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From Cleosson José Pirani de Souza <cso...@daitangroup.com>
Subject ApplicationMaster memory with Fair Scheduler
Date Wed, 24 Aug 2016 21:31:31 GMT

 I am using Spark 1.6.2 and Hadoop 2.7.2 in a single node cluster (Pseudo-Distributed Operation
settings for testing propose).
    # spark-defaults.conf
    spark.driver.memory                512m
    spark.yarn.am.memory               512m
    spark.executor.memory              512m
    spark.executor.cores               2
    spark.dynamicAllocation.enabled    true
    spark.shuffle.service.enabled   true
    # yarn-site.xml
    yarn.scheduler.maximum-allocation-vcores    32
    yarn.scheduler.minimum-allocation-vcores    1
    yarn.scheduler.maximum-allocation-mb        16384
    yarn.scheduler.minimum-allocation-mb        64
    yarn.scheduler.fair.preemption              true
    yarn.resourcemanager.scheduler.class        org.apache.hadoop.yarn.server.resourcemanager.scheduler.fair.FairScheduler
    yarn.nodemanager.aux-services               spark_shuffle
    # mapred-site.xml
    yarn.app.mapreduce.am.resource.mb           512
    yarn.app.mapreduce.am.resource.cpu-vcores   1
    yarn.app.mapreduce.am.command-opts          -Xmx384
    mapreduce.map.memory.mb                     1024
    mapreduce.map.java.opts                     -Xmx768m
    mapreduce.reduce.memory.mb                  1024
    mapreduce.reduce.java.opts                  -Xmx768m

 For every spark application that I submit I always get:
  - ApplicationMaster with 1024 MB of RAM and 1 vcore
  - One container with 1024 MB of RAM and 1 vcore

  I have three questions using dynamic allocation and Fair Scheduler:
  1) How do I change ApplicationMaster max memory to 512m ?
  2) How do I get more than one container running per application ? (using dynamic allocation
I cannot set the spark.executor.instances)
  3) I noticed that YARN ignores yarn.app.mapreduce.am.resource.mb, yarn.app.mapreduce.am.resource.cpu-vcores
and yarn.app.mapreduce.am.command-opts when the scheduler is Fair, am I right ?



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