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Epic 16: Kubernetes Integration - Phase 1 Complete ✅

Date: January 26, 2026
Phase: Database Schema
Status: Complete

✅ Completed Tasks

Database Migration

  • ✅ Created Alembic migration 20260126_1600_create_kubernetes_tables.py
  • ✅ Executed migration successfully in PostgreSQL
  • ✅ All tables, partitions, indexes, and views created

Tables Created (8 core tables)

  1. k8s_clusters - Kubernetes cluster registry

    • Stores encrypted kubeconfig
    • Tracks cluster health and status
    • Provider metadata (EKS, GKE, AKS, k3s, vanilla)
    • Cost tracking per cluster

  2. k8s_namespaces - Namespace tracking

    • Resource quotas and limits
    • Team/cost-center attribution
    • Usage metrics (CPU, memory, pods)

  3. k8s_nodes - Cluster node inventory

    • Capacity and allocatable resources
    • Current usage metrics
    • Labels, taints, conditions

  4. k8s_deployments - Deployment tracking

    • Replica counts and health status
    • Container specifications
    • Deployment strategies

  5. k8s_pods (PARTITIONED) - Pod instance tracking

    • 12 monthly partitions for 2026
    • Pod lifecycle and status
    • Resource requests/limits
    • Container status tracking

  6. k8s_resource_metrics (PARTITIONED) - Time-series metrics

    • 12 monthly partitions for 2026
    • CPU, memory, network, storage metrics
    • Multi-level (cluster, namespace, deployment, pod, node)
    • OOM kills and restart tracking

  7. k8s_helm_releases - Helm chart deployments

    • Chart name and version
    • Release status and revision
    • Values configuration

  8. k8s_cost_attribution - Cost tracking

    • Hourly and daily cost aggregation
    • Resource-level breakdown (CPU, memory, storage, network)
    • Team and cost-center attribution

Partitions Created (24 total)

  • ✅ 12 partitions for k8s_pods (Jan-Dec 2026)
  • ✅ 12 partitions for k8s_resource_metrics (Jan-Dec 2026)

Views Created (2 views)

  1. v_k8s_cluster_health - Cluster health dashboard

    • Aggregated health metrics
    • Node and deployment counts
    • Resource usage totals
    • Cost estimates

  2. v_k8s_namespace_costs - Namespace cost summary

    • Last 30 days cost totals
    • Average daily costs
    • Resource usage hours
    • Team attribution

Indexes Created (30+ indexes)

  • Cluster organization and status lookups
  • Namespace filtering by team/cost-center
  • Deployment health monitoring
  • Time-series partition pruning
  • Cost attribution queries

📊 Database Statistics

-- Total K8s Tables: 32
- 8 core tables
- 24 partition tables (12 pods + 12 metrics)

-- Total Indexes: 30+
-- Total Views: 2
-- Total Constraints: 15+ (FK, UNIQUE)

🔍 Verification

# All tables created
docker exec ops-center-postgresql psql -U unicorn -d unicorn_db -c "\dt k8s_*"

# Partition verification
docker exec ops-center-postgresql psql -U unicorn -d unicorn_db -c "
  SELECT tablename FROM pg_tables 
  WHERE tablename LIKE 'k8s_pods_%' 
  ORDER BY tablename;"

# Views verification  
docker exec ops-center-postgresql psql -U unicorn -d unicorn_db -c "\dv v_k8s_*"

# Index count
docker exec ops-center-postgresql psql -U unicorn -d unicorn_db -c "
  SELECT COUNT(*) FROM pg_indexes 
  WHERE tablename LIKE 'k8s_%';"

🎯 Next Steps: Phase 2 - Backend Implementation

  1. Dependencies

    # Add to backend/requirements.txt
kubernetes==29.0.0
kubernetes-asyncio==29.0.0

  2. Core Files to Create

    • backend/k8s_cluster_manager.py - Business logic
    • backend/k8s_sync_worker.py - Background sync (30s)
    • backend/k8s_cost_calculator.py - Cost attribution
    • backend/k8s_api.py - REST API (19 endpoints)

  3. Key Features

    • Cluster registration with kubeconfig encryption
    • Real-time health monitoring
    • Namespace/deployment/pod synchronization
    • Metrics collection and storage
    • Cost calculation engine

  4. Integration Points

    • Add router to server.py
    • Start workers in startup_event()
    • Stop workers in shutdown_event()

📝 Migration Details

File: alembic/versions/20260126_1600_create_kubernetes_tables.py
Revision: 20260126_1600
Previous: 20260126_1500 (Fleet Management)
Execution: Direct SQL via Docker exec

Rollback Available: Yes, downgrade() function drops all tables

💡 Key Design Decisions

  1. Partitioning Strategy: Monthly partitions for pods and metrics

    • Efficient pruning of old data
    • Optimized time-range queries
    • Automatic partition routing

  2. Cost Attribution: Separate table with hourly/daily aggregation

    • Flexible team/cost-center tracking
    • Historical cost analysis
    • Budget forecasting support

  3. Encryption: Kubeconfig stored encrypted

    • Uses BYOK_ENCRYPTION_KEY
    • Never exposed in API responses
    • Decrypted only for K8s API calls

  4. Views: Pre-computed aggregations

    • Fast dashboard queries
    • No N+1 query problems
    • Consistent data representation

Phase 1 Complete! Ready for Phase 2: Backend Implementation 🚀