Start by clarifying requirements and edge cases, then design the data model with appropriate data structures (e.g., hash maps for drivers and deliveries, and a running balance for unpaid amounts). Implement the class with methods for adding drivers, recording deliveries, computing total costs, and processing payments, ensuring efficiency and correctness. Test with scenarios like partial payments, overpayments, and multiple deliveries.
Pro tip: Emphasize immutability and thread-safety where appropriate, and discuss how you would handle concurrent payments or deliveries—this shows you think beyond basic functionality and consider real-world production concerns.
Ask questions to understand expected behavior: Are driver IDs unique? Can deliveries overlap? Should payments be applied to specific deliveries or just reduce overall balance? What are the performance requirements?
Choose data structures: a map from driver ID to driver object containing total cost and unpaid balance, and optionally a list of deliveries per driver. Consider using a running total for efficiency.
Write methods: addDriver(id), recordDelivery(driverId, startTime, endTime, cost), getTotalCost(driverId), and makePayment(driverId, amount). Ensure payments reduce unpaid balance and handle edge cases like overpayment.
Address scenarios: unknown driver, negative cost or payment, payment exceeding balance, and concurrent modifications. Decide on error handling (exceptions vs. return values).
Walk through test cases: multiple deliveries, partial payments, full payment, overpayment. Discuss time/space complexity and possible optimizations (e.g., lazy computation vs. eager updates).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Clarify the problem first: define 'active delivery' (start/end timestamps), whether the window is inclusive, and what 'continuously sustained' means (e.g., the peak count must hold for a non-zero duration). Then propose a sweep-line algorithm: create events for delivery starts (+1) and ends (-1), sort them, and scan to find the maximum count and the interval where it persists. Discuss trade-offs like handling simultaneous events, tie-breaking, and whether to return the first or longest peak interval.
Pro tip: Mention that you'd confirm with the interviewer whether the peak interval should be the longest one or any one, and whether deliveries that end exactly when another starts count as overlapping. This shows attention to edge cases and real-world ambiguity.
Ask about input format, definition of active delivery, inclusivity of window boundaries, and how to handle simultaneous start/end events. Confirm whether the peak interval must be maximal or just any interval with the peak count.
Propose a sweep-line approach: create events (start: +1, end: -1), sort by time, and scan to track current active count. Record the maximum count and the time range where it occurs.
Decide on tie-breaking: process all events at the same timestamp together (e.g., ends before starts or vice versa) to avoid incorrect counts. Track the start and end of the peak interval by noting when the count reaches the max and when it drops below.
State time complexity O(n log n) due to sorting, space O(n). Discuss alternatives like segment trees or difference arrays if the time range is small, and trade-offs between simplicity and performance.
Walk through a small example, including cases with no deliveries, all overlapping, and multiple peaks. Verify that the returned interval is correct and handles boundaries properly.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.