The base logic wasn't too bad but the double-pay windows tripped me up for a minute.
Start by clarifying the data model and business rules: what fields each record contains, how time and rate determine pay, what the flat fee is, and how special time windows are defined. Then outline an algorithm that iterates through records, applies the correct rule based on status and time, and accumulates the total. Discuss trade-offs like time complexity, edge cases, and potential optimizations.
Pro tip: Mention that you would confirm the definition of 'special time windows' (e.g., overlapping windows, time zone handling) and whether the doubled pay applies to the entire payout or just the time-based component. This shows attention to detail and avoids incorrect assumptions.
Ask about the structure of delivery order records (fields like status, start/end time, rate, etc.) and the exact payout rules for completed, cancelled, and special time windows.
For each record, determine the base pay: for completed orders, compute time * rate; for cancelled, use flat fee. Then check if the delivery falls within a special time window and apply doubling if needed.
Iterate through records, accumulate total payout. Consider edge cases: overlapping special windows, deliveries spanning multiple windows, missing data, and time zone consistency.
Discuss time and space complexity (likely O(n) time, O(1) space). Mention potential optimizations like pre-processing special windows or using interval trees if many windows.
Walk through a few examples to verify correctness, including normal, cancelled, and special window cases. Suggest unit tests for boundary conditions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the business rules and requirements for edge cases, then propose a robust design that handles them systematically. Emphasize correctness, maintainability, and testability, and discuss trade-offs between different approaches.
Pro tip: Mention the importance of idempotency and auditability to prevent double payments and ensure traceability. Also, suggest writing comprehensive unit tests for each edge case to validate the logic.
Ask questions to understand the exact business rules for overlapping windows, cancellations, and bonuses. Confirm expected behavior for each edge case.
Define clear states and transitions for orders, deliveries, and bonus windows. Identify invariants and constraints to prevent conflicts.
Propose a step-by-step algorithm that checks for overlaps, cancellations, and eligibility. Use data structures like interval trees or priority queues if needed.
Explicitly address each edge case: double-pay window overlap, cancelled order during bonus, etc. Explain how your design resolves them.
Compare alternative approaches (e.g., real-time vs batch processing) and justify your choice based on scalability, latency, and complexity.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Pulled from stuff I actually deal with at work so this part felt easier than the coding.
Structure your answer by first clarifying the requirements and constraints, then addressing scalability and reliability separately. For scalability, discuss partitioning, batching, and asynchronous processing; for reliability, cover retries, circuit breakers, and fallback mechanisms. Emphasize trade-offs and monitoring.
Pro tip: Demonstrate a proactive mindset by mentioning how you would design the system to handle these issues from the start, rather than just reacting to them. Also, relate your answer to DoorDash's specific domain, such as handling peak order times or ensuring accurate payouts.
Ask questions to understand the scale of data, latency requirements, and the criticality of payout accuracy. This shows you don't jump to solutions without context.
Discuss strategies like horizontal scaling (sharding by region or time), batch processing, and using distributed systems like Kafka or Spark. Mention the trade-offs between real-time and batch processing.
Propose resilience patterns: retries with exponential backoff, circuit breakers, fallback to cached or stale data, and queuing for later processing. Emphasize idempotency to avoid duplicate payouts.
Explain how you would maintain correctness, such as using transactional outbox patterns, reconciliation jobs, and auditing. Highlight the importance of exactly-once processing for financial calculations.
Describe how you would monitor system health, set up alerts for failures, and continuously improve based on metrics. Mention load testing and chaos engineering.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.