daoudjahdou edited a comment on issue #15978: URL: https://github.com/apache/airflow/issues/15978#issuecomment-878909135
Just tried it on both 2.1.1 and 1.10.15 same behaviour, i even tried the example found here: https://airflow.apache.org/docs/apache-airflow/1.10.15/_modules/airflow/example_dags/example_subdag_operator.html ```py from airflow.example_dags.subdags.subdag import subdag from airflow.models import DAG from airflow.operators.dummy_operator import DummyOperator from airflow.operators.subdag_operator import SubDagOperator from airflow.utils.dates import days_ago DAG_NAME = 'example_subdag_operator' args = { 'owner': 'Airflow', 'start_date': days_ago(2), } dag = DAG( dag_id=DAG_NAME, default_args=args, schedule_interval="@once", tags=['example'] ) start = DummyOperator( task_id='start', dag=dag, ) section_1 = SubDagOperator( task_id='section-1', subdag=subdag(DAG_NAME, 'section-1', args), dag=dag, ) some_other_task = DummyOperator( task_id='some-other-task', dag=dag, ) section_2 = SubDagOperator( task_id='section-2', subdag=subdag(DAG_NAME, 'section-2', args), dag=dag, ) end = DummyOperator( task_id='end', dag=dag, ) start >> section_1 >> some_other_task >> section_2 >> end ``` The log is the following: ``` ____________ _____________ ____ |__( )_________ __/__ /________ __ ____ /| |_ /__ ___/_ /_ __ /_ __ \_ | /| / / ___ ___ | / _ / _ __/ _ / / /_/ /_ |/ |/ / _/_/ |_/_/ /_/ /_/ /_/ \____/____/|__/ Starting flask * Serving Flask app "airflow.utils.serve_logs" (lazy loading) * Environment: production WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead. * Debug mode: off [2021-07-13 08:57:29,682] {_internal.py:113} INFO - * Running on http://0.0.0.0:8793/ (Press CTRL+C to quit) [2021-07-13 08:57:29,690] {scheduler_job.py:1270} INFO - Starting the scheduler [2021-07-13 08:57:29,691] {scheduler_job.py:1275} INFO - Processing each file at most -1 times [2021-07-13 08:57:29,696] {dag_processing.py:254} INFO - Launched DagFileProcessorManager with pid: 129 [2021-07-13 08:57:29,697] {scheduler_job.py:1839} INFO - Resetting orphaned tasks for active dag runs [2021-07-13 08:57:29,701] {settings.py:52} INFO - Configured default timezone Timezone('UTC') [2021-07-13 08:57:29,711] {dag_processing.py:532} WARNING - Because we cannot use more than 1 thread (parsing_processes = 2 ) when using sqlite. So we set parallelism to 1. [2021-07-13 08:57:37,907] {scheduler_job.py:964} INFO - 1 tasks up for execution: <TaskInstance: example_subdag_operator.section-1 2021-07-11 00:00:00+00:00 [scheduled]> [2021-07-13 08:57:37,909] {scheduler_job.py:998} INFO - Figuring out tasks to run in Pool(name=default_pool) with 128 open slots and 1 task instances ready to be queued [2021-07-13 08:57:37,909] {scheduler_job.py:1025} INFO - DAG example_subdag_operator has 0/16 running and queued tasks [2021-07-13 08:57:37,909] {scheduler_job.py:1086} INFO - Setting the following tasks to queued state: <TaskInstance: example_subdag_operator.section-1 2021-07-11 00:00:00+00:00 [scheduled]> [2021-07-13 08:57:37,911] {scheduler_job.py:1128} INFO - Sending TaskInstanceKey(dag_id='example_subdag_operator', task_id='section-1', execution_date=datetime.datetime(2021, 7, 11, 0, 0, tzinfo=Timezone('UTC')), try_number=1) to executor with priority 4 and queue default [2021-07-13 08:57:37,911] {base_executor.py:82} INFO - Adding to queue: ['airflow', 'tasks', 'run', 'example_subdag_operator', 'section-1', '2021-07-11T00:00:00+00:00', '--local', '--pool', 'default_pool', '--subdir', '/opt/airflow/dags/monitor.py'] [2021-07-13 08:57:37,922] {sequential_executor.py:59} INFO - Executing command: ['airflow', 'tasks', 'run', 'example_subdag_operator', 'section-1', '2021-07-11T00:00:00+00:00', '--local', '--pool', 'default_pool', '--subdir', '/opt/airflow/dags/monitor.py'] [2021-07-13 08:57:39,332] {dagbag.py:496} INFO - Filling up the DagBag from /opt/airflow/dags/monitor.py Running <TaskInstance: example_subdag_operator.section-1 2021-07-11T00:00:00+00:00 [queued]> on host 969b77ead72f ``` -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
