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Spark 和 Java：在 awaitResult 中抛出异常

社区维基
1
新手上路，请多包涵

我正在尝试使用 Java 应用程序中的 IP 10.20.30.50 和端口 7077 连接虚拟机中运行的 Spark 集群，并运行字数统计示例：

 SparkConf conf = new SparkConf().setMaster("spark://10.20.30.50:7077").setAppName("wordCount");
JavaSparkContext sc = new JavaSparkContext(conf);
JavaRDD<String> textFile = sc.textFile("hdfs://localhost:8020/README.md");
String result = Long.toString(textFile.count());
JavaRDD<String> words = textFile.flatMap((FlatMapFunction<String, String>) s -> Arrays.asList(s.split(" ")).iterator());
JavaPairRDD<String, Integer> pairs = words.mapToPair((PairFunction<String, String, Integer>) s -> new Tuple2<>(s, 1));
JavaPairRDD<String, Integer> counts = pairs.reduceByKey((Function2<Integer, Integer, Integer>) (a, b) -> a + b);
counts.saveAsTextFile("hdfs://localhost:8020/tmp/output");
sc.stop();
return result;

Java 应用程序显示以下堆栈跟踪：

 Running Spark version 2.0.1
Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Changing view acls to: lii5ka
Changing modify acls to: lii5ka
Changing view acls groups to:
Changing modify acls groups to:
SecurityManager: authentication disabled; ui acls disabled; users  with view permissions: Set(lii5ka); groups with view permissions: Set(); users  with modify permissions: Set(lii5ka); groups with modify permissions: Set()
Successfully started service 'sparkDriver' on port 61267.
Registering MapOutputTracker
Registering BlockManagerMaster
Created local directory at /private/var/folders/4k/h0sl02993_99bzt0dzv759000000gn/T/blockmgr-51de868d-3ba7-40be-8c53-f881f97ced63
MemoryStore started with capacity 2004.6 MB
Registering OutputCommitCoordinator
Logging initialized @48403ms
jetty-9.2.z-SNAPSHOT
Started o.s.j.s.ServletContextHandler@1316e7ec{/jobs,null,AVAILABLE}
Started o.s.j.s.ServletContextHandler@782de006{/jobs/json,null,AVAILABLE}
Started o.s.j.s.ServletContextHandler@2d0353{/jobs/job,null,AVAILABLE}
Started o.s.j.s.ServletContextHandler@381e24a0{/jobs/job/json,null,AVAILABLE}
Started o.s.j.s.ServletContextHandler@1c138dc8{/stages,null,AVAILABLE}
Started o.s.j.s.ServletContextHandler@b29739c{/stages/json,null,AVAILABLE}
Started o.s.j.s.ServletContextHandler@63f6de31{/stages/stage,null,AVAILABLE}
Started o.s.j.s.ServletContextHandler@2a04ddcb{/stages/stage/json,null,AVAILABLE}
Started o.s.j.s.ServletContextHandler@2af9688e{/stages/pool,null,AVAILABLE}
Started o.s.j.s.ServletContextHandler@6a0c5bde{/stages/pool/json,null,AVAILABLE}
Started o.s.j.s.ServletContextHandler@3f5e17f8{/storage,null,AVAILABLE}
Started o.s.j.s.ServletContextHandler@33b86f5d{/storage/json,null,AVAILABLE}
Started o.s.j.s.ServletContextHandler@5264dcbc{/storage/rdd,null,AVAILABLE}
Started o.s.j.s.ServletContextHandler@5a3ebf85{/storage/rdd/json,null,AVAILABLE}
Started o.s.j.s.ServletContextHandler@159082ed{/environment,null,AVAILABLE}
Started o.s.j.s.ServletContextHandler@6522c585{/environment/json,null,AVAILABLE}
Started o.s.j.s.ServletContextHandler@115774a1{/executors,null,AVAILABLE}
Started o.s.j.s.ServletContextHandler@3e3a3399{/executors/json,null,AVAILABLE}
Started o.s.j.s.ServletContextHandler@2f2c5959{/executors/threadDump,null,AVAILABLE}
Started o.s.j.s.ServletContextHandler@5c51afd4{/executors/threadDump/json,null,AVAILABLE}
Started o.s.j.s.ServletContextHandler@76893a83{/static,null,AVAILABLE}
Started o.s.j.s.ServletContextHandler@19c07930{/,null,AVAILABLE}
Started o.s.j.s.ServletContextHandler@54eb0dc0{/api,null,AVAILABLE}
Started o.s.j.s.ServletContextHandler@5953786{/stages/stage/kill,null,AVAILABLE}
Started ServerConnector@2eeb8bd6{HTTP/1.1}{0.0.0.0:4040}
Started @48698ms
Successfully started service 'SparkUI' on port 4040.
Bound SparkUI to 0.0.0.0, and started at http://192.168.0.104:4040
Connecting to master spark://10.20.30.50:7077...
Successfully created connection to /10.20.30.50:7077 after 25 ms (0 ms spent in bootstraps)
Connecting to master spark://10.20.30.50:7077...
Still have 2 requests outstanding when connection from /10.20.30.50:7077 is closed
Failed to connect to master 10.20.30.50:7077

