Took me a minute to realize you have to handle each user independently before summing anything up.
Clarify the input format and edge cases, then propose sorting events by userId and timestamp. For each user, iterate through sorted events and start a new session whenever the gap exceeds the timeout, incrementing a global counter. Analyze time and space complexity, and discuss potential optimizations like using a hash map for grouping.
Pro tip: Mention that timestamps might need normalization (e.g., to milliseconds) and that using a stable sort preserves order for equal timestamps. Also, consider if events are already grouped by user to avoid sorting overhead.
Ask about input format, timestamp units, whether events are pre-sorted, and how to handle empty lists or single events. Confirm that sessions are per-user and gaps are strictly greater than timeout.
Decide to sort events by userId and timestamp, then iterate. Use a hash map to group events by user if sorting by userId is costly, but sorting is simpler. Maintain a session count.
For each user, initialize session count to 1 if events exist. Iterate through sorted events, and when the gap between current and previous event exceeds timeout, increment session count.
State time complexity O(n log n) due to sorting, space O(n) for sorted array or O(k) for hash map. Discuss if we can avoid sorting by using a map and sorting each user's events individually.
Walk through a small example, test empty input, single user with multiple sessions, and multiple users. Verify that gaps exactly equal to timeout do not start a new session.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.