Problem Statement
Design a production system capable of ingesting a high-volume stream of delivery-order events and continuously computing each driver's daily pay.
System Requirements
The system must support:
- Ingestion of JSON events from upstream publishers.
- Application of peak-period multipliers, which may necessitate splitting work intervals at specific boundary times for accurate calculation.
- Robust handling of malformed, duplicate, or late-arriving events without compromising data integrity or accuracy.
- Processing data volumes large enough that a full day's raw events cannot fit into a single machine's memory.
- Seamless recovery from consumer or service outages, ensuring continuous operation and data consistency.
- Safe and resilient interaction with upstream APIs in a production environment.
Architectural Considerations
Candidates should discuss the end-to-end architecture, covering aspects such as:
- Publisher/consumer data flow.
- Strategies for achieving idempotency under at-least-once delivery guarantees.
- Partitioning strategy for events and state.
- Approaches to streaming computation for real-time aggregation.
- Persistence mechanisms for event data and computed aggregates.
- Mechanisms for event replay and reconciliation.
- Methods for handling large data volumes, potentially involving external sorting or batch recomputation.
- Operational protections, including retries, circuit breakers, rate limiting, and backpressure, to ensure system stability and reliability.
AI interview prep
Core dilemma
Balancing real-time, continuous computation of complex, stateful financial aggregates (daily pay with dynamic multipliers and interval splitting) against the challenges of high-volume, unreliable event streams (duplicates, late, malformed) and the need for absolute financial accuracy, auditability, and fault tolerance.
3 tradeoffs and 2 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Problem Statement
Design a production system capable of ingesting a high-volume stream of delivery-order events and continuously computing each driver's daily pay.
System Requirements
The system must support:
- Ingestion of JSON events from upstream publishers.
- Application of peak-period multipliers, which may necessitate splitting work intervals at specific boundary times for accurate calculation.
- Robust handling of malformed, duplicate, or late-arriving events without compromising data integrity or accuracy.
- Processing data volumes large enough that a full day's raw events cannot fit into a single machine's memory.
- Seamless recovery from consumer or service outages, ensuring continuous operation and data consistency.
- Safe and resilient interaction with upstream APIs in a production environment.
Architectural Considerations
Candidates should discuss the end-to-end architecture, covering aspects such as:
- Publisher/consumer data flow.
- Strategies for achieving idempotency under at-least-once delivery guarantees.
- Partitioning strategy for events and state.
- Approaches to streaming computation for real-time aggregation.
- Persistence mechanisms for event data and computed aggregates.
- Mechanisms for event replay and reconciliation.
- Methods for handling large data volumes, potentially involving external sorting or batch recomputation.
- Operational protections, including retries, circuit breakers, rate limiting, and backpressure, to ensure system stability and reliability.