**RANK()**: Same rank for ties; skips subsequent ranks (e.g., 1, 2, 2, 4, 5). **DENSE_RANK()**: Same rank for ties; no gaps (e.g., 1, 2, 2, 3, 4). **Why it matters**: RANK preserves "position" semantics (e.g., 4th place); DENSE_RANK gives consecutive integers useful for...
Red Flag: Mixing up which one skips and which doesn't. Pro-Move: Mention PARTITION BY for 'top N per category' and how it reduces sort memory in distributed engines.
This medium-level SQL question appears frequently in data engineering interviews at companies like Capco, Impetus, KPMG, and 1 others. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (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.
RANK(): Same rank for ties; skips subsequent ranks (e.g., 1, 2, 2, 4, 5). DENSE_RANK(): Same rank for ties; no gaps (e.g., 1, 2, 2, 3, 4). Why it matters: RANK preserves "position" semantics (e.g., 4th place); DENSE_RANK gives consecutive integers useful for filtering (e.g., TOP 10). Example: SELECT name, salary, RANK() OVER (ORDER BY salary DESC) AS rk, DENSE_RANK() OVER (ORDER BY salary DESC) AS dense_rk FROM employee. Scalability: Both are window functions; on large datasets, partitioning (PARTITION BY) limits sort scope and improves performance. Cost: Same computational cost; choice is semantic. Use RANK for leaderboards; DENSE_RANK when you need predictable top-N without gaps.
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