Thanks for linking that PR Kimoon.
It actually does mostly address the issue I was referring to. As the issue I linked in my first email states, one physical cpu might not be enough to execute a task in a performant way. So if I set spark.executor.cores=1 and spark.task.cpus=1 , I will get 1 core from Kubernetes and execute one task per Executor and run into performance problems. Being able to specify `spark.kubernetes.executor.cores=1.2` would fix the issue (1.2 is just an example). I am curious as to why you, Yinan, would want to use this property to request less than 1 physical cpu (that is how it sounds to me on the PR). Do you have testing that indicates that less than 1 physical CPU is enough for executing tasks? In the end it boils down to the question proposed by Yinan: > A relevant question is should Spark on Kubernetes really be opinionated on > how to set the cpu request and limit and even try to determine this > automatically? And I completely agree with your answer Kimoon, we should provide sensible defaults and make it configurable, as Yinan’s PR does. The only remaining question would then be what a sensible default for spark.kubernetes.executor.cores would be. Seeing that I wanted more than 1 and Yinan wants less, leaving it at 1 night be best. Thanks, David From: Kimoon Kim <kim...@pepperdata.com> Date: Friday, March 30, 2018 at 4:28 PM To: Yinan Li <liyinan...@gmail.com> Cc: David Vogelbacher <dvogelbac...@palantir.com>, "dev@spark.apache.org" <dev@spark.apache.org> Subject: Re: [Kubernetes] Resource requests and limits for Driver and Executor Pods I see. Good to learn the interaction between spark.task.cpus and spark.executor.cores. But am I right to say that PR #20553 can be still used as an additional knob on top of those two? Say a user wants 1.5 core per executor from Kubernetes, not the rounded up integer value 2? > A relevant question is should Spark on Kubernetes really be opinionated on > how to set the cpu request and limit and even try to determine this > automatically? Personally, I don't see how this can be auto-determined at all. I think the best we can do is to come up with sensible default values for the most common case, and provide and well-document other knobs for edge cases. Thanks, Kimoon On Fri, Mar 30, 2018 at 12:37 PM, Yinan Li <liyinan...@gmail.com> wrote: PR #20553 [github.com] is more for allowing users to use a fractional value for cpu requests. The existing spark.executor.cores is sufficient for specifying more than one cpus. > One way to solve this could be to request more than 1 core from Kubernetes > per task. The exact amount we should request is unclear to me (it largely > depends on how many threads actually get spawned for a task). A good indication is spark.task.cpus, and on average how many tasks are expected to run by a single executor at any point in time. If each executor is only expected to run one task at most at any point in time, spark.executor.cores can be set to be equal to spark.task.cpus. A relevant question is should Spark on Kubernetes really be opinionated on how to set the cpu request and limit and even try to determine this automatically? On Fri, Mar 30, 2018 at 11:40 AM, Kimoon Kim <kim...@pepperdata.com> wrote: > Instead of requesting `[driver,executor].memory`, we should just request > `[driver,executor].memory + [driver,executor].memoryOverhead `. I think this > case is a bit clearer than the CPU case, so I went ahead and filed an issue > [issues.apache.org] with more details and made a PR [github.com]. I think this suggestion makes sense. > One way to solve this could be to request more than 1 core from Kubernetes > per task. The exact amount we should request is unclear to me (it largely > depends on how many threads actually get spawned for a task). I wonder if this is being addressed by PR #20553 [github.com] written by Yinan. Yinan? Thanks, Kimoon On Thu, Mar 29, 2018 at 5:14 PM, David Vogelbacher <dvogelbac...@palantir.com> wrote: Hi, At the moment driver and executor pods are created using the following requests and limits: CPUMemory Request[driver,executor].cores[driver,executor].memory LimitUnlimited (but can be specified using spark.[driver,executor].cores)[driver,executor].memory + [driver,executor].memoryOverhead Specifying the requests like this leads to problems if the pods only get the requested amount of resources and nothing of the optional (limit) resources, as it can happen in a fully utilized cluster. For memory: Let’s say we have a node with 100GiB memory and 5 pods with 20 GiB memory and 5 GiB memoryOverhead. At the beginning all 5 pods use 20 GiB of memory and all is well. If a pod then starts using its overhead memory it will get killed as there is no more memory available, even though we told spark that it can use 25 GiB of memory. Instead of requesting `[driver,executor].memory`, we should just request `[driver,executor].memory + [driver,executor].memoryOverhead `. I think this case is a bit clearer than the CPU case, so I went ahead and filed an issue [issues.apache.org] with more details and made a PR [github.com]. For CPU: As it turns out, there can be performance problems if we only have `executor.cores` available (which means we have one core per task). This was raised here [github.com] and is the reason that the cpu limit was set to unlimited. This issue stems from the fact that in general there will be more than one thread per task, resulting in performance impacts if there is only one core available. However, I am not sure that just setting the limit to unlimited is the best solution because it means that even if the Kubernetes cluster can perfectly satisfy the resource requests, performance might be very bad. I think we should guarantee that an executor is able to do its work well (without performance issues or getting killed - as could happen in the memory case) with the resources it gets guaranteed from Kubernetes. One way to solve this could be to request more than 1 core from Kubernetes per task. The exact amount we should request is unclear to me (it largely depends on how many threads actually get spawned for a task). We would need to find a way to determine this somehow automatically or at least come up with a better default value than 1 core per task. Does somebody have ideas or thoughts on how to solve this best? Best, David
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