logging-observability

Structured logging uses JSON format with standard fields like timestamp, level, message, trace_id, and span_id to enable consistent log collection across services. Emit logs with these fields to a central system where you can correlate events by trace_id and analyze performance or errors systematically.

Trace IDs and correlation IDs link related logs across multiple services and requests, enabling you to follow a single transaction through your entire system. They're essential in microservices and distributed systems to reconstruct execution flow and debug issues that span multiple components.

Distributed tracing works by propagating trace_id and span_id through service calls and including them in every log entry. This creates a queryable chain of logs that shows the path a request took, timing at each step, and where failures occurred.

Yes, structured logging applies to batch jobs, cloud-native applications, and microservices. The same JSON format with trace_id and standard fields works across all contexts, enabling consistent observability whether logs run continuously or on schedule.

Structured logs may contain sensitive data like user_id or request context. Security considerations include restricting log access, scrubbing PII before storage, and controlling who can query trace data across services.

Standard log levels indicate severity and context: DEBUG for detailed diagnostics, INFO for important events, WARN for recoverable issues, and ERROR for failures. Choosing the right level reduces log volume while keeping actionable data for debugging and monitoring.