The schema clarification part took longer than I expected.
First, clarify the problem constraints and assumptions (e.g., event types, peak windows, pay rates). Then, outline a sweep-line algorithm that processes all interval endpoints (delivery starts/ends and peak starts/ends) in sorted order, splitting intervals at peak boundaries and accumulating pay with the appropriate multiplier. Finally, discuss edge cases and complexity.
Pro tip: Mention that you would confirm whether peak windows can overlap and how to handle them (e.g., merge or apply max multiplier) before coding, as this ambiguity often trips up candidates.
Ask about event types (only deliveries?), peak window definitions (fixed daily windows?), pay structure (base rate per hour?), and whether peak windows can overlap. Confirm that intervals are half-open [start, end) to avoid double-counting at boundaries.
Use a sweep-line approach: collect all critical time points (delivery starts/ends, peak starts/ends), sort them, and iterate through consecutive segments. For each segment, determine if it's within any peak window and apply the correct pay multiplier.
For each delivery interval, split it at peak boundaries. For each sub-interval, compute duration and multiply by the base rate, applying 2x if the sub-interval falls within a peak window. Sum all sub-interval pays.
Consider: deliveries that start/end exactly at peak boundaries, zero-duration intervals, multiple peak windows, overlapping peak windows (merge or take max multiplier), and gaps between deliveries (no pay).
Sorting endpoints takes O(n log n) where n is total number of events and peak boundaries. Sweeping takes O(n). Discuss potential optimizations like merging overlapping peaks first or using a priority queue for dynamic peak windows.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.