Kafka：ZK+Kafka+Spark Streaming集群环境搭建（六）针对spark2.2.1以yarn方式启动spark-shell抛出异常：ERROR cluster.YarnSchedulerBackend$YarnSchedulerEndpoint: Sending RequestExecutors(0,0,Map(),Set()) to AM was unsuccessful

时间：2024-01-17 12:41:14

Spark以yarn方式运行时抛出异常：

[spark@master bin]$ cd /opt/spark-2.2.-bin-hadoop2./bin

[spark@master bin]$ ./spark-shell --master yarn-client

Warning: Master yarn-client is deprecated since 2.0. Please use master "yarn" with specified deploy mode instead.

Setting default log level to "WARN".

To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).

// :: WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable

// :: WARN yarn.Client: Neither spark.yarn.jars nor spark.yarn.archive is set, falling back to uploading libraries under SPARK_HOME.

Spark context Web UI available at http://192.168.0.120:4040

Spark context available as 'sc' (master = yarn, app id = application_1530369937777_0003).

Spark session available as 'spark'.

Welcome to

      ____              __

     / __/__  ___ _____/ /__

    _\ \/ _ \/ _ `/ __/  '_/

   /___/ .__/\_,_/_/ /_/\_\   version 2.2.

      /_/

Using Scala version 2.11. (Java HotSpot(TM) -Bit Server VM, Java 1.8.0_171)

Type in expressions to have them evaluated.

Type :help for more information.

scala> // :: ERROR cluster.YarnClientSchedulerBackend: Yarn application has already exited with state FAILED!

// :: ERROR client.TransportClient: Failed to send RPC  to /192.168.0.121:: java.nio.channels.ClosedChannelException

java.nio.channels.ClosedChannelException

        at io.netty.channel.AbstractChannel$AbstractUnsafe.write(...)(Unknown Source)

// :: ERROR cluster.YarnSchedulerBackend$YarnSchedulerEndpoint: Sending RequestExecutors(,,Map(),Set()) to AM was unsuccessful

java.io.IOException: Failed to send RPC  to /192.168.0.121:: java.nio.channels.ClosedChannelException

        at org.apache.spark.network.client.TransportClient.lambda$sendRpc$(TransportClient.java:)

        at io.netty.util.concurrent.DefaultPromise.notifyListener0(DefaultPromise.java:)

        at io.netty.util.concurrent.DefaultPromise.notifyListenersNow(DefaultPromise.java:)

        at io.netty.util.concurrent.DefaultPromise.notifyListeners(DefaultPromise.java:)

        at io.netty.util.concurrent.DefaultPromise.tryFailure(DefaultPromise.java:)

        at io.netty.channel.AbstractChannel$AbstractUnsafe.safeSetFailure(AbstractChannel.java:)

        at io.netty.channel.AbstractChannel$AbstractUnsafe.write(AbstractChannel.java:)

        at io.netty.channel.DefaultChannelPipeline$HeadContext.write(DefaultChannelPipeline.java:)

        at io.netty.channel.AbstractChannelHandlerContext.invokeWrite0(AbstractChannelHandlerContext.java:)

        at io.netty.channel.AbstractChannelHandlerContext.invokeWrite(AbstractChannelHandlerContext.java:)

        at io.netty.channel.AbstractChannelHandlerContext.access$(AbstractChannelHandlerContext.java:)

        at io.netty.channel.AbstractChannelHandlerContext$AbstractWriteTask.write(AbstractChannelHandlerContext.java:)

        at io.netty.channel.AbstractChannelHandlerContext$WriteAndFlushTask.write(AbstractChannelHandlerContext.java:)

        at io.netty.channel.AbstractChannelHandlerContext$AbstractWriteTask.run(AbstractChannelHandlerContext.java:)

        at io.netty.util.concurrent.SingleThreadEventExecutor.runAllTasks(SingleThreadEventExecutor.java:)

        at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:)

        at io.netty.util.concurrent.SingleThreadEventExecutor$.run(SingleThreadEventExecutor.java:)

        at io.netty.util.concurrent.DefaultThreadFactory$DefaultRunnableDecorator.run(DefaultThreadFactory.java:)

        at java.lang.Thread.run(Thread.java:)

