org.apache.spark.SparkException: Could not find CoarseGrainedScheduler or it has been stopped.
at org.apache.spark.rpc.netty.Dispatcher.postMessage(Dispatcher.scala:163)
at org.apache.spark.rpc.netty.Dispatcher.postOneWayMessage(Dispatcher.scala:133)
at org.apache.spark.rpc.netty.NettyRpcEnv.send(NettyRpcEnv.scala:192)
at org.apache.spark.rpc.netty.NettyRpcEndpointRef.send(NettyRpcEnv.scala:516)
at org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend.reviveOffers(CoarseGrainedSchedulerBackend.scala:356)
at org.apache.spark.scheduler.TaskSchedulerImpl.executorLost(TaskSchedulerImpl.scala:494)
at org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend$DriverEndpoint.disableExecutor(CoarseGrainedSchedulerBackend.scala:301)
at org.apache.spark.scheduler.cluster.YarnSchedulerBackend$YarnDriverEndpoint$$anonfun$onDisconnected$1.apply(YarnSchedulerBackend.scala:121)
at org.apache.spark.scheduler.cluster.YarnSchedulerBackend$YarnDriverEndpoint$$anonfun$onDisconnected$1.apply(YarnSchedulerBackend.scala:120)
at scala.Option.foreach(Option.scala:236)
at org.apache.spark.scheduler.cluster.YarnSchedulerBackend$YarnDriverEndpoint.onDisconnected(YarnSchedulerBackend.scala:120)
at org.apache.spark.rpc.netty.Inbox$$anonfun$process$1.apply$mcV$sp(Inbox.scala:142)
at org.apache.spark.rpc.netty.Inbox.safelyCall(Inbox.scala:204)
at org.apache.spark.rpc.netty.Inbox.process(Inbox.scala:100)
at org.apache.spark.rpc.netty.Dispatcher$MessageLoop.run(Dispatcher.scala:217)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
18/10/14 22:23:26 ERROR netty.Inbox: Ignoring error
org.apache.spark.SparkException: Could not find CoarseGrainedScheduler or it has been stopped.
at org.apache.spark.rpc.netty.Dispatcher.postMessage(Dispatcher.scala:163)
at org.apache.spark.rpc.netty.Dispatcher.postOneWayMessage(Dispatcher.scala:133)
at org.apache.spark.rpc.netty.NettyRpcEnv.send(NettyRpcEnv.scala:192)
at org.apache.spark.rpc.netty.NettyRpcEndpointRef.send(NettyRpcEnv.scala:516)
at org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend.reviveOffers(CoarseGrainedSchedulerBackend.scala:356)
at org.apache.spark.scheduler.TaskSchedulerImpl.executorLost(TaskSchedulerImpl.scala:494)
at org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend$DriverEndpoint.disableExecutor(CoarseGrainedSchedulerBackend.scala:301)
at org.apache.spark.scheduler.cluster.YarnSchedulerBackend$YarnDriverEndpoint$$anonfun$onDisconnected$1.apply(YarnSchedulerBackend.scala:121)
at org.apache.spark.scheduler.cluster.YarnSchedulerBackend$YarnDriverEndpoint$$anonfun$onDisconnected$1.apply(YarnSchedulerBackend.scala:120)
at scala.Option.foreach(Option.scala:236)
at org.apache.spark.scheduler.cluster.YarnSchedulerBackend$YarnDriverEndpoint.onDisconnected(YarnSchedulerBackend.scala:120)
at org.apache.spark.rpc.netty.Inbox$$anonfun$process$1.apply$mcV$sp(Inbox.scala:142)
at org.apache.spark.rpc.netty.Inbox.safelyCall(Inbox.scala:204)
at org.apache.spark.rpc.netty.Inbox.process(Inbox.scala:100)
at org.apache.spark.rpc.netty.Dispatcher$MessageLoop.run(Dispatcher.scala:217)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745) ...... java.util.concurrent.RejectedExecutionException: Task scala.concurrent.impl.CallbackRunnable@708dfce7 rejected from java.util.concurrent.ThreadPoolExecutor@346be0ef[Terminated, pool size = 0, active threads = 0, queued tasks = 0, completed tasks = 224]
at java.util.concurrent.ThreadPoolExecutor$AbortPolicy.rejectedExecution(ThreadPoolExecutor.java:2048)
at java.util.concurrent.ThreadPoolExecutor.reject(ThreadPoolExecutor.java:821)
at java.util.concurrent.ThreadPoolExecutor.execute(ThreadPoolExecutor.java:1372)
at scala.concurrent.impl.ExecutionContextImpl.execute(ExecutionContextImpl.scala:122)
at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:40)
at scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:248)
at scala.concurrent.Promise$class.complete(Promise.scala:55)
at scala.concurrent.impl.Promise$DefaultPromise.complete(Promise.scala:153)
at scala.concurrent.Future$$anonfun$recover$1.apply(Future.scala:324)
