apache-spark - 为什么 Spark 作业失败并出现 org.apache.spark.shuffle.MetadataFetchFailedException : Missing an output location for shuffle 0 in speculation mode?

Question

我正在以推测模式运行 Spark 作业。我有大约 500 个任务和大约 500 个 1 GB gz 压缩文件。我不断地进入每项作业，对于 1-2 个任务，附加错误，然后它会重新运行数十次(阻止作业完成)。

org.apache.spark.shuffle.MetadataFetchFailedException: Missing an output location for shuffle 0

知道这个问题的含义是什么以及如何克服它吗？

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:384)
    at org.apache.spark.MapOutputTracker$$anonfun$org$apache$spark$MapOutputTracker$$convertMapStatuses$1.apply(MapOutputTracker.scala:381)
    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:380)
    at org.apache.spark.MapOutputTracker.getServerStatuses(MapOutputTracker.scala:176)
    at org.apache.spark.shuffle.hash.BlockStoreShuffleFetcher$.fetch(BlockStoreShuffleFetcher.scala:42)
    at org.apache.spark.shuffle.hash.HashShuffleReader.read(HashShuffleReader.scala:40)
    at org.apache.spark.rdd.ShuffledRDD.compute(ShuffledRDD.scala:92)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:263)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:230)
    at org.apache.spark.rdd.MappedRDD.compute(MappedRDD.scala:31)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:263)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:230)
    at org.apache.spark.rdd.FlatMappedRDD.compute(FlatMappedRDD.scala:33)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:263)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:230)
    at org.apache.spark.rdd.MappedRDD.compute(MappedRDD.scala:31)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:263)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:230)
    at org.apache.spark.rdd.MappedRDD.compute(MappedRDD.scala:31)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:263)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:230)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:61)
    at org.apache.spark.scheduler.Task.run(Task.scala:56)
    at org.apache.spark.executor.Executor$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:722)

Answer 1

当我为工作节点提供的内存比它拥有的内存多时，就发生了这种情况。由于它没有交换空间，spark 在尝试存储对象进行混洗时崩溃了，而内存已耗尽。

解决方案是添加交换，或者配置工作程序/执行程序以使用更少的内存，此外还对多个持久性使用 MEMORY_AND_DISK 存储级别。