GROUP BY aggregates summarize rows and return one row per group. Window functions calculate values across related rows but keep each query row, so you can see a detail row alongside its group average, rank, or running total. The practical choice is whether you want to replace detail with a summary or add a calculation to the detail.
At a glance: what changes in the result?
| Question | Aggregate with GROUP BY |
Window function with OVER |
|---|---|---|
| Output shape | One row per group; detail rows are summarized. | One result alongside each row at the query’s current grain. |
| Typical syntax | AVG(salary) with GROUP BY department |
AVG(salary) OVER (PARTITION BY department) |
| Best for | Summaries such as revenue by country or average salary by department. | Rankings, running calculations, or group context displayed beside individual rows. |
| Filtering the result | Use HAVING to filter groups after aggregation. |
Usually calculate in a subquery or CTE, then filter in an outer query. |
| Portability | Check aggregate function support in your database. | Check function, frame, and syntax support for your database and version. |
PostgreSQL defines a window function as one that “performs a calculation across a set of table rows that are somehow related to the current row.” Its key distinction from an ordinary grouped aggregate is that the window result does not collapse those rows. See the PostgreSQL window functions documentation.
What does GROUP BY do?
GROUP BY changes the grain—the level of detail—of the query result. If you group employees by department and calculate an average salary, the result contains a department and its average, not a separate row for every employee.
SELECT department, AVG(salary) AS department_avg
FROM employees
GROUP BY department;This is the right shape when the question is “What is the average salary in each department?” If you also select employee-level columns, the database generally requires them to be grouped or aggregated, because the output represents groups rather than individual employees.
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What does a window function do?
A window function computes across a set of rows related to the current row, then returns its result on that row. Add OVER to an aggregate such as AVG, and use PARTITION BY to define the groups used for the calculation:
SELECT department, employee_id, salary,
AVG(salary) OVER (PARTITION BY department) AS department_avg
FROM employees;This returns each employee’s department, ID, and salary, with that department’s average repeated beside each employee. The PostgreSQL documentation uses this same essential distinction: its department average window calculation preserves one output row for each employee.
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PARTITION BY versus GROUP BY
Both clauses can organize rows into groups, but they affect the result differently. GROUP BY produces a group-level result and reduces the number of rows. PARTITION BY divides the rows considered by a window calculation; it does not, by itself, combine the rows in the output.
- Choose
GROUP BY departmentwhen you want one result for each department. - Choose
OVER (PARTITION BY department)when you want each detail row plus a calculation for its department. - Leave out
PARTITION BYwhen the window should use all rows in the query result as one partition. MySQL documents that an emptyOVER()uses all query rows and repeats the calculation for each row in its window function concepts and syntax reference.
How to read OVER, ORDER BY, and frames
OVER identifies a calculation as a window calculation. Inside it, PARTITION BY sets the related groups, while its ORDER BY defines order for calculations that depend on sequence. This window ordering is separate from the query’s final ORDER BY, which sorts the displayed result.
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A frame can restrict an ordered window to a subset of its partition, which is useful for running or moving calculations. Frame syntax and default behavior depend on the database, so specify the intended frame deliberately and consult the relevant engine documentation rather than assuming identical behavior across SQL dialects. PostgreSQL and MySQL describe window syntax in their respective references: PostgreSQL and MySQL 8.4.
Which one should you use?
- One summary per group: use an aggregate with
GROUP BY, such as revenue by country. - Detail plus a group statistic: use an aggregate with
OVER (PARTITION BY ...), such as every transaction alongside its department total. - Rank or row number within a group: use a ranking window function and define the ranking order inside
OVER. - Running or moving total or average: use an aggregate window with
OVER (ORDER BY ...), selecting a frame appropriate to the calculation. - Top rows within each group: use a ranking window calculation, then filter its result in an outer query.
Microsoft lists moving averages, cumulative aggregates, running totals, and top-N-per-group among uses of the OVER clause in SQL Server.
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Why can’t I filter a window result in WHERE?
Window calculations happen after the query’s WHERE, GROUP BY, and HAVING processing. They are allowed in the SELECT list and final ORDER BY, but not directly in WHERE, GROUP BY, or HAVING in PostgreSQL. To keep, for example, only the top-ranked employees in each department, calculate the rank first and filter it in an outer query:
WITH ranked_employees AS (
SELECT department, employee_id, salary,
ROW_NUMBER() OVER (
PARTITION BY department
ORDER BY salary DESC
) AS position
FROM employees
)
SELECT department, employee_id, salary, position
FROM ranked_employees
WHERE position <= 3;The ranking is available to the outer query as a regular column. PostgreSQL’s window-function documentation shows this general subquery approach, and MySQL 8.4 likewise places window processing after WHERE, GROUP BY, and HAVING.
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Can you aggregate first and then use a window function?
Yes. Since window processing follows ordinary aggregation, a query can first form grouped results and then calculate a window value over those results. For example, you can summarize sales by country and then compare each country’s total with the overall total. The reverse nesting is not generally interchangeable: PostgreSQL documents that an ordinary aggregate call may be an argument to a window function, but a window function cannot be an argument to an ordinary aggregate call.
Does every aggregate work as a window function?
No. The general pattern is to use an aggregate with OVER, but support varies by database, function, and version. Microsoft’s SQL Server aggregate-function documentation lists STRING_AGG, GROUPING, and GROUPING_ID as exceptions to aggregate functions that may take OVER. Check the manual for your target engine before relying on a particular function or frame option; SQL Server’s aggregate functions reference and OVER clause reference document its rules.
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