How to Create an Online Python Compiler Using Docker: Step-by-Step Guide

Are you looking to create an online Python compiler? In this guide, we’ll walk you through building a Python compiler using Docker. This setup ensures a secure, consistent environment where users can write, run, and test their Python code directly from a browser.

Why Build an Online Python Compiler?

An online Python compiler is incredibly useful for:

• Learning and Teaching: Ideal for educators and students to practice coding.
• Interview Platforms: Conduct coding interviews with real-time code execution.
• Community Projects: Enable users to share and test code snippets.

Building an online compiler is simpler than you think, thanks to Docker’s ability to run isolated, secure containers for code execution.

What is Docker?

Docker is a platform that allows developers to:

• Package applications and their dependencies into containers.
• Run these containers consistently across different environments.
• Isolate code execution securely, preventing security risks and conflicts.

For this project, Docker ensures each Python script is executed in an isolated environment, safeguarding the server from malicious code.

Prerequisites

To follow along, you’ll need:

• Basic knowledge of Python and Docker.
• Docker installed on your system. If not, download Docker.
• A text editor or IDE like VSCode, more.

Step 1: Set Up the Project Structure

Create a directory for your project and navigate into it:

mkdir online-python-compiler
cd online-python-compiler

Inside the directory, create the following files:

- Dockerfile
- app.py
- requirements.txt

Step 2: Create the Dockerfile

A Dockerfile defines the environment for the Python compiler. Here’s a simple one for running Python code:

# Use the official Python base image
FROM python:3.9

# Set the working directory inside the container
WORKDIR /app

# Copy the current directory contents to the container
COPY . /app

# Install the required dependencies
RUN pip install -r requirements.txt

# Run the application
CMD ["python", "app.py"]

Explanation:

• FROM python:3.9: Uses the official Python image with version 3.9.
• WORKDIR /app: Sets the working directory inside the container.
• COPY . /app: Copies the project files into the container.
• RUN pip install -r requirements.txt: Installs required dependencies.
• CMD [“python”, “app.py”]: Starts the Python script.

Step 3: Write the Python Script (app.py)

This script will:

• Accept Python code as input.
• Execute the code.
• Return the output or errors.

from flask import Flask, request, jsonify
import subprocess

app = Flask(__name__)

@app.route('/run', methods=['POST'])
def run_code():
    code = request.json.get('code')
    
    # Save the code to a temporary file
    with open('temp_code.py', 'w') as f:
        f.write(code)
    
    try:
        # Run the code and capture the output
        result = subprocess.run(['python', 'temp_code.py'], capture_output=True, text=True, timeout=5)
        output = result.stdout if result.returncode == 0 else result.stderr
    except Exception as e:
        output = str(e)
    
    return jsonify({'output': output})

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=5000)

Explanation:

• Flask Framework: Used to create a web server that accepts POST requests.
• subprocess.run(): Executes the Python code securely.
• Timeout: Limits the execution time to prevent infinite loops.

Step 4: Install Dependencies (requirements.txt)

Specify the required Python packages:

flask

Step 5: Build and Run the Docker Container

Build the Docker image:

docker build -t online-python-compiler .

Run the container:

docker run -d -p 5000:5000 online-python-compiler

Explanation:

• -d: Runs the container in detached mode (background).
• -p 5000:5000: Maps the container port 5000 to the host’s port 5000.

Step 6: Test the Online Python Compiler

You can test the compiler using Postman or curl. Here’s an example using curl:

curl -X POST -H "Content-Type: application/json" -d '{"code": "print(\"Hello, World!\")"}' http://localhost:5000/run

The output should be:

{"output": "Hello, World!\n"}

Step 7: Security Considerations

Running user-submitted code involves risks. Ensure:

• Isolation: Each code execution is isolated in a Docker container.
• Resource Limits: Use Docker resource limits to prevent abuse:

docker run -d --memory="256m" --cpus="1.0" -p 5000:5000 online-python-compiler

• Sanitization: Sanitize inputs to prevent code injection.
• Timeouts: Limit execution time to prevent infinite loops.

Step 8: Deploying the Online Compiler

• Deploy the Docker container on cloud platforms like AWS, Google Cloud, or DigitalOcean.
• Use Docker Compose if scaling with multiple services (e.g., adding a frontend).

Step 9: Adding a Frontend (Optional)

For a complete online compiler experience, you can create a frontend using:

• HTML/CSS/JavaScript for a simple UI.
• Ace Editor or Monaco Editor for syntax highlighting.

Example UI Features:

• Code Editor
• Language Selector
• Input Box
• Output Display
• Run Button

Benefits of Using Docker for Online Compilers

• Consistency: Docker ensures the same environment across development, testing, and production.
• Portability: Run the compiler anywhere Docker is supported.
• Security: Isolate user code to protect the host system.
• Scalability: Easily scale by running multiple containers.

Conclusion

Therefore You’ve successfully built a secure, scalable online Python compiler using Docker. This setup is perfect for educational purposes and can serve as a foundation for competitive coding platforms and technical interview systems. Ready to deploy your project? Host it securely and efficiently with Hostinger today!

This project demonstrates:

• How to use Docker for isolated code execution.
• Building a Flask API to handle user requests.
• Security best practices for running untrusted code.

FAQs

1. Why use Docker for an online Python compiler?

Docker provides a consistent and isolated environment for code execution, ensuring security and preventing conflicts between dependencies.

2. Can I support multiple programming languages?

Yes! Simply create different Docker images for each language and configure the API to select the appropriate container.

3. Is it safe to run user-submitted code?

Yes, if proper security measures are implemented:

• Isolate execution in Docker containers.
• Limit resources (CPU, memory).
• Set execution timeouts.

Ready to Build More?

This online Python compiler is just the beginning! Expand its functionality by:

• Supporting more programming languages.
• Adding a beautiful frontend with real-time output.
• Integrating with authentication systems for user profiles and history tracking.