Window function (SQL) explained

In SQL, a window function or analytic function[1] is a function which uses values from one or multiple rows to return a value for each row. (This contrasts with an aggregate function, which returns a single value for multiple rows.) Window functions have an OVER clause; any function without an OVER clause is not a window function, but rather an aggregate or single-row (scalar) function.[2]

Example

As an example, here is a query which uses a window function to compare the salary of each employee with the average salary of their department (example from the PostgreSQL documentation):[3] SELECT depname, empno, salary, avg(salary) OVER (PARTITION BY depname) FROM empsalary;Output: 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 PARTITION BY clause groups rows into partitions, and the function is applied to each partition separately. If the PARTITION BY clause is omitted (such as with an empty OVER clause), then the entire result set is treated as a single partition.[4] For this query, the average salary reported would be the average taken over all rows.

Window functions are evaluated after aggregation (after the GROUP BY clause and non-window aggregate functions, for example).

Syntax

According to the PostgreSQL documentation, a window function has the syntax of one of the following:function_name ([expression [, expression ... ]]) OVER window_namefunction_name ([expression [, expression ... ]]) OVER (window_definition)function_name (*) OVER window_namefunction_name (*) OVER (window_definition)where window_definition has syntax:[existing_window_name ][PARTITION BY expression [, ...] ][ORDER BY expression [ ASC | DESC | USING operator ] [NULLS { FIRST | LAST } ] [, ...] ][frame_clause ]frame_clause has the syntax of one of the following: frame_start [frame_exclusion ] BETWEEN frame_start AND frame_end [frame_exclusion ]frame_start and frame_end can be UNBOUNDED PRECEDING, offset PRECEDING, CURRENT ROW, offset FOLLOWING, or UNBOUNDED FOLLOWING. frame_exclusion can be EXCLUDE CURRENT ROW, EXCLUDE GROUP, EXCLUDE TIES, or EXCLUDE NO OTHERS.

expression refers to any expression that does not contain a call to a window function.

Notation:

Example

Window functions allow access to data in the records right before and after the current record.[5] [6] [7] [8] A window function defines a frame or window of rows with a given length around the current row, and performs a calculation across the set of data in the window.[9] [10] NAME | ------------ Aaron| <-- Preceding (unbounded) Andrew| Amelia| James| Jill| Johnny| <-- 1st preceding row Michael| <-- Current row Nick| <-- 1st following row Ophelia| Zach| <-- Following (unbounded)In the above table, the next query extracts for each row the values of a window with one preceding and one following row: SELECT LAG(name, 1) OVER(ORDER BY name) "prev", name, LEAD(name, 1) OVER(ORDER BY name) "next" FROM people ORDER BY nameThe result query contains the following values: | PREV | NAME | NEXT | |----------|----------|----------| | (null)| Aaron| Andrew| | Aaron| Andrew| Amelia| | Andrew| Amelia| James| | Amelia| James| Jill| | James| Jill| Johnny| | Jill| Johnny| Michael| | Johnny| Michael| Nick| | Michael| Nick| Ophelia| | Nick| Ophelia| Zach| | Ophelia| Zach| (null)|

History

Window functions were introduced to the standard and had functionality expanded in later specifications.[11]

Support for particular database implementations was added as follows:

See also

Notes and References

  1. Web site: Analytic function concepts in Standard SQL BigQuery. 2021-03-23. Google Cloud. en.
  2. Web site: Window Functions. 2021-03-23. sqlite.org.
  3. Web site: 2021-02-11. 3.5. Window Functions. 2021-03-23. PostgreSQL Documentation. en.
  4. Web site: 2021-02-11. 4.2. Value Expressions. 2021-03-23. PostgreSQL Documentation. en.
  5. Leis. Viktor. Kundhikanjana. Kan. Kemper. Alfons. Neumann. Thomas. June 2015. Efficient Processing of Window Functions in Analytical SQL Queries. Proc. VLDB Endow.. 8. 10. 1058–1069. 10.14778/2794367.2794375. 2150-8097.
  6. Cao. Yu. Chan. Chee-Yong. Li. Jie. Tan. Kian-Lee. July 2012. Optimization of Analytic Window Functions. Proc. VLDB Endow.. 5. 11. 1244–1255. 1208.0086. 10.14778/2350229.2350243. 2150-8097.
  7. News: 2013-11-03. Probably the Coolest SQL Feature: Window Functions. en-US. Java, SQL and jOOQ.. 2017-09-26.
  8. News: 2013-10-31. Window Functions in SQL - Simple Talk. en-US. Simple Talk. 2017-09-26.
  9. Web site: SQL Window Functions Introduction. Apache Drill.
  10. Web site: PostgreSQL: Documentation: Window Functions. 2020-04-04. www.postgresql.org. en.
  11. Web site: Window Functions Overview. 2021-03-23. MariaDB KnowledgeBase.
  12. Web site: PostgreSQL Release 8.4 . 2024-03-10 . www.postgresql.org. 24 July 2014 .
  13. Web site: MySQL :: What's New in MySQL 8.0? (Generally Available) . 2022-11-21 . dev.mysql.com.
  14. Web site: MySQL :: MySQL 8.0 Reference Manual :: 12.21.2 Window Function Concepts and Syntax . dev.mysql.com.
  15. Web site: MariaDB 10.2.0 Release Notes . 2024-03-10 . mariadb.com.

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