Caused by: java.nio.channels.ClosedChannelException

        at io.netty.channel.AbstractChannel$AbstractUnsafe.write(...)(Unknown Source)

// :: ERROR util.Utils: Uncaught exception in thread Yarn application state monitor

org.apache.spark.SparkException: Exception thrown in awaitResult:

        at org.apache.spark.util.ThreadUtils$.awaitResult(ThreadUtils.scala:)

        at org.apache.spark.rpc.RpcTimeout.awaitResult(RpcTimeout.scala:)

        at org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend.requestTotalExecutors(CoarseGrainedSchedulerBackend.scala:)

        at org.apache.spark.scheduler.cluster.YarnSchedulerBackend.stop(YarnSchedulerBackend.scala:)

        at org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.stop(YarnClientSchedulerBackend.scala:)

        at org.apache.spark.scheduler.TaskSchedulerImpl.stop(TaskSchedulerImpl.scala:)

        at org.apache.spark.scheduler.DAGScheduler.stop(DAGScheduler.scala:)

        at org.apache.spark.SparkContext$$anonfun$stop$.apply$mcV$sp(SparkContext.scala:)

        at org.apache.spark.util.Utils$.tryLogNonFatalError(Utils.scala:)

        at org.apache.spark.SparkContext.stop(SparkContext.scala:)

        at org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend$MonitorThread.run(YarnClientSchedulerBackend.scala:)

Caused by: java.io.IOException: Failed to send RPC  to /192.168.0.121:: java.nio.channels.ClosedChannelException

        at org.apache.spark.network.client.TransportClient.lambda$sendRpc$(TransportClient.java:)

        at io.netty.util.concurrent.DefaultPromise.notifyListener0(DefaultPromise.java:)

        at io.netty.util.concurrent.DefaultPromise.notifyListenersNow(DefaultPromise.java:)

        at io.netty.util.concurrent.DefaultPromise.notifyListeners(DefaultPromise.java:)

        at io.netty.util.concurrent.DefaultPromise.tryFailure(DefaultPromise.java:)

        at io.netty.channel.AbstractChannel$AbstractUnsafe.safeSetFailure(AbstractChannel.java:)

        at io.netty.channel.AbstractChannel$AbstractUnsafe.write(AbstractChannel.java:)

        at io.netty.channel.DefaultChannelPipeline$HeadContext.write(DefaultChannelPipeline.java:)

        at io.netty.channel.AbstractChannelHandlerContext.invokeWrite0(AbstractChannelHandlerContext.java:)

        at io.netty.channel.AbstractChannelHandlerContext.invokeWrite(AbstractChannelHandlerContext.java:)

        at io.netty.channel.AbstractChannelHandlerContext.access$(AbstractChannelHandlerContext.java:)

        at io.netty.channel.AbstractChannelHandlerContext$AbstractWriteTask.write(AbstractChannelHandlerContext.java:)

        at io.netty.channel.AbstractChannelHandlerContext$WriteAndFlushTask.write(AbstractChannelHandlerContext.java:)

        at io.netty.channel.AbstractChannelHandlerContext$AbstractWriteTask.run(AbstractChannelHandlerContext.java:)

        at io.netty.util.concurrent.SingleThreadEventExecutor.runAllTasks(SingleThreadEventExecutor.java:)

        at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:)

        at io.netty.util.concurrent.SingleThreadEventExecutor$.run(SingleThreadEventExecutor.java:)

        at io.netty.util.concurrent.DefaultThreadFactory$DefaultRunnableDecorator.run(DefaultThreadFactory.java:)

        at java.lang.Thread.run(Thread.java:)

Caused by: java.nio.channels.ClosedChannelException

        at io.netty.channel.AbstractChannel$AbstractUnsafe.write(...)(Unknown Source)

解决方案：

主要是给节点分配的内存少，yarn kill了spark application。
给yarn-site.xml增加配置：

<property>

    <name>yarn.nodemanager.pmem-check-enabled</name>

    <value>false</value>

</property>

<property>

    <name>yarn.nodemanager.vmem-check-enabled</name>

    <value>false</value>

    <description>Whether virtual memory limits will be enforced for containers</description>

</property>

<property>

    <name>yarn.nodemanager.vmem-pmem-ratio</name>

    <value>4</value>

    <description>Ratio between virtual memory to physical memory when setting memory limits for containers</description>

</property>

重启hadoop。然后再重新执行./spark-shell --master yarn-client即可。

问题解决过程记录：

1）在master上将hadoop,spark服务停掉

[spark@master hadoop]$ cd /opt/hadoop-2.9.

