This tool generates Airflow DAG files from a JSON configuration file, with a user-friendly web interface for creating and visualizing DAGs.

.
├── ui/                  # Frontend React application
│   ├── src/              # React source code
│   ├── public/           # Static files
│   └── package.json      # Frontend dependencies
├── dag_generator/        # Backend Python package
│   ├── core/            # Core generation logic
│   ├── utils/           # Utility functions
│   └── tests/           # Unit tests
├── main.py              # Main entry point
├── requirements.txt     # Python dependencies
└── README.md           # This file

1. Python 3.8 or higher
2. Node.js 14 or higher
3. npm or yarn

1. Create and activate a Python virtual environment (recommended):

# Windows
python -m venv venv
venv\Scripts\activate

# Linux/Mac
python3 -m venv venv
source venv/bin/activate

2. Install the required Python dependencies:

pip install -r requirements.txt

3. Verify the installation:

python -m main -jfp sample_dag.json

1. Navigate to the frontend directory:

cd ui

2. Install Node.js dependencies:

npm install
# or if using yarn
yarn install

1. Start the frontend development server (in one terminal):

cd ui
npm start
# or
yarn start

2. The backend will be used when generating DAGs from the UI.

1. Use the web interface to:

   • Create tasks by selecting operators and configuring them
   • Define dependencies between tasks by connecting them
   • Set DAG-level configurations (schedule, start date, etc.)

2. Export the DAG configuration:

   • Click the "Generate DAG JSON" button to download the JSON configuration
   • Save the file (e.g., my_dag.json)

3. Generate the DAG file:

python -m main -jfp my_dag.json

The generated DAG file will be created in the output/dags directory.

The JSON configuration should follow this structure:

{
  "dag_id": "your_dag_id",
  "description": "DAG description",
  "schedule_interval": "@daily",
  "start_date": "YYYY-MM-DD",
  "catchup": false,
  "tasks": [
    {
      "task_id": "task_name",
      "operator_type": "OperatorType",
      "config": {
        // Operator specific configuration
      }
    }
  ],
  "dependencies": [
    {
      "source": "task_id_1",
      "target": "task_id_2"
    }
  ]
}

• PythonOperator
• BashOperator
• PostgresOperator
• EmailOperator
• HttpOperator
• S3Hook
• FileSensor
• TimeSensor
• SqlSensor
• DockerOperator
• SlackWebhookOperator

# Backend tests
pytest dag_generator/tests/

# Frontend tests (from ui directory)
cd ui
npm test
# or
yarn test

The project follows PEP 8 style guidelines for Python and ESLint for JavaScript. Use linters to ensure code quality:

# Python linting
flake8 dag_generator/

# JavaScript linting (from ui directory)
cd ui
npm run lint
# or
yarn lint

1. If the UI fails to start:

   • Ensure Node.js is installed
   • Check if port 3000 is available
   • Verify all frontend dependencies are installed
   • Try clearing npm cache: npm cache clean --force

2. If DAG generation fails:

   • Check if all required Python packages are installed
   • Verify the JSON configuration format
   • Ensure the output directory is writable
   • Check Python virtual environment is activated

3. Common issues:

   • Missing dependencies: Run pip install -r requirements.txt again
   • Port conflicts: Change the frontend port in package.json
   • Permission errors: Check directory permissions in output/
   • Node modules issues: Delete node_modules and run npm install again