dheerajturaga commented on code in PR #71425:
URL: https://github.com/apache/airflow/pull/71425#discussion_r4209233212
##########
airflow-core/src/airflow/models/taskinstance.py:
##########
@@ -1245,6 +1239,9 @@ def complete_restart(self, *, session: Session) ->
TaskInstance:
if self.state != TaskInstanceState.RESTARTING or self.working_set is
not True:
raise ValueError("Only a current restarting task instance can
complete a restart")
successor = self.prepare_db_for_next_try(session)
+ # Keep the terminated attempt's version; the successor follows the
run's current code.
+ if dag_version_id := DBDagBag._version_from_dag_run(self.dag_run,
session=session):
+ successor.dag_version_id = dag_version_id
Review Comment:
**Minor, and fine to handle in a follow-up PR; it doesn't block this one.**
When a run has been moved to a newer version, the successor now gets that
version's `dag_version_id`. Both callers of `complete_restart` set `self.task`
from `get_dag_for_run(ti.dag_run)`, so `max_tries` follows the new version too.
The other task-derived columns don't follow it. `prepare_db_for_next_try`
copies `queue`, `pool`, `pool_slots`, `priority_weight`, `executor`,
`executor_config`, `run_as_user` and `operator` from the terminated attempt's
row. `schedule_tis` only calls `refresh_from_task` for `UP_FOR_RETRY` task
instances, so a successor in state `None` keeps the old values. If the new
version moves the task to a different `executor`, `queue` or `pool`, the
restarted attempt runs the new code with the old routing.
The non-running branch of `clear_task_instances` already handles this with
`ti.refresh_from_task(task, dag_run=dr)`. Something like this would make the
two paths match:
```python
if self.task is not None:
successor.refresh_from_task(self.task, dag_run=self.dag_run)
successor.max_tries = self.try_number + self.task.retries
```
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