josh-fell commented on a change in pull request #19248:
URL: https://github.com/apache/airflow/pull/19248#discussion_r738479770
##########
File path: airflow/providers/google/cloud/example_dags/example_dataproc.py
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@@ -252,3 +263,40 @@
# Task dependency created via `XComArgs`:
# spark_task_async >> spark_task_async_sensor
+
+with models.DAG(
+ "example_gcp_batch_dataproc",
+ schedule_interval='@once',
+ start_date=days_ago(1),
Review comment:
```suggestion
"example_gcp_batch_dataproc",
schedule_interval='@once',
start_date=datetime(2021, 1, 1),
catchup=False,
```
There is an almost-finished effort to transition away from using
`start_date=days_ago(n)` in example DAGs to using a static value as best
practice. New example DAGs should follow the static start_date approach. The
value used doesn't matter as long as it's static.
Also adding `catchup=False` has been recently discussed for all example DAGs
to help with any accidental DAG run explosions if new users mutate the
`schedule_interval` without fully understanding `catchup=True` is the default.
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