How do you solve Game Play Analysis I on LeetCode?

Game Play Analysis I (LeetCode #511) 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: Using aggregation functions like MIN can significantly reduce the complexity of the problem.

What is the time complexity of Game Play Analysis I?

The optimal solution for Game Play Analysis I runs in O(n) time and uses O(n) space. This complexity is linear because we only need to scan through the records once to find the minimum date for each player.

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

The brute force approach for Game Play Analysis I is Brute Force, with O(n²) time complexity and O(1) space complexity. In this approach, we will check each player's login dates and find the earliest one by comparing each date. This is straightforward but inefficient for larger datasets. The optimal Optimal Solution approach improves this to O(n).

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

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

What are the interview tips for Game Play Analysis I?

Always think about how to leverage built-in functions for efficiency. Be clear about the structure of your data and how to manipulate it effectively. Practice writing SQL queries to become familiar with common patterns.

What are common mistakes when solving Game Play Analysis I?

Not using GROUP BY correctly, which can lead to incorrect results. Failing to consider players with no games played, which could affect the results.

#511

Game Play Analysis I

Easy

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)

Using SQL's aggregation functions, we can efficiently find the first login date for each player in a single query, leveraging the database's ability to handle large datasets effectively.

⚙️

Algorithm

3 steps

1Step 1: Use the SQL MIN function to find the earliest event_date for each player_id.
2Step 2: Group the results by player_id to ensure we get one result per player.
3Step 3: Return the player_id along with their corresponding first_login date.

solution.py1 lines

1SELECT player_id, MIN(event_date) AS first_login FROM Activity GROUP BY player_id;

ℹ

Complexity note: This complexity is linear because we only need to scan through the records once to find the minimum date for each player.

1Using aggregation functions like MIN can significantly reduce the complexity of the problem.
2Grouping results by player_id allows us to efficiently gather data for each player.

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