Hello Folks, Happy new year to one and all. I’m from the EMR on EKS<https://aws.amazon.com/emr/features/eks/> team. We help customers to run Spark workloads on Kubernetes. My team had similar ideas, and we have also sourced requirements from customers who use EMR on EKS / Spark on EKS. Would love to participate in the design to help solve the problem for the vast majority of Spark on Kubernetes users.
Any guidance on how to best contribute? Best, Janak From: Mich Talebzadeh <mich.talebza...@gmail.com> Sent: Tuesday, January 4, 2022 2:12 AM To: Yikun Jiang <yikunk...@gmail.com> Cc: dev <dev@spark.apache.org>; Weiwei Yang <w...@apache.org>; Holden Karau <hol...@pigscanfly.ca>; wang.platf...@gmail.com; Prasad Paravatha <prasad.parava...@gmail.com>; John Zhuge <jzh...@apache.org>; Chenya Zhang <chenyazhangche...@gmail.com>; Chaoran Yu <yuchaoran2...@gmail.com>; Wilfred Spiegelenburg <wilfr...@apache.org>; Klaus Ma <klaus1982...@gmail.com> Subject: RE: [EXTERNAL] [DISCUSSION] SPIP: Support Volcano/Alternative Schedulers Proposal CAUTION: This email originated from outside of the organization. Do not click links or open attachments unless you can confirm the sender and know the content is safe. Interesting,thanks Do you have any indication of the ballpark figure (a rough numerical estimate) of adding Volcano as an alternative scheduler is going to improve Spark on k8s performance? Thanks [https://docs.google.com/uc?export=download&id=1-q7RFGRfLMObPuQPWSd9sl_H1UPNFaIZ&revid=0B1BiUVX33unjMWtVUWpINWFCd0ZQTlhTRHpGckh4Wlg4RG80PQ] 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 Tue, 4 Jan 2022 at 09:43, Yikun Jiang <yikunk...@gmail.com<mailto:yikunk...@gmail.com>> wrote: Hi, folks! Wishing you all the best in 2022. I'd like to share the current status on "Support Customized K8S Scheduler in Spark". https://docs.google.com/document/d/1xgQGRpaHQX6-QH_J9YV2C2Dh6RpXefUpLM7KGkzL6Fg/edit#heading=h.1quyr1r2kr5n Framework/Common support - Volcano and Yunikorn team join the discussion and complete the initial doc on framework/common part. - SPARK-37145<https://issues.apache.org/jira/browse/SPARK-37145> (under reviewing): We proposed to extend the customized scheduler by just using a custom feature step, it will meet the requirement of customized scheduler after it gets merged. After this, the user can enable featurestep and scheduler like: spark-submit \ --conf spark.kubernete.scheduler.name<http://spark.kubernete.scheduler.name> volcano \ --conf spark.kubernetes.driver.pod.featureSteps org.apache.spark.deploy.k8s.features.scheduler.VolcanoFeatureStep --conf spark.kubernete.job.queue xxx (such as above, the VolcanoFeatureStep will help to set the the spark scheduler queue according user specified conf) - SPARK-37331<https://issues.apache.org/jira/browse/SPARK-37331>: Added the ability to create kubernetes resources before driver pod creation. - SPARK-36059<https://issues.apache.org/jira/browse/SPARK-36059>: Add the ability to specify a scheduler in driver/executor After above all, the framework/common support would be ready for most of customized schedulers Volcano part: - SPARK-37258<https://issues.apache.org/jira/browse/SPARK-37258>: Upgrade kubernetes-client to 5.11.1 to add volcano scheduler API support. - SPARK-36061<https://issues.apache.org/jira/browse/SPARK-36061>: Add a VolcanoFeatureStep to help users to create a PodGroup with user specified minimum resources required, there is also a WIP commit to show the preview of this<https://github.com/Yikun/spark/pull/45/commits/81bf6f98edb5c00ebd0662dc172bc73f980b6a34>. Yunikorn part: - @WeiweiYang is completing the doc of the Yunikorn part and implementing the Yunikorn part. Regards, Yikun Weiwei Yang <w...@apache.org<mailto:w...@apache.org>> 于2021年12月2日周四 02:00写道： Thank you Yikun for the info, and thanks for inviting me to a meeting to discuss this. I appreciate your effort to put these together, and I agree that the purpose is to make Spark easy/flexible enough to support other K8s schedulers (not just for Volcano). As discussed, could you please help to abstract out the things in common and allow Spark to plug different implementations? I'd be happy to work with you guys on this issue. On Tue, Nov 30, 2021 at 6:49 PM Yikun Jiang <yikunk...@gmail.com<mailto:yikunk...