Reviewed by Aditya Kumar · Last reviewed 2026-03-24
To identify duplicates by composite key in SQL, use GROUP BY with HAVING COUNT(*) > 1, or window functions. Example: SELECT order_id, product_id, COUNT(*) cnt FROM orders GROUP BY order_id, product_id HAVING COUNT(*) > 1. Window approach: SELECT * FROM (SELECT *, ROW_NUMBER()...
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 (partition, sql, 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.
To identify duplicates by composite key in SQL, use GROUP BY with HAVING COUNT() > 1, or window functions. Example: SELECT order_id, product_id, COUNT() cnt FROM orders GROUP BY order_id, product_id HAVING COUNT() > 1. Window approach: SELECT FROM (SELECT *, ROW_NUMBER() OVER (PARTITION BY order_id, product_id ORDER BY created_at) rn FROM orders) t WHERE rn > 1. The GROUP BY method returns only the composite key and count; the window method returns full rows of duplicates. For removal, use DELETE with a CTE: WITH dups AS (SELECT order_id, product_id, ROW_NUMBER() OVER (PARTITION BY order_id, product_id ORDER BY created_at DESC) rn FROM orders) DELETE FROM orders WHERE (order_id, product_id) IN (SELECT order_id, product_id FROM dups WHERE rn > 1); 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: Assuming pipeline success means data correctness. Pro-Move: 'We added row checksums and reconciliation—caught 0.02% drift that success status missed.'
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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.