Spent the first few minutes just making sure I understood the 'otherwise' case correctly.
Clarify the data model and query requirements first, then propose an efficient algorithm that preprocesses flights per user and uses binary search to find the relevant flight for a given timestamp. Discuss trade-offs between preprocessing time, query time, and memory, and consider edge cases like multiple flights, missing data, and time zones.
Pro tip: Mention that you would sort each user's flights by departure time and use binary search to find the latest flight with departure <= timestamp; then check if arrival > timestamp to determine in-flight status. This shows you can optimize for repeated queries.
Ask about data size, query frequency, time zone handling, and whether flights can overlap or have gaps. Confirm that 'most recently completed flight' means the flight with the latest arrival time before the timestamp.
Propose grouping flights by user ID and sorting each user's flights by departure time. For efficient lookup, consider an array of flights per user with binary search, or an interval tree if flights can overlap.
For a given user and timestamp, binary search for the latest flight with departure <= timestamp. If that flight's arrival > timestamp, the user is in-flight; otherwise, the user is at the destination airport of that flight.
Address cases where no prior flight exists (user at origin airport or unknown), multiple flights with same departure, and time zone conversions. Discuss caching or indexing for frequent queries.
State time complexity: O(log n) per query after O(n log n) preprocessing. Compare with alternative approaches like scanning all flights (O(n) per query) and explain why binary search is better for large datasets.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.