Start by clarifying requirements and assumptions, then walk through the system design in a structured manner covering data flow, APIs, storage, and processing. Emphasize idempotency, event ordering, and consistency trade-offs, and discuss how to handle bad data and ensure reliability.
Pro tip: Proactively discuss how you would handle late-arriving events and out-of-order timestamps, as this is a common real-world challenge in event-driven systems. Also, mention the importance of idempotent payout calculations to avoid double-paying drivers.
Ask questions to understand the scope: expected event volume, latency requirements, payout rules, and consistency needs. State assumptions about event ordering, data retention, and failure modes.
Outline how events are ingested (e.g., Kafka), processed (streaming vs batch), and how to handle out-of-order and late events. Discuss windowing and watermarks if using stream processing.
Specify payout rules based on order lifecycle (e.g., base pay + tips + bonuses). Design APIs for drivers to query earnings and for internal systems to trigger payouts. Ensure idempotency in payout calculations.
Choose appropriate storage for events (e.g., immutable log), payout records (e.g., relational DB with transactions), and driver earnings summaries. Discuss indexing and query patterns.
Explain how to handle failures (retries, dead-letter queues), ensure exactly-once processing (idempotency keys, deduplication), and deal with bad data (validation, quarantine). Discuss consistency trade-offs (e.g., eventual vs strong).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went async via a message queue pretty quickly, that felt right for decoupling.
Start by clarifying the requirements and constraints of the event-driven system, such as event types, delivery guarantees, and data consistency needs. Then, propose a versioned API contract (e.g., using OpenAPI or AsyncAPI) that defines the event schema and the earnings query response, ensuring it is extensible and backward-compatible. Finally, discuss trade-offs and how the design supports scalability and reliability.
Pro tip: Emphasize idempotency and exactly-once processing for payment events to prevent duplicate earnings, and suggest using a schema registry to manage event evolution. This shows you understand real-world payment system challenges.
Ask about event volume, latency requirements, delivery guarantees (at-least-once, exactly-once), and data consistency needs. This ensures your design aligns with business and technical constraints.
Specify the event schema (e.g., JSON) with fields like event ID, timestamp, dasher ID, amount, currency, and type. Choose a protocol (e.g., Kafka, HTTP webhooks) and include metadata for idempotency and versioning.
Outline a response structure that includes total earnings, breakdown by period (daily, weekly), and details per delivery. Include pagination, filtering, and aggregation options for flexibility.
Discuss how to handle failures, retries, and idempotency. Mention partitioning, load balancing, and caching to ensure the system scales with dasher count and query volume.
Explain choices like synchronous vs. asynchronous communication, schema evolution strategies, and how to maintain backward compatibility. Highlight potential bottlenecks and mitigations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I fumbled the timestamp attribution question.
Start by clarifying the business context and the distinction between order lifecycle events and financial settlement. Then, propose a state machine for payouts, specifying rules for each terminal state (accepted, fulfilled, cancelled) and the timestamp attribution (event time vs. processing time). Finally, discuss trade-offs and how to handle edge cases like late cancellations or partial fulfillments.
Pro tip: Emphasize the importance of idempotency and auditability in payout systems, as financial transactions must be exactly-once and traceable. Also, mention that timestamp attribution should align with accounting principles (e.g., revenue recognition) and may require event sourcing for accuracy.
Ask clarifying questions about the platform's payout policies: Does acceptance trigger a payout? Is fulfillment required? What are the cancellation policies (e.g., before pickup, after pickup)? This ensures you address the actual requirements.
Model the order lifecycle as a state machine with states like CREATED, ACCEPTED, FULFILLED, CANCELLED. Define transitions and the payout rules associated with each terminal state.
For each terminal state, specify the payout amount and recipient (e.g., full payout on fulfillment, partial or no payout on cancellation). Consider scenarios like cancellation after acceptance but before fulfillment.
Decide which timestamp to use for payout attribution: the time the event occurred (event time) or the time the payout is processed (processing time). Discuss implications for financial reporting and reconciliation.
Discuss handling of late events, out-of-order events, and idempotency. Trade-offs include consistency vs. latency, and complexity vs. accuracy.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where the bad data section came in.
Start by explaining the general strategies for handling late and out-of-order events in distributed systems, such as event time vs. processing time, watermarks, and buffering. Then, address the specific scenario of a FULFILL event without a prior ACCEPT, discussing detection, handling (e.g., dead-letter queue, reconciliation), and prevention. Emphasize the importance of idempotency, ordering guarantees, and business impact.
