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From Russell Jurney <russell.jur...@gmail.com>
Subject Re: PySpark saving to MongoDB: expected zero arguments for construction of ClassDict (for pyspark.sql.types._create_row)
Date Tue, 29 Mar 2016 03:08:31 GMT
btw, they can't be saved to BSON either. This seems a generic issue, can
anyone else reproduce this?

On Mon, Mar 28, 2016 at 8:02 PM, Russell Jurney <russell.jurney@gmail.com>
wrote:

> I created a JIRA: https://issues.apache.org/jira/browse/SPARK-14229
>
> On Mon, Mar 28, 2016 at 7:43 PM, Russell Jurney <russell.jurney@gmail.com>
> wrote:
>
>> Ted, I am using the .rdd method, see above, but for some reason these
>> RDDs can't be saved to MongoDB or ElasticSearch.
>>
>> I think this is a bug in PySpark/DataFrame. I can't think of another
>> explanation... somehow DataFrame.rdd RDDs are not able to be stored to an
>> arbitrary Hadoop OutputFormat. When I do this:
>>
>> on_time_lines =
>> sc.textFile("../data/On_Time_On_Time_Performance_2015.jsonl.gz")
>> on_time_performance = on_time_lines.map(lambda x: json.loads(x))
>>
>>
>> on_time_performance.saveToMongoDB('mongodb://localhost:27017/agile_data_science.on_time_performance')
>>
>>
>> It works. Same data, but loaded as textFile instead of DataFrame (via
>> json/parquet dataframe loading).
>>
>> It is the DataFrame.rdd bit that is broken. I will file a JIRA.
>>
>> Does anyone know a workaround?
>>
>> On Mon, Mar 28, 2016 at 7:28 PM, Ted Yu <yuzhihong@gmail.com> wrote:
>>
>>> See this method:
>>>
>>>   lazy val rdd: RDD[T] = {
>>>
>>> On Mon, Mar 28, 2016 at 6:30 PM, Russell Jurney <
>>> russell.jurney@gmail.com> wrote:
>>>
>>>> Ok, I'm also unable to save to Elasticsearch using a dataframe's RDD.
>>>> This seems related to DataFrames. Is there a way to convert a DataFrame's
>>>> RDD to a 'normal' RDD?
>>>>
>>>>
>>>> On Mon, Mar 28, 2016 at 6:20 PM, Russell Jurney <
>>>> russell.jurney@gmail.com> wrote:
>>>>
>>>>> I filed a JIRA <https://jira.mongodb.org/browse/HADOOP-276> in
the
>>>>> mongo-hadoop project, but I'm curious if anyone else has seen this issue.
>>>>> Anyone have any idea what to do? I can't save to Mongo from PySpark.
A
>>>>> contrived example works, but a dataframe does not.
>>>>>
>>>>> I activate pymongo_spark and load a dataframe:
>>>>>
>>>>> import pymongo
>>>>> import pymongo_spark
>>>>> # Important: activate pymongo_spark.
