LeetCode 512: Game Play Analysis II Solution in SQL – A Step-by-Step Guide

What Is LeetCode 512: Game Play Analysis II?

In LeetCode 512: Game Play Analysis II, you’re given an Activity table with columns player_id, device_id, event_date, and games_played, recording player actions. Your task is to find the device_id each player used on their first login date. For example, if a player logged in on 2023-01-01 with device 2 and later on 2023-01-03 with device 1, you report device 2. This problem builds on LeetCode 511: Game Play Analysis I, adding the device detail.

Problem Statement

• Input: Table Activity:
• player_id (int): Player identifier.
• device_id (int): Device identifier.
• event_date (date): Date of activity.
• games_played (int): Games played that day.
• Output: Table with player_id and device_id—device used on first login.
• Rules: One row per player; find device on earliest event_date.

Constraints

• At least one row in the table.
• Dates are valid (e.g., up to 2025-04-03).
• Multiple rows per player possible; ties in date may occur (assume first device).

Examples

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

Understanding the Problem: Pinpointing First Devices

To solve LeetCode 512: Game Play Analysis II in SQL (or via Python with a database library), we need a query that identifies each player’s first login date and retrieves the device_id used on that day. If a player logs in multiple times on their first day, we typically take the first device (assuming order or problem intent). We’ll explore:

• Best Solution (Subquery with GROUP BY): O(n) time, O(m) space (m players)—clean and efficient.
• Alternative Solution (Window Function): O(n) time, O(n) space—modern but more complex.

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

Best Solution: SQL with Subquery and GROUP BY

Why Subquery with GROUP BY Wins

The subquery with GROUP BY is the best for LeetCode 512 because it’s straightforward and optimized. It uses GROUP BY to find the first login date per player, then joins back to get the device_id, running in O(n) time (n rows) and O(m) space (m players). It’s like asking the database to tag each player’s debut device in one smooth pass!

How It Works

Think of this as a two-part detective mission:

• Step 1: Find First Login Dates:
• Use GROUP BY player_id with MIN(event_date) to get each player’s earliest date.
• Step 2: Match Devices:
• Join the result with the original table to fetch the device_id on that date.
• Step 3: Handle Ties:
• If multiple devices on the first day, take the first (via ordering or assumption).
• Why It Works:
• SQL optimizes joins and grouping.
• Subquery isolates the date logic cleanly.

It’s like a first-device spotlight!

Step-by-Step Example

Example:

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

• Step 1: Group by player_id:
• Player 1: MIN(event_date) = 2023-01-01.
• Player 2: MIN(event_date) = 2023-02-01.
• Temp table: (1, 2023-01-01), (2, 2023-02-01).
• Step 2: Join with Activity:
• (1, 2023-01-01) → matches row with device_id = 2.
• (2, 2023-02-01) → matches row with device_id = 3.
• Step 3: Output:
``` +-----------+-----------+ | player_id | device_id | +-----------+-----------+ | 1 | 2 | | 2 | 3 | +-----------+-----------+ ```

SQL Query with Detailed Explanation

Here’s the SQL query, explained for beginners:

Copy codeSELECT 
    a1.player_id,
    a1.device_id
FROM 
    Activity a1
INNER JOIN (
    SELECT 
        player_id,
        MIN(event_date) AS first_date
    FROM 
        Activity
    GROUP BY 
        player_id
) a2
ON 
    a1.player_id = a2.player_id 
    AND a1.event_date = a2.first_date;

• Lines 2-3: Select player_id and device_id from outer table.
• Line 5: Alias a1 for Activity.
• Lines 7-13: Subquery:
• player_id and MIN(event_date) as first_date.
• GROUP BY player_id for earliest date per player.
• Lines 15-17: Join conditions:
• Match player_id and event_date to first date.
• Time Complexity: O(n)—single scan with join.
• Space Complexity: O(m)—stores m players’ results.

It’s like a device-ID treasure hunt!

Alternative Solution: SQL with Window Function

Why an Alternative Approach?

The window function solution uses ROW_NUMBER() to rank each player’s events by date, selecting the first. It’s O(n) time and O(n) space—modern and powerful but less intuitive for beginners. It’s great for learning advanced SQL features!

How It Works

Picture this as a ranked timeline:

• Step 1: Assign row numbers per player by date.
• Step 2: Filter for row number 1 (first event).
• Step 3: Select player_id and device_id.

It’s like pinning a ribbon on each player’s first play!

Step-by-Step Example

Example: Same table as above

• -Step 1: Add row numbers:
``` +-----------+-----------+------------+--------------+----------+ | player_id | device_id | event_date | games_played | row_num | +-----------+-----------+------------+--------------+----------+ | 1 | 2 | 2023-01-01 | 5 | 1 | | 1 | 1 | 2023-01-03 | 6 | 2 | | 2 | 3 | 2023-02-01 | 1 | 1 | +-----------+-----------+------------+--------------+----------+ ```
• Step 2: Filter row_num = 1:
• Player 1: device_id = 2.
• Player 2: device_id = 3.
• Step 3: Output:
``` +-----------+-----------+ | player_id | device_id | +-----------+-----------+ | 1 | 2 | | 2 | 3 | +-----------+-----------+ ```

SQL Query for Window Function Approach

Copy codeSELECT 
    player_id,
    device_id
FROM (
    SELECT 
        player_id,
        device_id,
        ROW_NUMBER() OVER (PARTITION BY player_id ORDER BY event_date) AS rn
    FROM 
        Activity
) t
WHERE 
    rn = 1;

• Lines 2-3: Select player_id and device_id.
• Lines 6-8: Subquery with ROW_NUMBER():
• Partition by player_id, order by event_date.
• Assigns 1 to earliest date per player.
• Line 12: Filter for first row.
• Time Complexity: O(n)—window function scan.
• Space Complexity: O(n)—temporary table.

It’s a ranked first-device finder!

Comparing the Two Solutions

• Subquery with GROUP BY (Best):
• Pros: O(n), clear, widely supported.
• Cons: Join syntax.
• Window Function (Alternative):
• Pros: O(n), modern, concise.
• Cons: Less intuitive, newer SQL feature.

Subquery wins for simplicity!

Additional Examples and Edge Cases

• Single Row: [1, 2, 2023-01-01, 0] → (1, 2).
• Same Day: [1, 2, 2023-01-01, 5], [1, 3, 2023-01-01, 0] → (1, 2) (first row).
• Multiple Players: [3, 4, 2024-01-01, 1] → (3, 4).

Subquery handles them all!

Complexity Recap

• Subquery: Time O(n), Space O(m).
• Window: Time O(n), Space O(n).

Subquery’s the lean champ!

Key Takeaways

• GROUP BY: Join for precision—learn SQL at Python Basics (or SQL docs)!
• Windows: Rank with flair.
• SQL: Queries unlock insights.
• Fun: Gaming data rules!

Final Thoughts: Track Those Devices!

LeetCode 512: Game Play Analysis II in SQL is a practical database challenge. Subquery with GROUP BY is your fast track, while window functions add modern flair. Want more? Try LeetCode 511: Game Play Analysis I or LeetCode 550: Game Play Analysis IV. Ready to query? Head to Solve LeetCode 512 on LeetCode and pinpoint those first devices today!