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https://issues.apache.org/jira/browse/SPARK-3803?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14161281#comment-14161281
]
Xiangrui Meng commented on SPARK-3803:
--------------------------------------
In `computeCovariance`, we generate a warning message if `numCols > 10000`.
https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala#L307
We could do the same in `Gram`, or we can throw an exception if `numCols` is
too big.
> 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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