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I am trying to do some text mining tasks with pySpark. I am new to Spark and I've been following this example http://mccarroll.net/blog/pyspark2/index.html to build the bag of words for my data.

Originally my data looked something like this

df.show(5)
+------------+---------+----------------+--------------------+
|Title |Month | Author | Document|
+------------+---------+----------------+--------------------+
| a | Jan| John |This is a document |
| b | Feb| Mary |A book by Mary |
| c | Mar| Luke |Newspaper article |
+------------+---------+----------------+--------------------+

So far I have extracted the terms of each document with

bow0 = df.rdd
 .map( lambda x: x.Document.replace(',',' ').replace('.',' ').replace('-',' ').lower())
 .flatMap(lambda x: x.split())
 .map(lambda x: (x, 1))

Which gives me

[('This', 1),
 ('is', 1),
 ('a', 1),
 ('document', 1)]

But when I try to compute the frequency with reduceByKey and try to see the result

bow0.reduceByKey(lambda x,y:x+y).take(50)

I get this error:

---------------------------------------------------------------------------
Py4JJavaError Traceback (most recent call last)
<ipython-input-53-966f90775397> in <module>()
----> 1 bow0.reduceByKey(lambda x,y:x+y).take(50)

/usr/local/spark/python/pyspark/rdd.py in take(self, num)
 1341 
 1342 p = range(partsScanned, min(partsScanned + numPartsToTry, totalParts))
-> 1343 res = self.context.runJob(self, takeUpToNumLeft, p)
 1344 
 1345 items += res

/usr/local/spark/python/pyspark/context.py in runJob(self, rdd, partitionFunc, partitions, allowLocal)
 990 # SparkContext#runJob.
 991 mappedRDD = rdd.mapPartitions(partitionFunc)
--> 992 port = self._jvm.PythonRDD.runJob(self._jsc.sc(), mappedRDD._jrdd, partitions)
 993 return list(_load_from_socket(port, mappedRDD._jrdd_deserializer))
 994 

/usr/local/spark/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/spark/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/spark/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 012.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.runJob.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 1 in stage 31.0 failed 4 times, most recent failure: Lost task 1.3 in stage 31.0 (TID 84, 9.242.64.15, executor 7): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
 File "/usr/local/spark/python/lib/pyspark.zip/pyspark/worker.py", line 177, in main
 process()
 File "/usr/local/spark/python/lib/pyspark.zip/pyspark/worker.py", line 172, in process
 serializer.dump_stream(func(split_index, iterator), outfile)
 File "/usr/local/spark/python/pyspark/rdd.py", line 2423, in pipeline_func
 return func(split, prev_func(split, iterator))
 File "/usr/local/spark/python/pyspark/rdd.py", line 2423, in pipeline_func
 return func(split, prev_func(split, iterator))
 File "/usr/local/spark/python/pyspark/rdd.py", line 346, in func
 return f(iterator)
 File "/usr/local/spark/python/pyspark/rdd.py", line 1842, in combineLocally
 merger.mergeValues(iterator)
 File "/usr/local/spark/python/lib/pyspark.zip/pyspark/shuffle.py", line 236, in mergeValues
 for k, v in iterator:
 File "<ipython-input-48-5c0753c6b152>", line 1, in <lambda>
AttributeError: 'NoneType' object has no attribute 'replace'

 at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRDD.scala:193)
 at org.apache.spark.api.python.PythonRunner$$anon$1.<init>(PythonRDD.scala:234)
 at org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:152)
 at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:63)
 at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
 at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
 at org.apache.spark.api.python.PairwiseRDD.compute(PythonRDD.scala:404)
 at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
 at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
 at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:96)
 at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53)
 at org.apache.spark.scheduler.Task.run(Task.scala:108)
 at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:338)
 at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
 at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
 at java.lang.Thread.run(Thread.java:748)

