本地Kafka应用程序失败:NoSuchMethodError:createEphemeral

Question

我试图以spark本地模式运行我的应用程序.为了全部设置,我按照本教程:http://blog.d2-si.fr/2015/11/05/apache-kafka-3/,(法语)显示构建本地kafka/ zookeeper环境的每个步骤.

而且,我使用IntelliJ以下配置:

val sparkConf = new SparkConf().setAppName("zumbaApp").setMaster("local[2]")

我的运行配置,为消费者:

"127.0.0.1:2181" "zumbaApp-gpId" "D2SI" "1"

而对于制片人:

"127.0.0.1:9092" "D2SI" "my\Input\File.csv" 300

在此之前,我检查如果消费者从默认的生产者收到的意见console-producer和console-consumer的kafka_2.10-0.9.0.1; 它确实.

但是,我面临以下错误:

java.lang.NoSuchMethodError: org.I0Itec.zkclient.ZkClient.createEphemeral(Ljava/lang/String;Ljava/lang/Object;Ljava/util/List;)V
at kafka.utils.ZkPath$.createEphemeral(ZkUtils.scala:921)
at kafka.utils.ZkUtils.createEphemeralPath(ZkUtils.scala:348)
at kafka.utils.ZkUtils.createEphemeralPathExpectConflict(ZkUtils.scala:363)
at kafka.consumer.ZookeeperConsumerConnector$ZKRebalancerListener$$anonfun$18.apply(ZookeeperConsumerConnector.scala:839)
at kafka.consumer.ZookeeperConsumerConnector$ZKRebalancerListener$$anonfun$18.apply(ZookeeperConsumerConnector.scala:833)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
at scala.collection.mutable.HashMap$$anonfun$foreach$1.apply(HashMap.scala:98)
at scala.collection.mutable.HashMap$$anonfun$foreach$1.apply(HashMap.scala:98)
at scala.collection.mutable.HashTable$class.foreachEntry(HashTable.scala:226)
at scala.collection.mutable.HashMap.foreachEntry(HashMap.scala:39)
at scala.collection.mutable.HashMap.foreach(HashMap.scala:98)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
at scala.collection.AbstractTraversable.map(Traversable.scala:105)
at kafka.consumer.ZookeeperConsumerConnector$ZKRebalancerListener.reflectPartitionOwnershipDecision(ZookeeperConsumerConnector.scala:833)
at kafka.consumer.ZookeeperConsumerConnector$ZKRebalancerListener.kafka$consumer$ZookeeperConsumerConnector$ZKRebalancerListener$$rebalance(ZookeeperConsumerConnector.scala:721)
at kafka.consumer.ZookeeperConsumerConnector$ZKRebalancerListener$$anonfun$syncedRebalance$1$$anonfun$apply$mcV$sp$1.apply$mcVI$sp(ZookeeperConsumerConnector.scala:636)
at scala.collection.immutable.Range.foreach$mVc$sp(Range.scala:141)
at kafka.consumer.ZookeeperConsumerConnector$ZKRebalancerListener$$anonfun$syncedRebalance$1.apply$mcV$sp(ZookeeperConsumerConnector.scala:627)
at kafka.consumer.ZookeeperConsumerConnector$ZKRebalancerListener$$anonfun$syncedRebalance$1.apply(ZookeeperConsumerConnector.scala:627)
at kafka.consumer.ZookeeperConsumerConnector$ZKRebalancerListener$$anonfun$syncedRebalance$1.apply(ZookeeperConsumerConnector.scala:627)
at kafka.metrics.KafkaTimer.time(KafkaTimer.scala:33)
at kafka.consumer.ZookeeperConsumerConnector$ZKRebalancerListener.syncedRebalance(ZookeeperConsumerConnector.scala:626)
at kafka.consumer.ZookeeperConsumerConnector.kafka$consumer$ZookeeperConsumerConnector$$reinitializeConsumer(ZookeeperConsumerConnector.scala:967)
at kafka.consumer.ZookeeperConsumerConnector.consume(ZookeeperConsumerConnector.scala:254)
at kafka.consumer.ZookeeperConsumerConnector.createMessageStreams(ZookeeperConsumerConnector.scala:156)
at org.apache.spark.streaming.kafka.KafkaReceiver.onStart(KafkaInputDStream.scala:111)
at org.apache.spark.streaming.receiver.ReceiverSupervisor.startReceiver(ReceiverSupervisor.scala:148)
at org.apache.spark.streaming.receiver.ReceiverSupervisor.start(ReceiverSupervisor.scala:130)
at org.apache.spark.streaming.scheduler.ReceiverTracker$ReceiverTrackerEndpoint$$anonfun$9.apply(ReceiverTracker.scala:575)
at org.apache.spark.streaming.scheduler.ReceiverTracker$ReceiverTrackerEndpoint$$anonfun$9.apply(ReceiverTracker.scala:565)
at org.apache.spark.SparkContext$$anonfun$37.apply(SparkContext.scala:1992)
at org.apache.spark.SparkContext$$anonfun$37.apply(SparkContext.scala:1992)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
at org.apache.spark.scheduler.Task.run(Task.scala:89)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
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)

我没有成功解决这个问题.我认为这是一个zookeeper-config错误,但在与具有相同配置文件的另一台机器上的应用程序的工作版本进行比较之后,它似乎不再存在了.

Answer 1

看起来你有一个依赖问题.

在我们的类路径中检查库com.101tec.zkclient的版本.卡夫卡需要0.7版本

此外,由于kafka_2.10-0.9.0.1生产者和消费者的API不再使用zookeeper.在你的情况下,Spark-streaming似乎使用0.8版本的Kafka.