Github user tgravescs commented on the issue:
https://github.com/apache/spark/pull/17238
If you aren't adding in machines to rack and configuring yarn properly
before adding it to your cluster that is a process issue you should fix on your
end. I would assume a unracking/racking a node means putting in a new node?
If that is the case you have to install hadoop and hadoop configuration on that
node. I would expect you to fix the configuration or have a generic rack aware
script/java class that would be able to just figure it out, but that is going
to be pretty configuration specific. I would also assume if you have that
configuration wrong then your HDFS is also not being optimal as it could get
the replication wrong.
You can specify your own class/script to do the rack resolution so you
could change that to handle this case: see
https://hadoop.apache.org/docs/r2.7.2/hadoop-project-dist/hadoop-common/RackAwareness.html
I'll try to check on tez/mr today and get back to you (was to busy
yesterday). I know tez didn't have any explicit references to DEFAULT_RACK in
the code but want to look a bit more.
---
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
