Find Followers Count — LeetCode #1729 (Easy)
Tags: Database
Related patterns: Hash Map, Array
Brute Force approach
Time complexity: O(n²). Space complexity: O(1).
In the brute force approach, we will iterate through each user and count their followers by checking every entry in the Followers table. This is straightforward but inefficient for larger datasets.
The time complexity is O(n²) because for each user, we check all followers, leading to a nested loop. Space complexity is O(1) as we are not using any additional data structures.
- Step 1: Initialize an empty result list to store user_id and their follower count.
- Step 2: For each user_id in the Followers table, iterate through all rows to count how many times the user_id appears as a follower_id.
- Step 3: Append the user_id and the count of followers to the result list.
- Step 4: Sort the result list by user_id in ascending order.
- Step 5: Return the result list.
1. Input: [(0, 1), (1, 0), (2, 0), (2, 1)]
2. Count followers for user 0: 1 (follower_id 1)
3. Count followers for user 1: 1 (follower_id 0)
4. Count followers for user 2: 2 (follower_id 0, 1)
5. Result list: [(0, 1), (1, 1), (2, 2)]
6. Sorted result: [(0, 1), (1, 1), (2, 2)]
Optimal Solution approach
Time complexity: O(n). Space complexity: O(n).
The optimal approach leverages SQL's GROUP BY and COUNT functions to efficiently aggregate the follower counts in a single query, which is much faster than the brute force method.
The time complexity is O(n) because we are scanning through the table once to count followers. Space complexity is O(n) due to the storage of results.
- Step 1: Use the SQL SELECT statement to choose user_id and count the number of follower_id for each user.
- Step 2: Use GROUP BY to group results by user_id to aggregate follower counts.
- Step 3: Order the results by user_id in ascending order.
- Step 4: Return the aggregated results.
1. Input: [(0, 1), (1, 0), (2, 0), (2, 1)]
2. Group by user_id: {0: [1], 1: [0], 2: [0, 1]}
3. Count followers for user 0: 1
4. Count followers for user 1: 1
5. Count followers for user 2: 2
6. Result: [(0, 1), (1, 1), (2, 2)]
Key Insights
- Using GROUP BY and COUNT is much more efficient than manual counting.
- Understanding how to leverage SQL functions can significantly reduce complexity.
Common Mistakes
- Not grouping results correctly, leading to incorrect counts.
- Forgetting to order results as specified in the problem.
Interview Tips
- Always clarify the requirements before diving into the solution.
- Practice writing SQL queries as they are common in data-related interviews.
- Be prepared to explain your thought process and the reasoning behind your approach.
