Python Spark 1.4.0挂起运行

我正在AWS EC2上运行Spark 1.4.0，使用MLLIB对随机林进行测试。经过几个阶段后，它一直挂着。我试着在netty和nio之间切换，但没有效果

查看这些任务，我看到以下工作已完成

collect at DecisionTree.scala:977   
count at DecisionTreeMetadata.scala:111 
take at DecisionTreeMetadata.scala:110  
take at DecisionTreeMetadata.scala:110  
runJob at PythonRDD.scala:366   
runJob at PythonRDD.scala:366 

看看存储，我看到了两个RDD：PythonRDD和MapPartitionsRDD，分别为27MB和47.5MB

据sparkUI报告，我有1个Master和10个Worker，总共550GB的存储空间，我的数据要小得多。当我在UI中查看作业时，它被列为正在进行中，所有任务都已完成。但是，它不会进入下一步

我运行随机林的代码非常简单：

(trainingData, testData) = data.randomSplit([0.7, 0.3])
    model = RandomForest.trainClassifier(trainingData, numClasses=2, categoricalFeaturesInfo={},
                                     numTrees=3, featureSubsetStrategy="auto",
                                     impurity='gini', maxDepth=4, maxBins=32)

下面是它挂起来的地方：

15/09/17 01:16:47 INFO scheduler.TaskSetManager: Finished task 121.0 in stage 7.0 (TID 346) in 279 ms on 10.0.29.252 (158/160)
15/09/17 01:16:47 INFO scheduler.TaskSetManager: Finished task 45.0 in stage 7.0 (TID 254) in 319 ms on 10.0.28.53 (159/160)
15/09/17 01:16:47 INFO scheduler.TaskSetManager: Finished task 91.0 in stage 7.0 (TID 316) in 302 ms on 10.0.29.252 (160/160)
15/09/17 01:16:47 INFO scheduler.TaskSchedulerImpl: Removed TaskSet 7.0, whose tasks have all completed, from pool 
15/09/17 01:16:47 INFO scheduler.DAGScheduler: ResultStage 7 (collect at DecisionTree.scala:977) finished in 0.339 s
15/09/17 01:16:47 INFO scheduler.DAGScheduler: Job 7 finished: collect at DecisionTree.scala:977, took 0.370436 s
15/09/17 01:17:03 INFO storage.BlockManagerInfo: Removed broadcast_7_piece0 on 10.0.28.139:47025 in memory (size: 4.7 KB, free: 45.5 GB)
15/09/17 01:17:03 INFO storage.BlockManagerInfo: Removed broadcast_7_piece0 on 10.0.29.154:38339 in memory (size: 4.7 KB, free: 50.5 GB)
...
...
15/09/17 01:17:03 INFO storage.BlockManagerInfo: Removed broadcast_1_piece0 on 10.0.28.139:47025 in memory (size: 4.2 KB, free: 45.5 GB)
15/09/17 01:17:03 INFO storage.BlockManagerInfo: Removed broadcast_1_piece0 on 10.0.28.111:47295 in memory (size: 4.2 KB, free: 50.5 GB)
15/09/17 01:17:03 INFO storage.BlockManagerInfo: Removed broadcast_1_piece0 on 10.0.28.211:35071 in memory (size: 4.2 KB, free: 50.5 GB)
15/09/17 01:17:03 INFO storage.BlockManagerInfo: Removed broadcast_1_piece0 on 10.0.29.155:53837 in memory (size: 4.2 KB, free: 50.5 GB)
15/09/17 01:17:03 INFO storage.BlockManagerInfo: Removed broadcast_1_piece0 on 10.0.29.158:45929 in memory (size: 4.2 KB, free: 50.5 GB)

编辑： 有关如何创建我的数据的其他信息： 我的数据是来自MLLIB的LabeledPoint类型的RDD。每个标记的点对象都包含SparseVector类型的标签和特征

>>> type(data.first())
<class 'pyspark.mllib.regression.LabeledPoint'>
>>> type(data.first().features)
<class 'pyspark.mllib.linalg.SparseVector'>

