一键导入
observability
Implement structured logging, distributed tracing, and metrics for production-ready backend services.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
菜单
Implement structured logging, distributed tracing, and metrics for production-ready backend services.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
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 |