我有一个20GB的文件和一个400MB的文件，我将每个文件映射到每个项目的6个属性。然后我创建一个K，V RDD，创建一个带有部分属性的哈希（名字的前2个字母和姓氏的前4个字母）。
所以我现在a: RDD[K,V]和b: RDD[K,V]有一个共同密钥，所以我想加入他们
a.join(b).map(x=> [check commonality in the attributes]).SaveAsTextFile(fileout)
奇怪的是，我在我的16GB Macbook上运行HDFS，它可以在大约16分钟内运行。当我将它放在我们的3个工作节点集群上时，每个集群有96GB，我会重复FetchFailed异常。
这真的可以归结为我的Mac上的HDFS都是同一个SSD而没有网络IO，还是我还能看到其他东西？
我使用Cloudera 5.3.1并在Yarn上运行spark，执行程序日志信息有限，我还没有弄清楚如何调整执行程序的日志记录级别以获取更多信息。知道怎么做吗？
下面的示例堆栈;
FetchFailed(null, shuffleId=0, mapId=-1, reduceId=6, message=
org.apache.spark.shuffle.MetadataFetchFailedException: Missing an output location for shuffle 0
at org.apache.spark.MapOutputTracker$$anonfun$org$apache$spark$MapOutputTracker$$convertMapStatuses$1.apply(MapOutputTracker.scala:386)
at org.apache.spark.MapOutputTracker$$anonfun$org$apache$spark$MapOutputTracker$$convertMapStatuses$1.apply(MapOutputTracker.scala:383)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
at scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:108)
at org.apache.spark.MapOutputTracker$.org$apache$spark$MapOutputTracker$$convertMapStatuses(MapOutputTracker.scala:382)
at org.apache.spark.MapOutputTracker.getServerStatuses(MapOutputTracker.scala:178)
at org.apache.spark.shuffle.hash.BlockStoreShuffleFetcher$.fetch(BlockStoreShuffleFetcher.scala:42)
at org.apache.spark.shuffle.hash.HashShuffleReader.read(HashShuffleReader.scala:40)
at org.apache.spark.rdd.CoGroupedRDD$$anonfun$compute$2.apply(CoGroupedRDD.scala:137)
at org.apache.spark.rdd.CoGroupedRDD$$anonfun$compute$2.apply(CoGroupedRDD.scala:127)
at scala.collection.TraversableLike$WithFilter$$anonfun$foreach$1.apply(TraversableLike.scala:772)
at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108)
at scala.collection.TraversableLike$WithFilter.foreach(TraversableLike.scala:771)
at org.apache.spark.rdd.CoGroupedRDD.compute(CoGroupedRDD.scala:127)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:263)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:230)
at org.apache.spark.rdd.MappedValuesRDD.compute(MappedValuesRDD.scala:31)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:263)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:230)
at org.apache.spark.rdd.FlatMappedValuesRDD.compute(FlatMappedValuesRDD.scala:31)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:263)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:230)
at org.apache.spark.rdd.MappedRDD.compute(MappedRDD.scala:31)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:263)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:230)
at org.apache.spark.rdd.FilteredRDD.compute(FilteredRDD.scala:34)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:263)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:230)
at org.apache.spark.rdd.MappedRDD.compute(MappedRDD.scala:31)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:263)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:230)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:61)
at org.apache.spark.scheduler.Task.run(Task.scala:56)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:196)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
)