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.
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