spark kafka 生产环境 商可序列化

我想出了一个例外：

错误yarn.ApplicationMaster：用户类抛出异常：org.apache.spark.SparkException：任务不可序列org.apache.spark.SparkException：任务不能序列在org.apache.spark.util.ClosureCleaner $ .ensureSerializable（ClosureCleaner.scala ：304）在org.apache.spark.util.ClosureCleaner $ .ORG $阿帕奇$火花$ UTIL $ ClosureCleaner $$干净（ClosureCleaner.scala：在org.apache.spark.util.ClosureCleaner $清洁机壳294）（ClosureCleaner . scala：122）org.apache.spark.SparkContext.clean（SparkContext.scala：2032）atg.apache.spark.rdd.RDD $$ anonfun $ foreach $ 1.apply（RDD.scala：889）at org.apache .spark.rdd.RDD $$ anonfun $ $的foreach 1.适用（RDD.scala：888）在org.apache.spark.rdd.RDDOperationScope $ .withScope（RDDOperationScope.scala：147）在org.apache.spark.rdd . RDDOperationScope $ .withScope（RDDOperationScope.scala：108）在org.apache.spark.rdd.RDD.withScope（RDD.scala：306）在org.apache.spark.rdd.RDD.foreach（RDD.scala：888）在com.Boot $ .test（Boot.scala：60）at com.Boot $ .main（Boot.scala：36）at com.Boot.main（Boot.scal）一）在sun.reflect.NativeMethodAccessorImpl.invoke0（本机方法）在sun.reflect.NativeMethodAccessorImpl.invoke（NativeMethodAccessorImpl.java:57）在sun.reflect.DelegatingMethodAccessorImpl.invoke（DelegatingMethodAccessorImpl.java:43）在java.lang.reflect中.Method.invoke（Method.java:606）at org.apache.spark.deploy.yarn.ApplicationMaster $$ anon $ 2.run（ApplicationMaster.scala：525）引起：java.io.NotSerializableException：org.apache.kafka .clients.producer.KafkaProducer序列化堆栈： - 对象不是可序列化（等级：org.apache.kafka.clients.producer.KafkaProducer，值：org.apache.kafka.clients.producer.KafkaProducer@77624599） - 场（类：COM .Boot $$ anonfun $ test $ 1，name：producer $ 1，type：class org.apache.kafka.clients.producer.KafkaProducer） - object（class com.Boot $$ anonfun $ test $ 1，）org.apache.spark .serializer.SerializationDebugger $ .improveException（SerializationDebugger.scala：40）at org.apache.spark.serializer.JavaSerializationStream.writeObject（JavaSerializer.scala：47） org.apache.spark.serializer.JavaSerializerInstance.serialize（JavaSerializer.scala：84）at org.apache.spark.util.ClosureCleaner $ .ensureSerializable（ClosureCleaner.scala：301）

//    @transient
val sparkConf = new SparkConf()

sparkConf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")

//    @transient
val sc = new SparkContext(sparkConf)

val requestSet: RDD[String] = sc.textFile(s"hdfs:/user/bigdata/ADVERTISE-IMPRESSION-STAT*/*")

//    @transient
val props = new HashMap[String, Object]()
props.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, NearLineConfig.kafka_brokers)
//    props.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, "org.apache.kafka.common.serialization.ByteArraySerializer");
//    props.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, "org.apache.kafka.common.serialization.ByteArraySerializer");
props.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, "org.apache.kafka.common.serialization.StringSerializer")
props.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, "org.apache.kafka.common.serialization.StringSerializer")
props.put("producer.type", "async")
props.put(ProducerConfig.BATCH_SIZE_CONFIG, "49152")

//    @transient
val producer: KafkaProducer[String, String] = new KafkaProducer[String, String](props)

requestSet.foreachPartition((partisions: Iterator[String]) => {
  partisions.foreach((line: String) => {
    try {
      producer.send(new ProducerRecord[String, String]("testtopic", line))
    } catch {
      case ex: Exception => {
        log.warn(ex.getMessage, ex)
      }
    }
  })
})

producer.close()

在这个程序中，我尝试从hdfs路径读取记录并将它们保存到kafka中 . 问题是，当我删除有关向kafka发送记录的代码时，它运行良好 . 我错过了什么？