[jira] [Updated] (AIRFLOW-4510) Timezone set incorrectly if multiple DAGs defined in the same file

["Ash Berlin-Taylor (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/AIRFLOW-4510?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Ash Berlin-Taylor updated AIRFLOW-4510:
---------------------------------------
    Fix Version/s:     (was: 2.0.0)
                   1.10.4

> Timezone set incorrectly if multiple DAGs defined in the same file
> ------------------------------------------------------------------
>
>                 Key: AIRFLOW-4510
>                 URL: https://issues.apache.org/jira/browse/AIRFLOW-4510
>             Project: Apache Airflow
>          Issue Type: Bug
>          Components: DAG
>    Affects Versions: 1.10.3
>            Reporter: Abhishek Ray
>            Assignee: Abhishek Ray
>            Priority: Major
>             Fix For: 1.10.4
>
>         Attachments: Screen Shot 2019-05-13 at 2.41.54 PM.png, Screen Shot 2019-05-13 at 6.45.25 PM.png
>
>
> If multiple DAGs are defined in the same file and they share the same default_args, then the subsequent DAGs have an incorrect timezone.
>  
> Steps to reproduce:
>  
> Set the default_timezone to be non-UTC in airflow.cfg
>  
> {noformat}
> default_timezone = America/New_York{noformat}
>  
> DAG definition which has multiple DAGs in the same file:
>  
>  
> {code:java}
> from airflow import DAG
> from airflow.operators.bash_operator import BashOperator
> from datetime import datetime, timedelta
> default_args = {
>  'owner': 'airflow',
>  'depends_on_past': False,
>  'start_date': datetime(2019, 5, 11),
> }
> def make_dynamic_dag(schedule_interval, dag_name):
>  dag = DAG(f"tutorial_{dag_name}", default_args=default_args, schedule_interval=schedule_interval)
>  t1 = BashOperator(task_id='print_date', bash_command='date', dag=dag)
>  return dag
> test_dag_1 = make_dynamic_dag("00 15 * * *", “1”)
> test_dag_2 = make_dynamic_dag("00 18 * * *", “2”)
> {code}
>  
>  
> test_dag_1 is expected to run at 15:00 EST or 19:00 UTC and test_dag_2 is expected to run at 18:00 EST or 22:00 UTC.
>  
> However, test_dag_2 runs at 18:00 UTC which seems to point at it losing timezone information:
> !Screen Shot 2019-05-13 at 2.41.54 PM.png!
>  
> I added some logging in the Airflow code around the default_args initialization and it confirmed the hypothesis that the default_args were being mutated:
>  
> {noformat}
> [2019-05-13 18:40:10,409] {__init__.py:3045} INFO - default_args for DAG tutorial_1: {'owner': 'airflow', 'start_date': datetime.datetime(2019, 5, 11, 0, 0)}
> [2019-05-13 18:40:10,410] {__init__.py:3045} INFO - default_args for DAG tutorial_2: {'owner': 'airflow', 'start_date': <Pendulum [2019-05-11T04:00:00+00:00]>}
> {noformat}
>  
>  
> As a simple fix, I changed the DAG definition to:
> {noformat}
> dag = DAG(f"tutorial_{dag_name}", default_args=default_args, schedule_interval=schedule_interval){noformat}
> and this seems to fix the problem:
>  
> {noformat}
> [2019-05-13 18:44:44,674] {__init__.py:3045} INFO - default_args for DAG tutorial_1: {'owner': 'airflow', 'start_date': datetime.datetime(2019, 5, 11, 0, 0)}
> [2019-05-13 18:44:44,676] {__init__.py:3045} INFO - default_args for DAG tutorial_2: {'owner': 'airflow', 'start_date': datetime.datetime(2019, 5, 11, 0, 0)}
> {noformat}
>  
> !Screen Shot 2019-05-13 at 6.45.25 PM.png!
> I want to add a fix to create a deep-copy of default_args here: [https://github.com/apache/airflow/blob/master/airflow/models/dag.py#L197]



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