hardeybisey commented on code in PR #44960:
URL: https://github.com/apache/airflow/pull/44960#discussion_r1890303998
##########
providers/src/airflow/providers/databricks/operators/databricks.py:
##########
@@ -1127,19 +1134,17 @@ def _get_current_databricks_task(self) -> dict[str,
Any]:
# building the {task_key: task} map below.
sorted_task_runs = sorted(tasks, key=lambda x: x["start_time"])
- return {task["task_key"]: task for task in sorted_task_runs}[
- self._get_databricks_task_id(self.task_id)
- ]
+ return {task["task_key"]: task for task in
sorted_task_runs}[self.databricks_task_key]
def _convert_to_databricks_workflow_task(
self, relevant_upstreams: list[BaseOperator], context: Context | None
= None
) -> dict[str, object]:
"""Convert the operator to a Databricks workflow task that can be a
task in a workflow."""
base_task_json = self._get_task_base_json()
result = {
- "task_key": self._get_databricks_task_id(self.task_id),
+ "task_key": self.databricks_task_key,
"depends_on": [
- {"task_key": self._get_databricks_task_id(task_id)}
+ {"task_key": self._generate_databricks_task_key(task_id)}
for task_id in self.upstream_task_ids
if task_id in relevant_upstreams
],
Review Comment:
@rawwar The reason `_generate_databricks_task_key` is implemented as a
separate function is due to how the `task_key` is generated on line 1147. At
that point, we only have access to the `task_id` and not the Operator object.
Therefore, `_generate_databricks_task_key` is called with the task_id as its
input.
Please let me know if you have any suggestion on what's best to do here.
I also noticed a potential issue: `relevant_upstreams` contains as a list of
subclass of BaseOperators according to this function signature. However, we are
checking for the presence of `task_id`, which is a string, within this list.
This seems like an unintended bug.
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