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Posted to issues@spark.apache.org by "Joel Croteau (JIRA)" <ji...@apache.org> on 2017/10/05 20:26:00 UTC
[jira] [Created] (SPARK-22209) PySpark does not recognize imports
from submodules
Joel Croteau created SPARK-22209:
------------------------------------
Summary: 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
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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