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Start by acknowledging that scaling can introduce new bottlenecks and that a systematic, data-driven approach is key. Outline a structured troubleshooting process: gather metrics, identify the bottleneck, form hypotheses, and test them. Emphasize that the goal is to find the root cause, not just treat symptoms.
Pro tip: Mention that scaling often shifts bottlenecks to shared resources like databases or coordination services, so focus on those first. Also, highlight the importance of comparing performance before and after scaling to isolate the issue.
Collect metrics from all layers (application, database, network, infrastructure) to understand where the slowdown occurs. Use monitoring tools to compare performance before and after scaling.
Analyze the data to pinpoint the resource or component that is saturated or behaving abnormally. Look for increased latency, error rates, or resource utilization.
Based on the bottleneck, hypothesize potential causes such as contention, increased coordination overhead, or inefficient load distribution.
Design experiments or make targeted changes to test each hypothesis, such as adjusting configurations, adding caching, or optimizing queries. Measure the impact.
Once the root cause is identified and fixed, implement the solution and continue monitoring to ensure the issue is resolved and no new bottlenecks appear.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.