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My first instinct was to just count connections as a number per user, which works until you realize disconnects can happen and you need to avoid double-counting.
Use a hash map to track each user's current connection count, incrementing on connect and decrementing on disconnect. After processing all events, iterate through the map and partition users into two groups based on whether their count is less than N or at least N. Ensure only users who appeared in at least one event are included.
Pro tip: Clarify edge cases upfront, such as duplicate disconnections or self-connections, and discuss how you'd handle them to show attention to detail. Also, mention that the order of events matters and that you assume the stream is valid (e.g., no disconnection without a prior connection).
Ask about the definition of a connection (e.g., bidirectional means both users are connected), what happens with duplicate events, and whether N is inclusive. Confirm that only users appearing in events should be included.
Use a hash map (dictionary) to map each user ID to their current connection count. This allows O(1) updates per event and efficient iteration at the end.
Iterate through each event: for a connection, increment the count for both users; for a disconnection, decrement the count for both users. If a user is not in the map, initialize their count to 0 before updating.
After processing all events, iterate through the map and add each user to either the 'fewer than N' group or the 'N or more' group based on their final count.
State that time complexity is O(E) for E events and space is O(U) for U unique users. Mention that this is optimal for a single pass, and discuss potential streaming or distributed approaches if the data is too large.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.