Calculate Daily Driver Pay
Company: DoorDash
Role: Software Engineer
Category: Coding & Algorithms
Difficulty: hard
Interview Round: Technical Screen
You are given a one-day event log for a delivery driver. Each input record is a JSON event with fields such as `order_id`, `event_type`, `timestamp`, and `base_rate_per_minute`.
For each order, a valid pair of events defines a paid work interval: the order starts at its start event and ends at its completion event. The pay for that order is:
`duration_in_minutes * base_rate_per_minute`
Compute the driver's total pay for the day across all orders.
Follow-up: some time ranges are marked as peak periods, during which the pay rate is doubled. If a paid interval partially overlaps a peak period, you must split the interval at every peak boundary and apply the doubled rate only to the overlapping sub-intervals.
Assume timestamps are already normalized to comparable numeric values; timestamp parsing itself is not the focus. Also explain how you would handle malformed input such as missing events, duplicate events, or invalid event ordering.
Quick Answer: This question evaluates event-driven time-interval processing, interval-overlap arithmetic, rate computation, and robustness to malformed, duplicate, or unordered events when aggregating a driver's pay from a day's log.
Part 1: Total Driver Pay from Valid Order Intervals
You are given an unsorted list of event records for one delivery driver. Each record is a JSON-like Python dictionary with fields such as 'order_id', 'event_type', 'timestamp', and sometimes 'base_rate_per_minute'. A valid paid order interval is defined by exactly one 'start' event and exactly one 'complete' event for the same order, with complete_timestamp > start_timestamp. The pay for that order is (complete_timestamp - start_timestamp) * base_rate_per_minute, where the rate comes from the 'start' event. Ignore malformed orders entirely: orders with a missing start or completion event, duplicate start or completion events, a missing rate on the start event, or invalid ordering where completion time is not after start time. Ignore unrelated event types.
Constraints
- 0 <= len(events) <= 200000
- Timestamps and base rates are integers in the range [0, 10^9]
- Events may appear in any order
- Ignore malformed orders: missing start or complete, duplicate start/complete, missing start rate, or complete timestamp <= start timestamp
Examples
Input: [{'order_id': 'A', 'event_type': 'complete', 'timestamp': 25}, {'order_id': 'B', 'event_type': 'start', 'timestamp': 0, 'base_rate_per_minute': 3}, {'order_id': 'A', 'event_type': 'start', 'timestamp': 10, 'base_rate_per_minute': 2}, {'order_id': 'B', 'event_type': 'complete', 'timestamp': 5}]