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From "Xiangrui Meng (JIRA)" <j...@apache.org>
Subject [jira] [Created] (SPARK-4023) PySpark's stat.Statistics is broken
Date Tue, 21 Oct 2014 01:00:36 GMT
Xiangrui Meng created SPARK-4023:
------------------------------------

             Summary: PySpark's stat.Statistics is broken
                 Key: SPARK-4023
                 URL: https://issues.apache.org/jira/browse/SPARK-4023
             Project: Spark
          Issue Type: Bug
          Components: MLlib, PySpark
    Affects Versions: 1.2.0
            Reporter: Xiangrui Meng
            Priority: Critical


{code}
from pyspark.mllib.stat import Statistics
from pyspark.mllib.random import RandomRDDs
data = RandomRDDs.uniformVectorRDD(sc, 100000, 10, 10)
Statistics.colStats(data)
{code}

throws 

{code}

Py4JJavaError: An error occurred while calling o37.colStats.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 2 in stage 0.0 failed
1 times, most recent failure: Lost task 2.0 in stage 0.0 (TID 2, localhost): net.razorvine.pickle.PickleException:
expected zero arguments for construction of ClassDict (for numpy.core.multiarray._reconstruct)
        net.razorvine.pickle.objects.ClassDictConstructor.construct(ClassDictConstructor.java:23)
        net.razorvine.pickle.Unpickler.load_reduce(Unpickler.java:617)
        net.razorvine.pickle.Unpickler.dispatch(Unpickler.java:170)
        net.razorvine.pickle.Unpickler.load(Unpickler.java:84)
        net.razorvine.pickle.Unpickler.loads(Unpickler.java:97)
        org.apache.spark.mllib.api.python.SerDe$$anonfun$pythonToJava$1$$anonfun$apply$1.apply(PythonMLLibAPI.scala:695)
        org.apache.spark.mllib.api.python.SerDe$$anonfun$pythonToJava$1$$anonfun$apply$1.apply(PythonMLLibAPI.scala:694)
        scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
        scala.collection.Iterator$class.foreach(Iterator.scala:727)
        scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
        scala.collection.TraversableOnce$class.foldLeft(TraversableOnce.scala:144)
        scala.collection.AbstractIterator.foldLeft(Iterator.scala:1157)
        scala.collection.TraversableOnce$class.aggregate(TraversableOnce.scala:201)
        scala.collection.AbstractIterator.aggregate(Iterator.scala:1157)
        org.apache.spark.mllib.rdd.RDDFunctions$$anonfun$4.apply(RDDFunctions.scala:99)
        org.apache.spark.mllib.rdd.RDDFunctions$$anonfun$4.apply(RDDFunctions.scala:99)
        org.apache.spark.mllib.rdd.RDDFunctions$$anonfun$5.apply(RDDFunctions.scala:100)
        org.apache.spark.mllib.rdd.RDDFunctions$$anonfun$5.apply(RDDFunctions.scala:100)
        org.apache.spark.rdd.RDD$$anonfun$13.apply(RDD.scala:599)
        org.apache.spark.rdd.RDD$$anonfun$13.apply(RDD.scala:599)
        org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35)
        org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
        org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
        org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35)
        org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
        org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
        org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:68)
        org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
        org.apache.spark.scheduler.Task.run(Task.scala:56)
        org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:181)
        java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
        java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
        java.lang.Thread.run(Thread.java:744)
{code}



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