Github user mridulm commented on the pull request:
https://github.com/apache/spark/pull/900#issuecomment-45780405
Hit submit by mistake, to continue ...
The side effect of not having sufficient executors are different from #892.
For example,
a) the default parallelism in yarn is based on number of executors,
b) the number of intermediate files per node for shuffle (this can bring
the node down btw)
c) and amount of memory consumed on a node for rdd MEMORY persisted data
(making the job fail if disk is not specified : like some of the mllib algos ?)
and so on ...
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