pySpark - Spark DF 到 Pandas DF - java.lang.IllegalArgumentException

Question

一些基本信息：

• Python：2.7
• 操作系统：Mac 10.13.2 High Sierra
• Anaconda-Navigator：1.7.0 版

我的基本工作流程如下：

1. 使用 pySpark 从 HDFS 执行一些初始数据拉取和转换 和 Spark 数据帧
2. 将 Spark 数据框转换为 Panda 数据框，以使用 Seaborn 等库绘制图形。这里我使用了函数.toPandas()，但它会抛出一个非常奇怪的错误。

例如，这是我测试的一个非常小的 Spark Dataframe，它与我的较大 Dataframe 引发相同的错误：

sampleList = [('john', 10000.0),('sally', 3.0),('dude', 10.0)]

sparkTestDF = sqlContext.createDataFrame(sampleList, schema=['name','denominator'])

sparkTestDF.toPandas()

这最终会引发以下错误。关于（a）这意味着什么以及（b）如何修复它/解决它的任何想法？

    Py4JJavaErrorTraceback (most recent call last)
<ipython-input-15-b151034bf9ad> in <module>()
      1 sampleList = [('john', 10000.0),('sally', 3.0),('dude', 10.0)]
      2 sparkTestDF = sqlContext.createDataFrame(sampleList, schema=['name','denominator'])
----> 3 sparkTestDF.toPandas()

/anaconda2/lib/python2.7/site-packages/pyspark/sql/dataframe.pyc in toPandas(self)
   1964                 raise RuntimeError("%s\n%s" % (_exception_message(e), msg))
   1965         else:
-> 1966             pdf = pd.DataFrame.from_records(self.collect(), columns=self.columns)
   1967 
   1968             dtype = {}

/anaconda2/lib/python2.7/site-packages/pyspark/sql/dataframe.pyc in collect(self)
    464         """
    465         with SCCallSiteSync(self._sc) as css:
--> 466             port = self._jdf.collectToPython()
    467         return list(_load_from_socket(port, BatchedSerializer(PickleSerializer())))
    468 

/anaconda2/lib/python2.7/site-packages/py4j/java_gateway.pyc in __call__(self, *args)
   1158         answer = self.gateway_client.send_command(command)
   1159         return_value = get_return_value(
-> 1160             answer, self.gateway_client, self.target_id, self.name)
   1161 
   1162         for temp_arg in temp_args:

/anaconda2/lib/python2.7/site-packages/pyspark/sql/utils.pyc in deco(*a, **kw)
     61     def deco(*a, **kw):
     62         try:
---> 63             return f(*a, **kw)
     64         except py4j.protocol.Py4JJavaError as e:
     65             s = e.java_exception.toString()

/anaconda2/lib/python2.7/site-packages/py4j/protocol.pyc in get_return_value(answer, gateway_client, target_id, name)
    318                 raise Py4JJavaError(
    319                     "An error occurred while calling {0}{1}{2}.\n".
--> 320                     format(target_id, ".", name), value)
    321             else:
    322                 raise Py4JError(

Py4JJavaError: An error occurred while calling o155.collectToPython.
: java.lang.IllegalArgumentException
    at org.apache.xbean.asm5.ClassReader.<init>(Unknown Source)
    at org.apache.xbean.asm5.ClassReader.<init>(Unknown Source)
    at org.apache.xbean.asm5.ClassReader.<init>(Unknown Source)
    at org.apache.spark.util.ClosureCleaner$.getClassReader(ClosureCleaner.scala:46)
    at org.apache.spark.util.FieldAccessFinder$$anon$3$$anonfun$visitMethodInsn$2.apply(ClosureCleaner.scala:449)
    at org.apache.spark.util.FieldAccessFinder$$anon$3$$anonfun$visitMethodInsn$2.apply(ClosureCleaner.scala:432)
    at scala.collection.TraversableLike$WithFilter$$anonfun$foreach$1.apply(TraversableLike.scala:733)
    at scala.collection.mutable.HashMap$$anon$1$$anonfun$foreach$2.apply(HashMap.scala:103)
    at scala.collection.mutable.HashMap$$anon$1$$anonfun$foreach$2.apply(HashMap.scala:103)
    at scala.collection.mutable.HashTable$class.foreachEntry(HashTable.scala:230)
    at scala.collection.mutable.HashMap.foreachEntry(HashMap.scala:40)
    at scala.collection.mutable.HashMap$$anon$1.foreach(HashMap.scala:103)
    at scala.collection.TraversableLike$WithFilter.foreach(TraversableLike.scala:732)
    at org.apache.spark.util.FieldAccessFinder$$anon$3.visitMethodInsn(ClosureCleaner.scala:432)
    at org.apache.xbean.asm5.ClassReader.a(Unknown Source)
    at org.apache.xbean.asm5.ClassReader.b(Unknown Source)
    at org.apache.xbean.asm5.ClassReader.accept(Unknown Source)
    at org.apache.xbean.asm5.ClassReader.accept(Unknown Source)
    at org.apache.spark.util.ClosureCleaner$$anonfun$org$apache$spark$util$ClosureCleaner$$clean$14.apply(ClosureCleaner.scala:262)
    at org.apache.spark.util.ClosureCleaner$$anonfun$org$apache$spark$util$ClosureCleaner$$clean$14.apply(ClosureCleaner.scala:261)
    at scala.collection.immutable.List.foreach(List.scala:381)
    at org.apache.spark.util.ClosureCleaner$.org$apache$spark$util$ClosureCleaner$$clean(ClosureCleaner.scala:261)
    at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:159)
    at org.apache.spark.SparkContext.clean(SparkContext.scala:2292)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2066)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2092)
    at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:939)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
    at org.apache.spark.rdd.RDD.withScope(RDD.scala:363)
    at org.apache.spark.rdd.RDD.collect(RDD.scala:938)
    at org.apache.spark.sql.execution.SparkPlan.executeCollect(SparkPlan.scala:297)
    at org.apache.spark.sql.Dataset$$anonfun$collectToPython$1.apply$mcI$sp(Dataset.scala:3195)
    at org.apache.spark.sql.Dataset$$anonfun$collectToPython$1.apply(Dataset.scala:3192)
    at org.apache.spark.sql.Dataset$$anonfun$collectToPython$1.apply(Dataset.scala:3192)
    at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:77)
    at org.apache.spark.sql.Dataset.withNewExecutionId(Dataset.scala:3225)
    at org.apache.spark.sql.Dataset.collectToPython(Dataset.scala:3192)
    at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
    at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.base/java.lang.reflect.Method.invoke(Method.java:564)
    at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
    at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
    at py4j.Gateway.invoke(Gateway.java:282)
    at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
    at py4j.commands.CallCommand.execute(CallCommand.java:79)
    at py4j.GatewayConnection.run(GatewayConnection.java:214)
    at java.base/java.lang.Thread.run(Thread.java:844)

Answer 1

我遇到了同样的问题，并通过将 JAVA_HOME 环境变量设置为指向 Java SDK 8 来解决它。这个错误的关键部分是

at org.apache.spark.sql.Dataset.collectToPython(Dataset.scala:3192)

然后进入 Java 错误。这是一个已知问题 (see this related Stack Overflow link)。

你可以在你的 bashrc，spark 的 conf 文件，甚至在你的 notebook 中设置 JAVA_HOME，例如对于 Ubuntu：

%env JAVA_HOME=/usr/lib/jvm/java-1.8.0-openjdk-amd64/

对于 Mac 来说，可能是这样的：

%env JAVA_HOME=/Library/Java/JavaVirtualMachines/jdk1.8.0_162.jdk/Contents/Home/