Github user MLnick commented on a diff in the pull request:
https://github.com/apache/spark/pull/17845#discussion_r115425530
--- Diff: mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala
---
@@ -372,11 +385,43 @@ class ALSModel private[ml] (
num: Int): DataFrame = {
import srcFactors.sparkSession.implicits._
- val ratings = srcFactors.crossJoin(dstFactors)
- .select(
- srcFactors("id"),
- dstFactors("id"),
- predict(srcFactors("features"), dstFactors("features")))
+ val srcFactorsBlocked = blockify(srcFactors.as[(Int, Array[Float])])
+ val dstFactorsBlocked = blockify(dstFactors.as[(Int, Array[Float])])
+ val ratings = srcFactorsBlocked.crossJoin(dstFactorsBlocked)
+ .as[(Seq[(Int, Array[Float])], Seq[(Int, Array[Float])])]
+ .flatMap { case (srcIter, dstIter) =>
+ val m = srcIter.size
+ val n = math.min(dstIter.size, num)
+ val output = new Array[(Int, Int, Float)](m * n)
+ var j = 0
--- End diff --
Anyway the `iter.next()` code is a bit ugly and since it's at most `k`
elements it's not really performance critical, so could just use `foreach` I
think
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