Hi Martijn, Thank you for your comments. I will answer the questions one by one.
"" * Regarding the motivation, it mentions that the development trend is that Flink supports both batch and stream processing. I think the vision and trend is that we have unified batch- and stream processing. What I'm missing is the vision on what's the impact for customized Kubernetes schedulers on stream processing. Could there be some elaboration on that? "" >> We very much agree with you and the dev trend that Flink supports both batch and stream processing. Actually, using the K8S customized scheduler is beneficial for streaming scenarios too, such as avoiding resource deadlock and other problems, for example, the remaining resources in the K8S cluster are only enough for one job running, but we submitted two. At this time, both jobs will be prevented and hang from requesting resources at the same time when using the default K8S scheduler, but in this case, the customized scheduler Volcano won’t schedule overcommit pods if the idle can not fit all following pods setup. So the benefits mentioned in FLIP are not only for batch jobs. In fact, the said 4 scheduling capabilities mentioned in FLIP are all required for stream processing. YARN has some of those scheduling features too, such as priority scheduling, min/max resource constraint and etc... "" * While the FLIP talks about customized schedulers, it focuses on Volcano. Why is the choice made to only focus on Volcano and not on other schedulers like Apache YuniKorn? Can we not also provide an implementation for YuniKorn at the same time? Spark did the same with SPARK-36057 [1] "" >> Let's make it more clear about this. The FLIP consists of two parts: 1. Introducing Flink K8S supports the customized scheduler plugin mechanism. This aspect is a general consideration. 2. Introducing ONE reference implementation for the customized scheduler, volcano is just one of them, if other schedulers or people are interested, the integration of other schedulers can also be easily completed. "" * We still have quite a lot of tech debt on testing for Kubernetes [2]. I think that this FLIP would be a great improvement for Flink, but I am worried that we will add more tech debt to those tests. Can we somehow improve this situation? "" >> Yeah, We will pay attention to the test problems, which are related to Flink K8S and we are happy to improve it. ;-) BR, Bo Zhao Martijn Visser <martijnvis...@apache.org> 于2022年7月13日周三 15:19写道： > Hi all, > > Thanks for the FLIP. I have a couple of remarks/questions: > > * Regarding the motivation, it mentions that the development trend is that > Flink supports both batch and stream processing. I think the vision and > trend is that we have unified batch- and stream processing. What I'm > missing is the vision on what's the impact for customized Kubernetes > schedulers on stream processing. Could there be some elaboration on that? > * While the FLIP talks about customized schedulers, it focuses on Volcano. > Why is the choice made to only focus on Volcano and not on other schedulers > like Apache YuniKorn? Can we not also provide an implementation for > YuniKorn at the same time? Spark did the same with SPARK-36057 [1] > * We still have quite a lot of tech debt on testing for Kubernetes [2]. I > think that this FLIP would be a great improvement for Flink, but I am > worried that we will add more tech debt to those tests. Can we somehow > improve this situation? > > Best regards, > > Martijn > > [1] https://issues.apache.org/jira/browse/SPARK-36057 > [2] https://issues.apache.org/jira/browse/FLINK-20392 > > Op wo 13 jul. 2022 om 04:11 schreef 王正 <cswangzh...@gmail.com>: > > > +1 > > > > On 2022/07/07 01:15:13 bo zhaobo wrote: > > > Hi, all. > > > > > > I would like to raise a discussion in Flink dev ML about Support > > Customized > > > Kubernetes Schedulers. > > > Currentlly, Kubernetes becomes more and more polular for Flink Cluster > > > deployment, and its ability is better, especially, it supports > > customized > > > scheduling. > > > Essentially, in high-performance workloads, we need to apply new > > scheduling > > > policies for meeting the new requirements. And now Flink native > > Kubernetes > > > solution is using Kubernetes default scheduler to work with all > > scenarios, > > > the default scheduling policy might be difficult to apply in some > extreme > > > cases, so > > > we need to improve the Flink Kubernetes for coupling those Kubernetes > > > customized schedulers with Flink native Kubernetes, provides a way for > > Flink > > > administrators or users to use/apply their Flink Clusters on Kubernetes > > > more flexibility. > > > > > > The proposal will introduce the customized K8S schdulers plugin > mechanism > > > and a reference implementation 'Volcano' in Flink. More details see > [1]. > > > > > > Looking forward to your feedback. > > > > > > [1] > > > > > > https://cwiki.apache.org/confluence/display/FLINK/FLIP-250%3A+Support+Customized+Kubernetes+Schedulers+Proposal > > > > > > Thanks, > > > BR > > > >
