logging-observability

Structured logging outputs JSON-formatted logs with embedded correlation IDs to track requests across services. This enables unified log aggregation, easier parsing by monitoring systems, and automatic correlation of related events in distributed systems without manual log tailing.

Logging captures discrete events as JSON records; metrics aggregate numerical data over time; tracing follows request paths across services with spans and timing. Together they form complete observability, with logs for debugging, metrics for trends, and traces for understanding latency and dependencies.

OpenTelemetry provides unified APIs and SDKs for all three signals—logs, metrics, and traces—enabling a single instrumentation approach. This eliminates vendor lock-in and allows you to export all telemetry to the same backend or split across multiple compatible systems.

Trace propagation embeds correlation IDs and span context in request headers or message metadata so each service links its logs and spans to the same logical request. OpenTelemetry handles this automatically, creating an end-to-end trace across your microservice architecture.

Structured logging applies to any architecture—microservices, batch jobs, and event-driven systems all benefit from JSON logs with correlation IDs. Batch tasks and event handlers can emit structured logs and propagate trace context, giving operators visibility into asynchronous and long-running workloads.

Alert rules query metrics thresholds or log patterns and trigger notifications and runbooks when conditions are met. Structured metrics and JSON logs integrate directly with alerting pipelines, enabling operators to define clear escalation policies and automated remediation workflows.