Reviewed by Aditya Kumar · Last reviewed 2026-03-24
**Architectural Logic:** Window functions compute values over a set of rows related to the current row (defined by OVER) without collapsing rows like GROUP BY. **Why windows over self-joins:** Single scan, no explosion; optimal for rankings, running totals, period-over-period....
This medium-level SQL question appears frequently in data engineering interviews at companies like Citi, Freecharge. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (join, partition, window) will help you answer variations of this question confidently.
Break this problem into components. Identify the core trade-offs involved, then walk the interviewer through your reasoning step by step. Demonstrate awareness of edge cases and production considerations - this is what separates good answers from great ones.
Architectural Logic: Window functions compute values over a set of rows related to the current row (defined by OVER) without collapsing rows like GROUP BY. Why windows over self-joins: Single scan, no explosion; optimal for rankings, running totals, period-over-period. Scalability: PARTITION BY and ORDER BY affect sort/spill; large windows with RANGE BETWEEN UNBOUNDED PRECEDING can spill to disk. Cost: ROWS vs. RANGE—RANGE requires sorting by frame boundaries; ROWS is typically more efficient. Example: SELECT employee_id, department, salary, AVG(salary) OVER (PARTITION BY department) AS dept_avg, RANK() OVER (PARTITION BY department ORDER BY salary DESC) AS salary_rank FROM employees. Use LAG/LEAD for YoY; explicit frame (ROWS/RANGE) when behavior matters.
RED FLAG: Using RANGE UNBOUNDED PRECEDING without understanding sort cost. PRO MOVE: 'For running totals on 10B rows we switched from RANGE to ROWS BETWEEN 100 PRECEDING AND CURRENT ROW—reduced spill by 80% because the frame was bounded.'
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According to DataEngPrep.tech, this is one of the most frequently asked SQL interview questions, reported at 2 companies. DataEngPrep.tech maintains an editor-reviewed database of 1,863 data engineering interview questions across 7 categories.