我想将转换后的流写入elasticsearch索引，如下所示：

transformed.foreachRDD(rdd => {
  if (!rdd.isEmpty()) {
    val messages = rdd.map(prepare)
    messages.saveAsNewAPIHadoopFile("-", classOf[NullWritable], classOf[MapWritable], classOf[EsOutputFormat], ec)
  }
})

线路 val messages = rdd.map(prepare) 抛出错误（见下文）。我一直在尝试不同的方法来解决这个问题（例如添加 @transient 旁边 val conf )，但似乎什么都不管用。
6/06/28 19:23:00错误jobscheduler:运行作业流作业146713480000 ms.0 org.apache.spark.sparkexception时出错：无法在org.apache.spark.util.closurecleaner$.ensureserializable（closurecleaner）上序列化任务。scala:304)在org.apache.spark.util.closurecleaner$.org$apache$spark$util$closurecleaner$$clean（closurecleaner）。scala:294)在org.apache.spark.util.closurecleaner$.clean（closurecleaner。scala:122)在org.apache.spark.sparkcontext.clean（sparkcontext。scala:2055)在org.apache.spark.rdd.rdd$$anonfun$map$1.apply（rdd。scala:324)在org.apache.spark.rdd.rdd$$anonfun$map$1.apply（rdd。scala:323)在org.apache.spark.rdd.rddoperationscope$.withscope（rddoperationscope。scala:150)在org.apache.spark.rdd.rddoperationscope$.withscope（rddoperationscope。scala:111)在org.apache.spark.rdd.rdd.withscope（rdd。scala:316)在org.apache.spark.rdd.rdd.map（rdd。scala:323)在de.kp.spark.elastic.stream.esstream$$anonfun$运行$1.apply（esstream。scala:77)在de.kp.spark.elastic.stream.esstream$$anonfun$运行$1.apply（esstream。scala:75)在org.apache.spark.streaming.dstream$$anonfun$foreachrdd$1$$anonfun$apply$mcv$sp$3.apply（dstream。scala:661)在org.apache.spark.streaming.dstream$$anonfun$foreachrdd$1$$anonfun$apply$mcv$sp$3.apply（dstream。scala:661)在org.apache.spark.streaming.dstream.foreachdstream$$anonfun$1$$anonfun$apply$mcv$sp$1.apply$mcv$sp（foreachdstream）。scala:50)在org.apache.spark.streaming.dstream.foreachdstream$$anonfun$1$$anonfun$apply$mcv$sp$1.apply（foreachdstream。scala:50)在org.apache.spark.streaming.dstream.foreachdstream$$anonfun$1$$anonfun$apply$mcv$sp$1.apply（foreachdstream。scala:50)在org.apache.spark.streaming.dstream.dstream.createrddwithlocalproperties（dstream。scala:426)在org.apache.spark.streaming.dstream.foreachdstream$$anonfun$1.apply$mcv$sp（foreachdstream）。scala:49)在org.apache.spark.streaming.dstream.foreachdstream$$anonfun$1.apply（foreachdstream。scala:49)在org.apache.spark.streaming.dstream.foreachdstream$$anonfun$1.apply（foreachdstream。scala:49)在scala.util.try$.apply（try。scala:161)在org.apache.spark.streaming.scheduler.job.run（作业。scala:39)在org.apache.spark.streaming.scheduler.jobscheduler$jobhandler$$anonfun$run$1.apply$mcv$sp（作业调度器）。scala:224)在org.apache.spark.streaming.scheduler.jobscheduler$jobhandler$$anonfun$run$1.apply（作业调度器）。scala:224)在org.apache.spark.streaming.scheduler.jobscheduler$jobhandler$$anonfun$run$1.apply（作业调度器）。scala:224)在scala.util.dynamicvariable.withvalue（dynamicvariable。scala:57)在org.apache.spark.streaming.scheduler.jobscheduler$jobhandler.run（jobscheduler。scala:223)位于java.util.concurrent.threadpoolexecutor.runworker（threadpoolexecutor。java:1142)在java.util.concurrent.threadpoolexecutor$worker.run（threadpoolexecutor。java:617)在java.lang.thread.run（线程。java:745)引起原因：java.io.notserializableeexception:org.apache.hadoop.conf.configuration序列化堆栈：-对象不可序列化（类：org.apache.hadoop.conf.configuration，值：configuration:core-default.xml，core-site.xml，mapred-default.xml，mapred-site.xml，yarn-default.xml，yarn site.xml）-字段（类：de.kp.spark.elastic.stream.esstream，名称：de$kp$spark$elastic$stream$esstream$$conf，类型：class org.apache.hadoop.conf.conf）-对象（类：de.kp.spark.elastic.stream.esstream，de.kp.spark.elastic.stream）。esstream@6b156e9a)-字段（类：de.kp.spark.elastic.stream.esstream$$anonfun$run$1，名称：$outer，类型：class de.kp.spark.elastic.stream.esstream）-对象（class de.kp.spark.elastic.stream.esstream$$anonfun$run$1，）-字段（class:de.kp.spark.elastic.stream.esstream$$anonfun$run$1$$anonfun$2，名称：$outer，类型：class de.kp.spark.elastic.stream.esstream$$anonfun$run$1）-对象（class de.kp.spark.elastic.stream.esstream$$anonfun$run$1$$anonfun$2，）位于org.apache.spark.serializer.serializationdebugger$.improveexception（serializationdebugger）。scala:40)位于org.apache.spark.serializer.javaserializationstream.writeobject（javaserializer。scala:47)在org.apache.spark.serializer.javaserializerinstance.serialize（javaserializer。