structured-logging

Structured logging is enforced by defining a standard JSON schema with mandatory fields like time, level, msg, service, and trace_id. This ensures consistent, machine-readable logs that improve observability, debugging, and security across distributed services.

Trace context propagation involves passing correlation identifiers like trace_id and span_id across HTTP boundaries, message queues, and background jobs. This ensures that related log events can be linked together for incident response and debugging across services.

Sensitive data masking in logs is handled by implementing masking policies that redact or hash protected fields before output. This prevents data leakage while maintaining useful audit trails and observability for incident response and debugging purposes.

Log sampling rules are configured to selectively drop or retain log events based on criteria like level or trace_id. This reduces data volume and storage costs while maintaining necessary observability for audit trails and incident response.

Structured logging does not require a specific framework, as it defines a standard JSON log schema with mandatory fields. You instrument your services to emit JSON logs to stdout with per-request correlation identifiers, independent of specific frameworks.