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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 {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.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
738
asked Oct 28 '25 02:10
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|
#+---------+-----+
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