我使用java spark-streaming api从kafka主题中读取新蜜蜂。 写的代码片段：
SparkConf sparkConf = new SparkConf().setMaster(args[4]).setAppName("JavaKafkaWordCount");
// Create the context with 2 seconds batch size
JavaStreamingContext jssc = new JavaStreamingContext(sparkConf, new Duration(1000));
int numThreads = Integer.parseInt(args[3]);
Map<String, Integer> topicMap = new HashMap<>();
String[] topics = args[2].split(",");
for (String topic: topics) {
topicMap.put(topic, numThreads);
}
JavaPairReceiverInputDStream<String, String> messages =
KafkaUtils.createStream(jssc, args[0], args[1], topicMap);
运行命令：
./bin/spark-class -jar ../runnable/spark-0.0.1-SNAPSHOT.jar localhost:9092 "" incoming 1 spark://localhost:7077
然后抛出以下异常：
16/10/31 11:50:05 INFO StandaloneAppClient$ClientEndpoint: Connecting to master spark://localhost:7077...
16/10/31 11:50:05 INFO TransportClientFactory: Successfully created connection to localhost:7077 after 28 ms (0 ms spent in bootstraps)
16/10/31 11:50:05 WARN StandaloneAppClient$ClientEndpoint: Failed to connect to master localhost:7077
org.apache.spark.SparkException: Exception thrown in awaitResult
at org.apache.spark.rpc.RpcTimeout$$anonfun$1.applyOrElse(RpcTimeout.scala:77)
at org.apache.spark.rpc.RpcTimeout$$anonfun$1.applyOrElse(RpcTimeout.scala:75)
at scala.runtime.AbstractPartialFunction.apply(AbstractPartialFunction.scala:33)
at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:59)
at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:59)
at scala.PartialFunction$OrElse.apply(PartialFunction.scala:162)
at org.apache.spark.rpc.RpcTimeout.awaitResult(RpcTimeout.scala:83)
at org.apache.spark.rpc.RpcEnv.setupEndpointRefByURI(RpcEnv.scala:88)
at org.apache.spark.rpc.RpcEnv.setupEndpointRef(RpcEnv.scala:96)
at org.apache.spark.deploy.client.StandaloneAppClient$ClientEndpoint$$anonfun$tryRegisterAllMasters$1$$anon$1.run(StandaloneAppClient.scala:109)
at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)
at java.util.concurrent.FutureTask.run(FutureTask.java:266)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
Caused by: java.lang.RuntimeException: java.io.InvalidClassException: org.apache.spark.rpc.netty.RequestMessage; local class incompatible: stream classdesc serialVersionUID = -2221986757032131007, local class serialVersionUID = -5447855329526097695
at java.io.ObjectStreamClass.initNonProxy(ObjectStreamClass.java:616)
at java.io.ObjectInputStream.readNonProxyDesc(ObjectInputStream.java:1623)
at java.io.ObjectInputStream.readClassDesc(ObjectInputStream.java:1518)
at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1774)
at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1351)
at java.io.ObjectInputStream.readObject(ObjectInputStream.java:371)
at org.apache.spark.serializer.JavaDeserializationStream.readObject(JavaSerializer.scala:75)
at org.apache.spark.serializer.JavaSerializerInstance.deserialize(JavaSerializer.scala:108)
at org.apache.spark.rpc.netty.NettyRpcEnv$$anonfun$deserialize$1$$anonfun$apply$1.apply(NettyRpcEnv.scala:259)
at scala.util.DynamicVariable.withValue(DynamicVariable.scala:58)
at org.apache.spark.rpc.netty.NettyRpcEnv.deserialize(NettyRpcEnv.scala:308)
at org.apache.spark.rpc.netty.NettyRpcEnv$$anonfun$deserialize$1.apply(NettyRpcEnv.scala:258)
at scala.util.DynamicVariable.withValue(DynamicVariable.scala:58)
at org.apache.spark.rpc.netty.NettyRpcEnv.deserialize(NettyRpcEnv.scala:257)
at org.apache.spark.rpc.netty.NettyRpcHandler.internalReceive(NettyRpcEnv.scala:578)
at org.apache.spark.rpc.netty.NettyRpcHandler.receive(NettyRpcEnv.scala:563)
at org.apache.spark.network.server.TransportRequestHandler.processRpcRequest(TransportRequestHandler.java:159)
at org.apache.spark.network.server.TransportRequestHandler.handle(TransportRequestHandler.java:107)
at org.apache.spark.network.server.TransportChannelHandler.channelRead0(TransportChannelHandler.java:119)
at org.apache.spark.network.server.TransportChannelHandler.channelRead0(TransportChannelHandler.java:51)
at io.netty.channel.SimpleChannelInboundHandler.channelRead(SimpleChannelInboundHandler.java:105)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:308)
