My first instinct was to just flatten everything into one big list and slide a window of 3, which is obviously wrong since sequences shouldn't cross user boundaries.
Clarify the input format and constraints, then design an algorithm that groups events by user, extracts all consecutive length-3 sequences, counts their frequencies across users, and finally selects the most frequent sequence with lexicographic tie-breaking. Discuss time and space complexity, and consider edge cases such as fewer than three events per user or multiple users with identical sequences.
Pro tip: Demonstrate awareness of data scale: if the log is huge, propose a streaming approach with a hash map to avoid loading everything into memory, and mention that lexicographic tie-breaking can be handled by sorting the keys or using a custom comparator.
Ask about input format (e.g., list of events with user ID and timestamp), size of data, and whether sequences must be strictly consecutive in time per user. Confirm tie-breaking rules and output format.
Propose grouping events by user, then for each user, iterate through their sorted events to extract all length-3 consecutive sequences. Use a hash map to count frequencies of each sequence across all users.
After counting, find the maximum frequency. Collect all sequences with that frequency, then select the lexicographically smallest one. Return the sequence and its count.
Discuss time complexity O(N) where N is total events, and space O(U * L) for sequences. Mention edge cases: users with <3 events, empty log, all sequences unique, etc.
Walk through a small example to verify correctness, including tie-breaking. Consider writing unit tests for edge cases.
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