Pro tip: Mention that you would instrument and monitor the frequency of such anomalies to drive improvements in upstream systems, showing a proactive and data-driven mindset.
Ask about the expected event ordering guarantees, latency requirements, and business impact of out-of-order events. This shows you consider the context before diving into solutions.
Discuss techniques like event time processing, watermarks, buffering with timeouts, and reordering using sequence numbers or timestamps. Mention trade-offs between latency and completeness.
Describe how to detect such events (e.g., state validation), and handle them: dead-letter queue for manual inspection, automatic reconciliation by querying upstream, or emitting a compensating event. Emphasize idempotency and avoiding duplicate processing.
Suggest ways to reduce occurrence: ensuring upstream services emit events in order, using a saga pattern, or implementing a state machine that rejects invalid transitions. Also mention monitoring and alerting.
Conclude by reiterating how your approach maintains data consistency and minimizes customer impact, and how you would measure success (e.g., reduced anomaly rate).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Dedup table keyed on (orderId, status) with a processed flag.
Start by acknowledging that at-least-once delivery means duplicates are inevitable, so idempotency must be enforced at the payment layer. Then describe a concrete mechanism like idempotency keys tied to a unique business event (e.g., delivery ID) and how you'd store and check them atomically. Finally, discuss trade-offs around storage, latency, and failure modes, and how you'd handle retries and reconciliation.
Pro tip: Emphasize that idempotency should be enforced at the payment processor or ledger level, not just in application code, because distributed systems can fail in ways that bypass application-level checks. Also mention that you'd monitor for duplicate payment attempts and have a reconciliation process to catch any that slip through.
Restate that at-least-once delivery means events can be duplicated, and double-paying a driver is unacceptable. Ask about scale, latency requirements, and existing infrastructure (e.g., payment provider, database).
Propose using a unique idempotency key derived from the business event (e.g., delivery ID + payment type) that remains constant across retries. Explain that the key must be generated by the producer and passed through the entire payment flow.
Describe storing the idempotency key in a durable, atomic store (e.g., database with unique constraint, Redis with SETNX) before processing payment. If the key exists, return the previous result instead of reprocessing.
Explain how to handle partial failures: if payment succeeds but recording the key fails, you need a reconciliation process. Use transactions or two-phase commit where possible, and design for idempotent retries at every step.
Talk about trade-offs: storage overhead, latency of deduplication check, and complexity. Mention monitoring for duplicate attempts and a reconciliation job to detect and resolve any discrepancies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Said streaming for near-real-time visibility to drivers, batch finalization at pay period close to catch any late events and do a consistency check.
Start by clarifying the requirements and constraints of payout computation, then compare streaming and batch processing based on latency, accuracy, and cost. Explain how you would combine both approaches for backfills and end-of-period reconciliation, emphasizing correctness and idempotency.
Pro tip: Emphasize that payouts require strong consistency and auditability, so batch processing is often the source of truth while streaming provides near-real-time estimates. Mention that backfills must be idempotent and reconciliation should be automated with alerts for discrepancies.
Ask about latency needs, accuracy requirements, volume, and regulatory constraints to determine the appropriate processing model.
Discuss trade-offs: streaming offers low latency but may sacrifice accuracy and increase complexity; batch provides high accuracy and is easier to audit but has higher latency.
Propose using streaming for real-time estimates and batch for final payouts, ensuring consistency between the two.
Explain how to reprocess historical data idempotently, possibly using batch jobs that overwrite or correct previous results.
Describe end-of-period reconciliation: compare streaming and batch results, detect discrepancies, and trigger alerts or corrections.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements and constraints, then sketch a high-level architecture for event ingestion and payout calculation, focusing on data flow and key classes. Next, detail the API design for querying earnings, explicitly addressing timezone and DST handling to ensure correctness.
Pro tip: Demonstrate awareness of edge cases like DST transitions and timezone database updates, and propose idempotent event processing to avoid double-counting in payouts.
Ask about scale, event types, payout rules, and query patterns. Confirm the need for timezone-aware earnings queries and DST handling.
Sketch classes like EventCollector, EventProcessor, and EventStore. Emphasize idempotency, ordering, and scalability using queues and partitions.
Outline PayoutCalculator, EarningsAggregator, and RuleEngine. Discuss batch vs. real-time processing and how to handle late events.
Define endpoints like GET /dashers/{id}/earnings?start=...&end=...&timezone=.... Explain how to resolve local pay period boundaries using timezone data.
Explain storing timestamps in UTC, converting to local time for queries, and using libraries like IANA tz database. Discuss DST edge cases (e.g., ambiguous times).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.