And I have 2 TB free space on C driver.
On Sat, Mar 14, 2015 at 8:29 PM, Peng Xia <sparkpeng...@gmail.com> wrote:
> 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?
>>
>
>
