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  1. Spark
  2. SPARK-22209

PySpark does not recognize imports from submodules

    Details

    • Type: Bug
    • Status: Resolved
    • Priority: Minor
    • Resolution: Fixed
    • Affects Version/s: 2.2.0, 2.3.0
    • Fix Version/s: 2.3.0
    • Component/s: PySpark
    • Labels:
      None
    • Environment:

      Anaconda 4.4.0, Python 3.6, Hadoop 2.7, CDH 5.3.3, JDK 1.8, Centos 6

      Description

      Using submodule syntax inside a PySpark job seems to create issues. For example, the following:

      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()
      

      produces this error:

      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)
      

      But if this is changed to:

      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()
      

      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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              • Assignee:
                Unassigned
                Reporter:
                TV4Fun Joel Croteau
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                Dates

                • Created:
                  Updated:
                  Resolved: