated specifiers. See [Window function syntax summary](c_Window_functions.md#r_Window_function_synopsis).
## Data types
The argument types supported by the SUM function are SMALLINT, INTEGER, BIGINT, NUMERIC, DECIMAL, REAL, and DOUBLE PRECISION.
The return types supported by the SUM function are:
+ BIGINT for SMALLINT or INTEGER arguments
+ NUMERIC for BIGINT arguments
+ DOUBLE PRECISION for floating-point arguments
## Examples
The following example creates a cumulative (rolling) sum of sales quantities ordered by date and sales ID:
```
select salesid, dateid, sellerid, qty,
sum(qty) over (order by dateid, salesid rows unbounded preceding) as sum
from winsales
order by 2,1;
salesid | dateid | sellerid | qty | sum
---------+------------+----------+-----+-----
30001 | 2003-08-02 | 3 | 10 | 10
10001 | 2003-12-24 | 1 | 10 | 20
10005 | 2003-12-24 | 1 | 30 | 50
40001 | 2004-01-09 | 4 | 40 | 90
10006 | 2004-01-18 | 1 | 10 | 100
20001 | 2004-02-12 | 2 | 20 | 120
40005 | 2004-02-12 | 4 | 10 | 130
20002 | 2004-02-16 | 2 | 20 | 150
30003 | 2004-04-18 | 3 | 15 | 165
30004 | 2004-04-18 | 3 | 20 | 185
30007 | 2004-09-07 | 3 | 30 | 215
(11 rows)
```
For a description of the WINSALES table, see [Sample table for window function examples](c_Window_functions.md#r_Window_function_example).
The following example creates a cumulative (rolling) sum of sales quantities by date, partition the results by seller ID, and order the results by date and sales ID within the partition:
```
select salesid, dateid, sellerid, qty,
sum(qty) over (partition by sellerid
order by dateid, salesid rows unbounded preceding) as sum
from winsales
order by 2,1;
salesid | dateid | sellerid | qty | sum
---------+------------+----------+-----+-----
30001 | 2003-08-02 | 3 | 10 | 10
10001 | 2003-12-24 | 1 | 10 | 10
10005 | 2003-12-24 | 1 | 30 | 40
40001 | 2004-01-09 | 4 | 40 | 40
10006 | 2004-01-18 | 1 | 10 | 50
20001 | 2004-02-12 | 2 | 20 | 20
40005 | 2004-02-12 | 4 | 10 | 50
20002 | 2004-02-16 | 2 | 20 | 40
30003 | 2004-04-18 | 3 | 15 | 25
30004 | 2004-04-18 | 3 | 20 | 45
30007 | 2004-09-07 | 3 | 30 | 75
(11 rows)
```
The following example numbers all of the rows sequentially in the result set, ordered by the SELLERID and SALESID columns:
```
select salesid, sellerid, qty,
sum(1) over (order by sellerid, salesid rows unbounded preceding) as rownum
from winsales
order by 2,1;
salesid | sellerid | qty | rownum
--------+----------+------+--------
10001 | 1 | 10 | 1
10005 | 1 | 30 | 2
10006 | 1 | 10 | 3
20001 | 2 | 20 | 4
20002 | 2 | 20 | 5
30001 | 3 | 10 | 6
30003 | 3 | 15 | 7
30004 | 3 | 20 | 8
30007 | 3 | 30 | 9
40001 | 4 | 40 | 10
40005 | 4 | 10 | 11
(11 rows)
```
For a description of the WINSALES table, see [Sample table for window function examples](c_Window_functions.md#r_Window_function_example).
The following example numbers all rows sequentially in the result set, partition the results by SELLERID, and order the results by SELLERID and SALESID within the partition:
```
select salesid, sellerid, qty,
sum(1) over (partition by sellerid
order by sellerid, salesid rows unbounded preceding) as rownum
from winsales
order by 2,1;
salesid | sellerid | qty | rownum
---------+----------+-----+--------
10001 | 1 | 10 | 1
10005 | 1 | 30 | 2
10006 | 1 | 10 | 3
20001 | 2 | 20 | 1
20002 | 2 | 20 | 2
30001 | 3 | 10 | 1
30003 | 3 | 15 | 2
30004 | 3 | 20 | 3
30007 | 3 | 30 | 4
40001 | 4 | 40 | 1
40005 | 4 | 10 | 2
(11 rows)
```