Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/7121#discussion_r33561355
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
---
@@ -89,10 +89,14 @@ class MatrixFactorizationModel(
*/
def predict(usersProducts: RDD[(Int, Int)]): RDD[Rating] = {
val users = userFeatures.join(usersProducts).map {
- case (user, (uFeatures, product)) => (product, (user, uFeatures))
+ case (user, (uFeatures, product)) => ((user, product), uFeatures)
}
- users.join(productFeatures).map {
- case (product, ((user, uFeatures), pFeatures)) =>
+ val productUsers = usersProducts.map(up => (up._2, up._1))
+ val products = productFeatures.join(productUsers).map {
--- End diff --
.. but this creates an entire other `join` to compute. This is going to be
significantly slower, right? How does this relate to the JIRA though -- this is
a more expensive version of a simple repartitioning, which would also solve it,
but the point seemed to be to avoid that.
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