>>>>> pymongo_spark.activate()
>>>>>
>>>>> on_time_dataframe =
>>>>> sqlContext.read.parquet('../data/on_time_performance.parquet')
>>>>>
>>>>> Then I try saving to MongoDB in two ways:
>>>>>
>>>>>
>>>>> on_time_dataframe.rdd.saveToMongoDB('mongodb://localhost:27017/agile_data_science.on_time_performance')
>>>>>
>>>>> on_time_dataframe.rdd.saveAsNewAPIHadoopFile(
>>>>>   path='file://unused',
>>>>>   outputFormatClass='com.mongodb.hadoop.MongoOutputFormat',
>>>>>   keyClass='org.apache.hadoop.io.Text',
>>>>>   valueClass='org.apache.hadoop.io.MapWritable',
>>>>>   conf={"mongo.output.uri":
>>>>> "mongodb://localhost:27017/agile_data_science.on_time_performance"}
>>>>> )
>>>>>
>>>>>
>>>>> But I always get this error:
>>>>>
>>>>> In [7]:
>>>>> on_time_rdd.saveToMongoDB('mongodb://localhost:27017/agile_data_science.on_time_performance')
>>>>>
>>>>> 16/03/28 18:04:06 INFO mapred.FileInputFormat: Total input paths to
>>>>> process : 1
>>>>>
>>>>> 16/03/28 18:04:06 INFO spark.SparkContext: Starting job: runJob at
>>>>> PythonRDD.scala:393
>>>>>
>>>>> 16/03/28 18:04:06 INFO scheduler.DAGScheduler: Got job 2 (runJob at
>>>>> PythonRDD.scala:393) with 1 output partitions
>>>>>
>>>>> 16/03/28 18:04:06 INFO scheduler.DAGScheduler: Final stage:
>>>>> ResultStage 2 (runJob at PythonRDD.scala:393)
>>>>>
>>>>> 16/03/28 18:04:06 INFO scheduler.DAGScheduler: Parents of final stage:
>>>>> List()
>>>>>
>>>>> 16/03/28 18:04:06 INFO scheduler.DAGScheduler: Missing parents: List()
>>>>>
>>>>> 16/03/28 18:04:06 INFO scheduler.DAGScheduler: Submitting ResultStage
>>>>> 2 (PythonRDD[13] at RDD at PythonRDD.scala:43), which has no missing
parents
>>>>>
>>>>> 16/03/28 18:04:06 INFO storage.MemoryStore: Block broadcast_5 stored
>>>>> as values in memory (estimated size 19.3 KB, free 249.2 KB)
>>>>>
>>>>> 16/03/28 18:04:06 INFO storage.MemoryStore: Block broadcast_5_piece0
>>>>> stored as bytes in memory (estimated size 9.7 KB, free 258.9 KB)
>>>>>
>>>>> 16/03/28 18:04:06 INFO storage.BlockManagerInfo: Added
>>>>> broadcast_5_piece0 in memory on localhost:59881 (size: 9.7 KB, free:
511.1
>>>>> MB)
>>>>>
>>>>> 16/03/28 18:04:06 INFO spark.SparkContext: Created broadcast 5 from
>>>>> broadcast at DAGScheduler.scala:1006
>>>>>
>>>>> 16/03/28 18:04:06 INFO scheduler.DAGScheduler: Submitting 1 missing
>>>>> tasks from ResultStage 2 (PythonRDD[13] at RDD at PythonRDD.scala:43)
>>>>>
>>>>> 16/03/28 18:04:06 INFO scheduler.TaskSchedulerImpl: Adding task set
>>>>> 2.0 with 1 tasks
>>>>>
>>>>> 16/03/28 18:04:06 INFO scheduler.TaskSetManager: Starting task 0.0 in
>>>>> stage 2.0 (TID 2, localhost, partition 0,PROCESS_LOCAL, 2666 bytes)
>>>>>
>>>>> 16/03/28 18:04:06 INFO executor.Executor: Running task 0.0 in stage
>>>>> 2.0 (TID 2)
>>>>>
>>>>> 16/03/28 18:04:06 INFO rdd.HadoopRDD: Input split:
>>>>> file:/Users/rjurney/Software/Agile_Data_Code_2/data/On_Time_On_Time_Performance_2015.csv.gz:0+312456777
>>>>>
>>>>> 16/03/28 18:04:06 INFO compress.CodecPool: Got brand-new decompressor
>>>>> [.gz]
>>>>>
>>>>> 16/03/28 18:04:07 INFO python.PythonRunner: Times: total = 1310, boot
>>>>> = 1249, init = 58, finish = 3
>>>>>
>>>>> 16/03/28 18:04:07 INFO executor.Executor: Finished task 0.0 in stage
>>>>> 2.0 (TID 2). 4475 bytes result sent to driver
>>>>>
