如何修复"java.io.NotSerializableException:org.apache.kafka.clients.consumer.ConsumerRecord"在Spark Streaming Kafka Consumer中? [英] How to fix "java.io.NotSerializableException: org.apache.kafka.clients.consumer.ConsumerRecord" in Spark Streaming Kafka Consumer?

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本文介绍了如何修复"java.io.NotSerializableException:org.apache.kafka.clients.consumer.ConsumerRecord"在Spark Streaming Kafka Consumer中?的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！

问题描述

  • 火花2.0.0
  • Apache Kafka 0.10.1.0
  • scala 2.11.8

当我使用使用kafka进行火花流和kafka集成时具有以下Scala代码的代理版本0.10.1.0 会失败，并具有以下异常:

When I use spark streaming and kafka integration with kafka broker version 0.10.1.0 with the following Scala code it fails with the following exception:

16/11/13 12:55:20 ERROR Executor: Exception in task 0.0 in stage 0.0 (TID 0)
java.io.NotSerializableException: org.apache.kafka.clients.consumer.ConsumerRecord
Serialization stack:
    - object not serializable (class: org.apache.kafka.clients.consumer.ConsumerRecord, value: ConsumerRecord(topic = local1, partition = 0, offset = 10000, CreateTime = 1479012919187, checksum = 1713832959, serialized key size = -1, serialized value size = 1, key = null, value = a))
    - element of array (index: 0)
    - array (class [Lorg.apache.kafka.clients.consumer.ConsumerRecord;, size 11)
    at org.apache.spark.serializer.SerializationDebugger$.improveException(SerializationDebugger.scala:40)

为什么?如何解决?

代码:

import org.apache.kafka.clients.consumer.ConsumerRecord
import org.apache.kafka.common.serialization.StringDeserializer
import org.apache.spark.streaming.kafka010._
import org.apache.spark.streaming.kafka010.LocationStrategies.PreferConsistent
import org.apache.spark.streaming.kafka010.ConsumerStrategies.Subscribe
import org.apache.spark._
import org.apache.commons.codec.StringDecoder
import org.apache.spark.streaming._

object KafkaConsumer_spark_test {
  def main(args: Array[String]) {
    val conf = new SparkConf().setAppName("KafkaConsumer_spark_test").setMaster("local[4]")
    val ssc = new StreamingContext(conf, Seconds(1))
    ssc.checkpoint("./checkpoint")
    val kafkaParams =Map[String, Object](
      "bootstrap.servers" -> "localhost:9092",
      "key.deserializer" -> classOf[StringDeserializer],
      "value.deserializer" -> classOf[StringDeserializer],
      "group.id" -> "example",
      "auto.offset.reset" -> "latest",
      "enable.auto.commit" -> (false: java.lang.Boolean)
    )

    val topics = Array("local1")
    val stream = KafkaUtils.createDirectStream[String, String](
      ssc,
      PreferConsistent,
      Subscribe[String, String](topics, kafkaParams)
    )
    stream.map(record => (record.key, record.value))
    stream.print()

    ssc.start()
    ssc.awaitTermination()
  }
}

例外:

Exception:

