Python中安装Spark后运行run-example SparkPi 2正常，但pyspark报错的原因是什么？

下边是错误日志，这是为啥？有人指定么，感谢感谢！

Python 3.5.2+ (default, Sep 22 2016, 12:18:14)
[GCC 6.2.0 20160927] on linux
Type "help", "copyright", "credits" or "license" for more information.
2018-04-04 14:44:34 WARN  Utils:66 - Your hostname, ubuntu resolves to a loopback address: 127.0.1.1; using 172.16.0.2 instead (on interface enp2s0)
2018-04-04 14:44:34 WARN  Utils:66 - Set SPARK_LOCAL_IP if you need to bind to another address
2018-04-04 14:44:42 WARN  NativeCodeLoader:62 - Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /__ / .__/\_,_/_/ /_/\_\   version 2.3.0
      /_/
Using Python version 3.5.2+ (default, Sep 22 2016 12:18:14)
SparkSession available as ‘spark’.
>>> text_file = sc.textFile("/home/sujian/log.log")
>>> text_file.count()
Traceback (most recent call last):
File “<stdin>”, line 1, in <module>
File “/usr/local/spark/python/pyspark/rdd.py”, line 1056, in count
return self.mapPartitions(lambda i: [sum(1 for _ in i)]).sum()
File “/usr/local/spark/python/pyspark/rdd.py”, line 1047, in sum
return self.mapPartitions(lambda x: [sum(x)]).fold(0, operator.add)
File “/usr/local/spark/python/pyspark/rdd.py”, line 921, in fold
vals = self.mapPartitions(func).collect()
File “/usr/local/spark/python/pyspark/rdd.py”, line 824, in collect
port = self.ctx._jvm.PythonRDD.collectAndServe(self._jrdd.rdd())
File “/usr/local/spark/python/lib/py4j-0.10.6-src.zip/py4j/java_gateway.py”, line 1160, in call
File “/usr/local/spark/python/pyspark/sql/utils.py”, line 63, in deco
return f(*a, **kw)
File “/usr/local/spark/python/lib/py4j-0.10.6-src.zip/py4j/protocol.py”, line 320, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.collectAndServe.
: java.lang.NullPointerException
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$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:363)
at org.apache.spark.rdd.RDD.collect(RDD.scala:938)
at org.apache.spark.api.python.PythonRDD$.collectAndServe(PythonRDD.scala:153)
at org.apache.spark.api.python.PythonRDD.collectAndServe(PythonRDD.scala)
at jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(java.base@9-Ubuntu/Native Method)
at jdk.internal.reflect.NativeMethodAccessorImpl.invoke(java.base@9-Ubuntu/NativeMethodAccessorImpl.java:62)
at jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(java.base@9-Ubuntu/DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(java.base@9-Ubuntu/Method.java:535)
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.lang.Thread.run(java.base@9-Ubuntu/Thread.java:843)
>>>