[jira] [Updated] (SPARK-3803) ArrayIndexOutOfBoundsException found in executing computePrincipalComponents
[
https://issues.apache.org/jira/browse/SPARK-3803?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Tony Stevenson updated SPARK-3803:
--
Assignee: Sean Owen (was: Sean Owen)
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
Assignee: Sean Owen
Fix For: 1.2.0
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
[jira] [Updated] (SPARK-3803) ArrayIndexOutOfBoundsException found in executing computePrincipalComponents
[
https://issues.apache.org/jira/browse/SPARK-3803?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Xiangrui Meng updated SPARK-3803:
-
Assignee: Sean Owen
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
Assignee: Sean Owen
Fix For: 1.2.0
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
[jira] [Updated] (SPARK-3803) ArrayIndexOutOfBoundsException found in executing computePrincipalComponents
[
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.
{quote}
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)
{quote}
The RowMatrix instance was generated from the result of TF-IDF like the
following.
{quote}
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)
{quote}
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.
{quote}
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$1.apply(TraversableOnce.scala:144)
[jira] [Updated] (SPARK-3803) ArrayIndexOutOfBoundsException found in executing computePrincipalComponents
[
https://issues.apache.org/jira/browse/SPARK-3803?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Masaru Dobashi updated SPARK-3803:
--
Description:
When I executed computePrincipalComponents method of RowMatrix, I got
java.lang.ArrayIndexOutOfBoundsException.
{code}
14/10/05 20:16:31 INFO DAGScheduler: Failed to run reduce at
RDDFunctions.scala:111
org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in
stage 31.0 failed 1 times, most recent failure: Lost task 0.0 in stage 31.0
(TID 611, localhost): java.lang.ArrayIndexOutOfBoundsException: 4878161
org.apache.spark.mllib.linalg.distributed.RowMatrix$.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$$anonfun$13.apply(RDD.scala:596)
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35)
org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35)
org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:68)
org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
org.apache.spark.scheduler.Task.run(Task.scala:54)
org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:177)
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
java.lang.Thread.run(Thread.java:745)
{code}
The RowMatrix instance was generated from the result of TF-IDF like the
following.
{code}
scala val hashingTF = new HashingTF()
scala val tf = hashingTF.transform(texts)
scala import org.apache.spark.mllib.feature.IDF
scala tf.cache()
scala val idf = new IDF().fit(tf)
scala val tfidf: RDD[Vector] = idf.transform(tf)
scala import org.apache.spark.mllib.linalg.distributed.RowMatrix
scala val mat = new RowMatrix(tfidf)
scala val pc = mat.computePrincipalComponents(2)
{code}
was:
When I executed computePrincipalComponents method of RowMatrix, I got
java.lang.ArrayIndexOutOfBoundsException.
{quote}
14/10/05 20:16:31 INFO DAGScheduler: Failed to run reduce at
RDDFunctions.scala:111
org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in
stage 31.0 failed 1 times, most recent failure: Lost task 0.0 in stage 31.0
(TID 611, localhost): java.lang.ArrayIndexOutOfBoundsException: 4878161
org.apache.spark.mllib.linalg.distributed.RowMatrix$.org$apache$spark$mllib$linalg$distributed$RowMatrix$$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)
[jira] [Updated] (SPARK-3803) ArrayIndexOutOfBoundsException found in executing computePrincipalComponents
[
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:
[jira] [Updated] (SPARK-3803) ArrayIndexOutOfBoundsException found in executing computePrincipalComponents
[
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 does not seems to be 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:
[jira] [Updated] (SPARK-3803) ArrayIndexOutOfBoundsException found in executing computePrincipalComponents
[
https://issues.apache.org/jira/browse/SPARK-3803?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Masaru Dobashi updated SPARK-3803:
--
Description:
GrWhen 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.
was:
GrWhen 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:
[jira] [Updated] (SPARK-3803) ArrayIndexOutOfBoundsException found in executing computePrincipalComponents
[
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 may not be fundamental solution.
was:
GrWhen 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
