Structure your answer around the key production concerns: correctness, performance, observability, and maintainability. Start by clarifying requirements and scale, then walk through hardening the algorithm, API, error handling, and testing. Emphasize trade-offs and how you'd validate the component in a real system.
Pro tip: Show that you think about failure modes and operational aspects early—like how you'd detect cycles in production and alert on them—rather than just focusing on the algorithm. This demonstrates production maturity.
Ask about graph size, update frequency, latency requirements, and whether cycles are expected or indicate misconfiguration. This shapes your design decisions.
Discuss iterative vs recursive DFS to avoid stack overflow, efficient cycle reporting, and handling of large graphs with memory constraints. Consider Tarjan's algorithm for SCCs if needed.
Define clear inputs/outputs, error types (e.g., cycle detected with path), and idempotency. Ensure the API is easy to use and integrates well with existing systems.
Instrument metrics (latency, cycle count), logging, and tracing. Write unit tests for edge cases, property-based tests, and integration tests with realistic graphs.
Allow pluggable algorithms or graph representations, and optimize for scale (e.g., incremental detection, caching, parallelism). Discuss trade-offs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.