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From Mich Talebzadeh <mich.talebza...@gmail.com>
Subject Re: Spark on Kubernetes scheduler variety
Date Thu, 24 Jun 2021 16:22:53 GMT
Hi Holden,

Thank you for your points. I guess coming from a corporate world I had an
oversight on how an open source project like Spark does leverage resources
and interest :).

As @KlausMa kindly volunteered it would be good to hear scheduling ideas on
Spark on Kubernetes and of course as I am sure you have some inroads/ideas
on this subject as well, then truly I guess love would be in the air for
Kubernetes <https://www.youtube.com/watch?v=NNC0kIzM1Fo>

HTH



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<https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/>



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On Thu, 24 Jun 2021 at 16:59, Holden Karau <holden@pigscanfly.ca> wrote:

> Hi Mich,
>
> I certainly think making Spark on Kubernetes run well is going to be a
> challenge. However I think, and I could be wrong about this as well, that
> in terms of cluster managers Kubernetes is likely to be our future. Talking
> with people I don't hear about new standalone, YARN or mesos deployments of
> Spark, but I do hear about people trying to migrate to Kubernetes.
>
> To be clear I certainly agree that we need more work on structured
> streaming, but its important to remember that the Spark developers are not
> all fully interchangeable, we work on the things that we're interested in
> pursuing so even if structured streaming needs more love if I'm not super
> interested in structured streaming I'm less likely to work on it. That
> being said I am certainly spinning up a bit more in the Spark SQL area
> especially around our data source/connectors because I can see the need
> there too.
>
> On Wed, Jun 23, 2021 at 8:26 AM 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.):
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>>> YouTube Live Streams: https://www.youtube.com/user/holdenkarau
>>>
>>
>
> --
> 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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