**Systematic approach**: (1) **Profile**: EXPLAIN ANALYZE—identify seq scans on large tables, costly hash joins, sort spills. (2) **Baseline**: Current runtime, row counts, data distribution. (3) **Index**: Add B-tree/hash on filter and join columns; verify usage in plan. (4)...
This medium-level SQL question appears frequently in data engineering interviews at companies like Amazon. 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.
Systematic approach: (1) Profile: EXPLAIN ANALYZE—identify seq scans on large tables, costly hash joins, sort spills. (2) Baseline: Current runtime, row counts, data distribution. (3) Index: Add B-tree/hash on filter and join columns; verify usage in plan. (4) Statistics: Run ANALYZE; planner accuracy affects join order. (5) Rewrite: Replace correlated subqueries with JOINs or window functions; push predicates. (6) Partition: Prune by partition key. (7) Tune: Work_mem, parallel workers. Scalability: At billion-row scale, missing partition filter can scan 100× more data. Cost: Index maintenance trade-off; materialized views for repeated aggregates.
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Analyze My Answer — FreeAccording to DataEngPrep.tech, this is one of the most frequently asked SQL interview questions, reported at 1 company. DataEngPrep.tech maintains a curated database of 1,863+ real data engineering interview questions across 7 categories, verified by industry professionals.