Start by clarifying requirements and edge cases, then design a mock API that generates order events with accept and fulfill times. For computing pay, use a sweep-line algorithm: create events for each order start and end, sort them, and sweep through time to maintain the count of active orders, accumulating pay per minute based on the overlap count.
Pro tip: Discuss how you would handle large-scale data and real-time updates, and mention that the sweep-line approach can be extended to process streaming events incrementally, which is crucial for production systems.
Ask questions to understand the expected input format, whether orders can have zero duration, if times are inclusive/exclusive, and the scale of data. Clarify that pay is computed per minute and that overlapping orders multiply the rate.
Outline a simple mock API that returns a list of orders with accept and fulfill timestamps. Consider using a fixed dataset or generating random orders for testing, and ensure it mimics the upstream API's behavior.
Select a sweep-line algorithm: convert each order into two events (start and end), sort events by time, and sweep through while maintaining a count of active orders. At each minute, add pay = 0.30 * active_count.
Write code to process the events, compute total pay, and test with various scenarios including no overlap, full overlap, and partial overlap. Validate against a brute-force method for small inputs.
Explain how the solution can handle large datasets by processing events in a stream, using efficient data structures, and potentially parallelizing. Mention time complexity O(n log n) due to sorting.
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