Mark sure airflow get the right job status after log stream interrupt when schedule spark job on k8s

[dawany]

#8963

Description

I am using airflow SparkSubmitOperator to schedule my spark jobs on kubernetes cluster.

But for some reason, kubernetes often throw 'too old resource version' exception which will interrupt spark watcher, then airflow will lost the log stream and could not get 'Exit Code' eventually. So airflow will mark job failed once log stream lost but the job is still running.

This is a solution about an simple retry mechanism which is when the log stream is interrupted, then airflow try to get 'Exit Code' by 'kubectl describe pod xxxx-driver ' command.

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update

There is no method like cmd 'describe pod ' in kubernetes python client api, so call 'read_namespaced_pod()' to get spark driver pod status instead.

Target Github ISSUE

#8963

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[dawany]

Now I am trying kubernetes client invoked by airflow instead of shell cmd, working on it

[dawany]

This pr is closed ,and instead by a new pull request #9081