kunaljubce opened a new issue, #67643:
URL: https://github.com/apache/airflow/issues/67643
### Under which category would you file this issue?
Providers
### Apache Airflow version
3.3
### What happened and how to reproduce it?
When we try to run a dag with `executor_config={"KubernetesExecutor":
{"config_file": "/some/path/kubeconfig.yaml"}}`, the task gets stuck in a
`Queued` state and never actually triggers or fails. Further inspecting the
scheduler logs shows the below:
```
→ export
KUBECONFIG=/Users/81045729/Documents/constant_variables/airflow/.build/.k8s-clusters/airflow-python-3.10-v1.30.13/.kubeconfig
&& kubectl logs -n airflow airflow-scheduler-58dcc9f6cf-qnbvj -c scheduler
2>&1 | grep -i "Invalid
executor_config\|TypeError\|error.*executor\|execute_async\|run_next\|Cannot
convert" | tail -20
2026-05-28T11:06:02.185119Z [error ] Invalid executor_config for
TaskInstanceKey(dag_id='test_k8s_executor_config_legacy',
task_id='check_for_failure', run_id='manual__2026-05-28T11:06:01.899410+00:00',
try_number=1, map_index=-1). Executor_config: {'KubernetesExecutor':
{'config_file': '/some/path/kubeconfig.yaml'}}
[airflow.providers.cncf.kubernetes.executors.kubernetes_executor.KubernetesExecutor]
loc=kubernetes_executor.py:221
```
### What you think should happen instead?
Task should fail gracefully or continue with a deprecation warning to switch
to using `pod_override` attribute inside `executor_config`.
### Operating System
MacOS
### Deployment
Virtualenv installation
### Apache Airflow Provider(s)
cncf-kubernetes
### Versions of Apache Airflow Providers
Issue not restricted to a specific provider version
### Official Helm Chart version
Not Applicable
### Kubernetes Version
_No response_
### Helm Chart configuration
_No response_
### Docker Image customizations
_No response_
### Anything else?
To reproduce, you can create the below dag, place it in
`airflow-core/src/airflow/example_dags/`, and deploy the airflow server with
`KubernetesExecutor` as mentioned in [contribution
docs](https://github.com/kunaljubce/airflow/blob/main/contributing-docs/testing/k8s_tests.rst#typical-testing-pattern-for-kubernetes-tests):
```
from airflow.providers.standard.operators.python import PythonOperator
from airflow.sdk import DAG
import pendulum
def useless_func():
pass
with DAG(
dag_id = "test_k8s_executor_config_legacy",
schedule = None,
start_date=pendulum.datetime(2021, 1, 1, tz="UTC"),
catchup=False,
) as dag:
run_this = PythonOperator(
task_id = "check_for_failure",
python_callable = useless_func,
executor_config={"KubernetesExecutor": {"config_file":
"/some/path/kubeconfig.yaml"}}
)
run_this
```
Once all commands have executed successfully, you should see the URL for the
airflow UI in the console logs, looking like below (your port might be
different to mine, verify from console logs):
```
KinD Cluster API server URL: http://localhost:44498
Connecting to localhost:34814. Num try: 1
Error when connecting to localhost:34814 : ('Connection aborted.',
RemoteDisconnected('Remote end closed connection without response'))
Sleeping for 5 seconds.
Connecting to localhost:34814. Num try: 2
Error when connecting to localhost:34814 : ('Connection aborted.',
RemoteDisconnected('Remote end closed connection without response'))
Sleeping for 5 seconds.
Connecting to localhost:34814. Num try: 3
Established connection to api server at
http://localhost:34814/api/v2/monitor/health and it is healthy.
Airflow API server URL: http://localhost:34814 (admin/admin)
<<<<---- Airflow UI URL
NEXT STEP: You might now run tests or interact with airflow via shell
(kubectl, pytest etc.) or k9s commands:
```
Copy and paste the URL on your browser, trigger the DAG and watch it go into
queued state
### 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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