@gmail.com>> wrote: @Weiwei @Chenya > Thanks for bringing this up. This is quite interesting, we definitely should > participate more in the discussions. Thanks for your reply and welcome to join the discussion, I think the input from Yunikorn is very critical. > The main thing here is, the Spark community should make Spark pluggable in > order to support other schedulers, not just for Volcano. It looks like this > proposal is pushing really hard for adopting PodGroup, which isn't part of > K8s yet, that to me is problematic. Definitely yes, we are on the same page. I think we have the same goal: propose a general and reasonable mechanism to make spark on k8s with a custom scheduler more usable. But for the PodGroup, just allow me to do a brief introduction: - The PodGroup definition has been approved by Kubernetes officially in KEP-583. [1] - It can be regarded as a general concept/standard in Kubernetes rather than a specific concept in Volcano, there are also others to implement it, such as [2][3]. - Kubernetes recommends using CRD to do more extension to implement what they want. [4] - Volcano as extension provides an interface to maintain the life cycle PodGroup CRD and use volcano-scheduler to complete the scheduling. [1] https://github.com/kubernetes/enhancements/tree/master/keps/sig-scheduling/583-coscheduling [2] https://github.com/kubernetes-sigs/scheduler-plugins/tree/master/pkg/coscheduling#podgroup [3] https://github.com/kubernetes-sigs/kube-batch [4] https://kubernetes.io/docs/tasks/extend-kubernetes/custom-resources/custom-resource-definitions/ Regards, Yikun Weiwei Yang <w...@apache.org<mailto:w...@apache.org>> 于2021年12月1日周三 上午5:57写道： Hi Chenya Thanks for bringing this up. This is quite interesting, we definitely should participate more in the discussions. The main thing here is, the Spark community should make Spark pluggable in order to support other schedulers, not just for Volcano. It looks like this proposal is pushing really hard for adopting PodGroup, which isn't part of K8s yet, that to me is problematic. On Tue, Nov 30, 2021 at 9:21 AM Prasad Paravatha <prasad.parava...@gmail.com<mailto:prasad.parava...@gmail.com>> wrote: This is a great feature/idea. I'd love to get involved in some form (testing and/or documentation). This could be my 1st contribution to Spark! On Tue, Nov 30, 2021 at 10:46 PM John Zhuge <jzh...@apache.org<mailto:jzh...@apache.org>> wrote: +1 Kudos to Yikun and the community for starting the discussion! On Tue, Nov 30, 2021 at 8:47 AM Chenya Zhang <chenyazhangche...@gmail.com<mailto:chenyazhangche...@gmail.com>> wrote: Thanks folks for bringing up the topic of natively integrating Volcano and other alternative schedulers into Spark! +Weiwei, Wilfred, Chaoran. We would love to contribute to the discussion as well. From our side, we have been using and improving on one alternative resource scheduler, Apache YuniKorn (https://yunikorn.apache.org/), for Spark on Kubernetes in production at Apple with solid results in the past year. It is capable of supporting Gang scheduling (similar to PodGroups), multi-tenant resource queues (similar to YARN), FIFO, and other handy features like bin packing to enable efficient autoscaling, etc. Natively integrating with Spark would provide more flexibility for users and reduce the extra cost and potential inconsistency of maintaining different layers of resource strategies. One interesting topic we hope to discuss more about is dynamic allocation, which would benefit from native coordination between Spark and resource schedulers in K8s & cloud environment for an optimal resource efficiency. On Tue, Nov 30, 2021 at 8:10 AM Holden Karau <hol...@pigscanfly.ca<mailto:hol...@pigscanfly.ca>> wrote: Thanks for putting this together, I’m really excited for us to add better batch scheduling integrations. On Tue, Nov 30, 2021 at 12:46 AM Yikun Jiang <yikunk...@gmail.com<mailto:yikunk...@gmail.com>> wrote: Hey everyone, I'd like to start a discussion on "Support Volcano/Alternative Schedulers Proposal". This SPIP is proposed to make spark k8s schedulers provide more YARN like features (such as queues and minimum resources before scheduling jobs) that many folks want on Kubernetes. The goal of this SPIP is to improve current spark k8s scheduler implementations, add the ability of batch scheduling and support volcano as one of implementations. Design doc: https://docs.google.com/document/d/1xgQGRpaHQX6-QH_J9YV2C2Dh6RpXefUpLM7KGkzL6Fg JIRA: https://issues.apache.org/jira/browse/SPARK-36057 Part of PRs: Ability to create resources https://github.com/apache/spark/pull/34599 Add PodGroupFeatureStep: https://github.com/apache/spark/pull/34456 Regards, Yikun -- 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 -- John Zhuge -- Regards, Prasad Paravatha
