Question

我在8节点火花簇上运行逻辑回归算法，每个节点有8个核心和56 GB Ram（每个节点运行windows系统）。火花安装驱动器的容量为1.9 TB。我正在训练的数据集有大约4千万条记录，大约6600个特征。但是我总是在培训过程中遇到这个错误：

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)

代码如下：

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)

任何人都可以分享这方面的经验吗？

Answer 1

RDD的大小并不总是与输入文件大小匹配。在您的情况下，您的数据集的RDD大于Spark用来保存它的文件系统的容量。

尝试使用较小的数据集并逐渐增加大小，同时检查Spark Web UI的RDD大小信息。（要查找spark web UI URL，请在Spark日志的输出中搜索＆＃34; http＆＃34;）

spark java.io.IOException：磁盘上没有足够的空间

1 个答案: