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Sujith edited comment on SPARK-25073 at 8/23/18 3:48 PM: --------------------------------------------------------- Yes, in the executor memory validation check we are displaying the proper message considering both yarn.nodemanager.resource.memory-mb and yarn.scheduler.maximum-allocation-mb in org.apache.spark.deploy.yarn.Client class as below, where as for AM container memory allocation validation only yarn.scheduler.maximum-allocation-mb is mentioned if (executorMem > maxMem) { throw new IllegalArgumentException(s"Required executor memory ($executorMemory" + s"+$executorMemoryOverhead MB) is above the max threshold ($maxMem MB) of this cluster! " + "Please check the values of 'yarn.scheduler.maximum-allocation-mb' and/or " + "'yarn.nodemanager.resource.memory-mb and increase the memory appropriately." ) } i think is we can mention about both yarn.nodemanager.resource.memory-mb and yarn.scheduler.maximum-allocation-mb parameters for am memory validation as well,even though this issue sounds to be more kind of negative scenario Please correct me if i am missing something. was (Author: s71955): Yes, in the executor memory validation check we are displaying the proper message considering both yarn.nodemanager.resource.memory-mb and yarn.scheduler.maximum-allocation-mb in org.apache.spark.deploy.yarn.Client class as below, where as for AM container memory allocation validation only yarn.scheduler.maximum-allocation-mb is mentioned if (executorMem > maxMem) { throw new IllegalArgumentException(s"Required executor memory ($executorMemory" + s"+$executorMemoryOverhead MB) is above the max threshold ($maxMem MB) of this cluster! " + "Please check the values of 'yarn.scheduler.maximum-allocation-mb' and/or " + "'yarn.nodemanager.resource.memory-mb and increase the memory appropriately." ) } i think is we can mention about both yarn.nodemanager.resource.memory-mb and yarn.scheduler.maximum-allocation-mb parameters for am memory validation as well, > Spark-submit on Yarn Task : When the yarn.nodemanager.resource.memory-mb > and/or yarn.scheduler.maximum-allocation-mb is insufficient, Spark always > reports an error request to adjust yarn.scheduler.maximum-allocation-mb > -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- > > Key: SPARK-25073 > URL: https://issues.apache.org/jira/browse/SPARK-25073 > Project: Spark > Issue Type: Bug > Components: Spark Submit > Affects Versions: 2.3.0, 2.3.1 > Reporter: vivek kumar > Priority: Minor > > When the yarn.nodemanager.resource.memory-mb and/or > yarn.scheduler.maximum-allocation-mb is insufficient, Spark *always* reports > an error request to adjust Yarn.scheduler.maximum-allocation-mb. Expecting > the error request to be more around yarn.scheduler.maximum-allocation-mb' > and/or 'yarn.nodemanager.resource.memory-mb'. > > Scenario 1. yarn.scheduler.maximum-allocation-mb =4g and > yarn.nodemanager.resource.memory-mb =8G > # Launch shell on Yarn with am.memory less than nodemanager.resource memory > but greater than yarn.scheduler.maximum-allocation-mb > eg; spark-shell --master yarn --conf spark.yarn.am.memory 5g > Error: java.lang.IllegalArgumentException: Required AM memory (5120+512 MB) > is above the max threshold (4096 MB) of this cluster! Please increase the > value of 'yarn.scheduler.maximum-allocation-mb'. > at > org.apache.spark.deploy.yarn.Client.verifyClusterResources(Client.scala:325) > > *Scenario 2*. yarn.scheduler.maximum-allocation-mb =15g and > yarn.nodemanager.resource.memory-mb =8g > a. Launch shell on Yarn with am.memory greater than nodemanager.resource > memory but less than yarn.scheduler.maximum-allocation-mb > eg; *spark-shell --master yarn --conf spark.yarn.am.memory=10g* > Error : > java.lang.IllegalArgumentException: Required AM memory (10240+1024 MB) is > above the max threshold (*8096 MB*) of this cluster! *Please increase the > value of 'yarn.scheduler.maximum-allocation-mb'.* > at > org.apache.spark.deploy.yarn.Client.verifyClusterResources(Client.scala:325) > > b. Launch shell on Yarn with am.memory greater than nodemanager.resource > memory and yarn.scheduler.maximum-allocation-mb > eg; *spark-shell --master yarn --conf spark.yarn.am.memory=17g* > Error: > java.lang.IllegalArgumentException: Required AM memory (17408+1740 MB) is > above the max threshold (*8096 MB*) of this cluster! *Please increase the > value of 'yarn.scheduler.maximum-allocation-mb'.* > at > org.apache.spark.deploy.yarn.Client.verifyClusterResources(Client.scala:325) > > *Expected* : Error request for scenario2 should be more around > yarn.scheduler.maximum-allocation-mb' and/or > 'yarn.nodemanager.resource.memory-mb'. -- This message was sent by Atlassian JIRA (v7.6.3#76005) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org