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https://issues.apache.org/jira/browse/SPARK-19368?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15842581#comment-15842581
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Ohad Raviv commented on SPARK-19368:
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well, not with the same elegant code. the main problem is that Sparse Vector is
very inefficient to manipulate. from Breeze's site:
{quote}
You should not be adding lots of values to a SparseVector if you want good
speed. SparseVectors have to maintain the invariant that the index array is
always sorted, which makes insertions expensive.
{quote}
and then they suggest to use VectorBuilder for instead, but that is only good
for SparseVector. with DenseVector the current implementation is better.
so if you want I can just create two different functions for Sparse/Desne cases.
> Very bad performance in BlockMatrix.toIndexedRowMatrix()
> --------------------------------------------------------
>
> Key: SPARK-19368
> URL: https://issues.apache.org/jira/browse/SPARK-19368
> Project: Spark
> Issue Type: Improvement
> Components: MLlib
> Affects Versions: 2.0.0, 2.1.0
> Reporter: Ohad Raviv
> Priority: Minor
> Attachments: profiler snapshot.png
>
>
> In SPARK-12869, this function was optimized for the case of dense matrices
> using Breeze. However, I have a case with very very sparse matrices which
> suffers a great deal from this optimization. A process we have that took
> about 20 mins now takes about 6.5 hours.
> Here is a sample code to see the difference:
> {quote}
> val n = 40000
> val density = 0.0002
> val rnd = new Random(123)
> val rndEntryList = (for (i <- 0 until (n*n*density).toInt) yield
> (rnd.nextInt\(n\), rnd.nextInt\(n\), rnd.nextDouble()))
> .groupBy(t => (t._1,t._2)).map\(t => t._2.last).map\{ case
> (i,j,d) => (i,(j,d)) }.toSeq
> val entries: RDD\[(Int, (Int, Double))] = sc.parallelize(rndEntryList, 10)
> val indexedRows = entries.groupByKey().map(e => IndexedRow(e._1,
> Vectors.sparse(n, e._2.toSeq)))
> val mat = new IndexedRowMatrix(indexedRows, nRows = n, nCols = n)
> val t1 = System.nanoTime()
>
> println(mat.toBlockMatrix(10000,10000).toCoordinateMatrix().toIndexedRowMatrix().rows.map(_.vector.numActives).sum())
> val t2 = System.nanoTime()
> println("took: " + (t2 - t1) / 1000 / 1000 + " ms")
> println("============================================================")
>
> println(mat.toBlockMatrix(10000,10000).toIndexedRowMatrix().rows.map(_.vector.numActives).sum())
> val t3 = System.nanoTime()
> println("took: " + (t3 - t2) / 1000 / 1000 + " ms")
> println("============================================================")
> {quote}
> I get:
> {quote}
> took: 9404 ms
> ============================================================
> took: 57350 ms
> ============================================================
> {quote}
> Looking at it a little with a profiler, I see that the problem is with the
> SliceVector.update() and SparseVector.apply.
> I currently work-around this by doing:
> {quote}
> blockMatrix.toCoordinateMatrix().toIndexedRowMatrix()
> {quote}
> like it was in version 1.6.
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