بنقرة واحدة
observability
Implement structured logging, distributed tracing, and metrics for production-ready backend services.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
القائمة
Implement structured logging, distributed tracing, and metrics for production-ready backend services.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
REST API design decisions — URL conventions, error contracts, versioning, pagination, idempotency, auth patterns.
API design principles and decision-making. REST vs GraphQL vs tRPC selection, response formats, versioning, pagination.
Orchestrate full-stack application scaffolding — determine project type, select tech stack, coordinate agents, scaffold structure.
Project scaffolding templates for new applications. Use when creating new projects from scratch. Contains 12 templates for various tech stacks.
Specialized skill for Python backend development using FastAPI, SQLAlchemy, and Pydantic.
Automates end-to-end and system validation tests. Runs services via Docker, feeds inputs, executes scenarios, and verifies outputs.
| 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":"v0.5.1","updated_by":"v0.6.0"} |
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 |