refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조
에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
This commit is contained in:
Antigravity Agent
@@ -0,0 +1,171 @@
|
||||
---
|
||||
id: wiki-2026-0508-call-stack-analysis
|
||||
title: Call Stack Analysis
|
||||
category: 10_Wiki/Topics
|
||||
status: verified
|
||||
canonical_id: self
|
||||
aliases: [Call Stack, Stack Trace Analysis, Flame Graph, Profiling Stack]
|
||||
duplicate_of: none
|
||||
source_trust_level: A
|
||||
confidence_score: 0.95
|
||||
verification_status: applied
|
||||
tags: [profiling, performance, flame-graph, debugging, observability]
|
||||
raw_sources: []
|
||||
last_reinforced: 2026-05-10
|
||||
github_commit: pending
|
||||
tech_stack:
|
||||
language: Polyglot
|
||||
framework: perf/eBPF/pprof
|
||||
---
|
||||
|
||||
# Call Stack Analysis
|
||||
|
||||
## 매 한 줄
|
||||
> **"매 performance bug 의 95% 는 'where is CPU time spent?' — 매 call stack sampling 이 답한다."**. 매 stack trace 를 statistical 하게 sampling → flame graph 로 visualize 하면 hot path 가 즉시 보임. 매 2026 표준 stack 은 Linux perf + eBPF, 매 inferno / pyroscope / Datadog Continuous Profiler.
|
||||
|
||||
## 매 핵심
|
||||
|
||||
### 매 sampling vs instrumentation
|
||||
- **Sampling profiler**: 매 N Hz (보통 99/999Hz) 마다 stack capture → low overhead, statistical.
|
||||
- **Instrumented profiler**: 매 every entry/exit hook → exact, but 10-100x overhead.
|
||||
- **현대 default**: 매 sampling — 매 production-safe.
|
||||
|
||||
### 매 stack source
|
||||
- **Frame pointer (RBP) walk**: 매 fastest, requires `-fno-omit-frame-pointer`.
|
||||
- **DWARF unwind**: 매 .eh_frame 사용 — frame pointer 불필요하나 expensive.
|
||||
- **ORC unwinder**: 매 Linux kernel 의 lightweight DWARF subset.
|
||||
- **eBPF stackmap**: 매 user+kernel stack 통합.
|
||||
|
||||
### 매 visualization
|
||||
- **Flame graph (Brendan Gregg)**: 매 x=share of samples, y=stack depth, width=hot.
|
||||
- **Icicle graph**: 매 flipped flame — root at top.
|
||||
- **Differential flame graph**: 매 두 profile diff — perf regression 사냥.
|
||||
|
||||
### 매 응용
|
||||
1. **CPU bottleneck 진단**: 매 hot function 식별.
|
||||
2. **Lock contention**: 매 off-CPU profile + futex stack.
|
||||
3. **GC pressure**: 매 alloc-stack profile.
|
||||
4. **Cold start**: 매 startup phase flame graph.
|
||||
5. **Continuous profiling**: 매 prod 24/7 sample → regression alerting.
|
||||
|
||||
## 💻 패턴
|
||||
|
||||
### Linux perf — basic
|
||||
```bash
|
||||
# 30s sample at 99Hz
|
||||
perf record -F 99 -a -g --call-graph dwarf -- sleep 30
|
||||
perf script > out.stack
|
||||
# render
|
||||
git clone https://github.com/brendangregg/FlameGraph
|
||||
./FlameGraph/stackcollapse-perf.pl out.stack | \
|
||||
./FlameGraph/flamegraph.pl > flame.svg
|
||||
```
|
||||
|
||||
### eBPF profile (BCC)
|
||||
```bash
|
||||
profile-bpfcc -F 99 -f 30 > out.folded
|
||||
flamegraph.pl out.folded > flame.svg
|
||||
# advantages: lower overhead, kernel+user merged
|
||||
```
|
||||
|
||||
### Go pprof
|
||||
```go
|
||||
import _ "net/http/pprof"
|
||||
func main() {
|
||||
go http.ListenAndServe(":6060", nil)
|
||||
// ... app
|
||||
}
|
||||
```
|
||||
```bash
|
||||
go tool pprof -http=:8080 http://localhost:6060/debug/pprof/profile?seconds=30
|
||||
# interactive flame graph in browser
|
||||
```
|
||||
|
||||
### Python py-spy (no code change)
|
||||
```bash
|
||||
py-spy record -o flame.svg -d 30 --pid $(pgrep -f myapp.py)
