[ https://issues.apache.org/jira/browse/SPARK-3803?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Masaru Dobashi updated SPARK-3803:
----------------------------------
Description:
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 seems to be not fundamental solution.
was:
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$Worker.run(ThreadPoolExecutor.java:615)
java.lang.Thread.run(Thread.java:745)
{code}
The RowMatrix instance was generated from the result of TF-IDF like the following.
{code}
scala> val hashingTF = new HashingTF()
scala> val tf = hashingTF.transform(texts)
scala> import org.apache.spark.mllib.feature.IDF
scala> tf.cache()
scala> val idf = new IDF().fit(tf)
scala> val tfidf: RDD[Vector] = idf.transform(tf)
scala> import org.apache.spark.mllib.linalg.distributed.RowMatrix
scala> val mat = new RowMatrix(tfidf)
scala> val pc = mat.computePrincipalComponents(2)
{code}
> ArrayIndexOutOfBoundsException found in executing computePrincipalComponents
> ----------------------------------------------------------------------------
>
> Key: SPARK-3803
> URL: https://issues.apache.org/jira/browse/SPARK-3803
> Project: Spark
> Issue Type: Bug
> Components: MLlib
> Affects Versions: 1.1.0
> Reporter: Masaru Dobashi
>
> When I executed computePrincipalComponents method of RowMatrix, I got java.lang.ArrayIndexOutOfBoundsException.
> {code}
> 14/10/05 20:16:31 INFO DAGScheduler: Failed to run reduce at RDDFunctions.scala:111
> org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 31.0
failed 1 times, most recent failure: Lost task 0.0 in stage 31.0 (TID 611, localhost): java.lang.ArrayIndexOutOfBoundsException:
4878161
> org.apache.spark.mllib.linalg.distributed.RowMatrix$.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 seems to be not fundamental solution.
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