我一直在Kafka消费者群体中尝试多个消费者实例，但它总是因kafka.common.NotLeaderForPartitionException而失败。
我的Kafka群集由3个代理组成，主题包含PartitionCount:2和ReplicationFactor:3。
SparkConsumer.java
public class SparkConsumer {
private static Function2<Integer, Integer, Integer> MyReducerFunc = (a, b) -> a + b;
public static void main(String[] args) throws Exception {
if (args.length < 2) {
System.err.println("Usage: SparkConsumer <brokers> <topics>\n" +
" <brokers> is a list of one or more Kafka brokers\n" +
" <topics> is a list of one or more kafka topics to consume from\n\n");
System.exit(1);
}
//StreamingExamples.setStreamingLogLevels();
String brokers = args[0];
String topics = args[1];
SparkConf sparkConf = new SparkConf().setMaster("local[5]").setAppName("SparkConsumer").set("spark.driver.host", "localhost");
JavaSparkContext sc = new JavaSparkContext(sparkConf);
// Create a StreamingContext with a 2 second batch size
JavaStreamingContext jssc = new JavaStreamingContext(sc, Durations.seconds(2));
Set<String> topicsSet = new HashSet<>(Arrays.asList(topics.split(",")));
Map<String, String> kafkaParams = new HashMap<>();
kafkaParams.put("metadata.broker.list", brokers);
//kafkaParams.put("auto.offset.reset", "smallest");
kafkaParams.put("group.id", "SparkConsumerGrp");
kafkaParams.put("zookeeper.connect", "localhost:2181");
// Create direct kafka stream with brokers and topics
JavaPairInputDStream<String, String> messages = KafkaUtils.createDirectStream(
jssc,
String.class,
String.class,
StringDecoder.class,
StringDecoder.class,
kafkaParams,
topicsSet
);
//Aggregate data every 30 sec
JavaPairDStream<String, String> messages2 =
messages.window(Durations.seconds(30), Durations.seconds(30));
messages2.foreachRDD(rdd -> {
long numHits = rdd.count();
if(numHits == 0)
System.out.println("No new data fetched in last 30 sec");
//Do Processing
else{
System.out.println("\n\n----------------------------------Data fetched in the last 30 seconds: " + rdd.partitions().size()
+ " partitions and " + numHits + " records------------------\n\n");
//Convert to java log object
JavaRDD<ApacheAccessLog> logs = rdd.map(x-> x._2)
.map(ApacheAccessLog::parseFromLogLine)
.cache();
//Find the bot ip addresses
JavaRDD<String> iprdd = logs.mapToPair(ip-> new Tuple2<>(ip.getIpAddress(),1))
.reduceByKey(MyReducerFunc)
.filter(botip-> botip._2 > 50)
.keys();
//If we find something, we store it in results dir on hdfs
if(iprdd.count() > 0)
{
sc.hadoopConfiguration().set("fs.hdfs.impl", org.apache.hadoop.hdfs.DistributedFileSystem.class.getName());
sc.hadoopConfiguration().set("fs.file.impl", org.apache.hadoop.fs.LocalFileSystem.class.getName());
String timeStamp = new SimpleDateFormat("yyyy.MM.dd.HH.mm.ss").format(new Date());
//sc.hadoopConfiguration().set("mapreduce.output.basename", timeStamp);
iprdd.coalesce(1).saveAsTextFile("hdfs://quickstart.cloudera:8020/results/"+timeStamp);
JobConf jobConf=new JobConf();
System.out.println("\n\n-------------Resuts successfully written to /results on hdfs-------------\n");
}
}
});
jssc.start();
jssc.awaitTermination();
}
}
我对documentation的理解是，我们可以使用相同的groupid启动另一个使用者进程，以将该实例添加到现有组。因此，我在两个独立的终端上运行此代码。但是，这是我总是得到的错误：
第一次：
17/06/06 00:53:26 ERROR DirectKafkaInputDStream: ArrayBuffer(kafka.common.NotLeaderForPartitionException, org.apache.spark.SparkException: Couldn't find leader offsets for Set([topic5,1]))
17/06/06 00:53:26 ERROR DirectKafkaInputDStream: ArrayBuffer(org.apache.spark.SparkException: Couldn't find leaders for Set([topic5,0], [topic5,1]))
17/06/06 00:53:27 ERROR JobScheduler: Error generating jobs for time 1496735582000 ms
org.apache.spark.SparkException: ArrayBuffer(org.apache.spark.SparkException: Couldn't find leaders for Set([topic5,0], [topic5,1]))
at org.apache.spark.streaming.kafka.DirectKafkaInputDStream.latestLeaderOffsets(DirectKafkaInputDStream.scala:133)
at org.apache.spark.streaming.kafka.DirectKafkaInputDStream.compute(DirectKafkaInputDStream.scala:158)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1$$anonfun$apply$7.apply(DStream.scala:341)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1$$anonfun$apply$7.apply(DStream.scala:341)
at scala.util.DynamicVariable.withValue(DynamicVariable.scala:58)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1.apply(DStream.scala:340)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1.apply(DStream.scala:340)
at org.apache.spark.streaming.dstream.DStream.createRDDWithLocalProperties(DStream.scala:415)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:335)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:333)
at scala.Option.orElse(Option.scala:289)