编辑2： 我让它坐了一个多小时，终于坏了。这是断点处的错误输出

15/09/17 19:36:13 INFO storage.BlockManagerInfo: Removed broadcast_4_piece0 on 10.0.28.233:38432 in memory (size: 4.4 KB, free: 45.5 GB)
15/09/17 19:36:13 INFO storage.BlockManagerInfo: Removed broadcast_4_piece0 on 10.0.28.28:58416 in memory (size: 4.4 KB, free: 50.5 GB)
15/09/17 19:36:13 INFO storage.BlockManager: Removing RDD 10
15/09/17 19:36:13 INFO spark.ContextCleaner: Cleaned RDD 10
15/09/17 19:36:13 INFO storage.BlockManagerInfo: Removed broadcast_3_piece0 on 10.0.28.233:38432 in memory (size: 4.2 KB, free: 45.5 GB)
15/09/17 19:36:13 INFO storage.BlockManagerInfo: Removed broadcast_3_piece0 on 10.0.28.28:58416 in memory (size: 4.2 KB, free: 50.5 GB)
15/09/17 19:36:13 INFO storage.BlockManagerInfo: Removed broadcast_2_piece0 on 10.0.28.233:38432 in memory (size: 3.7 KB, free: 45.5 GB)
15/09/17 19:36:13 INFO storage.BlockManagerInfo: Removed broadcast_2_piece0 on 10.0.28.28:58416 in memory (size: 3.7 KB, free: 50.5 GB)
15/09/17 19:36:13 INFO storage.BlockManagerInfo: Removed broadcast_2_piece0 on 10.0.28.31:56554 in memory (size: 3.7 KB, free: 50.5 GB)
15/09/17 20:33:43 INFO rdd.MapPartitionsRDD: Removing RDD 28 from persistence list
15/09/17 20:33:43 INFO storage.BlockManager: Removing RDD 28
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "random_forest_spark.py", line 144, in trainModel
    impurity='gini', maxDepth=4, maxBins=32)
  File "/root/spark/python/pyspark/mllib/tree.py", line 352, in trainClassifier
    maxDepth, maxBins, seed)
  File "/root/spark/python/pyspark/mllib/tree.py", line 270, in _train
    maxDepth, maxBins, seed)
  File "/root/spark/python/pyspark/mllib/common.py", line 128, in callMLlibFunc
    return callJavaFunc(sc, api, *args)
  File "/root/spark/python/pyspark/mllib/common.py", line 121, in callJavaFunc
    return _java2py(sc, func(*args))
  File "/root/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/java_gateway.py", line 538, in __call__
  File "/root/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/protocol.py", line 300, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o104.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$BlockDataOutputStream.drain(ObjectOutputStream.java:1876)
        at java.io.ObjectOutputStream$BlockDataOutputStream.setBlockDataMode(ObjectOutputStream.java:1785)
        at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1188)
        at java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:347)
        at org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:44)
        at org.apache.spark.serializer.JavaSerializerInstance.serialize(JavaSerializer.scala:81)
        at org.apache.spark.util.ClosureCleaner$.ensureSerializable(ClosureCleaner.scala:312)
        at org.apache.spark.util.ClosureCleaner$.org$apache$spark$util$ClosureCleaner$$clean(ClosureCleaner.scala:305)
        at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:132)
        at org.apache.spark.SparkContext.clean(SparkContext.scala:1891)
        at org.apache.spark.rdd.RDD$$anonfun$map$1.apply(RDD.scala:294)
        at org.apache.spark.rdd.RDD$$anonfun$map$1.apply(RDD.scala:293)
        at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:148)
        at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:109)
        at org.apache.spark.rdd.RDD.withScope(RDD.scala:286)
        at org.apache.spark.rdd.RDD.map(RDD.scala:293)
        at org.apache.spark.mllib.tree.impl.TreePoint$.convertToTreeRDD(TreePoint.scala:65)
        at org.apache.spark.mllib.tree.RandomForest.run(RandomForest.scala:160)
        at org.apache.spark.mllib.tree.RandomForest$.trainClassifier(RandomForest.scala:289)
        at org.apache.spark.mllib.api.python.PythonMLLibAPI.trainRandomForestModel(PythonMLLibAPI.scala:666)
        at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
        at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
        at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
        at java.lang.reflect.Method.invoke(Method.java:606)
        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)