[spark@master hadoop]$ sbin/stop-all.sh

[spark@master hadoop]$ cd /opt/spark-2.2.-bin-hadoop2.

[spark@master hadoop]$ sbin/stop-all.sh

2）在master上修改yarn-site.xml

[spark@master hadoop]$ cd /opt/hadoop-2.9./etc/hadoop

[spark@master hadoop]$ ls

capacity-scheduler.xml  hadoop-env.cmd              hadoop-policy.xml        httpfs-signature.secret  kms-log4j.properties  mapred-env.sh               slaves                  yarn-env.sh

configuration.xsl       hadoop-env.sh               hdfs-site.xml            httpfs-site.xml          kms-site.xml          mapred-queues.xml.template  ssl-client.xml.example  yarn-site.xml

container-executor.cfg  hadoop-metrics2.properties  httpfs-env.sh            kms-acls.xml             log4j.properties      mapred-site.xml             ssl-server.xml.example

core-site.xml           hadoop-metrics.properties   httpfs-log4j.properties  kms-env.sh               mapred-env.cmd        mapred-site.xml.template    yarn-env.cmd

[spark@master hadoop]$ vi yarn-site.xml

<?xml version="1.0"?>

<!--

  Licensed under the Apache License, Version 2.0 (the "License");

  you may not use this file except in compliance with the License.

  You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

  Unless required by applicable law or agreed to in writing, software

  distributed under the License is distributed on an "AS IS" BASIS,

  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

  See the License for the specific language governing permissions and

  limitations under the License. See accompanying LICENSE file.

-->

<configuration>

    <property>

        <name>yarn.nodemanager.aux-services</name>

        <value>mapreduce_shuffle</value>

    </property>

    <property>

        <name>yarn.nodemanager.aux-services.mapreduce.shuffle.class</name>

        <value>org.apache.hadoop.mapred.ShuffleHandler</value>

    </property>

    <property>

        <name>yarn.resourcemanager.address</name>

        <value>master:</value>

    </property>

    <property>

        <name>yarn.resourcemanager.scheduler.address</name>

        <value>master:</value>

    </property>

    <property>

        <name>yarn.resourcemanager.resource-tracker.address</name>

        <value>master:</value>

    </property>

    <property>

        <name>yarn.resourcemanager.admin.address</name>

        <value>master:</value>

    </property>

    <property>

        <name>yarn.resourcemanager.webapp.address</name>

        <value>master:</value>

    </property>

    <property>

        <name>yarn.resourcemanager.hostname</name>

        <value>master</value>

    </property>

    <property>

        <name>yarn.nodemanager.resource.memory-mb</name>

        <value></value>

    </property>

    <property>

        <name>yarn.nodemanager.pmem-check-enabled</name>

        <value>false</value>

    </property>

    <property>

        <name>yarn.nodemanager.vmem-check-enabled</name>

        <value>false</value>

        <description>Whether virtual memory limits will be enforced for containers</description>

    </property>

    <property>

        <name>yarn.nodemanager.vmem-pmem-ratio</name>

        <value></value>

        <description>Ratio between virtual memory to physical memory when setting memory limits for containers</description>

    </property>

</configuration>

~

~

~

"yarn-site.xml" 66L, 2285C written

3）将master上将l修改后的yarn-site.xm文件覆盖到各个slaves节点

[spark@master hadoop]$ scp -r /opt/hadoop-2.9./etc/hadoop/yarn-site.xml spark@slave1:/opt/hadoop-2.9./etc/hadoop/

yarn-site.xml                                                                                                                                                             %    .6KB/s   :

[spark@master hadoop]$ scp -r /opt/hadoop-2.9./etc/hadoop/yarn-site.xml spark@slave2:/opt/hadoop-2.9./etc/hadoop/

yarn-site.xml                                                                                                                                                             %    .3KB/s   :

[spark@master hadoop]$ scp -r /opt/hadoop-2.9./etc/hadoop/yarn-site.xml spark@slave3:/opt/hadoop-2.9./etc/hadoop/

yarn-site.xml                                                                                                                                                             %      .5MB/s   :

4）重新启动hadoop,spark服务

[spark@master hadoop]$ cd /opt/hadoop-2.9.

