deployment-config.txt

# docker-compose.yml - For local development
# This is like setting up your chess board for practice
version: '3.8'

services:
  # Main application - The King piece (most important)
  app:
    build:
      context: .
      dockerfile: Dockerfile
    ports:
      - "3000:3000"
    environment:
      - NODE_ENV=development
      - DATABASE_URL=postgresql://chess:password@postgres:5432/chess_learning
      - REDIS_URL=redis://redis:6379
      - JWT_SECRET=your-secret-key-here
    depends_on:
      - postgres
      - redis
    volumes:
      - ./src:/app/src  # Hot reload for development
      - ./prisma:/app/prisma

  # PostgreSQL database - The Queen (powerful and versatile)
  postgres:
    image: postgres:15-alpine
    environment:
      - POSTGRES_USER=chess
      - POSTGRES_PASSWORD=password
      - POSTGRES_DB=chess_learning
    volumes:
      - postgres_data:/var/lib/postgresql/data
    ports:
      - "5432:5432"

  # Redis for caching and WebSocket scaling - The Knights (quick and agile)
  redis:
    image: redis:7-alpine
    command: redis-server --appendonly yes
    volumes:
      - redis_data:/data
    ports:
      - "6379:6379"

  # Stockfish service - The Bishops (deep analysis)
  stockfish:
    build:
      context: ./stockfish
      dockerfile: Dockerfile
    deploy:
      replicas: 4  # Multiple engines for parallel analysis
    environment:
      - STOCKFISH_THREADS=2
      - STOCKFISH_HASH=256

volumes:
  postgres_data:
  redis_data:

# Dockerfile - Building our application container
FROM node:18-alpine AS builder

# Install dependencies for native modules
RUN apk add --no-cache python3 make g++

WORKDIR /app

# Copy package files
COPY package*.json ./
COPY prisma ./prisma/

# Install dependencies
RUN npm ci

# Generate Prisma client
RUN npx prisma generate

# Copy source code
COPY . .

# Build the application
RUN npm run build

# Production stage
FROM node:18-alpine

# Install Stockfish (for local analysis option)
RUN apk add --no-cache stockfish

WORKDIR /app

# Copy built application
COPY --from=builder /app/dist ./dist
COPY --from=builder /app/node_modules ./node_modules
COPY --from=builder /app/prisma ./prisma
COPY --from=builder /app/package*.json ./

# Expose port
EXPOSE 3000

# Health check - Ensuring our King is safe
HEALTHCHECK --interval=30s --timeout=3s --start-period=40s --retries=3 \
  CMD node healthcheck.js

# Start the application
CMD ["npm", "run", "start:prod"]

# kubernetes/deployment.yaml - For production scaling
# This is like organizing a chess tournament with multiple boards
apiVersion: apps/v1
kind: Deployment
metadata:
  name: chess-learning-api
  labels:
    app: chess-learning
spec:
  replicas: 3  # Start with 3 instances
  selector:
    matchLabels:
      app: chess-learning-api
  template:
    metadata:
      labels:
        app: chess-learning-api
    spec:
      containers:
      - name: api
        image: your-registry/chess-learning:latest
        ports:
        - containerPort: 3000
        env:
        - name: NODE_ENV
          value: "production"
        - name: DATABASE_URL
          valueFrom:
            secretKeyRef:
              name: chess-secrets
              key: database-url
        - name: REDIS_URL
          valueFrom:
            secretKeyRef:
              name: chess-secrets
              key: redis-url
        resources:
          requests:
            memory: "512Mi"
            cpu: "500m"
          limits:
            memory: "1Gi"
            cpu: "1000m"
        livenessProbe:
          httpGet:
            path: /health
            port: 3000
          initialDelaySeconds: 30
          periodSeconds: 10
        readinessProbe:
          httpGet:
            path: /ready
            port: 3000
          initialDelaySeconds: 5
          periodSeconds: 5

---
# Horizontal Pod Autoscaler - Scales based on load
# Like adding more chess boards when more players arrive
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: chess-learning-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: chess-learning-api
  minReplicas: 3
  maxReplicas: 20
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70
  - type: Resource
    resource:
      name: memory
      target:
        type: Utilization
        averageUtilization: 80
  behavior:
    scaleUp:
      stabilizationWindowSeconds: 60
      policies:
      - type: Percent
        value: 100  # Double pods
        periodSeconds: 60
    scaleDown:
      stabilizationWindowSeconds: 300  # Wait 5 minutes before scaling down
      policies:
      - type: Percent
        value: 50  # Remove half
        periodSeconds: 60

---
# Service - Load balancer for our API
apiVersion: v1
kind: Service
metadata:
  name: chess-learning-service
spec:
  type: LoadBalancer
  selector:
    app: chess-learning-api
  ports:
  - port: 80
    targetPort: 3000
  sessionAffinity: ClientIP  # Important for WebSocket connections

---
# Redis deployment for caching and WebSocket scaling
apiVersion: apps/v1
kind: Deployment
metadata:
  name: redis
spec:
  selector:
    matchLabels:
      app: redis
  template:
    metadata:
      labels:
        app: redis
    spec:
      containers:
      - name: redis
        image: redis:7-alpine
        ports:
        - containerPort: 6379
        resources:
          requests:
            memory: "256Mi"
            cpu: "250m"
          limits:
            memory: "512Mi"
            cpu: "500m"