You cannot fix what you cannot see. Before observability, a production problem looks like a customer email and a guess. After it, the service tells you what broke, where, and how often. Three tools cover most of the ground: Sentry, Prometheus, and Grafana.
Three questions, three tools
- Sentry answers what broke: the exception, the stack trace, the request that caused it.
- Prometheus answers what the numbers say: request rate, latency, error counts.
- Grafana answers show me over time: the dashboards and trends built on those numbers.
Sentry catches the exceptions
import os
import sentry_sdk
from sentry_sdk.integrations.django import DjangoIntegration
sentry_sdk.init(
dsn=os.environ["SENTRY_DSN"],
integrations=[DjangoIntegration()],
traces_sample_rate=0.1,
environment=os.environ.get("ENV", "production"),
)From here, every unhandled exception arrives grouped, counted, and attached to the request that triggered it.
Prometheus exposes the metrics
Add django-prometheus and the app exposes a
/metrics endpoint with request latency, response counts by
status, and database timing. Prometheus scrapes it on an interval and
keeps the history.
Grafana turns metrics into a story
A dashboard with p95 latency, error rate, and throughput, side by side, turns raw numbers into a shape you can read at a glance. When something is wrong, the shape changes before the emails start.
Alert on symptoms, not causes
Alert on what the user feels: rising error rate, slow responses, requests failing. High CPU is not an incident on its own, it is a detail you look at after a symptom alert fires. Alerting on causes produces noise that teams learn to ignore.
Add context to every error
An exception with no context is a puzzle. Attach the user, the tenant, the relevant ids, and a tag for the area of the system. A well-tagged error is often fixed from the report alone, with no reproduction step needed.
Instrument as you build
The worst time to add observability is during an incident, in the dark. Wire it in as each feature ships, so the first production problem is met with a dashboard and a stack trace rather than a guess.