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Posted to issues@spark.apache.org by "Apache Spark (JIRA)" <ji...@apache.org> on 2014/10/21 06:38:33 UTC
[jira] [Commented] (SPARK-4023) PySpark's stat.Statistics is broken
[ https://issues.apache.org/jira/browse/SPARK-4023?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14177934#comment-14177934 ]
Apache Spark commented on SPARK-4023:
-------------------------------------
User 'davies' has created a pull request for this issue:
https://github.com/apache/spark/pull/2870
> 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
> Assignee: Davies Liu
> 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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