所以，我碰上了磁盘空间问题，在Spark工作继续处理中。最终与洗牌我tmp目录的写入和读取洗牌将在写入，超过磁盘使用情况。因此，我的ML算法没有终止，引发了异常，并且我的Spark作业终止了。我期待减少我的磁盘使用情况。我可以做些优化磁盘使用的事情吗？我不想删除任何可能有价值的Spark RDD值。
异常这里：
Exception in thread "main" org.apache.spark.SparkException: Exception thrown in awaitResult:
at org.apache.spark.util.ThreadUtils$.awaitResult(ThreadUtils.scala:205)
at org.apache.spark.ml.tuning.CrossValidator$$anonfun$4$$anonfun$6.apply(CrossValidator.scala:164)
at org.apache.spark.ml.tuning.CrossValidator$$anonfun$4$$anonfun$6.apply(CrossValidator.scala:164)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:186)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
at scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:186)
at org.apache.spark.ml.tuning.CrossValidator$$anonfun$4.apply(CrossValidator.scala:164)
at org.apache.spark.ml.tuning.CrossValidator$$anonfun$4.apply(CrossValidator.scala:144)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:186)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
at scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:186)
at org.apache.spark.ml.tuning.CrossValidator.fit(CrossValidator.scala:144)
at HyperParamSelection$.main(HyperParamSelection.scala:169)
at HyperParamSelection.main(HyperParamSelection.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at org.apache.spark.deploy.JavaMainApplication.start(SparkApplication.scala:52)
at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:894)
at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:198)
at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:228)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:137)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
Caused by: org.apache.spark.SparkException: Job aborted due to stage failure: Task 10 in stage 16688.0 failed 1 times, most recent failure: Lost task 10.0 in stage 16688.0 (TID 921360, localhost, executor driver): java.io.IOException: No space left on device
at java.io.FileOutputStream.writeBytes(Native Method)
at java.io.FileOutputStream.write(FileOutputStream.java:326)
at org.apache.spark.storage.TimeTrackingOutputStream.write(TimeTrackingOutputStream.java:58)
at java.io.BufferedOutputStream.flushBuffer(BufferedOutputStream.java:82)
at java.io.BufferedOutputStream.write(BufferedOutputStream.java:126)
at net.jpountz.lz4.LZ4BlockOutputStream.flushBufferedData(LZ4BlockOutputStream.java:220)
at net.jpountz.lz4.LZ4BlockOutputStream.write(LZ4BlockOutputStream.java:173)
at java.io.ObjectOutputStream$BlockDataOutputStream.drain(ObjectOutputStream.java:1877)
at java.io.ObjectOutputStream$BlockDataOutputStream.writeByte(ObjectOutputStream.java:1915)
at java.io.ObjectOutputStream.writeFatalException(ObjectOutputStream.java:1576)
at java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:351)
at org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:43)
at org.apache.spark.serializer.SerializationStream.writeValue(Serializer.scala:134)
at org.apache.spark.storage.DiskBlockObjectWriter.write(DiskBlockObjectWriter.scala:241)
at org.apache.spark.util.collection.WritablePartitionedPairCollection$$anon$1.writeNext(WritablePartitionedPairCollection.scala:56)
at org.apache.spark.util.collection.ExternalSorter.writePartitionedFile(ExternalSorter.scala:699)
at org.apache.spark.shuffle.sort.SortShuffleWriter.write(SortShuffleWriter.scala:72)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:96)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53)
at org.apache.spark.scheduler.Task.run(Task.scala:109)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1602)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1590)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1589)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1589)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:831)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:831)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:831)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1823)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1772)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1761)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:642)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2034)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2131)
at org.apache.spark.rdd.RDD$$anonfun$fold$1.apply(RDD.scala:1092)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:363)
at org.apache.spark.rdd.RDD.fold(RDD.scala:1086)
at org.apache.spark.rdd.RDD$$anonfun$treeAggregate$1.apply(RDD.scala:1155)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:363)
at org.apache.spark.rdd.RDD.treeAggregate(RDD.scala:1131)
at org.apache.spark.ml.optim.loss.RDDLossFunction.calculate(RDDLossFunction.scala:61)
at org.apache.spark.ml.optim.loss.RDDLossFunction.calculate(RDDLossFunction.scala:47)
at breeze.optimize.CachedDiffFunction.calculate(CachedDiffFunction.scala:23)
at breeze.optimize.LineSearch$$anon$1.calculate(LineSearch.scala:41)
at breeze.optimize.LineSearch$$anon$1.calculate(LineSearch.scala:30)
