java.io.NotSerializableException：org.apache.spark.InterruptibleIterator在spark java中执行mapPartition（）时

时间：2016-12-06 14:14:06

标签： hadoop apache-spark iterator rdd partitioning

我正在尝试对样本数据执行简单的Spark RDD转换mapPartition（）。但是在这个过程中，我得到java.io.NotSerializableException: org.apache.spark.InterruptibleIterator 异常。

这是我的例外：

java.io.NotSerializableException: org.apache.spark.InterruptibleIterator
Serialization stack:
    - object not serializable (class: org.apache.spark.InterruptibleIterator, value: non-empty iterator)
    - field (class: scala.collection.convert.Wrappers$IteratorWrapper, name: underlying, type: interface scala.collection.Iterator)
    - object (class scala.collection.convert.Wrappers$IteratorWrapper, IteratorWrapper(non-empty iterator))
    - element of array (index: 0)
    - array (class [Ljava.lang.Object;, size 2)
    at org.apache.spark.serializer.SerializationDebugger$.improveException(SerializationDebugger.scala:40)
    at org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:47)
    at org.apache.spark.serializer.JavaSerializerInstance.serialize(JavaSerializer.scala:101)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:265)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
    at java.lang.Thread.run(Thread.java:745)
16/12/06 19:36:24 ERROR TaskSetManager: Task 0.0 in stage 0.0 (TID 0) had a not serializable result: org.apache.spark.InterruptibleIterator
Serialization stack:
    - object not serializable (class: org.apache.spark.InterruptibleIterator, value: non-empty iterator)
    - field (class: scala.collection.convert.Wrappers$IteratorWrapper, name: underlying, type: interface scala.collection.Iterator)
    - object (class scala.collection.convert.Wrappers$IteratorWrapper, IteratorWrapper(non-empty iterator))
    - element of array (index: 0)
    - array (class [Ljava.lang.Object;, size 2); not retrying
16/12/06 19:36:24 INFO TaskSchedulerImpl: Removed TaskSet 0.0, whose tasks have all completed, from pool 
16/12/06 19:36:24 INFO TaskSchedulerImpl: Cancelling stage 0
16/12/06 19:36:24 INFO DAGScheduler: ResultStage 0 (collect at MapPartition.java:18) failed in 0.168 s
16/12/06 19:36:24 INFO DAGScheduler: Job 0 failed: collect at MapPartition.java:18, took 0.529927 s
Exception in thread "main" org.apache.spark.SparkException: Job aborted due to stage failure: Task 0.0 in stage 0.0 (TID 0) had a not serializable result: org.apache.spark.InterruptibleIterator
Serialization stack:
    - object not serializable (class: org.apache.spark.InterruptibleIterator, value: non-empty iterator)
    - field (class: scala.collection.convert.Wrappers$IteratorWrapper, name: underlying, type: interface scala.collection.Iterator)
    - object (class scala.collection.convert.Wrappers$IteratorWrapper, IteratorWrapper(non-empty iterator))
    - element of array (index: 0)
    - array (class [Ljava.lang.Object;, size 2)
    at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1431)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1419)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1418)
    at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
    at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1418)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)
    at scala.Option.foreach(Option.scala:257)
    at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:799)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1640)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1599)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1588)
    at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
    at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:620)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1832)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1845)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1858)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1929)
    at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:927)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111)
    at org.apache.spark.rdd.RDD.withScope(RDD.scala:316)
    at org.apache.spark.rdd.RDD.collect(RDD.scala:926)
    at org.apache.spark.api.java.JavaRDDLike$class.collect(JavaRDDLike.scala:339)
    at org.apache.spark.api.java.AbstractJavaRDDLike.collect(JavaRDDLike.scala:46)
    at in.inndata.sparkbasics.MapPartition.main(MapPartition.java:18)

这是我的代码：

SparkConf conf = new SparkConf().setAppName("MapPartition").setMaster("local");
        JavaSparkContext sc = new JavaSparkContext(conf);
        List<Integer> list = new ArrayList<Integer>(Arrays.asList(10,20,30,40,50,60,70,80,90,100));

        JavaRDD<Integer> lines = sc.parallelize(list,1);
        JavaRDD<Object> mappartitions = lines.mapPartitions(f -> Arrays.asList(f,f));
        System.out.println(mappartitions.collect());

如果我执行mappartitions.foreach(f -> System.out.println(f));获取

IteratorWrapper(non-empty iterator)

1 个答案:

答案 0 :(得分：2)

<强>问题： 这里：

 JavaRDD<Object> mappartitions = lines.mapPartitions(f -> Arrays.asList(f,f));

您正在创建两个元素的列表，它们都是相同的迭代器。 Iterator不可序列化且无法发送 - 这就是您收到此错误的原因。

解决方法： 如果要从iterator创建列表（包含迭代器中元素的列表），请使用：

lines.mapPartitions(f -> {
List<String> list = new LinkedList<String>();

while(iter.hasNext()) {
    list.add(iter.next());
}
return Arrays.asList(list).iterator(); // this will create iterator with only one element - our list of all elements in partition
});

//无法检查类型，如果我犯了错误，请通知我

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