All,
We have a Apache Spark v3.12 + Yarn on AKS (SQLServer 2019 BDC). We ran a refactored python code to Pyspark which resulted in the error below:
Application application_1635264473597_0181 failed 1 times (global limit =2; local limit is =1) due to AM Container for appattempt_1635264473597_0181_000001 exited with exitCode: -104
Failing this attempt.Diagnostics: [2021-11-12 15:00:16.915]Container [pid=12990,containerID=container_1635264473597_0181_01_000001] is running 7282688B beyond the 'PHYSICAL' memory limit. Current usage: 2.0 GB of 2 GB physical memory used; 4.9 GB of 4.2 GB virtual memory used. Killing container.
Dump of the process-tree for container_1635264473597_0181_01_000001 :
|- PID PPID PGRPID SESSID CMD_NAME USER_MODE_TIME(MILLIS) SYSTEM_TIME(MILLIS) VMEM_USAGE(BYTES) RSSMEM_USAGE(PAGES) FULL_CMD_LINE
|- 13073 12999 12990 12990 (python3) 7333 112 1516236800 235753 /opt/bin/python3 /var/opt/hadoop/temp/nm-local-dir/usercache/grajee/appcache/application_1635264473597_0181/container_1635264473597_0181_01_000001/tmp/3677222184783620782
|- 12999 12990 12990 12990 (java) 6266 586 3728748544 289538 /opt/mssql/lib/zulu-jre-8/bin/java -server -XX:ActiveProcessorCount=1 -Xmx1664m -Djava.io.tmpdir=/var/opt/hadoop/temp/nm-local-dir/usercache/grajee/appcache/application_1635264473597_0181/container_1635264473597_0181_01_000001/tmp -Dspark.yarn.app.container.log.dir=/var/log/yarnuser/userlogs/application_1635264473597_0181/container_1635264473597_0181_01_000001 org.apache.spark.deploy.yarn.ApplicationMaster --class org.apache.livy.rsc.driver.RSCDriverBootstrapper --properties-file /var/opt/hadoop/temp/nm-local-dir/usercache/grajee/appcache/application_1635264473597_0181/container_1635264473597_0181_01_000001/spark_conf/spark_conf.properties --dist-cache-conf /var/opt/hadoop/temp/nm-local-dir/usercache/grajee/appcache/application_1635264473597_0181/container_1635264473597_0181_01_000001/spark_conf/spark_dist_cache.properties
|- 12990 12987 12990 12990 (bash) 0 0 4304896 775 /bin/bash -c /opt/mssql/lib/zulu-jre-8/bin/java -server -XX:ActiveProcessorCount=1 -Xmx1664m -Djava.io.tmpdir=/var/opt/hadoop/temp/nm-local-dir/usercache/grajee/appcache/application_1635264473597_0181/container_1635264473597_0181_01_000001/tmp -Dspark.yarn.app.container.log.dir=/var/log/yarnuser/userlogs/application_1635264473597_0181/container_1635264473597_0181_01_000001 org.apache.spark.deploy.yarn.ApplicationMaster --class 'org.apache.livy.rsc.driver.RSCDriverBootstrapper' --properties-file /var/opt/hadoop/temp/nm-local-dir/usercache/grajee/appcache/application_1635264473597_0181/container_1635264473597_0181_01_000001/spark_conf/spark_conf.properties --dist-cache-conf /var/opt/hadoop/temp/nm-local-dir/usercache/grajee/appcache/application_1635264473597_0181/container_1635264473597_0181_01_000001/spark_conf/spark_dist_cache.properties 1> /var/log/yarnuser/userlogs/application_1635264473597_0181/container_1635264473597_0181_01_000001/stdout 2> /var/log/yarnuser/userlogs/application_1635264473597_0181/container_1635264473597_0181_01_000001/stderr
[2021-11-12 15:00:16.921]Container killed on request. Exit code is 143
[2021-11-12 15:00:16.940]Container exited with a non-zero exit code 143.
For more detailed output, check the application tracking page: https://sparkhead-0.mssql-cluster.everestre.net:8090/cluster/app/application_1635264473597_0181 Then click on links to logs of each attempt.
. Failing the application.
The default setting is as below and there are no runtime settings:
"settings": {
"spark-defaults-conf.spark.driver.cores": "1",
"spark-defaults-conf.spark.driver.memory": "1664m",
"spark-defaults-conf.spark.driver.memoryOverhead": "384",
"spark-defaults-conf.spark.executor.instances": "1",
"spark-defaults-conf.spark.executor.cores": "2",
"spark-defaults-conf.spark.executor.memory": "3712m",
"spark-defaults-conf.spark.executor.memoryOverhead": "384",
"yarn-site.yarn.nodemanager.resource.memory-mb": "12288",
"yarn-site.yarn.nodemanager.resource.cpu-vcores": "6",
"yarn-site.yarn.scheduler.maximum-allocation-mb": "12288",
"yarn-site.yarn.scheduler.maximum-allocation-vcores": "6",
"yarn-site.yarn.scheduler.capacity.maximum-am-resource-percent": "0.34".
}
Is the AM Container mentioned the Application Master Container or Application Manager (of YARN). If this is the case, then in a Cluster Mode setting, the Driver and the Application Master run in the same Container?
What runtime parameter do I change to make the Pyspark code successfully.
Thanks,
grajee
