SRJahirTech

Docker Full Tutorial — Part 12: Real-World Docker Project

By Suraj Ahir December 15, 2025 6 min read

This is the final part of the Docker tutorial series. Instead of new concepts, we are going to build a complete, production-ready application from scratch using everything we have learned across all twelve parts. This is how Docker is actually used in the real world — not isolated examples, but a complete system working together.

What We Are Building

A full-stack web application with four components:

• Nginx — Reverse proxy and static file server (Port 80)
• Flask API — Backend REST API (internal, not exposed directly)
• PostgreSQL — Database with persistent storage
• Redis — Caching layer for performance

All four run as Docker containers, managed by Docker Compose, with proper networking, volumes, secrets, health checks, and a GitHub Actions CI/CD pipeline.

Project Directory Structure

my-docker-app/
├── .github/
│   └── workflows/
│       └── deploy.yml
├── backend/
│   ├── Dockerfile
│   ├── requirements.txt
│   └── app.py
├── nginx/
│   ├── Dockerfile
│   └── nginx.conf
├── docker-compose.yml
├── docker-compose.dev.yml
├── .env.example
└── .dockerignore

Backend Dockerfile — Multi-Stage

FROM python:3.11-slim AS base
WORKDIR /app
RUN groupadd -r appuser && useradd -r -g appuser appuser
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

FROM base AS production
COPY --chown=appuser:appuser . .
USER appuser
EXPOSE 5000
HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3   CMD curl -f http://localhost:5000/health || exit 1
CMD ["gunicorn", "app:app", "--bind", "0.0.0.0:5000", "--workers", "4"]

Docker Compose — Production

version: '3.8'

services:
  nginx:
    build: ./nginx
    ports:
      - "80:80"
    depends_on:
      backend:
        condition: service_healthy
    networks: [app-net]
    restart: unless-stopped

  backend:
    build:
      context: ./backend
      target: production
    environment:
      DATABASE_URL: postgresql://appuser:${DB_PASSWORD}@db:5432/appdb
      REDIS_URL: redis://redis:6379/0
    depends_on:
      db:
        condition: service_healthy
      redis:
        condition: service_healthy
    networks: [app-net]
    restart: unless-stopped

  db:
    image: postgres:15-alpine
    environment:
      POSTGRES_DB: appdb
      POSTGRES_USER: appuser
      POSTGRES_PASSWORD: ${DB_PASSWORD}
    volumes:
      - db-data:/var/lib/postgresql/data
    healthcheck:
      test: ["CMD-SHELL", "pg_isready -U appuser -d appdb"]
      interval: 10s
      timeout: 5s
      retries: 5
    networks: [app-net]
    restart: unless-stopped

  redis:
    image: redis:7-alpine
    command: redis-server --maxmemory 256mb --maxmemory-policy allkeys-lru
    healthcheck:
      test: ["CMD", "redis-cli", "ping"]
      interval: 10s
      timeout: 5s
      retries: 3
    networks: [app-net]
    restart: unless-stopped

volumes:
  db-data:

networks:
  app-net:
    driver: bridge

Nginx Configuration

upstream backend {
    server backend:5000;
}

server {
    listen 80;
    server_name _;

    location /api {
        proxy_pass http://backend;
        proxy_set_header Host $host;
        proxy_set_header X-Real-IP $remote_addr;
        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
        proxy_connect_timeout 60s;
        proxy_read_timeout 60s;
    }

    location /health {
        proxy_pass http://backend/health;
    }

    location / {
        root /usr/share/nginx/html;
        try_files $uri $uri/ /index.html;
    }
}

Deployment Commands

# Copy .env.example to .env and fill in values
cp .env.example .env
nano .env

# Build all images
docker compose build

# Start everything
docker compose up -d

# Check all services are healthy
docker compose ps

# View logs
docker compose logs -f

# Deploy new version with zero downtime
docker compose pull
docker compose up -d --no-deps --build backend

# Rollback if needed
docker compose up -d --no-deps my-app:previous-tag

What You Have Learned in This Series

Over twelve parts, you have gone from understanding what Docker is to building a production-ready multi-container application. You understand images and containers, Dockerfiles and layers, volumes and networking, Docker Compose, registries, multi-stage builds, security, CI/CD, and Swarm orchestration. This is a complete foundation. The next step is to take a real project you are building and containerize it. That hands-on experience will solidify everything from this series into real skill.

From here, the natural progression is learning Kubernetes for more advanced orchestration, and cloud-native deployment patterns on AWS, GCP, or Azure.

Production Deployment Checklist

Before deploying a Dockerized application to production, work through these essential checks. Images should use specific version tags, not latest. All sensitive configuration (passwords, API keys) should come from environment variables or secrets management, not baked into the image. Resource limits (memory, CPU) should be set for every container. Health checks should be defined so orchestration tools know when a container is ready and when it needs restarting. The application should run as a non-root user inside the container. Images should be scanned for vulnerabilities. Logging should be configured to send to a centralized location rather than relying on docker logs. Volumes should be used for any data that needs to persist across container restarts.

Observability in Docker Environments

Running applications in containers introduces new observability challenges. Container logs are accessible via docker logs container_name but this does not scale to many containers. In production, configure a logging driver to forward logs to a centralized system — the awslogs driver sends logs to AWS CloudWatch, the fluentd driver sends to Fluentd. For metrics, Prometheus with cAdvisor provides container-level CPU, memory, and network metrics. Grafana dashboards visualize these metrics. For distributed tracing across multiple containers, tools like Jaeger or Zipkin help trace requests as they flow through services. Investing in observability from the beginning saves enormous debugging time when issues occur in production.

Practice Exercise

Build a complete production-ready application stack: a web API with a database backend, configured using Docker Compose with proper health checks, resource limits, environment variable management via .env file, named volumes for data persistence, and a reverse proxy (nginx) in front of the API. Write a README.md documenting how to build, run, and deploy the stack. This final project brings together everything from the Docker tutorial series into a realistic, deployable application.

Building Cloud Intuition Over Time

Cloud computing is a domain where deep intuition — the ability to make good architectural decisions quickly, to diagnose problems efficiently, and to anticipate how systems will behave under load — develops through accumulated hands-on experience. Every project you build on cloud infrastructure teaches you something that cannot be learned from documentation alone. The cost surprises, the permission errors, the networking debugging sessions, the performance investigations — these are not obstacles to learning, they are the learning. The engineers who have built genuinely deep cloud intuition have usually accumulated it through many projects over several years, not from any single course or certification. Start building things, make mistakes safely in learning environments, and accumulate that experience deliberately.