spark shell 运行异常

知行合一1 发布于 2016/08/26 10:51
阅读 483
收藏 0
Association with remote system [akka.tcp://sparkMaster@master :7077] has failed, address is now gated for [5000] ms. Reason: [Association failed with [akka.tcp://sparkMaster@master :7077]] Caused by: [拒绝连接: master/192.168.2.230:7077]
16/08/25 19:50:44 WARN AppClient$ClientEndpoint: Failed to connect to master master:7077
akka.actor.ActorNotFound: Actor not found for: ActorSelection[Anchor(akka.tcp://sparkMaster@master :7077/), Path(/user/Master)]
 at akka.actor.ActorSelection$$anonfun$resolveOne$1.apply(ActorSelection.scala:65)
 at akka.actor.ActorSelection$$anonfun$resolveOne$1.apply(ActorSelection.scala:63)
 at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:32)
 at akka.dispatch.BatchingExecutor$AbstractBatch.processBatch(BatchingExecutor.scala:55)
 at akka.dispatch.BatchingExecutor$Batch.run(BatchingExecutor.scala:73)
 at akka.dispatch.ExecutionContexts$sameThreadExecutionContext$.unbatchedExecute(Future.scala:74)
 at akka.dispatch.BatchingExecutor$class.execute(BatchingExecutor.scala:120)
 at akka.dispatch.ExecutionContexts$sameThreadExecutionContext$.execute(Future.scala:73)
 at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:40)
 at scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:248)
 at akka.pattern.PromiseActorRef.$bang(AskSupport.scala:266)
 at akka.actor.EmptyLocalActorRef.specialHandle(ActorRef.scala:533)
 at akka.actor.DeadLetterActorRef.specialHandle(ActorRef.scala:569)
 at akka.actor.DeadLetterActorRef.$bang(ActorRef.scala:559)
 at akka.remote.RemoteActorRefProvider$RemoteDeadLetterActorRef.$bang(RemoteActorRefProvider.scala:87)
 at akka.remote.EndpointWriter.postStop(Endpoint.scala:557)
 at akka.actor.Actor$class.aroundPostStop(Actor.scala:477)
 at akka.remote.EndpointActor.aroundPostStop(Endpoint.scala:411)
 at akka.actor.dungeon.FaultHandling$class.akka$actor$dungeon$FaultHandling$$finishTerminate(FaultHandling.scala:210)
 at akka.actor.dungeon.FaultHandling$class.terminate(FaultHandling.scala:172)
 at akka.actor.ActorCell.terminate(ActorCell.scala:369)
 at akka.actor.ActorCell.invokeAll$1(ActorCell.scala:462)
 at akka.actor.ActorCell.systemInvoke(ActorCell.scala:478)
 at akka.dispatch.Mailbox.processAllSystemMessages(Mailbox.scala:263)
 at akka.dispatch.Mailbox.run(Mailbox.scala:219)
 at akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:397)
 at scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260)
 at scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339)
加载中
0
NickWilde
NickWilde
看看你master起来没有,网页能不能访问……
0
感谢分享
感谢分享
深入浅出Spark机器学习实战(用户行为分析)
课程观看地址:http://www.xuetuwuyou.com/course/144
课程出自学途无忧网:http://www.xuetuwuyou.com

一、课程目标
 熟练掌握SparkSQL的各种操作,深入了解Spark内部实现原理
 深入了解SparkML机器学习各种算法模型的构建和运行
 熟练Spark的API并能灵活运用
 能掌握Spark在工作当中的运用

二、适合人群
 适合给,有java,scala基础,想往大数据spark机器学习这块发展
 适合给想学习spark,往数据仓库,大数据挖掘机器学习,方向发展的学员


三、课程用到的软件及版本:
Spark2.0,Spark1.6.2,STS,maven,Linux Centos6.5,mysql,mongodb3.2




四、课程目录:


课时1:Spark介绍
课时2:Spark2集群安装 
课时3:Spark RDD操作 
课时4:SparkRDD原理剖析
课时5:Spark2sql从mysql中导入 
课时6:Spark1.6.2sql与mysql数据交互
课时7:SparkSQL java操作mysql数据
课时8:Spark统计用户的收藏转换率 
课时9:Spark梳理用户的收藏以及订单转换率
课时10:最终获取用户的收藏以及订单转换率 
课时11:Spark Pipeline构建随机森林回归预测模型 
课时12:Spark 随机森林回归预测结果并存储进mysql 
课时13:Spark对收藏转预测换率与真正的转换率对比,以及决策树模型构建
课时14:Spark机器学习对各种监督与非监督分类学习详细介绍 
课时15:Spark协同过滤算法,构建用户与产品模型 
课时16:Spark协同算法完成给用户推荐产品
课时17:mongodb的安装以及其基本操作 
课时18:Spark与mongodb整合 
课时19:Spark预测收藏以及给用户推荐的产品存储进mongodb 
课时20:操作RDD需要注意点,以及Spark内存分配资源调优
课时21:Spark整个学习过程及其总结


推荐组合学习:《国内首部系统性介绍Scala语言培训课程》
课程观看地址:http://www.xuetuwuyou.com/course/12
返回顶部
顶部