[GitHub] [spark] LuciferYang commented on a diff in pull request #41384: [SPARK-43297][ML]Use scala parallel collection ParVector to accelarate LocalKMeans.

["LuciferYang (via GitHub)" <gi...@apache.org>]

LuciferYang commented on code in PR #41384:
URL: https://github.com/apache/spark/pull/41384#discussion_r1210393260


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mllib/src/main/scala/org/apache/spark/mllib/clustering/LocalKMeans.scala:
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@@ -46,7 +47,9 @@ private[mllib] object LocalKMeans extends Logging {
 
     // Initialize centers by sampling using the k-means++ procedure.
     centers(0) = pickWeighted(rand, points, weights).toDense
-    val costArray = points.map(EuclideanDistanceMeasure.fastSquaredDistance(_, centers(0)))
+    val pointsParVector = new ParVector(points.toVector)

Review Comment:
   Can you find the threshold for `points.length` that can get performance improvement?
   
   Not sure this always can get performance improvement, `points.toVector` will also be some costs,  it will create a new Vector and add all elements using tail recursion.
   



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