Save money with our transparent approach to pricing Rapid Assessment & Migration Program (RAMP) Migrate from PaaS: Cloud Foundry, OpenshiftĬOVID-19 Solutions for the Healthcare Industry Running a Data Analytics DAG in Google Cloud Using Data from Azure.Running a Data Analytics DAG in Google Cloud Using Data from AWS.Running a Data Analytics DAG in Google Cloud.Running a Hadoop wordcount job on a Cloud Dataproc cluster.Launching Dataflow pipelines with Cloud Composer.Automating infrastructure with Cloud Composer.Otherwise, the Airflow dashboard won't be able to render much about it. To retain access to the task after it runs, you'd have to keep the new DAG definition stable and accessible on future dashboard updates / DagBag collection. Use something like Jinja templating to write a new Python file in the dags/ folder.If you dynamically generate DAGs from a list of config files, add a new config file to wherever you're pulling config files from, so that a new DAG gets generated on the next DAG collection.If you dynamically generate DAGs from a Variable ( like so), modify the Variable.That said, here are the approaches that would accomplish what the question is asking, no matter how bad of an idea it is, in the increasing degree of ham-handedness: y2k-shubham provides an excellent example of such a setup, and I'm grateful for his guidance in the comments on this question. Any DAG you're tempted to custom-create in a task should probably instead be a static, heavily parametrized, conditionally-triggered DAG. In short: if the task writes where the DagBag reads from, yes, but it's best to avoid a pattern that requires this. Proper way to create dynamic workflows in Airflow - accepted answer dynamically creates tasks, not DAGs, via a complicated XCom setup.Run Airflow DAG for each file and Airflow: Proper way to run DAG for each file: identical use case, but the accepted answer uses two static DAGs, presumably with different parameters.Is that the best pattern here? Other related StackOverflow threads This does mean that I can no longer regulate the frequency of folder polling with the poke_interval parameter of FileSensor instead, Airflow will poll the folder every time it collects DAGs. Per Airflow dynamic DAG and task Ids, I can achieve what I'm trying to do by omitting the FileSensor task altogether and just letting Airflow generate the per-file task at each scheduler heartbeat, replacing the Sensor_DAG with just executing generate_dags_for_files: Update: Nevermind - while this does create a DAG in the dashboard, actual execution runs into the "DAG seems to be missing" issue: generate_dags_for_files() New globals after parse time are irrelevant. Start_task > sensor_task > process_creator_task > stop_taskīut that doesn't work, because by the time process_creator_task runs, the globals have already been parsed by Airflow. Sensor_task = FileSensor(task_id="my_file_sensor_task", Stop_task = DummyOperator(task_id="stop") Start_task = DummyOperator(task_id="start") Schedule_interval= "50 * * * *", catchup=False, ) as sensor_dag: With DAG("Sensor_DAG", default_args=default_args, The main DAG then invokes this logic via a PythonOperator: # File-sensing DAG # Try to place the DAG into globals(), which doesn't work # Dynamic DAG generation task code, for the Sensor_DAG belowĭef generate_dags_for_files(location=UPLOAD_LOCATION, **kwargs):įor filepath in Path(location).glob('*'):ĭag_task = DummyOperator(dag=dag, task_id=f"start_") In my initial implementation, CreateDAGTask was a PythonOperator that created DAG globals, by placing them in the global namespace ( see this SO answer), like so: from airflow import DAGįrom _operator import DummyOperatorįrom _operator import PythonOperatorįrom _sensor import FileSensor Conceptually: Sensor_DAG (FileSensor -> CreateDAGTask) My first thought was to do this with a FileSensor that monitors the upload folder and, conditional on presence of new files, triggers a task that creates the separate DAGs. For each uploaded file, I want to spawn a DAG that is specific to that file. I have an upload folder that gets irregular uploads.
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