Spent the first few minutes just listing event types (offer, accept, pickup, dropoff, cancel) and the pay components (base, per-mile, per-minute, tips, promos).
Start by clarifying requirements and scale, then design an event-driven pipeline that ingests delivery lifecycle events, computes earnings idempotently, and schedules payouts. Focus on data modeling for events and earnings, and discuss trade-offs around consistency, latency, and fault tolerance.
Pro tip: Emphasize idempotency and exactly-once processing to avoid double payments, and discuss how to handle late or out-of-order events gracefully—these are critical in financial systems.
Ask about expected event volume, payout frequency, latency requirements, and regulatory constraints. Define what constitutes a delivery lifecycle event and how earnings are calculated (base pay, tips, promotions, adjustments).
Propose a scalable ingestion layer (e.g., Kafka) to collect events from drivers and the delivery platform. Store raw events in a durable, append-only log or data lake for replayability and auditing.
Design a stream processing job that consumes events, aggregates per-delivery earnings, and writes to a ledger. Ensure idempotency using unique event IDs and deduplication, and handle late events with watermarks or windowing.
Create a payout service that reads from the earnings ledger, batches payments per driver on a schedule (e.g., weekly), and integrates with a payment provider. Include retry logic and reconciliation.
Discuss consistency models (eventual vs. strong), monitoring for anomalies, and trade-offs between latency and accuracy. Cover failure recovery, audit trails, and compliance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the scale and requirements (e.g., transaction volume, latency, regulatory needs). Then describe a ledger design that uses integer arithmetic, double-entry bookkeeping, and idempotent operations, with consistency guarantees like ACID transactions and eventual consistency for reporting. Finally, discuss auditability through immutable logs and reconciliation processes.
Pro tip: Emphasize that correctness at scale requires a combination of strong consistency for critical writes and eventual consistency for read-heavy operations, and that auditability is achieved through append-only logs and cryptographic hashing. Mention that you'd validate with property-based testing and chaos engineering.
Ask about scale (transactions per second), latency requirements, regulatory constraints, and consistency needs (e.g., strong vs eventual).
Use integer cents, double-entry bookkeeping, idempotency keys, and ACID transactions for critical writes to prevent double-spending and ensure balance invariants.
Choose strong consistency (e.g., serializable isolation) for core ledger operations, and eventual consistency for derived data like analytics, with clear boundaries.
Maintain an immutable, append-only log of all transactions with timestamps and actor info, and use cryptographic hashing for tamper-evidence.
Run periodic reconciliation against external systems, set up alerts for discrepancies, and use property-based testing to validate invariants.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the event flow and where duplicates can occur, then propose an idempotent consumer design using a unique event ID and a deduplication store. Emphasize transactional guarantees between deduplication and payment updates, and discuss how to handle retries and failures gracefully.
Pro tip: Mention that idempotency should be enforced at the payment service boundary, not just the event consumer, to protect against duplicates from any source. Also, highlight the importance of monitoring duplicate rates and having a reconciliation process for edge cases.
Ask questions to understand how events are produced, delivered, and consumed, and where duplicates might originate (e.g., at-least-once delivery, retries).
Propose using a unique event ID (e.g., delivery ID) and a deduplication store (e.g., Redis or database) to track processed events. Ensure the check-and-set is atomic.
Use a transaction or a two-phase approach to guarantee that marking an event as processed and updating driver payment happen atomically, preventing partial failures.
Discuss how to handle cases where the deduplication store is unavailable or the payment update fails, including retry policies and dead-letter queues.
Mention the need for monitoring duplicate rates, alerting on anomalies, and having a reconciliation job to detect and correct any missed duplicates.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Didn't see this coming as a system design question.
Start by clarifying the scenario and then walk through a systematic investigation, from data validation to resolution. Emphasize a user-centric approach while balancing technical and business constraints, and propose scalable solutions.
Pro tip: Show empathy for the driver while maintaining objectivity; mention that you'd use this as an opportunity to identify systemic issues and improve the platform, not just resolve one dispute.
Ask specific questions to understand the driver's claim: which delivery, what pay was expected vs. received, and any evidence they have. This ensures you address the right problem.
Pull relevant data from multiple sources: delivery records, GPS logs, timestamps, pay calculation rules, and any customer feedback. Verify the accuracy and completeness of the data.
Compare expected vs. actual pay, check for common issues like missing tips, incorrect distance calculation, or system errors. Determine if it's a one-off or systemic issue.
If the dispute is valid, correct the pay promptly and inform the driver. If not, explain clearly with evidence. Always communicate respectfully and transparently.
Propose improvements to prevent similar disputes: better pay transparency, automated checks, or driver education. Consider scalable solutions like ML models to detect anomalies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.