observability-setup

To implement structured JSON logging, emit logs with required fields including timestamp, level, message, service, and traceId while avoiding sensitive data. This enables reliable correlation across distributed services for diagnosing production issues.

RED metrics define request rate, error rate, and latency per endpoint, capturing duration with p50, p95, and p99 percentiles. They help monitor API latency and diagnose performance bottlenecks by measuring traffic patterns and response times across services.

Propagating traceId across microservices requires OpenTelemetry-based instrumentation to create spans for meaningful operations. This correlates logs, metrics, and traces across distributed services, enabling you to trace execution paths and pinpoint failure locations.

OpenTelemetry supports instrumentation for various stacks to implement end-to-end observability across distributed systems. It enables structured logs, RED metrics, and distributed tracing with propagated trace IDs for supported service environments.

The best way to correlate failures across microservices is combining structured JSON logs with traceId propagation and RED metrics per endpoint. This lets you link error spikes and latency anomalies to specific distributed operations for faster diagnosis.

RED metrics target request-driven endpoints, so use USE patterns instead for resource monitoring. When you need to track CPU, memory, and connection saturation signals rather than request rate or latency, USE patterns provide the appropriate resource utilization data.