SQL window functions calculate across related rows without collapsing those rows into a single result. Use the OVER clause to define the rows a calculation can see, then add PARTITION BY, ordering, and—where needed—a frame to control its scope. The examples below use PostgreSQL.
What makes a window function different?
A window function performs a calculation across a set of rows and returns a value for each row. Its defining syntax is an OVER clause immediately after the function call. PostgreSQL’s official tutorial describes this syntax and the way window functions preserve individual rows.
That makes a window calculation useful when you need both detail and context—for example, each employee’s salary alongside the average salary in that employee’s department. A grouped aggregate such as GROUP BY department instead produces a result for each department, rather than retaining every employee row.
SELECT department,
employee_id,
salary,
avg(salary) OVER (PARTITION BY department) AS department_average
FROM employees;Each output row still identifies an employee. The average is calculated over rows in the same department and shown beside that employee’s salary.
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How does the OVER clause define the calculation?
Think of OVER as specifying a window: the rows available to a function as it produces a result for the current row. Its main controls are partitioning, ordering, and, for frame-sensitive calculations, the frame.
Partition: where the calculation restarts
PARTITION BY divides the available rows into groups for the calculation. A function operates separately within each partition; without PARTITION BY, all available rows belong to one partition. Partitioning changes the calculation’s scope, not the number of detail rows returned.
Window ordering: the sequence used by the calculation
ORDER BY inside OVER sets the order used by the window calculation. It does not necessarily set the order in which the query returns rows. Add a query-level ORDER BY when the presentation order matters.
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For ranking functions such as row_number, rows tied on the specified ordering expressions may be numbered in an unspecified order. If that order must be repeatable, add a stable tie-breaker, such as a unique identifier.
Frame: which partition rows a calculation sees
A frame is the subset of a partition considered for the current row by a frame-sensitive calculation. Partitioning identifies the group; the frame narrows which rows in that group contribute to a particular result.
In PostgreSQL, when a window has an ORDER BY but no explicit frame, the default extends from the start of the partition through the current row and any peers tied on the ordering expressions. Consequently, sum(value) OVER (PARTITION BY account_id ORDER BY event_time) usually produces a cumulative sum. Rows sharing the same ordering value receive the same peer-inclusive cumulative result. See PostgreSQL’s window-function reference for frame behavior and whole-partition aggregation.
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For an aggregate over the entire partition, omit the window ORDER BY or specify a frame that includes following rows as well. An explicit whole-partition frame makes that intent clear:
sum(value) OVER (
PARTITION BY account_id
ORDER BY event_time
ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING
)Use an explicit frame when you want to avoid the ordered default’s cumulative behavior, or when the intended boundary should be visible in the SQL.
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How do you rank rows within each group?
Use a ranking function with a partition for each group and an ordering expression for the rank sequence. This PostgreSQL example numbers employees within each department, with higher salaries first:
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SELECT department,
employee_id,
salary,
row_number() OVER (
PARTITION BY department
ORDER BY salary DESC, employee_id
) AS position
FROM employees;Numbering restarts for each department. The employee identifier breaks salary ties; for deterministic numbering, it should uniquely identify an employee within the rows being ranked.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How can you filter on a window result?
In PostgreSQL, window functions can appear in the SELECT list and query-level ORDER BY, but not directly in WHERE, GROUP BY, or HAVING. Calculate the value in an inner query, then filter it in an outer query:
WITH ranked AS (
SELECT department,
employee_id,
salary,
row_number() OVER (
PARTITION BY department
ORDER BY salary DESC, employee_id
) AS position
FROM employees
)
SELECT department, employee_id, salary, position
FROM ranked
WHERE position <= 3;The CTE computes the position for each eligible row first. The outer query then returns up to three rows per department. To change which rows can contribute to the ranking, apply a filter in the inner query before the window calculation; rows removed there cannot participate.
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Which rows are available to a window function?
Window functions operate on the query’s virtual table after FROM, WHERE, GROUP BY, and HAVING have been applied. A row filtered out before the window stage cannot contribute to the calculation. A single SELECT can also contain multiple window functions with different OVER specifications, all working from that same virtual table.
How does this apply outside PostgreSQL?
SQL Server has an analogous OVER clause, documented in Microsoft Learn’s SQL Server 15 view of the Transact-SQL reference. The examples and default-frame details here are specifically for PostgreSQL; syntax and behavior should be checked against the documentation for the database engine and version you use.
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