I went straight for a GROUP BY and COUNT approach, comparing the number of distinct products each customer bought against the total rows in the catalog table.
Use a GROUP BY with HAVING COUNT(DISTINCT product_id) equal to the total number of products in the catalog. This assumes each customer-product pair is unique in the purchases table; if not, use COUNT(DISTINCT) to avoid duplicates. Alternatively, use a NOT EXISTS subquery to find customers who have no missing products.
Pro tip: Clarify whether the purchases table can have duplicate entries for the same customer and product; if so, use COUNT(DISTINCT product_id) to ensure accurate counting. Also, consider performance implications: the GROUP BY approach is often more efficient than multiple subqueries, but indexing on customer_id and product_id can further optimize.
Identify the relevant columns: customer_id and product_id in purchases, and product_id in catalog. Confirm that 'every product' means all products currently in the catalog.
Decide between aggregation (GROUP BY with HAVING) or set-based (NOT EXISTS) methods. Consider data size, duplicates, and performance.
For aggregation: SELECT customer_id FROM purchases GROUP BY customer_id HAVING COUNT(DISTINCT product_id) = (SELECT COUNT(*) FROM catalog). For NOT EXISTS: SELECT customer_id FROM purchases p WHERE NOT EXISTS (SELECT 1 FROM catalog c WHERE NOT EXISTS (SELECT 1 FROM purchases p2 WHERE p2.customer_id = p.customer_id AND p2.product_id = c.product_id)).
Consider empty catalog (should return no customers or all? clarify), customers with no purchases, and duplicate purchase records. Use DISTINCT or appropriate joins.
Add indexes on customer_id and product_id if needed. Test with sample data to verify correctness, especially for customers who bought all products and those who missed some.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.