Game Play Analysis I

You are tasked with analyzing player activity for a gaming company. Understanding when players first start their gaming journey is crucial for marketing, onboarding, and retention strategies. This challenge focuses on identifying the very first login date for each player.

Problem Description

Given a table of player activity, you need to find the earliest login date for each unique player. This information is fundamental for understanding player acquisition timelines and can inform decisions about engagement campaigns.

What needs to be achieved: For each distinct player ID, identify the date of their very first login.

Key requirements:

• Process a dataset containing player login events.
• Group events by player ID.
• Within each group, find the minimum login date.

Expected behavior: The output should be a table containing two columns: player_id and first_login_date. Each row should represent a unique player and their earliest login date.

Important edge cases to consider:

• A player might have multiple login entries on the same earliest date. The output should still reflect that single earliest date.
• The input table might be empty.

Examples

Example 1:

Input:
PlayerLogins Table:
| player_id | event_date | event_type |
|-----------|------------|------------|
| 1         | 2019-08-01 | PlayerStarted |
| 1         | 2019-08-02 | QuestStarted |
| 2         | 2019-08-01 | PlayerStarted |
| 2         | 2019-08-02 | PlayerStarted |
| 2         | 2019-08-03 | QuestStarted |
| 3         | 2019-08-01 | PlayerStarted |
| 3         | 2019-08-02 | QuestStarted |
| 3         | 2019-08-01 | QuestStarted |

Output:
PlayerFirstLogin Table:
| player_id | first_login_date |
|-----------|------------------|
| 1         | 2019-08-01       |
| 2         | 2019-08-01       |
| 3         | 2019-08-01       |

Explanation:
- Player 1's earliest login date is 2019-08-01.
- Player 2's earliest login date is 2019-08-01 (even though they have multiple entries on this date).
- Player 3's earliest login date is 2019-08-01.

Example 2:

Input:
PlayerLogins Table:
| player_id | event_date | event_type |
|-----------|------------|------------|
| 10        | 2020-01-15 | PlayerStarted |
| 10        | 2020-01-10 | PlayerStarted |
| 10        | 2020-01-20 | QuestStarted |
| 11        | 2020-01-12 | PlayerStarted |

Output:
PlayerFirstLogin Table:
| player_id | first_login_date |
|-----------|------------------|
| 10        | 2020-01-10       |
| 11        | 2020-01-12       |

Explanation:
- Player 10's earliest login date is 2020-01-10.
- Player 11's earliest login date is 2020-01-12.

Example 3 (Edge Case - Empty Table):

Input:
PlayerLogins Table:
(empty)

Output:
PlayerFirstLogin Table:
(empty)

Explanation:
If there are no login events, the output table should also be empty.

Constraints

• The PlayerLogins table will contain at least one row, or zero rows if the table is empty.
• player_id is an integer.
• event_date is a date type (e.g., YYYY-MM-DD).
• The output table will have at most N rows, where N is the number of unique player_ids in the input.
• The total number of rows in PlayerLogins will not exceed 100,000.
• The solution should be efficient and complete the operation within reasonable time limits.

Notes

• You will need to group the data by player_id.
• For each group, you will need to find the minimum event_date.
• Consider how to handle dates. Standard date comparison logic applies.
• The event_type column is not directly used to determine the first login, but it's part of the available data. The problem specifically asks for the earliest event_date associated with a player_id.