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  1. Correctness:

    • For "Customers who bought all the products.sql", the solution correctly identifies customers who bought all products by comparing the count of distinct product keys per customer with the total count of distinct product keys. This is a standard approach for this type of problem.
    • For "Product Sales Analysis III.sql", the solution correctly finds the first year each product was sold using a subquery with MIN(year). This is also a correct approach.
  2. Time Complexity:

    • For the first query: O(n) where n is the number of records in the Customer table, as it needs to scan all records and perform grouping.
    • For the second query: O(n) where n is the number of records in the Sales table, as it needs to scan all records for the subquery and then filter the main table.
  3. Space Complexity:

    • Both queries have O(m) space complexity where m is the number of distinct groups (customer_id in the first query, product_id in the second query) as they need to store intermediate grouping results.
  4. Code Quality:

    • The queries are well-structured and readable.
    • The use of DISTINCT in the first query ensures correct counting of unique products.
    • The second query could be improved by using a JOIN instead of IN for better readability and potentially better performance.
    • The column alias 'num_product_key' in the first query is unnecessary since it's not used in the output.
    • The column alias 'first_year' in the second query is appropriate and improves readability.
  5. Efficiency:

    • Both queries are efficient for their respective problems.
    • For the second query, consider using a JOIN with a derived table instead of IN for potentially better performance with large datasets:
      SELECT s.product_id, s.year AS first_year, s.quantity, s.price 
      FROM Sales s
      JOIN (SELECT product_id, MIN(year) as min_year FROM Sales GROUP BY product_id) t
      ON s.product_id = t.product_id AND s.year = t.min_year
      

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