无法在 foreachRDD 中序列化 SparkContext

Question

我正在尝试将流数据从 Kafka 保存到 cassandra。我能够读取和解析数据，但是当我调用下面的行来保存数据时，我得到了 Task not Serializable 异常。我的课程正在扩展可序列化但不知道为什么我会看到这个错误，在谷歌搜索 3 小时后没有得到太多帮助，有人可以给出任何指示吗？

val collection = sc.parallelize(Seq((obj.id, obj.data)))
collection.saveToCassandra("testKS", "testTable ", SomeColumns("id", "data"))` 


import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.sql.SaveMode
import org.apache.spark.streaming.Seconds
import org.apache.spark.streaming.StreamingContext
import org.apache.spark.streaming.kafka.KafkaUtils
import com.datastax.spark.connector._

import kafka.serializer.StringDecoder
import org.apache.spark.rdd.RDD
import com.datastax.spark.connector.SomeColumns
import java.util.Formatter.DateTime

object StreamProcessor extends Serializable {
  def main(args: Array[String]): Unit = {
    val sparkConf = new SparkConf().setMaster("local[2]").setAppName("StreamProcessor")
      .set("spark.cassandra.connection.host", "127.0.0.1")

    val sc = new SparkContext(sparkConf)

    val ssc = new StreamingContext(sc, Seconds(2))

    val sqlContext = new SQLContext(sc)

    val kafkaParams = Map("metadata.broker.list" -> "localhost:9092")

    val topics = args.toSet

    val stream = KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder](
      ssc, kafkaParams, topics)

    stream.foreachRDD { rdd =>

      if (!rdd.isEmpty()) {
        try {

          rdd.foreachPartition { iter =>
            iter.foreach {
              case (key, msg) =>

                val obj = msgParseMaster(msg)

                val collection = sc.parallelize(Seq((obj.id, obj.data)))
                collection.saveToCassandra("testKS", "testTable ", SomeColumns("id", "data"))

            }
          }

        }

      }
    }

    ssc.start()
    ssc.awaitTermination()

  }

  import org.json4s._
  import org.json4s.native.JsonMethods._
  case class wordCount(id: Long, data: String) extends serializable
  implicit val formats = DefaultFormats
  def msgParseMaster(msg: String): wordCount = {
    val m = parse(msg).extract[wordCount]
    return m

  }

}

我来了

org.apache.spark.SparkException：任务不可序列化

以下是完整的日志

16/08/06 10:24:52 错误 JobScheduler：运行作业流作业时出错 1470504292000 ms.0 org.apache.spark.SparkException：任务不可序列化 在 org.apache.spark.util.ClosureCleaner$.ensureSerializable(ClosureCleaner.scala:304) 在 org.apache.spark.util.ClosureCleaner$.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$foreachPartition$1.apply(RDD.scala:919) 在 org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1.apply(RDD.scala:918) 在 org.apache.spark.rdd.RDDOperationScope$.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.foreachPartition(RDD.scala:918) 在

Answer 1

您不能在传递给foreachPartition 的函数中调用sc.parallelize - 该函数必须被序列化并发送到每个执行程序，并且SparkContext（故意）不可序列化（它应该只驻留在驱动程序中应用程序，而不是执行者）。

Answer 2

SparkContext 不可序列化，你不能在foreachRDD 中使用它，并且从你的图表的使用中你不需要它。相反，您可以简单地映射每个 RDD，解析出相关数据并将新的 RDD 保存到 cassandra：

stream
  .map { 
    case (_, msg) => 
      val result = msgParseMaster(msg)
      (result.id, result.data)
   }
  .foreachRDD(rdd => if (!rdd.isEmpty)
                       rdd.saveToCassandra("testKS",
                                           "testTable",
                                            SomeColumns("id", "data")))