Github user vanzin commented on a diff in the pull request:
https://github.com/apache/spark/pull/17480#discussion_r110801458
--- Diff:
core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala ---
@@ -249,7 +249,14 @@ private[spark] class ExecutorAllocationManager(
* yarn-client mode when AM re-registers after a failure.
*/
def reset(): Unit = synchronized {
- initializing = true
+ /**
+ * When some tasks need to be scheduled and initial executor = 0,
resetting the initializing
+ * field may cause it to not be set to false in yarn.
+ * SPARK-20079: https://issues.apache.org/jira/browse/SPARK-20079
+ */
+ if (maxNumExecutorsNeeded() == 0) {
+ initializing = true
--- End diff --
Sorry but that doesn't really explain much. Why is it bad to ramp up
quickly? At which point are things not "initializing" anymore?
Isn't the AM restarting the definition of "I should ramp up quickly because
I might be in the middle of a big job being run"?
---
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 infrastruct...@apache.org or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org
For additional commands, e-mail: reviews-h...@spark.apache.org
