Logging Strategies
Practices for implementing effective logging in distributed systems: structured logging, levels, correlation, and centralized aggregation.
What it is
Effective logging in distributed systems goes beyond console.log. It requires structure, cross-service correlation, appropriate levels, and centralized aggregation to be useful for debugging and auditing.
Structured Logging
Logs in JSON format instead of plain text:
{"level":"info","msg":"Order created","orderId":"123","userId":"456","traceId":"abc","timestamp":"2026-03-16T10:00:00Z"}Benefits: efficient search, field filtering, automatic parsing.
Levels
| Level | Use |
|---|---|
| DEBUG | Development detail |
| INFO | Normal business events |
| WARN | Unexpected non-critical situations |
| ERROR | Failures requiring attention |
Correlation
Include traceId and requestId in every log to trace a request across multiple services.
Aggregation tools
- ELK Stack (Elasticsearch, Logstash, Kibana)
- Grafana Loki
- AWS CloudWatch Logs
- Datadog Logs
Why it matters
Logs are the first diagnostic tool when something fails. Structured logs, with appropriate levels and cross-service correlation, turn hours of debugging into minutes. Unstructured logs are noise that hides the signal.
References
- Structured Logging — Reference Python library.
- OpenTelemetry Logs — OpenTelemetry, 2024. Open standard for logs.
- Elastic Common Schema — Elastic, 2024. Standard schema for structured logs.