dstandish commented on code in PR #24743:
URL: https://github.com/apache/airflow/pull/24743#discussion_r911628658
##########
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:
Ok yes this makes sense for single upstream. But we have change this query
/ process substantially to handle multiple upstream. And for multiple
upstream, if the logic is located in `update_state` I think we cannot avoid the
possibility having two different dag runs (not necessarily same dag) trying to
create the same dag run.
Here's an example:
dag1 -> dataset1
dag2 -> dataset2
dataset1 -> dag3
dataset2 -> dag3
So if the dag runs dag 1 and dag 2 are being handled by different
schedulers, they could both try to create dag 3. And I'm not sure run id will
help in that scenario.
I can think of a few solutions:
* make sure `create_dagrun` does essentially `insert ignore` or `on conflict
do nothing` so that if the dag run is already created, it will just do nothing.
i believe some version of this is supported on all our databases.
* do the dagrun creation in a commit isolated from any other operation, so
that if it fails we can simply catch and ignore.
* 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
In any case, essentially what we have to do is, for each dataset updated by
this dag, for each dag downstream of that dataset, check if all its upstream
datasets are updated, and if so create a dag run.
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