Docker Containerization & DevOps Deployment Guide
Package applications with all dependencies into lightweight, portable containers that run identically on your local machine and in cloud production environments.
1. Images vs Containers vs Volumes
An Image is a read-only blueprint. A Container is a running instance of an image. A Volume persists data outside the container lifecycle.
# Build an image from current directory
docker build -t myapp:1.0 .
# Run container in detached mode with port forwarding and restart policy
docker run -d -p 8000:8000 --name api-service --restart unless-stopped myapp:1.0
# View running containers and logs
docker ps
docker logs -f api-service
# Clean up unused containers, images, and networks
docker system prune -f2. Writing a Production Dockerfile
Follow best practices: use minimal base images, non-root users, and cache layer dependencies.
# Use lightweight official Python runtime
FROM python:3.11-slim
# Set working directory and avoid .pyc files
WORKDIR /app
ENV PYTHONDONTWRITEBYTECODE=1 PYTHONUNBUFFERED=1
# Install dependencies first to leverage layer caching
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
# Copy application code
COPY . .
# Run as non-root user for security
RUN useradd -m appuser && chown -R appuser /app
USER appuser
EXPOSE 8000
CMD ["gunicorn", "--bind", "0.0.0.0:8000", "config.wsgi:application"]Frequently Asked Questions
What is the difference between a Virtual Machine and a Docker Container?
Virtual machines emulate full hardware including a guest operating system kernel, making them heavy. Docker containers share the host OS kernel and isolate user space, making them start in seconds and use a fraction of RAM.
Why use multi-stage Docker builds?
Multi-stage builds allow you to compile code or install build tools in an initial heavy stage, and then copy only the compiled binaries into a tiny final production image (shrinking image sizes from 1GB+ down to under 50MB).
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