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As already explained seeral times - You might experience other issue, but in order to be able to act on it, some logs, indication of what is going on and reproducible case need to be provided. Screenshots and logs from the tasks showing the problem you see should be added in order to see what really the problem is. |
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Apache Airflow version
Other Airflow 2 version (please specify below)
What happened
We have spined up the Airflow from Helm chart 1.6.0. We have defined AirflowExcecutor as "KubernetesExecutor" in our config map.
System works fine when number of parallel runs are less than, but when parallel runs increase - few of the PODS randomly runs on "LocalExecutor" and these task are not visible on Airflow DAG Run UI screen. After the long debugging we identified that it was dues to the environment variable "AIRFLOW__CORE__EXECUTOR " hard-coded to "LocalExecutor" in chart/files/pod-template-file.kubernetes-helm-yaml.
We tried to hard-coded as "KubernetesExecutor", then all the Jobs are working as expected.
What you think should happen instead
Like other configuration, Variable AIRFLOW__CORE__EXECUTOR in the pod template file should not be hardcoded (there is a possibility to add it via .Values.<variable_name>).
How to reproduce
Install the helm chart and define - "AIRFLOW__CORE__EXECUTOR == "KubernetesExecutor" in ConfigMap.
Run single job multiple times (atleast 20+ runs) within 10 seconds. You may observe that - not all job instances are visible on the screen. If we go on Task level details - you may find task are available but the Job are not register on Airflow UI/Database.
Operating System
Linux
Versions of Apache Airflow Providers
2.3.1
Deployment
Other 3rd-party Helm chart
Deployment details
Official Helm Chart version: 1.6.0
Anything else
No response
Are you willing to submit PR?
Code of Conduct
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