I knew a linked list was involved pretty early but kept second-guessing myself on how to handle the duplicates cleanly.
Clarify the requirements and constraints, then propose a design using a hash map to track login counts and a doubly linked list to maintain the order of users who have logged in exactly once. Explain how to achieve O(1) for both operations by updating the list and map on each login, and discuss edge cases and trade-offs.
Pro tip: Mention that the solution must handle multiple logins per user and that the order is based on the first login time among users with exactly one login. Also, discuss how to handle deletions from the linked list efficiently using a hash map to store node references.
Ask clarifying questions to confirm assumptions: e.g., whether 'earliest-logged-in' refers to the first login event among users who have logged in exactly once, and whether the stream is continuous with no deletions.
Select a hash map to store each user's login count and a doubly linked list to maintain the order of users with exactly one login. Also, use a second hash map to store references to nodes in the linked list for O(1) removal.
For recordLogin(user): increment the user's count; if count becomes 1, append the user to the linked list; if count becomes 2, remove the user from the linked list. For getEarliestSingleLogin(): return the head of the linked list or null if empty.
Explain that both operations run in O(1) time because hash map lookups and linked list insertions/removals are constant time. Space complexity is O(n) for n unique users.
Cover scenarios like no users with exactly one login, multiple logins by the same user, and potential memory overhead. Mention alternative approaches (e.g., using a queue with lazy deletion) and their trade-offs.
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