Docker Lab Starter

Welcome to the Docker Lab Starter project! This repository is designed to help you get hands-on experience with containerizing an application using Docker. The goal is to take an existing application, test it locally, and then create a Dockerfile to containerize the application.

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Objectives

By completing this assessment, you will:

1. Understand the application's functionality by testing it locally.
2. Learn to create a Dockerfile to containerize the application.
3. Run the application inside a Docker container.
4. Use Docker Compose to manage a multi-container setup.

Prerequisites

Before you begin, make sure you have the following installed:

• Python (version 3.9 or later)
• Pip
• Docker
• Docker Compose

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Step 0: Clone the Repository

First, clone the project to your local machine:

git clone https://github.com/mo-salah1998/Docker-Lab-Starter.git
cd Docker-Lab-Starter

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Step 2: Run the Application Locally

To understand the application's functionality:

1. Navigate to the project directory:

   cd Docker-Lab-Starter

2. Install the required Python dependencies:

   pip install -r requirements.txt

3. Run the application:

   python main.py

4. Open your browser and go to http://127.0.0.1:5000 to interact with the application.

Take a moment to explore the app and understand what it does.

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Step 3: Containerize the Application

Your task is to create a Dockerfile to containerize this application. Follow these steps:

1. Create a Dockerfile

   • Inside the project directory, create a file named Dockerfile.
   • Write a Dockerfile to containerize the application. Here are some tips:
     • Use a Python base image (e.g., python:3.11-slim).
     • Copy the application files into the container.
     • Install dependencies using pip.
     • Expose port 5000.
     • Set the command to run main.py.

2. Build the Docker Image
   Build the image using the following command:

   docker build -t docker-lab-starter .

3. Run the Container
   Run the application in a container:

   docker run -d -p 5000:5000 docker-lab-starter

4. Test the Application in a Container
   Open your browser and go to http://127.0.0.1:5000 to verify that the application runs correctly in a container.

5. Push the Image to a dockerHub new repo

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Step 4: Adapt the Application to Use MySQL

To enhance the application, you will persist tasks in a MySQL database.

1. Modify the Flask App
   Update the Flask application (main.py) to connect to a MySQL database and store tasks. Replace the in-memory tasks list with database operations.

   Example changes:

   • Use mysql.connector to connect to the database.
   • Fetch tasks from the database for display.
   • Insert new tasks into the database.

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Step 5: Create a Docker Compose Setup

1. Define docker-compose.yml
   Create a docker-compose.yml file to manage both the Flask app and the MySQL database.

2. Build and Start Containers
   Use Docker Compose to build and start the containers:

   docker-compose up --build

3. Test the Application
   Open your browser and go to http://127.0.0.1:5000. Verify that tasks are stored in the MySQL database.

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Submission

Once you have successfully containerized the application:

1. Make sure your Dockerfile is included in the project directory.
2. Document your steps in a README.md file inside the project.
3. Push your changes to a GitHub repository and share the link.

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Hints and Tips

• Refer to the Docker Documentation if you need help with Docker commands.
• Check for errors during the build or run process and debug accordingly.
• If you are new to Flask or Python, take some time to review the code in main.py.
• Test database connectivity using the mysql CLI or a GUI client like MySQL Workbench.
• Check for errors during container startup and debug logs with docker-compose logs.

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Learning Outcomes

By completing this assessment, you will gain hands-on experience in:

• Testing a Python web application.
• Writing and understanding Dockerfile syntax.
• Building and running Docker containers.
• Containerizing a real-world application.
• Using Docker Compose for multi-container setups.
• Persisting data in a relational database.
• Managing real-world application workflows with Docker.

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🚀 Happy coding & learning ! 🚀