本文介绍了我的星火的工人无法连接Master.Something错阿卡？的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我想安装星火并且standlone模式与我的两个虚拟机集群。结果
随着火花0.9.1彬hadoop1的版本，我在每个虚拟机成功执行火花外壳。我按照使一个虚拟机（IP：XX.XX .xx.223）作为主服务器和工人，并令对方（IP：xx.xx.xx.224）作为唯一的工人结果
但224-IP虚拟机无法连接的223-IP虚拟机。
其次是223（主）的主日志：
I want to install Spark Standlone mode to a Cluster with my two virtual machines.
With the version of spark-0.9.1-bin-hadoop1, I execute spark-shell successfully in each vm. I follow the offical document to make one vm(ip:xx.xx.xx.223) as both Master and Worker and to make the other(ip:xx.xx.xx.224) as Worker only.
But the 224-ip vm cannot connect the 223-ip vm.Followed is 223(Master)'s master log:
[@tc-52-223 logs]# tail -100f spark-root-org.apache.spark.deploy.master.Master-1-tc-52-223.out
Spark Command: /usr/local/jdk/bin/java -cp :/data/test/spark-0.9.1-bin-hadoop1/conf:/data/test/spark-0.9.1-bin-hadoop1/assembly/target/scala-2.10/spark-assembly_2.10-0.9.1-hadoop1.0.4.jar -Dspark.akka.logLifecycleEvents=true -Djava.library.path= -Xms512m -Xmx512m org.apache.spark.deploy.master.Master --ip 10.11.52.223 --port 7077 --webui-port 8080
log4j:WARN No appenders could be found for logger (akka.event.slf4j.Slf4jLogger).
log4j:WARN Please initialize the log4j system properly.
log4j:WARN See http://logging.apache.org/log4j/1.2/faq.html#noconfig for more info.
14/04/14 22:17:03 INFO Master: Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
14/04/14 22:17:03 INFO Master: Starting Spark master at spark://10.11.52.223:7077
14/04/14 22:17:03 INFO MasterWebUI: Started Master web UI at http://tc-52-223:8080
14/04/14 22:17:03 INFO Master: I have been elected leader! New state: ALIVE
14/04/14 22:17:06 INFO Master: Registering worker tc-52-223:20599 with 1 cores, 4.0 GB RAM
14/04/14 22:17:06 INFO Master: Registering worker tc_52_224:21371 with 1 cores, 4.0 GB RAM
14/04/14 22:17:06 INFO RemoteActorRefProvider$RemoteDeadLetterActorRef: Message [org.apache.spark.deploy.DeployMessages$RegisteredWorker] from Actor[akka://sparkMaster/user/Master#1972530850] to Actor[akka://sparkMaster/deadLetters] was not delivered. [1] dead letters encountered. This logging can be turned off or adjusted with configuration settings 'akka.log-dead-letters' and 'akka.log-dead-letters-during-shutdown'.
14/04/14 22:17:26 INFO Master: Registering worker tc_52_224:21371 with 1 cores, 4.0 GB RAM
14/04/14 22:17:26 INFO RemoteActorRefProvider$RemoteDeadLetterActorRef: Message [org.apache.spark.deploy.DeployMessages$RegisterWorkerFailed] from Actor[akka://sparkMaster/user/Master#1972530850] to Actor[akka://sparkMaster/deadLetters] was not delivered. [2] dead letters encountered. This logging can be turned off or adjusted with configuration settings 'akka.log-dead-letters' and 'akka.log-dead-letters-during-shutdown'.
14/04/14 22:17:46 INFO Master: Registering worker tc_52_224:21371 with 1 cores, 4.0 GB RAM
14/04/14 22:17:46 INFO RemoteActorRefProvider$RemoteDeadLetterActorRef: Message [org.apache.spark.deploy.DeployMessages$RegisterWorkerFailed] from Actor[akka://sparkMaster/user/Master#1972530850] to Actor[akka://sparkMaster/deadLetters] was not delivered. [3] dead letters encountered. This logging can be turned off or adjusted with configuration settings 'akka.log-dead-letters' and 'akka.log-dead-letters-during-shutdown'.
14/04/14 22:18:06 INFO Master: akka.tcp://sparkWorker@tc_52_224:21371 got disassociated, removing it.
14/04/14 22:18:06 INFO Master: akka.tcp://sparkWorker@tc_52_224:21371 got disassociated, removing it.
14/04/14 22:18:06 INFO LocalActorRef: Message [akka.remote.transport.ActorTransportAdapter$DisassociateUnderlying] from Actor[akka://sparkMaster/deadLetters] to Actor[akka://sparkMaster/system/transports/akkaprotocolmanager.tcp0/akkaProtocol-tcp%3A%2F%2FsparkMaster%4010.11.52.224%3A61550-1#646150938] was not delivered. [4] dead letters encountered. This logging can be turned off or adjusted with configuration settings 'akka.log-dead-letters' and 'akka.log-dead-letters-during-shutdown'.
14/04/14 22:18:06 INFO Master: akka.tcp://sparkWorker@tc_52_224:21371 got disassociated, removing it.
14/04/14 22:18:06 ERROR EndpointWriter: AssociationError [akka.tcp://[email protected]:7077] -> [akka.tcp://sparkWorker@tc_52_224:21371]: Error [Association failed with [akka.tcp://sparkWorker@tc_52_224:21371]] [
akka.remote.EndpointAssociationException: Association failed with [akka.tcp://sparkWorker@tc_52_224:21371]
Caused by: akka.remote.transport.netty.NettyTransport$$anonfun$associate$1$$anon$2: Connection refused: tc_52_224/10.11.52.224:21371
]
14/04/14 22:18:06 INFO Master: akka.tcp://sparkWorker@tc_52_224:21371 got disassociated, removing it.
