observability

OpenTelemetry setup requires trace-context propagation across services to correlate logs, metrics, and distributed traces. You must instrument your application to export structured logs and metrics alongside traces, ensuring request flows can be followed end-to-end.

Dashboard design using RED and USE principles structures panels to explain service health and resource health. RED focuses on Rate, Errors, and Duration for services, while USE tracks Utilization, Saturation, and Errors for resources, creating consistent operational visibility.

SLO burn-rate alert rules trigger pages based on the rate at which your error budget is consumed relative to your service level objectives. This approach ensures runbooks are enforced for page-level alerts, prioritizing actionable incidents over raw threshold breaches.

Preventing high-cardinality metrics in Prometheus requires avoiding unbounded labels like user IDs or request paths in metric definitions. You must design metric labels carefully to prevent explosive time-series growth that degrades database performance and query efficiency.

Logs are for discrete events, metrics are for aggregated measurements, and traces are for request flow. Routing tasks to the right signal ensures decision-ready observability workflows, allowing you to query the most efficient data source for your specific debugging context.

Trace-log correlation requires propagating trace context into structured log entries to link them with distributed traces. Production safety checks must be enforced to actively prevent PII and secrets from being exported into logging pipelines.