How do you solve Game Play Analysis IV on LeetCode?

Game Play Analysis IV (LeetCode #550) 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 how to track unique player logins is crucial for solving this problem.

What is the brute force solution for Game Play Analysis IV?

The brute force approach for Game Play Analysis IV is Brute Force, with O(n²) time complexity and O(1) space complexity. The brute force approach checks each player's login dates against all other login dates to see if they logged in the day after their first login. This is straightforward but inefficient. The optimal Optimal Solution approach improves this to O(n).

What algorithm or data structure is used to solve Game Play Analysis IV?

Game Play Analysis IV uses the following concepts: Database. The recommended patterns to study are: Hash Map, Array.

What are the interview tips for Game Play Analysis IV?

Always clarify the requirements and constraints of the problem before diving into coding. Think about edge cases, such as players who only log in once or on consecutive days. Practice writing SQL queries, as they are commonly used in data-related interviews.

What are common mistakes when solving Game Play Analysis IV?

Failing to account for players who logged in on the same day multiple times. Not properly handling date comparisons, which can lead to incorrect results.

#550

Game Play Analysis IV

Medium

DatabaseHash MapArray

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 uses a single pass through the data to track first logins and check for subsequent logins, which is more efficient than the brute force method.

⚙️

Algorithm

5 steps

1Step 1: Create a dictionary to store the first login date for each player.
2Step 2: Iterate through the Activity table to populate this dictionary.
3Step 3: Create a set to track players who logged in the day after their first login.
4Step 4: Iterate through the Activity table again to check if players logged in on the day after their first login date.
5Step 5: Calculate the fraction of players who logged in again and round to 2 decimal places.

solution.py14 lines

1# Full working Python code
2WITH FirstLogins AS (
3    SELECT player_id, MIN(event_date) AS first_login
4    FROM Activity
5    GROUP BY player_id
6), SubsequentLogins AS (
7    SELECT DISTINCT a.player_id
8    FROM Activity a
9    JOIN FirstLogins fl ON a.player_id = fl.player_id
10    WHERE a.event_date = DATE_ADD(fl.first_login, INTERVAL 1 DAY)
11)
12SELECT ROUND(COUNT(DISTINCT sl.player_id) * 1.0 / COUNT(DISTINCT fl.player_id), 2) AS fraction
13FROM FirstLogins fl
14LEFT JOIN SubsequentLogins sl ON fl.player_id = sl.player_id;

ℹ

Complexity note: This complexity is due to the need to store first login dates and track subsequent logins, which requires linear space and time.

1Understanding how to track unique player logins is crucial for solving this problem.
2Using date functions effectively can simplify the logic for checking subsequent logins.

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