at breeze.optimize.StrongWolfeLineSearch.breeze$optimize$StrongWolfeLineSearch$$phi$1(StrongWolfe.scala:76)
at breeze.optimize.StrongWolfeLineSearch$$anonfun$minimizeWithBound$1.apply$mcVI$sp(StrongWolfe.scala:149)
at scala.collection.immutable.Range.foreach$mVc$sp(Range.scala:160)
at breeze.optimize.StrongWolfeLineSearch.minimizeWithBound(StrongWolfe.scala:148)
at breeze.optimize.StrongWolfeLineSearch.minimize(StrongWolfe.scala:62)
at breeze.optimize.LBFGS.determineStepSize(LBFGS.scala:76)
at breeze.optimize.LBFGS.determineStepSize(LBFGS.scala:39)
at breeze.optimize.FirstOrderMinimizer$$anonfun$infiniteIterations$1.apply(FirstOrderMinimizer.scala:64)
at breeze.optimize.FirstOrderMinimizer$$anonfun$infiniteIterations$1.apply(FirstOrderMinimizer.scala:62)
at scala.collection.Iterator$$anon$7.next(Iterator.scala:129)
at breeze.util.IteratorImplicits$RichIterator$$anon$2.next(Implicits.scala:71)
at org.apache.spark.ml.classification.LogisticRegression.train(LogisticRegression.scala:809)
at org.apache.spark.ml.classification.LogisticRegression.train(LogisticRegression.scala:488)
at org.apache.spark.ml.classification.LogisticRegression.train(LogisticRegression.scala:278)
at org.apache.spark.ml.Predictor.fit(Predictor.scala:118)
at org.apache.spark.ml.Predictor.fit(Predictor.scala:82)
at org.apache.spark.ml.Pipeline$$anonfun$fit$2.apply(Pipeline.scala:153)
at org.apache.spark.ml.Pipeline$$anonfun$fit$2.apply(Pipeline.scala:149)
at scala.collection.Iterator$class.foreach(Iterator.scala:893)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
at scala.collection.IterableViewLike$Transformed$class.foreach(IterableViewLike.scala:44)
at scala.collection.SeqViewLike$AbstractTransformed.foreach(SeqViewLike.scala:37)
at org.apache.spark.ml.Pipeline.fit(Pipeline.scala:149)
at org.apache.spark.ml.Pipeline.fit(Pipeline.scala:96)
at org.apache.spark.ml.Estimator.fit(Estimator.scala:61)
at org.apache.spark.ml.tuning.CrossValidator$$anonfun$4$$anonfun$5$$anonfun$apply$1.apply$mcD$sp(CrossValidator.scala:152)
at org.apache.spark.ml.tuning.CrossValidator$$anonfun$4$$anonfun$5$$anonfun$apply$1.apply(CrossValidator.scala:151)
at org.apache.spark.ml.tuning.CrossValidator$$anonfun$4$$anonfun$5$$anonfun$apply$1.apply(CrossValidator.scala:151)
at scala.concurrent.impl.Future$PromiseCompletingRunnable.liftedTree1$1(Future.scala:24)
at scala.concurrent.impl.Future$PromiseCompletingRunnable.run(Future.scala:24)
at org.spark_project.guava.util.concurrent.MoreExecutors$SameThreadExecutorService.execute(MoreExecutors.java:293)
at scala.concurrent.impl.ExecutionContextImpl$$anon$1.execute(ExecutionContextImpl.scala:136)
at scala.concurrent.impl.Future$.apply(Future.scala:31)
at scala.concurrent.Future$.apply(Future.scala:494)
at org.apache.spark.ml.tuning.CrossValidator$$anonfun$4$$anonfun$5.apply(CrossValidator.scala:160)
at org.apache.spark.ml.tuning.CrossValidator$$anonfun$4$$anonfun$5.apply(CrossValidator.scala:150)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:186)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
at scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:186)
at org.apache.spark.ml.tuning.CrossValidator$$anonfun$4.apply(CrossValidator.scala:150)
... 20 more
Caused by: java.io.IOException: No space left on device
at java.io.FileOutputStream.writeBytes(Native Method)
at java.io.FileOutputStream.write(FileOutputStream.java:326)
at org.apache.spark.storage.TimeTrackingOutputStream.write(TimeTrackingOutputStream.java:58)
at java.io.BufferedOutputStream.flushBuffer(BufferedOutputStream.java:82)
at java.io.BufferedOutputStream.write(BufferedOutputStream.java:126)
at net.jpountz.lz4.LZ4BlockOutputStream.flushBufferedData(LZ4BlockOutputStream.java:220)
at net.jpountz.lz4.LZ4BlockOutputStream.write(LZ4BlockOutputStream.java:173)
at java.io.ObjectOutputStream$BlockDataOutputStream.drain(ObjectOutputStream.java:1877)
at java.io.ObjectOutputStream$BlockDataOutputStream.writeByte(ObjectOutputStream.java:1915)
at java.io.ObjectOutputStream.writeFatalException(ObjectOutputStream.java:1576)
at java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:351)
at org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:43)
at org.apache.spark.serializer.SerializationStream.writeValue(Serializer.scala:134)
at org.apache.spark.storage.DiskBlockObjectWriter.write(DiskBlockObjectWriter.scala:241)
at org.apache.spark.util.collection.WritablePartitionedPairCollection$$anon$1.writeNext(WritablePartitionedPairCollection.scala:56)
at org.apache.spark.util.collection.ExternalSorter.writePartitionedFile(ExternalSorter.scala:699)
at org.apache.spark.shuffle.sort.SortShuffleWriter.write(SortShuffleWriter.scala:72)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:96)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53)
at org.apache.spark.scheduler.Task.run(Task.scala:109)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
我使用的存储级别是：
/** Cache prepped data **/
allTraining.persist(StorageLevel.MEMORY_AND_DISK_SER_2)
testingData.persist(StorageLevel.MEMORY_AND_DISK_SER_2)