Github user ksakellis commented on the pull request:
https://github.com/apache/spark/pull/4051#issuecomment-71558537
LGTM
I agree with @sryza that maxExecutors should default to Integer.MAX_VALUE.
It is not the app's responsibility to play fair but rather to ask the resource
manager, YARN, for its optimal amount of resources and it take care of
enforcing the admin policies. @vanzin I also don't see the point with the app
setting the max to #nm. If you default maxExecutors to #nm on startup, what
happens when the administrator adds more node managers while the spark job is
running? It would be good for Spark to take advantage of the spare capacity.
Although I don't like yet another configuration option, initialExecutors, I
don't think there is a way around it given the hive on spark use case. At least
none of the three are required which is the most important part. A default
value of 0 for minExecutors seems like a good default for the reasons described
by @vanzin. Defaulting initialExecutors to minExecutors seems fine also.
@andrewor14 I don't like the idea of using --num-executors as the
initialExecutors. Like @vanzin we want at some point to make dynamic allocation
the default so having consistent configuration make sense. Reusing
--num-executors would just add confusion now and later.
@andrewor14 why do we increase the number of executors we request once a
minute? is this to reduce load on the YARN resource manager? I think we might
want to revisit how we ramp up the cluster but that is beyond the scope of this
PR.
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