Reviewed by Aditya Kumar · Last reviewed 2026-03-24
**Architectural Logic**: WHERE filters rows before aggregation; HAVING filters groups after aggregation. Execution order: FROM → WHERE → GROUP BY → aggregates → HAVING → SELECT. **Why**: WHERE enables predicate pushdown—filtering early reduces rows before shuffle and...
Red Flag: Putting non-aggregate filters in HAVING (e.g., HAVING region = 'US')—wastes compute. Pro-Move: 'I always ask: can this filter run before GROUP BY? If yes, it goes in WHERE.'
This medium-level SQL question appears frequently in data engineering interviews at companies like Presidio, Swiggy. While less common, it tests deeper understanding that distinguishes strong candidates.
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: WHERE filters rows before aggregation; HAVING filters groups after aggregation. Execution order: FROM → WHERE → GROUP BY → aggregates → HAVING → SELECT. Why: WHERE enables predicate pushdown—filtering early reduces rows before shuffle and aggregation, saving memory and I/O. HAVING operates on aggregates; pushing filter logic into WHERE when possible is critical for performance. Scalability: A filter in HAVING forces full aggregation of all groups; moving filterable predicates to WHERE (e.g., date range) can reduce groups by orders of magnitude. Cost: HAVING SUM(salary) > 100000 scans all dept groups; WHERE dept IN (...) reduces groups first. Example: SELECT dept, SUM(salary) FROM emp WHERE age > 25 GROUP BY dept HAVING SUM(salary) > 100000—WHERE prunes rows; HAVING prunes departments.
Red Flag: Putting non-aggregate filters in HAVING (e.g., HAVING region = 'US')—wastes compute. Pro-Move: 'I always ask: can this filter run before GROUP BY? If yes, it goes in WHERE.'
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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.