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https://issues.apache.org/jira/browse/SPARK-31149?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Sean R. Owen resolved SPARK-31149.
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Resolution: Won't Fix
> PySpark job not killing Spark Daemon processes after the executor is killed
> due to OOM
> --------------------------------------------------------------------------------------
>
> Key: SPARK-31149
> URL: https://issues.apache.org/jira/browse/SPARK-31149
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 2.4.5
> Reporter: Arsenii Venherak
> Priority: Major
>
> {code:java}
> 2020-03-10 10:15:00,257 INFO
> org.apache.hadoop.yarn.server.nodemanager.containermanager.monitor.ContainersMonitorImpl:
> Memory usage of ProcessTree 327523 for container-id container_e25_1583
> 485217113_0347_01_000042: 1.9 GB of 2 GB physical memory used; 39.5 GB of 4.2
> GB virtual memory used
> 2020-03-10 10:15:05,135 INFO
> org.apache.hadoop.yarn.server.nodemanager.containermanager.monitor.ContainersMonitorImpl:
> Memory usage of ProcessTree 327523 for container-id container_e25_1583
> 485217113_0347_01_000042: 3.6 GB of 2 GB physical memory used; 41.1 GB of 4.2
> GB virtual memory used
> 2020-03-10 10:15:05,136 WARN
> org.apache.hadoop.yarn.server.nodemanager.containermanager.monitor.ContainersMonitorImpl:
> Process tree for container: container_e25_1583485217113_0347_01_000042
> has processes older than 1 iteration running over the configured limit.
> Limit=2147483648, current usage = 3915513856
> 2020-03-10 10:15:05,136 WARN
> org.apache.hadoop.yarn.server.nodemanager.containermanager.monitor.ContainersMonitorImpl:
> Container [pid=327523,containerID=container_e25_1583485217113_0347_01_
> 000042] is running beyond physical memory limits. Current usage: 3.6 GB of 2
> GB physical memory used; 41.1 GB of 4.2 GB virtual memory used. Killing
> container.
> Dump of the process-tree for container_e25_1583485217113_0347_01_000042 :
> |- 327535 327523 327523 327523 (java) 1611 111 4044427264 172306
> /usr/lib/jvm/java-1.8.0-openjdk-1.8.0.242.b08-0.el7_7.x86_64/jre/bin/java
> -server -Xmx1024m -Djava.io.tmpdir=/data/s
> cratch/yarn/usercache/u689299/appcache/application_1583485217113_0347/container_e25_1583485217113_0347_01_000042/tmp
> -Dspark.ssl.trustStore=/opt/mapr/conf/ssl_truststore -Dspark.authenticat
> e.enableSaslEncryption=true -Dspark.driver.port=40653
> -Dspark.network.timeout=7200 -Dspark.ssl.keyStore=/opt/mapr/conf/ssl_keystore
> -Dspark.network.sasl.serverAlwaysEncrypt=true -Dspark.ssl
> .enabled=true -Dspark.ssl.protocol=TLSv1.2 -Dspark.ssl.fs.enabled=true
> -Dspark.ssl.ui.enabled=false -Dspark.authenticate=true
> -Dspark.yarn.app.container.log.dir=/opt/mapr/hadoop/hadoop-2.7.
> 0/logs/userlogs/application_1583485217113_0347/container_e25_1583485217113_0347_01_000042
> -XX:OnOutOfMemoryError=kill %p
> org.apache.spark.executor.CoarseGrainedExecutorBackend --driver-url
> spark://coarsegrainedschedu...@bd02slse0201.wellsfargo.com:40653
> --executor-id 40 --hostname bd02slsc0519.wellsfargo.com --cores 1 --app-id
> application_1583485217113_0347 --user-class-path
> file:/data/scratch/yarn/usercache/u689299/appcache/application_1583485217113_0347/container_e25_1583485217113_0347_01_000042/__app__.jar
> {code}
>
>
> After that, there are lots of pyspark.daemon process left.
> eg:
> /apps/anaconda3-5.3.0/bin/python -m pyspark.daemon
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