>>>>> 16/03/28 18:04:07 INFO scheduler.TaskSetManager: Finished task 0.0 in
>>>>> stage 2.0 (TID 2) in 1345 ms on localhost (1/1)
>>>>>
>>>>> 16/03/28 18:04:07 INFO scheduler.TaskSchedulerImpl: Removed TaskSet
>>>>> 2.0, whose tasks have all completed, from pool
>>>>>
>>>>> 16/03/28 18:04:07 INFO scheduler.DAGScheduler: ResultStage 2 (runJob
>>>>> at PythonRDD.scala:393) finished in 1.346 s
>>>>>
>>>>> 16/03/28 18:04:07 INFO scheduler.DAGScheduler: Job 2 finished: runJob
>>>>> at PythonRDD.scala:393, took 1.361003 s
>>>>>
>>>>> 16/03/28 18:04:07 INFO spark.SparkContext: Starting job: take at
>>>>> SerDeUtil.scala:231
>>>>>
>>>>> 16/03/28 18:04:07 INFO scheduler.DAGScheduler: Got job 3 (take at
>>>>> SerDeUtil.scala:231) with 1 output partitions
>>>>>
>>>>> 16/03/28 18:04:07 INFO scheduler.DAGScheduler: Final stage:
>>>>> ResultStage 3 (take at SerDeUtil.scala:231)
>>>>>
>>>>> 16/03/28 18:04:07 INFO scheduler.DAGScheduler: Parents of final stage:
>>>>> List()
>>>>>
>>>>> 16/03/28 18:04:07 INFO scheduler.DAGScheduler: Missing parents: List()
>>>>>
>>>>> 16/03/28 18:04:07 INFO scheduler.DAGScheduler: Submitting ResultStage
>>>>> 3 (MapPartitionsRDD[15] at mapPartitions at SerDeUtil.scala:146), which
has
>>>>> no missing parents
>>>>>
>>>>> 16/03/28 18:04:07 INFO storage.MemoryStore: Block broadcast_6 stored
>>>>> as values in memory (estimated size 19.6 KB, free 278.4 KB)
>>>>>
>>>>> 16/03/28 18:04:07 INFO storage.MemoryStore: Block broadcast_6_piece0
>>>>> stored as bytes in memory (estimated size 9.8 KB, free 288.2 KB)
>>>>>
>>>>> 16/03/28 18:04:07 INFO storage.BlockManagerInfo: Added
>>>>> broadcast_6_piece0 in memory on localhost:59881 (size: 9.8 KB, free:
511.1
>>>>> MB)
>>>>>
>>>>> 16/03/28 18:04:07 INFO spark.SparkContext: Created broadcast 6 from
>>>>> broadcast at DAGScheduler.scala:1006
>>>>>
>>>>> 16/03/28 18:04:07 INFO scheduler.DAGScheduler: Submitting 1 missing
>>>>> tasks from ResultStage 3 (MapPartitionsRDD[15] at mapPartitions at
>>>>> SerDeUtil.scala:146)
>>>>>
>>>>> 16/03/28 18:04:07 INFO scheduler.TaskSchedulerImpl: Adding task set
>>>>> 3.0 with 1 tasks
>>>>>
>>>>> 16/03/28 18:04:07 INFO scheduler.TaskSetManager: Starting task 0.0 in
>>>>> stage 3.0 (TID 3, localhost, partition 0,PROCESS_LOCAL, 2666 bytes)
>>>>>
>>>>> 16/03/28 18:04:07 INFO executor.Executor: Running task 0.0 in stage
>>>>> 3.0 (TID 3)
>>>>>
>>>>> 16/03/28 18:04:07 INFO rdd.HadoopRDD: Input split:
>>>>> file:/Users/rjurney/Software/Agile_Data_Code_2/data/On_Time_On_Time_Performance_2015.csv.gz:0+312456777
>>>>>
>>>>> 16/03/28 18:04:07 INFO compress.CodecPool: Got brand-new decompressor
>>>>> [.gz]
>>>>>
>>>>> 16/03/28 18:04:07 ERROR executor.Executor: Exception in task 0.0 in
>>>>> stage 3.0 (TID 3)
>>>>>
>>>>> net.razorvine.pickle.PickleException: expected zero arguments for
>>>>> construction of ClassDict (for pyspark.sql.types._create_row)
>>>>>
>>>>> at
>>>>> net.razorvine.pickle.objects.ClassDictConstructor.construct(ClassDictConstructor.java:23)
>>>>>
>>>>> at net.razorvine.pickle.Unpickler.load_reduce(Unpickler.java:707)
>>>>>
>>>>> at net.razorvine.pickle.Unpickler.dispatch(Unpickler.java:175)
>>>>>
>>>>> at net.razorvine.pickle.Unpickler.load(Unpickler.java:99)
>>>>>
>>>>> at net.razorvine.pickle.Unpickler.loads(Unpickler.java:112)
