2nd/10_Wiki/Topics/Architecture/Logging_Diagnostics.md

---
id: wiki-2026-0508-logging-diagnostics
title: Logging Diagnostics
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Logging, Application Logging, Diagnostic Logging]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [observability, logging, diagnostics, sre]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: typescript
  framework: pino
---

# Logging Diagnostics

## 매 한 줄
> **"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.

## 매 핵심

### 매 3 pillars (observability)
- **Logs**: discrete events, high cardinality, narrative.
- **Metrics**: aggregated time-series, low cardinality, dashboards/alerts.
- **Traces**: causal chain across services.
- 매 modern unified backbone: OpenTelemetry → Loki/Tempo/Prometheus 또는 Datadog/Honeycomb.

### 매 log levels
- **TRACE**: 매 fine-grained internal state (off in prod).
- **DEBUG**: 매 development diagnostics (sampled or off in prod).
- **INFO**: 매 business event, lifecycle (default level).
- **WARN**: 매 degraded but recoverable.
- **ERROR**: 매 actionable failure — page-worthy candidate.
- **FATAL**: 매 process-terminating.

### 매 structured logging principles
1. JSON output (or logfmt).
2. 매 fixed schema: `timestamp, level, service, trace_id, span_id, message, ...attrs`.
3. Correlation IDs propagated (W3C Trace Context).
4. 매 PII redaction at source.
5. Sampling for high-volume paths.
6. 매 cardinality discipline — no unbounded values in indexed fields.

### 매 응용
1. Incident investigation (search by trace_id).
2. Audit trail (compliance — separate stream).
3. Business event analytics (BI pipeline ingestion).
4. SLO error budget calculation.
5. Anomaly detection input.

## 💻 패턴

### 1. Pino (Node.js, fast structured logging)
```typescript
import pino from "pino";

export const logger = pino({
  level: process.env.LOG_LEVEL ?? "info",
  redact: ["password", "*.authorization", "creditCard"],
  formatters: {
    level: (label) => ({ level: label }),
  },
  base: {
    service: "checkout",
    env: process.env.NODE_ENV,
    version: process.env.GIT_SHA,
  },
});

logger.info({ userId, orderId, amount }, "order placed");
// {"level":"info","time":1710000000,"service":"checkout","userId":"u1",...}
```

### 2. OpenTelemetry log correlation
```typescript
import { trace } from "@opentelemetry/api";
import pino from "pino";

const baseLogger = pino();

export function getLogger() {
  const span = trace.getActiveSpan();
  const ctx = span?.spanContext();
  return baseLogger.child({
    trace_id: ctx?.traceId,
    span_id: ctx?.spanId,
  });
}

// Usage
getLogger().error({ err }, "payment failed");
// Now searchable by trace_id across services.
```

### 3. Python structlog
```python
import structlog

structlog.configure(
    processors=[
        structlog.contextvars.merge_contextvars,
        structlog.processors.add_log_level,
        structlog.processors.TimeStamper(fmt="iso"),
        structlog.processors.JSONRenderer(),
    ],
)

log = structlog.get_logger()

structlog.contextvars.bind_contextvars(request_id=req_id, user_id=uid)
log.info("checkout_started", cart_size=len(cart))
```

### 4. Sampling (head + tail)
```typescript
// Head sampling: decide at request entry
function shouldLog(req: Request): boolean {
  if (req.url.startsWith("/health")) return false;       // drop healthchecks
  if (req.headers["x-debug"]) return true;               // force on
  return Math.random() < 0.01;                           // 1% sample for /api/*
}

// Tail sampling (in OTel collector): keep all errors + slow + 1% baseline
```

### 5. Error logging with stack + cause
```typescript
try {
  await chargeCard(order);
} catch (err) {
  logger.error({
    err: { message: err.message, stack: err.stack, cause: err.cause },
    orderId: order.id,
    customerId: order.customerId,
  }, "charge failed");
  throw err;
}
```

### 6. Redaction (PII safety)
```typescript
const SENSITIVE = /(\b\d{16}\b|\b\d{3}-\d{2}-\d{4}\b)/g;  // CC, SSN

function sanitize(obj: any): any {
  const json = JSON.stringify(obj);
  return JSON.parse(json.replace(SENSITIVE, "[REDACTED]"));
}

logger.info(sanitize(payload), "received webhook");
```

### 7. Audit log (separate stream)
```typescript
const audit = pino({
  level: "info",
  base: { stream: "audit" },
  // separate transport → tamper-evident store (S3 + object lock)
});

audit.info({
  actor: user.id,
  action: "user.delete",
  target: targetId,
  ip: req.ip,
  outcome: "success",
}, "audit");
```

### 8. Go slog (stdlib, 1.21+)
```go
import "log/slog"

logger := slog.New(slog.NewJSONHandler(os.Stdout, &slog.HandlerOptions{
    Level: slog.LevelInfo,
}))
slog.SetDefault(logger)

slog.Info("order placed",
    "user_id", userID,
    "order_id", orderID,
    "amount", amount,
)
```

## 매 결정 기준
| 상황 | Approach |
|---|---|
| 새 service | Structured JSON + OTel correlation. |
| High-volume path (>1k rps) | Head sampling + tail sampling. |
| Compliance / audit | Separate audit stream + immutable store. |
| Legacy plain-text logs | Parse → enrich → forward (Vector, Fluent Bit). |
| Edge / IoT | Compact binary (CBOR) + batched upload. |
| Real-time alerting on log content | Stream → Loki/ELK with regex rules. |

**기본값**: 2026 의 새 서비스는 매 OTel logs + structured JSON + Pino/structlog/slog. 매 plain text 의 X.

## 🔗 Graph
- 부모: [[Observability]]
- Adjacent: [[OpenTelemetry]] · [[Distributed Tracing]]

## 🤖 LLM 활용
**언제**: log schema design, sampling strategy, log-to-trace correlation, redaction policy review.
**언제 X**: 매 metric/trace 만 필요한 경우 (logs 의 cost > value), 매 single-developer side project (basic console.log 충분).

## ❌ 안티패턴
- **String interpolation logs**: `log.info("user " + id + " did " + action)` — 매 unparseable, 매 search 불가. 매 structured fields 사용.
- **PII leak**: 매 redaction 부재 → 매 GDPR breach.
- **Unbounded cardinality**: 매 user_email 을 indexed field 로 → 매 storage explosion.
- **Logging in tight loop**: 매 hot path 의 매 iter 마다 log → 매 IO bottleneck.
- **Catch and silent log**: `} catch (e) { logger.error(e); }` 에서 매 context 부재 — orderId/userId 같이 add.
- **Plain text in 2026**: 매 grep-only logs — search/correlation 매 painful.
- **No trace correlation**: 매 service 마다 isolated logs — 매 incident 시 매 cross-service narrative 의 manual 재구성.

## 🧪 검증 / 중복
- Verified (OpenTelemetry Logs spec, Google SRE Book Ch.6, Pino/structlog official docs).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — structured logging + OTel correlation + sampling/redaction patterns |