uranusjr commented on code in PR #27106:
URL: https://github.com/apache/airflow/pull/27106#discussion_r997965953


##########
airflow/cli/commands/task_command.py:
##########
@@ -126,10 +126,12 @@ def _get_dag_run(
         dag_run = DagRun(dag.dag_id, run_id=exec_date_or_run_id, 
execution_date=dag_run_execution_date)
         return dag_run, True
     elif create_if_necessary == "db":
+        data_interval = 
dag.infer_automated_data_interval(logical_date=dag_run_execution_date)

Review Comment:
   This would not always work since infer_automated_data_interval` is only 
designed to provide backward compatibility for DAG runs that are created before 
the data interval concept was introduced, and a DAG using a custom timetable 
would not work correctly with this method. I think `infer_manual_data_interval` 
is probably a better choice; it is not technically correct (the call below 
created a scheduled run), but the exact value of the data interval does not 
actually matter much here anyway since the run is only supposed to be a stub.



##########
airflow/cli/commands/task_command.py:
##########
@@ -126,10 +126,12 @@ def _get_dag_run(
         dag_run = DagRun(dag.dag_id, run_id=exec_date_or_run_id, 
execution_date=dag_run_execution_date)
         return dag_run, True
     elif create_if_necessary == "db":
+        data_interval = 
dag.infer_automated_data_interval(logical_date=dag_run_execution_date)

Review Comment:
   This would not always work since `infer_automated_data_interval` is only 
designed to provide backward compatibility for DAG runs that are created before 
the data interval concept was introduced, and a DAG using a custom timetable 
would not work correctly with this method. I think `infer_manual_data_interval` 
is probably a better choice; it is not technically correct (the call below 
created a scheduled run), but the exact value of the data interval does not 
actually matter much here anyway since the run is only supposed to be a stub.



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