|
||||
# zero instrumentation, samples a running process
|
||||
```
|
||||
|
||||
### Node.js / V8
|
||||
```bash
|
||||
node --prof app.js
|
||||
# ... run workload ...
|
||||
node --prof-process isolate-0xNNN-v8.log > profile.txt
|
||||
# or 0x: npx 0x -- node app.js
|
||||
```
|
||||
|
||||
### JVM async-profiler
|
||||
```bash
|
||||
# attach to running JVM, 60s flame graph
|
||||
asprof -d 60 -f flame.html $PID
|
||||
# also captures lock contention, alloc, wall-clock
|
||||
```
|
||||
|
||||
### Rust — pprof crate
|
||||
```rust
|
||||
use pprof::ProtoBuf;
|
||||
let guard = pprof::ProfilerGuardBuilder::default()
|
||||
.frequency(999)
|
||||
.blocklist(&["libc", "libgcc", "pthread"])
|
||||
.build()?;
|
||||
// ... workload ...
|
||||
let report = guard.report().build()?;
|
||||
let mut file = std::fs::File::create("profile.pb")?;
|
||||
report.pprof()?.encode(&mut file)?;
|
||||
```
|
||||
|
||||
### Continuous profiling (Pyroscope / Grafana)
|
||||
```yaml
|
||||
# pyroscope agent — runs alongside app
|
||||
pyroscope:
|
||||
server: http://pyroscope:4040
|
||||
app_name: api-prod
|
||||
spy_name: ebpfspy # auto-detect language
|
||||
sample_rate: 100
|
||||
```
|
||||
|
||||
### Differential flame graph
|
||||
```bash
|
||||
./FlameGraph/difffolded.pl before.folded after.folded | \
|
||||
./FlameGraph/flamegraph.pl > diff.svg
|
||||
# red = got slower, blue = got faster
|
||||
```
|
||||
|
||||
## 매 결정 기준
|
||||
| 상황 | Tool |
|
||||
|---|---|
|
||||
| Linux native (C/C++/Rust/Go) | perf + FlameGraph |
|
||||
| Container / k8s (no SYS_ADMIN) | pprof endpoint |
|
||||
| Python prod | py-spy |
|
||||
| JVM prod | async-profiler |
|
||||
| Continuous 24/7 | Pyroscope / Datadog |
|
||||
| Off-CPU (lock/IO) | offcputime-bpfcc |
|
||||
|
||||
**기본값**: 99Hz sampling → folded → flamegraph.pl. 매 첫 5분 안에 hot path 보임.
|
||||
|
||||
## 🔗 Graph
|
||||
- 부모: [[Observability]]
|
||||
- 변형: [[Flame_Graph]]
|
||||
- Adjacent: [[eBPF]]
|
||||
|
||||
## 🤖 LLM 활용
|
||||
**언제**: hot function 분석, regression diff, profile 결과 해석.
|
||||
**언제 X**: tail latency / distributed trace (분산 환경은 OpenTelemetry).
|
||||
|
||||
## ❌ 안티패턴
|
||||
- **Time.time printf 로그 profiling**: 매 statistical 안 되고 hot loop 망침.
|
||||
- **Frame pointer 없는 build**: 매 unwind 망가짐 — `-fno-omit-frame-pointer` 필수.
|
||||
- **너무 낮은 sample rate (10Hz)**: 매 30초 = 300 samples — noise dominate.
|
||||
- **너무 높은 rate (10kHz)**: 매 self-overhead 가 측정 결과 왜곡.
|
||||
- **Single-run profile 만 보기**: 매 variance — minimum 5 runs 권장.
|
||||
|
||||
## 🧪 검증 / 중복
|
||||
- Verified (Brendan Gregg "Systems Performance" 2nd ed 2020, Linux perf docs, async-profiler README 2024).
|
||||
- 신뢰도 A.
|
||||
|
||||
## 🕓 Changelog
|
||||
| 날짜 | 변경 |
|
||||
|---|---|
|
||||
| 2026-05-08 | Phase 1 |
|
||||
| 2026-05-10 | Manual cleanup — sampling profilers, flame graphs, multi-language tooling |
|
||||
Reference in New Issue
Block a user