为什么spark会抛出notserializableexception org.apache.hadoop.io.nullwritable和序列文件

wqnecbli 于 2021-06-02 发布在 Hadoop

关注(0)|答案(2)|浏览(367)

为什么Spark会抛出
NotSerializableException org.apache.hadoop.io.NullWritable 有序列文件吗？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)

hadoop hdfs IO apache-spark

来源：https://stackoverflow.com/questions/24224392/why-does-spark-throw-notserializableexception-org-apache-hadoop-io-nullwritable

2条答案

按热度按时间

m1m5dgzv1#

因此，将不可序列化的类型读入rdd是可能的，也就是说，有一个不可序列化的rdd（这似乎是违反直觉的）。但是一旦您希望在rdd上执行一个要求对象可序列化的操作，比如 repartition 它需要序列化。此外，这些奇怪的类是可写的，尽管它们的发明仅仅是为了序列化事物，但实际上并不是可序列化的：（。因此，必须将这些内容Map到字节数组，然后再Map回来：

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

另请参见：https://stackoverflow.com/a/22594142/1586965

赞(0）回复(0）举报 2021-06-03

5ktev3wc2#

在spark中，如果您尝试使用不可序列化的第三方类，它会引发notserializable异常。这是因为spark的闭包属性，即您尝试在转换操作内部访问的任何示例变量（在转换操作外部定义）spark会尝试序列化它以及所有该对象的依赖类。

赞(0）回复(0）举报 2021-06-03