Thank you Peter for your comment,

> Since they didn't share profiling results or heat maps, we can only guess 
> what part of Capacity Scheduler is deemed slow or a possible bottleneck.

Agreed. I want to know the profiling results or heat maps from the
authors just for curiosity.

-Akira

On Thu, Aug 19, 2021 at 12:05 AM Peter Bacsko <pbac...@cloudera.com> wrote:
>
> Hi Akira,
>
> from the article, it's not clear to me what they mean by saying 
> "sophisticated features". It is true that the container assignment code path 
> is very complicated and understanding it takes quite a bit of time and 
> effort. So in order to speed up container assignment in large clusters, it 
> might be necessary to rewrite that, also losing certain features in the 
> process - but what those might be is not elaborated. But they didn't take 
> this path and instead opted for multiple Hadoop clusters.
>
> Since they didn't share profiling results or heat maps, we can only guess 
> what part of Capacity Scheduler is deemed slow or a possible bottleneck.
>
> Peter
>
> On Thu, Aug 12, 2021 at 9:48 AM Akira Ajisaka <aajis...@apache.org> wrote:
>>
>> Hi folks,
>>
>> I read Uber's article
>> https://eng.uber.com/cost-efficient-big-data-platform/. This article
>> is very interesting for me, and now I have some questions.
>>
>> > For example, we identified that the Capacity Scheduler has some complex 
>> > logic that slows down task assignment. However, code changes to get rid of 
>> > those won’t be able to merge into Apache Hadoop trunk, since those 
>> > sophisticated features may be needed by other companies.
>>
>> - What are those sophisticated features in the Capacity Scheduler?
>> - In the future, can we turn off the features by some flags in Apache Hadoop?
>> - Is there any other examples like this?
>>
>> Thanks and regards,
>> Akira
>>
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