apache-airflow-dragdrop-plugin 1.1.1

A custom Apache Airflow plugin to integrate a React UI

Apache Airflow Drag-and-Drop Plugin 🚀

The Apache Airflow Drag-and-Drop Plugin enhances the Apache Airflow UI by allowing users to create and manage workflows using a drag-and-drop interface. This plugin simplifies the process of creating DAGs (Directed Acyclic Graphs) by providing an intuitive visual interface. 🎉

Features ✨

✅ Drag-and-Drop Interface – Easily create and modify DAGs visually. ✅ Predefined Templates – Use templates for common workflows. ✅ Custom Operators – Extend the palette with custom operators. ✅ Real-Time Validation – Validate workflows before deployment. ✅ Export DAG Code – Save workflows as .py file.

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Installation 🛠️

🔹 For Non-Dockerized Airflow Setup

1️⃣ Install the Plugin via pip:

pip install apache-airflow-dragdrop-plugin

2️⃣ Restart Airflow Services:

airflow webserver --reload
airflow scheduler --daemon

3️⃣ Access the Plugin:

Open the Airflow UI and navigate to the "Drag-and-Drop" tab.

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🐳 For Dockerized Airflow Setup

Option 1️⃣: Add the Plugin to requirements.txt

1. Add the following line to your requirements.txt file:

   apache-airflow-dragdrop-plugin
   

2. Rebuild your Docker image:

   docker-compose build -t your-image-name
   

3. Restart your Docker containers:

   docker-compose up -d
   

Option 2️⃣: Install the Plugin Directly Inside the Running Container

1. Install the plugin inside the running Airflow container:

   docker exec -it <container_id> pip install apache-airflow-dragdrop-plugin
   

2. Restart the Airflow webserver and scheduler inside the container:

   docker exec -it <container_id> airflow webserver --reload
   docker exec -it <container_id> airflow scheduler --daemon
   

3. If needed, restart the container:

   docker restart <container_id>
   

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Usage 🖥️

🚀 Creating a New Workflow

1️⃣ Open the Drag-and-Drop Interface : Navigate to the "Drag-and-Drop" tab in the Airflow UI. 

2️⃣ Add Nodes : Drag operators onto the canvas and connect them. 

3️⃣ Configure Nodes : Click on each node to set its properties. 

4️⃣ Validate and Save : Ensure your workflow is error-free and save it as a DAG.

🔄 Exporting and Importing Workflows

• Export: Click "Export" to save your DAG as a Python file.

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Contributing 🤝

We welcome contributions! Follow these steps: 1️⃣ Fork the repository 2️⃣ Create a new branch for your feature or bugfix. 3️⃣ Submit a pull request with a detailed description.

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License 📜

This project is licensed under the Apache License 2.0. See the LICENSE file for details.

Support 🆘

If you encounter any issues or have questions, please open an issue on our GitHub repository.

Acknowledgments 🙏

Special thanks to the Apache Airflow community for their support and inspiration.

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🌎 Connect with Us

[e-mail] https://www.linkedin.com/in/akshaythakare3

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🚀 Happy Workflow Building! 🚀