15/09/17 19:36:13信息存储.BlockManagerInfo:删除了10.0.28.233:38432内存中的广播片段0（大小：4.4KB，可用空间：45.5GB）
2017年9月15日19:36:13信息存储.BlockManagerInfo:删除了10.0.28.28:58416内存中的广播片段0（大小：4.4KB，可用空间：50.5GB）
15/09/17 19:36:13信息存储.BlockManager:删除RDD 10
2017年9月15日19:36:13信息spark.ContextCleaner:已清洁RDD 10
2017年9月15日19:36:13信息存储.BlockManagerInfo:在10.0.28.233:38432内存中删除了广播片段0（大小：4.2 KB，可用空间：45.5 GB）
2017年9月15日19:36:13信息存储.BlockManagerInfo:在10.0.28.28:58416内存中删除了广播片段0（大小：4.2 KB，可用空间：50.5 GB）
2017年9月15日19:36:13信息存储.BlockManagerInfo:在10.0.28.233:38432内存中删除了广播片段0（大小：3.7KB，可用空间：45.5GB）
2017年9月15日19:36:13信息存储.BlockManagerInfo:在10.0.28.28:58416内存中删除了广播片段0（大小：3.7KB，可用空间：50.5GB）
2017年9月15日19:36:13信息存储.BlockManagerInfo:在10.0.28.31:56554内存中删除了广播片段0（大小：3.7KB，可用空间：50.5GB）
15/09/17 20:33:43信息rdd.MapPartitionsRDD:从持久性列表中删除rdd 28
15/09/17 20:33:43信息存储。BlockManager:删除RDD 28
回溯（最近一次呼叫最后一次）：
文件“”，第1行，在
trainModel中第144行的文件“random\u forest\u spark.py”
杂质（基尼），最大深度=4，最大宾数=32）
trainClassifier中的文件“/root/spark/python/pyspark/mllib/tree.py”，第352行
maxDepth、maxBins、种子）
文件“/root/spark/python/pyspark/mllib/tree.py”，第270行，在
maxDepth、maxBins、种子）
callMLlibFunc中的文件“/root/spark/python/pyspark/mllib/common.py”，第128行
返回callJavaFunc（sc、api、*args）
callJavaFunc中的文件“/root/spark/python/pyspark/mllib/common.py”，第121行
return _java2py（sc，func（*args））
文件“/root/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/java_gateway.py”，第538行，在__
文件“/root/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/protocol.py”，第300行，在get_return_值中
py4j.protocol.Py4JJavaError:调用o104.trainRandomForestModel时出错。
：java.lang.OutOfMemoryError
位于java.io.ByteArrayOutputStream.hugeCapacity（ByteArrayOutputStream.java:123）
在java.io.ByteArrayOutputStream.grow中（ByteArrayOutputStream.java:117）
在java.io.ByteArrayOutputStream.ensureCapacity（ByteArrayOutputStream.java:93）
在java.io.ByteArrayOutputStream.write处（ByteArrayOutputStream.java:153）
位于java.io.ObjectOutputStream$BlockDataOutputStream.drain（ObjectOutputStream.java:1876）
位于java.io.ObjectOutputStream$BlockDataOutputStream.setBlockDataMode（ObjectOutputStream.java:1785）
位于java.io.ObjectOutputStream.writeObject0（ObjectOutputStream.java:1188）
位于java.io.ObjectOutputStream.writeObject（ObjectOutputStream.java:347）
位于org.apache.spark.serializer.JavaSerializationStream.writeObject（JavaSerializer.scala:44）
位于org.apache.spark.serializer.JavaSerializerInstance.serialize（JavaSerializer.scala:81）
位于org.apache.spark.util.ClosureCleaner$.ensureSerializable（ClosureCleaner.scala:312）
位于org.apache.spark.util.ClosureCleaner$.org$apache$spark$util$ClosureCleaner$$clean（ClosureCleaner.scala:305）
位于org.apache.spark.util.ClosureCleaner$.clean（ClosureCleaner.scala:132）
位于org.apache.spark.SparkContext.clean（SparkContext.scala:1891）
位于org.apache.spark.rdd.rdd$$anonfun$map$1.apply（rdd.scala:294）
位于org.apache.spark.rdd.rdd$$anonfun$map$1.apply（rdd.scala:293）
位于org.apache.spark.rdd.RDDOperationScope$.withScope（RDDOperationScope.scala:148）
位于org.apache.spark.rdd.RDDOperationScope$.withScope（RDDOperationScope.scala:109）
位于org.apache.spark.rdd.rdd.withScope（rdd.scala:286）
位于org.apache.spark.rdd.rdd.map（rdd.scala:293）
位于org.apache.spark.mllib.tree.impl.TreePoint$.converttotreedd（TreePoint.scala:65）
在org.apache.spark.mllib.tree.RandomForest.run上（RandomForest.scala:160）
在org.apache.spark.mllib.tree.RandomForest$.trainClassifier（RandomForest.scala:289）上
位于org.apache.spark.mllib.api.PythonMLLibAPI.trainRandomForestModel（PythonMLLibAPI.scala:666）
在sun.reflect.NativeMethodAccessorImpl.invoke0（本机方法）处
在sun.reflect.NativeMethodAccessorImpl.invoke（NativeMethodAccessorImpl.java:57）中
在sun.reflect.DelegatingMethodAccessorImpl.invoke（DelegatingMethodAccessorImpl.java:43）中
位于java.lang.reflect.Method.invoke（Method.java:606）
位于py4j.reflection.MethodInvoker.invoke（MethodInvoker.java:231）
位于py4j.reflection.ReflectionEngine.invoke（ReflectionEngine.java:379）
在py4j.Gateway.invoke处（Gateway.java:259）
位于py4j.commands.AbstractCommand.invokeMethod（AbstractCommand.java:133）
在py4j.commands.CallCommand.execute（CallCommand.java:79）
在py4j.GatewayConnection.run(
