How do you solve Confirmation Rate on LeetCode?

Confirmation Rate (LeetCode #1934) can be solved using Brute Force or Optimal Solution. The optimal approach is Optimal Solution with O(n) time complexity and O(n) space complexity. Key insight: Understanding the difference between confirmed and total requests is crucial.

What is the brute force solution for Confirmation Rate?

The brute force approach for Confirmation Rate is Brute Force, with O(n²) time complexity and O(1) space complexity. In the brute force approach, we will iterate through each user in the Signups table and then check their corresponding entries in the Confirmations table to count the number of confirmed and total requests. This method is straightforward but inefficient as it involves nested loops. The optimal Optimal Solution approach improves this to O(n).

What algorithm or data structure is used to solve Confirmation Rate?

Confirmation Rate uses the following concepts: Database. The recommended patterns to study are: Hash Map, Aggregation.

What are the interview tips for Confirmation Rate?

Clearly explain your thought process while writing the query. Be prepared to discuss the trade-offs between different approaches. Practice writing SQL queries on sample datasets.

What are common mistakes when solving Confirmation Rate?

Not handling users with no confirmation requests properly. Forgetting to round the confirmation rate.

#1934

Confirmation Rate

Medium

DatabaseHash MapAggregation

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

	Brute Force	Optimal Solution★
Time	O(n²)	O(n)
Space	O(1)	O(n)

💡

Intuition

Time O(n)Space O(n)

The optimal solution leverages SQL's aggregation capabilities to compute the confirmation rates in a single query. By using a LEFT JOIN and aggregation functions, we can efficiently calculate the required rates without nested loops.

⚙️

Algorithm

6 steps

1Step 1: Perform a LEFT JOIN between the Signups and Confirmations tables on user_id.
2Step 2: Use aggregation functions to count confirmed actions and total actions for each user.
3Step 3: Calculate the confirmation rate using the counts obtained.
4Step 4: Use COALESCE to handle users with no confirmation requests, ensuring the rate is 0.
5Step 5: Round the confirmation rate to two decimal places.
6Step 6: Return the user_id and confirmation rate.

solution.py5 lines

1# Full working Python code
2SELECT s.user_id, ROUND(COALESCE(SUM(CASE WHEN c.action = 'confirmed' THEN 1 ELSE 0 END) / COUNT(c.action), 0), 2) AS confirmation_rate
3FROM Signups s
4LEFT JOIN Confirmations c ON s.user_id = c.user_id
5GROUP BY s.user_id;

ℹ

Complexity note: The time complexity is O(n) because we are processing each row in the Signups and Confirmations tables once, leveraging SQL's built-in aggregation functions.

1Understanding the difference between confirmed and total requests is crucial.
2Using SQL's aggregation functions can significantly reduce complexity.

Solutions and explanations are original Tejav content. Problem titles © LeetCode — use the LeetCode button above for the full problem statement.