SameerMesiah97 opened a new issue, #62392:
URL: https://github.com/apache/airflow/issues/62392
### Apache Airflow Provider(s)
databricks
### Versions of Apache Airflow Providers
`apache-airflow-providers-databricks >= 7.9.1`
### Apache Airflow version
main
### Operating System
Debian GNU/Linux 12 (bookworm)
### Deployment
Other
### Deployment details
_No response_
### What happened
When using `DatabricksRunNowOperator` with `cancel_previous_runs=True` and
without specifying either `job_id` or `job_name`, the task fails at execution
time with a `KeyError`.
The failure occurs immediately when the task is triggered.
### What you think should happen instead
If `cancel_previous_runs=True` is set without specifying either `job_id` or
`job_name`, the operator should raise a clear `AirflowException` explaining
that a job identifier is required.
Validation errors related to operator configuration should surface as
explicit Airflow exceptions rather than raw Python exceptions.
### How to reproduce
Create and trigger the following DAG:
```
from datetime import datetime
from airflow import DAG
from airflow.providers.databricks.operators.databricks import
DatabricksRunNowOperator
with DAG(
dag_id="databricks_cancel_previous_runs_keyerror_repro",
start_date=datetime(2025, 1, 1),
schedule=None,
catchup=False,
) as dag:
DatabricksRunNowOperator(
task_id="trigger_keyerror",
cancel_previous_runs=True,
)
```
**Observed Behaviour**
The task fails immediately with a `KeyError`.
### Anything else
While the configuration is invalid, the current behavior surfaces a raw
`KeyError`, which is an internal Python exception. Other validation paths in
`DatabricksRunNowOperator` raise `AirflowException` for invalid or conflicting
parameters.
Raising a consistent `AirflowException` in this scenario would align this
branch with the operator’s existing validation behavior and provide clearer
feedback to DAG authors.
### Are you willing to submit PR?
- [x] Yes I am willing to submit a PR!
### Code of Conduct
- [x] I agree to follow this project's [Code of
Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)
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