15/09/17 19:36:13 INFO storage.BlockManagerInfo: Removed broadcast_4_piece0 on 10.0.28.233:38432 in memory (size: 4.4 KB, free: 45.5 GB)
15/09/17 19:36:13 INFO storage.BlockManagerInfo: Removed broadcast_4_piece0 on 10.0.28.28:58416 in memory (size: 4.4 KB, free: 50.5 GB)
15/09/17 19:36:13 INFO storage.BlockManager: Removing RDD 10
15/09/17 19:36:13 INFO spark.ContextCleaner: Cleaned RDD 10
15/09/17 19:36:13 INFO storage.BlockManagerInfo: Removed broadcast_3_piece0 on 10.0.28.233:38432 in memory (size: 4.2 KB, free: 45.5 GB)
15/09/17 19:36:13 INFO storage.BlockManagerInfo: Removed broadcast_3_piece0 on 10.0.28.28:58416 in memory (size: 4.2 KB, free: 50.5 GB)
15/09/17 19:36:13 INFO storage.BlockManagerInfo: Removed broadcast_2_piece0 on 10.0.28.233:38432 in memory (size: 3.7 KB, free: 45.5 GB)
15/09/17 19:36:13 INFO storage.BlockManagerInfo: Removed broadcast_2_piece0 on 10.0.28.28:58416 in memory (size: 3.7 KB, free: 50.5 GB)
15/09/17 19:36:13 INFO storage.BlockManagerInfo: Removed broadcast_2_piece0 on 10.0.28.31:56554 in memory (size: 3.7 KB, free: 50.5 GB)
15/09/17 20:33:43 INFO rdd.MapPartitionsRDD: Removing RDD 28 from persistence list
15/09/17 20:33:43 INFO storage.BlockManager: Removing RDD 28
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "random_forest_spark.py", line 144, in trainModel
    impurity='gini', maxDepth=4, maxBins=32)
  File "/root/spark/python/pyspark/mllib/tree.py", line 352, in trainClassifier
    maxDepth, maxBins, seed)
  File "/root/spark/python/pyspark/mllib/tree.py", line 270, in _train
    maxDepth, maxBins, seed)
  File "/root/spark/python/pyspark/mllib/common.py", line 128, in callMLlibFunc
    return callJavaFunc(sc, api, *args)
  File "/root/spark/python/pyspark/mllib/common.py", line 121, in callJavaFunc
    return _java2py(sc, func(*args))
  File "/root/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/java_gateway.py", line 538, in __call__
  File "/root/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/protocol.py", line 300, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o104.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:1876)
        at java.io.ObjectOutputStream$BlockDataOutputStream.setBlockDataMode(ObjectOutputStream.java:1785)
        at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1188)
        at java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:347)
        at org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:44)
        at org.apache.spark.serializer.JavaSerializerInstance.serialize(JavaSerializer.scala:81)
        at org.apache.spark.util.ClosureCleaner$.ensureSerializable(ClosureCleaner.scala:312)
        at org.apache.spark.util.ClosureCleaner$.org$apache$spark$util$ClosureCleaner$$clean(ClosureCleaner.scala:305)
        at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:132)
        at org.apache.spark.SparkContext.clean(SparkContext.scala:1891)
        at org.apache.spark.rdd.RDD$$anonfun$map$1.apply(RDD.scala:294)
        at org.apache.spark.rdd.RDD$$anonfun$map$1.apply(RDD.scala:293)
        at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:148)
        at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:109)
        at org.apache.spark.rdd.RDD.withScope(RDD.scala:286)
        at org.apache.spark.rdd.RDD.map(RDD.scala:293)
        at org.apache.spark.mllib.tree.impl.TreePoint$.convertToTreeRDD(TreePoint.scala:65)
        at org.apache.spark.mllib.tree.RandomForest.run(RandomForest.scala:160)
        at org.apache.spark.mllib.tree.RandomForest$.trainClassifier(RandomForest.scala:289)
        at org.apache.spark.mllib.api.python.PythonMLLibAPI.trainRandomForestModel(PythonMLLibAPI.scala:666)
        at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
        at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
        at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
        at java.lang.reflect.Method.invoke(Method.java:606)
        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)