>>>>>
>>>>> at
>>>>> org.apache.spark.api.python.SerDeUtil$$anonfun$pythonToJava$1$$anonfun$apply$1.apply(SerDeUtil.scala:150)
>>>>>
>>>>> at
>>>>> org.apache.spark.api.python.SerDeUtil$$anonfun$pythonToJava$1$$anonfun$apply$1.apply(SerDeUtil.scala:149)
>>>>>
>>>>> at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
>>>>>
>>>>> at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:308)
>>>>>
>>>>> at scala.collection.Iterator$class.foreach(Iterator.scala:727)
>>>>>
>>>>> at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
>>>>>
>>>>> at
>>>>> scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
>>>>>
>>>>> at
>>>>> scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
>>>>>
>>>>> at
>>>>> scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)
>>>>>
>>>>> at scala.collection.TraversableOnce$class.to
>>>>> (TraversableOnce.scala:273)
>>>>>
>>>>> at scala.collection.AbstractIterator.to(Iterator.scala:1157)
>>>>>
>>>>> at
>>>>> scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)
>>>>>
>>>>> at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)
>>>>>
>>>>> at
>>>>> scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)
>>>>>
>>>>> at scala.collection.AbstractIterator.toArray(Iterator.scala:1157)
>>>>>
>>>>> at
>>>>> org.apache.spark.rdd.RDD$$anonfun$take$1$$anonfun$28.apply(RDD.scala:1328)
>>>>>
>>>>> at
>>>>> org.apache.spark.rdd.RDD$$anonfun$take$1$$anonfun$28.apply(RDD.scala:1328)
>>>>>
>>>>> at
>>>>> org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
>>>>>
>>>>> at
>>>>> org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
>>>>>
>>>>> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
>>>>>
>>>>> at org.apache.spark.scheduler.Task.run(Task.scala:89)
>>>>>
>>>>> at
>>>>> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
>>>>>
>>>>> at
>>>>> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
>>>>>
>>>>> at
>>>>> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
>>>>>
>>>>> at java.lang.Thread.run(Thread.java:745)
>>>>>
>>>>> 16/03/28 18:04:07 WARN scheduler.TaskSetManager: Lost task 0.0 in
>>>>> stage 3.0 (TID 3, localhost): net.razorvine.pickle.PickleException:
>>>>> expected zero arguments for construction of ClassDict (for
>>>>> pyspark.sql.types._create_row)
>>>>>
>>>>> at
>>>>> net.razorvine.pickle.objects.ClassDictConstructor.construct(ClassDictConstructor.java:23)
>>>>>
>>>>> at net.razorvine.pickle.Unpickler.load_reduce(Unpickler.java:707)
>>>>>
>>>>> at net.razorvine.pickle.Unpickler.dispatch(Unpickler.java:175)
>>>>>
>>>>> at net.razorvine.pickle.Unpickler.load(Unpickler.java:99)
>>>>>
>>>>> at net.razorvine.pickle.Unpickler.loads(Unpickler.java:112)
>>>>>
>>>>> at
>>>>> org.apache.spark.api.python.SerDeUtil$$anonfun$pythonToJava$1$$anonfun$apply$1.apply(SerDeUtil.scala:150)
>>>>>
>>>>> at
>>>>> org.apache.spark.api.python.SerDeUtil$$anonfun$pythonToJava$1$$anonfun$apply$1.apply(SerDeUtil.scala:149)
>>>>>
>>>>> at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
>>>>>
>>>>> at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:308)
>>>>>
>>>>> at scala.collection.Iterator$class.foreach(Iterator.scala:727)
>>>>>
>>>>> at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
>>>>>
>>>>> at
>>>>> scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
