Docker in Production: Lessons from Scaling to Millions

Introduction

Running Docker in production at scale requires careful planning and proven strategies. In this article, I'll share lessons learned from scaling Dockerized applications to serve millions of users.

1. Multi-Stage Builds

Use multi-stage builds to create smaller, more secure images:

# Dockerfile
FROM node:18-alpine AS builder
WORKDIR /app
COPY package*.json ./
RUN npm ci
COPY . .
RUN npm run build

FROM node:18-alpine AS production
WORKDIR /app
COPY --from=builder /app/dist ./dist
COPY --from=builder /app/node_modules ./node_modules
COPY --from=builder /app/package.json ./
USER node
EXPOSE 3000
CMD ["npm", "start"]

2. Resource Management

Set resource limits to prevent container sprawl:

# docker-compose.yml
version: '3.8'
services:
  app:
    image: my-app:latest
    deploy:
      resources:
        limits:
          memory: 512M
          cpus: '1.0'
        reservations:
          memory: 256M
          cpus: '0.5'

3. Health Checks

Implement proper health checks:

# Dockerfile
HEALTHCHECK --interval=30s --timeout=3s --start-period=5s --retries=3 \
  CMD curl -f http://localhost:3000/health || exit 1

4. Logging Strategy

Implement centralized logging:

# docker-compose.yml
version: '3.8'
services:
  app:
    image: my-app:latest
    logging:
      driver: "json-file"
      options:
        max-size: "10m"
        max-file: "3"

5. Security Best Practices

Run containers as non-root users:

# Dockerfile
FROM node:18-alpine
RUN addgroup -g 1001 -S nodejs
RUN adduser -S nextjs -u 1001
USER nextjs

6. Orchestration with Kubernetes

For large-scale deployments, use Kubernetes:

# deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-app
spec:
  replicas: 3
  selector:
    matchLabels:
      app: my-app
  template:
    metadata:
      labels:
        app: my-app
    spec:
      containers:
      - name: my-app
        image: my-app:latest
        ports:
        - containerPort: 3000
        resources:
          limits:
            memory: "512Mi"
            cpu: "500m"

Conclusion

Docker in production requires a holistic approach focusing on security, monitoring, and scalability. By implementing these strategies, you can build robust, scalable containerized applications.