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This approach leverages SQL aggregate functions to determine the first login date for each player. We use the MIN function to get the earliest event_date for each player_id. This is accomplished by grouping the results based on the player_id field.
Time complexity: O(n) due to a single pass through the Activity table.
Space complexity: O(k), where k is the number of unique player_ids.
1SELECT player_id, MIN(event_date) AS first_login FROM Activity GROUP BY player_id;The SQL query selects the smallest event_date for each player_id, yielding the first login date. The GROUP BY clause groups the data based on player_id, enabling the MIN function to operate within each group independently.
This approach involves loading the data into a data structure that optimizes lookup times, such as a dictionary or a map, and then iterating over the list to find the earliest event_date for each player_id. This functionally mimics the behavior of the SQL approach but executed in a programming environment rather than within a database.
Time complexity: O(n), with a single traversal of the records list.
Space complexity: O(k), where k is the number of unique player_ids.
1def first_login(records):
2 first_login_map = This Python function processes a list of tuples each containing the fields from the Activity table. It iterates through the records, updating a map that tracks the earliest event_date for each player_id. After processing all records, it outputs a list of tuples with the player_id and their respective first_login date.