scala:101)在org.apache.spark.util.closurecleaner$.ensureserializable（closurecleaner。scala:301) ... 线程“main”org.apache.spark.sparkexception中还有30个异常：任务不能在org.apache.spark.util.closurecleaner$.ensureserializable（closurecleaner）上序列化。scala:304)在org.apache.spark.util.closurecleaner$.org$apache$spark$util$closurecleaner$$clean（closurecleaner）。scala:294)在org.apache.spark.util.closurecleaner$.clean（closurecleaner。scala:122)在org.apache.spark.sparkcontext.clean（sparkcontext。scala:2055)在org.apache.spark.rdd.rdd$$anonfun$map$1.apply（rdd。scala:324)在org.apache.spark.rdd.rdd$$anonfun$map$1.apply（rdd。scala:323)在org.apache.spark.rdd.rddoperationscope$.withscope（rddoperationscope。scala:150)在org.apache.spark.rdd.rddoperationscope$.withscope（rddoperationscope。scala:111)在org.apache.spark.rdd.rdd.withscope（rdd。scala:316)在org.apache.spark.rdd.rdd.map（rdd。scala:323)在de.kp.spark.elastic.stream.esstream$$anonfun$运行$1.apply（esstream。scala:77)在de.kp.spark.elastic.stream.esstream$$anonfun$运行$1.apply（esstream。scala:75)在org.apache.spark.streaming.dstream$$anonfun$foreachrdd$1$$anonfun$apply$mcv$sp$3.apply（dstream。scala:661)在org.apache.spark.streaming.dstream$$anonfun$foreachrdd$1$$anonfun$apply$mcv$sp$3.apply（dstream。scala:661)在org.apache.spark.streaming.dstream.foreachdstream$$anonfun$1$$anonfun$apply$mcv$sp$1.apply$mcv$sp（foreachdstream）。scala:50)在org.apache.spark.streaming.dstream.foreachdstream$$anonfun$1$$anonfun$apply$mcv$sp$1.apply（foreachdstream。scala:50)在org.apache.spark.streaming.dstream.foreachdstream$$anonfun$1$$anonfun$apply$mcv$sp$1.apply（foreachdstream。scala:50)在org.apache.spark.streaming.dstream.dstream.createrddwithlocalproperties（dstream。scala:426)在org.apache.spark.streaming.dstream.foreachdstream$$anonfun$1.apply$mcv$sp（foreachdstream）。scala:49)在org.apache.spark.streaming.dstream.foreachdstream$$anonfun$1.apply（foreachdstream。scala:49)在org.apache.spark.streaming.dstream.foreachdstream$$anonfun$1.apply（foreachdstream。scala:49)在scala.util.try$.apply（try。scala:161)在org.apache.spark.streaming.scheduler.job.run（作业。scala:39)在org.apache.spark.streaming.scheduler.jobscheduler$jobhandler$$anonfun$run$1.apply$mcv$sp（作业调度器）。scala:224)在org.apache.spark.streaming.scheduler.jobscheduler$jobhandler$$anonfun$run$1.apply（作业调度器）。scala:224)在org.apache.spark.streaming.scheduler.jobscheduler$jobhandler$$anonfun$run$1.apply（作业调度器）。scala:224)在scala.util.dynamicvariable.withvalue（dynamicvariable。scala:57)在org.apache.spark.streaming.scheduler.jobscheduler$jobhandler.run（jobscheduler。scala:223)位于java.util.concurrent.threadpoolexecutor.runworker（threadpoolexecutor。java:1142)在java.util.concurrent.threadpoolexecutor$worker.run（threadpoolexecutor。java:617)在java.lang.thread.run（线程。java:745)引起原因：java.io.notserializableeexception:org.apache.hadoop.conf.configuration序列化堆栈：-对象不可序列化（类：org.apache.hadoop.conf.configuration，值：configuration:core-default.xml，core-site.xml，mapred-default.xml，mapred-site.xml，yarn-default.xml，yarn site.xml）-字段（类：de.kp.spark.elastic.stream.esstream，名称：de$kp$spark$elastic$stream$esstream$$conf，类型：class org.apache.hadoop.conf.conf）-对象（类：de.kp.spark.elastic.stream.esstream，de.kp.spark.elastic.stream）。esstream@6b156e9a)-字段（类：de.kp.spark.elastic.stream.esstream$$anonfun$run$1，名称：$outer，类型：class de.kp.spark.elastic.stream.esstream）-对象（class de.kp.spark.elastic.stream.esstream$$anonfun$run$1，）-字段（class:de.kp.spark.elastic.stream.esstream$$anonfun$run$1$$anonfun$2，名称：$outer，类型：class de.kp.spark.elastic.stream.esstream$$anonfun$run$1）-对象（class de.kp.spark.elastic.stream.esstream$$anonfun$run$1$$anonfun$2，）位于org.apache.spark.serializer.serializationdebugger$.improveexception（serializationdebugger）。scala:40)位于org.apache.spark.serializer.javaserializationstream.writeobject（javaserializer。scala:47)在org.apache.spark.serializer.javaserializerinstance.serialize（javaserializer。scala:101)在org.apache.spark.util.closurecleaner$.ensureserializable（closurecleaner。scala:301) ... 30多个
它是否与hadoop的配置有关(我指的是这个信息： class: org.apache.hadoop.conf.Configuration, value: Configuration: core-default.xml, core-site.xml, mapred-default.xml, mapred-site.xml, yarn-default.xml, yarn-site.xml )
更新：

