The setup was a shared screen with a HackerRank-style environment.
First, clarify the skeleton's structure and the timeout detection mechanism. Then, design the conditional branching by adding a decision node that checks timeout status and routes to either refund or normal continuation. Finally, implement the refund node and ensure proper error handling and idempotency.
Pro tip: Emphasize idempotency and exactly-once semantics for refunds, as payment operations must be safe against retries. Also, discuss how you would test the timeout path, including edge cases like partial timeouts or race conditions.
Review the provided DAG skeleton to identify node types, execution flow, and how timeouts are represented. Clarify whether timeout detection is built-in or needs to be added.
Introduce a decision node that evaluates the timeout condition and routes to either a refund node or the next normal node. Ensure the DAG remains acyclic and the branch is clearly defined.
Create a refund node that performs the refund operation, with idempotency keys and error handling. Consider retries and dead-letter queues for failures.
Wire the new nodes into the DAG, ensuring dependencies are correct. Validate the flow with unit tests covering both timeout and non-timeout paths, and integration tests for end-to-end scenarios.
Explain trade-offs such as synchronous vs. asynchronous refunds, and how to handle partial timeouts, duplicate events, and consistency guarantees.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the existing node-based system's architecture and the requirements for new flow types. Then propose a design that abstracts common flow logic and allows pluggable node types, ensuring extensibility and maintainability. Finally, discuss trade-offs and how to handle edge cases like partial refunds and cancellations.
Pro tip: Emphasize idempotency and state management for financial flows, as these are critical in payment systems like DoorDash. Also, mention the importance of backward compatibility and incremental rollout to avoid disrupting existing flows.
Ask questions to understand the current system, expected flow types, and non-functional requirements like scalability and consistency. Confirm what 'partial refund' and 'cancellation' entail in the business context.
Analyze the existing node-based system to find common patterns across flows. Propose abstractions such as a base node interface, flow definition DSL, or a state machine that can be extended for new flow types.
Outline how to add new node types (e.g., refund node, cancellation node) and compose them into flows. Consider using a plugin architecture, configuration-driven flows, or a graph-based execution engine.
Discuss trade-offs between flexibility and complexity, performance implications, and how to handle partial failures, idempotency, and compensation logic. Mention testing and monitoring strategies.
Suggest a migration plan, such as adding new flows alongside existing ones, feature flags, and gradual rollout to minimize risk. Highlight the importance of observability and rollback capabilities.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.