[spark@master hadoop]$ sbin/start-all.sh

[spark@master hadoop]$ cd /opt/spark-2.2.-bin-hadoop2.

[spark@master spark-2.2.-bin-hadoop2.]$ sbin/start-all.sh

[spark@master spark-2.2.-bin-hadoop2.]$ jps

 ResourceManager

 Master

 SecondaryNameNode

 Jps

 NameNode

5）验证spark on yarn是否正常运行

[spark@master spark-2.2.-bin-hadoop2.]$ cd /opt/spark-2.2.-bin-hadoop2./bin

[spark@master bin]$ ./spark-shell --master yarn-client

Warning: Master yarn-client is deprecated since 2.0. Please use master "yarn" with specified deploy mode instead.

Setting default log level to "WARN".

To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).

// :: WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable

// :: WARN yarn.Client: Neither spark.yarn.jars nor spark.yarn.archive is set, falling back to uploading libraries under SPARK_HOME.

Spark context Web UI available at http://192.168.0.120:4040

Spark context available as 'sc' (master = yarn, app id = application_1530373644791_0001).

Spark session available as 'spark'.

Welcome to

      ____              __

     / __/__  ___ _____/ /__

    _\ \/ _ \/ _ `/ __/  '_/

   /___/ .__/\_,_/_/ /_/\_\   version 2.2.

      /_/

Using Scala version 2.11. (Java HotSpot(TM) -Bit Server VM, Java 1.8.0_171)

Type in expressions to have them evaluated.

Type :help for more information.

scala> 

[spark@master bin]$ ./spark-shell --master yarn

Setting default log level to "WARN".

To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).

// :: WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable

// :: WARN yarn.Client: Neither spark.yarn.jars nor spark.yarn.archive is set, falling back to uploading libraries under SPARK_HOME.

Spark context Web UI available at http://192.168.0.120:4040

Spark context available as 'sc' (master = yarn, app id = application_1530373644791_0002).

Spark session available as 'spark'.

Welcome to

      ____              __

     / __/__  ___ _____/ /__

    _\ \/ _ \/ _ `/ __/  '_/

   /___/ .__/\_,_/_/ /_/\_\   version 2.2.

      /_/

Using Scala version 2.11. (Java HotSpot(TM) -Bit Server VM, Java 1.8.0_171)

Type in expressions to have them evaluated.

Type :help for more information.

scala>

spark on yarn启动spark-shell后，可以在yarn管理界面看到一个Runing Application

Kafka：ZK+Kafka+Spark Streaming集群环境搭建（六）针对spark2.2.1以yarn方式启动spark-shell抛出异常：ERROR cluster.YarnSchedulerBackend$YarnSchedulerEndpoint: Sending RequestExecutors(0,0,Map(),Set()) to AM was unsuccessful

6）继续验证：以yarn-cluster方式运行一个spark任务测试是否正常

[spark@master bin]$ cd /opt/spark-2.2.-bin-hadoop2./

[spark@master spark-2.2.-bin-hadoop2.]$ ./bin/spark-submit \

> --class org.apache.spark.examples.SparkPi \

> --master yarn \

> /opt/spark-2.2.-bin-hadoop2./examples/jars/spark-examples_2.-2.2..jar \

>

// :: INFO spark.SparkContext: Running Spark version 2.2.

// :: WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable

// :: INFO spark.SparkContext: Submitted application: Spark Pi

// :: INFO spark.SecurityManager: Changing view acls to: spark

// :: INFO spark.SecurityManager: Changing modify acls to: spark

// :: INFO spark.SecurityManager: Changing view acls groups to:

// :: INFO spark.SecurityManager: Changing modify acls groups to:

// :: INFO spark.SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users  with view permissions: Set(spark); groups with view permissions: Set(); users  with modify permissions: Set(spark); groups with modify permissions: Set()

// :: INFO util.Utils: Successfully started service 'sparkDriver' on port .

