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Posted to issues@spark.apache.org by "Bryan Cutler (JIRA)" <ji...@apache.org> on 2017/10/20 17:02:02 UTC
[jira] [Comment Edited] (SPARK-22209) PySpark does not recognize
imports from submodules
[ https://issues.apache.org/jira/browse/SPARK-22209?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16212890#comment-16212890 ]
Bryan Cutler edited comment on SPARK-22209 at 10/20/17 5:01 PM:
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It does seem like a bug to me so it should be fixed, I suspect it is in Sparks cloudpickle module. Would you be able to submit a patch for this? If not, I can try to take a look. cc [~holden.karau@gmail.com]
was (Author: bryanc):
It does seem like a bug to me so it should be fixed, I suspect it is in Sparks cloudpickle module. Would you be able to submit a patch for this? If not, I can try to take a look.
> 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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