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  1. Correctness:

    • Problem 1 (Consecutive Numbers): The solution correctly identifies consecutive numbers by joining the Logs table three times and checking for consecutive IDs and matching numbers. This approach is correct for the problem.
    • Problem 2 (Number of Passengers in Each Bus): The solution uses a CTE to find the earliest bus arrival time for each passenger and then counts passengers per bus. This logic is correct.
    • Problem 3 (User Activity): The solution correctly counts distinct users for each day within the specified date range.
    • Problem 4 (Dynamic Pivoting of a Table): The solution dynamically generates a pivot table using GROUP_CONCAT and prepared statements, which is correct for the problem.
  2. Time Complexity:

    • Problem 1: O(n^3) due to the triple join, which could be inefficient for large datasets.
    • Problem 2: O(n log n) due to the JOIN and GROUP BY operations.
    • Problem 3: O(n) for scanning the Activity table.
    • Problem 4: O(n) for generating the dynamic SQL, but the execution time depends on the size of the pivoted data.
  3. Space Complexity:

    • Problem 1: O(n) for storing the intermediate results of the joins.
    • Problem 2: O(n) for the CTE and JOIN results.
    • Problem 3: O(n) for storing the grouped results.
    • Problem 4: O(n) for storing the dynamic SQL and results.
  4. Code Quality:

    • The code is generally well-structured and readable.
    • Comments are minimal but sufficient for understanding the purpose of each query.
    • The use of CTE in Problem 2 is a good practice for readability.
    • The dynamic SQL in Problem 4 is well-handled with proper use of GROUP_CONCAT and prepared statements.
  5. Efficiency:

    • Problem 1 could be optimized by using window functions (e.g., LEAD/LAG) to avoid the triple join.
    • Problem 2 and 3 are already efficient for their respective problems.
    • Problem 4 is efficient for dynamic pivoting, but the GROUP_CONCAT_MAX_LEN setting could be documented for clarity.

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