本地kafka应用程序失败,错误为:nosuchmethoderror:createephemeral

o2g1uqev  于 2021-06-07  发布在  Kafka
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我在试着跑我的车 spark 本地模式下的应用程序。为了进行设置,我遵循了以下教程:http://blog.d2-si.fr/2015/11/05/apache-kafka-3/,(法语)显示建立本地 kafka / zookeeper 环境。
此外,我使用 IntelliJ 具有以下配置:

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

以及我的运行配置,对于消费者:

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

对于制作人:

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

事先,我检查了消费者是否从生产者那里收到了默认的输入 console-producer 以及 console-consumerkafka_2.10-0.9.0.1 ; 是的。
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)

我没能解决这个问题。我以为这是个秘密 zookeeper -配置错误,但与另一台机器上具有相同配置文件的应用程序的工作版本进行比较后,它似乎不再是了。

inkz8wg9

inkz8wg91#

你好像有依赖问题。
在我们的类路径中检查库com.101tec.zkclient的版本。Kafka需要0.7版本
另外,由于kafkaèu2.10-0.9.0.1,生产者和消费者的api不再使用zookeeper。似乎spark streaming在您的案例中使用了Kafka的0.8版本。

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