at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:294)
at io.netty.handler.timeout.IdleStateHandler.channelRead(IdleStateHandler.java:266)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:308)
at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:294)
at io.netty.handler.codec.MessageToMessageDecoder.channelRead(MessageToMessageDecoder.java:103)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:308)
at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:294)
at org.apache.spark.network.util.TransportFrameDecoder.channelRead(TransportFrameDecoder.java:85)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:308)
at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:294)
at io.netty.channel.DefaultChannelPipeline.fireChannelRead(DefaultChannelPipeline.java:846)
at io.netty.channel.nio.AbstractNioByteChannel$NioByteUnsafe.read(AbstractNioByteChannel.java:131)
at io.netty.channel.nio.NioEventLoop.processSelectedKey(NioEventLoop.java:511)
at io.netty.channel.nio.NioEventLoop.processSelectedKeysOptimized(NioEventLoop.java:468)
at io.netty.channel.nio.NioEventLoop.processSelectedKeys(NioEventLoop.java:382)
at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:354)
at io.netty.util.concurrent.SingleThreadEventExecutor$2.run(SingleThreadEventExecutor.java:111)
at java.lang.Thread.run(Thread.java:745)
at org.apache.spark.network.client.TransportResponseHandler.handle(TransportResponseHandler.java:190)
at org.apache.spark.network.server.TransportChannelHandler.channelRead0(TransportChannelHandler.java:121)
at org.apache.spark.network.server.TransportChannelHandler.channelRead0(TransportChannelHandler.java:51)
at io.netty.channel.SimpleChannelInboundHandler.channelRead(SimpleChannelInboundHandler.java:105)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:308)
at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:294)
at io.netty.handler.timeout.IdleStateHandler.channelRead(IdleStateHandler.java:266)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:308)
at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:294)
at io.netty.handler.codec.MessageToMessageDecoder.channelRead(MessageToMessageDecoder.java:103)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:308)
at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:294)
at org.apache.spark.network.util.TransportFrameDecoder.channelRead(TransportFrameDecoder.java:85)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:308)
at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:294)
at io.netty.channel.DefaultChannelPipeline.fireChannelRead(DefaultChannelPipeline.java:846)
at io.netty.channel.nio.AbstractNioByteChannel$NioByteUnsafe.read(AbstractNioByteChannel.java:131)
at io.netty.channel.nio.NioEventLoop.processSelectedKey(NioEventLoop.java:511)
at io.netty.channel.nio.NioEventLoop.processSelectedKeysOptimized(NioEventLoop.java:468)
at io.netty.channel.nio.NioEventLoop.processSelectedKeys(NioEventLoop.java:382)
at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:354)
at io.netty.util.concurrent.SingleThreadEventExecutor$2.run(SingleThreadEventExecutor.java:111)
... 1 more
它连接但是断开连接，究竟是什么意思？我不认为有版本错配。 pom.xml中：
<dependencies>
<dependency>
<groupId>junit</groupId>
<artifactId>junit</artifactId>
<version>3.8.1</version>
<scope>test</scope>
</dependency>
<dependency>
<groupId>org.apache.storm</groupId>
<artifactId>storm-core</artifactId>
<version>0.10.0</version>
<scope>provided</scope>
<exclusions>
<exclusion>
<groupId>org.slf4j</groupId>
<artifactId>slf4j-log4j12</artifactId>
</exclusion>
</exclusions>
</dependency>
<dependency>
<groupId>org.apache.storm</groupId>
<artifactId>storm-kafka</artifactId>
<version>0.10.0-beta1</version>
</dependency>
<!-- https://mvnrepository.com/artifact/org.apache.spark/spark-streaming_2.10 -->
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming_2.10</artifactId>
<version>2.0.0</version>
<exclusions>
<exclusion>
<groupId>org.apache.avro</groupId>
<artifactId>avro-mapred</artifactId>
</exclusion>
<exclusion>
<groupId>com.twitter</groupId>
<artifactId>chill_2.10</artifactId>
</exclusion>
<exclusion>
<groupId>com.twitter</groupId>
<artifactId>chill-java</artifactId>
</exclusion>
