LeetCode 511: Game Play Analysis I Solution in SQL – A Step-by-Step Guide

What Is LeetCode 511: Game Play Analysis I?

In LeetCode 511: Game Play Analysis I, you’re given a table called Activity with columns player_id, device_id, event_date, and games_played, tracking player activities. Your task is to find each player’s first login date. For example, if a player logged in on 2023-01-01 and 2023-01-03, you report 2023-01-01. This problem introduces SQL aggregation and filtering, similar to LeetCode 512: Game Play Analysis II.

Problem Statement

• Input: Table Activity:
• player_id (int): Player identifier.
• device_id (int): Device used.
• event_date (date): Date of activity.
• games_played (int): Games played that day.
• Output: Table with player_id and first_login (date)—earliest event date per player.
• Rules: Return one row per player; find the minimum event date.

Constraints

• At least one row in the table.
• Dates are valid and in range (e.g., 2023-01-01 to 2025-04-03).

Examples

• Input:
``` +-----------+-----------+------------+--------------+ | player_id | device_id | event_date | games_played | +-----------+-----------+------------+--------------+ | 1 | 2 | 2023-01-01 | 5 | | 1 | 2 | 2023-01-03 | 6 | | 2 | 3 | 2023-02-01 | 1 | +-----------+-----------+------------+--------------+ ```
• Output:
``` +-----------+-------------+ | player_id | first_login | +-----------+-------------+ | 1 | 2023-01-01 | | 2 | 2023-02-01 | +-----------+-------------+ ```
• Input:
``` +-----------+-----------+------------+--------------+ | player_id | device_id | event_date | games_played | +-----------+-----------+------------+--------------+ | 1 | 1 | 2023-03-01 | 0 | +-----------+-----------+------------+--------------+ ```
• Output:
``` +-----------+-------------+ | player_id | first_login | +-----------+-------------+ | 1 | 2023-03-01 | +-----------+-------------+ ```

Understanding the Problem: Tracking First Logins

To solve LeetCode 511: Game Play Analysis I in SQL (or via Python with a database library), we need a query that groups player activities and finds the earliest event_date for each player_id. A naive approach might scan the table multiple times, but SQL’s aggregation functions make this a breeze. We’ll explore:

• Best Solution (GROUP BY): O(n) time (database scan), O(m) space (m players)—clean and efficient.
• Alternative Solution (Subquery): O(n log n) time (possible sorting), O(n) space—more explicit but slower.

Let’s dive into the GROUP BY solution with a friendly breakdown!

Best Solution: SQL with GROUP BY

Why GROUP BY Wins

The GROUP BY solution is the best for LeetCode 511 because it’s concise, efficient, and leverages SQL’s built-in aggregation. Using MIN(event_date) with GROUP BY player_id, it scans the table once to find each player’s first login, making it O(n) time (where n is rows) and O(m) space (m unique players). It’s like asking the database to hand you a neatly sorted list of firsts!

How It Works

Think of this as a player roll call:

• Step 1: Select Columns:
• Pick player_id and the minimum event_date as first_login.
• Step 2: Group by Player:
• Use GROUP BY player_id to bundle all rows per player.
• Step 3: Aggregate:
• MIN(event_date) finds the earliest date in each group.
• Why It Works:
• SQL engines optimize grouping and min operations.
• One pass gets all answers.

It’s like a first-login spotlight!

Step-by-Step Example

Example:

Copy code+-----------+-----------+------------+--------------+
| player_id | device_id | event_date | games_played |
+-----------+-----------+------------+--------------+
| 1         | 2         | 2023-01-01 | 5            |
| 1         | 2         | 2023-01-03 | 6            |
| 2         | 3         | 2023-02-01 | 1            |
+-----------+-----------+------------+--------------+