16/11/13 12:55:20 ERROR Executor: Exception in task 0.0 in stage 0.0 (TID 0)
java.io.NotSerializableException: org.apache.kafka.clients.consumer.ConsumerRecord
Serialization stack:
    - object not serializable (class: org.apache.kafka.clients.consumer.ConsumerRecord, value: ConsumerRecord(topic = local1, partition = 0, offset = 10000, CreateTime = 1479012919187, checksum = 1713832959, serialized key size = -1, serialized value size = 1, key = null, value = a))
    - element of array (index: 0)
    - array (class [Lorg.apache.kafka.clients.consumer.ConsumerRecord;, size 11)
    at org.apache.spark.serializer.SerializationDebugger$.improveException(SerializationDebugger.scala:40)
    at org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:46)
    at org.apache.spark.serializer.JavaSerializerInstance.serialize(JavaSerializer.scala:100)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:313)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
    at java.lang.Thread.run(Thread.java:745)
16/11/13 12:55:20 ERROR TaskSetManager: Task 0.0 in stage 0.0 (TID 0) had a not serializable result: org.apache.kafka.clients.consumer.ConsumerRecord
Serialization stack:
    - object not serializable (class: org.apache.kafka.clients.consumer.ConsumerRecord, value: ConsumerRecord(topic = local1, partition = 0, offset = 10000, CreateTime = 1479012919187, checksum = 1713832959, serialized key size = -1, serialized value size = 1, key = null, value = a))
    - element of array (index: 0)
    - array (class [Lorg.apache.kafka.clients.consumer.ConsumerRecord;, size 11); not retrying
16/11/13 12:55:20 ERROR JobScheduler: Error running job streaming job 1479012920000 ms.0
org.apache.spark.SparkException: Job aborted due to stage failure: Task 0.0 in stage 0.0 (TID 0) had a not serializable result: org.apache.kafka.clients.consumer.ConsumerRecord
Serialization stack:
    - object not serializable (class: org.apache.kafka.clients.consumer.ConsumerRecord, value: ConsumerRecord(topic = local1, partition = 0, offset = 10000, CreateTime = 1479012919187, checksum = 1713832959, serialized key size = -1, serialized value size = 1, key = null, value = a))
    - element of array (index: 0)
    - array (class [Lorg.apache.kafka.clients.consumer.ConsumerRecord;, size 11)
    at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1450)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1438)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1437)
    at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
    at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1437)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:811)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:811)
    at scala.Option.foreach(Option.scala:257)
    at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:811)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1659)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1618)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1607)
    at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
    at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:632)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1871)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1884)
    at org.apache.spark.streaming.kafka010.KafkaRDD.take(KafkaRDD.scala:122)
    at org.apache.spark.streaming.kafka010.KafkaRDD.take(KafkaRDD.scala:50)
    at org.apache.spark.streaming.dstream.DStream$$anonfun$print$2$$anonfun$foreachFunc$3$1.apply(DStream.scala:734)
    at org.apache.spark.streaming.dstream.DStream$$anonfun$print$2$$anonfun$foreachFunc$3$1.apply(DStream.scala:733)
    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$sp(JobScheduler.scala:245)
    at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:245)
    at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:245)
    at scala.util.DynamicVariable.withValue(DynamicVariable.scala:58)
    at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler.run(JobScheduler.scala:244)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
    at java.lang.Thread.run(Thread.java:745)
Exception in thread "main" org.apache.spark.SparkException: Job aborted due to stage failure: Task 0.0 in stage 0.0 (TID 0) had a not serializable result: org.apache.kafka.clients.consumer.ConsumerRecord
Serialization stack:
    - object not serializable (class: org.apache.kafka.clients.consumer.ConsumerRecord, value: ConsumerRecord(topic = local1, partition = 0, offset = 10000, CreateTime = 1479012919187, checksum = 1713832959, serialized key size = -1, serialized value size = 1, key = null, value = a))
    - element of array (index: 0)
    - array (class [Lorg.apache.kafka.clients.consumer.ConsumerRecord;, size 11)
    at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1450)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1438)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1437)
    at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
    at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1437)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:811)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:811)
    at scala.Option.foreach(Option.scala:257)
    at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:811)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1659)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1618)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1607)
    at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
    at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:632)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1871)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1884)
    at org.apache.spark.streaming.kafka010.KafkaRDD.take(KafkaRDD.scala:122)
    at org.apache.spark.streaming.kafka010.KafkaRDD.take(KafkaRDD.scala:50)
    at org.apache.spark.streaming.dstream.DStream$$anonfun$print$2$$anonfun$foreachFunc$3$1.apply(DStream.scala:734)
    at org.apache.spark.streaming.dstream.DStream$$anonfun$print$2$$anonfun$foreachFunc$3$1.apply(DStream.scala:733)
    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$sp(JobScheduler.scala:245)
    at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:245)
    at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:245)
    at scala.util.DynamicVariable.withValue(DynamicVariable.scala:58)
    at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler.run(JobScheduler.scala:244)
    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)

推荐答案

从Dstream接收到使用者记录对象.当您尝试打印它时，它会出错，因为该对象无法进行序列化.相反，您应该从ConsumerRecord对象获取值并进行打印.

The Consumer record object is received from Dstream. When you try to print it, it gives error because that object is not serailizable. Instead you should get values from ConsumerRecord object and print it.

代替stream.print()，请执行以下操作:

instead of stream.print(), do:

stream.map(record=>(record.value().toString)).print

这应该可以解决您的问题.

This should solve your problem.

GOTCHA

对于其他任何看到此异常的人，任何对checkpoint的调用都会调用带有storageLevel = MEMORY_ONLY_SER的persist，因此在您调用map

For anyone else seeing this exception, any call to checkpoint will call a persist with storageLevel = MEMORY_ONLY_SER, so don't call checkpoint until you call map

这篇关于如何修复"java.io.NotSerializableException:org.apache.kafka.clients.consumer.ConsumerRecord"在Spark Streaming Kafka Consumer中?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！

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