Added implementation steps for this FLIP on the wiki page [1].
Thank you~ Xintong Song [1] https://cwiki.apache.org/confluence/display/FLINK/FLIP-49%3A+Unified+Memory+Configuration+for+TaskExecutors On Mon, Aug 19, 2019 at 10:29 PM Xintong Song <tonysong...@gmail.com> wrote: > Hi everyone, > > As Till suggested, the original "FLIP-53: Fine Grained Resource > Management" splits into two separate FLIPs, > > - FLIP-53: Fine Grained Operator Resource Management [1] > - FLIP-56: Dynamic Slot Allocation [2] > > We'll continue using this discussion thread for FLIP-53. For FLIP-56, I > just started a new discussion thread [3]. > > Thank you~ > > Xintong Song > > > [1] > https://cwiki.apache.org/confluence/display/FLINK/FLIP-53%3A+Fine+Grained+Operator+Resource+Management > > [2] > https://cwiki.apache.org/confluence/display/FLINK/FLIP-56%3A+Dynamic+Slot+Allocation > > [3] > http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/DISCUSS-FLIP-56-Dynamic-Slot-Allocation-td31960.html > > On Mon, Aug 19, 2019 at 2:55 PM Xintong Song <tonysong...@gmail.com> > wrote: > >> Thinks for the comments, Yang. >> >> Regarding your questions: >> >> 1. How to calculate the resource specification of TaskManagers? Do they >>> have them same resource spec calculated based on the configuration? I >>> think >>> we still have wasted resources in this situation. Or we could start >>> TaskManagers with different spec. >>> >> I agree with you that we can further improve the resource utility by >> customizing task executors with different resource specifications. However, >> I'm in favor of limiting the scope of this FLIP and leave it as a future >> optimization. The plan for that part is to move the logic of deciding task >> executor specifications into the slot manager and make slot manager >> pluggable, so inside the slot manager plugin we can have different logics >> for deciding the task executor specifications. >> >> >>> 2. If a slot is released and returned to SlotPool, does it could be >>> reused by other SlotRequest that the request resource is smaller than >>> it? >>> >> No, I think slot pool should always return slots if they do not exactly >> match the pending requests, so that resource manager can deal with the >> extra resources. >> >>> - If it is yes, what happens to the available resource in the >> >> TaskManager. >>> - What is the SlotStatus of the cached slot in SlotPool? The >>> AllocationId is null? >>> >> The allocation id does not change as long as the slot is not returned >> from the job master, no matter its occupied or available in the slot pool. >> I think we have the same behavior currently. No matter how many tasks the >> job master deploy into the slot, concurrently or sequentially, it is one >> allocation from the cluster to the job until the slot is freed from the job >> master. >> >>> 3. In a session cluster, some jobs are configured with operator >>> resources, meanwhile other jobs are using UNKNOWN. How to deal with >>> this >>> situation? >> >> As long as we do not mix unknown / specified resource profiles within the >> same job / slot, there shouldn't be a problem. Resource manager converts >> unknown resource profiles in slot requests to specified default resource >> profiles, so they can be dynamically allocated from task executors' >> available resources just as other slot requests with specified resource >> profiles. >> >> Thank you~ >> >> Xintong Song >> >> >> >> On Mon, Aug 19, 2019 at 11:39 AM Yang Wang <danrtsey...@gmail.com> wrote: >> >>> Hi Xintong, >>> >>> >>> Thanks for your detailed proposal. I think many users are suffering from >>> waste of resources. The resource spec of all task managers are same and >>> we >>> have to increase all task managers to make the heavy one more stable. So >>> we >>> will benefit from the fine grained resource management a lot. We could >>> get >>> better resource utilization and stability. >>> >>> >>> Just to share some thoughts. >>> >>> >>> >>> 1. How to calculate the resource specification of TaskManagers? Do >>> they >>> have them same resource spec calculated based on the configuration? I >>> think >>> we