Thanks for pointing out this feature to me. I will have a look when I get
there.

Mich Talebzadeh,
Solutions Architect/Engineering Lead
London
United Kingdom


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On Tue, 8 Aug 2023 at 11:44, roryqi(齐赫) <ror...@tencent.com> wrote:

> Spark 3.5 have added an method `supportsReliableStorage`  in the `
> ShuffleDriverComponents` which indicate whether writing  shuffle data to
> a distributed filesystem or persisting it in a remote shuffle service.
>
> Uniffle is a general purpose remote shuffle service (
> https://github.com/apache/incubator-uniffle).  It can enhance the
> experience of Spark on K8S. After Spark 3.5 is released, Uniffle will
> support the `ShuffleDriverComponents`.  you can see [1].
>
> If you have interest about more details of Uniffle, you can  see [2]
>
>
> [1] https://github.com/apache/incubator-uniffle/issues/802.
>
> [2]
> https://uniffle.apache.org/blog/2023/07/21/Uniffle%20-%20New%20chapter%20for%20the%20shuffle%20in%20the%20cloud%20native%20era
>
>
>
> *发件人**: *Mich Talebzadeh <mich.talebza...@gmail.com>
> *日期**: *2023年8月8日 星期二 06:53
> *抄送**: *dev <dev@spark.apache.org>
> *主题**: *[Internet]Re: Improving Dynamic Allocation Logic for Spark 4+
>
>
>
> On the subject of dynamic allocation, is the following message a cause for
> concern when running Spark on k8s?
>
>
>
> INFO ExecutorAllocationManager: Dynamic allocation is enabled without a
> shuffle service.
>
>
> Mich Talebzadeh,
>
> Solutions Architect/Engineering Lead
>
> London
>
> United Kingdom
>
>
>
>    view my Linkedin profile
> <https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/>
>
>
>
>  https://en.everybodywiki.com/Mich_Talebzadeh
>
>
>
> *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 Mon, 7 Aug 2023 at 23:42, Mich Talebzadeh <mich.talebza...@gmail.com>
> wrote:
>
>
>
> Hi,
>
>
>
> From what I have seen spark on a serverless cluster has hard up getting
> the driver going in a timely manner
>
>
>
> Annotations:  autopilot.gke.io/resource-adjustment:
>
>
> {"input":{"containers":[{"limits":{"memory":"1433Mi"},"requests":{"cpu":"1","memory":"1433Mi"},"name":"spark-kubernetes-driver"}]},"output...
>
>               autopilot.gke.io/warden-version: 2.7.41
>
>
>
> This is on spark 3.4.1 with Java 11 both the host running spark-submit and
> the docker itself
>
>
>
> I am not sure how relevant this is to this discussion but it looks like a
> kind of blocker for now. What config params can help here and what can be
> done?
>
>
>
> Thanks
>
>
>
> Mich Talebzadeh,
>
> Solutions Architect/Engineering Lead
>
> London
>
> United Kingdom
>
>
>
>    view my Linkedin profile
> <https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/>
>
>
>
>  https://en.everybodywiki.com/Mich_Talebzadeh
>
>
>
> *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 Mon, 7 Aug 2023 at 22:39, Holden Karau <hol...@pigscanfly.ca> wrote:
>
> Oh great point
>
>
>
> On Mon, Aug 7, 2023 at 2:23 PM bo yang <bobyan...@gmail.com> wrote:
>
> Thanks Holden for bringing this up!
>
>
>
> Maybe another thing to think about is how to make dynamic allocation more
> friendly with Kubernetes and disaggregated shuffle storage?
>
>
>
>
>
>
>
> On Mon, Aug 7, 2023 at 1:27 PM Holden Karau <hol...@pigscanfly.ca> wrote:
>
> So I wondering if there is interesting in revisiting some of how Spark is
> doing it's dynamica allocation for Spark 4+?
>
>
>
> Some things that I've been thinking about:
>
>
>
> - Advisory user input (e.g. a way to say after X is done I know I need Y
> where Y might be a bunch of GPU machines)
>
> - Configurable tolerance (e.g. if we have at most Z% over target no-op)
>
> - Past runs of same job (e.g. stage X of job Y had a peak of K)
>
> - Faster executor launches (I'm a little fuzzy on what we can do here but,
> one area for example is we setup and tear down an RPC connection to the
> driver with a blocking call which does seem to have some locking inside of
> the driver at first glance)
>
>
>
> Is this an area other folks are thinking about? Should I make an epic we
> can track ideas in? Or are folks generally happy with today's dynamic
> allocation (or just busy with other things)?
>
>
>
> --
>
> 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
>
> --
>
> 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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