at org.apache.spark.streaming.dstream.DStream.getOrCompute(DStream.scala:330)
at org.apache.spark.streaming.dstream.DStream$$anonfun$slice$2$$anonfun$apply$29.apply(DStream.scala:900)
at org.apache.spark.streaming.dstream.DStream$$anonfun$slice$2$$anonfun$apply$29.apply(DStream.scala:899)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at scala.collection.Iterator$class.foreach(Iterator.scala:893)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
at scala.collection.IterableLike$class.foreach(IterableLike.scala:72)
at scala.collection.AbstractIterable.foreach(Iterable.scala:54)
at scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:241)
at scala.collection.AbstractTraversable.flatMap(Traversable.scala:104)
at org.apache.spark.streaming.dstream.DStream$$anonfun$slice$2.apply(DStream.scala:899)
at org.apache.spark.streaming.dstream.DStream$$anonfun$slice$2.apply(DStream.scala:877)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.SparkContext.withScope(SparkContext.scala:701)
at org.apache.spark.streaming.StreamingContext.withScope(StreamingContext.scala:264)
at org.apache.spark.streaming.dstream.DStream.slice(DStream.scala:877)
at org.apache.spark.streaming.dstream.DStream$$anonfun$slice$1.apply(DStream.scala:871)
at org.apache.spark.streaming.dstream.DStream$$anonfun$slice$1.apply(DStream.scala:871)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.SparkContext.withScope(SparkContext.scala:701)
at org.apache.spark.streaming.StreamingContext.withScope(StreamingContext.scala:264)
at org.apache.spark.streaming.dstream.DStream.slice(DStream.scala:870)
at org.apache.spark.streaming.dstream.WindowedDStream.compute(WindowedDStream.scala:65)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1$$anonfun$apply$7.apply(DStream.scala:341)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1$$anonfun$apply$7.apply(DStream.scala:341)
at scala.util.DynamicVariable.withValue(DynamicVariable.scala:58)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1.apply(DStream.scala:340)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1.apply(DStream.scala:340)
at org.apache.spark.streaming.dstream.DStream.createRDDWithLocalProperties(DStream.scala:415)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:335)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:333)
at scala.Option.orElse(Option.scala:289)
at org.apache.spark.streaming.dstream.DStream.getOrCompute(DStream.scala:330)
at org.apache.spark.streaming.dstream.ForEachDStream.generateJob(ForEachDStream.scala:48)
at org.apache.spark.streaming.DStreamGraph$$anonfun$1.apply(DStreamGraph.scala:117)
at org.apache.spark.streaming.DStreamGraph$$anonfun$1.apply(DStreamGraph.scala:116)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:241)
at scala.collection.AbstractTraversable.flatMap(Traversable.scala:104)
at org.apache.spark.streaming.DStreamGraph.generateJobs(DStreamGraph.scala:116)
at org.apache.spark.streaming.scheduler.JobGenerator$$anonfun$3.apply(JobGenerator.scala:249)
at org.apache.spark.streaming.scheduler.JobGenerator$$anonfun$3.apply(JobGenerator.scala:247)
at scala.util.Try$.apply(Try.scala:192)
at org.apache.spark.streaming.scheduler.JobGenerator.generateJobs(JobGenerator.scala:247)
at org.apache.spark.streaming.scheduler.JobGenerator.org$apache$spark$streaming$scheduler$JobGenerator$$processEvent(JobGenerator.scala:183)
at org.apache.spark.streaming.scheduler.JobGenerator$$anon$1.onReceive(JobGenerator.scala:89)
at org.apache.spark.streaming.scheduler.JobGenerator$$anon$1.onReceive(JobGenerator.scala:88)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
Exception in thread "main" org.apache.spark.SparkException: ArrayBuffer(org.apache.spark.SparkException: Couldn't find leaders for Set([topic5,0], [topic5,1]))
at org.apache.spark.streaming.kafka.DirectKafkaInputDStream.latestLeaderOffsets(DirectKafkaInputDStream.scala:133)
at org.apache.spark.streaming.kafka.DirectKafkaInputDStream.compute(DirectKafkaInputDStream.scala:158)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1$$anonfun$apply$7.apply(DStream.scala:341)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1$$anonfun$apply$7.apply(DStream.scala:341)
at scala.util.DynamicVariable.withValue(DynamicVariable.scala:58)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1.apply(DStream.scala:340)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1.apply(DStream.scala:340)
at org.apache.spark.streaming.dstream.DStream.createRDDWithLocalProperties(DStream.scala:415)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:335)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:333)
at scala.Option.orElse(Option.scala:289)