still have wasted resources in this situation. Or we could start >>> TaskManagers with different spec. >>> 2. If a slot is released and returned to SlotPool, does it could be >>> reused by other SlotRequest that the request resource is smaller than >>> it? >>> - If it is yes, what happens to the available resource in the >>> TaskManager. >>> - What is the SlotStatus of the cached slot in SlotPool? The >>> AllocationId is null? >>> 3. In a session cluster, some jobs are configured with operator >>> resources, meanwhile other jobs are using UNKNOWN. How to deal with >>> this >>> situation? >>> >>> >>> >>> Best, >>> Yang >>> >>> Xintong Song <tonysong...@gmail.com> 于2019年8月16日周五 下午8:57写道： >>> >>> > Thanks for the feedbacks, Yangze and Till. >>> > >>> > Yangze, >>> > >>> > I agree with you that we should make scheduling strategy pluggable and >>> > optimize the strategy to reduce the memory fragmentation problem, and >>> > thanks for the inputs on the potential algorithmic solutions. However, >>> I'm >>> > in favor of keep this FLIP focusing on the overall mechanism design >>> rather >>> > than strategies. Solving the fragmentation issue should be considered >>> as an >>> > optimization, and I agree with Till that we probably should tackle this >>> > afterwards. >>> > >>> > Till, >>> > >>> > - Regarding splitting the FLIP, I think it makes sense. The operator >>> > resource management and dynamic slot allocation do not have much >>> dependency >>> > on each other. >>> > >>> > - Regarding the default slot size, I think this is similar to FLIP-49 >>> [1] >>> > where we want all the deriving happens at one place. I think it would >>> be >>> > nice to pass the default slot size into the task executor in the same >>> way >>> > that we pass in the memory pool sizes in FLIP-49 [1]. >>> > >>> > - Regarding the return value of TaskExecutorGateway#requestResource, I >>> > think you're right. We should avoid using null as the return value. I >>> think >>> > we probably should thrown an exception here. >>> > >>> > Thank you~ >>> > >>> > Xintong Song >>> > >>> > >>> > [1] >>> > >>> > >>> https://cwiki.apache.org/confluence/display/FLINK/FLIP-49%3A+Unified+Memory+Configuration+for+TaskExecutors >>> > >>> > On Fri, Aug 16, 2019 at 2:18 PM Till Rohrmann <trohrm...@apache.org> >>> > wrote: >>> > >>> > > Hi Xintong, >>> > > >>> > > thanks for drafting this FLIP. I think your proposal helps to >>> improve the >>> > > execution of batch jobs more efficiently. Moreover, it enables the >>> proper >>> > > integration of the Blink planner which is very important as well. >>> > > >>> > > Overall, the FLIP looks good to me. I was wondering whether it >>> wouldn't >>> > > make sense to actually split it up into two FLIPs: Operator resource >>> > > management and dynamic slot allocation. I think these two FLIPs >>> could be >>> > > seen as orthogonal and it would decrease the scope of each individual >>> > FLIP. >>> > > >>> > > Some smaller comments: >>> > > >>> > > - I'm not sure whether we should pass in the default slot size via an >>> > > environment variable. Without having unified the way how Flink >>> components >>> > > are configured [1], I think it would be better to pass it in as part >>> of >>> > the >>> > > configuration. >>> > > - I would avoid returning a null value from >>> > > TaskExecutorGateway#requestResource if it cannot be fulfilled. >>> Either we >>> > > should introduce an explicit return value saying this or throw an >>> > > exception. >>> > > >>> > > Concerning Yangze's comments: I think you are right that it would be >>> > > helpful to make the selection strategy pluggable. Also batching slot >>> > > requests to the RM could be a good optimization. For the sake of >>> keeping >>> > > the scope of this FLIP smaller I would try to tackle these things >>> after >>> > the >>> > > initial version has been completed (without spoiling these >>> optimization >>> > > opportunities). In particular batching the slot requests depends on >>> the >>> > > current scheduler refactoring and could also be realized on the RM >>> side >>> > > only. >>> > > >>> > > [1] >>> > > >>> > > >>> > >>> https://cwiki.apache.org/confluence/display/FLINK/FLIP-54%3A+Evolve+ConfigOption+and+Configuration >>> > > >>> > > Cheers, >>> > > Till >>> > > >>> > > >>> > > >>> > > On Fri, Aug 16, 2019 at 11:11 AM Yangze Guo <karma...