// :: INFO spark.SparkEnv: Registering MapOutputTracker

// :: INFO spark.SparkEnv: Registering BlockManagerMaster

// :: INFO storage.BlockManagerMasterEndpoint: Using org.apache.spark.storage.DefaultTopologyMapper for getting topology information

// :: INFO storage.BlockManagerMasterEndpoint: BlockManagerMasterEndpoint up

// :: INFO storage.DiskBlockManager: Created local directory at /opt/spark-2.2.-bin-hadoop2./blockmgr-121559e6-2f03-4f68--faf513bca0ac

// :: INFO memory.MemoryStore: MemoryStore started with capacity 366.3 MB

// :: INFO spark.SparkEnv: Registering OutputCommitCoordinator

// :: INFO util.log: Logging initialized @1288ms

// :: INFO server.Server: jetty-9.3.z-SNAPSHOT

// :: INFO server.Server: Started @1345ms

// :: INFO server.AbstractConnector: Started ServerConnector@596df867{HTTP/1.1,[http/1.1]}{0.0.0.0:}

// :: INFO util.Utils: Successfully started service 'SparkUI' on port .

// :: INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@425357dd{/jobs,null,AVAILABLE,@Spark}

// :: INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@52eacb4b{/jobs/json,null,AVAILABLE,@Spark}

// :: INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@2a551a63{/jobs/job,null,AVAILABLE,@Spark}

// :: INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@ec2bf82{/jobs/job/json,null,AVAILABLE,@Spark}

// :: INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@6cc0bcf6{/stages,null,AVAILABLE,@Spark}

// :: INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@32f61a31{/stages/json,null,AVAILABLE,@Spark}

// :: INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@669253b7{/stages/stage,null,AVAILABLE,@Spark}

// :: INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@49a64d82{/stages/stage/json,null,AVAILABLE,@Spark}

// :: INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@66d23e4a{/stages/pool,null,AVAILABLE,@Spark}

// :: INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@4d9d1b69{/stages/pool/json,null,AVAILABLE,@Spark}

// :: INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@251f7d26{/storage,null,AVAILABLE,@Spark}

// :: INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@52d10fb8{/storage/json,null,AVAILABLE,@Spark}

// :: INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@1fe8d51b{/storage/rdd,null,AVAILABLE,@Spark}

// :: INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@22680f52{/storage/rdd/json,null,AVAILABLE,@Spark}

// :: INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@39c11e6c{/environment,null,AVAILABLE,@Spark}

// :: INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@503d56b5{/environment/json,null,AVAILABLE,@Spark}

// :: INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@433ffad1{/executors,null,AVAILABLE,@Spark}

// :: INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@2575f671{/executors/json,null,AVAILABLE,@Spark}

// :: INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@ecf9fb3{/executors/threadDump,null,AVAILABLE,@Spark}

// :: INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@27f9e982{/executors/threadDump/json,null,AVAILABLE,@Spark}

// :: INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@37d3d232{/static,null,AVAILABLE,@Spark}

// :: INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@{/,null,AVAILABLE,@Spark}

// :: INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@c8b96ec{/api,null,AVAILABLE,@Spark}

// :: INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@58a55449{/jobs/job/kill,null,AVAILABLE,@Spark}

// :: INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@6e0ff644{/stages/stage/kill,null,AVAILABLE,@Spark}

// :: INFO ui.SparkUI: Bound SparkUI to 0.0.0.0, and started at http://192.168.0.120:4040

// :: INFO spark.SparkContext: Added JAR file:/opt/spark-2.2.-bin-hadoop2./examples/jars/spark-examples_2.-2.2..jar at spark://192.168.0.120:41922/jars/spark-examples_2.11-2.2.1.jar with timestamp 1530375071834

// :: INFO client.RMProxy: Connecting to ResourceManager at master/192.168.0.120:

// :: INFO yarn.Client: Requesting a new application from cluster with  NodeManagers

// :: INFO yarn.Client: Verifying our application has not requested more than the maximum memory capability of the cluster ( MB per container)

// :: INFO yarn.Client: Will allocate AM container, with  MB memory including  MB overhead

// :: INFO yarn.Client: Setting up container launch context for our AM

// :: INFO yarn.Client: Setting up the launch environment for our AM container

// :: INFO yarn.Client: Preparing resources for our AM container

// :: WARN yarn.Client: Neither spark.yarn.jars nor spark.yarn.archive is set, falling back to uploading libraries under SPARK_HOME.

