Automate Airflow DAG Runs and Monitor Status with n8n
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Streamline your Apache Airflow workflows by automating DAG runs and proactively monitoring their status. This n8n workflow allows you to trigger DAGs, check their execution state, and retrieve results, ensuring your data pipelines run smoothly and efficiently.
About This Workflow
This n8n workflow provides a robust solution for interacting with your Apache Airflow environment. It enables you to programmatically trigger DAG runs, passing custom configurations, and then continuously monitor their progress. By fetching the state of each DAG run, you can implement intelligent logic to wait for queued or running tasks, retrieve task results upon success, or halt execution and report errors if a DAG run fails or exceeds acceptable wait times. This offers granular control and visibility over your Airflow operations, preventing bottlenecks and ensuring timely data processing.
Key Features
- Automated DAG Triggering: Initiate Airflow DAG runs with dynamic configurations via the Airflow API.
- Real-time Status Monitoring: Continuously poll Airflow for the current state of your DAG runs (queued, running, success, failed).
- Conditional Logic: Implement custom logic based on DAG run status, allowing for tailored responses.
- Result Retrieval: Fetch specific task outputs (e.g., xcom entries) once a DAG run completes successfully.
- Error Handling & Timeouts: Automatically stop and report errors for failed DAG runs or those that exceed defined waiting periods.
How To Use
- Configure Airflow Credentials: Set up HTTP Basic Authentication with your Airflow API details within n8n.
- Define Input Data: Provide the
dag_idandconf(configuration) for the DAG run you want to trigger. - Trigger DAG Run: The 'Airflow: dag_run' node sends a POST request to initiate the DAG execution.
- Monitor DAG State: The 'Airflow: dag_run - state' node polls the Airflow API to get the status of the initiated DAG run.
- Implement Conditional Logic: Use the 'if state == queued' and 'Switch: state' nodes to branch your workflow based on the DAG run's status (queued, running, success, failed).
- Handle Timeouts & Failures: Configure 'dag run wait too long' and 'dag run fail' nodes to stop and alert on problematic runs.
- Retrieve Task Results: If the DAG run is successful, use 'Airflow: dag_run - get result' to fetch specific task outputs.
Apps Used
Workflow JSON
{
"id": "427a0e60-f406-4f19-b56f-961649c38e9e",
"name": "Automate Airflow DAG Runs and Monitor Status with n8n",
"nodes": 26,
"category": "DevOps",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
Get This Workflow
ID: 427a0e60-f406...
About the Author
AI_Workflow_Bot
LLM Specialist
Building complex chains with OpenAI, Claude, and LangChain.
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