Observability
Learn how to use K8sGPT's observability features to monitor and analyze your Kubernetes cluster
Overview
K8sGPT provides comprehensive observability features to help you monitor, analyze, and troubleshoot your Kubernetes clusters. These features enable you to gain insights into cluster health, performance, and potential issues.
Metrics and Monitoring
K8sGPT collects and analyzes various metrics from your Kubernetes cluster:
- Resource utilization (CPU, memory, storage)
- Pod and container health
- Network performance
- API server latency
- Node status and capacity
Logging Integration
K8sGPT can integrate with your existing logging infrastructure:
- Aggregate logs from pods and containers
- Correlate logs with Kubernetes events
- Identify patterns and anomalies
- Provide context for troubleshooting
Tracing and Debugging
Advanced tracing capabilities help you debug complex issues:
- Request tracing across services
- Latency analysis
- Dependency mapping
- Error correlation
Dashboards and Visualization
K8sGPT provides customizable dashboards for visualizing cluster data:
- Real-time metrics display
- Historical trend analysis
- Custom dashboard creation
- Export capabilities
Alerting and Notifications
Configure alerts based on various conditions:
- Threshold-based alerts
- Anomaly detection
- Multi-channel notifications
- Alert aggregation and deduplication
Integration with Other Tools
K8sGPT can integrate with popular observability tools:
- Prometheus and Grafana
- Elasticsearch and Kibana
- Jaeger and OpenTelemetry
- Datadog and New Relic
Best Practices
- Define clear observability objectives
- Collect relevant metrics only
- Set appropriate retention periods
- Regularly review and adjust alert thresholds
- Document observability setup and procedures
Next Steps
To get started with K8sGPT observability:
- Configure metrics collection
- Set up logging integration
- Create initial dashboards
- Define alert rules
- Integrate with existing tools