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https://issues.apache.org/jira/browse/SPARK-27927?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16884339#comment-16884339
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Edwin Biemond commented on SPARK-27927:
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thanks again, indeed it looks like this commit can be the issue
[https://github.com/apache/spark/commit/03e90f65bfdad376400a4ae4df31a82c05ed4d4b#diff-2952082eba54dc17cd6f73a3260e8f2d]
it is related to dag-scheduler-event-loop blocked thread and hitting my
sparkcontext, spark session issue.
> driver pod hangs with pyspark 2.4.3 and master on kubenetes
> -----------------------------------------------------------
>
> Key: SPARK-27927
> URL: https://issues.apache.org/jira/browse/SPARK-27927
> Project: Spark
> Issue Type: Bug
> Components: Kubernetes, PySpark
> Affects Versions: 3.0.0, 2.4.3
> Environment: k8s 1.11.9
> spark 2.4.3 and master branch.
> Reporter: Edwin Biemond
> Priority: Major
> Attachments: driver_threads.log, executor_threads.log
>
>
> When we run a simple pyspark on spark 2.4.3 or 3.0.0 the driver pods hangs
> and never calls the shutdown hook.
> {code:java}
> #!/usr/bin/env python
> from __future__ import print_function
> import os
> import os.path
> import sys
> # Are we really in Spark?
> from pyspark.sql import SparkSession
> spark = SparkSession.builder.appName('hello_world').getOrCreate()
> print('Our Spark version is {}'.format(spark.version))
> print('Spark context information: {} parallelism={} python version={}'.format(
> str(spark.sparkContext),
> spark.sparkContext.defaultParallelism,
> spark.sparkContext.pythonVer
> ))
> {code}
> When we run this on kubernetes the driver and executer are just hanging. We
> see the output of this python script.
> {noformat}
> bash-4.2# cat stdout.log
> Our Spark version is 2.4.3
> Spark context information: <SparkContext
> master=k8s://https://kubernetes.default.svc:443 appName=hello_world>
> parallelism=2 python version=3.6{noformat}
> What works
> * a simple python with a print works fine on 2.4.3 and 3.0.0
> * same setup on 2.4.0
> * 2.4.3 spark-submit with the above pyspark
>
>
>
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