Yes, the PR allows you to set say 1.5. The New configuration property defaults to spark.executor.cores, which defaults to 1.
On Fri, Mar 30, 2018, 3:03 PM Kimoon Kim <kim...@pepperdata.com> wrote: > David, glad it helped! And thanks for your clear example. > > > 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. > > 1 as default SGTM. > > Thanks, > Kimoon > > On Fri, Mar 30, 2018 at 1:38 PM, David Vogelbacher < > dvogelbac...@palantir.com> wrote: > >> Thanks for linking that PR Kimoon. >> >> >> It actually does mostly address the issue I was referring to. As the >> issue <https://github.com/apache-spark-on-k8s/spark/issues/352> 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] >> <https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_apache_spark_pull_20553&d=DwMFaQ&c=izlc9mHr637UR4lpLEZLFFS3Vn2UXBrZ4tFb6oOnmz8&r=BFXcJr3WTIvmlY-gtaiCO5QK4bLix2sgwDDpPfrZKoE&m=TrCA4oIVKyN3M_ExqpHr7bbhi14uvoEaspPwclIJI4M&s=jqIG5lO5tnV3K3SDPPxw2bEHs0i6cltoaLh8K39JTTQ&e=> >> 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] >> <https://urldefense.proofpoint.com/v2/url?u=https-3A__issues.apache.org_jira_browse_SPARK-2D23825&d=DwMFaQ&c=izlc9mHr637UR4lpLEZLFFS3Vn2UXBrZ4tFb6oOnmz8&r=BFXcJr3WTIvmlY-gtaiCO5QK4bLix2sgwDDpPfrZKoE&m=TrCA4oIVKyN3M_ExqpHr7bbhi14uvoEaspPwclIJI4M&s=hA8h-KIeJ_6Khjx1JzFZF55ZH3GnSrB4HEkHc1I-yBc&e=> >> with >> more details and made a PR [github.com] >> <https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_apache_spark_pull_20943&d=DwMFaQ&c=izlc9mHr637UR4lpLEZLFFS3Vn2UXBrZ4tFb6oOnmz8&r=BFXcJr3WTIvmlY-gtaiCO5QK4bLix2sgwDDpPfrZKoE&m=TrCA4oIVKyN3M_ExqpHr7bbhi14uvoEaspPwclIJI4M&s=qZFhxef7FgsA9UfijbVtKAIDuchcTf9wQxYIKL87SsU&e=> >> . >> >> 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] >> <https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_apache_spark_pull_20553&d=DwMFaQ&c=izlc9mHr637UR4lpLEZLFFS3Vn2UXBrZ4tFb6oOnmz8&r=BFXcJr3WTIvmlY-gtaiCO5QK4bLix2sgwDDpPfrZKoE&m=TrCA4oIVKyN3M_ExqpHr7bbhi14uvoEaspPwclIJI4M&s=jqIG5lO5tnV3K3SDPPxw2bEHs0i6cltoaLh8K39JTTQ&e=> >> 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: >> >> >> >> *CPU* >> >> *Memory* >> >> *Request* >> >> [driver,executor].cores >> >> [driver,executor].memory >> >> *Limit* >> >> Unlimited (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] >> <https://urldefense.proofpoint.com/v2/url?u=https-3A__issues.apache.org_jira_browse_SPARK-2D23825&d=DwMFaQ&c=izlc9mHr637UR4lpLEZLFFS3Vn2UXBrZ4tFb6oOnmz8&r=BFXcJr3WTIvmlY-gtaiCO5QK4bLix2sgwDDpPfrZKoE&m=TrCA4oIVKyN3M_ExqpHr7bbhi14uvoEaspPwclIJI4M&s=hA8h-KIeJ_6Khjx1JzFZF55ZH3GnSrB4HEkHc1I-yBc&e=> >> with more details and made a PR [github.com] >> <https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_apache_spark_pull_20943&d=DwMFaQ&c=izlc9mHr637UR4lpLEZLFFS3Vn2UXBrZ4tFb6oOnmz8&r=BFXcJr3WTIvmlY-gtaiCO5QK4bLix2sgwDDpPfrZKoE&m=TrCA4oIVKyN3M_ExqpHr7bbhi14uvoEaspPwclIJI4M&s=qZFhxef7FgsA9UfijbVtKAIDuchcTf9wQxYIKL87SsU&e=> >> . >> >> >> >> *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] >> <https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_apache-2Dspark-2Don-2Dk8s_spark_issues_352&d=DwMFaQ&c=izlc9mHr637UR4lpLEZLFFS3Vn2UXBrZ4tFb6oOnmz8&r=BFXcJr3WTIvmlY-gtaiCO5QK4bLix2sgwDDpPfrZKoE&m=TrCA4oIVKyN3M_ExqpHr7bbhi14uvoEaspPwclIJI4M&s=uTMrl29jkJRlc_N1S_6lvwCjkovzrsan8zIczzxDZGM&e=> >> 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 >> >> >> >> >> >> >> > >