>>>>>
>>>>> at
>>>>> scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
>>>>>
>>>>> at
>>>>> scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)
>>>>>
>>>>> at scala.collection.TraversableOnce$class.to
>>>>> (TraversableOnce.scala:273)
>>>>>
>>>>> at scala.collection.AbstractIterator.to(Iterator.scala:1157)
>>>>>
>>>>> at
>>>>> scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)
>>>>>
>>>>> at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)
>>>>>
>>>>> at
>>>>> scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)
>>>>>
>>>>> at scala.collection.AbstractIterator.toArray(Iterator.scala:1157)
>>>>>
>>>>> at
>>>>> org.apache.spark.rdd.RDD$$anonfun$take$1$$anonfun$28.apply(RDD.scala:1328)
>>>>>
>>>>> at
>>>>> org.apache.spark.rdd.RDD$$anonfun$take$1$$anonfun$28.apply(RDD.scala:1328)
>>>>>
>>>>> at
>>>>> org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
>>>>>
>>>>> at
>>>>> org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
>>>>>
>>>>> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
>>>>>
>>>>> at org.apache.spark.scheduler.Task.run(Task.scala:89)
>>>>>
>>>>> at
>>>>> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
>>>>>
>>>>> at
>>>>> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
>>>>>
>>>>> at
>>>>> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
>>>>>
>>>>> at java.lang.Thread.run(Thread.java:745)
>>>>>
>>>>>
>>>>> 16/03/28 18:04:07 ERROR scheduler.TaskSetManager: Task 0 in stage 3.0
>>>>> failed 1 times; aborting job
>>>>>
>>>>> 16/03/28 18:04:07 INFO scheduler.TaskSchedulerImpl: Removed TaskSet
>>>>> 3.0, whose tasks have all completed, from pool
>>>>>
>>>>> 16/03/28 18:04:07 INFO scheduler.TaskSchedulerImpl: Cancelling stage
3
>>>>>
>>>>> 16/03/28 18:04:07 INFO scheduler.DAGScheduler: ResultStage 3 (take at
>>>>> SerDeUtil.scala:231) failed in 0.117 s
>>>>>
>>>>> 16/03/28 18:04:07 INFO scheduler.DAGScheduler: Job 3 failed: take at
>>>>> SerDeUtil.scala:231, took 0.134593 s
>>>>>
>>>>>
>>>>> ---------------------------------------------------------------------------
>>>>>
>>>>> Py4JJavaError                             Traceback (most recent call
>>>>> last)
>>>>>
>>>>> <ipython-input-7-d1f984f17e27> in <module>()
>>>>>
>>>>> ----> 1 on_time_rdd.saveToMongoDB
>>>>> ('mongodb://localhost:27017/agile_data_science.on_time_performance')
>>>>>
>>>>>
>>>>> /Users/rjurney/Software/Agile_Data_Code_2/lib/pymongo_spark.pyc in
>>>>> saveToMongoDB(self, connection_string, config)
>>>>>
>>>>>     104         keyConverter='com.mongodb.spark.pickle.NoopConverter',
>>>>>
>>>>>     105         valueConverter
>>>>> ='com.mongodb.spark.pickle.NoopConverter',
>>>>>
>>>>> --> 106         conf=conf)
>>>>>
>>>>>     107
>>>>>
>>>>>     108
>>>>>
>>>>>
>>>>> /Users/rjurney/Software/Agile_Data_Code_2/spark/python/pyspark/rdd.pyc
>>>>> in saveAsNewAPIHadoopFile(self, path, outputFormatClass, keyClass,
>>>>> valueClass, keyConverter, valueConverter, conf)
>>>>>
>>>>>    1372
>>>>> outputFormatClass,
>>>>>
>>>>>    1373
>>>>> keyClass, valueClass,
>>>>>
>>>>> -> 1374
>>>>> keyConverter, valueConverter, jconf)
>>>>>
>>>>>    1375
>>>>>
>>>>>    1376     def saveAsHadoopDataset(self, conf, keyConverter=None,
>>>>> valueConverter=None):
>>>>>
>>>>>
>>>>>
