qa-observability

Distributed tracing tracks requests across microservices using OpenTelemetry instrumentation. Deploy a tracing backend like Jaeger, Tempo, or Zipkin, instrument your services with OpenTelemetry SDKs to emit trace data, and configure exporters to send traces to your backend. This reveals request paths, latency bottlenecks, and failure points across your cloud-native system.

Use Prometheus to scrape metrics from instrumented services via exposed /metrics endpoints, then visualize dashboards in Grafana. Define SLOs and SLIs alongside key metrics like latency and error rates. This provides real-time visibility into system health and performance across your infrastructure.

Yes. Emit structured logs in JSON format with embedded trace context (trace IDs and span IDs) from your application. This links logs to distributed traces and enables correlation across your observability stack, improving incident diagnosis and root-cause analysis in production environments.

SLOs define target service-level objectives; SLIs measure actual performance against them. Define SLOs using metrics (availability, latency, error rate), configure Prometheus alerts when SLIs breach thresholds, and track performance budgets. This quantifies reliability expectations and guides capacity planning decisions.

Yes. OpenTelemetry integrates with Jaeger, Tempo, Zipkin for tracing; Prometheus and Grafana for metrics; and many commercial APM platforms. Its vendor-neutral design lets you switch backends without re-instrumenting code, providing flexibility across cloud-native architectures.

Start with distributed tracing to map service dependencies and request flows, add Prometheus metrics collection for latency and error rates, define baseline SLOs, implement structured logging with trace context, then layer in capacity planning and performance profiling as scale increases.