org.apache.spark.SparkException: Exception thrown in awaitResult
        at org.apache.spark.rpc.RpcTimeout$$anonfun$1.applyOrElse(RpcTimeout.scala:77) ~[spark-core_2.11-2.0.1.jar:2.0.1]
        at org.apache.spark.rpc.RpcTimeout$$anonfun$1.applyOrElse(RpcTimeout.scala:75) ~[spark-core_2.11-2.0.1.jar:2.0.1]
        at scala.runtime.AbstractPartialFunction.apply(AbstractPartialFunction.scala:36) ~[scala-library-2.11.8.jar:na]
        at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:59) ~[spark-core_2.11-2.0.1.jar:2.0.1]
        at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:59) ~[spark-core_2.11-2.0.1.jar:2.0.1]
        at scala.PartialFunction$OrElse.apply(PartialFunction.scala:167) ~[scala-library-2.11.8.jar:na]
        at org.apache.spark.rpc.RpcTimeout.awaitResult(RpcTimeout.scala:83) ~[spark-core_2.11-2.0.1.jar:2.0.1]
        at org.apache.spark.rpc.RpcEnv.setupEndpointRefByURI(RpcEnv.scala:88) ~[spark-core_2.11-2.0.1.jar:2.0.1]
        at org.apache.spark.rpc.RpcEnv.setupEndpointRef(RpcEnv.scala:96) ~[spark-core_2.11-2.0.1.jar:2.0.1]
        at org.apache.spark.deploy.client.StandaloneAppClient$ClientEndpoint$$anonfun$tryRegisterAllMasters$1$$anon$1.run(StandaloneAppClient.scala:106) ~[spark-core_2.11-2.0.1.jar:2.0.1]
        at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511) [na:1.8.0_102]
        at java.util.concurrent.FutureTask.run(FutureTask.java:266) [na:1.8.0_102]
        at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) [na:1.8.0_102]
        at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) [na:1.8.0_102]
        at java.lang.Thread.run(Thread.java:745) [na:1.8.0_102]
Caused by: java.io.IOException: Connection from /10.20.30.50:7077 closed
        at org.apache.spark.network.client.TransportResponseHandler.channelInactive(TransportResponseHandler.java:128) ~[spark-network-common_2.11-2.0.1.jar:2.0.1]
        at org.apache.spark.network.server.TransportChannelHandler.channelInactive(TransportChannelHandler.java:109) ~[spark-network-common_2.11-2.0.1.jar:2.0.1]
        at io.netty.channel.AbstractChannelHandlerContext.invokeChannelInactive(AbstractChannelHandlerContext.java:208) ~[netty-all-4.0.29.Final.jar:4.0.29.Final]
        at io.netty.channel.AbstractChannelHandlerContext.fireChannelInactive(AbstractChannelHandlerContext.java:194) ~[netty-all-4.0.29.Final.jar:4.0.29.Final]
        at io.netty.channel.ChannelInboundHandlerAdapter.channelInactive(ChannelInboundHandlerAdapter.java:75) ~[netty-all-4.0.29.Final.jar:4.0.29.Final]
        at io.netty.handler.timeout.IdleStateHandler.channelInactive(IdleStateHandler.java:257) ~[netty-all-4.0.29.Final.jar:4.0.29.Final]
        at io.netty.channel.AbstractChannelHandlerContext.invokeChannelInactive(AbstractChannelHandlerContext.java:208) ~[netty-all-4.0.29.Final.jar:4.0.29.Final]
        at io.netty.channel.AbstractChannelHandlerContext.fireChannelInactive(AbstractChannelHandlerContext.java:194) ~[netty-all-4.0.29.Final.jar:4.0.29.Final]
        at io.netty.channel.ChannelInboundHandlerAdapter.channelInactive(ChannelInboundHandlerAdapter.java:75) ~[netty-all-4.0.29.Final.jar:4.0.29.Final]
        at io.netty.channel.AbstractChannelHandlerContext.invokeChannelInactive(AbstractChannelHandlerContext.java:208) ~[netty-all-4.0.29.Final.jar:4.0.29.Final]
        at io.netty.channel.AbstractChannelHandlerContext.fireChannelInactive(AbstractChannelHandlerContext.java:194) ~[netty-all-4.0.29.Final.jar:4.0.29.Final]
        at io.netty.channel.ChannelInboundHandlerAdapter.channelInactive(ChannelInboundHandlerAdapter.java:75) ~[netty-all-4.0.29.Final.jar:4.0.29.Final]
        at org.apache.spark.network.util.TransportFrameDecoder.channelInactive(TransportFrameDecoder.java:182) ~[spark-network-common_2.11-2.0.1.jar:2.0.1]
        at io.netty.channel.AbstractChannelHandlerContext.invokeChannelInactive(AbstractChannelHandlerContext.java:208) ~[netty-all-4.0.29.Final.jar:4.0.29.Final]
        at io.netty.channel.AbstractChannelHandlerContext.fireChannelInactive(AbstractChannelHandlerContext.java:194) ~[netty-all-4.0.29.Final.jar:4.0.29.Final]
        at io.netty.channel.DefaultChannelPipeline.fireChannelInactive(DefaultChannelPipeline.java:828) ~[netty-all-4.0.29.Final.jar:4.0.29.Final]
        at io.netty.channel.AbstractChannel$AbstractUnsafe$7.run(AbstractChannel.java:621) ~[netty-all-4.0.29.Final.jar:4.0.29.Final]
        at io.netty.util.concurrent.SingleThreadEventExecutor.runAllTasks(SingleThreadEventExecutor.java:357) ~[netty-all-4.0.29.Final.jar:4.0.29.Final]
        at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:357) ~[netty-all-4.0.29.Final.jar:4.0.29.Final]
        at io.netty.util.concurrent.SingleThreadEventExecutor$2.run(SingleThreadEventExecutor.java:111) ~[netty-all-4.0.29.Final.jar:4.0.29.Final]
        ... 1 common frames omitted