>>>>> /Users/rjurney/Software/Agile_Data_Code_2/spark/python/lib/py4j-0.9-src.zip/py4j/java_gateway.py
>>>>> in __call__(self, *args)
>>>>>
>>>>>     811         answer = self.gateway_client.send_command(command)
>>>>>
>>>>>     812         return_value = get_return_value(
>>>>>
>>>>> --> 813             answer, self.gateway_client, self.target_id,
>>>>> self.name)
>>>>>
>>>>>     814
>>>>>
>>>>>     815         for temp_arg in temp_args:
>>>>>
>>>>>
>>>>>
>>>>> /Users/rjurney/Software/Agile_Data_Code_2/spark/python/pyspark/sql/utils.pyc
>>>>> in deco(*a, **kw)
>>>>>
>>>>>      43     def deco(*a, **kw):
>>>>>
>>>>>      44         try:
>>>>>
>>>>> ---> 45             return f(*a, **kw)
>>>>>
>>>>>      46         except py4j.protocol.Py4JJavaError as e:
>>>>>
>>>>>      47             s = e.java_exception.toString()
>>>>>
>>>>>
>>>>>
>>>>> /Users/rjurney/Software/Agile_Data_Code_2/spark/python/lib/py4j-0.9-src.zip/py4j/protocol.py
>>>>> in get_return_value(answer, gateway_client, target_id, name)
>>>>>
>>>>>     306                 raise Py4JJavaError(
>>>>>
>>>>>     307                     "An error occurred while calling
>>>>> {0}{1}{2}.\n".
>>>>>
>>>>> --> 308                     format(target_id, ".", name), value)
>>>>>
>>>>>     309             else:
>>>>>
>>>>>     310                 raise Py4JError(
>>>>>
>>>>>
>>>>> Py4JJavaError: An error occurred while calling
>>>>> z:org.apache.spark.api.python.PythonRDD.saveAsNewAPIHadoopFile.
>>>>>
>>>>> : org.apache.spark.SparkException: Job aborted due to stage failure:
>>>>> Task 0 in stage 3.0 failed 1 times, most recent failure: Lost task 0.0
in
>>>>> stage 3.0 (TID 3, localhost): net.razorvine.pickle.PickleException:
>>>>> expected zero arguments for construction of ClassDict (for
>>>>> pyspark.sql.types._create_row)
>>>>>
>>>>> at
>>>>> net.razorvine.pickle.objects.ClassDictConstructor.construct(ClassDictConstructor.java:23)
>>>>>
>>>>> at net.razorvine.pickle.Unpickler.load_reduce(Unpickler.java:707)
>>>>>
>>>>> at net.razorvine.pickle.Unpickler.dispatch(Unpickler.java:175)
>>>>>
>>>>> at net.razorvine.pickle.Unpickler.load(Unpickler.java:99)
>>>>>
>>>>> at net.razorvine.pickle.Unpickler.loads(Unpickler.java:112)
>>>>>
>>>>> at
>>>>> org.apache.spark.api.python.SerDeUtil$$anonfun$pythonToJava$1$$anonfun$apply$1.apply(SerDeUtil.scala:150)
>>>>>
>>>>> at
>>>>> org.apache.spark.api.python.SerDeUtil$$anonfun$pythonToJava$1$$anonfun$apply$1.apply(SerDeUtil.scala:149)
>>>>>
>>>>> at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
>>>>>
>>>>> at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:308)
>>>>>
>>>>> at scala.collection.Iterator$class.foreach(Iterator.scala:727)
>>>>>
>>>>> at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
>>>>>
>>>>> at
>>>>> scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
>>>>>
>>>>> at
>>>>> scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
>>>>>
>>>>> at
>>>>> scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)
>>>>>
>>>>> at scala.collection.TraversableOnce$class.to
>>>>> (TraversableOnce.scala:273)
>>>>>
>>>>> at scala.collection.AbstractIterator.to(Iterator.scala:1157)
>>>>>
>>>>> at
>>>>> scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)
>>>>>
>>>>> at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)