at org.apache.spark.streaming.dstream.DStream.getOrCompute(DStream.scala:330)
at org.apache.spark.streaming.dstream.DStream$$anonfun$slice$2$$anonfun$apply$29.apply(DStream.scala:900)
at org.apache.spark.streaming.dstream.DStream$$anonfun$slice$2$$anonfun$apply$29.apply(DStream.scala:899)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at scala.collection.Iterator$class.foreach(Iterator.scala:893)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
at scala.collection.IterableLike$class.foreach(IterableLike.scala:72)
at scala.collection.AbstractIterable.foreach(Iterable.scala:54)
at scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:241)
at scala.collection.AbstractTraversable.flatMap(Traversable.scala:104)
at org.apache.spark.streaming.dstream.DStream$$anonfun$slice$2.apply(DStream.scala:899)
at org.apache.spark.streaming.dstream.DStream$$anonfun$slice$2.apply(DStream.scala:877)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.SparkContext.withScope(SparkContext.scala:701)
at org.apache.spark.streaming.StreamingContext.withScope(StreamingContext.scala:264)
at org.apache.spark.streaming.dstream.DStream.slice(DStream.scala:877)
at org.apache.spark.streaming.dstream.DStream$$anonfun$slice$1.apply(DStream.scala:871)
at org.apache.spark.streaming.dstream.DStream$$anonfun$slice$1.apply(DStream.scala:871)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.SparkContext.withScope(SparkContext.scala:701)
at org.apache.spark.streaming.StreamingContext.withScope(StreamingContext.scala:264)
at org.apache.spark.streaming.dstream.DStream.slice(DStream.scala:870)
at org.apache.spark.streaming.dstream.WindowedDStream.compute(WindowedDStream.scala:65)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1$$anonfun$apply$7.apply(DStream.scala:341)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1$$anonfun$apply$7.apply(DStream.scala:341)
at scala.util.DynamicVariable.withValue(DynamicVariable.scala:58)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1.apply(DStream.scala:340)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1.apply(DStream.scala:340)
at org.apache.spark.streaming.dstream.DStream.createRDDWithLocalProperties(DStream.scala:415)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:335)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:333)
at scala.Option.orElse(Option.scala:289)
at org.apache.spark.streaming.dstream.DStream.getOrCompute(DStream.scala:330)
at org.apache.spark.streaming.dstream.ForEachDStream.generateJob(ForEachDStream.scala:48)
at org.apache.spark.streaming.DStreamGraph$$anonfun$1.apply(DStreamGraph.scala:117)
at org.apache.spark.streaming.DStreamGraph$$anonfun$1.apply(DStreamGraph.scala:116)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:241)
at scala.collection.AbstractTraversable.flatMap(Traversable.scala:104)
at org.apache.spark.streaming.DStreamGraph.generateJobs(DStreamGraph.scala:116)
at org.apache.spark.streaming.scheduler.JobGenerator$$anonfun$3.apply(JobGenerator.scala:249)
at org.apache.spark.streaming.scheduler.JobGenerator$$anonfun$3.apply(JobGenerator.scala:247)
at scala.util.Try$.apply(Try.scala:192)
at org.apache.spark.streaming.scheduler.JobGenerator.generateJobs(JobGenerator.scala:247)
at org.apache.spark.streaming.scheduler.JobGenerator.org$apache$spark$streaming$scheduler$JobGenerator$$processEvent(JobGenerator.scala:183)
at org.apache.spark.streaming.scheduler.JobGenerator$$anon$1.onReceive(JobGenerator.scala:89)
at org.apache.spark.streaming.scheduler.JobGenerator$$anon$1.onReceive(JobGenerator.scala:88)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
在第二个实例:(似乎在我调用rdd.count()时发生，并在第一个实例崩溃后立即发生）
Exception in thread "streaming-job-executor-0" java.lang.Error: java.lang.InterruptedException
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1148)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:748)
Caused by: java.lang.InterruptedException
at java.util.concurrent.locks.AbstractQueuedSynchronizer.doAcquireSharedInterruptibly(AbstractQueuedSynchronizer.java:998)
at java.util.concurrent.locks.AbstractQueuedSynchronizer.acquireSharedInterruptibly(AbstractQueuedSynchronizer.java:1304)
at scala.concurrent.impl.Promise$DefaultPromise.tryAwait(Promise.scala:202)
at scala.concurrent.impl.Promise$DefaultPromise.ready(Promise.scala:218)
at scala.concurrent.impl.Promise$DefaultPromise.ready(Promise.scala:153)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:619)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1925)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1938)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1951)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1965)