class EsStream(name:String,conf:HConf) extends SparkBase with Serializable {

  /* Elasticsearch configuration */ 
  val ec = getEsConf(conf)               

  /* Kafka configuration */
  val (kc,topics) = getKafkaConf(conf)

  def run() {

    val ssc = createSSCLocal(name,conf)

    /*
     * The KafkaInputDStream returns a Tuple where only the second component
     * holds the respective message; we therefore reduce to a DStream[String]
     */
    val stream = KafkaUtils.createStream[String,String,StringDecoder,StringDecoder](ssc,kc,topics,StorageLevel.MEMORY_AND_DISK).map(_._2)
    /*
     * Inline transformation of the incoming stream by any function that maps 
     * a DStream[String] onto a DStream[String]
     */
    val transformed = transform(stream)
    /*
     * Write transformed stream to Elasticsearch index
     */
    transformed.foreachRDD(rdd => {
      if (!rdd.isEmpty()) {
        val messages = rdd.map(prepare)
        messages.saveAsNewAPIHadoopFile("-", classOf[NullWritable], classOf[MapWritable], classOf[EsOutputFormat], ec)
      }
    })

    ssc.start()
    ssc.awaitTermination()    

  }

  def transform(stream:DStream[String]) = stream

  private def getEsConf(config:HConf):HConf = {

    val _conf = new HConf()

    _conf.set("es.nodes", conf.get("es.nodes"))
    _conf.set("es.port", conf.get("es.port"))

    _conf.set("es.resource", conf.get("es.resource"))

    _conf

  }

  private def getKafkaConf(config:HConf):(Map[String,String],Map[String,Int]) = {

    val cfg = Map(
      "group.id" -> conf.get("kafka.group"),

      "zookeeper.connect" -> conf.get("kafka.zklist"),
      "zookeeper.connection.timeout.ms" -> conf.get("kafka.timeout")

    )

    val topics = conf.get("kafka.topics").split(",").map((_,conf.get("kafka.threads").toInt)).toMap   

    (cfg,topics)

  }

  private def prepare(message:String):(Object,Object) = {

    val m = JSON.parseFull(message) match {
      case Some(map) => map.asInstanceOf[Map[String,String]]
      case None => Map.empty[String,String]
    }

    val kw = NullWritable.get

    val vw = new MapWritable
    for ((k, v) <- m) vw.put(new Text(k), new Text(v))

    (kw, vw)

  }

}