// :: INFO yarn.Client: Uploading resource file:/opt/spark-2.2.-bin-hadoop2./spark-c4987503--4e4c-b170-301856a36773/__spark_libs__7066117465738289067.zip -> hdfs://master:9000/user/spark/.sparkStaging/application_1530373644791_0003/__spark_libs__7066117465738289067.zip

// :: INFO yarn.Client: Uploading resource file:/opt/spark-2.2.-bin-hadoop2./spark-c4987503--4e4c-b170-301856a36773/__spark_conf__2688610535686541958.zip -> hdfs://master:9000/user/spark/.sparkStaging/application_1530373644791_0003/__spark_conf__.zip

// :: INFO spark.SecurityManager: Changing view acls to: spark

// :: INFO spark.SecurityManager: Changing modify acls to: spark

// :: INFO spark.SecurityManager: Changing view acls groups to:

// :: INFO spark.SecurityManager: Changing modify acls groups to:

// :: INFO spark.SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users  with view permissions: Set(spark); groups with view permissions: Set(); users  with modify permissions: Set(spark); groups with modify permissions: Set()

// :: INFO yarn.Client: Submitting application application_1530373644791_0003 to ResourceManager

// :: INFO impl.YarnClientImpl: Submitted application application_1530373644791_0003

// :: INFO cluster.SchedulerExtensionServices: Starting Yarn extension services with app application_1530373644791_0003 and attemptId None

// :: INFO yarn.Client: Application report for application_1530373644791_0003 (state: ACCEPTED)

// :: INFO yarn.Client:

         client token: N/A

         diagnostics: AM container is launched, waiting for AM container to Register with RM

         ApplicationMaster host: N/A

         ApplicationMaster RPC port: -

         queue: default

         start time:

         final status: UNDEFINED

         tracking URL: http://master:8088/proxy/application_1530373644791_0003/

         user: spark

// :: INFO yarn.Client: Application report for application_1530373644791_0003 (state: ACCEPTED)

// :: INFO yarn.Client: Application report for application_1530373644791_0003 (state: ACCEPTED)

// :: INFO yarn.Client: Application report for application_1530373644791_0003 (state: ACCEPTED)

// :: INFO yarn.Client: Application report for application_1530373644791_0003 (state: ACCEPTED)

// :: INFO cluster.YarnSchedulerBackend$YarnSchedulerEndpoint: ApplicationMaster registered as NettyRpcEndpointRef(spark-client://YarnAM)

// :: INFO cluster.YarnClientSchedulerBackend: Add WebUI Filter. org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter, Map(PROXY_HOSTS -> master, PROXY_URI_BASES -> http://master:8088/proxy/application_1530373644791_0003), /proxy/application_1530373644791_0003

// :: INFO ui.JettyUtils: Adding filter: org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter

// :: INFO yarn.Client: Application report for application_1530373644791_0003 (state: RUNNING)

// :: INFO yarn.Client:

         client token: N/A

         diagnostics: N/A

         ApplicationMaster host: 192.168.0.121

         ApplicationMaster RPC port:

         queue: default

         start time:

         final status: UNDEFINED

         tracking URL: http://master:8088/proxy/application_1530373644791_0003/

         user: spark

// :: INFO cluster.YarnClientSchedulerBackend: Application application_1530373644791_0003 has started running.

// :: INFO util.Utils: Successfully started service 'org.apache.spark.network.netty.NettyBlockTransferService' on port .

// :: INFO netty.NettyBlockTransferService: Server created on 192.168.0.120:

// :: INFO storage.BlockManager: Using org.apache.spark.storage.RandomBlockReplicationPolicy for block replication policy

// :: INFO storage.BlockManagerMaster: Registering BlockManager BlockManagerId(driver, 192.168.0.120, , None)

// :: INFO storage.BlockManagerMasterEndpoint: Registering block manager 192.168.0.120: with 366.3 MB RAM, BlockManagerId(driver, 192.168.0.120, , None)

// :: INFO storage.BlockManagerMaster: Registered BlockManager BlockManagerId(driver, 192.168.0.120, , None)

// :: INFO storage.BlockManager: Initialized BlockManager: BlockManagerId(driver, 192.168.0.120, , None)

