Logging Events & Monitoring Metrics
Quick Reference: Anomaly Detection
Quick Reference
| Component | Purpose | Tools |
|---|---|---|
| Logging | Event records | ELK, Splunk, CloudWatch |
| Metrics | Numerical measurements | Prometheus, Grafana, Datadog |
| Tracing | Request flow | Jaeger, Zipkin, OpenTelemetry |
Clear Definition
Logging records discrete events. Metrics measure system performance over time. Tracing tracks requests across services. Together they provide observability into system behavior.
š” Key Insight: Logs tell you what happened, metrics tell you how much, tracing tells you where.
Core Concepts
Logging
- Levels: DEBUG, INFO, WARN, ERROR
- Structured Logging: JSON format
- Centralized: Aggregate logs
- Retention: Store appropriately
Metrics
- Counters: Incrementing values
- Gauges: Current values
- Histograms: Distribution
- Dashboards: Visualize metrics
Distributed Tracing
- Spans: Individual operations
- Traces: Request flows
- Context Propagation: Track across services
Best Practices
- Structured Logging: Use JSON
- Appropriate Levels: Don't log everything
- Metrics: Track business and technical metrics
- Sampling: Sample traces for performance
Quick Reference Summary
Logging: Record events for debugging and auditing.
Metrics: Measure performance and system health.
Tracing: Track requests across distributed systems.
Key: Combine all three for complete observability.
Next Topic: Anomaly Detection ā
Back to: Step 9 Overview | Main Index