Github user holdenk commented on a diff in the pull request:
https://github.com/apache/spark/pull/11919#discussion_r61710810
--- 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 --
The test case has multiple iterations inside of it. Node failure recovery
is handled from the checkpoint files. If the shuffle files are gone we should
be able to fall back to recomputing the stage that created the shuffle files
no? I can certainly change it though (and I'll run a job tomorrow to verify).
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at [email protected] or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