// :: INFO handler.ContextHandler: Started o.s.j.s.ServletContextHandler@58aa1d72{/metrics/json,null,AVAILABLE,@Spark}

// :: INFO cluster.YarnSchedulerBackend$YarnDriverEndpoint: Registered executor NettyRpcEndpointRef(spark-client://Executor) (192.168.0.123:59710) with ID 1

// :: INFO storage.BlockManagerMasterEndpoint: Registering block manager slave3: with 366.3 MB RAM, BlockManagerId(, slave3, , None)

// :: INFO cluster.YarnSchedulerBackend$YarnDriverEndpoint: Registered executor NettyRpcEndpointRef(spark-client://Executor) (192.168.0.122:36090) with ID 2

// :: INFO cluster.YarnClientSchedulerBackend: SchedulerBackend is ready for scheduling beginning after reached minRegisteredResourcesRatio: 0.8

// :: INFO storage.BlockManagerMasterEndpoint: Registering block manager slave2: with 366.3 MB RAM, BlockManagerId(, slave2, , None)

// :: INFO spark.SparkContext: Starting job: reduce at SparkPi.scala:

// :: INFO scheduler.DAGScheduler: Got job  (reduce at SparkPi.scala:) with  output partitions

// :: INFO scheduler.DAGScheduler: Final stage: ResultStage  (reduce at SparkPi.scala:)

// :: INFO scheduler.DAGScheduler: Parents of final stage: List()

// :: INFO scheduler.DAGScheduler: Missing parents: List()

// :: INFO scheduler.DAGScheduler: Submitting ResultStage  (MapPartitionsRDD[] at map at SparkPi.scala:), which has no missing parents

// :: INFO memory.MemoryStore: Block broadcast_0 stored as values in memory (estimated size 1832.0 B, free 366.3 MB)

// :: INFO memory.MemoryStore: Block broadcast_0_piece0 stored as bytes in memory (estimated size 1172.0 B, free 366.3 MB)

// :: INFO storage.BlockManagerInfo: Added broadcast_0_piece0 in memory on 192.168.0.120: (size: 1172.0 B, free: 366.3 MB)

// :: INFO spark.SparkContext: Created broadcast  from broadcast at DAGScheduler.scala:

// :: INFO scheduler.DAGScheduler: Submitting  missing tasks from ResultStage  (MapPartitionsRDD[] at map at SparkPi.scala:) (first  tasks are for partitions Vector(, , , , , , , , , ))

// :: INFO cluster.YarnScheduler: Adding task set 0.0 with  tasks

// :: INFO scheduler.TaskSetManager: Starting task 0.0 in stage 0.0 (TID , slave2, executor , partition , PROCESS_LOCAL,  bytes)

// :: INFO scheduler.TaskSetManager: Starting task 1.0 in stage 0.0 (TID , slave3, executor , partition , PROCESS_LOCAL,  bytes)

// :: INFO storage.BlockManagerInfo: Added broadcast_0_piece0 in memory on slave3: (size: 1172.0 B, free: 366.3 MB)

// :: INFO storage.BlockManagerInfo: Added broadcast_0_piece0 in memory on slave2: (size: 1172.0 B, free: 366.3 MB)

// :: INFO scheduler.TaskSetManager: Starting task 2.0 in stage 0.0 (TID , slave3, executor , partition , PROCESS_LOCAL,  bytes)

// :: INFO scheduler.TaskSetManager: Finished task 1.0 in stage 0.0 (TID ) in  ms on slave3 (executor ) (/)

// :: INFO scheduler.TaskSetManager: Starting task 3.0 in stage 0.0 (TID , slave3, executor , partition , PROCESS_LOCAL,  bytes)

// :: INFO scheduler.TaskSetManager: Finished task 2.0 in stage 0.0 (TID ) in  ms on slave3 (executor ) (/)

// :: INFO scheduler.TaskSetManager: Starting task 4.0 in stage 0.0 (TID , slave3, executor , partition , PROCESS_LOCAL,  bytes)

// :: INFO scheduler.TaskSetManager: Finished task 3.0 in stage 0.0 (TID ) in  ms on slave3 (executor ) (/)

// :: INFO scheduler.TaskSetManager: Starting task 5.0 in stage 0.0 (TID , slave3, executor , partition , PROCESS_LOCAL,  bytes)

