Я пытаюсь использовать искру для какой-то простой задачи машинного обучения. Я использовал pyspark и spark 1.2.0 для решения простой задачи логистической регрессии. У меня есть 1,2 миллиона записей для обучения, и я хэшировал особенности записей. Когда я устанавливаю количество хэшированных функций равным 1024, программа работает нормально, но когда я устанавливаю количество хэшированных функций равным 16384, программа несколько раз завершается сбоем со следующей ошибкой:

Py4JJavaError: An error occurred while calling o84.trainLogisticRegressionModelWithSGD.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 1 in stage 4.0 failed 4 times, most recent failure: Lost task 1.3 in stage 4.0 (TID 9, workernode0.sparkexperience4a7.d5.internal.cloudapp.net): java.lang.RuntimeException: java.lang.IllegalArgumentException: Size exceeds Integer.MAX_VALUE
    at sun.nio.ch.FileChannelImpl.map(FileChannelImpl.java:828)
    at org.apache.spark.storage.DiskStore.getBytes(DiskStore.scala:123)
    at org.apache.spark.storage.DiskStore.getBytes(DiskStore.scala:132)
    at org.apache.spark.storage.BlockManager.doGetLocal(BlockManager.scala:517)
    at org.apache.spark.storage.BlockManager.getBlockData(BlockManager.scala:307)
    at org.apache.spark.network.netty.NettyBlockRpcServer$anonfun$2.apply(NettyBlockRpcServer.scala:57)
    at org.apache.spark.network.netty.NettyBlockRpcServer$anonfun$2.apply(NettyBlockRpcServer.scala:57)
    at scala.collection.TraversableLike$anonfun$map$1.apply(TraversableLike.scala:244)
    at scala.collection.TraversableLike$anonfun$map$1.apply(TraversableLike.scala:244)
    at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
    at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108)
    at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
    at scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:108)
    at org.apache.spark.network.netty.NettyBlockRpcServer.receive(NettyBlockRpcServer.scala:57)
    at org.apache.spark.network.server.TransportRequestHandler.processRpcRequest(TransportRequestHandler.java:124)
    at org.apache.spark.network.server.TransportRequestHandler.handle(TransportRequestHandler.java:97)
    at org.apache.spark.network.server.TransportChannelHandler.channelRead0(TransportChannelHandler.java:91)
    at org.apache.spark.network.server.TransportChannelHandler.channelRead0(TransportChannelHandler.java:44)
    at io.netty.channel.SimpleChannelInboundHandler.channelRead(SimpleChannelInboundHandler.java:105)
    at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:333)
    at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:319)
    at io.netty.handler.codec.MessageToMessageDecoder.channelRead(MessageToMessageDecoder.java:103)
    at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:333)
    at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:319)
    at io.netty.handler.codec.ByteToMessageDecoder.channelRead(ByteToMessageDecoder.java:163)
    at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:333)
    at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:319)
    at io.netty.channel.DefaultChannelPipeline.fireChannelRead(DefaultChannelPipeline.java:787)
    at io.netty.channel.nio.AbstractNioByteChannel$NioByteUnsafe.read(AbstractNioByteChannel.java:130)
    at io.netty.channel.nio.NioEventLoop.processSelectedKey(NioEventLoop.java:511)
    at io.netty.channel.nio.NioEventLoop.processSelectedKeysOptimized(NioEventLoop.java:468)
    at io.netty.channel.nio.NioEventLoop.processSelectedKeys(NioEventLoop.java:382)
    at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:354)
    at io.netty.util.concurrent.SingleThreadEventExecutor$2.run(SingleThreadEventExecutor.java:116)
    at java.lang.Thread.run(Thread.java:745)

    at org.apache.spark.network.client.TransportResponseHandler.handle(TransportResponseHandler.java:156)
    at org.apache.spark.network.server.TransportChannelHandler.channelRead0(TransportChannelHandler.java:93)
    at org.apache.spark.network.server.TransportChannelHandler.channelRead0(TransportChannelHandler.java:44)
    at io.netty.channel.SimpleChannelInboundHandler.channelRead(SimpleChannelInboundHandler.java:105)
    at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:333)
    at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:319)
    at io.netty.handler.codec.MessageToMessageDecoder.channelRead(MessageToMessageDecoder.java:103)
    at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:333)
    at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:319)
    at io.netty.handler.codec.ByteToMessageDecoder.channelRead(ByteToMessageDecoder.java:163)
    at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:333)
    at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:319)
    at io.netty.channel.DefaultChannelPipeline.fireChannelRead(DefaultChannelPipeline.java:787)
    at io.netty.channel.nio.AbstractNioByteChannel$NioByteUnsafe.read(AbstractNioByteChannel.java:130)
    at io.netty.channel.nio.NioEventLoop.processSelectedKey(NioEventLoop.java:511)
    at io.netty.channel.nio.NioEventLoop.processSelectedKeysOptimized(NioEventLoop.java:468)
    at io.netty.channel.nio.NioEventLoop.processSelectedKeys(NioEventLoop.java:382)
    at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:354)
    at io.netty.util.concurrent.SingleThreadEventExecutor$2.run(SingleThreadEventExecutor.java:116)
    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)

Эта ошибка происходит, когда я тренирую LogisticRegressionWithSGD после передачи данных в LabeledPoint.

У кого-нибудь есть идеи по этому поводу?

Мой код выглядит следующим образом (для этого я использую IPython Notebook):

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", "50g").set("spark.cores.max", 30)
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)

Ошибка происходит при выполнении последней строки.