广播是我在Spark中收到“缺少用于混洗的输出位置”异常的原因吗？

Question

我的代码分为三个阶段。在第一阶段中，我读取数据并将其减少，在第二阶段中，我进行计算，而在第三阶段中，我对第二阶段的结果进行cartesian transformation。

为了提高性能，我在第二阶段更改了实现，以消除joins之间的一些rdds。相反，我向驱动程序读取了一个文件，并将其broadcast读取到workers，进行了计算，然后转到第三阶段。

当我有大约60.000个值/批次时，一切似乎都还可以。但是，当我将其扩展到每批100.000时，在第三阶段会出现org.apache.spark.shuffle.MetadataFetchFailedException: Missing an output location for shuffle异常。

为什么会这样？这是由broadcast引起的内存问题吗？

错误是：

18/10/06 18:36:35 ERROR YarnScheduler: Lost executor 3 on clu26.softnet.tuc.gr: Container marked as failed: container_e30_1532029170450_0254_01_000004 on host: clu26.softnet.tuc.gr. Exit status: 143. Diagnostics: Container killed on request. Exit code is 143
Container exited with a non-zero exit code 143. 
Killed by external signal

18/10/06 18:36:35 WARN TaskSetManager: Lost task 0.0 in stage 23.1 (TID 15, clu26.softnet.tuc.gr, executor 3): ExecutorLostFailure (executor 3 exited caused by one of the running tasks) Reason: Container marked as failed: container_e30_1532029170450_0254_01_000004 on host: clu26.softnet.tuc.gr. Exit status: 143. Diagnostics: Container killed on request. Exit code is 143
Container exited with a non-zero exit code 143. 
Killed by external signal

18/10/06 18:36:35 WARN YarnSchedulerBackend$YarnSchedulerEndpoint: Container marked as failed: container_e30_1532029170450_0254_01_000004 on host: clu26.softnet.tuc.gr. Exit status: 143. Diagnostics: Container killed on request. Exit code is 143
Container exited with a non-zero exit code 143. 
Killed by external signal

18/10/06 18:36:35 WARN TaskSetManager: Lost task 0.1 in stage 23.1 (TID 16, clu03.softnet.tuc.gr, executor 2): FetchFailed(null, shuffleId=0, mapId=-1, reduceId=0, message=
org.apache.spark.shuffle.MetadataFetchFailedException: Missing an output location for shuffle 0
    at org.apache.spark.MapOutputTracker$$anonfun$org$apache$spark$MapOutputTracker$$convertMapStatuses$2.apply(MapOutputTracker.scala:697)
    at org.apache.spark.MapOutputTracker$$anonfun$org$apache$spark$MapOutputTracker$$convertMapStatuses$2.apply(MapOutputTracker.scala:693)
    at scala.collection.TraversableLike$WithFilter$$anonfun$foreach$1.apply(TraversableLike.scala:733)
    at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
    at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:186)
    at scala.collection.TraversableLike$WithFilter.foreach(TraversableLike.scala:732)
    at org.apache.spark.MapOutputTracker$.org$apache$spark$MapOutputTracker$$convertMapStatuses(MapOutputTracker.scala:693)
    at org.apache.spark.MapOutputTracker.getMapSizesByExecutorId(MapOutputTracker.scala:147)
    at org.apache.spark.shuffle.BlockStoreShuffleReader.read(BlockStoreShuffleReader.scala:49)
    at org.apache.spark.rdd.ShuffledRDD.compute(ShuffledRDD.scala:105)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
    at org.apache.spark.rdd.CartesianRDD.compute(CartesianRDD.scala:75)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:96)
    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53)
    at org.apache.spark.scheduler.Task.run(Task.scala:108)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:338)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
    at java.lang.Thread.run(Thread.java:745)

)

我在UI中失败的工作如下：