Github user srowen commented on the pull request:
https://github.com/apache/spark/pull/1391#issuecomment-48835727
Yes of course, lots of settings' best or even usable values are ultimately
app-specific. Ideally, defaults work for lots of cases. A flat value is the
simplest of models, and anecdotally, the current default value does not work in
medium- to large-memory YARN jobs. You can increase the default, but then the
overhead gets silly for small jobs -- 1GB? And all of these are not-uncommon
use cases.
None of that implies the overhead logically scales with container memory.
Empirically, it may do, and that's useful. Until the magic explanatory variable
is found, which one is less problematic for end users -- a flat constant that
frequently has to be tuned, or an imperfect model that could get it right in
more cases?
That said it is kind of a developer API change and feels like something to
not keep reimagining.
Niskham can you share any anecdotal evidence about how the overhead
changes. If executor memory is the only variable changing, that seems to be
evidence against it being driven by other factors. but I don't know if that's
what we know.
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