Logging
Logs are your system's narrative: what happened, when it happened, and the state at that moment. Unlike metrics (which aggregate) and traces (which follow requests), logs capture fine-grained events in sequence, often with rich context.
Good logging discipline enables:
- Troubleshooting: Quickly understand what went wrong in production
- Compliance: Maintain audit trails for security and regulatory requirements
- Performance Analysis: Identify slow operations and bottlenecks
- Alerting: Trigger on error patterns or anomalies
- Business Intelligence: Extract insights from application events
But logging has costs: storage, ingestion, and retention. Unstructured logs create signal-to-noise problems. This section covers logging as a practice: structuring logs for queryability, managing log levels, controlling retention, and protecting sensitive data.
📄️ Structured Logs, Context, and Correlation IDs
Log structured data to enable queries, use correlation IDs to track requests, and maintain context across async boundaries.
📄️ Log Levels and Governance
Use log levels strategically, enforce consistency across teams, and optimize storage costs while maintaining debuggability.
📄️ Log Retention and Privacy
Manage log lifecycle responsibly: comply with regulations, protect sensitive data, and optimize retention periods.