at scala.concurrent.Future$$anonfun$recover$1.apply(Future.scala:324)
at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:32)
at org.spark-project.guava.util.concurrent.MoreExecutors$SameThreadExecutorService.execute(MoreExecutors.java:293)
at scala.concurrent.impl.ExecutionContextImpl$$anon$1.execute(ExecutionContextImpl.scala:133)
at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:40)
at scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:248)
at scala.concurrent.Promise$class.complete(Promise.scala:55)
at scala.concurrent.impl.Promise$DefaultPromise.complete(Promise.scala:153)
at scala.concurrent.Future$$anonfun$map$1.apply(Future.scala:235)
at scala.concurrent.Future$$anonfun$map$1.apply(Future.scala:235)
at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:32)
at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.processBatch$1(Future.scala:643)
at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply$mcV$sp(Future.scala:658)
at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply(Future.scala:635)
at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply(Future.scala:635)
at scala.concurrent.BlockContext$.withBlockContext(BlockContext.scala:72)
at scala.concurrent.Future$InternalCallbackExecutor$Batch.run(Future.scala:634)
at scala.concurrent.Future$InternalCallbackExecutor$.scala$concurrent$Future$InternalCallbackExecutor$$unbatchedExecute(Future.scala:694)
at scala.concurrent.Future$InternalCallbackExecutor$.execute(Future.scala:685)
at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:40)
at scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:248)
at scala.concurrent.Promise$class.tryFailure(Promise.scala:112)
at scala.concurrent.impl.Promise$DefaultPromise.tryFailure(Promise.scala:153)
at org.apache.spark.rpc.netty.NettyRpcEnv.org$apache$spark$rpc$netty$NettyRpcEnv$$onFailure$1(NettyRpcEnv.scala:208)
at org.apache.spark.rpc.netty.NettyRpcEnv$$anonfun$2.apply(NettyRpcEnv.scala:230)
at org.apache.spark.rpc.netty.NettyRpcEnv$$anonfun$2.apply(NettyRpcEnv.scala:230)
at org.apache.spark.rpc.netty.RpcOutboxMessage.onFailure(Outbox.scala:71)
at org.apache.spark.network.client.TransportResponseHandler.failOutstandingRequests(TransportResponseHandler.java:110)
at org.apache.spark.network.client.TransportResponseHandler.channelUnregistered(TransportResponseHandler.java:124)
at org.apache.spark.network.server.TransportChannelHandler.channelUnregistered(TransportChannelHandler.java:94)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelUnregistered(AbstractChannelHandlerContext.java:158)
at io.netty.channel.AbstractChannelHandlerContext.fireChannelUnregistered(AbstractChannelHandlerContext.java:144)
at io.netty.channel.ChannelInboundHandlerAdapter.channelUnregistered(ChannelInboundHandlerAdapter.java:53)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelUnregistered(AbstractChannelHandlerContext.java:158)
at io.netty.channel.AbstractChannelHandlerContext.fireChannelUnregistered(AbstractChannelHandlerContext.java:144)
at io.netty.channel.ChannelInboundHandlerAdapter.channelUnregistered(ChannelInboundHandlerAdapter.java:53)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelUnregistered(AbstractChannelHandlerContext.java:158)
at io.netty.channel.AbstractChannelHandlerContext.fireChannelUnregistered(AbstractChannelHandlerContext.java:144)
at io.netty.channel.ChannelInboundHandlerAdapter.channelUnregistered(ChannelInboundHandlerAdapter.java:53)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelUnregistered(AbstractChannelHandlerContext.java:158)
at io.netty.channel.AbstractChannelHandlerContext.fireChannelUnregistered(AbstractChannelHandlerContext.java:144)
at io.netty.channel.DefaultChannelPipeline.fireChannelUnregistered(DefaultChannelPipeline.java:739)
at io.netty.channel.AbstractChannel$AbstractUnsafe$8.run(AbstractChannel.java:659)
at io.netty.util.concurrent.SingleThreadEventExecutor.runAllTasks(SingleThreadEventExecutor.java:328)
at io.netty.util.concurrent.SingleThreadEventExecutor.confirmShutdown(SingleThreadEventExecutor.java:627)
at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:362)

