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observability
Implement structured logging, distributed tracing, and metrics for production-ready backend services.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
메뉴
Implement structured logging, distributed tracing, and metrics for production-ready backend services.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Design and implement REST APIs with consistent conventions, versioning, error contracts, and security.
Design and implement async task queues, message consumers, and background job patterns.
Design relational schemas, write efficient queries, plan indexes, and implement safe migrations.
Core development tools used across any project — git, docker, make, CI/CD, linting, formatting, pre-commit hooks.
Systematic backend debugging — reproduce, isolate root cause, implement fix with regression test.
| name | observability |
| type | skill |
| description | Implement structured logging, distributed tracing, and metrics for production-ready backend services. |
| related-rules | ["architecture.md"] |
| allowed-tools | Read, Write, Edit, Bash |
| agentic | {"generated_by":"agentic","source":"areas/software/backend/skills/observability/SKILL.md","repository":"https://github.com/sawrus/agent-guides","created_by":"v0.4.0","updated_by":"v0.5.1"} |
Expertise: Structured JSON logging, OpenTelemetry distributed tracing, Prometheus/RED metrics, alert design.
import structlog
import logging
# Configure once at app startup
structlog.configure(
processors=[
structlog.contextvars.merge_contextvars,
structlog.processors.add_log_level,
structlog.processors.TimeStamper(fmt="iso"),
structlog.processors.JSONRenderer(), # machine-parseable
],
logger_factory=structlog.PrintLoggerFactory(),
)
log = structlog.get_logger()
# Bind context per request (FastAPI middleware)
@app.middleware("http")
async def logging_middleware(request: Request, call_next):
request_id = request.headers.get("X-Request-ID") or str(uuid4())
structlog.contextvars.bind_contextvars(
request_id=request_id,
method=request.method,
path=request.url.path,
)
response = await call_next(request)
structlog.contextvars.unbind_contextvars("request_id", "method", "path")
return response
# Usage in service/repository layer
log.info("order.created", order_id=order.id, user_id=user.id, amount=str(order.total))
log.warning("payment.retry", order_id=order_id, attempt=attempt, reason=str(error))
log.error("db.query_failed", table="orders", query_type="insert", exc_info=True)
# ❌ Never log PII or secrets
log.info("user.login", email=user.email) # PII — omit or hash
log.debug("auth.token", token=access_token) # secret — never
# ✅ Log identifiers, not values
log.info("user.login", user_id=user.id)
log.info("auth.issued", token_jti=token_payload["jti"])
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.trace.export import BatchSpanProcessor
# Setup (once at startup)
provider = TracerProvider()
provider.add_span_processor(BatchSpanProcessor(OTLPSpanExporter()))
trace.set_tracer_provider(provider)
tracer = trace.get_tracer("order-service")
# Instrument a service method
async def create_order(self, user_id: int, items: list) -> Order:
with tracer.start_as_current_span("order.create") as span:
span.set_attribute("user.id", user_id)
span.set_attribute("items.count", len(items))
try:
order = await self.repo.create(user_id, items)
span.set_attribute("order.id", order.id)
return order
except Exception as e:
span.record_exception(e)
span.set_status(trace.StatusCode.ERROR)
raise
RED method: Rate · Error rate · Duration for every service boundary.
from prometheus_client import Counter, Histogram, start_http_server
# Define metrics at module level (not inside functions)
REQUEST_COUNT = Counter(
"http_requests_total",
"Total HTTP requests",
["method", "endpoint", "status_code"]
)
REQUEST_DURATION = Histogram(
"http_request_duration_seconds",
"HTTP request duration",
["method", "endpoint"],
buckets=[0.01, 0.05, 0.1, 0.25, 0.5, 1.0, 2.5, 5.0]
)
DB_QUERY_DURATION = Histogram(
"db_query_duration_seconds",
"DB query duration",
["operation", "table"],
buckets=[0.001, 0.005, 0.01, 0.05, 0.1, 0.5]
)
# FastAPI middleware
@app.middleware("http")
async def metrics_middleware(request: Request, call_next):
start = time.monotonic()
response = await call_next(request)
duration = time.monotonic() - start
endpoint = request.url.path
REQUEST_COUNT.labels(request.method, endpoint, response.status_code).inc()
REQUEST_DURATION.labels(request.method, endpoint).observe(duration)
return response
@router.get("/health", include_in_schema=False)
async def health(db: AsyncSession = Depends(get_db)):
checks = {}
try:
await db.execute(text("SELECT 1"))
checks["database"] = "ok"
except Exception as e:
checks["database"] = f"error: {e}"
is_healthy = all(v == "ok" for v in checks.values())
return JSONResponse(
{"status": "ok" if is_healthy else "degraded", "checks": checks},
status_code=200 if is_healthy else 503
)
| Signal | Threshold | Severity |
|---|---|---|
| Error rate | > 1% over 5 min | P1 |
| p99 latency | > 2s over 5 min | P1 |
| p95 latency | > 500ms over 15 min | P2 |
| DB connection pool saturation | > 80% for 5 min | P2 |
| Health check failures | 2 consecutive | P1 |