Start by clarifying requirements and constraints, then outline a data structure that supports efficient round-robin selection with dynamic membership. Discuss how to handle health checks and concurrency, and finally analyze trade-offs and edge cases.
Pro tip: Mention that you would use a read-write lock to allow concurrent reads while safely handling membership changes, and that you'd consider consistent hashing if the backends were stateful.
Ask about expected scale, concurrency, health check mechanism, and whether the backend list is static or dynamic. Confirm if the router should be thread-safe and if there are latency requirements.
Propose using a circular array or linked list of healthy servers with an index pointer. For dynamic membership, consider a concurrent data structure like a copy-on-write list or a lock-protected list, and explain how to update the index when servers are added/removed.
Describe how to maintain a list of healthy servers, possibly using a background thread that periodically checks health and updates the list. Explain how to skip unhealthy servers during selection and avoid infinite loops if all are down.
Discuss thread-safety: use atomic operations for the index, and read-write locks or concurrent collections for the server list. Mention potential contention and how to minimize it.
Compare round-robin with other algorithms (e.g., least connections, consistent hashing) and discuss when round-robin is appropriate. Cover edge cases like empty server list, all servers unhealthy, and rapid membership changes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The failing tests were actually a nice hint.
Start by understanding the expected routing behavior and the failing tests, then trace through the round-robin algorithm to identify where the logic deviates. Use a systematic debugging approach: reproduce the failure, isolate the faulty component, hypothesize the bug, and verify the fix with tests.
Pro tip: Demonstrate a test-driven debugging mindset: before fixing, write a minimal test that reproduces the bug, then fix and ensure all tests pass. This shows you value regression prevention and clear verification.
Review the test cases and requirements to clarify what correct round-robin routing should do, including edge cases like empty server lists or uneven weights.
Walk through the round-robin implementation with sample inputs, tracking the state (e.g., current index) and comparing against expected outputs to spot discrepancies.
Pinpoint the root cause, such as off-by-one errors, incorrect index wrapping, or failure to update state after selection.
Apply a minimal correction, then run the failing tests and additional edge-case tests to confirm the fix and prevent regressions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I'm decent at writing tests but the health flapping case tripped me up.
Start by clarifying the system under test: a load balancer or service discovery component that routes requests to multiple servers. Then outline a test plan that covers each scenario, focusing on deterministic simulation of server states and request routing. Use a combination of unit tests for logic and integration tests for end-to-end behavior, ensuring edge cases like flapping are handled with appropriate backoff or circuit-breaking.
Pro tip: Demonstrate awareness of production concerns by discussing how to avoid flaky tests in health flapping scenarios—e.g., using controlled clocks or mocked health check intervals—and emphasize the importance of testing failure modes, not just happy paths.
Ask questions to understand the routing component: Is it a load balancer, service mesh, or custom router? What are the health check semantics and expected behavior during failures?
Map each requirement to specific test cases: single server (happy path), multiple servers (load distribution), server removal (graceful and abrupt), and health flapping (rapid state changes).
Decide between unit tests (mocking server states) and integration tests (spinning up real servers). Use dependency injection to simulate health checks and control time for flapping tests.
Write tests that assert routing decisions, error handling, and recovery. For flapping, verify that the system doesn't oscillate excessively and applies backoff or circuit-breaking as needed.
Highlight trade-offs like test speed vs. realism, and mention edge cases such as concurrent server removal, partial failures, and network partitions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clearly stating the time and space complexity of your implementation using Big-O notation, then explain the reasoning behind each. Next, discuss thread-safety by identifying shared mutable state and describing the synchronization mechanisms or design choices you used to ensure correctness under concurrency, tying it back to DoorDash's high-throughput, real-time systems.
Pro tip: Always relate complexity and thread-safety to real-world impact at DoorDash—e.g., how O(n) vs O(log n) affects order dispatch latency, or how lock contention could bottleneck peak-hour throughput. This shows you think beyond code and consider business implications.
Begin by explicitly stating the time and space complexity of your implementation in Big-O notation, covering best, average, and worst cases if relevant.
Walk through the key operations (loops, recursion, data structure operations) that contribute to the complexity, justifying each component.
Point out any shared mutable data structures or resources that multiple threads could access concurrently.
Explain how you ensure thread safety—e.g., using locks, atomic variables, immutable objects, thread-local storage, or lock-free data structures—and discuss trade-offs.
Discuss how your complexity and thread-safety choices affect scalability, latency, and throughput in a production environment like DoorDash.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.