Github user mridulm commented on a diff in the pull request:
https://github.com/apache/spark/pull/16788#discussion_r105319503
--- Diff: core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
---
@@ -1028,7 +1028,7 @@ class DAGScheduler(
val locs = taskIdToLocations(id)
new ResultTask(stage.id, stage.latestInfo.attemptId,
taskBinary, part, locs, id, properties,
serializedTaskMetrics,
- Option(jobId), Option(sc.applicationId),
sc.applicationAttemptId)
+ Option(jobId), Option(sc.applicationId),
sc.applicationAttemptId, Option(tokens))
}
--- End diff --
Current spark model in yarn for managing tokens is to do it out of band
with the actual tasks (unlike tez/MR iirc : where then the execution model is
itself different).
The tasks themselves do not propagate the credentials - the executors
directly update the credentials based on driver updates.
This allows for very long running spark tasks (> 24 hours for example) to
run - which per task tokens might not allow.
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