Uptime is not enough
A technically available application can still be commercially broken. The API may return HTTP 200 while payment failures rise, inventory reservations time out, or checkout becomes slow enough to reduce conversion.
Observability therefore needs both technical and business signals.
Structured logging
logger.LogInformation(
"Order {OrderId} confirmed for Shop {ShopId} with Total {Total}",
order.Id,
order.ShopId,
order.Total);Structured fields allow aggregation. Instead of reading log files line by line, the team can ask how many payment failures occurred for one provider, shop or deployment version.
Distributed traces answer 'where was the time spent?'
A trace can show that a 2.8-second checkout spent 70 ms in the API, 130 ms in PostgreSQL and 2.4 seconds waiting for a payment dependency. That immediately changes the investigation path.
Metrics show system shape
Technical metrics
- Request duration
- Error rate
- DB connections
- Cache hit ratio
- Queue depth
Business metrics
- Checkout success
- Payment failure
- Orders per minute
- Booking failures
- Refund volume
Alert on symptoms that matter
Alerts should be actionable. A warning that CPU reached 71% may be noise; an alert that checkout success fell from its normal range while payment-provider errors rose is operationally useful.
Connect telemetry to deployments
Every release should be identifiable in telemetry. When latency changes immediately after deployment, the team needs to correlate the production behaviour with the exact version that introduced it.
Business value
Why it matters to the business
- Faster incident diagnosis and shorter outages.
- Evidence for capacity and performance decisions.
- Ability to monitor customer-impacting failures, not only infrastructure.
- Safer releases because changes can be compared against baseline behaviour.
- Better accountability with traceable operational events.
Practical considerations
- Telemetry retention has cost.
- Dashboards need ownership to remain useful.