Driver stacktrace:
 at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1517)
 at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1505)
 at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1504)
 at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
 at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
 at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1504)
 at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)
 at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)
 at scala.Option.foreach(Option.scala:257)
 at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:814)
 at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1732)
 at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1687)
 at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1676)
 at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
 at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:630)
 at org.apache.spark.SparkContext.runJob(SparkContext.scala:2029)
 at org.apache.spark.SparkContext.runJob(SparkContext.scala:2050)
 at org.apache.spark.SparkContext.runJob(SparkContext.scala:2069)
 at org.apache.spark.api.python.PythonRDD$.runJob(PythonRDD.scala:455)
 at org.apache.spark.api.python.PythonRDD.runJob(PythonRDD.scala)
 at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
 at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
 at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
 at java.lang.reflect.Method.invoke(Method.java:498)
 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.lang.Thread.run(Thread.java:748)
Caused by: org.apache.spark.api.python.PythonException: Traceback (most recent call last):
 File "/usr/local/spark/python/lib/pyspark.zip/pyspark/worker.py", line 177, in main
 process()
 File "/usr/local/spark/python/lib/pyspark.zip/pyspark/worker.py", line 172, in process
 serializer.dump_stream(func(split_index, iterator), outfile)
 File "/usr/local/spark/python/pyspark/rdd.py", line 2423, in pipeline_func
 return func(split, prev_func(split, iterator))
 File "/usr/local/spark/python/pyspark/rdd.py", line 2423, in pipeline_func
 return func(split, prev_func(split, iterator))
 File "/usr/local/spark/python/pyspark/rdd.py", line 346, in func
 return f(iterator)
 File "/usr/local/spark/python/pyspark/rdd.py", line 1842, in combineLocally
 merger.mergeValues(iterator)
 File "/usr/local/spark/python/lib/pyspark.zip/pyspark/shuffle.py", line 236, in mergeValues
 for k, v in iterator:
 File "<ipython-input-48-5c0753c6b152>", line 1, in <lambda>
AttributeError: 'NoneType' object has no attribute 'replace'

 at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRDD.scala:193)
 at org.apache.spark.api.python.PythonRunner$$anon$1.<init>(PythonRDD.scala:234)
 at org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:152)
 at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:63)
 at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
 at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
 at org.apache.spark.api.python.PairwiseRDD.compute(PythonRDD.scala:404)
 at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
 at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
 at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:96)
 at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53)
 at org.apache.spark.scheduler.Task.run(Task.scala:108)
 at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:338)
 at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
 at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
 ... 1 more

To expand on my comment, the error you are receiving is due to the presence of a null value in your Document column. Here's a small example to demonstrate:

data = [
 ['a', 'Jan', 'John', 'This is a document'],
 ['b', 'Feb', 'Mary', 'A book by Mary'],
 ['c', 'Mar', 'Luke', 'Newspaper article'],
 ['d', 'Apr', 'Mark', None]
]
columns = ['Title', 'Month', 'Author', 'Document']
df = spark.createDataFrame(data, columns)
df.show()
#+-----+-----+------+------------------+
#|Title|Month|Author| Document|
#+-----+-----+------+------------------+
#| a| Jan| John|This is a document|
#| b| Feb| Mary| A book by Mary|
#| c| Mar| Luke| Newspaper article|
#| d| Apr| Mark| null|
#+-----+-----+------+------------------+

For the last row, the value in the Document column is null. When you compute bow0 as in your question, when the map function operates on that row it tries to call x.Document.replace where x is None. This results in AttributeError: 'NoneType' object has no attribute 'replace'.

One way to overcome this is to filter out the bad values before calling map:

bow0 = df.rdd
 .filter(lambda x: x.Document)
 .map( lambda x: x.Document.replace(',',' ').replace('.',' ').replace('-',' ').lower())
 .flatMap(lambda x: x.split())
 .map(lambda x: (x, 1))
bow0.reduceByKey(lambda x,y:x+y).take(50)
#[(u'a', 2),
# (u'this', 1),
# (u'is', 1),
# (u'newspaper', 1),
# (u'article', 1),
# (u'by', 1),
# (u'book', 1),
# (u'mary', 1),
# (u'document', 1)]

Or you can build in the check for None condition inside of your map function. In general, it is good practice to make your map function robust to bad inputs.

As an aside, you can do the same thing using the DataFrame API functions. In this case:

from pyspark.sql.functions import explode, split, regexp_replace, col, lower
df.select(explode(split(regexp_replace("Document", "[,.-]", " "), "s+")).alias("word"))
 .groupby(lower(col("word")).alias("lower"))
 .count()
 .show()
#+---------+-----+
#| lower|count|
#+---------+-----+
#| document| 1|
#| by| 1|
#|newspaper| 1|
#| article| 1|
#| mary| 1|
#| is| 1|
#| a| 2|
#| this| 1|
#| book| 1|
#+---------+-----+