在 10.20.30.50 的 Spark Master 日志中，我收到以下错误消息：

 16/11/05 14:47:20 ERROR OneForOneStrategy: Error while decoding incoming Akka PDU of length: 1298
akka.remote.transport.AkkaProtocolException: Error while decoding incoming Akka PDU of length: 1298
Caused by: akka.remote.transport.PduCodecException: Decoding PDU failed.
    at akka.remote.transport.AkkaPduProtobufCodec$.decodePdu(AkkaPduCodec.scala:167)
    at akka.remote.transport.ProtocolStateActor.akka$remote$transport$ProtocolStateActor$$decodePdu(AkkaProtocolTransport.scala:580)
    at akka.remote.transport.ProtocolStateActor$$anonfun$4.applyOrElse(AkkaProtocolTransport.scala:375)
    at akka.remote.transport.ProtocolStateActor$$anonfun$4.applyOrElse(AkkaProtocolTransport.scala:343)
    at scala.runtime.AbstractPartialFunction.apply(AbstractPartialFunction.scala:33)
    at akka.actor.FSM$class.processEvent(FSM.scala:604)
    at akka.remote.transport.ProtocolStateActor.processEvent(AkkaProtocolTransport.scala:269)
    at akka.actor.FSM$class.akka$actor$FSM$$processMsg(FSM.scala:598)
    at akka.actor.FSM$$anonfun$receive$1.applyOrElse(FSM.scala:592)
    at akka.actor.Actor$class.aroundReceive(Actor.scala:467)
    at akka.remote.transport.ProtocolStateActor.aroundReceive(AkkaProtocolTransport.scala:269)
    at akka.actor.ActorCell.receiveMessage(ActorCell.scala:516)
    at akka.actor.ActorCell.invoke(ActorCell.scala:487)
    at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:238)
    at akka.dispatch.Mailbox.run(Mailbox.scala:220)
    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)
    at scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979)
    at scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107)
Caused by: com.google.protobuf.InvalidProtocolBufferException: Protocol message contained an invalid tag (zero).
    at com.google.protobuf.InvalidProtocolBufferException.invalidTag(InvalidProtocolBufferException.java:89)
    at com.google.protobuf.CodedInputStream.readTag(CodedInputStream.java:108)
    at akka.remote.WireFormats$AkkaProtocolMessage.<init>(WireFormats.java:6643)
    at akka.remote.WireFormats$AkkaProtocolMessage.<init>(WireFormats.java:6607)
    at akka.remote.WireFormats$AkkaProtocolMessage$1.parsePartialFrom(WireFormats.java:6703)
