Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/9980#discussion_r46220915
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
---
@@ -275,16 +276,13 @@ object MatrixFactorizationModel extends
Loader[MatrixFactorizationModel] {
num: Int): RDD[(Int, Array[(Int, Double)])] = {
val srcBlocks = blockify(rank, srcFeatures)
val dstBlocks = blockify(rank, dstFeatures)
+ val output = new ArrayBuffer[(Int, (Int, Double))]()
val ratings = srcBlocks.cartesian(dstBlocks).flatMap {
case ((srcIds, srcFactors), (dstIds, dstFactors)) =>
- val m = srcIds.length
- val n = dstIds.length
val ratings = srcFactors.transpose.multiply(dstFactors)
- val output = new Array[(Int, (Int, Double))](m * n)
- var k = 0
+ output.clear()
ratings.foreachActive { (i, j, r) =>
--- End diff --
We don't need `output` to hold the buffer. The following should work,
though it doesn't really fix the GC problem:
~~~scala
for (i <- 0 until m; j <- 0 until n) yield {
(srcIds(i), dstIds(j), ratings(i, j))
}
~~~
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