Latency Analysis & Optimization
Understanding and reducing response time across the request path: where latency accumulates (compute, I/O, serialization, network hops, queuing), percentile-based reasoning (p50/p95/p99/p999), tail-latency behavior (why the tail diverges from the median, fan-out amplification across dependencies, hedged/speculative requests and other targeted mitigations), latency budgets and SLOs, and critical-path or distributed-trace span analysis that pinpoints where a request's wall-clock time actually goes. Focuses on end-to-end latency as a measured, engineered property rather than an incidental one. This is distinct from profiling a codebase to find CPU or memory hotspots with flame graphs, sampling, or instrumentation tooling (that is performance-optimization-and-bottleneck-analysis), and distinct from the reactive process of triaging, mitigating, and running the postmortem for a live production incident (that is performance-optimization-and-incident-response).
