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From "Varshney, Vaibhav" <vaibhav.varsh...@siemens.com>
Subject RE: [Spark 3.0 Kubernetes] Does Spark 3.0 support production deployment
Date Fri, 10 Jul 2020 15:59:49 GMT
Hi Prashant,

It sounds encouraging. During scale down of the cluster, probably few of the spark jobs are
impacted due to re-computation of shuffle data. This is not of supreme importance for us for
now.
Is there any reference deployment architecture available, which is HA , scalable and dynamic-allocation-enabled
for deploying Spark on K8s? Any suggested github repo or link?

Thanks,
Vaibhav V


From: Prashant Sharma <scrapcodes@gmail.com>
Sent: Friday, July 10, 2020 12:57 AM
To: user@spark.apache.org
Cc: Sean Owen <srowen@gmail.com>; Ramani, Sai (DI SW CAS MP AFC ARC) <sai.ramani@siemens.com>;
Varshney, Vaibhav (DI SW CAS MP AFC ARC) <vaibhav.varshney@siemens.com>
Subject: Re: [Spark 3.0 Kubernetes] Does Spark 3.0 support production deployment

Hi,

Whether it is a blocker or not, is upto you to decide. But, spark k8s cluster supports dynamic
allocation, through a different mechanism, that is, without using an external shuffle service.
https://issues.apache.org/jira/browse/SPARK-27963. There are pros and cons of both approaches.
The only disadvantage of scaling without external shuffle service is, when the cluster scales
down or it loses executors due to some external cause ( for example losing spot instances),
we lose the shuffle data (data that was computed as an intermediate to some overall computation)
on that executor. This situation may not lead to data loss, as spark can recompute the lost
shuffle data.

Dynamically, scaling up and down scaling, is helpful when the spark cluster is running off,
"spot instances on AWS" for example or when the size of data is not known in advance. In other
words, we cannot estimate how much resources would be needed to process the data. Dynamic
scaling, lets the cluster increase its size only based on the number of pending tasks, currently
this is the only metric implemented.

I don't think it is a blocker for my production use cases.

Thanks,
Prashant

On Fri, Jul 10, 2020 at 2:06 AM Varshney, Vaibhav <vaibhav.varshney@siemens.com<mailto:vaibhav.varshney@siemens.com>>
wrote:
Thanks for response. We have tried it in dev env. For production, if Spark 3.0 is not leveraging
k8s scheduler, then would Spark Cluster in K8s be "static"?
As per https://issues.apache.org/jira/browse/SPARK-24432 it seems it is still blocker for
production workloads?

Thanks,
Vaibhav V

-----Original Message-----
From: Sean Owen <srowen@gmail.com<mailto:srowen@gmail.com>>
Sent: Thursday, July 9, 2020 3:20 PM
To: Varshney, Vaibhav (DI SW CAS MP AFC ARC) <vaibhav.varshney@siemens.com<mailto:vaibhav.varshney@siemens.com>>
Cc: user@spark.apache.org<mailto:user@spark.apache.org>; Ramani, Sai (DI SW CAS MP AFC
ARC) <sai.ramani@siemens.com<mailto:sai.ramani@siemens.com>>
Subject: Re: [Spark 3.0 Kubernetes] Does Spark 3.0 support production deployment

I haven't used the K8S scheduler personally, but, just based on that comment I wouldn't worry
too much. It's been around for several versions and AFAIK works fine in general. We sometimes
aren't so great about removing "experimental" labels. That said I know there are still some
things that could be added to it and more work going on, and maybe people closer to that work
can comment. But yeah you shouldn't be afraid to try it.

On Thu, Jul 9, 2020 at 3:18 PM Varshney, Vaibhav <vaibhav.varshney@siemens.com<mailto:vaibhav.varshney@siemens.com>>
wrote:
>
> Hi Spark Experts,
>
>
>
> We are trying to deploy spark on Kubernetes.
>
> As per doc http://spark.apache.org/docs/latest/running-on-kubernetes.html, it looks like
K8s deployment is experimental.
>
> "The Kubernetes scheduler is currently experimental ".
>
>
>
> Spark 3.0 does not support production deployment using k8s scheduler?
>
> What’s the plan on full support of K8s scheduler?
>
>
>
> Thanks,
>
> Vaibhav V
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