Github user markhamstra commented on a diff in the pull request:
https://github.com/apache/spark/pull/4909#discussion_r26001955
--- Diff: core/src/main/scala/org/apache/spark/deploy/master/Master.scala
---
@@ -467,7 +467,9 @@ private[spark] class Master(
* two executors on the same worker).
*/
def canUse(app: ApplicationInfo, worker: WorkerInfo): Boolean = {
- worker.memoryFree >= app.desc.memoryPerSlave &&
!worker.hasExecutor(app)
+ worker.memoryFree >= app.desc.memoryPerSlave &&
+ !worker.hasExecutor(app) &&
+ !app.removedExecutors.exists(_.worker == worker)
--- End diff --
I agree with @mateiz, and Spark Streaming is by no means the only Spark
application for which a common use case is to have the entire cluster
essentially running a single long-lived application. To have transiently
failing Executors perpetually banned from doing work for that long-lived
application is not acceptable.
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