>>>>>
>>>>> at
>>>>> scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)
>>>>>
>>>>> at scala.collection.AbstractIterator.toArray(Iterator.scala:1157)
>>>>>
>>>>> at
>>>>> org.apache.spark.rdd.RDD$$anonfun$take$1$$anonfun$28.apply(RDD.scala:1328)
>>>>>
>>>>> at
>>>>> org.apache.spark.rdd.RDD$$anonfun$take$1$$anonfun$28.apply(RDD.scala:1328)
>>>>>
>>>>> at
>>>>> org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
>>>>>
>>>>> at
>>>>> org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
>>>>>
>>>>> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
>>>>>
>>>>> at org.apache.spark.scheduler.Task.run(Task.scala:89)
>>>>>
>>>>> at
>>>>> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
>>>>>
>>>>> at
>>>>> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
>>>>>
>>>>> at
>>>>> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
>>>>>
>>>>> at java.lang.Thread.run(Thread.java:745)
>>>>>
>>>>>
>>>>> Driver stacktrace:
>>>>>
>>>>> at org.apache.spark.scheduler.DAGScheduler.org
>>>>> $apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1431)
>>>>>
>>>>> at
>>>>> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1419)
>>>>>
>>>>> at
>>>>> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1418)
>>>>>
>>>>> at
>>>>> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
>>>>>
>>>>> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
>>>>>
>>>>> at
>>>>> org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1418)
>>>>>
>>>>> at
>>>>> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)
>>>>>
>>>>> at
>>>>> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)
>>>>>
>>>>> at scala.Option.foreach(Option.scala:236)
>>>>>
>>>>> at
>>>>> org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:799)
>>>>>
>>>>> at
>>>>> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1640)
>>>>>
>>>>> at
>>>>> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1599)
>>>>>
>>>>> at
>>>>> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1588)
>>>>>
>>>>> at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
>>>>>
>>>>> at
>>>>> org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:620)
>>>>>
>>>>> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1832)
>>>>>
>>>>> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1845)
>>>>>
>>>>> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1858)
>>>>>
>>>>> at org.apache.spark.rdd.RDD$$anonfun$take$1.apply(RDD.scala:1328)
>>>>>
>>>>> at
>>>>> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
>>>>>
>>>>> at
>>>>> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111)
>>>>>
>>>>> at org.apache.spark.rdd.RDD.withScope(RDD.scala:316)
>>>>>
>>>>> at org.apache.spark.rdd.RDD.take(RDD.scala:1302)
>>>>>
>>>>> at
>>>>> org.apache.spark.api.python.SerDeUtil$.pythonToPairRDD(SerDeUtil.scala:231)
>>>>>
>>>>> at
>>>>> org.apache.spark.api.python.PythonRDD$.saveAsNewAPIHadoopFile(PythonRDD.scala:775)
>>>>>
>>>>> at
>>>>> org.apache.spark.api.python.PythonRDD.saveAsNewAPIHadoopFile(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:497)
>>>>>
>>>>> at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231)
>>>>>
>>>>> at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:381)
>>>>>
>>>>> at py4j.Gateway.invoke(Gateway.java:259)
>>>>>
