Explain SQL Window Functions.
SQL window functions perform calculations across a related set of rows (the “window”) while still returning results for every individual row, unlike aggregate functions that collapse rows.
When to Use
Use window functions when you need rankings, running totals, moving averages, or percentiles without losing row-level detail. They’re widely used in analytics, reporting, and performance dashboards.
Example
A PostgreSQL query like:
SELECT date, sales, SUM(sales) OVER (ORDER BY date) AS running_total FROM daily_sales;
This shows each day’s sales with a running total across time.
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Why Is It Important
They let you perform advanced analytics in a single query, reducing the need for subqueries or multiple joins. This makes SQL both cleaner and faster to maintain.
Interview Tips
Explain clearly that window functions do not reduce row count like GROUP BY. Be ready to mention PARTITION BY and ORDER BY as the core syntax. Use ranking or running total examples to stand out.
Trade-offs
They simplify logic but can be expensive on very large datasets, requiring careful indexing.
Pitfalls
Common mistakes include forgetting PARTITION BY, misusing ORDER BY, or assuming results are aggregated like GROUP BY.
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