Hi, what about supporting for solving the disk space problem of "device space isn't enough"? I think it's same as OOM exception.
kalyan <[email protected]> 于2024年1月27日周六 13:00写道： > Hi all, > > Sorry for the delay in getting the first draft of (my first) SPIP out. > > https://docs.google.com/document/d/1hxEPUirf3eYwNfMOmUHpuI5dIt_HJErCdo7_yr9htQc/edit?pli=1 > > Let me know what you think. > > Regards > kalyan. > > On Sat, Jan 20, 2024 at 8:19 AM Ashish Singh <[email protected]> wrote: > >> Hey all, >> >> Thanks for this discussion, the timing of this couldn't be better! >> >> At Pinterest, we recently started to look into reducing OOM failures >> while also reducing memory consumption of spark applications. We considered >> the following options. >> 1. Changing core count on executor to change memory available per task in >> the executor. >> 2. Changing resource profile based on task failures and gc metrics to >> grow or shrink executor memory size. We do this at application level based >> on the app's past runs today. >> 3. K8s vertical pod autoscaler >> <https://github.com/kubernetes/autoscaler/tree/master/vertical-pod-autoscaler> >> >> Internally, we are mostly getting aligned on option 2. We would love to >> make this happen and are looking forward to the SPIP. >> >> >> On Wed, Jan 17, 2024 at 9:34 AM Mridul Muralidharan <[email protected]> >> wrote: >> >>> >>> Hi, >>> >>> We are internally exploring adding support for dynamically changing >>> the resource profile of a stage based on runtime characteristics. >>> This includes failures due to OOM and the like, slowness due to >>> excessive GC, resource wastage due to excessive overprovisioning, etc. >>> Essentially handles scale up and scale down of resources. >>> Instead of baking these into the scheduler directly (which is already >>> complex), we are modeling it as a plugin - so that the 'business logic' of >>> how to handle task events and mutate state is pluggable. >>> >>> The main limitation I find with mutating only the cores is the limits it >>> places on what kind of problems can be solved with it - and mutating >>> resource profiles is a much more natural way to handle this >>> (spark.task.cpus predates RP). >>> >>> Regards, >>> Mridul >>> >>> On Wed, Jan 17, 2024 at 9:18 AM Tom Graves <[email protected]> >>> wrote: >>> >>>> It is interesting. I think there are definitely some discussion points >>>> around this. reliability vs performance is always a trade off and its >>>> great it doesn't fail but if it doesn't meet someone's SLA now that could >>>> be as bad if its hard to figure out why. I think if something like this >>>> kicks in, it needs to be very obvious to the user so they can see that it >>>> occurred. Do you have something in place on UI or something that indicates >>>> this? The nice thing is also you aren't wasting memory by increasing it for >>>> all tasks when maybe you only need it for one or two. The downside is you >>>> are only finding out after failure. >>>> >>>> I do also worry a little bit that in your blog post, the error you >>>> pointed out isn't a java OOM but an off heap memory issue (overhead + heap >>>> usage). You don't really address heap memory vs off heap in that article. >>>> Only thing I see mentioned is spark.executor.memory which is heap memory. >>>> Obviously adjusting to only run one task is going to give that task more >>>> overall memory but the reasons its running out in the first place could be >>>> different. If it was on heap memory for instance with more tasks I would >>>> expect to see more GC and not executor OOM. If you are getting executor >>>> OOM you are likely using more off heap memory/stack space, etc then you >>>> allocated. Ultimately it would be nice to know why that is happening and >>>> see if we can address it to not fail in the first place. That could be >>>> extremely difficult though, especially if using software outside Spark that >>>> is using that memory. >>>> >>>> As Holden said, we need to make sure this would play nice with the >>>> resource profiles, or potentially if we can use the resource profile >>>> functionality. Theoretically you could extend this to try to get new >>>> executor if using dynamic allocation for instance. >>>> >>>> I agree doing a SPIP would be a good place to start to have more >>>> discussions. >>>> >>>> Tom >>>> >>>> On Wednesday, January 17, 2024 at 12:47:51 AM CST, kalyan < >>>> [email protected]> wrote: >>>> >>>> >>>> Hello All, >>>> >>>> At Uber, we had recently, done some work on improving the reliability >>>> of spark applications in scenarios of fatter executors going out of memory >>>> and leading to application failure. Fatter executors are those that have >>>> more than 1 task running on it at a given time concurrently. This has >>>> significantly improved the reliability of many spark applications for us at >>>> Uber. We made a blog about this recently. Link: >>>> https://www.uber.com/en-US/blog/dynamic-executor-core-resizing-in-spark/ >>>> >>>> At a high level, we have done the below changes: >>>> >>>> 1. When a Task fails with the OOM of an executor, we update the >>>> core requirements of the task to max executor cores. >>>> 2. When the task is picked for rescheduling, the new attempt of the >>>> task happens to be on an executor where no other task can run >>>> concurrently. >>>> All cores get allocated to this task itself. >>>> 3. This way we ensure that the configured memory is completely at >>>> the disposal of a single task. Thus eliminating contention of memory. >>>> >>>> The best part of this solution is that it's reactive. It kicks in only >>>> when the executors fail with the OOM exception. >>>> >>>> We understand that the problem statement is very common and we expect >>>> our solution to be effective in many cases. >>>> >>>> There could be more cases that can be covered. Executor failing with >>>> OOM is like a hard signal. The framework(making the driver aware of >>>> what's happening with the executor) can be extended to handle scenarios of >>>> other forms of memory pressure like excessive spilling to disk, etc. >>>> >>>> While we had developed this on Spark 2.4.3 in-house, we would like to >>>> collaborate and contribute this work to the latest versions of Spark. >>>> >>>> What is the best way forward here? Will an SPIP proposal to detail the >>>> changes help? >>>> >>>> Regards, >>>> Kalyan. >>>> Uber India. >>>> >>>
