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$YarnSchedulerEndpoint: Sending RequestExecutors(0,0,Map(),Set()) to AM was unsuccessful
Spark以yarn方式运行时抛出异常：
[spark@master bin]$ 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
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$OutputCommitCoordinatorEndpoint: OutputCommitCoordinator stopped!
// :: INFO spark.SparkContext: Successfully stopped SparkContext
// :: INFO util.ShutdownHookManager: Shutdown hook called
// :: INFO util.ShutdownHookManager: Deleting directory /opt/spark-2.2.-bin-hadoop2./spark-c4987503--4e4c-b170-301856a36773
通过yarn resource manager界面查看任务运行状态：
参考《https://blog.csdn.net/rongyongfeikai2/article/details/69361333》
《https://blog.csdn.net/chengyuqiang/article/details/77864246》
Kafka：ZK+Kafka+Spark Streaming集群环境搭建（六）针对spark2.2.1以yarn方式启动spark-shell抛出异常：ERROR cluster.YarnSchedulerBackend$YarnSchedulerEndpoint: Sending RequestExecutors(0,0,Map(),Set()) to AM was unsuccessful的更多相关文章
- Kafka：ZK+Kafka+Spark Streaming集群环境搭建（二十一）NIFI1.7.1安装
一.nifi基本配置 1. 修改各节点主机名,修改/etc/hosts文件内容. 192.168.0.120 master 192.168.0.121 slave1 192.168.0.122 sla ...
- Kafka：ZK+Kafka+Spark Streaming集群环境搭建（十三）kafka+spark streaming打包好的程序提交时提示虚拟内存不足（Container is running beyond virtual memory limits. Current usage: 119.5 MB of 1 GB physical memory used; 2.2 GB of 2.1 G）
异常问题:Container is running beyond virtual memory limits. Current usage: 119.5 MB of 1 GB physical mem ...
- Kafka：ZK+Kafka+Spark Streaming集群环境搭建（十二）VMW安装四台CentOS，并实现本机与它们能交互，虚拟机内部实现可以上网。
Centos7出现异常:Failed to start LSB: Bring up/down networking. 按照<Kafka:ZK+Kafka+Spark Streaming集群环境搭 ...
- Kafka：ZK+Kafka+Spark Streaming集群环境搭建（十一）定制一个arvo格式文件发送到kafka的topic，通过Structured Streaming读取kafka的数据
将arvo格式数据发送到kafka的topic 第一步:定制avro schema: { "type": "record", "name": ...
- Kafka：ZK+Kafka+Spark Streaming集群环境搭建（十）安装hadoop2.9.0搭建HA
如何搭建配置centos虚拟机请参考<Kafka:ZK+Kafka+Spark Streaming集群环境搭建(一)VMW安装四台CentOS,并实现本机与它们能交互,虚拟机内部实现可以上网.& ...
- Kafka：ZK+Kafka+Spark Streaming集群环境搭建（九）安装kafka_2.11-1.1.0
如何搭建配置centos虚拟机请参考<Kafka:ZK+Kafka+Spark Streaming集群环境搭建(一)VMW安装四台CentOS,并实现本机与它们能交互,虚拟机内部实现可以上网.& ...
- Kafka：ZK+Kafka+Spark Streaming集群环境搭建（八）安装zookeeper-3.4.12
如何搭建配置centos虚拟机请参考<Kafka:ZK+Kafka+Spark Streaming集群环境搭建(一)VMW安装四台CentOS,并实现本机与它们能交互,虚拟机内部实现可以上网.& ...
- Kafka：ZK+Kafka+Spark Streaming集群环境搭建（三）安装spark2.2.1
如何搭建配置centos虚拟机请参考<Kafka:ZK+Kafka+Spark Streaming集群环境搭建(一)VMW安装四台CentOS,并实现本机与它们能交互,虚拟机内部实现可以上网.& ...
- Kafka：ZK+Kafka+Spark Streaming集群环境搭建（二）安装hadoop2.9.0
如何搭建配置centos虚拟机请参考<Kafka:ZK+Kafka+Spark Streaming集群环境搭建(一)VMW安装四台CentOS,并实现本机与它们能交互,虚拟机内部实现可以上网.& ...
随机推荐
- ELASTIC制图等高级使用
基于上一个安装部署的文档后(ELASTIC 5.2部署并收集nginx日志) http://www.cnblogs.com/kerwinC/p/6387073.html 本次带来一些使用的分享. ki ...
- Redis使用小结
Redis官方没有windows版本的,对于Windows环境的redis,有如下两个方案 微软的移植版本,但只支持到3.2 下载地址 win10及以上的版本直接通过win10的liunx子系统执行 ...
- 使用Puppeteer进行数据抓取(三)——简单的示例
本文以一个示例简单的介绍一下puppeteer的用法,我们的目的是:获取我博客上的文章的前十页的所有随笔的标题和链接.由于puppeteer本身是自动化chorme,因此这里我们的步骤和手动操作浏览器 ...
- ARM JTAG 信号 RTCK 应该如何处理？
用户在调试内嵌可综合内核的 CPU 如 ARM7TDMI-S 时,需要通过打开仿真器的自适应时钟功能. 此时,ARM仿真器根据 RTCK 时钟信号的频率,产生可用于 CPU 内核当前时钟主频的最快的 ...
- 看opengl 写代码（4） 画一个圆
opengl 编程指南 P30 以下代码 是 用 直线 连起来 画一个圆. // circle.cpp : 定义控制台应用程序的入口点. // #include "stdafx.h" ...
- lufylegend:图形变形1
HTML5中的几种变形 HTML5中的变形,共有以下几种方法 scale() 缩放 rotate() 旋转 translate() 平移 transform() 矩阵变形 setTransform() ...
- 转: Linux --- Supervisor的作用与配置
supervisor管理进程,是通过fork/exec的方式将这些被管理的进程当作supervisor的子进程来启动,所以我们只需要将要管理进程的可执行文件的路径添加到supervisor的配置文件中 ...
- 7z压缩与解压命令
在写很多工具的时候,可能会用到7z命令来进行压缩与解压操作.这里记录二个比较常用的操作:压缩.解压. 在dos窗口下输入7z命令,会显示7z的使用参数详情: 7-Zip 9.10 beta Co ...
- 用PopupWindow实现弹出菜单（弹出的菜单采用自定义布局）
用PopupWindow实现弹出菜单是一个比较好的方式.当然我们还有一个类PopupMenu也能实现弹出菜单,但那个太过于局限了,所以不是很推荐. 这个实例的效果是这样的:点击按钮后,一个菜 ...
- easyui-combobox 设置option内容不换行
使用easyui 1.4.4 1 2 3 4 5 6 7 8 <select id="hotalid" class="easyui-combobox" d ...