解决办法:

  1. 在提交代码的时候添加配置spark-submit  ...  –conf spark.dynamicAllocation.enabled=false
  2. 也可以在代码中通过SparkConf设置:conf.set(“spark.dynamicAllocation.enabled”,”false”)

SparkException: Could not find CoarseGrainedScheduler or it has been stopped.的更多相关文章

  1. org.apache.spark.rpc.RpcTimeout$$anonfun$1.applyOrElse

    跑sparkPis示例程序 [root@node01 bin]# ./spark-submit --master spark://node01:7077 --class org.apache.spar ...

  2. spark streaming 1: SparkContex

    StreamingContext 和SparkContex的用途是差不多的,作为spark stream的入口,提供配置.生成DStream等功能. 总体来看,spark stream包括如下模块: ...

  3. 【原创】大叔问题定位分享(10)提交spark任务偶尔报错 org.apache.spark.SparkException: A master URL must be set in your configuration

    spark 2.1.1 一 问题重现 问题代码示例 object MethodPositionTest { val sparkConf = new SparkConf().setAppName(&qu ...

  4. spark出现task不能序列化错误的解决方法 org.apache.spark.SparkException: Task not serializable

    import org.elasticsearch.cluster.routing.Murmur3HashFunction; import org.elasticsearch.common.math.M ...

  5. Spark运行程序异常信息: org.apache.spark.SparkException: Task not serializable 解决办法

    错误信息: 17/05/20 18:51:39 ERROR JobScheduler: Error running job streaming job 1495277499000 ms.0 org.a ...

  6. SparkException: Master removed our application

    come from https://stackoverflow.com/questions/32245498/sparkexception-master-removed-our-application ...

  7. 错误:Caused by:org.apache.spark.SparkException: Kryo serialization failed: Buffer overflow.Available: 0, required: 21. To avoid this,

    这个是写入Redis时用的序列化器,然后错误提示是超过了大小限制,把配置调大即可. .set("spark.kryoserializer.buffer.max","128 ...

  8. Spark On YARN内存分配

    本文转自:http://blog.javachen.com/2015/06/09/memory-in-spark-on-yarn.html?utm_source=tuicool 此文解决了Spark ...

  9. spark on yarn 内存分配

    Spark On YARN内存分配 本文主要了解Spark On YARN部署模式下的内存分配情况,因为没有深入研究Spark的源代码,所以只能根据日志去看相关的源代码,从而了解“为什么会这样,为什么 ...

随机推荐

  1. HTML5 缓存: cache manifest

    ---恢复内容开始--- 1:MIME TYPE:text/cache-manifest 服务器配置MIME类型2:需要由你创建的:NAME.manifest 创建manifest文件3:给 < ...

  2. linux 基础知识(三)

    抽空把Linux的一些基础的东西再补充一下,安全的东西真的很多都是要自己不断的学习,很多还是今天学习了一点时间过后不用就会忘记.所以学习的东西就是要不断地往复. 有时候感觉有时候快就是慢,慢就是快. ...

  3. codeforce 240E 最小树形图+路径记录更新

    最小树形图的路径是在不断建立新图的过程中更新的,因此需要开一个结构体cancle记录那些被更新的边,保存可能会被取消的边和边在旧图中的id 在朱刘算法最后添加了一个从后往前遍历新建边的循环,这可以理解 ...

  4. java常见错误总结

    1. 现象:将数组转为List后进行removeAll()操作,报java.lang.UnsupportedOperationException错误. 代码: /** * 获取标记ID * @retu ...

  5. base | Tread类

    Tread类 Linux中,每个进程有一个pid,类型pid_t,由getpid()取得.Linux下的POSIX线程也有一个id,类型 pthread_t,由pthread_self()取得,该id ...

  6. Beyond-Compare 4 -linux 破解

    key失效了可以去https://www.serials.be/serial/Beyond_Compare_4_Linux_68803632.html生成 Crack-Beyond-Compare-l ...

  7. 连接Oracle时报错ORA-12541: TNS: 无监听程序

    从开始菜单中打开“Oracle Net Configuration Assistance”,选择“监听程序配置”,如下图所示,点击下一步.   选择“重新配置”,如下图所示,点击下一步.   选择监听 ...

  8. Tomcat8 启动慢 Creation of SecureRandom instance for session ID generation using [SHA1PRNG] took [53,161] milliseconds

    修改$JAVA_PATH/jre/lib/security/java.security文件 将 securerandom.source=file:/dev/random 修改为 securerando ...

  9. Doracle.jdbc.J2EE13Compliant=true

    To make the Oracle driver behave in a Java EE-compliant manner, you must define the following JVM pr ...

  10. FATAL ERROR: please install the following Perl modules before executing ./mysql_install_db: Data::Dumper

    今天安装本地数据库,所遇到的错误 FATAL ERROR: please install the following Perl modules before executing ./mysql_ins ...