docker learn by doing

A simple project to learn Docker by doing.
This example uses a small Python app to explain how to build Docker images, run containers, map ports, use environment variables, and persist data with volumes.

---

Project Structure

docker-basics-101/
│
├── app.py              # FastAPI app with one endpoint
├── requirements.txt    # Python dependencies
├── Dockerfile          # Instructions to build the image
├── .dockerignore       
└── README.md           

---

What the App Does

• Starts a FastAPI web server
• Displays a message (customizable with an environment variable)
• Logs every visit into data/visits.log using a mounted volume

Once running, open: http://localhost:8000

---

Why Docker?

• Solves the "works on my machine" problem
• Provides the same setup across laptops, servers, and CI/CD pipelines
• Isolates dependencies from your local environment
• Makes it easy to run and scale multiple services
• Widely used in Data/AI projects (dbt, Spark, Airflow, ML models, etc.)

---

Key Docker Concepts

• Image – The blueprint built from the Dockerfile
• Container – A running instance of an image
• Port mapping – Connects the container port to your machine (-p 8000:8000)
• Environment variables – Configure runtime values (-e APP_MESSAGE=...)
• Volumes – Store data outside the container (-v ./data:/app/data)

---

Step 1: Build the Image

Run this command in the project folder:

docker build -t docker-learn-by-doing .

---

Step 2: Run the Container

On Mac/Linux:

docker run -p 8000:8000 --name docker-basics   -e APP_MESSAGE="Hello from Docker"   -v $(pwd)/data:/app/data   docker-learn-by-doing

Now open: http://localhost:8000

---

Useful Docker Commands

docker ps                 # list running containers
docker ps -a              # list all containers
docker images             # list available images
docker logs -f <id>       # view logs of a container
docker exec -it <id> sh   # open a shell inside a container
docker stop <id>          # stop a container
docker rm <id>            # remove a stopped container
docker rmi <image>        # remove an image
docker system prune       # clean up unused containers/images (caustion adviced)

---

Workflow Diagram

Dockerfile  --->  Image  --->  Container
                          | 
                          |---> Port mapping (-p)
                          |---> Environment variables (-e)
                          |---> Volume mounts (-v)

---

Next Steps

• Add a database container (e.g., Postgres) and connect the app to it
• Use .env files to manage environment variables
• Try multi-stage builds to optimize image size
• Run containers as non-root users for better security

---

Some quick notes

This project is a beginner-friendly and help with basic setup and commands.
Experiment, try commands, and once you are comfortable, move on to more advanced Docker and data/AI use cases.