# count > 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: count 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/count 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/count.adoc page-edit-url: https://github.com/redpanda-data/cloud-docs/edit/main/modules/reference/pages/sql/sql-functions/window-functions/count.adoc description: The `count()` window function retrieves the number of records that meet a specific criteria. page-topic-type: reference page-git-created-date: "2026-05-26" page-git-modified-date: "2026-05-26" --- The `count()` window function retrieves the number of records that meet a specific criteria. When using it with the `RANGE` clause, it performs counts within a defined range based on the values of the current row. This function can be used with all [data types supported by Redpanda SQL](https://docs.redpanda.com/cloud-data-platform/reference/sql/sql-data-types/). ## [](#syntax)Syntax There are two available variants of that function: - `COUNT(*)`: Counts all rows in the target table, regardless of whether they contain NULL values or not. - `COUNT(expression)`: Counts the number of non-NULL values in a specific column or expression. The syntax for this function is: ```sql COUNT(expression) OVER ( [PARTITION BY partition_expression] ORDER BY sort_expression [ROWS | RANGE BETWEEN start_value AND end_value] ) ``` The `count()` window function always return `bigint` as an output, which represents the total number of rows in a table irrespective of the input types. ## [](#parameters)Parameters - `expression`: Column or expression. - `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 a `winsales` table that stores the 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); ``` ### [](#count)COUNT(\*) This example executes the variant of this function that counts all rows in the target table: ```sql SELECT salesid, qty, COUNT(*) OVER (ORDER BY salesid rows unbounded preceding) AS count FROM winsales ORDER BY salesid; ``` The output displays the sales ID, quantity and the count of all rows from the start of the data window: ```sql salesid | qty | count ---------+-----+------- 10001 | 10 | 1 10005 | 30 | 2 10006 | 10 | 3 20001 | 20 | 4 20002 | 20 | 5 30001 | 10 | 6 30003 | 15 | 7 30004 | 20 | 8 30007 | 30 | 9 40001 | 40 | 10 40005 | 10 | 11 (11 rows) ``` ### [](#countexpression)Count(expression) This example executes the variant of this function that counts the number of non-NULL values in a specific expression: ```sql SELECT salesid, qty, qty_shipped, COUNT(qty_shipped) OVER (ORDER BY salesid rows unbounded preceding) AS count FROM winsales ORDER BY salesid; ``` The query returns: ```sql salesid | qty | qty_shipped | count ---------+-----+-------------+------- 10001 | 10 | 10 | 1 10005 | 30 | | 1 10006 | 10 | | 1 20001 | 20 | 20 | 2 20002 | 20 | 20 | 3 30001 | 10 | 10 | 4 30003 | 15 | | 4 30004 | 20 | | 4 30007 | 30 | | 4 40001 | 40 | | 4 40005 | 10 | 10 | 5 (11 rows) ``` ### [](#time-series-count-with-range-for-last-90-days)Time series: COUNT(\*) with RANGE for last 90 days This example demonstrates counting the number of sales within a 90-day window prior to each sale, based on `dateid`: ```sql SELECT salesid, dateid, qty, COUNT(*) OVER ( ORDER BY dateid RANGE BETWEEN INTERVAL '90 days' PRECEDING AND CURRENT ROW ) AS sales_count_90d FROM winsales ORDER BY dateid; ``` This query counts the number of sales transactions within a 90-day window before each `dateid`, including the current sale: ```sql salesid | dateid | qty | sales_count_90d ---------+------------+-----+----------------- 30001 | 2003-08-02 | 10 | 1 10001 | 2003-12-24 | 10 | 2 10005 | 2003-12-24 | 30 | 2 40001 | 2004-01-09 | 40 | 3 10006 | 2004-01-18 | 10 | 4 20001 | 2004-02-12 | 20 | 6 40005 | 2004-02-12 | 10 | 6 20002 | 2004-02-16 | 20 | 7 30003 | 2004-04-18 | 15 | 5 30004 | 2004-04-18 | 20 | 5 30007 | 2004-09-07 | 30 | 1 ```