<exclusion>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-client</artifactId>
</exclusion>
<exclusion>
<groupId>org.glassfish.jersey.core</groupId>
<artifactId>jersey-client</artifactId>
</exclusion>
<exclusion>
<groupId>org.glassfish.jersey.core</groupId>
<artifactId>jersey-common</artifactId>
</exclusion>
<exclusion>
<groupId>org.glassfish.jersey.core</groupId>
<artifactId>jersey-server</artifactId>
</exclusion>
<exclusion>
<groupId>org.glassfish.jersey.containers</groupId>
<artifactId>jersey-container-servlet</artifactId>
</exclusion>
<exclusion>
<groupId>org.glassfish.jersey.containers</groupId>
<artifactId>jersey-container-servlet-core</artifactId>
</exclusion>
<!-- <exclusion>
<groupId>org.scala-lang</groupId>
<artifactId>scala-library</artifactId>
</exclusion> -->
<exclusion>
<groupId>org.json4s</groupId>
<artifactId>json4s-jackson_2.10</artifactId>
</exclusion>
<exclusion>
<groupId>com.fasterxml.jackson.module</groupId>
<artifactId>jackson-module-scala_2.10</artifactId>
</exclusion>
</exclusions>
</dependency>
<!-- https://mvnrepository.com/artifact/org.apache.spark/spark-streaming-kafka_2.10 -->
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming-kafka_2.10</artifactId>
<version>1.6.2</version>
</dependency>
<!-- <dependency>
<groupId>org.scala-lang</groupId>
<artifactId>scala-xml</artifactId>
<version>2.11.0-M4</version>
</dependency> -->
<dependency>
<groupId>org.scala-lang</groupId>
<artifactId>scala-library</artifactId>
<version>2.10.6</version>
</dependency>
<!-- https://mvnrepository.com/artifact/org.json/json -->
<dependency>
<groupId>org.json</groupId>
<artifactId>json</artifactId>
<version>20160810</version>
</dependency>
<!--<dependency> <groupId>org.apache.storm</groupId> <artifactId>storm-hdfs</artifactId>
<version>0.10.0-beta1</version> </dependency> <dependency> <groupId>org.apache.solr</groupId>
<artifactId>solr-solrj</artifactId> <version>5.3.1</version> </dependency> -->
<dependency>
<groupId>org.mongodb</groupId>
<artifactId>mongo-java-driver</artifactId>
<version>3.0.1</version>
</dependency>
<dependency>
<groupId>com.googlecode.json-simple</groupId>
<artifactId>json-simple</artifactId>
<version>1.1</version>
</dependency>
</dependencies>
使用sharde插件构建：
<build>
<sourceDirectory>src</sourceDirectory>
<plugins>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-shade-plugin</artifactId>
<version>2.3</version>
<configuration>
<createDependencyReducedPom>true</createDependencyReducedPom>
</configuration>
<executions>
<execution>
<phase>package</phase>
<goals>
<goal>shade</goal>
</goals>
<configuration>
<transformers>
<transformer
implementation="org.apache.maven.plugins.shade.resource.ServicesResourceTransformer" />
<transformer
implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer">
<mainClass>org.labs.analytics.spark.streaming.StreamingKafkaConsumer</mainClass>
</transformer>
</transformers>
</configuration>
</execution>
</executions>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-resources-plugin</artifactId>
<version>2.7</version>
</plugin>
<plugin>
<artifactId>maven-compiler-plugin</artifactId>
<version>3.3</version>
<configuration>
<source>1.8</source>
<target>1.8</target>
</configuration>
</plugin>
</plugins>
<resources>
<resource>
<directory>src/main/java</directory>
<includes>
<include> **/*.properties</include>
</includes>
</resource>
</resources>
</build>
请建议一个提供spark，kafka，mongoDb，Zookeeper的虚拟机。
答案 0 :(得分：0)
这是一个版本与版本不匹配的问题.Scala来自spark（已安装）与库的使用。 我考虑过重新安装2.0.1 spark，scala-2.11.4和pom.xml，如下所示：
<!-- https://mvnrepository.com/artifact/org.apache.spark/spark-streaming_2.10 -->
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming_2.11</artifactId>
<version>${spark.version}</version>
</dependency>
<!-- https://mvnrepository.com/artifact/org.apache.spark/spark-streaming-kafka_2.10 -->
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming-kafka_2.11</artifactId>
<version>1.6.2</version>
</dependency>
<dependency>
<groupId>org.scala-lang</groupId>
<artifactId>scala-library</artifactId>
<version>2.11.1</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-core_2.11</artifactId>
<version>${spark.version}</version>
</dependency>