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📊 Analysis Views Overview

K8s-reporter provides multiple specialized analysis views, each designed to provide insights into different aspects of your Kubernetes cluster. This guide explains each view and how to use them effectively.

Available Views

📊 Cluster Overview

Purpose: High-level cluster health and resource distribution

Key Metrics: - Total resources and namespaces - Health ratio percentage - Resource type distribution - Top namespaces by resource count - Issues summary

Use Cases: - Daily cluster health checks - Executive dashboards - Initial cluster assessment - Resource planning

Key Features: - Resource distribution pie charts - Health metrics with visual indicators - Top namespaces ranking - Quick issue identification

🔒 Security Analysis

Purpose: Security posture assessment and RBAC analysis

Key Metrics: - Service account usage patterns - Pod security context evaluation - Privileged container detection - RBAC permissions analysis - ConfigMaps and Secrets overview

Use Cases: - Security audits - Compliance reporting - Vulnerability assessment - RBAC optimization

Key Features: - Security recommendations - Privileged pod identification - Service account analysis - Configuration security assessment

🏷️ Namespace Analysis

Purpose: Detailed per-namespace resource breakdown

Key Metrics: - Resource count per namespace - Health distribution by namespace - Resource types within namespaces - Namespace-specific issues

Use Cases: - Multi-tenant cluster management - Team-based resource allocation - Namespace-level troubleshooting - Resource quota planning

Key Features: - Interactive namespace selection - Per-namespace health metrics - Resource type distribution - Cross-namespace comparison

❤️ Health Dashboard

Purpose: Resource health monitoring and issue tracking

Key Metrics: - Health status distribution - Resources with issues - Health trends over time - Issue categorization

Use Cases: - Continuous monitoring - Incident response - Health trend analysis - Proactive maintenance

Key Features: - Real-time health monitoring - Issue categorization and filtering - Health trend visualization - Detailed issue descriptions

🔗 Relationship Analysis

Purpose: Resource dependency mapping and relationship visualization

Key Metrics: - Resource relationship counts - Relationship type distribution - Dependency chains - Orphaned resources

Use Cases: - Architecture understanding - Impact analysis - Dependency mapping - Change planning

Key Features: - Interactive relationship matrix - Network graph visualization - Relationship type filtering - Dependency chain analysis

⚡ Resource Efficiency

Purpose: Resource optimization and efficiency analysis

Key Metrics: - Pods without resource requests - Pods without resource limits - Resource coverage percentages - Optimization opportunities

Use Cases: - Resource optimization - Cost management - Performance tuning - Capacity planning

Key Features: - Severity classification - Automated recommendations - Resource coverage metrics - Export capabilities for remediation

💾 Storage Analysis

Purpose: Storage consumption and capacity tracking

Key Metrics: - Total storage consumption - Storage class distribution - Volume status tracking - Per-namespace storage usage

Use Cases: - Capacity planning - Storage optimization - Cost management - Storage class analysis

Key Features: - Storage consumption charts - Capacity utilization metrics - Storage class breakdown - Volume status monitoring

⏰ Temporal Analysis

Purpose: Resource lifecycle and creation pattern analysis

Key Metrics: - Resource age distribution - Creation timeline patterns - Most active namespaces - Lifecycle statistics

Use Cases: - Resource lifecycle management - Creation pattern analysis - Cleanup planning - Activity monitoring

Key Features: - Age-based categorization - Timeline visualizations - Creation pattern analysis - Lifecycle statistics

🏷️ Label Analysis (New in v0.7.9)

Purpose: Comprehensive labeling governance and quality assessment

Key Metrics: - Label coverage percentage and quality scoring - Common labels identification and usage patterns - Multi-label resource analysis with statistical insights - Orphaned resource detection without proper labels

Use Cases: - Labeling governance and compliance - Resource organization and categorization - Cleanup and standardization initiatives - Application discovery and inventory

Key Features: - Label coverage metrics and quality scoring - Interactive label filtering and search - Orphaned resource identification - Label usage pattern analysis - Export functionality for governance reports

🚀 Application View (New in v0.7.9)