>>>>> at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)
>>>>>
>>>>> at py4j.commands.CallCommand.execute(CallCommand.java:79)
>>>>>
>>>>> at py4j.GatewayConnection.run(GatewayConnection.java:209)
>>>>>
>>>>> at java.lang.Thread.run(Thread.java:745)
>>>>>
>>>>> Caused by: net.razorvine.pickle.PickleException: expected zero
>>>>> arguments for construction of ClassDict (for pyspark.sql.types._create_row)
>>>>>
>>>>> at
>>>>> net.razorvine.pickle.objects.ClassDictConstructor.construct(ClassDictConstructor.java:23)
>>>>>
>>>>> at net.razorvine.pickle.Unpickler.load_reduce(Unpickler.java:707)
>>>>>
>>>>> at net.razorvine.pickle.Unpickler.dispatch(Unpickler.java:175)
>>>>>
>>>>> at net.razorvine.pickle.Unpickler.load(Unpickler.java:99)
>>>>>
>>>>> at net.razorvine.pickle.Unpickler.loads(Unpickler.java:112)
>>>>>
>>>>> at
>>>>> org.apache.spark.api.python.SerDeUtil$$anonfun$pythonToJava$1$$anonfun$apply$1.apply(SerDeUtil.scala:150)
>>>>>
>>>>> at
>>>>> org.apache.spark.api.python.SerDeUtil$$anonfun$pythonToJava$1$$anonfun$apply$1.apply(SerDeUtil.scala:149)
>>>>>
>>>>> at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
>>>>>
>>>>> at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:308)
>>>>>
>>>>> at scala.collection.Iterator$class.foreach(Iterator.scala:727)
>>>>>
>>>>> at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
>>>>>
>>>>> at
>>>>> scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
>>>>>
>>>>> at
>>>>> scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
>>>>>
>>>>> at
>>>>> scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)
>>>>>
>>>>> at scala.collection.TraversableOnce$class.to
>>>>> (TraversableOnce.scala:273)
>>>>>
>>>>> at scala.collection.AbstractIterator.to(Iterator.scala:1157)
>>>>>
>>>>> at
>>>>> scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)
>>>>>
>>>>> at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)
>>>>>
>>>>> at
>>>>> scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)
>>>>>
>>>>> at scala.collection.AbstractIterator.toArray(Iterator.scala:1157)
>>>>>
>>>>> at
>>>>> org.apache.spark.rdd.RDD$$anonfun$take$1$$anonfun$28.apply(RDD.scala:1328)
>>>>>
>>>>> at
>>>>> org.apache.spark.rdd.RDD$$anonfun$take$1$$anonfun$28.apply(RDD.scala:1328)
>>>>>
>>>>> at
>>>>> org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
>>>>>
>>>>> at
>>>>> org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
>>>>>
>>>>> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
>>>>>
>>>>> at org.apache.spark.scheduler.Task.run(Task.scala:89)
>>>>>
>>>>> at
>>>>> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
>>>>>
>>>>> at
>>>>> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
>>>>>
>>>>> at
>>>>> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
>>>>>
>>>>> ... 1 more
>>>>>
>>>>>
>>>>> --
>>>>> Russell Jurney twitter.com/rjurney russell.jurney@gmail.com relato.io
>>>>>
>>>>
>>>>
>>>>
>>>> --
>>>> Russell Jurney twitter.com/rjurney russell.jurney@gmail.com relato.io
>>>>
>>>
>>>
>>
>>
>> --
>> Russell Jurney twitter.com/rjurney russell.jurney@gmail.com relato.io
>>
>
>
>
> --
> Russell Jurney twitter.com/rjurney russell.jurney@gmail.com relato.io
>



-- 
Russell Jurney twitter.com/rjurney russell.jurney@gmail.com relato.io

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