Kubernetes Native Examples

This section contains examples of deploying applications to native Kubernetes clusters using Simple Container.

Available Examples

Streaming Platform

Deploy a streaming platform with hardcoded IPs, N8N integration, and zero-downtime configurations.

Use Case: Media streaming, real-time data processing, workflow automation

Configuration:

# .sc/stacks/streaming/client.yaml
schemaVersion: 1.0
stacks:
  production:
    type: cloud-compose
    parent: myorg/infrastructure
    config:
      uses: [redis-cache]
      runs: [streaming-server, n8n-workflow]

Docker Compose:

# docker-compose.yaml
version: '3.8'
services:
  streaming-server:
    build: ./streaming
    ports:
      - "8080:8080"
    environment:
      RTMP_PORT: "1935"
      HLS_PORT: "8080"
      REDIS_URL: ${REDIS_URL}
    volumes:
      - streaming-data:/var/streaming

  n8n-workflow:
    image: n8nio/n8n:latest
    ports:
      - "5678:5678"
    environment:
      N8N_BASIC_AUTH_ACTIVE: "true"
      N8N_BASIC_AUTH_USER: "${secret:N8N_USERNAME}"
      N8N_BASIC_AUTH_PASSWORD: "${secret:N8N_PASSWORD}"
      WEBHOOK_URL: "https://streaming.mycompany.com/webhook"
    volumes:
      - n8n-data:/home/node/.n8n

volumes:
  streaming-data:
  n8n-data:

Features:

• Hardcoded IP addresses for consistent access
• N8N workflow automation integration
• Zero-downtime deployment strategy
• RTMP and HLS streaming support
• Redis integration for session management
• Custom registry authentication
• SSL/TLS termination with Caddy

High-Resource Environment

Deploy applications requiring significant compute resources.

Use Case: AI/ML workloads, data processing, compute-intensive applications

Configuration:

# .sc/stacks/ai-workload/client.yaml
schemaVersion: 1.0
stacks:
  production:
    type: cloud-compose
    parent: myorg/infrastructure
    config:
      uses: [gpu-resources, storage-volumes]
      runs: [ai-processor]

Docker Compose:

# docker-compose.yaml
version: '3.8'
services:
  ai-processor:
    build: ./ai-processor
    ports:
      - "8080:8080"
    deploy:
      resources:
        requests:
          cpus: '8.0'
          memory: 32G
        limits:
          cpus: '16.0'
          memory: 64G
    environment:
      CUDA_VISIBLE_DEVICES: "0,1"
      MODEL_PATH: "/models"
      BATCH_SIZE: "32"
    volumes:
      - model-storage:/models
      - gpu-cache:/tmp/gpu-cache

volumes:
  model-storage:
    driver: local
    driver_opts:
      type: nfs
      o: addr=nfs.mycompany.com,rw
      device: ":/models"
  gpu-cache:

Features:

• High CPU and memory allocation (32GB/16CPU)
• GPU support for AI/ML workloads
• NFS storage for large model files
• Node selector for specific hardware
• Resource quotas and limits
• Optimized for compute-intensive tasks

Common Patterns

Zero-Downtime Deployment

# Deployment strategy for zero downtime
spec:
  strategy:
    type: RollingUpdate
    rollingUpdate:
      maxSurge: 50%
      maxUnavailable: 0
  template:
    spec:
      containers:
      - name: app
        readinessProbe:
          httpGet:
            path: /ready
            port: 8080
          initialDelaySeconds: 5
          periodSeconds: 5
        lifecycle:
          preStop:
            exec:
              command: ["/bin/sh", "-c", "sleep 15"]

Deployment Commands

Deploy to staging:

sc deploy -s myapp -e staging

Deploy to production:

sc deploy -s myapp -e production

Best Practices

• Use rolling updates for zero-downtime deployments
• Configure proper resource requests and limits for optimal scheduling
• Implement comprehensive health checks (readiness and liveness probes)
• Use network policies for security isolation
• Configure persistent storage for stateful applications
• Set up horizontal pod autoscaling based on metrics
• Use node selectors for hardware-specific workloads
• Implement proper logging and monitoring with Prometheus and Grafana
• Use secrets and configmaps for configuration management
• Configure RBAC for proper access control