SQL3 COMPLETED

[HaswathaSridharan]

No description provided.

[super30admin]

1. Problem 1 (Consecutive Numbers):

   • Correctness: The solution correctly identifies consecutive numbers by joining the table with itself and checking for consecutive IDs and matching numbers. This is a standard approach.
   • Time Complexity: O(n^3) due to the triple self-join, which could be inefficient for large datasets.
   • Space Complexity: O(n) for storing the intermediate results.
   • Code Quality: The code is readable but could benefit from using explicit JOIN syntax (INNER JOIN) instead of comma-separated joins for clarity.
   • Efficiency: Consider using window functions (LEAD/LAG) for better performance with large datasets.

2. Problem 2 (Number of Passengers in Each Bus):

   • Correctness: The solution correctly identifies the earliest bus each passenger can catch and counts passengers per bus. The LEFT JOIN ensures all buses are included.
   • Time Complexity: O(n*m) where n is passengers and m is buses, due to the JOIN and GROUP BY operations.
   • Space Complexity: O(n) for the CTE.
   • Code Quality: Well-structured with a CTE for clarity. Good use of LEFT JOIN to include all buses.
   • Efficiency: The solution is efficient for the problem constraints.

3. Problem 3 (User Activity):

   • Correctness: The solution correctly counts distinct users per day within the 30-day window. The DATEDIFF conditions are accurate.
   • Time Complexity: O(n) for scanning the Activity table.
   • Space Complexity: O(1) for the aggregation.
   • Code Quality: Clear and concise. The date filtering could be written more simply as activity_date BETWEEN DATE_SUB('2019-07-27', INTERVAL 29 DAY) AND '2019-07-27'.
   • Efficiency: The solution is optimal.

4. Problem 4 (Dynamic Pivoting of a Table):

   • Correctness: The solution dynamically pivots the table correctly using prepared statements. The use of GROUP_CONCAT is appropriate.
   • Time Complexity: O(n) for building the dynamic SQL, plus the execution time of the pivoted query.
   • Space Complexity: O(n) for storing the dynamic SQL.
   • Code Quality: Well-structured with proper use of prepared statements. The variable names could be more descriptive (e.g., @sql could be @dynamic_sql).
   • Efficiency: The solution is efficient for dynamic pivoting.

General Observations:

• The solutions are generally correct and follow standard approaches.
• The code is readable but could benefit from consistent formatting (e.g., spacing between problems).
• Consider adding comments for complex queries to improve maintainability.
• For Problem 1, using window functions would be a more modern and efficient approach.