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https://issues.apache.org/jira/browse/SPARK-5656?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Sean Owen resolved SPARK-5656.
------------------------------
Resolution: Fixed
Fix Version/s: 1.4.0
Issue resolved by pull request 4433
[https://github.com/apache/spark/pull/4433]
> NegativeArraySizeException in EigenValueDecomposition.symmetricEigs for large
> n and/or large k
> ----------------------------------------------------------------------------------------------
>
> Key: SPARK-5656
> URL: https://issues.apache.org/jira/browse/SPARK-5656
> Project: Spark
> Issue Type: Bug
> Components: MLlib
> Reporter: Mark Bittmann
> Priority: Minor
> Fix For: 1.4.0
>
>
> Large values of n or k in EigenValueDecomposition.symmetricEigs will fail
> with a NegativeArraySizeException. Specifically, this occurs when 2*n*k >
> Integer.MAX_VALUE. These values are currently unchecked and allow for the
> array to be initialized to a value greater than Integer.MAX_VALUE. I have
> written the below 'require' to fail this condition gracefully. I will submit
> a pull request.
> require(ncv * n.toLong < Integer.MAX_VALUE, "Product of 2*k*n must be smaller
> than " +
> s"Integer.MAX_VALUE. Found required eigenvalues k = $k and matrix
> dimension n = $n")
> Here is the exception that occurs from computeSVD with large k and/or n:
> Exception in thread "main" java.lang.NegativeArraySizeException
> at
> org.apache.spark.mllib.linalg.EigenValueDecomposition$.symmetricEigs(EigenValueDecomposition.scala:85)
> at
> org.apache.spark.mllib.linalg.distributed.RowMatrix.computeSVD(RowMatrix.scala:258)
> at
> org.apache.spark.mllib.linalg.distributed.RowMatrix.computeSVD(RowMatrix.scala:190)
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