过去几天我一直坚持这个问题：
我试图从MLLIB运行随机林，它通过大部分，但在执行mapPartition操作时中断。显示以下堆栈跟踪：
: An error occurred while calling o94.trainRandomForestModel.
: java.lang.OutOfMemoryError
at java.io.ByteArrayOutputStream.hugeCapacity(ByteArrayOutputStream.java:123)
at java.io.ByteArrayOutputStream.grow(ByteArrayOutputStream.java:117)
at java.io.ByteArrayOutputStream.ensureCapacity(ByteArrayOutputStream.java:93)
at java.io.ByteArrayOutputStream.write(ByteArrayOutputStream.java:153)
at java.io.ObjectOutputStream$BlockDataOutputStream.drain(ObjectOutputStream.java:1877)
at java.io.ObjectOutputStream$BlockDataOutputStream.setBlockDataMode(ObjectOutputStream.java:1786)
at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1189)
at java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:348)
at org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:44)
at org.apache.spark.serializer.JavaSerializerInstance.serialize(JavaSerializer.scala:84)
at org.apache.spark.util.ClosureCleaner$.ensureSerializable(ClosureCleaner.scala:301)
at org.apache.spark.util.ClosureCleaner$.org$apache$spark$util$ClosureCleaner$$clean(ClosureCleaner.scala:294)
at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:122)
at org.apache.spark.SparkContext.clean(SparkContext.scala:2021)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1.apply(RDD.scala:703)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1.apply(RDD.scala:702)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:147)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:108)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:306)
at org.apache.spark.rdd.RDD.mapPartitions(RDD.scala:702)
at org.apache.spark.mllib.tree.DecisionTree$.findBestSplits(DecisionTree.scala:625)
at org.apache.spark.mllib.tree.RandomForest.run(RandomForest.scala:235)
at org.apache.spark.mllib.tree.RandomForest$.trainClassifier(RandomForest.scala:291)
at org.apache.spark.mllib.api.python.PythonMLLibAPI.trainRandomForestModel(PythonMLLibAPI.scala:742)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:497)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:379)
at py4j.Gateway.invoke(Gateway.java:259)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:207)
at java.lang.Thread.run(Thread.java:745)
在我看来，它试图序列化mapPartitions闭包，但是这样做会耗尽空间。但是，当我给驱动程序提供大约190GB的45GB文件时，我不明白它是如何耗尽空间的。
我在AWS上设置了一个群集，这样我的主人就是一个r3.8xlarge以及两个r3.4xlarge worker。我有以下配置：
spark version: 1.5.0
-----------------------------------
spark.executor.memory 32000m
spark.driver.memory 230000m
spark.driver.cores 10
spark.executor.cores 5
spark.executor.instances 17
spark.driver.maxResultSize 0
spark.storage.safetyFraction 1
spark.storage.memoryFraction 0.9
spark.storage.shuffleFraction 0.05
spark.default.parallelism 128
主机有大约240 GB的柱塞，每个工人有大约120GB的柱塞。
我加载了一个相对较小的MLLIB LabeledPoint对象RDD，每个对象都包含稀疏向量。该RDD的总大小约为45MB。我的稀疏向量总长度约为1500万，而只有约3000左右是非零。