// :: INFO scheduler.TaskSetManager: Finished task 4.0 in stage 0.0 (TID ) in  ms on slave3 (executor ) (/)

// :: INFO scheduler.TaskSetManager: Starting task 6.0 in stage 0.0 (TID , slave2, executor , partition , PROCESS_LOCAL,  bytes)

// :: INFO scheduler.TaskSetManager: Finished task 0.0 in stage 0.0 (TID ) in  ms on slave2 (executor ) (/)

// :: INFO scheduler.TaskSetManager: Starting task 7.0 in stage 0.0 (TID , slave3, executor , partition , PROCESS_LOCAL,  bytes)

// :: INFO scheduler.TaskSetManager: Finished task 5.0 in stage 0.0 (TID ) in  ms on slave3 (executor ) (/)

// :: INFO scheduler.TaskSetManager: Starting task 8.0 in stage 0.0 (TID , slave2, executor , partition , PROCESS_LOCAL,  bytes)

// :: INFO scheduler.TaskSetManager: Finished task 6.0 in stage 0.0 (TID ) in  ms on slave2 (executor ) (/)

// :: INFO scheduler.TaskSetManager: Starting task 9.0 in stage 0.0 (TID , slave2, executor , partition , PROCESS_LOCAL,  bytes)

// :: INFO scheduler.TaskSetManager: Finished task 7.0 in stage 0.0 (TID ) in  ms on slave3 (executor ) (/)

// :: INFO scheduler.TaskSetManager: Finished task 8.0 in stage 0.0 (TID ) in  ms on slave2 (executor ) (/)

// :: INFO scheduler.TaskSetManager: Finished task 9.0 in stage 0.0 (TID ) in  ms on slave2 (executor ) (/)

// :: INFO cluster.YarnScheduler: Removed TaskSet 0.0, whose tasks have all completed, from pool

// :: INFO scheduler.DAGScheduler: ResultStage  (reduce at SparkPi.scala:) finished in 0.857 s

// :: INFO scheduler.DAGScheduler: Job  finished: reduce at SparkPi.scala:, took 1.104223 s

Pi is roughly 3.143763143763144

// :: INFO server.AbstractConnector: Stopped Spark@596df867{HTTP/1.1,[http/1.1]}{0.0.0.0:}

// :: INFO ui.SparkUI: Stopped Spark web UI at http://192.168.0.120:4040

// :: INFO cluster.YarnClientSchedulerBackend: Interrupting monitor thread

// :: INFO cluster.YarnClientSchedulerBackend: Shutting down all executors

// :: INFO cluster.YarnSchedulerBackend$YarnDriverEndpoint: Asking each executor to shut down

// :: INFO cluster.SchedulerExtensionServices: Stopping SchedulerExtensionServices

(serviceOption=None,

 services=List(),

 started=false)

// :: INFO cluster.YarnClientSchedulerBackend: Stopped

// :: INFO spark.MapOutputTrackerMasterEndpoint: MapOutputTrackerMasterEndpoint stopped!

// :: INFO memory.MemoryStore: MemoryStore cleared

// :: INFO storage.BlockManager: BlockManager stopped

// :: INFO storage.BlockManagerMaster: BlockManagerMaster stopped

// :: INFO scheduler.OutputCommitCoordinator$OutputCommitCoordinatorEndpoint: OutputCommitCoordinator stopped!

// :: INFO spark.SparkContext: Successfully stopped SparkContext

// :: INFO util.ShutdownHookManager: Shutdown hook called

// :: INFO util.ShutdownHookManager: Deleting directory /opt/spark-2.2.-bin-hadoop2./spark-c4987503--4e4c-b170-301856a36773

通过yarn resource manager界面查看任务运行状态：

Kafka：ZK+Kafka+Spark Streaming集群环境搭建（六）针对spark2.2.1以yarn方式启动spark-shell抛出异常：ERROR cluster.YarnSchedulerBackend$YarnSchedulerEndpoint: Sending RequestExecutors(0,0,Map(),Set()) to AM was unsuccessful

参考《https://blog.csdn.net/rongyongfeikai2/article/details/69361333》

《https://blog.csdn.net/chengyuqiang/article/details/77864246》

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