Spark-task执行过程中的序列化

2020-07-07  本文已影响0人  布莱安托

先看一个例子:

/*
  首先我们定义了一个Search对象,带有一个String类型的参数
  该类拥有三个成员方法:
  1)isMatch:判断参数字符串s是否包含子串query
  2)getMatchRdd1:使用isMatch方法获取匹配结果后的RDD
  3)getMatchRdd1:在filter中实现方法获取匹配结果后的RDD
 */

class Search(query: String) {
  def isMatch(s: String): Boolean = {
    s.contains(query)
  }

  def getMatchRdd1(rdd: RDD[String]): RDD[String] = {
    rdd.filter(isMatch)
  }

  def getMatchRdd2(rdd: RDD[String]): RDD[String] = {
    rdd.filter(_.contains(query))
  }

}

object SerializableDemo {
  def main(args: Array[String]): Unit = {

    val conf = new SparkConf().setMaster("local[4]").setAppName("SerializableDemo")
    val sc = new SparkContext(conf)

    val rdd = sc.parallelize(Array("hello", "world", "hello", "spark"))

    val search = new Search("h")

    val matchRdd = search.getMatchRdd2(rdd)
    matchRdd.collect().foreach(println)

    sc.stop()

  }
}

运行后结果:

Exception in thread "main" org.apache.spark.SparkException: Task not serializable
at org.apache.spark.util.ClosureCleaner.ensureSerializable(ClosureCleaner.scala:345) at org.apache.spark.util.ClosureCleaner.orgapachesparkutilClosureCleanerclean(ClosureCleaner.scala:335) at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:159) at org.apache.spark.SparkContext.clean(SparkContext.scala:2299) at org.apache.spark.rdd.RDDanonfunfilter1.apply(RDD.scala:388)
at org.apache.spark.rdd.RDD$$anonfunfilter1.apply(RDD.scala:387)
at org.apache.spark.rdd.RDDOperationScope.withScope(RDDOperationScope.scala:151) at org.apache.spark.rdd.RDDOperationScope.withScope(RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:363)
at org.apache.spark.rdd.RDD.filter(RDD.scala:387)
at adamlee.spark.Search.getMatchRdd2(SerializableDemo.scala:34)
at adamlee.spark.SerializableDemo$.main(SerializableDemo.scala:16)
at adamlee.spark.SerializableDemo.main(SerializableDemo.scala)
Caused by: java.io.NotSerializableException: adamlee.spark.Search
Serialization stack:

  • object not serializable (class: adamlee.spark.Search, value: adamlee.spark.Search@4cafa9aa)
  • field (class: adamlee.spark.Search$$anonfungetMatchRdd21, name: $outer, type: class adamlee.spark.Search)
  • object (class adamlee.spark.Search$$anonfungetMatchRdd21, <function1>)
    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.util.ClosureCleaner.ensureSerializable(ClosureCleaner.scala:342)
    ... 12 more

报错提示Task未能序列化,再看Caused By提示:object not serializable,告诉我们Search这个类的对象未能序列化。

原因就是search对象初始化是在Driver端进行的,当我们执行collect是,触发计算,Driver需要将任务下发至Executor,这时候就产生了进程间通信,Driver和Executor间通信是通过网络传输,网络上传输的是二进制的比特流,由于Search类并未继承Serializable类,所以这个类的对象就不能被序列化。

现在我们新建一个类Search1,继承了Serializable:

class Search1(query: String) extends Serializable {
  def isMatch(s: String): Boolean = {
    s.contains(query)
  }

  def getMatchRdd1(rdd: RDD[String]): RDD[String] = {
    rdd.filter(isMatch)
  }

  def getMatchRdd2(rdd: RDD[String]): RDD[String] = {
    rdd.filter(_.contains(query))
  }

}

object SerializableDemo {
  def main(args: Array[String]): Unit = {

    val conf = new SparkConf().setMaster("local[4]").setAppName("SerializableDemo")
    val sc = new SparkContext(conf)

    val rdd = sc.parallelize(Array("hello", "world", "hello", "spark"))

    val search1 = new Search1("h")

    val matchRdd = search1.getMatchRdd2(rdd)
    matchRdd.collect().foreach(println)

    sc.stop()

  }
}

运行后结果:

hello
hello

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