![]() When reset_dag_run=True and dag run exists, existing dag run will be cleared to rerun. When reset_dag_run=False and dag run exists, DagRunAlreadyExists will be raised. This is useful when backfill or rerun an existing dag run. Reset_dag_run ( bool) - Whether or not clear existing dag run if already exists. If not provided, a run ID will be automatically generated.Ĭonf ( dict) - Configuration for the DAG run.Įxecution_date ( str or datetime.datetime) - Execution date for the dag (templated). Trigger_run_id ( str) - The run ID to use for the triggered DAG run (templated). Trigger_dag_id ( str) - The dag_id to trigger (templated). Triggers a DAG run for a specified dag_id Parameters TriggerDagRunOperator ( *, trigger_dag_id : str, trigger_run_id : Optional = None, conf : Optional = None, execution_date : Optional ] = None, reset_dag_run : bool = False, wait_for_completion : bool = False, poke_interval : int = 60, allowed_states : Optional = None, failed_states : Optional = None, ** kwargs ) ¶ name = Triggered DAG ¶ get_link ( self, operator, dttm ) ¶ It allows users to accessĭAG triggered by task using TriggerDagRunOperator. XCOM_RUN_ID = trigger_run_id ¶ class _dagrun. XCOM_EXECUTION_DATE_ISO = trigger_execution_date_iso ¶ _dagrun. You can do this by using a secret, as described in the GitHub documentation on securing your webhooks. Also, remember to secure your webhook payloads to ensure that only authorized requests can trigger your DAGs. Please note that this is a high-level overview and the exact implementation may vary based on your specific use case and environment. Here's an example: from import trigger_dagįor more details, refer to the Airflow documentation on triggering DAGs. For example, you can use the trigger_dag function from the .trigger_dag module to trigger a DAG run. You can do this by using the Airflow API. Once your endpoint receives a webhook payload, it should trigger the appropriate DAG. Trigger the DAG in response to the webhook payload: For more details, refer to the Flask documentation on routing. You can use the Flask web framework, which Airflow is built on, to create this endpoint. This endpoint should be able to parse the payload and trigger the appropriate DAG based on the event type and other data in the payload. You'll need to create an HTTP endpoint in your Airflow instance that can receive the webhook payloads from GitHub. For more details, refer to the GitHub documentation on creating webhooks.Ĭreate an endpoint in your Airflow instance to receive the webhook payloads: You'll also need to select which events you want to trigger the webhook. Here, you'll need to specify the Payload URL, which is the URL of your Airflow instance that will receive the webhook payloads. In your GitHub repository, navigate to Settings > Webhooks > Add webhook. Set up a webhook in your GitHub repository: Here's a high-level overview of the steps you would need to follow: This can be useful for automating the execution of workflows in response to events such as code pushes or pull requests. You can use GitHub webhooks to trigger Airflow DAGs in a continuous integration pipeline by setting up a webhook in your GitHub repository to send an HTTP request to your Airflow instance whenever a specific event occurs.
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