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| from datetime import datetime, timedelta | |
| import pendulum | |
| from airflow import DAG | |
| from airflow.operators.python_operator import PythonOperator | |
| # gcloud composer environments run test --project PROJECT --location us-central1 backfill -- -s 2020-02-24 -e 2020-02-26 timezone | |
| default_args = {"owner": "airflow"} | |
| def f1(tzinfo: datetime.tzinfo = None, **context): | |
| if tzinfo is not None: | |
| print(("local time:", tzinfo.convert(context["execution_date"]))) | |
| for key in context: | |
| print((key, context[key])) | |
| dag_id = "timezone" | |
| with DAG( | |
| dag_id, | |
| default_args=default_args, | |
| catchup=False, | |
| start_date=datetime(2020, 2, 21), | |
| schedule_interval="@daily", | |
| ) as dag: | |
| # As of airflow 1.10.5, the marcos value is based on UTC. | |
| # For timezone aware task, I suggest to only use execution_date as a reference. | |
| # Don't use ds or prev_ds | |
| # See https://airflow.apache.org/timezone.html#templates | |
| task = PythonOperator( | |
| task_id="t1", | |
| provide_context=True, | |
| depends_on_past=True, | |
| python_callable=f1, | |
| dag=dag, | |
| ) |
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