Github user subrotosanyal commented on a diff in the pull request:
https://github.com/apache/spark/pull/13499#discussion_r65762991
--- Diff:
yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
---
@@ -64,7 +68,7 @@ private[spark] class YarnClientSchedulerBackend(
// SPARK-8851: In yarn-client mode, the AM still does the credentials
refresh. The driver
// reads the credentials from HDFS, just like the executors and
updates its own credentials
// cache.
- if (conf.contains("spark.yarn.credentials.file")) {
+ if (!conf.contains(PRINCIPAL.key) &&
conf.contains("spark.yarn.credentials.file")) {
--- End diff --
hi @vanzin
the method:
org.apache.spark.deploy.yarn.ExecutorDelegationTokenUpdater.updateCredentialsIfRequired()
invokes the same operation of updating the current user credential and correct
me if I am wrong; if user has logged in through a keytab file already then
ideally there shouldn't be any need to update the credentials at regular
intervals. This thread of updating the credential might be useful for executors
where there is only CREDENTAILS_FILE or delegation token.
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