Sample Answer
In that situation, I treat it as a production investigation, not a local debugging exercise. First, I’d verify the impact: which endpoint, what timeout rate, which clients, and at what QPS. I’d pull dashboards to look at p95/p99 latency, CPU, memory, DB connections, and thread pools around peak windows. Next, I’d enable or tighten distributed tracing and structured logging just for that API, sampling heavily during peak. I’ve had cases where this quickly exposed hidden contention, like a shared DB connection pool hitting its limit or a slow downstream dependency. I’d also compare behavior across instances to see if it’s noisy neighbors, GC pauses, or imbalance in load. From there, I’d run targeted experiments in production: temporarily increase timeouts and resource limits, add circuit breakers/bulkheads, and roll out small config changes behind feature flags. Once we isolate the root cause, I’d implement a durable fix, add specific alerts on the stress indicators we saw, and run a load test that mirrors the peak pattern before calling it done.
Keywords
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