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
