# sum > For the complete documentation index, see [llms.txt](https://docs.redpanda.com/llms.txt). Component-specific: [cloud-data-platform-full.txt](https://docs.redpanda.com/cloud-data-platform-full.txt) --- title: sum latest-operator-version: v26.1.4 latest-console-tag: v3.7.3 latest-connect-version: 4.93.0 latest-redpanda-tag: v26.1.9 docname: sql/sql-functions/window-functions/sum page-component-name: cloud-data-platform page-version: master page-component-version: master page-component-title: Cloud page-relative-src-path: sql/sql-functions/window-functions/sum.adoc page-edit-url: https://github.com/redpanda-data/cloud-docs/edit/main/modules/reference/pages/sql/sql-functions/window-functions/sum.adoc description: The `sum()` window function returns the sum of the input column or expression values. page-topic-type: reference page-git-created-date: "2026-05-26" page-git-modified-date: "2026-05-26" --- The `sum()` window function returns the sum of the input column or expression values. It can be used with a `RANGE` clause, that defines a logical frame of rows based on the values of the current row, rather than a fixed number of rows. ## [](#syntax)Syntax The syntax for this function is: ```sql SUM(expression) OVER ( [PARTITION BY partition_expression] ORDER BY sort_expression [ROWS | RANGE BETWEEN start_value AND end_value] ) ``` The expression’s argument types supported by the `sum` window function are `integer`, `bigint`, `real` and `double precision`. The return types of the `sum` function are: `bigint` for integer and `double precision` for floating-point arguments. > 📝 **NOTE** > > The `sum()` window function works with numeric values and ignores NULL ones ## [](#parameters)Parameters - `expression`: Column or expression to sum. - `PARTITION BY`: Optional. Divides the result set into partitions, each processed independently. If omitted, the entire result set is treated as a single partition. - `ROWS | RANGE BETWEEN`: Range-based window frame relative to the current row. ## [](#examples)Examples The following examples use the `winsales` table that stores details of some sales transactions: ```sql CREATE TABLE winsales( salesid int, dateid date, sellerid int, buyerid text, qty int, qty_shipped int); INSERT INTO winsales VALUES (30001, '8/2/2003', 3, 'b', 10, 10), (10001, '12/24/2003', 1, 'c', 10, 10), (10005, '12/24/2003', 1, 'a', 30, null), (40001, '1/9/2004', 4, 'a', 40, null), (10006, '1/18/2004', 1, 'c', 10, null), (20001, '2/12/2004', 2, 'b', 20, 20), (40005, '2/12/2004', 4, 'a', 10, 10), (20002, '2/16/2004', 2, 'c', 20, 20), (30003, '4/18/2004', 3, 'b', 15, null), (30004, '4/18/2004', 3, 'b', 20, null), (30007, '9/7/2004', 3, 'c', 30, null); ``` ### [](#sum-with-order-by)`sum()` with ORDER BY This example executes the `sum()` window function with `ORDER BY` keyword: ```sql SELECT salesid, dateid, sellerid, qty SUM(qty) OVER (ORDER BY dateid, salesid ROWS UNBOUNDED PRECEDING) FROM winsales ORDER BY 2,1; ``` The output from this query includes the sales ID, date ID, seller ID, quantity and quantity sum: ```sql 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) ``` ### [](#sum-with-order-by-and-rows-frame)`sum()` with ORDER BY and ROWS frame This example calculates the running total of `qty` ordered by dateid and salesid using a `ROWS UNBOUNDED PRECEDING` frame, which sums all rows from the start up to the current row: ```sql SELECT salesid, dateid, sellerid, qty, SUM(qty) OVER (ORDER BY dateid, salesid ROWS UNBOUNDED PRECEDING) AS running_qty_sum FROM winsales ORDER BY dateid, salesid; ``` The query returns: ```sql salesid | dateid | qty | running_qty_sum ---------+------------+-----+----------------- 30001 | 2003-08-02 | 10 | 10 10001 | 2003-12-24 | 10 | 20 10005 | 2003-12-24 | 30 | 50 40001 | 2004-01-09 | 40 | 90 10006 | 2004-01-18 | 10 | 100 20001 | 2004-02-12 | 20 | 120 40005 | 2004-02-12 | 10 | 130 20002 | 2004-02-16 | 20 | 150 30003 | 2004-04-18 | 15 | 165 30004 | 2004-04-18 | 20 | 185 30007 | 2004-09-07 | 30 | 215 (11 rows) ``` The `running_qty_sum` column shows the cumulative sum of `qty` ordered by `dateid` and `salesid`. For each row, it sums all `qty` values from the first row up to the current row in that order. ### [](#sum-with-order-by-and-partition-by)`sum()` with ORDER BY and PARTITION BY This example executes the `sum()` function with `ORDER BY` keyword and `PARTITION BY` clause: ```sql SELECT salesid, dateid, sellerid, qty SUM(qty) OVER (PARTITION BY sellerid ORDER BY dateid, sellerid ROWS UNBOUNDED PRECEDING) FROM winsales ORDER BY 3,2,1; ``` The query returns: ```sql salesid | dateid | sellerid | qty | sum ---------+------------+----------+-----+----- 10001 | 2003-12-24 | 1 | 10 | 10 10005 | 2003-12-24 | 1 | 30 | 40 10006 | 2004-01-18 | 1 | 10 | 50 20001 | 2004-02-12 | 2 | 20 | 20 20002 | 2004-02-16 | 2 | 20 | 40 30001 | 2003-08-02 | 3 | 10 | 10 30003 | 2004-04-18 | 3 | 15 | 25 30004 | 2004-04-18 | 3 | 20 | 45 30007 | 2004-09-07 | 3 | 30 | 75 40001 | 2004-01-09 | 4 | 40 | 40 40005 | 2004-02-12 | 4 | 10 | 50 (11 rows) ``` ### [](#time-series-sum-with-range-between-for-last-30-days)Time series: `sum()` with RANGE BETWEEN for last 30 days This example demonstrates a common time series use case: calculating the rolling sum of sales quantity over the last 30 days for each row, using the RANGE BETWEEN `interval` ‘30 days’ PRECEDING AND CURRENT `row` frame: ```sql SELECT salesid, dateid, qty, SUM(qty) OVER ( ORDER BY dateid RANGE BETWEEN INTERVAL '30 days' PRECEDING AND CURRENT ROW ) AS rolling_30d_qty_sum FROM winsales ORDER BY dateid; ``` The output from this query sums the `qty` of all sales within the 30-day window ending at the current row’s `dateid`: ```sql salesid | dateid | qty | rolling_30d_qty_sum ---------+------------+-----+--------------------- 30001 | 2003-08-02 | 10 | 10 10001 | 2003-12-24 | 10 | 40 10005 | 2003-12-24 | 30 | 40 40001 | 2004-01-09 | 40 | 80 10006 | 2004-01-18 | 10 | 90 20001 | 2004-02-12 | 20 | 40 40005 | 2004-02-12 | 10 | 40 20002 | 2004-02-16 | 20 | 60 30003 | 2004-04-18 | 15 | 35 30004 | 2004-04-18 | 20 | 35 30007 | 2004-09-07 | 30 | 30 (11 rows) ```