at org.apache.spark.rdd.RDD.count(RDD.scala:1158)
at org.apache.spark.api.java.JavaRDDLike$class.count(JavaRDDLike.scala:455)
at org.apache.spark.api.java.AbstractJavaRDDLike.count(JavaRDDLike.scala:45)
at hadoopTest.hadoopTest.SparkConsumer.lambda$1(SparkConsumer.java:85)
at org.apache.spark.streaming.api.java.JavaDStreamLike$$anonfun$foreachRDD$1.apply(JavaDStreamLike.scala:272)
at org.apache.spark.streaming.api.java.JavaDStreamLike$$anonfun$foreachRDD$1.apply(JavaDStreamLike.scala:272)
at org.apache.spark.streaming.dstream.DStream$$anonfun$foreachRDD$1$$anonfun$apply$mcV$sp$3.apply(DStream.scala:627)
at org.apache.spark.streaming.dstream.DStream$$anonfun$foreachRDD$1$$anonfun$apply$mcV$sp$3.apply(DStream.scala:627)
at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(ForEachDStream.scala:51)
at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:51)
at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:51)
at org.apache.spark.streaming.dstream.DStream.createRDDWithLocalProperties(DStream.scala:415)
at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply$mcV$sp(ForEachDStream.scala:50)
at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:50)
at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:50)
at scala.util.Try$.apply(Try.scala:192)
at org.apache.spark.streaming.scheduler.Job.run(Job.scala:39)
at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply$mcV$sp(JobScheduler.scala:256)
at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:256)
at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:256)
at scala.util.DynamicVariable.withValue(DynamicVariable.scala:58)
at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler.run(JobScheduler.scala:255)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
... 2 more
这是我的pom.xml
<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
<groupId>hadoopTest</groupId>
<artifactId>hadoopTest</artifactId>
<version>0.0.1-SNAPSHOT</version>
<packaging>jar</packaging>
<name>hadoopTest</name>
<url>http://maven.apache.org</url>
<properties>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
</properties>
<dependencies>
<dependency>
<groupId>junit</groupId>
<artifactId>junit</artifactId>
<version>3.8.1</version>
<scope>test</scope>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-hdfs</artifactId>
<version>2.6.0</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-auth</artifactId>
<version>2.6.0</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-common</artifactId>
<version>2.6.0</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-mapreduce-client-core</artifactId>
<version>2.6.0</version>
</dependency>
<dependency>
<groupId>org.apache.kafka</groupId>
<artifactId>kafka-clients</artifactId>
<version>0.8.2.1</version>
</dependency>
<dependency>
<groupId>org.apache.kafka</groupId>
<artifactId>kafka_2.11</artifactId>
<version>0.8.2.1</version>
<scope>compile</scope>
<exclusions>
<exclusion>
<artifactId>jmxri</artifactId>
<groupId>com.sun.jmx</groupId>
</exclusion>
<exclusion>
<artifactId>jms</artifactId>
<groupId>javax.jms</groupId>
</exclusion>
<exclusion>
<artifactId>jmxtools</artifactId>
<groupId>com.sun.jdmk</groupId>
</exclusion>
</exclusions>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-core_2.11</artifactId>
<version>2.1.1</version>
<exclusions>
<exclusion>
<groupId>javax.validation</groupId>
<artifactId>validation-api</artifactId>
</exclusion>
</exclusions>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-sql_2.11</artifactId>
<version>1.2.2</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming-kafka-0-8_2.11</artifactId>
<version>2.1.1</version>
</dependency>
<dependency>
<groupId>org.slf4j</groupId>
<artifactId>slf4j-log4j12</artifactId>
<version>1.7.5</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming_2.11</artifactId>
<version>2.1.1</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-tags_2.11</artifactId>
<version>2.1.1</version>
</dependency>
<dependency>
<groupId>org.scala-lang</groupId>
<artifactId>scala-library</artifactId>
<version>2.11.8</version>
</dependency>
<dependency>
<groupId>org.scala-lang</groupId>
<artifactId>scala-reflect</artifactId>
<version>2.10.2</version>
</dependency>
<dependency>
<groupId>net.jpountz.lz4</groupId>
<artifactId>lz4</artifactId>
<version>1.3</version>
</dependency>
</dependencies>
</project>
我一直试图调试这一整天而且不知所措。它说尽管我的所有经纪人都在努力，但它找不到领导者。我很感激任何帮助。谢谢！
答案 0 :(得分：0)
原来是因为我使用的是<?xml version="1.0" encoding="CP1257"?>
<Root-Element>
<Test>Łų߯ĒČ</Test>
</Root-Element>
而不是spark-streaming-kafka-0-8_2.11。您还需要将spark-streaming-kafka-0-10_2.11的版本更改为kafka-clients以与之匹配。