Spark作业对纱线客户端正常工作，但对纱线簇完全不工作

Question

我正面临一个问题，当提交一个火花作业罐子在纱。当我用-master yarn-client提交它时，它工作得很好，并给出了我预期的结果

命令如下所示；

./spark-submit--类main.mainclass--主纱--客户端--驱动程序--内存4G--执行器--内存4G--num-执行器4--执行器-核心2 job.jar其他--选项

 <property>
    <name>yarn.scheduler.minimum-allocation-mb</name>
    <value>128</value>
    <description>Minimum limit of memory to allocate to each container request at the Resource Manager.</description>
</property>
<property>
    <name>yarn.scheduler.maximum-allocation-mb</name>
    <value>20048</value>
    <description>Maximum limit of memory to allocate to each container request at the Resource Manager.</description>
</property>
<property>
    <name>yarn.scheduler.minimum-allocation-vcores</name>
    <value>1</value>
    <description>The minimum allocation for every container request at the RM, in terms of virtual CPU cores. Requests lower than this won't take effect, and the specified value will get allocated the minimum.</description>
</property>
<property>
    <name>yarn.scheduler.maximum-allocation-vcores</name>
    <value>2</value>
    <description>The maximum allocation for every container request at the RM, in terms of virtual CPU cores. Requests higher than this won't take effect, and will get capped to this value.</description>
</property>
<property>
    <name>yarn.nodemanager.resource.memory-mb</name>
    <value>24096</value>
    <description>Physical memory, in MB, to be made available to running containers</description>
</property>
<property>
    <name>yarn.nodemanager.resource.cpu-vcores</name>
    <value>4</value>
    <description>Number of CPU cores that can be allocated for containers.</description>
</property>
<property>
    <name>yarn.nodemanager.pmem-check-enabled</name>
    <value>false</value>
</property>
<property>
    <name>yarn.nodemanager.vmem-check-enabled</name>
    <value>false</value>
</property>

        17/03/23 03:30:44 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@3315fed4{/static,null,AVAILABLE}
    17/03/23 03:30:44 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@3e430b9a{/,null,AVAILABLE}
    17/03/23 03:30:44 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@77184f65{/api,null,AVAILABLE}
    17/03/23 03:30:44 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@643f7b84{/stages/stage/kill,null,AVAILABLE}
    17/03/23 03:30:44 INFO server.ServerConnector: Started ServerConnector@27614db2{HTTP/1.1}{0.0.0.0:37212}
    17/03/23 03:30:44 INFO server.Server: Started @7799ms
    17/03/23 03:30:44 INFO util.Utils: Successfully started service 'SparkUI' on port 37212.
    17/03/23 03:30:44 INFO ui.SparkUI: Bound SparkUI to 0.0.0.0, and started at http://50.31.66.56:37212
    17/03/23 03:30:44 INFO cluster.YarnClusterScheduler: Created YarnClusterScheduler
    17/03/23 03:30:44 INFO cluster.SchedulerExtensionServices: Starting Yarn extension services with app application_1490254182417_0001 and attemptId Some(appattempt_1490254182417_0001_000001)
    17/03/23 03:30:44 INFO util.Utils: Successfully started service 'org.apache.spark.network.netty.NettyBlockTransferService' on port 45469.
    17/03/23 03:30:44 INFO netty.NettyBlockTransferService: Server created on 50.31.66.56:45469
    17/03/23 03:30:44 INFO storage.BlockManagerMaster: Registering BlockManager BlockManagerId(driver, 50.31.66.56, 45469)
    17/03/23 03:30:44 INFO storage.BlockManagerMasterEndpoint: Registering block manager 50.31.66.56:45469 with 2004.6 MB RAM, BlockManagerId(driver, 50.31.66.56, 45469)
    17/03/23 03:30:44 INFO storage.BlockManagerMaster: Registered BlockManager BlockManagerId(driver, 50.31.66.56, 45469)
    17/03/23 03:30:44 INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@60245f4e{/metrics/json,null,AVAILABLE}
    17/03/23 03:30:49 INFO scheduler.EventLoggingListener: Logging events to hdfs://mecku-1:54310/spark/application_1490254182417_0001_1
    17/03/23 03:30:49 INFO cluster.YarnSchedulerBackend$YarnSchedulerEndpoint: ApplicationMaster registered as NettyRpcEndpointRef(spark://YarnAM@50.31.66.56:50465)
    17/03/23 03:30:49 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8030
    17/03/23 03:30:49 INFO yarn.YarnRMClient: Registering the ApplicationMaster
    17/03/23 03:30:49 INFO yarn.YarnAllocator: Will request 4 executor containers, each with 2 cores and 4505 MB memory including 409 MB overhead
    17/03/23 03:30:49 INFO yarn.YarnAllocator: Canceled 0 container requests (locality no longer needed)
    17/03/23 03:30:49 INFO yarn.YarnAllocator: Submitted container request (host: Any, capability: <memory:4505, vCores:2>)
    17/03/23 03:30:49 INFO yarn.YarnAllocator: Submitted container request (host: Any, capability: <memory:4505, vCores:2>)
    17/03/23 03:30:49 INFO yarn.YarnAllocator: Submitted container request (host: Any, capability: <memory:4505, vCores:2>)
    17/03/23 03:30:49 INFO yarn.YarnAllocator: Submitted container request (host: Any, capability: <memory:4505, vCores:2>)
    17/03/23 03:30:49 INFO yarn.ApplicationMaster: Started progress reporter thread with (heartbeat : 3000, initial allocation : 200) intervals
    17/03/23 03:30:49 INFO yarn.ApplicationMaster: Unregistering ApplicationMaster with SUCCEEDED
    17/03/23 03:30:49 INFO impl.AMRMClientImpl: Waiting for application to be successfully unregistered.
    17/03/23 03:30:49 INFO yarn.ApplicationMaster: Deleting staging directory hdfs://localhost:54310/user/root/.sparkStaging/application_1490254182417_0001
    17/03/23 03:30:49 INFO storage.DiskBlockManager: Shutdown hook called
    17/03/23 03:30:49 INFO util.ShutdownHookManager: Shutdown hook called
    17/03/23 03:30:49 INFO util.ShutdownHookManager: Deleting directory /tmp/hadoop-root/nm-local-dir/usercache/root/appcache/application_1490254182417_0001/spark-d77de654-4040-4b43-8155-efb155008b4b
    17/03/23 03:30:49 INFO util.ShutdownHookManager: Deleting directory /tmp/hadoop-root/nm-local-dir/usercache/root/appcache/application_1490254182417_0001/spark-d77de654-4040-4b43-8155-efb155008b4b/userFiles-d71596df-df26-4b88-b51e-f0b962daf84a
    17/03/23 03:30:40 INFO yarn.ApplicationMaster: ApplicationAttemptId: appattempt_1490254182417_0001_000001

Answer 1

也许，你的从节点不工作了。您应该检查下面的节点命令,

sudo -u yarn yarn node -list

如果你找不到所有的节点，你应该修复节点的设置。例如，selinux off（检查getenfore)，以及每个节点的yarn-site.xml和core-site.xml。