Github user holdenk commented on a diff in the pull request:
https://github.com/apache/spark/pull/11919#discussion_r61769074
--- Diff: mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala
---
@@ -1355,4 +1359,28 @@ object ALS extends DefaultParamsReadable[ALS] with
Logging {
* satisfies this requirement, we simply use a type alias here.
*/
private[recommendation] type ALSPartitioner =
org.apache.spark.HashPartitioner
+
+ /**
+ * Private function to clean up all of the shuffles files from the
dependencies and their parents.
+ */
+ private[spark] def cleanShuffleDependencies[T](sc: SparkContext, deps:
Seq[Dependency[_]],
+ blocking: Boolean = false): Unit = {
+ // If there is no reference tracking we skip clean up.
+ sc.cleaner.foreach{ cleaner =>
+ /**
+ * Clean the shuffles & all of its parents.
+ */
+ def cleanEagerly(dep: Dependency[_]): Unit = {
+ if (dep.isInstanceOf[ShuffleDependency[_, _, _]]) {
+ val shuffleId = dep.asInstanceOf[ShuffleDependency[_, _,
_]].shuffleId
+ cleaner.doCleanupShuffle(shuffleId, blocking)
--- End diff --
So my understanding is that previously the ttl cleaner could clean up check
point files and Spark would still need to handle recompute. Of course I've gone
ahead and gone with the safer choice - although I also modified the first test
to illustrate that recompute works fine (we clean up the shuffle files of a
non-cached RDD and then call count).
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