Kubernetes Deployment

This skill covers deploying applications to Kubernetes clusters, managing Persistent Volume Claims (PVCs), and working with Kubernetes jobs in this monorepo.

Overview

The monorepo uses Kubernetes for deploying the caelundas ephemeris calendar generator as a scheduled job. The deployment workflow involves:

1. •Building Docker images for linux/amd64
2. •Pushing to GitHub Container Registry (GHCR)
3. •Deploying to K8s using Helm charts
4. •Managing PVCs for input/output data persistence

For comprehensive deployment patterns and infrastructure details, see infrastructure/AGENTS.md.

Key Concepts

Helm Charts

The monorepo includes a reusable Helm chart at infrastructure/helm/kubernetes-job for deploying batch jobs. Key features:

• •Auto-generated names: Each deployment creates uniquely named resources
• •PVC management: Mounts input and output volumes
• •Image pull secrets: Supports private registries (GHCR)
• •Resource limits: Configurable CPU and memory constraints

Deployment Workflow

# Build Docker image for K8s platform
nx run caelundas:docker-build

# Deploy to K8s cluster (generates unique job name)
nx run caelundas:helm-upgrade

# Retrieve output files after job completion
nx run caelundas:kubernetes-copy-files

# Clean up completed jobs
nx run caelundas:kubernetes-uninstall

PVC Strategy

Applications use two PVCs:

• •Input PVC: Configuration files, environment-specific data (read-only)
• •Output PVC: Generated artifacts, logs, results (read-write)

The kubernetes-copy-files target synchronizes output files back to local filesystem after job completion.

Project-Specific Patterns

caelundas Deployment

See applications/caelundas/AGENTS.md for detailed caelundas deployment workflows, including:

• •Docker multi-stage build patterns
• •Environment variable management in K8s
• •Output file retrieval from completed jobs
• •Debugging failed K8s jobs

Key targets:

• •nx run caelundas:docker-build - Builds for linux/amd64
• •nx run caelundas:helm-upgrade - Deploys with auto-generated name
• •nx run caelundas:kubernetes-copy-files - Retrieves output from PVC
• •nx run caelundas:kubernetes-uninstall - Removes completed jobs

Infrastructure Configuration

Helm Values

Configure deployments via values.yaml:

image:
  repository: ghcr.io/jimmypaolini/caelundas
  tag: latest
  pullPolicy: Always

resources:
  limits:
    memory: 2Gi
    cpu: 1000m

volumes:
  input:
    name: caelundas-input
    mountPath: /app/input
  output:
    name: caelundas-output
    mountPath: /app/output

Kubernetes Context

Deployments use the K8s context from ~/Personal/lexico-prod-kubeconfig.yaml. Ensure the context is set before running deployment commands:

export KUBECONFIG=~/Personal/lexico-prod-kubeconfig.yaml
kubectl config current-context

Common Tasks

Deploy New Version

# Build and push image
nx run caelundas:docker-build

# Deploy to cluster
nx run caelundas:helm-upgrade

Check Job Status

# List all jobs
kubectl get jobs -n default

# Get job details
kubectl describe job <job-name>

# View logs
kubectl logs job/<job-name>

Retrieve Results

# Copy files from output PVC
nx run caelundas:kubernetes-copy-files

# Or manually
kubectl cp <pod-name>:/app/output ./output

Troubleshooting

Job fails to start:

• •Check image pull secrets: kubectl get secret ghcr-credentials
• •Verify image exists in registry: docker pull ghcr.io/jimmypaolini/caelundas:latest
• •Check PVC bindings: kubectl get pvc

PVC mount failures:

• •Ensure PVCs exist: kubectl get pvc
• •Check access modes (ReadWriteOnce vs ReadWriteMany)
• •Verify storage class supports dynamic provisioning

Resource constraints:

• •Check node capacity: kubectl describe nodes
• •Adjust resource limits in values.yaml
• •Consider node selectors for specific workloads

Related Documentation

• •infrastructure/AGENTS.md - Infrastructure architecture
• •applications/caelundas/AGENTS.md - Caelundas deployment workflow
• •infrastructure/helm/kubernetes-job/README.md - Helm chart details

Best Practices

1. •Always specify platform when building Docker images: --platform linux/amd64
2. •Use auto-generated names for jobs to avoid conflicts
3. •Set resource limits to prevent OOM kills
4. •Clean up completed jobs to avoid cluster bloat
5. •Use image pull secrets for private registries
6. •Monitor PVC usage to prevent disk pressure
7. •Tag images with commit SHA for traceability