Lee-W commented on code in PR #55068:
URL: https://github.com/apache/airflow/pull/55068#discussion_r2541293088
##########
airflow-core/src/airflow/models/taskinstance.py:
##########
@@ -1446,6 +1447,80 @@ def update_heartbeat(self):
.values(last_heartbeat_at=timezone.utcnow())
)
+ @property
+ def start_trigger_args(self) -> StartTriggerArgs | None:
+ if self.task:
+ if self.task.is_mapped:
+ context = self.get_template_context()
+ if self.task.expand_start_from_trigger(context=context):
Review Comment:
If `MappedOperator` does not works well now, should we raise a warning?
##########
airflow-core/src/airflow/models/taskinstance.py:
##########
@@ -1446,6 +1447,80 @@ def update_heartbeat(self):
.values(last_heartbeat_at=timezone.utcnow())
)
+ @property
+ def start_trigger_args(self) -> StartTriggerArgs | None:
+ if self.task:
+ if self.task.is_mapped:
+ context = self.get_template_context()
+ if self.task.expand_start_from_trigger(context=context):
+ return self.task.expand_start_trigger_args(context=context)
+ elif self.task.start_from_trigger:
+ return self.task.start_trigger_args
+ return None
+
+ # TODO: We have some code duplication here and in the
_create_ti_state_update_query_and_update_state
+ # method of the task_instances module in the execution api when a
TIDeferredStatePayload is being
+ # processed. This is because of a TaskInstance being updated
differently using SQLAlchemy.
+ # If we use the approach from the execution api as common code in
the DagRun schedule_tis method,
+ # the side effect is the changes done to the task instance aren't
picked up by the scheduler and
+ # thus the task instance isn't processed until the scheduler is
restarted.
+ @provide_session
+ def defer_task(self, session: Session = NEW_SESSION) -> bool:
+ """
+ Mark the task as deferred and sets up the trigger that is needed to
resume it when TaskDeferred is raised.
+
+ :meta: private
+ """
+ from airflow.models.trigger import Trigger
+
+ if TYPE_CHECKING:
+ assert isinstance(self.task, Operator)
+
+ if start_trigger_args := self.start_trigger_args:
+ trigger_kwargs = start_trigger_args.trigger_kwargs or {}
+ timeout = start_trigger_args.timeout
+
+ # Calculate timeout too if it was passed
+ if timeout is not None:
+ self.trigger_timeout = timezone.utcnow() + timeout
+ else:
+ self.trigger_timeout = None
+
+ trigger_row = Trigger(
+ classpath=start_trigger_args.trigger_cls,
+ kwargs=trigger_kwargs,
+ )
+
+ # First, make the trigger entry
+ session.add(trigger_row)
+ session.flush()
+
+ # Then, update ourselves so it matches the deferral request
+ # Keep an eye on the logic in
`check_and_change_state_before_execution()`
+ # depending on self.next_method semantics
+ self.state = TaskInstanceState.DEFERRED
+ self.trigger_id = trigger_row.id
+ self.next_method = start_trigger_args.next_method
+ self.next_kwargs = start_trigger_args.next_kwargs or {}
+
+ # If an execution_timeout is set, set the timeout to the minimum of
+ # it and the trigger timeout
+ execution_timeout = self.task.execution_timeout
+ if execution_timeout:
Review Comment:
```suggestion
if execution_timeout := self.task.execution_timeout:
```
##########
airflow-core/src/airflow/models/taskinstance.py:
##########
@@ -1446,6 +1447,80 @@ def update_heartbeat(self):
.values(last_heartbeat_at=timezone.utcnow())
)
+ @property
+ def start_trigger_args(self) -> StartTriggerArgs | None:
+ if self.task:
+ if self.task.is_mapped:
+ context = self.get_template_context()
+ if self.task.expand_start_from_trigger(context=context):
+ return self.task.expand_start_trigger_args(context=context)
+ elif self.task.start_from_trigger:
Review Comment:
```suggestion
elif self.task.start_from_trigger is True:
```
nit: for expliclity
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