[GitHub] [spark] wenwj0 commented on a diff in pull request #41384: [SPARK-43297][ML]Use scala parallel collection ParVector to accelarate LocalKMeans.

["wenwj0 (via GitHub)" <gi...@apache.org>]

wenwj0 commented on code in PR #41384:
URL: https://github.com/apache/spark/pull/41384#discussion_r1211708341


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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:
   Thanks for reply, I think the benefits of parallelism outweigh the costs of vector copying.
   It's not easy to find the threshold, because the points are two-dimensionsal.



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