Done HW3

[sagar1023]

No description provided.

[super30admin]

1. Problem 1 (Consecutive Numbers):

   • Correctness: The solution correctly identifies consecutive numbers by joining the Logs table three times and checking for consecutive IDs and matching numbers. This is a standard approach for this problem.
   • Time Complexity: O(n^3) due to the triple join, which could be inefficient for large datasets.
   • Space Complexity: O(n) for storing the intermediate results.
   • Code Quality: The code is concise and readable. Using table aliases (l1, l2, l3) is a good practice.
   • Efficiency: The solution could be optimized using window functions (e.g., LEAD/LAG) to reduce the time complexity to O(n).

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

   • Correctness: The solution correctly counts passengers for each bus by finding the earliest bus arrival time that is greater than or equal to the passenger's arrival time. The use of a CTE and LEFT JOIN ensures all buses are included.
   • Time Complexity: O(n*m) where n is the number of passengers and m is the number of buses, due to the join and group by operations.
   • Space Complexity: O(n) for the CTE.
   • Code Quality: The code is well-structured and readable. The CTE makes the logic clear.
   • Efficiency: The solution is efficient for moderate-sized datasets. No major optimizations are needed.

3. Problem 3 (User Activity):

   • Correctness: The solution has a logical error in the date filter. It checks for dates greater than '2019-06-27' AND less than or equal to '2019-06-27', which will return no results. The correct filter should be between '2019-06-28' and '2019-07-27' (or similar, depending on the problem's exact requirements).
   • Time Complexity: O(n) for scanning the Activity table.
   • Space Complexity: O(1) for storing the result.
   • Code Quality: The code is simple but contains a critical error in the date filter.
   • Efficiency: The query is efficient once the date filter is corrected.

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

   • Correctness: The solution dynamically generates a pivot table by creating a SQL statement that pivots the Scores table by course_id. This is a correct and elegant approach for dynamic pivoting.
   • Time Complexity: O(n) for generating the pivot table, where n is the number of distinct course_ids.
   • Space Complexity: O(n) for storing the dynamic SQL statement.
   • Code Quality: The code is well-structured and follows best practices for dynamic SQL. The use of GROUP_CONCAT and PREPARE/EXECUTE is appropriate.
   • Efficiency: The solution is efficient and handles dynamic pivoting well.

General Feedback:

• The student demonstrates a good understanding of SQL and problem-solving skills.
• The code is generally well-structured and readable.
• The main issue is in Problem 3, where the date filter logic is incorrect. This should be reviewed and corrected.
• For Problem 1, the student could consider using window functions for better performance with large datasets.