SQL3: First 3 Problems solved

[Feminto]

No description provided.

[BharathVuppala96]

Good Work

[super30admin]

1. Correctness:

   • For consecutive_numbers.sql: The solution correctly identifies consecutive numbers using the LEAD window function, which is an appropriate approach for this problem.
   • For num_of_passengers.sql: The solution correctly matches passengers to buses and counts them, handling the case where no passengers are assigned to a bus with IFNULL.
   • For user_activity.sql: The solution correctly counts active users within the last 30 days, filtering by activity_type and date range.

2. Time Complexity:

   • consecutive_numbers.sql: O(n) where n is the number of rows in logs, due to the window function scan.
   • num_of_passengers.sql: O(n log n) due to the GROUP BY and JOIN operations.
   • user_activity.sql: O(n) for scanning the activity table and applying filters.

3. Space Complexity:

   • consecutive_numbers.sql: O(n) for storing the intermediate results of the window functions.
   • num_of_passengers.sql: O(n) for the CTE and join results.
   • user_activity.sql: O(n) for storing the filtered results before grouping.

4. Code Quality:

   • The code is generally well-structured and readable.
   • Consistent use of aliases and indentation.
   • Could improve by adding comments explaining the logic, especially for complex queries like num_of_passengers.sql.
   • In user_activity.sql, the date filter could be more readable using BETWEEN or DATE_ADD instead of two DATEDIFF conditions.

5. Efficiency:

   • consecutive_numbers.sql: Efficient use of window functions.
   • num_of_passengers.sql: The solution is efficient but could consider indexing arrival_time for better performance on large datasets.
   • user_activity.sql: The query could benefit from an index on activity_date and activity_type.