Completed Mock_SQL2

[Paneripatel]

No description provided.

[super30admin]

1. Correctness:

   • For "CustomersWhoBoughtAllProducts.sql", the solution correctly identifies customers who have purchased all products by comparing the count of distinct product keys per customer with the total count of products. This is a standard and correct approach.
   • For "ProductSalesAnalysisIII.sql", the solution correctly identifies the first year each product was sold using a subquery with MIN(year). This is also a correct approach.

2. Time Complexity:

   • For "CustomersWhoBoughtAllProducts.sql", the GROUP BY and HAVING operations are O(n) where n is the number of records in the Customer table. The subquery is O(m) where m is the number of products. Overall, it's O(n + m).
   • For "ProductSalesAnalysisIII.sql", the subquery with GROUP BY is O(n) where n is the number of sales records. The main query's IN clause could be O(n^2) in the worst case if not optimized by the database engine.

3. Space Complexity:

   • Both solutions have O(k) space complexity where k is the number of groups (customers or products) being processed.

4. Code Quality:

   • The code is well-structured and readable.
   • Both solutions follow SQL best practices.
   • The use of DISTINCT in the first solution and the subquery in the second solution are appropriate.
   • Comments with problem URLs are helpful for reference.

5. Efficiency:

   • For "ProductSalesAnalysisIII.sql", consider using a JOIN instead of IN for potentially better performance, especially 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
     

[super30admin]

1. Correctness:

   • For "CustomersWhoBoughtAllProducts.sql", the solution correctly identifies customers who have bought all products by comparing the count of distinct product keys per customer with the total count of products. This is a standard approach for this type of problem.
   • For "ProductSalesAnalysisIII.sql", the solution correctly finds the first year each product was sold by using a subquery to get the minimum year per product. This is also a correct approach.

2. Time Complexity:

   • For "CustomersWhoBoughtAllProducts.sql", the time complexity is O(n) where n is the number of records in the Customer table, as it requires a full scan of the table for grouping and counting.
   • For "ProductSalesAnalysisIII.sql", the time complexity is O(n) for the subquery (to find min year per product) and O(n) for the main query, resulting in O(n) overall.

3. Space Complexity:

   • Both solutions have a space complexity of O(m) 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 results for grouping.

4. Code Quality:

   • The code is well-structured and readable.
   • Both solutions follow SQL best practices, using appropriate GROUP BY and HAVING clauses.
   • The use of DISTINCT in the first query ensures accurate counting of unique products.
   • The subquery in the second solution is clear and effective.

5. Efficiency:

   • Both solutions are efficient for their respective problems.
   • No significant optimizations are needed as the queries are already optimal for standard SQL implementations.

Potential Edge Cases:

• For "CustomersWhoBoughtAllProducts.sql", ensure the Product table isn't empty to avoid division by zero if the count is used in a division operation (though not applicable here).
• For "ProductSalesAnalysisIII.sql", consider if there could be multiple sales of the same product in the same year (the current solution handles this correctly by including all such records).