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From Klaus Ma <klaus1982...@gmail.com>
Subject Re: Spark on Kubernetes scheduler variety
Date Thu, 24 Jun 2021 01:52:50 GMT
Hi team,

I'm kube-batch/Volcano founder, and I'm excited to hear that the spark
community also has such requirements :)

Volcano provides several features for batch workload, e.g. fair-share,
queue, reservation, preemption/reclaim and so on.
It has been used in several product environments with Spark; if necessary,
I can give an overall introduction about Volcano's features and those use
cases :)

-- Klaus

On Wed, Jun 23, 2021 at 11:26 PM Mich Talebzadeh <mich.talebzadeh@gmail.com>
wrote:

>
>
> Please allow me to be diverse and express a different point of view on
> this roadmap.
>
>
> I believe from a technical point of view spending time and effort plus
> talent on batch scheduling on Kubernetes could be rewarding. However, if I
> may say I doubt whether such an approach and the so-called democratization
> of Spark on whatever platform is really should be of great focus.
>
> Having worked on Google Dataproc <https://cloud.google.com/dataproc> (A fully
> managed and highly scalable service for running Apache Spark, Hadoop and
> more recently other artefacts) for that past two years, and Spark on
> Kubernetes on-premise, I have come to the conclusion that Spark is not a
> beast that that one can fully commoditize it much like one can do with
> Zookeeper, Kafka etc. There is always a struggle to make some niche areas
> of Spark like Spark Structured Streaming (SSS) work seamlessly and
> effortlessly on these commercial platforms with whatever as a Service.
>
>
> Moreover, Spark (and I stand corrected) from the ground up has already a
> lot of resiliency and redundancy built in. It is truly an enterprise class
> product (requires enterprise class support) that will be difficult to
> commoditize with Kubernetes and expect the same performance. After all,
> Kubernetes is aimed at efficient resource sharing and potential cost saving
> for the mass market. In short I can see commercial enterprises will work on
> these platforms ,but may be the great talents on dev team should focus on
> stuff like the perceived limitation of SSS in dealing with chain of
> aggregation( if I am correct it is not yet supported on streaming datasets)
>
>
> These are my opinions and they are not facts, just opinions so to speak :)
>
>
>    view my Linkedin profile
> <https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/>
>
>
>
> *Disclaimer:* Use it at your own risk. Any and all responsibility for any
> loss, damage or destruction of data or any other property which may arise
> from relying on this email's technical content is explicitly disclaimed.
> The author will in no case be liable for any monetary damages arising from
> such loss, damage or destruction.
>
>
>
>
> On Fri, 18 Jun 2021 at 23:18, Holden Karau <holden@pigscanfly.ca> wrote:
>
>> I think these approaches are good, but there are limitations (eg dynamic
>> scaling) without us making changes inside of the Spark Kube scheduler.
>>
>> Certainly whichever scheduler extensions we add support for we should
>> collaborate with the people developing those extensions insofar as they are
>> interested. My first place that I checked was #sig-scheduling which is
>> fairly quite on the Kubernetes slack but if there are more places to look
>> for folks interested in batch scheduling on Kubernetes we should definitely
>> give it a shot :)
>>
>> On Fri, Jun 18, 2021 at 1:41 AM Mich Talebzadeh <
>> mich.talebzadeh@gmail.com> wrote:
>>
>>> Hi,
>>>
>>> Regarding your point and I quote
>>>
>>> "..  I know that one of the Spark on Kube operators
>>> supports volcano/kube-batch so I was thinking that might be a place I would
>>> start exploring..."
>>>
>>> There seems to be ongoing work on say Volcano as part of  Cloud Native
>>> Computing Foundation <https://cncf.io/> (CNCF). For example through
>>> https://github.com/volcano-sh/volcano
>>>
>> <https://github.com/volcano-sh/volcano>
>>>
>>> There may be value-add in collaborating with such groups through CNCF in
>>> order to have a collective approach to such work. There also seems to be
>>> some work on Integration of Spark with Volcano for Batch Scheduling.
>>> <https://github.com/GoogleCloudPlatform/spark-on-k8s-operator/blob/master/docs/volcano-integration.md>
>>>
>>>
>>>
>>> What is not very clear is the degree of progress of these projects. You
>>> may be kind enough to elaborate on KPI for each of these projects and where
>>> you think your contributions is going to be.
>>>
>>>
>>> HTH,
>>>
>>>
>>> Mich
>>>
>>>
>>>    view my Linkedin profile
>>> <https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/>
>>>
>>>
>>>
>>> *Disclaimer:* Use it at your own risk. Any and all responsibility for
>>> any loss, damage or destruction of data or any other property which may
>>> arise from relying on this email's technical content is explicitly
>>> disclaimed. The author will in no case be liable for any monetary damages
>>> arising from such loss, damage or destruction.
>>>
>>>
>>>
>>>
>>> On Fri, 18 Jun 2021 at 00:44, Holden Karau <holden@pigscanfly.ca> wrote:
>>>
>>>> Hi Folks,
>>>>
>>>> I'm continuing my adventures to make Spark on containers party and I
>>>> was wondering if folks have experience with the different batch
>>>> scheduler options that they prefer? I was thinking so that we can
>>>> better support dynamic allocation it might make sense for us to
>>>> support using different schedulers and I wanted to see if there are
>>>> any that the community is more interested in?
>>>>
>>>> I know that one of the Spark on Kube operators supports
>>>> volcano/kube-batch so I was thinking that might be a place I start
>>>> exploring but also want to be open to other schedulers that folks
>>>> might be interested in.
>>>>
>>>> Cheers,
>>>>
>>>> Holden :)
>>>>
>>>> --
>>>> Twitter: https://twitter.com/holdenkarau
>>>> Books (Learning Spark, High Performance Spark, etc.):
>>>> https://amzn.to/2MaRAG9
>>>> YouTube Live Streams: https://www.youtube.com/user/holdenkarau
>>>>
>>>> ---------------------------------------------------------------------
>>>> To unsubscribe e-mail: dev-unsubscribe@spark.apache.org
>>>>
>>>> --
>> Twitter: https://twitter.com/holdenkarau
>> Books (Learning Spark, High Performance Spark, etc.):
>> https://amzn.to/2MaRAG9  <https://amzn.to/2MaRAG9>
>> YouTube Live Streams: https://www.youtube.com/user/holdenkarau
>>
>

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