I created a JIRA: https://issues.apache.org/jira/browse/SPARK-14229
On Mon, Mar 28, 2016 at 7:43 PM, Russell Jurney <russell.jur...@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 <yuzhih...@gmail.com> wrote:
>
>> See this method:
>>
>> lazy val rdd: RDD[T] = {
>>
>> On Mon, Mar 28, 2016 at 6:30 PM, Russell Jurney <russell.jur...@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.jur...@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.jur...@gmail.com relato.io
>>>>
>>>
>>>
>>>
>>> --
>>> Russell Jurney twitter.com/rjurney russell.jur...@gmail.com relato.io
>>>
>>
>>
>
>
> --
> Russell Jurney twitter.com/rjurney russell.jur...@gmail.com relato.io
>
--
Russell Jurney twitter.com/rjurney russell.jur...@gmail.com relato.io
