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Failed Apache Spark applications are reported as successful.
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The entrypoint of the Spark images does not propagate the exit code of the Spark process, so the driver Pod always terminates with exit code `0` and the SparkApplication ends up in the `Succeeded` state even if the application failed.
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Until the fix is released, the driver logs must be checked to determine whether an application actually succeeded.
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Fixed in https://github.com/stackabletech/docker-images/pull/1595[docker-images#1595].
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* Failed Apache Spark applications are reported as successful.
627
+
The entrypoint of the Spark images does not propagate the exit code of the Spark process, so the driver Pod always terminates with exit code `0` and the SparkApplication ends up in the `Succeeded` state even if the application failed.
628
+
Until the fix is released, the driver logs must be checked to determine whether an application actually succeeded.
629
+
Fixed in https://github.com/stackabletech/docker-images/pull/1595[docker-images#1595].
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* Using Apache Airflow 3.2.2 with Kubernetes executors can lead to a crashlooping scheduler.
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The scheduler crashloops if it was stopped while unfinished tasks were still present.
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To recover, the offending records must be updated in Airflow's database.
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This is described in https://github.com/apache/airflow/issues/67813[apache/airflow#67813] and fixed in https://github.com/apache/airflow/pull/67850[apache/airflow#67850].
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Until the fix is backported to 3.2.2, we recommend using Airflow 3.1.6 or 3.0.6 instead, or switching to Celery executors.
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