Spark Java错误：大小超过Integer.MAX_VALUE

时间：2015-03-10 15:02:10

标签： java python apache-spark distributed-computing logistic-regression

我正在尝试使用spark进行一些简单的机器学习任务。 我使用pyspark和spark 1.2.0来做一个简单的逻辑回归问题。 我有120万条培训记录，我记录了记录的功能。 当我将散列函数的数量设置为1024时，程序运行正常，但是当我将散列函数的数量设置为16384时，程序会多次失败并出现以下错误：

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)

在将数据传输到LabeledPoint后训练LogisticRegressionWithSGD时会发生此错误。

有没有人对此有所了解？

我的代码如下（我正在使用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)

执行最后一行时发生错误。

3 个答案:

答案 0 :(得分：11)

Integer.MAX_INT限制与存储文件的大小有关。 1.2M行并不是一件大事，我不确定你的问题是“火花的极限”。更有可能的是，你工作的某些部分正在创造一些太大而无法由任何给定执行者处理的东西。

我不是Python编码器，但是当你“散列记录的特征”时，你可能会为一个样本拍摄一组非常稀疏的记录并创建一个非稀疏数组。这将意味着16384功能的大量内存。特别是当你做zip(line[1].indices, line[1].data)时。没有让你失去记忆的唯一原因就是你似乎配置了它的shitload（50G）。

可能有帮助的另一件事是增加分区。因此，如果您不能使您的行使用更少的内存，至少可以尝试在任何给定任务上使用更少的行。正在创建的任何临时文件都可能依赖于此，因此您不太可能达到文件限制。

并且，与错误完全无关，但与您尝试做的事情相关：

16384确实是大量的功能，在乐观的情况下，每个功能只是一个布尔功能，你总共需要2 ^ 16384个可能的排列来学习，这是一个巨大的数字（在这里尝试：{ {3}}）。

非常非常可能没有算法只能通过1.2M样本学习决策边界，您可能需要至少几万亿个示例才能减少这样的特征空间。机器学习有其局限性，所以如果你没有获得优于随机的准确性，不要感到惊讶。

我肯定会建议首先尝试某种降维!!

答案 1 :(得分：1)

在某些时候，它会尝试存储功能，并且1.2M * 16384大于Integer.MAX_INT，因此您尝试存储的功能超过Spark支持的最大功能。

你可能已经遇到了Apache Spark的极限。

答案 2 :(得分：0)

增加分区数可能会导致Active tasks is a negative number in Spark UI，这可能意味着分区数太高。

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