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[英]Kafka 0.9.0 and Spark Streaming 2.1.0: kafka.cluster.BrokerEndPoint cannot be cast to kafka.cluster.Broker
[英]kafka.cluster.BrokerEndPoint cannot be cast to kafka.cluster.Broker issue
我正在使用kafka2.11-0.11.0.1,scala 2.11和spark 2.2.0。 我在eclipse的java构建路径中添加了以下jar:
kafka-streams-0.11.0.1,
kafka-tools-0.11.0.1,
spark-streaming_2.11-2.2.0,
spark-streaming-kafka_2.11-1.6.3,
spark-streaming-kafka-0-10_2.11-2.2.0,
kafka_2.11-0.11.0.1.
我的代码如下:
import kafka.serializer.StringDecoder
import kafka.api._
import kafka.api.ApiUtils._
import org.apache.spark.SparkConf
import org.apache.spark._
import org.apache.spark.streaming._
import org.apache.spark.streaming.dstream._
import org.apache.spark.streaming.kafka
import org.apache.spark.streaming.kafka._
import org.apache.spark.streaming.kafka.KafkaUtils
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.storage.StorageLevel
import org.apache.spark.SparkContext._
object KafkaExample {
def main(args: Array[String]) {
val ssc = new StreamingContext("local[*]", "KafkaExample", Seconds(1))
val kafkaParams = Map("bootstrap.servers" -> "kafkaIP:9092")
val topics = List("logstash_log").toSet
val stream = KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder](ssc,kafkaParams,topics).map(_._2)
stream.print()
ssc.checkpoint("C:/checkpoint/")
ssc.start()
ssc.awaitTermination()
}
}
这是仅用于连接spark和kafka的非常简单的代码。 但是,我收到此错误:
Exception in thread "main" java.lang.ClassCastException: kafka.cluster.BrokerEndPoint cannot be cast to kafka.cluster.Broker
at org.apache.spark.streaming.kafka.KafkaCluster$$anonfun$2$$anonfun$3$$anonfun$apply$6$$anonfun$apply$7.apply(KafkaCluster.scala:90)
at scala.Option.map(Option.scala:146)
at org.apache.spark.streaming.kafka.KafkaCluster$$anonfun$2$$anonfun$3$$anonfun$apply$6.apply(KafkaCluster.scala:90)
at org.apache.spark.streaming.kafka.KafkaCluster$$anonfun$2$$anonfun$3$$anonfun$apply$6.apply(KafkaCluster.scala:87)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.WrappedArray.foreach(WrappedArray.scala:35)
at scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:241)
at scala.collection.AbstractTraversable.flatMap(Traversable.scala:104)
at org.apache.spark.streaming.kafka.KafkaCluster$$anonfun$2$$anonfun$3.apply(KafkaCluster.scala:87)
at org.apache.spark.streaming.kafka.KafkaCluster$$anonfun$2$$anonfun$3.apply(KafkaCluster.scala:86)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at scala.collection.immutable.Set$Set1.foreach(Set.scala:94)
at scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:241)
at scala.collection.AbstractTraversable.flatMap(Traversable.scala:104)
at org.apache.spark.streaming.kafka.KafkaCluster$$anonfun$2.apply(KafkaCluster.scala:86)
at org.apache.spark.streaming.kafka.KafkaCluster$$anonfun$2.apply(KafkaCluster.scala:85)
at scala.util.Either$RightProjection.flatMap(Either.scala:522)
at org.apache.spark.streaming.kafka.KafkaCluster.findLeaders(KafkaCluster.scala:85)
at org.apache.spark.streaming.kafka.KafkaCluster.getLeaderOffsets(KafkaCluster.scala:179)
at org.apache.spark.streaming.kafka.KafkaCluster.getLeaderOffsets(KafkaCluster.scala:161)
at org.apache.spark.streaming.kafka.KafkaCluster.getLatestLeaderOffsets(KafkaCluster.scala:150)
at org.apache.spark.streaming.kafka.KafkaUtils$$anonfun$5.apply(KafkaUtils.scala:215)
at org.apache.spark.streaming.kafka.KafkaUtils$$anonfun$5.apply(KafkaUtils.scala:211)
at scala.util.Either$RightProjection.flatMap(Either.scala:522)
at org.apache.spark.streaming.kafka.KafkaUtils$.getFromOffsets(KafkaUtils.scala:211)
at org.apache.spark.streaming.kafka.KafkaUtils$.createDirectStream(KafkaUtils.scala:484)
at com.defne.KafkaExample$.main(KafkaExample.scala:28)
at com.defne.KafkaExample.main(KafkaExample.scala)
我在哪里做错了?
注意:我尝试使用“ metadata.broker.list”而不是“ bootstrap.server”,但没有任何更改。
您的问题是您加载了太多的Kafka依赖项,而在运行时拾取的依赖项与Spark期望的版本不二进制兼容。
您的实际问题是PartitionMetadata
类。 在0.8.2中看起来像这样(这是从spark-streaming-kafka_2.11-1.6.3
获得spark-streaming-kafka_2.11-1.6.3
):
case class PartitionMetadata(partitionId: Int,
val leader: Option[Broker],
replicas: Seq[Broker],
isr: Seq[Broker] = Seq.empty,
errorCode: Short = ErrorMapping.NoError) extends Logging
并且在> 0.10.0.0中是这样的:
case class PartitionMetadata(partitionId: Int,
leader: Option[BrokerEndPoint],
replicas: Seq[BrokerEndPoint],
isr: Seq[BrokerEndPoint] = Seq.empty,
errorCode: Short = Errors.NONE.code) extends Logging
看看leader
如何从Option[Broker]
更改为Option[BrokerEndPoint]
? 这就是Spark大喊大叫的原因。
您必须清理依赖关系,您需要做的就是(如果使用的是Spark 2.2):
spark-streaming_2.11-2.2.0,
spark-streaming-kafka-0-10_2.11-2.2.0
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