Question

为什么spark NotSerializableException org.apache.hadoop.io.NullWritable包含序列文件？我的代码（非常简单）：

import org.apache.hadoop.io.{BytesWritable, NullWritable}
sc.sequenceFile[NullWritable, BytesWritable](in).repartition(1000).saveAsSequenceFile(out, None)

例外

org.apache.spark.SparkException: Job aborted: Task 1.0:66 had a not serializable result: java.io.NotSerializableException: org.apache.hadoop.io.NullWritable
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$org$apache$spark$scheduler$DAGScheduler$$abortStage$1.apply(DAGScheduler.scala:1028)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$org$apache$spark$scheduler$DAGScheduler$$abortStage$1.apply(DAGScheduler.scala:1026)
    at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
    at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$abortStage(DAGScheduler.scala:1026)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$processEvent$10.apply(DAGScheduler.scala:619)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$processEvent$10.apply(DAGScheduler.scala:619)
    at scala.Option.foreach(Option.scala:236)
    at org.apache.spark.scheduler.DAGScheduler.processEvent(DAGScheduler.scala:619)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$start$1$$anon$2$$anonfun$receive$1.applyOrElse(DAGScheduler.scala:207)
    at akka.actor.ActorCell.receiveMessage(ActorCell.scala:498)
    at akka.actor.ActorCell.invoke(ActorCell.scala:456)
    at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:237)
    at akka.dispatch.Mailbox.run(Mailbox.scala:219)
    at akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:386)
    at scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260)
    at scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339)
    at scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979)
    at scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107)

Answer 1

因此可以将不可序列化的类型读入RDD - 即具有不可序列化的RDD（这似乎是反直觉的）。但是，一旦您希望在该RDD上执行需要对象可序列化的操作，例如repartition，它就需要可序列化。而且事实证明，那些奇怪的类SomethingWritable，虽然发明了用于序列化事物的唯一目的但实际上并不是可序列化的:(。所以你必须将这些东西映射到字节数组然后再回来：

sc.sequenceFile[NullWritable, BytesWritable](in)
.map(_._2.copyBytes()).repartition(1000)
.map(a => (NullWritable.get(), new BytesWritable(a)))
.saveAsSequenceFile(out, None)

另见：https://stackoverflow.com/a/22594142/1586965

Answer 2

如果你试图使用一个不可序列化的第三方类，它会抛出NotSerializable异常。这是因为spark的closure属性，即任何实例变量（在转换操作之外定义））你试图在转换操作中访问spark尝试序列化它以及该对象的所有依赖类。

为什么spark会使用序列文件抛出NotSerializableException org.apache.hadoop.io.NullWritable

2 个答案: