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Github user liyinan926 commented on the issue: https://github.com/apache/spark/pull/20553 Agreed that `spark.kubernetes.executor.request.cores` is a better name. Will change it. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user mccheah commented on the issue: https://github.com/apache/spark/pull/20553 Ah never mind sorry - thought we were referring to changing thread count with `spark.kubernetes.executor.request.cores`. Think this config key makes sense to communicate the exact K8s semantics here. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user mccheah commented on the issue: https://github.com/apache/spark/pull/20553 `spark.executor.cores` is the standard used by all the other cluster managers, so we have to use that. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user dvogelbacher commented on the issue: https://github.com/apache/spark/pull/20553 Can we call this `spark.kubernetes.executor.request.cores`, similar to `spark.kubernetes.executor.limit.cores` ? I think this would make the naming a bit less confusing. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user liyinan926 commented on the issue: https://github.com/apache/spark/pull/20553 @foxish I think the confusion mostly comes from the property name. I don't think we need to change the semantics of `spark.executor.cores` nor the way dynamic resource allocation works in K8S mode. Like what I said above, `spark.executor.cores`/`spark.task.cpus` still determines the number of tasks that can run in an executor simultaneously given the cpus specified in `spark.kubernetes.executor.cores`. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user foxish commented on the issue: https://github.com/apache/spark/pull/20553 Oops - knew we tried that before. Just paged in the discussion from https://github.com/apache/spark/pull/20460. Looks like that approach might need revisiting. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user foxish commented on the issue: https://github.com/apache/spark/pull/20553 @cloud-fan @jiangxb1987 I assume you're referring to [ExecutorAllocationManager.scala#L114-L118](https://github.com/apache/spark/blob/fc6fe8a1d0f161c4788f3db94de49a8669ba3bcc/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala#L114-L118) @liyinan926, while this might be the least obtrusive way to get Kubernetes mode to have fractional executor cores and not change other backends, it seems to me like separating the notion of a pod's CPU request and `spark.executor.cores` might prove confusing to users. We don't expect Spark users to necessarily understand pods or containers. Elaborating on @srowen's point on the JIRA, in theory, could we relax `spark.executor.cores` to accept doubles and then push the check down to the individual resource managers - force conversion to int if needed? It might require a similar change to `spark.task.cpus` also possibly. Thoughts? --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark issue #20553: [SPARK-23285][K8S] Add a config property for specifying ...
Github user liyinan926 commented on the issue: https://github.com/apache/spark/pull/20553 > Do you mean `spark.kubernetes.executor.cores` will only be used with k8s for static allocation? It looks to me that if we wanna k8s work with Spark dynamic allocation better, we have to change the semantics of `spark.executor.cores` to support fraction. Or we introduce a new dynamic allocation module for k8s, which reads `spark.kubernetes.executor.cores`. Since it's only used to define the **physical** cpu request for executor pods, it can be used for both static and dynamic allocation in k8s mode. IMO, `spark.executor.cores` and `spark.task.cpus` are for defining the cpu resource availability and demands in a virtual sense. An executor with `100m` physical cpus allocated can still run 2 tasks if they fit in. This is equivalently to a scenario in which `spark.executor.cores=2` and `spark.task.cpus=1`. Task parallelism per executor and dynamic resource allocation are still based on the semantics of `spark.executor.cores` and `spark.task.cpus`. So I don't think we need to change the semantics of these two properties and we don't need a new version of dynamic resource allocation for k8s. IMO, it's unfortunate that `spark.executor.cores` is used both for defining the physical cpu request and for defining task parallelism. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark issue #20553: [SPARK-23285][K8S] Add a config property for specifying ...
Github user cloud-fan commented on the issue: https://github.com/apache/spark/pull/20553 > This is to avoid changing the semantics of spark.executor.cores and spark.task.cpus and their role in task scheduling, task parallelism, dynamic resource allocation, etc. The new configuration property only determines the physical CPU cores available to an executor. Do you mean `spark.kubernetes.executor.cores` will only be used with k8s for static allocation? It looks to me that if we wanna k8s work with Spark dynamic allocation better, we have to change the semantics of `spark.executor.cores` to support fraction. Or we introduce a new dynamic allocation module for k8s, which reads `spark.kubernetes.executor.cores`. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user liyinan926 commented on the issue: https://github.com/apache/spark/pull/20553 The value of `spark.executor.cores` will be used to set cpu request for the executor pods if `spark.kubernetes.executor.cores` is not set. `spark.driver.cores` already allows fractional values so we don't have a Kubernetes specific property for the driver pod. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user jerryshao commented on the issue: https://github.com/apache/spark/pull/20553 What is the default value if it is not configured, how do K8S control the CPU usage by default? Also it seems that user may configure how to differentiate between k8s executor cores and executor cores. BTW, do we also need similar k8s driver cores for cluster mode? --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user liyinan926 commented on the issue: https://github.com/apache/spark/pull/20553 `spark.kubernetes.executor.cores` has nothing to do with dynamic resource allocation. It's just a way of letting users specify a value for the cpu resource request that conforms to Kubernetes convention and is only read/used when determining the cpu request. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user jiangxb1987 commented on the issue: https://github.com/apache/spark/pull/20553 IIUC the `spark.kubernetes.executor.cores` here is just a special case for `spark.executor.cores`, for k8s backend, you shall still have to handle float values if you're to read the value of `spark.kubernetes.executor.cores`, for instance, in dynamic allocation. If that is the case here, I don't see much benefit of bringing in a new conf here. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user jiangxb1987 commented on the issue: https://github.com/apache/spark/pull/20553 also cc @cloud-fan @jerryshao --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user jiangxb1987 commented on the issue: https://github.com/apache/spark/pull/20553 How do we plan to support dynamic allocation with k8s? Should we read `spark.executor.cores` or `spark.kubernetes.executor.cores` ? --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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