• Step 1: Group by player_id:
• Player 1: Rows with dates [2023-01-01, 2023-01-03].
• Player 2: Row with date [2023-02-01].
• Step 2: Find min date:
• Player 1: MIN([2023-01-01, 2023-01-03]) = 2023-01-01.
• Player 2: MIN([2023-02-01]) = 2023-02-01.
• Step 3: Output:
``` +-----------+-------------+ | player_id | first_login | +-----------+-------------+ | 1 | 2023-01-01 | | 2 | 2023-02-01 | +-----------+-------------+ ```

SQL Query with Detailed Explanation

Here’s the SQL query, explained for beginners:

Copy codeSELECT 
    player_id,
    MIN(event_date) AS first_login
FROM 
    Activity
GROUP BY 
    player_id;

• Line 2: player_id—grouping key.
• Line 3: MIN(event_date)—earliest date per group, aliased as first_login.
• Line 5: FROM Activity—source table.
• Line 7: GROUP BY player_id—bundles rows by player.
• Time Complexity: O(n)—single table scan (database-dependent).
• Space Complexity: O(m)—stores m players’ results.

It’s like a first-date finder!

Alternative Solution: SQL with Subquery

Why an Alternative Approach?

The subquery solution uses a correlated subquery to find each player’s first login by comparing dates explicitly. It’s O(n log n) time (if sorting occurs) and O(n) space—more verbose and less efficient but shows another way to think about the problem. Great for understanding SQL’s flexibility!

How It Works

Picture this as a detective hunt:

• Step 1: For each player_id, find the min date.
• Step 2: Match rows where the date equals that min.
• Step 3: Select distinct player_id and date pairs.

It’s like double-checking each player’s debut!

Step-by-Step Example

Example: Same table as above

• Step 1: Subquery finds min dates:
• Player 1: MIN(event_date) = 2023-01-01.
• Player 2: MIN(event_date) = 2023-02-01.
• Step 2: Match rows:
• Row (1, 2023-01-01) matches min for 1.
• Row (2, 2023-02-01) matches min for 2.
• Step 3: Output:
``` +-----------+-------------+ | player_id | first_login | +-----------+-------------+ | 1 | 2023-01-01 | | 2 | 2023-02-01 | +-----------+-------------+ ```

SQL Query for Subquery Approach

Copy codeSELECT DISTINCT 
    player_id,
    event_date AS first_login
FROM 
    Activity a1
WHERE 
    event_date = (
        SELECT MIN(event_date)
        FROM Activity a2
        WHERE a2.player_id = a1.player_id
    );

• Line 1: DISTINCT—avoids duplicates (though unique here).
• Line 2-3: Select player_id and event_date.
• Line 5: Alias a1 for outer table.
• Line 7-10: Subquery finds min date per player, matched to outer row.
• Time Complexity: O(n log n)—subquery may sort per player.
• Space Complexity: O(n)—temporary storage.

It’s a meticulous first-login tracker!

Comparing the Two Solutions

• GROUP BY (Best):
• Pros: O(n), concise, optimized.
• Cons: Less explicit.
• Subquery (Alternative):
• Pros: O(n log n), clear logic.
• Cons: Slower, verbose.

GROUP BY wins for speed!

Additional Examples and Edge Cases

• Single Row: [1, 1, 2023-01-01, 0] → (1, 2023-01-01).
• Multiple per Day: [1, 1, 2023-01-01, 5], [1, 2, 2023-01-01, 3] → (1, 2023-01-01).
• Large Gaps: [2, 3, 2024-01-01, 1], [2, 3, 2025-01-01, 2] → (2, 2024-01-01).

GROUP BY handles them all!

Complexity Recap

• GROUP BY: Time O(n), Space O(m).
• Subquery: Time O(n log n), Space O(n).

GROUP BY’s the efficiency champ!

Key Takeaways

• GROUP BY: Aggregate magic—learn SQL at Python Basics (or SQL docs)!
• Subqueries: Explicit but costly.
• SQL: Databases simplify analysis.
• Fun: Player data rocks!

Final Thoughts: Analyze Those Logins!

LeetCode 511: Game Play Analysis I in SQL is a practical database challenge. GROUP BY is your fast track, while subqueries offer clarity. Want more? Try LeetCode 512: Game Play Analysis II or LeetCode 550: Game Play Analysis IV. Ready to query? Head to Solve LeetCode 511 on LeetCode and track those first logins today! (Note: Adjust link if 511 differs.)