Performance Debugging and Latency Investigation Questions
Finding the root cause of latency spikes: checking CPU/memory/disk/network utilization, profiling applications, querying slow logs, and identifying bottlenecks. Understanding the difference between resource exhaustion and an algorithmic problem. Using monitoring and tracing tools to narrow down where time is spent.
MediumTechnical
0 practiced
Design a concise performance incident runbook template that on-call teams can follow when p99 latency spikes for a critical service. Your runbook should include immediate checks, prioritized mitigation steps, safe rollbacks, and how to record findings for post-incident review.
HardTechnical
0 practiced
Implement an online change-point detection (e.g., CUSUM) in Python that ingests a stream of request latencies and raises an alert when a statistically significant upward shift in p99-level behavior occurs. Provide code and explain parameter choices (window size, thresholds) and how you avoid excessive false positives.
MediumTechnical
0 practiced
Describe a step-by-step workflow to correlate metrics, logs and traces to locate the root cause of a latency spike that affects only a subset of users. Include the order in which you'd consult each signal, what specific artifacts you look for in each, and how you reduce the search space to the impacted user subset.
EasyTechnical
0 practiced
What is a flamegraph, how is it generated from sampled stacks, and how do you interpret width, depth and hotspots on a flamegraph when investigating latency? Provide one example scenario where a flamegraph could be misleading and how you would validate its findings with other tools.
MediumTechnical
0 practiced
Write a PromQL query that computes the instant p99 request latency for job='frontend' over the last 5 minutes and returns a time series suitable for alerting when it exceeds 0.2s. Explain any assumptions about metric types and histogram naming conventions you used.
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