我设置了 Jupyter 4.3.0、Python 3.6.3 (Anaconda) 和 PySpark 2.2.1。
以下示例在通过 Jupyter 运行时将失败：
sc = SparkContext.getOrCreate()
rdd = sc.parallelize(['A','B','C'])
rdd.collect()
下面是完整的堆栈跟踪：
---------------------------------------------------------------------------
Py4JJavaError Traceback (most recent call last)
<ipython-input-35-0d4a2ca9edf4> in <module>()
2
3 rdd = sc.parallelize(['A','B','C'])
----> 4 rdd.collect()
/usr/local/Cellar/apache-spark/2.2.1/libexec/python/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/Cellar/apache-spark/2.2.1/libexec/python/lib/py4j-0.10.4-src.zip/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/Cellar/apache-spark/2.2.1/libexec/python/pyspark/sql/utils.py 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()
/usr/local/Cellar/apache-spark/2.2.1/libexec/python/lib/py4j-0.10.4-src.zip/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)
使用 pyspark 客户端成功运行同一示例。使用时它也可以成功运行（在 Jupyter 或 pyspark 客户端中）take()代替collect().
对可能发生的事情有什么想法吗？这个帖子 https://stackoverflow.com/questions/48243877/illegalargumentexception-thrown-when-count-and-collect-function-in-spark表明这可能是 Spark 2.2.1 中的一些错误。如果可能的话，我宁愿不按照建议降级到 Spark 2.2.0。
UPDATE:我在 macOS High Sierra (10.13.3) 上运行。这是输出sc._conf.getAll():
[('spark.sql.catalogImplementation', 'hive'),
('spark.app.id', 'local-1517752276379'),
('spark.rdd.compress', 'True'),
('spark.serializer.objectStreamReset', '100'),
('spark.master', 'local[*]'),
('spark.executor.id', 'driver'),
('spark.submit.deployMode', 'client'),
('spark.driver.port', '55920'),
('spark.app.name', 'pyspark-shell'),
('spark.driver.host', '192.168.1.5')]
以下是一些进一步的 Jupyter-PySpark 集成配置：
~/.ipython/profile_pyspark/startup/00-pyspark-setup.py
import os
import sys
spark_home = os.environ.get('SPARK_HOME', None)
if not spark_home:
raise ValueError('SPARK_HOME environment variable is not set')
sys.path.insert(0, os.path.join(spark_home, 'python'))
sys.path.insert(0, os.path.join(spark_home, 'python/lib/py4j-0.10.4-src.zip'))
exec(open(os.path.join(spark_home, 'python/pyspark/shell.py')).read())
~/Library/Jupyter/kernels/pyspark/kernel.json
{
"display_name": "PySpark (Spark 2.2.1)",
"language": "python",
"argv": [
"/Users/rodrygo/anaconda3/bin/python3",
"-m",
"ipykernel",
"--profile=pyspark",
"-f",
"{connection_file}"
],
"env": {
"CAPTURE_STANDARD_OUT": "true",
"CAPTURE_STANDARD_ERR": "true",
"SEND_EMPTY_OUTPUT": "false",
"SPARK_HOME": "/usr/local/Cellar/apache-spark/2.2.1/libexec/"
}
}
