FFCMSouza commented on issue #38935:
URL: https://github.com/apache/airflow/issues/38935#issuecomment-2054223724
Anyway, I managed to find a way to solve my problem overwriting the
`on_kill` method in my spark operator.
The only downside is that the `on_kill` method doesn't have access to the
task context. To solve this problem, I created local variables on the `execute`
method with the context information I need.
```
def on_kill(self):
logging.info('starting on_kill')
cancel_step(self.project, self.dag_name, self.run_id, self.task_id)
```
def execute(self, context):
self.log.info("starting SparkToDataLake.execute")
self.dag_name = context.get('task_instance').dag_id
self.run_id = context.get('run_id')
self.task_id = context.get('task_instance').task_id +
str(context.get('task_instance').map_index)
PS: The `on_kill` method is also called on the `AirflowTaskTimeout`
exception.
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