背景：
按照业务需求将数据拆分为60份，启动60个application分别运行对每一份数据，application的提交脚本如下：
#/bin/sh #LANG=zh_CN.utf8 #export LANG export SPARK_KAFKA_VERSION=0.10 export LANG=zh_CN.UTF-8 jarspath='' for file in `ls /home/dx/pro2.0/app01/sparkjars/*.jar` do jarspath=${file},$jarspath done jarspath=${jarspath%?} echo $jarspath ./bin/spark-submit.sh --jars $jarspath --properties-file ../conf/spark-properties.conf --verbose --master yarn --deploy-mode cluster --name Streaming-$2-$3-$4-$5-$1-Agg-Parser --driver-memory 9g --driver-cores 1 --num-executors 1 --executor-cores 12 --executor-memory 22g --driver-java-options "-XX:+TraceClassPaths" --class com.dx.app01.streaming.Main /home/dx/pro2.0/app01/lib/app01-streaming-driver.jar $1 $2 $3 $4 $5
运行集群包含的运行节点43个节点，每个节点配置信息如下：24VCores 64G
yarn配置情况：
| yarn.scheduler.minimum-allocation-mb | 单个容器可申请的最小内存 1G |
| yarn.scheduler.maximum-allocation-mb | 单个容器可申请的最大内存 51G |
| yarn.nodemanager.resource.cpu-vcores | NodeManager总的可用虚拟CPU个数 21vcores |
| yarn.nodemanager.resource.memory-mb | 每个节点可用的最大内存，RM中的两个值不应该超过此值 51G |
问题：
执行上边脚本启动了60个任务，但是经过测试发现最多只能提交24个任务，然后剩余还有一个部分任务都是处于 Accepted 状态，按照目前情况至少要执行43个任务。
通过yarn node -list命令查看当前节点上运行containers情况如下：
| Node-Id | Node-State | Node-Http-Address | Number-of-Running-Containers |
| node-53:45454 | RUNNING | node-53:8042 | 1 |
| node-62:45454 | RUNNING | node-62:8042 | 4 |
| node-44:45454 | RUNNING | node-44:8042 | 3 |
| node-37:45454 | RUNNING | node-37:8042 | 0 |
| node-35:45454 | RUNNING | node-35:8042 | 1 |
| node-07:45454 | RUNNING | node-07:8042 | 0 |
| node-30:45454 | RUNNING | node-30:8042 | 0 |
| node-56:45454 | RUNNING | node-56:8042 | 2 |
| node-47:45454 | RUNNING | node-47:8042 | 0 |
| node-42:45454 | RUNNING | node-42:8042 | 2 |
| node-03:45454 | RUNNING | node-03:8042 | 6 |
| node-51:45454 | RUNNING | node-51:8042 | 2 |
| node-33:45454 | RUNNING | node-33:8042 | 1 |
| node-04:45454 | RUNNING | node-04:8042 | 1 |
| node-48:45454 | RUNNING | node-48:8042 | 6 |
| node-39:45454 | RUNNING | node-39:8042 | 0 |
| node-60:45454 | RUNNING | node-60:8042 | 1 |
| node-54:45454 | RUNNING | node-54:8042 | 0 |
| node-45:45454 | RUNNING | node-45:8042 | 0 |
| node-63:45454 | RUNNING | node-63:8042 | 1 |
| node-09:45454 | RUNNING | node-09:8042 | 1 |
| node-01:45454 | RUNNING | node-01:8042 | 1 |
| node-36:45454 | RUNNING | node-36:8042 | 3 |
| node-06:45454 | RUNNING | node-06:8042 | 0 |
| node-61:45454 | RUNNING | node-61:8042 | 1 |
| node-31:45454 | RUNNING | node-31:8042 | 0 |
| node-40:45454 | RUNNING | node-40:8042 | 0 |
| node-57:45454 | RUNNING | node-57:8042 | 1 |
| node-59:45454 | RUNNING | node-59:8042 | 1 |
| node-43:45454 | RUNNING | node-43:8042 | 1 |
| node-52:45454 | RUNNING | node-52:8042 | 1 |
| node-34:45454 | RUNNING | node-34:8042 | 1 |
| node-38:45454 | RUNNING | node-38:8042 | 0 |
| node-50:45454 | RUNNING | node-50:8042 | 4 |
| node-46:45454 | RUNNING | node-46:8042 | 1 |
| node-08:45454 | RUNNING | node-08:8042 | 1 |
| node-55:45454 | RUNNING | node-55:8042 | 1 |
| node-32:45454 | RUNNING | node-32:8042 | 0 |
| node-41:45454 | RUNNING | node-41:8042 | 2 |
| node-05:45454 | RUNNING | node-05:8042 | 1 |
| node-02:45454 | RUNNING | node-02:8042 | 1 |
| node-58:45454 | RUNNING | node-58:8042 | 0 |
| node-49:45454 | RUNNING | node-49:8042 | 0 |
很明显，目前集群还有一部分节点未被使用，说明资源时充足的。
那么，至少应该能提交43个任务才对，但是目前只提交了24个任务，而且在Yarn上还提示错误信息：
[Tue Jul 30 16:33:29 +0000 2019] Application is added to the scheduler and is not yet activated.
Queue's AM resource limit exceeded. Details : AM Partition = <DEFAULT_PARTITION>;
AM Resource Request = <memory:9216MB(9G), vCores:1>;
Queue Resource Limit for AM = <memory:454656MB(444G), vCores:1>;
User AM Resource Limit of the queue = <memory:229376MB(224G), vCores:1>;
Queue AM Resource Usage = <memory:221184MB(216G), vCores:24>;
解决方案：
其中错误日志：“Queue AM Resource Usage = <memory:221184MB(216G), vCores:24>;”中正是指目前已经运行了24个app（yarn-cluster模式下，每个app包含一个driver，driver也就是等同于AM）：每个app的driver包含1个vcores，一共占用24vcores；每个app的driver内存为9G，9G*24=216G。
其中错误日志：“User AM Resource Limit of the queue = <memory:229376MB(224G), vCores:1>; ”中集群中用于运行应用程序ApplicationMaster的资源最大允许224G，这个值由参数”yarn.scheduler.capacity.maximum-am-resource-percent“决定。
|
yarn.scheduler.capacity.maximum-am-resource-percent / yarn.scheduler.capacity.<queue-path>.maximum-am-resource-percent |
集群中用于运行应用程序ApplicationMaster的资源比例上限，该参数通常用于限制处于活动状态的应用程序数目。该参数类型为浮点型，默认是0.1，表示10%。 所有队列的ApplicationMaster资源比例上限可通过参数yarn.scheduler.capacity. maximum-am-resource-percent设置（可看做默认值）， 而单个队列可通过参数yarn.scheduler.capacity.<queue-path>.maximum-am-resource-percent设置适合自己的值。 |
1）yarn.scheduler.capacity.maximum-am-resource-percent（调大）
<property> <!-- Maximum resources to allocate to application masters If this is too high application masters can crowd out actual work --> <name>yarn.scheduler.capacity.maximum-am-resource-percent</name> <value>0.5</value> </property>
2）降低 driver 内存。
关于Yarn Capacity更多，更官方问题请参考官网文档：《Hadoop: Capacity Scheduler》