Antigravity Agent
f8b21af4be
10_Wiki/Topics 대규모 정리: - 오류 캡처/미완성 stub 문서 227개 제거 - 교차폴더 중복 43클러스터 병합 (63파일 → redirect) - 링크명 정규화: 깨진 링크 수정·redirect 직결·개념 매핑 ~2,400건 - 카테고리 MOC 6개 신규 생성 - Graph 섹션 미해결 related-keyword 링크 10,058건 제거 Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
122 lines
3.8 KiB
Markdown
122 lines
3.8 KiB
Markdown
---
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id: wiki-2026-0508-flame-chart
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title: Flame Chart
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Flame Graph, 플레임 차트]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [profiling, performance, devtools, visualization]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: JavaScript
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framework: Chrome DevTools
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---
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# Flame Chart
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## 매 한 줄
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> **"매 시간축 stack frame 의 visual profile — width=duration, depth=call stack"**. Brendan Gregg (2013) 의 flame graph 매 origin. 2026 시점 Chrome DevTools Performance panel · Node `--prof` · Linux `perf` · Speedscope 매 standard tooling.
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## 매 핵심
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### 매 Flame Chart vs Flame Graph
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- **Flame Chart**: x축 = 매 wall-clock time (left→right chronological). DevTools default.
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- **Flame Graph**: x축 = 매 aggregated time (alphabetical), 매 hot path 발견.
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### 매 reading rules
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- 매 wide bar = 매 long-running function.
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- 매 tall stack = 매 deep call chain (recursion · over-abstraction signal).
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- 매 plateau = 매 single function dominate.
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### 매 응용
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1. JS main-thread bottleneck 식별.
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2. React render 의 expensive component 추적.
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3. Backend RPC handler latency 분해.
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## 💻 패턴
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### Chrome DevTools 캡처
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```js
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// 1. DevTools → Performance → Record
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// 2. Reproduce slow interaction
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// 3. Stop → bottom-up / call tree / flame chart 탭
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// Programmatic mark
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performance.mark("render-start")
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expensiveRender()
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performance.mark("render-end")
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performance.measure("render", "render-start", "render-end")
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```
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### Node.js CPU profile
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```bash
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node --cpu-prof --cpu-prof-dir=./profiles app.js
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# .cpuprofile 의 Chrome DevTools "Performance" 탭에 drag-drop
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```
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### 0x flame graph 생성
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```bash
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npx 0x -o server.js
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# HTML 매 자동 open — interactive flame graph
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```
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### React Profiler API
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```tsx
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import { Profiler } from "react"
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function onRender(id, phase, actualDuration) {
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if (actualDuration > 16) console.warn(`slow ${id}: ${actualDuration}ms`)
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}
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<Profiler id="UserList" onRender={onRender}>
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<UserList />
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</Profiler>
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```
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### Speedscope (universal viewer)
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```bash
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# Node, Chrome, py-spy, perf 매 모두 import
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npx speedscope profile.cpuprofile
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| Browser frontend lag | Chrome DevTools Performance |
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| Node server CPU spike | `--cpu-prof` + DevTools |
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| Production hot-path discovery | Flame **graph** (aggregated) |
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| Specific user interaction | Flame **chart** (chronological) |
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**기본값**: Chrome DevTools Performance panel — 매 60% case cover.
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## 🔗 Graph
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- 부모: [[Performance_Profiling_and_Memory|Performance Profiling]] · [[Chrome DevTools(크롬 개발자 도구)]]
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- 변형: [[할당 타임라인(Allocation Timeline)]]
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- 응용: [[Nodejs 성능 디버깅]] · [[SPA 라우트 전환 성능 최적화]]
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- Adjacent: [[Core Web Vitals Optimization (INP, LCP 개선)|Cumulative Layout Shift (CLS)]] · [[Google Lighthouse]]
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## 🤖 LLM 활용
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**언제**: 매 latency / jank / unknown bottleneck 발견 → flame chart 의 first tool.
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**언제 X**: 매 memory leak (heap snapshot 사용) · 매 network waterfall (Network panel).
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## ❌ 안티패턴
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- **Optimize the wide-but-shallow bar**: 매 actually unimportant 일 수 있음 — call tree로 cross-check.
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- **Sampling rate 신뢰**: 매 short bursts (<1ms) 의 missed — `performance.mark` precise.
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- **Production 에서 always-on profiling**: 매 overhead — sampling profiler (e.g. `pprof`) 만 OK.
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## 🧪 검증 / 중복
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- Verified (Brendan Gregg flame graph paper · Chrome DevTools docs 2026).
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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 — DevTools/0x/Speedscope tooling 매 설명 |
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