Github user mridulm commented on the pull request:
https://github.com/apache/spark/pull/1391#issuecomment-48835656
The basic issue is you are trying to model overhead using the wrong
variable... It has no correlation on executor memory actually (other than
vm overheads as heap increases)
On 13-Jul-2014 2:44 pm, "Mridul Muralidharan" <[email protected]> wrote:
> That would be a function of your jobs.
> Other apps would have a drastically different characteristics ... Which is
> why we can't generalize to a simple fraction of executor memory.
> It actually buys us nothing in general case ... Jobs will continue to fail
> when it is incorrect : while wasting a lot of memory
> On 13-Jul-2014 2:38 pm, "nishkamravi2" <[email protected]> wrote:
>
>> Yes, I'm aware of the discussion on this issue in the past. Experiments
>> confirm that overhead is a function of executor memory. Why and how can
be
>> figured out with due diligence and analysis. It may be a function of
other
>> parameters and the function may be fairly complex. However, the
>> proportionality is undeniable. Besides, we are only adjusting the default
>> value and making it a bit more resilient. The memory_overhead parameter
can
>> still be configured by the developer separately. The constant additive
>> factor makes little sense (empirically).
>>
>> â
>> Reply to this email directly or view it on GitHub
>> <https://github.com/apache/spark/pull/1391#issuecomment-48835500>.
>>
>
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