molcay commented on PR #37087:
URL: https://github.com/apache/airflow/pull/37087#issuecomment-1916616603
Hi @uranusjr,
I think the use case is a bit different from the datasets. Let's have an
following DAG as an example;
```python3
import random
from airflow import models
from airflow.operators.empty import EmptyOperator
from airflow.operators.python import BranchPythonOperator
from airflow.operators.trigger_dagrun import TriggerDagRunOperator
import pendulum
def check_for_file():
i = random.randint(1, 8)
if i % 2 == 1:
return "do_nothing"
else:
return "trigger_dag_again"
with models.DAG(dag_id="Trigger_dag_test",
start_date=pendulum.datetime(2021, 1, 1, tz="UTC"),
schedule_interval="*/3 * * * *",
catchup=False,
tags=['TriggerDagRunOperator']) as dag:
first_task = EmptyOperator(
task_id="first_task"
)
checkforfile = BranchPythonOperator(task_id='check_for_file',
python_callable=check_for_file)
trigger_dag_again = TriggerDagRunOperator(
task_id="trigger_dag_again",
trigger_dag_id="Trigger_dag_test",
wait_for_completion=False
)
do_nothing = EmptyOperator(
task_id="do_nothing"
)
final = EmptyOperator(
task_id="final"
)
first_task >> checkforfile >> [trigger_dag_again, do_nothing] >> final
```
For this DAG, every time the DAG run in scheduled fashion and select the
trigger_dag_again path, than we see `manual__<date>` as `run_id`, to
distinguish this programmatically triggered DAG runs (using
`TriggerDagRunOperator`) we need to introduce a new run type.
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