python - 在Spark中计数和收集函数时引发IllegalArgumentException

当我在PySpark中使用count()时抛出此异常时，我尝试在本地Spark上加载一个小的数据集(take()似乎可以正常工作)。我试图搜索此问题，但找不到原因。 RDD的分区似乎出了问题。有任何想法吗？先感谢您!

sc.stop()
sc = SparkContext("local[4]", "temp")
testfile1 = sc.textFile(localpath('part-00000-Copy1.xml'))
testfile1.filter(lambda x: x.strip().encode('utf-8').startswith(b'<row')).take(1) ## take function seems working

数据如下所示:

['  <row AcceptedAnswerId="15" AnswerCount="5" Body="&lt;p&gt;How should I elicit prior distributions from experts when fitting a Bayesian model?&lt;/p&gt;&#10;" CommentCount="1" CreationDate="2010-07-19T19:12:12.510" FavoriteCount="17" Id="1" LastActivityDate="2010-09-15T21:08:26.077" OwnerUserId="8" PostTypeId="1" Score="26" Tags="&lt;bayesian&gt;&lt;prior&gt;&lt;elicitation&gt;" Title="Eliciting priors from experts" ViewCount="1457" />']

这就是问题:

test1 = testfile1.filter(lambda x: (x.strip().encode('utf-8').startswith(b'<row'))).filter(lambda x: x is not None)
test1.count()

这是异常(exception):

    ---------------------------------------------------------------------------
Py4JJavaError                             Traceback (most recent call last)
<ipython-input-34-d7626ed81f56> in <module>()
----> 1 test1.count()

/usr/local/lib/python3.6/site-packages/pyspark/rdd.py in count(self)
   1039         3
   1040         """
-> 1041         return self.mapPartitions(lambda i: [sum(1 for _ in i)]).sum()
   1042
   1043     def stats(self):

/usr/local/lib/python3.6/site-packages/pyspark/rdd.py in sum(self)
   1030         6.0
   1031         """
-> 1032         return self.mapPartitions(lambda x: [sum(x)]).fold(0, operator.add)
   1033
   1034     def count(self):

/usr/local/lib/python3.6/site-packages/pyspark/rdd.py in fold(self, zeroValue, op)
    904         # zeroValue provided to each partition is unique from the one provided
    905         # to the final reduce call
--> 906         vals = self.mapPartitions(func).collect()
    907         return reduce(op, vals, zeroValue)
    908

/usr/local/lib/python3.6/site-packages/pyspark/rdd.py in collect(self)
    807         """
    808         with SCCallSiteSync(self.context) as css:
--> 809             port = self.ctx._jvm.PythonRDD.collectAndServe(self._jrdd.rdd())
    810         return list(_load_from_socket(port, self._jrdd_deserializer))
    811

/usr/local/lib/python3.6/site-packages/py4j/java_gateway.py in __call__(self, *args)
   1131         answer = self.gateway_client.send_command(command)
   1132         return_value = get_return_value(
-> 1133             answer, self.gateway_client, self.target_id, self.name)
   1134
   1135         for temp_arg in temp_args:

/usr/local/lib/python3.6/site-packages/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
    317                 raise Py4JJavaError(
    318                     "An error occurred while calling {0}{1}{2}.\n".
--> 319                     format(target_id, ".", name), value)
    320             else:
    321                 raise Py4JError(

Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.collectAndServe.
: 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:443)
    at org.apache.spark.util.FieldAccessFinder$$anon$3$$anonfun$visitMethodInsn$2.apply(ClosureCleaner.scala:426)
    at scala.collection.TraversableLike$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$$anon$3.visitMethodInsn(ClosureCleaner.scala:426)
    at org.apache.xbean.asm5.ClassReader.a(Unknown Source)
    at org.apache.xbean.asm5.ClassReader.b(Unknown Source)
    at org.apache.xbean.asm5.ClassReader.accept(Unknown Source)
    at org.apache.xbean.asm5.ClassReader.accept(Unknown Source)
    at org.apache.spark.util.ClosureCleaner$$anonfun$org$apache$spark$util$ClosureCleaner$$clean$14.apply(ClosureCleaner.scala:257)
    at org.apache.spark.util.ClosureCleaner$$anonfun$org$apache$spark$util$ClosureCleaner$$clean$14.apply(ClosureCleaner.scala:256)
    at scala.collection.immutable.List.foreach(List.scala:381)
    at org.apache.spark.util.ClosureCleaner$.org$apache$spark$util$ClosureCleaner$$clean(ClosureCleaner.scala:256)
    at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:156)
    at org.apache.spark.SparkContext.clean(SparkContext.scala:2294)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2068)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2094)
    at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:936)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
    at org.apache.spark.rdd.RDD.withScope(RDD.scala:362)
    at org.apache.spark.rdd.RDD.collect(RDD.scala:935)
    at org.apache.spark.api.python.PythonRDD$.collectAndServe(PythonRDD.scala:467)
    at org.apache.spark.api.python.PythonRDD.collectAndServe(PythonRDD.scala)
    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:280)
    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)

~~~~~~~~~~~~~
更新:
因此，我尝试在PySpark中创建一个最小的示例，并且count()问题仍然存在。我想知道此问题是否与jupyter笔记本中Spark的配置有关。这是一个最小的示例:

import pyspark
from pyspark import SparkContext
import findspark
findspark.init()
import random
sc = pyspark.SparkContext(appName="Pi")
num_samples = 100000000
def inside(p):
    x, y = random.random(), random.random()
    return x*x + y*y < 1
sc.parallelize(range(0, num_samples)).filter(inside).take(10)

这是输出:

[0, 1, 3, 4, 7, 9, 11, 12, 13, 14]

并再次执行count():

sc.parallelize(range(0, num_samples)).filter(inside).count()

输出看起来类似于前面的示例:

---------------------------------------------------------------------------
Py4JJavaError                             Traceback (most recent call last)
<ipython-input-23-5588c6957b1d> in <module>()
----> 1 sc.parallelize(range(0, num_samples)).filter(inside).count()

/usr/local/lib/python3.6/site-packages/pyspark/rdd.py in count(self)
   1039         3
   1040         """
-> 1041         return self.mapPartitions(lambda i: [sum(1 for _ in i)]).sum()
   1042
   1043     def stats(self):

/usr/local/lib/python3.6/site-packages/pyspark/rdd.py in sum(self)
   1030         6.0
   1031         """
-> 1032         return self.mapPartitions(lambda x: [sum(x)]).fold(0, operator.add)
   1033
   1034     def count(self):

/usr/local/lib/python3.6/site-packages/pyspark/rdd.py in fold(self, zeroValue, op)
    904         # zeroValue provided to each partition is unique from the one provided
    905         # to the final reduce call
--> 906         vals = self.mapPartitions(func).collect()
    907         return reduce(op, vals, zeroValue)
    908

/usr/local/lib/python3.6/site-packages/pyspark/rdd.py in collect(self)
    807         """
    808         with SCCallSiteSync(self.context) as css:
--> 809             port = self.ctx._jvm.PythonRDD.collectAndServe(self._jrdd.rdd())
    810         return list(_load_from_socket(port, self._jrdd_deserializer))
    811

/usr/local/lib/python3.6/site-packages/py4j/java_gateway.py in __call__(self, *args)
   1131         answer = self.gateway_client.send_command(command)
   1132         return_value = get_return_value(
-> 1133             answer, self.gateway_client, self.target_id, self.name)
   1134
   1135         for temp_arg in temp_args:

/usr/local/lib/python3.6/site-packages/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
    317                 raise Py4JJavaError(
    318                     "An error occurred while calling {0}{1}{2}.\n".
--> 319                     format(target_id, ".", name), value)
    320             else:
    321                 raise Py4JError(

Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.collectAndServe.
: 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:443)
    at org.apache.spark.util.FieldAccessFinder$$anon$3$$anonfun$visitMethodInsn$2.apply(ClosureCleaner.scala:426)
    at scala.collection.TraversableLike$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$$anon$3.visitMethodInsn(ClosureCleaner.scala:426)
    at org.apache.xbean.asm5.ClassReader.a(Unknown Source)
    at org.apache.xbean.asm5.ClassReader.b(Unknown Source)
    at org.apache.xbean.asm5.ClassReader.accept(Unknown Source)
    at org.apache.xbean.asm5.ClassReader.accept(Unknown Source)
    at org.apache.spark.util.ClosureCleaner$$anonfun$org$apache$spark$util$ClosureCleaner$$clean$14.apply(ClosureCleaner.scala:257)
    at org.apache.spark.util.ClosureCleaner$$anonfun$org$apache$spark$util$ClosureCleaner$$clean$14.apply(ClosureCleaner.scala:256)
    at scala.collection.immutable.List.foreach(List.scala:381)
    at org.apache.spark.util.ClosureCleaner$.org$apache$spark$util$ClosureCleaner$$clean(ClosureCleaner.scala:256)
    at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:156)
    at org.apache.spark.SparkContext.clean(SparkContext.scala:2294)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2068)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2094)
    at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:936)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
    at org.apache.spark.rdd.RDD.withScope(RDD.scala:362)
    at org.apache.spark.rdd.RDD.collect(RDD.scala:935)
    at org.apache.spark.api.python.PythonRDD$.collectAndServe(PythonRDD.scala:467)
    at org.apache.spark.api.python.PythonRDD.collectAndServe(PythonRDD.scala)
    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:280)
    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)

我注意到的一件事是，当我在jupyter笔记本上运行PySpark时，终端显示一些警告:

WARNING: An illegal reflective access operation has occurred
WARNING: Illegal reflective access by org.apache.hadoop.security.authentication.util.KerberosUtil (file:/usr/local/lib/python3.6/site-packages/pyspark/jars/hadoop-auth-2.7.3.jar) to method sun.security.krb5.Config.getInstance()
WARNING: Please consider reporting this to the maintainers of org.apache.hadoop.security.authentication.util.KerberosUtil
WARNING: Use --illegal-access=warn to enable warnings of further illegal reflective access operations
WARNING: All illegal access operations will be denied in a future release
18/01/15 14:05:35 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable

谷歌搜索显示此post讨论了最后一个警告。

最佳答案

您正在使用Java 9吗？现在，将Java9与Apache项目一起使用存在许多问题。

如果是这种情况，并且这是Mac OS，请执行以下操作:

cd /usr/libexec
./java_home -V

您将能够看到您拥有的Java版本。选择旧版本作为新的JAVA_HOME。

export JAVA_HOME="/Library/Java/JavaVirtualMachines/jdk1.8.{YourVersion}.jdk/Contents/Home"

如果可行(我不确定，因为信息丢失)，请将export命令添加到您的配置文件初始化脚本中。