Github user holdenk commented on the issue:
https://github.com/apache/spark/pull/15961
I think that warning gives a false sense of security - as I indicated above
this swallows all of the Py4J errors and there are a host of things which could
cause the Py4J bridge to break down. The warning text I would choose would be
more along the lines of: "Unable to cleanly shutdown Spark JVM process. It is
possible that the process has crashed or been killed, but may also be in a
zombie state."
This way the end user know to take a look and verify that the previous
Spark JVM did indeed exit.
I'm still not sure that we want to do this though - if the Spark JVM has
failed stopping and restarting the Spark context is going to have all of the
previous references to Java objects invalid (not just the RDDs/DataFrames which
happens on a stop/start of the Scala Spark context normally). It seems like the
correct action for the user to take when the Py4J bridge breaks is starting
over from scratch, either by exiting and re-running their notebook or otherwise
re-submitting there job.
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