Reviewed by Aditya Kumar · Last reviewed 2026-03-24
Optimizing multi-join/subquery queries: (1) Reduce join cardinality early—filter before joining. (2) Replace correlated subqueries with JOINs: e.g., SELECT * FROM a WHERE a.id IN (SELECT id FROM b WHERE ...) becomes JOIN (SELECT id FROM b WHERE ...) b ON a.id = b.id. (3) Use...
This medium-level SQL question appears frequently in data engineering interviews at companies like American Express. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (join) 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.
Optimizing multi-join/subquery queries: (1) Reduce join cardinality early—filter before joining. (2) Replace correlated subqueries with JOINs: e.g., SELECT FROM a WHERE a.id IN (SELECT id FROM b WHERE ...) becomes JOIN (SELECT id FROM b WHERE ...) b ON a.id = b.id. (3) Use CTEs for readability and to let optimizer materialize. (4) Ensure join columns are indexed. (5) Join smallest tables first when possible. (6) Avoid SELECT in subqueries. (7) Use EXPLAIN to detect full scans. Example: WITH filtered_orders AS (SELECT FROM orders WHERE status = 'completed'), filtered_items AS (SELECT FROM items WHERE category = 'electronics') SELECT * FROM filtered_orders o JOIN filtered_items i ON o.item_id = i.id; Why it matters: Design choices compound at scale—wrong approach can cause 100× overhead. Scalability trade-offs: Profile before optimizing; validate on sample then full. Cost implications: Suboptimal choices multiply at billion-row scale.
Red Flag: Optimizing without EXPLAIN or baseline. Pro-Move: 'EXPLAIN ANALYZE + composite index cut P99 80%; we measured write impact before rollout.'
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According to DataEngPrep.tech, this is one of the most frequently asked SQL interview questions, reported at 1 company. DataEngPrep.tech maintains an editor-reviewed database of 1,863 data engineering interview questions across 7 categories.