14/04/14 22:18:06 ERROR EndpointWriter: AssociationError [akka.tcp://[email protected]:7077] -> [akka.tcp://sparkWorker@tc_52_224:21371]: Error [Association failed with [akka.tcp://sparkWorker@tc_52_224:21371]] [
akka.remote.EndpointAssociationException: Association failed with [akka.tcp://sparkWorker@tc_52_224:21371]
Caused by: akka.remote.transport.netty.NettyTransport$$anonfun$associate$1$$anon$2: Connection refused: tc_52_224/10.11.52.224:21371
]
14/04/14 22:18:06 ERROR EndpointWriter: AssociationError [akka.tcp://[email protected]:7077] -> [akka.tcp://sparkWorker@tc_52_224:21371]: Error [Association failed with [akka.tcp://sparkWorker@tc_52_224:21371]] [
akka.remote.EndpointAssociationException: Association failed with [akka.tcp://sparkWorker@tc_52_224:21371]
Caused by: akka.remote.transport.netty.NettyTransport$$anonfun$associate$1$$anon$2: Connection refused: tc_52_224/10.11.52.224:21371
]
14/04/14 22:18:06 INFO Master: akka.tcp://sparkWorker@tc_52_224:21371 got disassociated, removing it.
14/04/14 22:19:03 WARN Master: Removing worker-20140414221705-tc_52_224-21371 because we got no heartbeat in 60 seconds
14/04/14 22:19:03 INFO Master: Removing worker worker-20140414221705-tc_52_224-21371 on tc_52_224:21371
其次
为223（工人）的工人日志：
Followed is 223(Worker)'s worker log:
14/04/14 22:17:06 INFO Worker: Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
14/04/14 22:17:06 INFO Worker: Starting Spark worker tc-52-223:20599 with 1 cores, 4.0 GB RAM
14/04/14 22:17:06 INFO Worker: Spark home: /data/test/spark-0.9.1-bin-hadoop1
14/04/14 22:17:06 INFO WorkerWebUI: Started Worker web UI at http://tc-52-223:8081
14/04/14 22:17:06 INFO Worker: Connecting to master spark://xx.xx.52.223:7077...
14/04/14 22:17:06 INFO Worker: Successfully registered with master spark://xx.xx.52.223:7077
其次
为224（工人）的工作日志：
Followed is 224(Worker)'s work log:
Spark Command: /usr/local/jdk/bin/java -cp :/data/test/spark-0.9.1-bin-hadoop1/conf:/data/test/spark-0.9.1-bin-hadoop1/assembly/target/scala-2.10/spark-assembly_2.10-0.9.1-hadoop1.0.4.jar -Dspark.akka.logLifecycleEvents=true -Djava.library.path= -Xms512m -Xmx512m org.apache.spark.deploy.worker.Worker spark://10.11.52.223:7077 --webui-port 8081
========================================
log4j:WARN No appenders could be found for logger (akka.event.slf4j.Slf4jLogger).
log4j:WARN Please initialize the log4j system properly.
log4j:WARN See http://logging.apache.org/log4j/1.2/faq.html#noconfig for more info.
14/04/14 22:17:06 INFO Worker: Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
14/04/14 22:17:06 INFO Worker: Starting Spark worker tc_52_224:21371 with 1 cores, 4.0 GB RAM
14/04/14 22:17:06 INFO Worker: Spark home: /data/test/spark-0.9.1-bin-hadoop1
14/04/14 22:17:06 INFO WorkerWebUI: Started Worker web UI at http://tc_52_224:8081
14/04/14 22:17:06 INFO Worker: Connecting to master spark://xx.xx.52.223:7077...
14/04/14 22:17:26 INFO Worker: Connecting to master spark://xx.xx.52.223:7077...
14/04/14 22:17:46 INFO Worker: Connecting to master spark://xx.xx.52.223:7077...
14/04/14 22:18:06 ERROR Worker: All masters are unresponsive! Giving up.
其次
是我spark-env.sh：
Followed is my spark-env.sh:
JAVA_HOME=/usr/local/jdk
export SPARK_MASTER_IP=tc-52-223
export SPARK_WORKER_CORES=1
export SPARK_WORKER_INSTANCES=1
export SPARK_MASTER_PORT=7077
export SPARK_WORKER_MEMORY=4g
export MASTER=spark://${SPARK_MASTER_IP}:${SPARK_MASTER_PORT}
export SPARK_LOCAL_IP=tc-52-223
我GOOGLE了许多解决方案，但他们不能工作。
请帮我。
I have googled many solutions, but they cant work.Please help me.
推荐答案
我不知道这是否是我遇到了同样的问题，但你可能要尝试设置 SPARK_MASTER_IP 一样的火花什么结合。在您的例子是看起来像这将是 10.11.52.223 ，而不是 TC-52-223 。
I'm not sure if this is the same issue I encountered but you may want to try setting SPARK_MASTER_IP the same as what spark binds to. In your example is looks like it would be 10.11.52.223 and not tc-52-223.
这应该是一样的，你看什么，当你参观8080有点像主节点的网络用户界面：在SPARK乐驰站长：//ec2-XX-XX-XXX-XXX.compute -1.amazonaws.com:7077
It should be the same as what you see when you visit the master node web UI on 8080. Something like: Spark Master at spark://ec2-XX-XX-XXX-XXX.compute-1.amazonaws.com:7077
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