I started with horizontal scaling and load balancers which was fine, but I jumped there too fast without talking about profiling first.
Start by acknowledging the symptom and outlining a systematic diagnostic process: check monitoring dashboards, logs, and system metrics to identify the bottleneck. Then propose immediate mitigations (e.g., scaling, rate limiting) and long-term fixes (e.g., caching, query optimization), emphasizing trade-offs and root cause analysis.
Pro tip: Mention the importance of having a runbook and post-mortem culture—showing you think beyond the immediate fix to prevent recurrence and improve team resilience.
Check monitoring tools (e.g., Prometheus, Datadog) for CPU, memory, I/O, and network metrics; review logs for errors or slow queries to pinpoint the bottleneck.
Determine if the spike is due to increased traffic, inefficient code, database contention, or external dependencies; use profiling and tracing to narrow down.
Implement quick fixes like horizontal scaling, rate limiting, caching, or load shedding to restore service and buy time for deeper analysis.
Address the root cause with optimizations (e.g., query tuning, code refactoring, adding caching layers) and consider architectural changes for scalability.
Verify fixes with load testing, update monitoring/alerting, and conduct a post-mortem to document lessons learned and prevent future incidents.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.