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From "Hyukjin Kwon (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (SPARK-22209) PySpark does not recognize imports from submodules
Date Fri, 06 Oct 2017 11:54:00 GMT

     [ https://issues.apache.org/jira/browse/SPARK-22209?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Hyukjin Kwon updated SPARK-22209:
---------------------------------
    Affects Version/s: 2.3.0

> PySpark does not recognize imports from submodules
> --------------------------------------------------
>
>                 Key: SPARK-22209
>                 URL: https://issues.apache.org/jira/browse/SPARK-22209
>             Project: Spark
>          Issue Type: Bug
>          Components: PySpark
>    Affects Versions: 2.2.0, 2.3.0
>         Environment: Anaconda 4.4.0, Python 3.6, Hadoop 2.7, CDH 5.3.3, JDK 1.8, Centos
6
>            Reporter: Joel Croteau
>            Priority: Minor
>
> Using submodule syntax inside a PySpark job seems to create issues. For example, the
following:
> {code:python}
> import scipy.sparse
> from pyspark import SparkContext, SparkConf
> def do_stuff(x):
>     y = scipy.sparse.dok_matrix((1, 1))
>     y[0, 0] = x
>     return y[0, 0]
> def init_context():
>     conf = SparkConf().setAppName("Spark Test")
>     sc = SparkContext(conf=conf)
>     return sc
> def main():
>     sc = init_context()
>     data = sc.parallelize([1, 2, 3, 4])
>     output_data = data.map(do_stuff)
>     print(output_data.collect())
> __name__ == '__main__' and main()
> {code}
> produces this error:
> {noformat}
> Driver stacktrace:
>         at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1499)
>         at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1487)
>         at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1486)
>         at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
>         at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
>         at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1486)
>         at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)
>         at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)
>         at scala.Option.foreach(Option.scala:257)
>         at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:814)
>         at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1714)
>         at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1669)
>         at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1658)
>         at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
>         at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:630)
>         at org.apache.spark.SparkContext.runJob(SparkContext.scala:2022)
>         at org.apache.spark.SparkContext.runJob(SparkContext.scala:2043)
>         at org.apache.spark.SparkContext.runJob(SparkContext.scala:2062)
>         at org.apache.spark.SparkContext.runJob(SparkContext.scala:2087)
>         at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:936)
>         at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
>         at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
>         at org.apache.spark.rdd.RDD.withScope(RDD.scala:362)
>         at org.apache.spark.rdd.RDD.collect(RDD.scala:935)
>         at org.apache.spark.api.python.PythonRDD$.collectAndServe(PythonRDD.scala:458)
>         at org.apache.spark.api.python.PythonRDD.collectAndServe(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:745)
> Caused by: org.apache.spark.api.python.PythonException: Traceback (most recent call last):
>   File "/home/matt/spark-2.2.0-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/worker.py",
line 177, in main
>     process()
>   File "/home/matt/spark-2.2.0-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/worker.py",
line 172, in process
>     serializer.dump_stream(func(split_index, iterator), outfile)
>   File "/home/matt/spark-2.2.0-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/serializers.py",
line 268, in dump_stream
>     vs = list(itertools.islice(iterator, batch))
>   File "/home/jcroteau/is/pel_selection/test_sparse.py", line 6, in dostuff
>     y = scipy.sparse.dok_matrix((1, 1))
> AttributeError: module 'scipy' has no attribute 'sparse'
>         at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRDD.scala:193)
>         at org.apache.spark.api.python.PythonRunner$$anon$1.<init>(PythonRDD.scala:234)
>         at org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:152)
>         at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:63)
>         at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
>         at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
>         at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
>         at org.apache.spark.scheduler.Task.run(Task.scala:108)
>         at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335)
>         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)
> {noformat}
> But if this is changed to:
> {code:python}
> import scipy.sparse as sp
> from pyspark import SparkContext, SparkConf
> def do_stuff(x):
>     y = sp.dok_matrix((1, 1))
>     y[0, 0] = x
>     return y[0, 0]
> def init_context():
>     conf = SparkConf().setAppName("Spark Test")
>     sc = SparkContext(conf=conf)
>     return sc
> def main():
>     sc = init_context()
>     data = sc.parallelize([1, 2, 3, 4])
>     output_data = data.map(do_stuff)
>     print(output_data.collect())
> __name__ == '__main__' and main()
> {code}
> It works fine. At the very least, this should be documented. I've looked through the
documentation, but I haven't found a mention of this anywhere.



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