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 LabeledPointfrom
pyspark.mllib.classification import LogisticRegressionWithSGDfrom
numpy import arrayfrom sklearn.feature_extraction import
FeatureHasherfrom 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?