    at akka.remote.WireFormats$AkkaProtocolMessage$1.parsePartialFrom(WireFormats.java:6698)
    at com.google.protobuf.AbstractParser.parsePartialFrom(AbstractParser.java:141)
    at com.google.protobuf.AbstractParser.parseFrom(AbstractParser.java:176)
    at com.google.protobuf.AbstractParser.parseFrom(AbstractParser.java:188)
    at com.google.protobuf.AbstractParser.parseFrom(AbstractParser.java:193)
    at com.google.protobuf.AbstractParser.parseFrom(AbstractParser.java:49)
    at akka.remote.WireFormats$AkkaProtocolMessage.parseFrom(WireFormats.java:6821)
    at akka.remote.transport.AkkaPduProtobufCodec$.decodePdu(AkkaPduCodec.scala:168)
    ... 19 more

附加信息

  • 当我使用 new SparkConf().setMaster("local") 时，该示例工作正常
  • 我可以在同一台机器上使用 spark-shell --master spark://10.20.30.50:7077 连接到 Spark Master

原文由 Michael Lihs 发布，翻译遵循 CC BY-SA 4.0 许可协议

Stack Overflow 翻译javascalaapache-sparkhdfsprotocol-buffers
阅读 1.7k
2 个回答
得票最新
社区维基
1
✓ 已被采纳

看起来首先是网络错误（但实际上不是），伪装成 spark 的版本不匹配。您可以指向正确版本的火花罐，主要是装配罐。

由于使用 Protobuffer 的 Hadoop RPC 调用中的版本不匹配，可能会发生此问题。

当正在解析的协议消息在某种程度上无效时，例如它包含格式错误的 varint 或负字节长度。

   override def decodePdu(raw: ByteString): AkkaPdu = {
      try {
        val pdu = AkkaProtocolMessage.parseFrom(raw.toArray)
        if (pdu.hasPayload) Payload(ByteString(pdu.getPayload.asReadOnlyByteBuffer()))
        else if (pdu.hasInstruction) decodeControlPdu(pdu.getInstruction)
        else throw new PduCodecException("Error decoding Akka PDU: Neither message nor control message were contained", null)
      } catch {
        case e: InvalidProtocolBufferException ⇒ throw new PduCodecException("Decoding PDU failed.", e)
      }
    }

但在你的情况下，由于它的版本不匹配，新的 protobuf 版本消息无法从旧版本的解析器解析……或者类似……

如果您正在使用 maven 其他依赖项，请。审查。

原文由 Ram Ghadiyaram 发布，翻译遵循 CC BY-SA 4.0 许可协议

社区维基
1

事实证明，我在虚拟机中运行的是 Spark 1.5.2 版，并使用 Java 中的 Spark 库 2.0.1 版。我通过在我的 pom.xml 中使用适当的Spark库版本解决了这个问题—

 <dependency>
    <groupId>org.apache.spark</groupId>
    <artifactId>spark-core_2.10</artifactId>
    <version>1.5.2</version>
</dependency>

另一个问题（后来发生）是，我还必须固定构建库所用的 Scala 版本。这是 artifactId 中的 _2.10 后缀。

基本上 @RamPrassad 的回答为我指出了正确的方向，但没有给出明确的建议我需要做什么来解决我的问题。

顺便说一下：我无法在虚拟机中更新 Spark，因为它是由 HortonWorks 发行版提供给我的…

原文由 Michael Lihs 发布，翻译遵循 CC BY-SA 3.0 许可协议

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