ashb commented on code in PR #24743:
URL: https://github.com/apache/airflow/pull/24743#discussion_r912820582
##########
airflow/models/dagrun.py:
##########
@@ -631,6 +631,32 @@ def update_state(
session.merge(self)
# We do not flush here for performance reasons(It increases queries
count by +20)
+ from airflow.models import Dataset
+ from airflow.models.dataset_dag_run_event import DatasetDagRunEvent as
DDRE
+ from airflow.models.serialized_dag import SerializedDagModel
+
+ datasets = []
+ for task in self.dag.tasks:
+ for outlet in getattr(task, '_outlets', []):
+ if isinstance(outlet, Dataset):
+ datasets.append(outlet)
+ dataset_ids = [x.get_dataset_id(session=session) for x in datasets]
+ events_to_process =
session.query(DDRE).filter(DDRE.dataset_id.in_(dataset_ids)).all()
Review Comment:
> add a new top level scheduler query that will partition dags using the
same kind of skip locked logic of the main scheduler query and create necessary
dag runs
I was hoping to explicitly not have to do this (as mentioned in the thread I
linked.)
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