Purpose: Application-centric cluster analysis using Kubernetes labels

Key Metrics: - Total applications discovered via standard labels - Application health status and resource breakdowns - Orphaned resources without application labels - Label coverage percentage for applications

Use Cases: - Application portfolio management - Resource governance and ownership - Development team reporting - Application lifecycle tracking

Key Features: - Automatic application discovery using standard Kubernetes labels - Application health and resource breakdowns - Orphaned resource identification and labeling recommendations - Detailed per-application resource inventory - Interactive label-based filtering and analysis - Export functionality for application reports

Common Filters Available Across Views

Namespace Filter: - Filter data by specific namespaces - "All" option to view cluster-wide data - Dynamically populated based on available data

Resource Type Filter: - Focus on specific Kubernetes resource types - Supports all detected resource kinds - Useful for targeted analysis

Health Status Filter: - Show only healthy, warning, or error resources - Helps focus on problematic areas - Supports multiple status selection

View-Specific Features

Each view provides specialized filtering and interaction options:

  • Interactive Charts: Click legends to toggle data series
  • Hover Tooltips: Detailed information on data points
  • Export Functions: Download filtered data as CSV
  • Search Capabilities: Find specific resources by name
  • Drill-down Options: Navigate from summary to detailed views

Best Practices

Daily Operations

  1. Start with Cluster Overview for general health assessment
  2. Use Health Dashboard to identify immediate issues
  3. Check Resource Efficiency for optimization opportunities
  4. Review Security Analysis for compliance monitoring

Incident Response

  1. Health Dashboard - Identify resources with issues
  2. Namespace Analysis - Scope the impact to specific namespaces
  3. Relationship Analysis - Understand dependency impacts
  4. Temporal Analysis - Check recent changes

Capacity Planning

  1. Storage Analysis - Monitor storage consumption trends
  2. Resource Efficiency - Identify optimization opportunities
  3. Namespace Analysis - Plan resource allocation
  4. Cluster Overview - Assess overall growth patterns

Security Audits

  1. Security Analysis - Comprehensive security assessment
  2. Namespace Analysis - Per-tenant security review
  3. Relationship Analysis - Understand access patterns
  4. Resource Efficiency - Identify security-relevant misconfigurations

Data Export and Integration

Export Options

Each view supports data export functionality: - CSV Export: Raw data for external analysis - Report Generation: Formatted reports for stakeholders - API Integration: Programmatic access to data

Integration Patterns

# Automated report generation
k8s-reporter --database cluster.db --headless --export-reports

# Custom dashboard integration
k8s-reporter --database cluster.db --api-mode

Customization

View Configuration

Views can be customized through: - Filter presets for common use cases - Custom metric thresholds - Personalized dashboard layouts - Scheduled report generation

Adding Custom Views

Developers can extend the analysis capabilities by: 1. Creating new view modules 2. Implementing custom analysis functions 3. Adding specialized visualizations 4. Integrating with external data sources

Performance Considerations

Large Clusters

  • Use namespace filtering to reduce data volume
  • Implement pagination for large result sets
  • Cache expensive computations
  • Use efficient database queries

Real-time Monitoring

  • Implement data refresh mechanisms
  • Use efficient update strategies
  • Monitor memory and CPU usage
  • Optimize rendering performance

Troubleshooting

Common Issues

Slow Loading: - Check database size and query complexity - Use filters to reduce data volume - Verify system resources

Missing Data: - Verify database integrity - Check k8s-analyzer export completeness - Validate filter settings

Visualization Issues: - Check browser compatibility - Verify JavaScript enablement - Test with different browsers

Future Enhancements

Planned Features

  • Custom Dashboard Builder: User-defined views
  • Advanced Analytics: Machine learning insights
  • Real-time Monitoring: Live cluster connection
  • Multi-cluster Views: Comparative analysis
  • Alert Integration: Notification systems

Community Contributions

  • Custom view templates
  • Specialized analysis functions
  • Industry-specific dashboards
  • Integration plugins

Support and Resources

  • Documentation: Comprehensive guides for each view
  • Examples: Sample dashboards and use cases
  • Community: User discussions and best practices
  • Development: Contribution guidelines and API reference