I went with Kahn's algorithm because BFS feels more intuitive to explain out loud than recursive DFS.
Model the services as a directed graph and use topological sorting (Kahn's algorithm or DFS) to produce a valid deployment order. If a cycle is detected, return an error indicating the cycle and the involved services. Analyze time and space complexity, typically O(N + E).
Pro tip: Mention that in real-world systems, you might want to return the actual cycle path for debugging, and discuss how to handle dynamic dependency changes or partial deployments.
Clarify that we need a linear ordering of services such that for every dependency (A, B), B comes before A. If no such order exists, detect and report a cycle.
Select topological sort using either Kahn's algorithm (BFS with in-degree) or DFS with recursion stack. Both are efficient and standard.
Build the graph and in-degree array. For Kahn's: repeatedly enqueue nodes with in-degree 0, decrement neighbors, and build order. For DFS: perform DFS and detect back edges.
If the topological order doesn't include all nodes (Kahn's) or a back edge is found (DFS), return an error with the cycle details.
Time complexity is O(N + E) where N is number of services and E is number of dependencies. Space complexity is O(N + E) for storing the graph and auxiliary data.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where the interview got interesting and also where I felt less prepared.
Start by framing the problem around idempotency, state management, and observability, then walk through a concrete example of a partial deployment and how you'd detect and recover from it. Emphasize trade-offs between consistency, availability, and latency, and tie your answer to Uber's scale and reliability requirements.
Pro tip: Mention that you'd design for failure from the start—using idempotent operations, circuit breakers, and canary deployments—and that you'd measure success with SLOs like error budgets and recovery time objectives (RTO).
Ask about the service dependency system's expected scale, consistency needs, and latency SLAs to tailor your approach. Confirm whether the system is stateful or stateless and what failure modes are most critical.
Ensure all operations are idempotent using unique request IDs and deduplication, and implement retries with exponential backoff and jitter. Discuss how to avoid retry storms and handle non-idempotent operations via compensation or sagas.
Use canary deployments and feature flags to roll out changes gradually, with automated rollback on error thresholds. Monitor key metrics (error rates, latency) and define clear rollback criteria.
Incorporate circuit breakers, bulkheads, and timeouts to isolate failures, and use dead-letter queues for poison messages. Design for graceful degradation and automatic recovery with health checks and self-healing.
Instrument distributed tracing, logging, and metrics to detect partial failures quickly. Conduct post-mortems and use chaos engineering to validate recovery strategies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.