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
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---
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id: wiki-2026-0508-logging-diagnostics
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title: Logging Diagnostics
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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: [Logging, Application Logging, Diagnostic Logging]
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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: [observability, logging, diagnostics, sre]
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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: typescript
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framework: pino
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---
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# Logging Diagnostics
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## 매 한 줄
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> **"매 structured event 의 production runtime 의 X-ray"**. Application logging 은 매 incident 의 forensic record + 매 system behavior 의 narrative. 2026 best practice: **structured JSON logs + OpenTelemetry semantic conventions + sampling at scale + cardinality discipline**. Plain text logs 는 매 deprecated; loggers 는 매 trace context 와 correlation.
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## 매 핵심
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### 매 3 pillars (observability)
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- **Logs**: discrete events, high cardinality, narrative.
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- **Metrics**: aggregated time-series, low cardinality, dashboards/alerts.
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- **Traces**: causal chain across services.
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- 매 modern unified backbone: OpenTelemetry → Loki/Tempo/Prometheus 또는 Datadog/Honeycomb.
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### 매 log levels
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- **TRACE**: 매 fine-grained internal state (off in prod).
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- **DEBUG**: 매 development diagnostics (sampled or off in prod).
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- **INFO**: 매 business event, lifecycle (default level).
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- **WARN**: 매 degraded but recoverable.
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- **ERROR**: 매 actionable failure — page-worthy candidate.
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- **FATAL**: 매 process-terminating.
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### 매 structured logging principles
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1. JSON output (or logfmt).
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2. 매 fixed schema: `timestamp, level, service, trace_id, span_id, message, ...attrs`.
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3. Correlation IDs propagated (W3C Trace Context).
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4. 매 PII redaction at source.
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5. Sampling for high-volume paths.
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6. 매 cardinality discipline — no unbounded values in indexed fields.
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### 매 응용
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1. Incident investigation (search by trace_id).
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2. Audit trail (compliance — separate stream).
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3. Business event analytics (BI pipeline ingestion).
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4. SLO error budget calculation.
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5. Anomaly detection input.
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## 💻 패턴
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### 1. Pino (Node.js, fast structured logging)
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```typescript
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import pino from "pino";
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export const logger = pino({
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level: process.env.LOG_LEVEL ?? "info",
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redact: ["password", "*.authorization", "creditCard"],
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formatters: {
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level: (label) => ({ level: label }),
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},
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base: {
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service: "checkout",
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env: process.env.NODE_ENV,
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version: process.env.GIT_SHA,
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},
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});
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logger.info({ userId, orderId, amount }, "order placed");
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// {"level":"info","time":1710000000,"service":"checkout","userId":"u1",...}
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```
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### 2. OpenTelemetry log correlation
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```typescript
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import { trace } from "@opentelemetry/api";
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import pino from "pino";
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const baseLogger = pino();
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export function getLogger() {
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const span = trace.getActiveSpan();
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const ctx = span?.spanContext();
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return baseLogger.child({
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trace_id: ctx?.traceId,
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span_id: ctx?.spanId,
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});
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}
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// Usage
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getLogger().error({ err }, "payment failed");
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// Now searchable by trace_id across services.
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```
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### 3. Python structlog
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```python
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import structlog
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structlog.configure(
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processors=[
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structlog.contextvars.merge_contextvars,
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structlog.processors.add_log_level,
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structlog.processors.TimeStamper(fmt="iso"),
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structlog.processors.JSONRenderer(),
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],
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)
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log = structlog.get_logger()
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structlog.contextvars.bind_contextvars(request_id=req_id, user_id=uid)
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log.info("checkout_started", cart_size=len(cart))
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```
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### 4. Sampling (head + tail)
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```typescript
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// Head sampling: decide at request entry
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function shouldLog(req: Request): boolean {
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if (req.url.startsWith("/health")) return false; // drop healthchecks
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if (req.headers["x-debug"]) return true; // force on
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return Math.random() < 0.01; // 1% sample for /api/*
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}
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// Tail sampling (in OTel collector): keep all errors + slow + 1% baseline
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```
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### 5. Error logging with stack + cause
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```typescript
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try {
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await chargeCard(order);
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} catch (err) {
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logger.error({
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err: { message: err.message, stack: err.stack, cause: err.cause },
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orderId: order.id,
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customerId: order.customerId,
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}, "charge failed");
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throw err;
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}
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```
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### 6. Redaction (PII safety)
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```typescript
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const SENSITIVE = /(\b\d{16}\b|\b\d{3}-\d{2}-\d{4}\b)/g; // CC, SSN
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function sanitize(obj: any): any {
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const json = JSON.stringify(obj);
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return JSON.parse(json.replace(SENSITIVE, "[REDACTED]"));
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}
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logger.info(sanitize(payload), "received webhook");
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```
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### 7. Audit log (separate stream)
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```typescript
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const audit = pino({
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level: "info",
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base: { stream: "audit" },
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// separate transport → tamper-evident store (S3 + object lock)
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});
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audit.info({
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actor: user.id,
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action: "user.delete",
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target: targetId,
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ip: req.ip,
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outcome: "success",
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}, "audit");
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```
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### 8. Go slog (stdlib, 1.21+)
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```go
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import "log/slog"
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logger := slog.New(slog.NewJSONHandler(os.Stdout, &slog.HandlerOptions{
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Level: slog.LevelInfo,
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}))
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slog.SetDefault(logger)
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slog.Info("order placed",
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"user_id", userID,
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"order_id", orderID,
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"amount", amount,
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)
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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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| 새 service | Structured JSON + OTel correlation. |
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| High-volume path (>1k rps) | Head sampling + tail sampling. |
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| Compliance / audit | Separate audit stream + immutable store. |
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| Legacy plain-text logs | Parse → enrich → forward (Vector, Fluent Bit). |
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| Edge / IoT | Compact binary (CBOR) + batched upload. |
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| Real-time alerting on log content | Stream → Loki/ELK with regex rules. |
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**기본값**: 2026 의 새 서비스는 매 OTel logs + structured JSON + Pino/structlog/slog. 매 plain text 의 X.
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## 🔗 Graph
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- 부모: [[Observability]]
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- Adjacent: [[OpenTelemetry]] · [[Distributed Tracing]]
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## 🤖 LLM 활용
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**언제**: log schema design, sampling strategy, log-to-trace correlation, redaction policy review.
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**언제 X**: 매 metric/trace 만 필요한 경우 (logs 의 cost > value), 매 single-developer side project (basic console.log 충분).
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## ❌ 안티패턴
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- **String interpolation logs**: `log.info("user " + id + " did " + action)` — 매 unparseable, 매 search 불가. 매 structured fields 사용.
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- **PII leak**: 매 redaction 부재 → 매 GDPR breach.
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- **Unbounded cardinality**: 매 user_email 을 indexed field 로 → 매 storage explosion.
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- **Logging in tight loop**: 매 hot path 의 매 iter 마다 log → 매 IO bottleneck.
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- **Catch and silent log**: `} catch (e) { logger.error(e); }` 에서 매 context 부재 — orderId/userId 같이 add.
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- **Plain text in 2026**: 매 grep-only logs — search/correlation 매 painful.
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- **No trace correlation**: 매 service 마다 isolated logs — 매 incident 시 매 cross-service narrative 의 manual 재구성.
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## 🧪 검증 / 중복
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- Verified (OpenTelemetry Logs spec, Google SRE Book Ch.6, Pino/structlog official 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 — structured logging + OTel correlation + sampling/redaction patterns |
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