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Posted to issues@spark.apache.org by "Hyukjin Kwon (Jira)" <ji...@apache.org> on 2019/10/08 05:42:17 UTC
[jira] [Resolved] (SPARK-8614) Row order preservation for
operations on MLlib IndexedRowMatrix
[ https://issues.apache.org/jira/browse/SPARK-8614?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Hyukjin Kwon resolved SPARK-8614.
---------------------------------
Resolution: Incomplete
> Row order preservation for operations on MLlib IndexedRowMatrix
> ---------------------------------------------------------------
>
> Key: SPARK-8614
> URL: https://issues.apache.org/jira/browse/SPARK-8614
> Project: Spark
> Issue Type: Bug
> Components: MLlib
> Reporter: Jan Luts
> Priority: Major
> Labels: bulk-closed
>
> In both IndexedRowMatrix.computeSVD and IndexedRowMatrix.multiply indices are dropped before calling the methods from RowMatrix. For example for IndexedRowMatrix.computeSVD:
> val svd = toRowMatrix().computeSVD(k, computeU, rCond)
> and for IndexedRowMatrix.multiply:
> val mat = toRowMatrix().multiply(B).
> After computing these results, they are zipped with the original indices, e.g. for IndexedRowMatrix.computeSVD
> val indexedRows = indices.zip(svd.U.rows).map { case (i, v) =>
> IndexedRow(i, v)
> }
> and for IndexedRowMatrix.multiply:
>
> val indexedRows = rows.map(_.index).zip(mat.rows).map { case (i, v) =>
> IndexedRow(i, v)
> }
> I have experienced that for IndexedRowMatrix.computeSVD().U and IndexedRowMatrix.multiply() (which both depend on RowMatrix.multiply) row indices can get mixed (when running Spark jobs with multiple executors/machines): i.e. the vectors and indices of the result do not seem to correspond anymore.
> To me it looks like this is caused by zipping RDDs that have a different ordering?
> For the IndexedRowMatrix.multiply I have observed that ordering within partitions is preserved, but that it seems to get mixed up between partitions. For example, for:
> part1Index1 part1Vector1
> part1Index2 part1Vector2
> part2Index1 part2Vector1
> part2Index2 part2Vector2
> I got:
> part2Index1 part1Vector1
> part2Index2 part1Vector2
> part1Index1 part2Vector1
> part1Index2 part2Vector2
> Another observation is that the mapPartitions in RowMatrix.multiply :
> val AB = rows.mapPartitions { iter =>
> had an "preservesPartitioning = true" argument in version 1.0, but this is no longer there.
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