[G1-Sync] Manual knowledge update
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Antigravity Agent
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id: wiki-2026-0508-analyze-runtime-performance
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title: Analyze runtime performance
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category: 10_Wiki/Topics
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status: merged
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redirect_to: 성능_프로파일링_및_메모리_관리_표준
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canonical_id: wiki-2026-0508-040
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aliases: []
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status: verified
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canonical_id: self
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aliases: [Performance Analysis, Profiling, Runtime Profiling]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.92
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tags: [uncategorized]
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confidence_score: 0.9
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verification_status: applied
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tags: [profiling, performance, devtools]
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raw_sources: []
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last_reinforced: 2026-05-08
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github_commit: pending
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inferred_by: Claude Opus 4.7 (auto-normalize 2026-05-08)
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last_reinforced: 2026-05-10
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github_commit: applied
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tech_stack:
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language: JavaScript/Python
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framework: Chrome DevTools/perf/py-spy
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---
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# Redirect
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# Analyze runtime performance
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이 문서는 Canonical 문서인 [[성능_프로파일링_및_메모리_관리_표준]]으로 통합되었습니다.
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모든 최신 지식과 세부 내용은 위 링크를 참조하십시오.
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## 매 한 줄
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> **"매 측정 없이 최적화 없다."**. Runtime performance 분석은 sampling profiler + tracing + flame graph + RAIL/Web Vitals metrics 의 stack 으로 매 hot path 와 long task 를 식별. 2026 stack: Chrome Performance panel (Insights AI), perf+FlameGraph, py-spy, eBPF/bpftrace, Datadog APM.
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## 매 핵심
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### 매 Profiler Type
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- **Sampling**: 매 N ms 마다 stack 캡처 — 매 low overhead, statistical (perf, py-spy, async-profiler).
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- **Instrumentation**: 매 fn enter/exit 기록 — 매 accurate, high overhead.
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- **Tracing**: 매 event timeline (Chrome trace, perfetto).
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- **Hardware counters**: PMU — IPC, cache miss, branch miss.
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### 매 RAIL Model (web)
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- **Response**: < 100ms input → feedback.
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- **Animation**: < 16ms / frame (60fps).
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- **Idle**: 50ms work blocks max.
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- **Load**: < 5s on 4G.
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### 매 Core Web Vitals 2026
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- **LCP** (Largest Contentful Paint) < 2.5s.
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- **INP** (Interaction to Next Paint) < 200ms — 매 FID 대체 (2024+).
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- **CLS** (Cumulative Layout Shift) < 0.1.
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### 매 응용
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1. Web app TTI/INP 개선.
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2. Backend p99 latency 추적.
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3. Game/XR frame budget.
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4. ML inference latency.
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5. CI pipeline 시간 단축.
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## 💻 패턴
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### Chrome Performance panel + AI insights
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```javascript
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// 매 manual marker — show up in Performance timeline
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performance.mark('ai-search:start');
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const r = await search(q);
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performance.mark('ai-search:end');
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performance.measure('ai-search', 'ai-search:start', 'ai-search:end');
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// 매 record trace in DevTools → AI Insights highlights bottleneck (2025+)
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```
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### User Timing API + reporter
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```javascript
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const obs = new PerformanceObserver(list => {
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list.getEntries().forEach(e => analytics('perf', { name: e.name, dur: e.duration }));
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});
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obs.observe({ entryTypes: ['measure', 'navigation', 'resource', 'longtask'] });
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```
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### py-spy live flame graph
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```bash
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# 매 attach to running PID, no code change
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py-spy record -o flame.svg --pid 12345 --duration 30
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# 매 live top-like
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py-spy top --pid 12345
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```
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### Linux perf flame graph
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```bash
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sudo perf record -F 99 -g -p $PID -- sleep 30
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sudo perf script | \
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./stackcollapse-perf.pl | ./flamegraph.pl > flame.svg
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```
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### Web Vitals INP measurement
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```javascript
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import { onINP, onLCP, onCLS } from 'web-vitals';
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onINP(m => beacon('inp', m.value, m.id));
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onLCP(m => beacon('lcp', m.value, m.id));
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onCLS(m => beacon('cls', m.value, m.id));
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```
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### Node --prof + --prof-process
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```bash
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node --prof server.js
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# 매 after run
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node --prof-process isolate-*.log > profile.txt
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```
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### eBPF (bpftrace) syscall latency
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```bash
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sudo bpftrace -e '
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tracepoint:syscalls:sys_enter_read { @s[tid] = nsecs; }
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tracepoint:syscalls:sys_exit_read /@s[tid]/ {
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@us = hist((nsecs - @s[tid]) / 1000);
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delete(@s[tid]);
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}'
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```
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## 매 결정 기준
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| 상황 | Tool |
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|---|---|
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| Web (Chromium) | DevTools Performance + Web Vitals |
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| Node.js | clinic.js / 0x / --prof |
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| Python | py-spy / scalene / cProfile |
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| JVM | async-profiler / JFR |
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| Go | pprof |
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| Linux native | perf + FlameGraph / eBPF |
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| Production APM | Datadog / NewRelic / OTel |
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**기본값**: 매 sampling profiler 우선, instrumentation 은 spot-check.
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## 🔗 Graph
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- 부모: [[Flame_Graphs]]
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- 변형: [[CPU Bottleneck]]
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- 응용: [[High Resolution Time]] · [[Memory Management]]
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- Adjacent: [[Page Experience Algorithm]] · [[Debugger_Techniques]]
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## 🤖 LLM 활용
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**언제**: flame graph 해석, perf insight 요약, regression hypothesis.
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**언제 X**: 매 nanosecond-level profiling — specialized tool 만이 정확.
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## ❌ 안티패턴
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- **Production 에 instrumenting profiler**: 매 overhead 폭발.
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- **Single-run 결론**: 매 statistical noise — multi-run 필요.
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- **Average only**: 매 p50 보다 p99 가 user 체감.
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- **Profile dev build**: 매 prod build 와 다름 — release/optimized 측정.
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## 🧪 검증 / 중복
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- Verified: web.dev Performance docs; Brendan Gregg "Systems Performance"; Chrome DevTools docs.
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — RAIL/INP + multi-language profilers |
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