- Check both CPU and Memory data availability before historical analysis
- If either CPU or Memory has insufficient data, add warning and skip analysis
- Prevent conflicting insufficient_historical_data and historical_analysis
- Ensure consistent data availability requirements for workload analysis
- Only proceed with P95/P99 calculations when both resources have sufficient data
- Fix insufficient_historical_data vs historical_analysis contradiction
- Add return statement when insufficient data to prevent P99 calculation
- Implement workload-based historical analysis instead of pod-based
- Add _extract_workload_name() to identify workload from pod names
- Add analyze_workload_historical_usage() for workload-level analysis
- Add _analyze_workload_metrics() with Prometheus workload queries
- Add validate_workload_resources_with_historical_analysis() method
- Update /cluster/status endpoint to use workload analysis by namespace
- Improve reliability by analyzing workloads instead of individual pods
- Maintain fallback to pod-level analysis if workload analysis fails
- Use regex pattern pod=~"{pod.name}.*" instead of exact match
- This allows matching pods with suffixes like resource-governance-78b77cc868-gchx7
- Apply fix to both CPU and Memory queries for usage, requests, and limits
- Should resolve issue where resource-governance pod data was not being retrieved
- Revert step calculation to 60s for better data retrieval
- Reduce threshold to 3 data points for insufficient data detection
- Add detailed logging for Prometheus query debugging
- Ensure historical data is properly retrieved from Prometheus
- Adjust Prometheus query step based on time range (5min for 24h)
- Reduce threshold from 10 to 5 data points for insufficient data detection
- Add debug logging to understand data point counts
- Improve step calculation: 30s for 1h, 5min for 24h, 30min for 7d
- Unify Prometheus queries between namespace analysis and historical analysis
- Fix efficiency calculations to prevent division by zero
- Remove duplicate validations in validation service
- Improve frontend data display with clear numerical values
- Add proper error handling for missing data
- Adicionar modelos para QoSClassification, ResourceQuota e ClusterHealth
- Implementar classificação automática de QoS (Guaranteed, Burstable, BestEffort)
- Criar análise de Resource Quotas com recomendações automáticas
- Adicionar dashboard principal com visão geral do cluster
- Implementar análise de overcommit com métricas visuais
- Adicionar top resource consumers com ranking
- Criar distribuição de QoS com estatísticas
- Adicionar novos endpoints API para cluster health e QoS
- Melhorar interface com design responsivo e intuitivo
- Alinhar com práticas Red Hat para gerenciamento de recursos
- Updated CPU usage query to use node_namespace_pod_container:container_cpu_usage_seconds_total:sum_irate
- Updated memory usage query to use container_memory_working_set_bytes with correct job and metrics_path
- Updated requests/limits queries to use kube_resourcequota with correct cluster and type parameters
- Applied fixes to both get_workload_historical_analysis and get_namespace_historical_analysis functions
- Queries now match the working queries from OpenShift console dashboard
- Migrate from DaemonSet to Deployment for better efficiency
- Add GitHub Actions for automatic build and deploy
- Add Blue-Green deployment strategy with health checks
- Add scripts for development and production workflows
- Update documentation with CI/CD flow
- Implementa ferramenta completa de governança de recursos
- Backend Python com FastAPI para coleta de dados
- Validações seguindo best practices Red Hat
- Integração com Prometheus e VPA
- UI web interativa para visualização
- Relatórios em JSON, CSV e PDF
- Deploy como DaemonSet com RBAC
- Scripts de automação para build e deploy
