IllegalArgumentException with Spark collect() on J

I have a setup with Jupyter 4.3.0, Python 3.6.3 (Anaconda), and PySpark 2.2.1.

The following example will fail when run through Jupyter:

sc = SparkContext.getOrCreate()

rdd = sc.parallelize(['A','B','C'])
rdd.collect()

Below is the complete stack trace:

---------------------------------------------------------------------------
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)

The same example runs successfully using the pyspark client. It also runs successfully (in either Jupyter or the pyspark client) when using take() instead of collect().

Any ideas of what might be going on? This post suggests it could be some bug in Spark 2.2.1. I'd rather not downgrade to Spark 2.2.0 as suggested if at all possible.

UPDATE: I'm running on macOS High Sierra (10.13.3). Here is the output of 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')]

And here are some further Jupyter-PySpark integration configuration:

~/.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/"
    }
}

回答1:

For those of you having the same problem, there seems to be an issue with Spark (as of version 2.2.1) and Java 9. I got my example code to work by setting JAVA_HOME back to Java 1.8.

回答2:

I had the same issue when loading text files. Apache Spark does not work with Java >= 9 yet. Switch back to Java 8. Remove your current Java SDK with these commands:

sudo rm -fr /Library/Internet\ Plug-Ins/JavaAppletPlugin.plugin 
sudo rm -fr /Library/PreferencePanes/JavaControlPanel.prefPane 
sudo rm -fr ~/Library/Application\ Support/Java
sudo rm -fr /Library/Java/JavaVirtualMachines/jdkmajor.minor.macro[_update].jdk(your version here)

Then download JAVA SDK 8 and install it. You should be good.