@gmail.com> >>> wrote: >>> > > >>> > > > Hi, Xintong >>> > > > >>> > > > Thanks to propose this FLIP. The general design looks good to me, >>> +1 >>> > > > for this feature. >>> > > > >>> > > > Since slots in the same task executor could have different resource >>> > > > profile, we will >>> > > > meet resource fragment problem. Think about this case: >>> > > > - request A want 1G memory while request B & C want 0.5G memory >>> > > > - There are two task executors T1 & T2 with 1G and 0.5G free >>> memory >>> > > > respectively >>> > > > If B come first and we cut a slot from T1 for B, A must wait for >>> the >>> > > > free resource from >>> > > > other task. But A could have been scheduled immediately if we cut a >>> > > > slot from T2 for B. >>> > > > >>> > > > The logic of findMatchingSlot now become finding a task executor >>> which >>> > > > has enough >>> > > > resource and then cut a slot from it. Current method could be seen >>> as >>> > > > "First-fit strategy", >>> > > > which works well in general but sometimes could not be the >>> optimization >>> > > > method. >>> > > > >>> > > > Actually, this problem could be abstracted as "Bin Packing >>> Problem"[1]. >>> > > > Here are >>> > > > some common approximate algorithms: >>> > > > - First fit >>> > > > - Next fit >>> > > > - Best fit >>> > > > >>> > > > But it become multi-dimensional bin packing problem if we take CPU >>> > > > into account. It hard >>> > > > to define which one is best fit now. Some research addressed this >>> > > > problem, such like Tetris[2]. >>> > > > >>> > > > Here are some thinking about it: >>> > > > 1. We could make the strategy of finding matching task executor >>> > > > pluginable. Let user to config the >>> > > > best strategy in their scenario. >>> > > > 2. We could support batch request interface in RM, because we have >>> > > > opportunities to optimize >>> > > > if we have more information. If we know the A, B, C at the same >>> time, >>> > > > we could always make the best decision. >>> > > > >>> > > > [1] http://www.or.deis.unibo.it/kp/Chapter8.pdf >>> > > > [2] >>> > https://www.cs.cmu.edu/~xia/resources/Documents/grandl_sigcomm14.pdf >>> > > > >>> > > > Best, >>> > > > Yangze Guo >>> > > > >>> > > > On Thu, Aug 15, 2019 at 10:40 PM Xintong Song < >>> tonysong...@gmail.com> >>> > > > wrote: >>> > > > > >>> > > > > Hi everyone, >>> > > > > >>> > > > > We would like to start a discussion thread on "FLIP-53: Fine >>> Grained >>> > > > > Resource Management"[1], where we propose how to improve Flink >>> > resource >>> > > > > management and scheduling. >>> > > > > >>> > > > > This FLIP mainly discusses the following issues. >>> > > > > >>> > > > > - How to support tasks with fine grained resource >>> requirements. >>> > > > > - How to unify resource management for jobs with / without >>> fine >>> > > > grained >>> > > > > resource requirements. >>> > > > > - How to unify resource management for streaming / batch jobs. >>> > > > > >>> > > > > Key changes proposed in the FLIP are as follows. >>> > > > > >>> > > > > - Unify memory management for operators with / without fine >>> > grained >>> > > > > resource requirements by applying a fraction based quota >>> > mechanism. >>> > > > > - Unify resource scheduling for streaming and batch jobs by >>> > setting >>> > > > slot >>> > > > > sharing groups for pipelined regions during compiling stage. >>> > > > > - Dynamically allocate slots from task executors' available >>> > > resources. >>> > > > > >>> > > > > Please find more details in the FLIP wiki document [1]. Looking >>> > forward >>> > > > to >>> > > > > your feedbacks. >>> > > > > >>> > > > > Thank you~ >>> > > > > >>> > > > > Xintong Song >>> > > > > >>> > > > > >>> > > > > [1] >>> > > > > >>> > > > >>> > > >>> > >>> https://cwiki.apache.org/confluence/display/FLINK/FLIP-53%3A+Fine+Grained+Resource+Management >>> > > > >>> > > >>> > >>> >>
