I went straight to unit tests per service handler with mocked downstreams, which felt solid.
Start by clarifying the system's behavior: sequential calls, field-level null on failure, and continued execution. Then outline a test strategy that covers unit tests for each service call and parse, integration tests for the orchestration logic, and contract tests to ensure downstream compatibility. Emphasize testing failure modes, partial failures, and the composite response's correctness under various scenarios.
Pro tip: Highlight the importance of testing the system's resilience and observability: ensure that failures are logged with enough context to debug, and that null fields are distinguishable from valid nulls. Also, consider using consumer-driven contract tests to catch breaking changes in downstream services early.
Confirm what 'failure' means for each call (timeout, HTTP error, parse error) and how null fields should be interpreted. Understand if there are SLAs or retry policies.
Mock each downstream service to test the API's handling of success, failure, and malformed responses for each field independently. Verify that failures result in null fields and execution continues.
Test the sequence of calls with real or stubbed services to ensure the composite response is correctly assembled. Cover scenarios where one, two, or all three calls fail.
Use consumer-driven contracts to verify that downstream services adhere to expected request/response formats, reducing the risk of parse failures in production.
Include tests for timeouts, retries, logging, and metrics. Ensure that failures are logged with sufficient detail and that null fields are traceable to specific failures.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Parallelizing the calls was the obvious first move and I said it immediately.
Focus on parallelizing the calls to the three independent downstream services to reduce overall latency, using techniques like concurrent requests, asynchronous I/O, or fan-out/fan-in patterns. Also consider caching, connection pooling, and graceful degradation to handle failures without impacting the entire bootstrap response. Emphasize trade-offs such as increased complexity, resource usage, and error handling.
Pro tip: Mention that you would measure the actual latency distribution of each downstream call to decide whether parallelization is worth the added complexity, and consider using a timeout budget to prevent slow services from blocking the entire bootstrap.
Explain that the current bootstrap API likely calls the three services sequentially, causing latency to add up. Since they are independent, this is unnecessary.
Propose making the three downstream calls concurrently using async I/O, threads, or a fan-out/fan-in pattern. This reduces total latency to the slowest call instead of the sum.
Discuss using timeouts, retries with backoff, and circuit breakers for each call. If one service fails, consider returning partial data or a degraded response instead of failing the entire bootstrap.
Mention connection pooling, keep-alive, and caching to reduce overhead. Also consider limiting concurrency to avoid overwhelming downstream services or the client.
Emphasize the importance of monitoring latency, error rates, and resource utilization. Use A/B testing or canary deployments to validate improvements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.