Hi Sean, Thank very much for your reply. I tried to config it from below code:
sf = SparkConf().setAppName("test").set("spark.executor.memory", "45g").set("spark.cores.max", 62),set("spark.local.dir", "C:\\tmp") But still get the error. Do you know how I can config this? Thanks, Best, Peng On Sat, Mar 14, 2015 at 3:41 AM, Sean Owen <so...@cloudera.com> wrote: > It means pretty much what it says. You ran out of space on an executor > (not driver), because the dir used for serialization temp files is > full (not all volumes). Set spark.local.dirs to something more > appropriate and larger. > > On Sat, Mar 14, 2015 at 2:10 AM, Peng Xia <sparkpeng...@gmail.com> wrote: > > Hi > > > > > > I was running a logistic regression algorithm on a 8 nodes spark cluster, > > each node has 8 cores and 56 GB Ram (each node is running a windows > system). > > And the spark installation driver has 1.9 TB capacity. The dataset I was > > training on are has around 40 million records with around 6600 features. > But > > I always get this error during the training process: > > > > Py4JJavaError: An error occurred while calling > > o70.trainLogisticRegressionModelWithLBFGS. > > : org.apache.spark.SparkException: Job aborted due to stage failure: Task > > 2709 in stage 3.0 failed 4 times, most recent failure: Lost task 2709.3 > in > > stage 3.0 (TID 2766, > > workernode0.rbaHdInsightCluster5.b6.internal.cloudapp.net): > > java.io.IOException: There is not enough space on the disk > > at java.io.FileOutputStream.writeBytes(Native Method) > > at java.io.FileOutputStream.write(FileOutputStream.java:345) > > at > java.io.BufferedOutputStream.write(BufferedOutputStream.java:122) > > at > > > org.xerial.snappy.SnappyOutputStream.dumpOutput(SnappyOutputStream.java:300) > > at > > > org.xerial.snappy.SnappyOutputStream.rawWrite(SnappyOutputStream.java:247) > > at > > org.xerial.snappy.SnappyOutputStream.write(SnappyOutputStream.java:107) > > at > > > java.io.ObjectOutputStream$BlockDataOutputStream.drain(ObjectOutputStream.java:1876) > > at > > > java.io.ObjectOutputStream$BlockDataOutputStream.writeByte(ObjectOutputStream.java:1914) > > at > > > java.io.ObjectOutputStream.writeFatalException(ObjectOutputStream.java:1575) > > at > > java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:350) > > at > > > org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:42) > > at > > > org.apache.spark.serializer.SerializationStream.writeAll(Serializer.scala:110) > > at > > > org.apache.spark.storage.BlockManager.dataSerializeStream(BlockManager.scala:1177) > > at > > org.apache.spark.storage.DiskStore.putIterator(DiskStore.scala:78) > > at > > org.apache.spark.storage.BlockManager.doPut(BlockManager.scala:787) > > at > > org.apache.spark.storage.BlockManager.putIterator(BlockManager.scala:638) > > at > > org.apache.spark.CacheManager.putInBlockManager(CacheManager.scala:145) > > at > org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:70) > > at org.apache.spark.rdd.RDD.iterator(RDD.scala:243) > > at org.apache.spark.rdd.FilteredRDD.compute(FilteredRDD.scala:34) > > at > org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:278) > > at org.apache.spark.rdd.RDD.iterator(RDD.scala:245) > > 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$TaskRunner.run(Executor.scala:200) > > at > > > java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145) > > at > > > java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615) > > at java.lang.Thread.run(Thread.java:745) > > > > Driver stacktrace: > > at > > org.apache.spark.scheduler.DAGScheduler.org > $apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1214) > > at > > > org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1203) > > at > > > org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1202) > > at > > > scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) > > at > > scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47) > > at > > > org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1202) > > at > > > org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:696) > > at > > > org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:696) > > at scala.Option.foreach(Option.scala:236) > > at > > > org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:696) > > at > > > org.apache.spark.scheduler.DAGSchedulerEventProcessActor$$anonfun$receive$2.applyOrElse(DAGScheduler.scala:1420) > > at akka.actor.Actor$class.aroundReceive(Actor.scala:465) > > at > > > org.apache.spark.scheduler.DAGSchedulerEventProcessActor.aroundReceive(DAGScheduler.scala:1375) > > at akka.actor.ActorCell.receiveMessage(ActorCell.scala:516) > > at akka.actor.ActorCell.invoke(ActorCell.scala:487) > > at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:238) > > at akka.dispatch.Mailbox.run(Mailbox.scala:220) > > at > > > akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:393) > > at > > scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260) > > at > > > scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339) > > at > > scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979) > > at > > > scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107) > > > > The code is below: > > > > from pyspark.mllib.regression import LabeledPoint > > from pyspark.mllib.classification import LogisticRegressionWithSGD > > from numpy import array > > from sklearn.feature_extraction import FeatureHasher > > from pyspark import SparkContext > > sf = SparkConf().setAppName("test").set("spark.executor.memory", > > "45g").set("spark.cores.max", 62) > > sc = SparkContext(conf=sf) > > training_file = sc.textFile("train_small.txt") > > def hash_feature(line): > > values = [0, dict()] > > for index, x in enumerate(line.strip("\n").split('\t')): > > if index == 0: > > values[0] = float(x) > > else: > > values[1][str(index)+"_"+x] = 1 > > return values > > n_feature = 2**14 > > hasher = FeatureHasher(n_features=n_feature) > > training_file_hashed = training_file.map(lambda line: > > [hash_feature(line)[0], hasher.transform([hash_feature(line)[1]])]) > > def build_lable_points(line): > > values = [0.0] * n_feature > > for index, value in zip(line[1].indices, line[1].data): > > values[index] = value > > return LabeledPoint(line[0], values) > > parsed_training_data = training_file_hashed.map(lambda line: > > build_lable_points(line)) > > model = LogisticRegressionWithSGD.train(parsed_training_data) > > > > Can anyone share any experience on this? >