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https://issues.apache.org/jira/browse/SPARK-3803?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14161982#comment-14161982
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Masaru Dobashi edited comment on SPARK-3803 at 10/7/14 3:14 PM:
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Thank you for your comments.
I agree with the idea to throw an exception.
This is because exiting with an appropriate exception and messages seems to be
kind for users of MLlib.
It helps users to recognize which part of application they should fix.
How about using sys.error() to throw RuntimeException in the same way as
handling of empty rows.
was (Author: dobachi):
Thank you for your comments.
I agree with the idea to throw an exception.
This is because exiting with appropriate exception and messages seems to be
kind for users of MLlib.
It helps users to recognize which part of application they should fix.
How about using sys.error() to throw RuntimeException in the same way as
handling of empty rows.
> ArrayIndexOutOfBoundsException found in executing computePrincipalComponents
> ----------------------------------------------------------------------------
>
> Key: SPARK-3803
> URL: https://issues.apache.org/jira/browse/SPARK-3803
> Project: Spark
> Issue Type: Bug
> Components: MLlib
> Affects Versions: 1.1.0
> Reporter: Masaru Dobashi
>
> When I executed computePrincipalComponents method of RowMatrix, I got
> java.lang.ArrayIndexOutOfBoundsException.
> {code}
> 14/10/05 20:16:31 INFO DAGScheduler: Failed to run reduce at
> RDDFunctions.scala:111
> org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in
> stage 31.0 failed 1 times, most recent failure: Lost task 0.0 in stage 31.0
> (TID 611, localhost): java.lang.ArrayIndexOutOfBoundsException: 4878161
>
> org.apache.spark.mllib.linalg.distributed.RowMatrix$.org$apache$spark$mllib$linalg$distributed$RowMatrix$$dspr(RowMatrix.scala:460)
>
> org.apache.spark.mllib.linalg.distributed.RowMatrix$$anonfun$3.apply(RowMatrix.scala:114)
>
> org.apache.spark.mllib.linalg.distributed.RowMatrix$$anonfun$3.apply(RowMatrix.scala:113)
>
> scala.collection.TraversableOnce$$anonfun$foldLeft$1.apply(TraversableOnce.scala:144)
>
> scala.collection.TraversableOnce$$anonfun$foldLeft$1.apply(TraversableOnce.scala:144)
> scala.collection.Iterator$class.foreach(Iterator.scala:727)
> scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
>
> scala.collection.TraversableOnce$class.foldLeft(TraversableOnce.scala:144)
> scala.collection.AbstractIterator.foldLeft(Iterator.scala:1157)
>
> scala.collection.TraversableOnce$class.aggregate(TraversableOnce.scala:201)
> scala.collection.AbstractIterator.aggregate(Iterator.scala:1157)
>
> org.apache.spark.mllib.rdd.RDDFunctions$$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:596)
> org.apache.spark.rdd.RDD$$anonfun$13.apply(RDD.scala:596)
>
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35)
> org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
> org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
>
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35)
> org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
> org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
>
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:68)
>
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
> org.apache.spark.scheduler.Task.run(Task.scala:54)
> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:177)
>
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
>
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
> java.lang.Thread.run(Thread.java:745)
> {code}
> The RowMatrix instance was generated from the result of TF-IDF like the
> following.
> {code}
> scala> val hashingTF = new HashingTF()
> scala> val tf = hashingTF.transform(texts)
> scala> import org.apache.spark.mllib.feature.IDF
> scala> tf.cache()
> scala> val idf = new IDF().fit(tf)
> scala> val tfidf: RDD[Vector] = idf.transform(tf)
> scala> import org.apache.spark.mllib.linalg.distributed.RowMatrix
> scala> val mat = new RowMatrix(tfidf)
> scala> val pc = mat.computePrincipalComponents(2)
> {code}
> I think this was because I created HashingTF instance with default
> numFeatures and Array is used in RowMatrix#computeGramianMatrix method
> like the following.
> {code}
> /**
> * Computes the Gramian matrix `A^T A`.
> */
> def computeGramianMatrix(): Matrix = {
> val n = numCols().toInt
> val nt: Int = n * (n + 1) / 2
> // Compute the upper triangular part of the gram matrix.
> val GU = rows.treeAggregate(new BDV[Double](new Array[Double](nt)))(
> seqOp = (U, v) => {
> RowMatrix.dspr(1.0, v, U.data)
> U
> }, combOp = (U1, U2) => U1 += U2)
> RowMatrix.triuToFull(n, GU.data)
> }
> {code}
> When the size of Vectors generated by TF-IDF is too large, it makes "nt" to
> have undesirable value (and undesirable size of Array used in treeAggregate),
> since n * (n + 1) / 2 exceeded Int.MaxValue.
> Is this surmise correct?
> And, of course, I could avoid this situation by creating instance of
> HashingTF with smaller numFeatures.
> But this may not be fundamental solution.
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