Observability patterns for Python applications. Triggers on: logging, metrics, tracing, opentelemetry, prometheus, observability, monitoring, structlog, correlation id.
Install with Tessl CLI
npx tessl i github:NeverSight/skills_feed --skill python-observability-patterns83
Does it follow best practices?
If you maintain this skill, you can automatically optimize it using the tessl CLI to improve its score:
npx tessl skill review --optimize ./path/to/skillValidation for skill structure
Logging, metrics, and tracing for production applications.
import structlog
# Configure structlog
structlog.configure(
processors=[
structlog.contextvars.merge_contextvars,
structlog.processors.add_log_level,
structlog.processors.TimeStamper(fmt="iso"),
structlog.processors.JSONRenderer(),
],
wrapper_class=structlog.make_filtering_bound_logger(logging.INFO),
context_class=dict,
logger_factory=structlog.PrintLoggerFactory(),
)
logger = structlog.get_logger()
# Usage
logger.info("user_created", user_id=123, email="test@example.com")
# Output: {"event": "user_created", "user_id": 123, "email": "test@example.com", "level": "info", "timestamp": "2024-01-15T10:00:00Z"}import structlog
from contextvars import ContextVar
from uuid import uuid4
request_id_var: ContextVar[str] = ContextVar("request_id", default="")
def bind_request_context(request_id: str | None = None):
"""Bind request ID to logging context."""
rid = request_id or str(uuid4())
request_id_var.set(rid)
structlog.contextvars.bind_contextvars(request_id=rid)
return rid
# FastAPI middleware
@app.middleware("http")
async def request_context_middleware(request, call_next):
request_id = request.headers.get("X-Request-ID") or str(uuid4())
bind_request_context(request_id)
response = await call_next(request)
response.headers["X-Request-ID"] = request_id
structlog.contextvars.clear_contextvars()
return responsefrom prometheus_client import Counter, Histogram, Gauge, generate_latest
from fastapi import FastAPI, Response
# Define metrics
REQUEST_COUNT = Counter(
"http_requests_total",
"Total HTTP requests",
["method", "endpoint", "status"]
)
REQUEST_LATENCY = Histogram(
"http_request_duration_seconds",
"HTTP request latency",
["method", "endpoint"],
buckets=[0.01, 0.05, 0.1, 0.5, 1.0, 5.0]
)
ACTIVE_CONNECTIONS = Gauge(
"active_connections",
"Number of active connections"
)
# Middleware to record metrics
@app.middleware("http")
async def metrics_middleware(request, call_next):
ACTIVE_CONNECTIONS.inc()
start = time.perf_counter()
response = await call_next(request)
duration = time.perf_counter() - start
REQUEST_COUNT.labels(
method=request.method,
endpoint=request.url.path,
status=response.status_code
).inc()
REQUEST_LATENCY.labels(
method=request.method,
endpoint=request.url.path
).observe(duration)
ACTIVE_CONNECTIONS.dec()
return response
# Metrics endpoint
@app.get("/metrics")
async def metrics():
return Response(
content=generate_latest(),
media_type="text/plain"
)from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter
# Setup
provider = TracerProvider()
processor = BatchSpanProcessor(OTLPSpanExporter(endpoint="localhost:4317"))
provider.add_span_processor(processor)
trace.set_tracer_provider(provider)
tracer = trace.get_tracer(__name__)
# Manual instrumentation
async def process_order(order_id: int):
with tracer.start_as_current_span("process_order") as span:
span.set_attribute("order_id", order_id)
with tracer.start_as_current_span("validate_order"):
await validate(order_id)
with tracer.start_as_current_span("charge_payment"):
await charge(order_id)| Library | Purpose |
|---|---|
| structlog | Structured logging |
| prometheus-client | Metrics collection |
| opentelemetry | Distributed tracing |
| Metric Type | Use Case |
|---|---|
| Counter | Total requests, errors |
| Histogram | Latencies, sizes |
| Gauge | Current connections, queue size |
./references/structured-logging.md - structlog configuration, formatters./references/metrics.md - Prometheus patterns, custom metrics./references/tracing.md - OpenTelemetry, distributed tracing./assets/logging-config.py - Production logging configurationPrerequisites:
python-async-patterns - Async context propagationRelated Skills:
python-fastapi-patterns - API middleware for metrics/tracingpython-cli-patterns - CLI logging patternsIntegration Skills:
python-database-patterns - Database query tracing5c15b3d
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.