Skip to main content

Documentation Index

Fetch the complete documentation index at: https://docs.localops.co/llms.txt

Use this file to discover all available pages before exploring further.

A complete walkthrough of instrumenting a Python service using the official prometheus_client library. See the overview for the general approach.

​
1. Add the dependency

pip install prometheus-client
Or in requirements.txt: 
prometheus-client>=0.20.0

​
2. Register custom metrics and expose /metrics

start_http_server spins up a dedicated HTTP server on the given port that serves /metrics. The default registry already includes process metrics (CPU, memory, open FDs) and Python runtime metrics (GC stats, threads). 
import time
from prometheus_client import Counter, start_http_server

emails_sent = Counter(
    'emails_sent_total',
    'Total number of emails sent',
    ['status'],
)

if __name__ == '__main__':
    start_http_server(9090)
    while True:
        emails_sent.labels(status='success').inc()
        time.sleep(1)
For ASGI apps (FastAPI, Starlette), use prometheus-fastapi-instrumentator to mount /metrics on your main app port. For Django, use django-prometheus. For Flask, use prometheus-flask-exporter.

​
3. Declare the metrics endpoint in ops.json

{
    "metrics": {
        "endpoint": "/metrics",
        "interval": 15,
        "port": 9090
    }
}

​
4. Visualize with a community dashboard

Python doesn’t have a single canonical community dashboard like JVM or Node.js, but the metrics exposed by the default registry (process_* and python_*) are well-supported by generic process dashboards.
DashboardIDNotes
Prometheus Stats2Generic dashboard for process_* metrics (CPU, memory, FDs) that work for any Python service using prometheus_client.
Node Exporter Full1860Host-level metrics if you also want OS-level context for your Python service.
For a Python-app-specific dashboard with panels for requests/sec, latency percentiles, error rates, app uptime, and interpreter version, see pilosus/prometheus-client-python-app-grafana-dashboard on GitHub — it’s a JSON dashboard you can import directly. You can also browse grafana.com/grafana/dashboards for more Python-related community dashboards. To import: in Grafana, go to Dashboards → New → Import, enter the dashboard ID (or upload the JSON), and select your LocalOps Prometheus data source.