IIRC you have to set that configuration on the Worker processes (for
standalone). The app can't override it (only for a client-mode
driver). YARN has a similar configuration, but I don't know the name
(shouldn't be hard to find, though).
On Thu, Mar 19, 2015 at 11:56 AM, Davies Liu <dav...@databricks.com> wrote:
> Is it possible that `spark.local.dir` is overriden by others? The docs say:
>
> NOTE: In Spark 1.0 and later this will be overriden by
> SPARK_LOCAL_DIRS (Standalone, Mesos) or LOCAL_DIRS (YARN)
>
> On Sat, Mar 14, 2015 at 5:29 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?
>>
>>
>
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