[SPARK-38135][K8S] Introduce job sheduling related configurations #35436
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martin-g
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Feb 8, 2022
resource-managers/kubernetes/core/src/main/scala/org/apache/spark/deploy/k8s/Config.scala
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martin-g
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| .version("3.3.0") | ||
| .intConf | ||
| .checkValue(value => value > 0, "The minimum number should be a positive integer") | ||
| .createWithDefault(1) |
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does this mean that the driver and the executor(s) will be on the same pod ?
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No, it's for driver, in the case of spot instance, we allow user only create driver pod first. So, it's equivalent to no limit by default.
martin-g
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dongjoon-hyun
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Sorry, but -1. Apache Spark doesn't add the configuration first like this.
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It seems that you are confused. Please provide a complete working PR with the test coverage. |
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What changes were proposed in this pull request?
This patch inroduces 5 scheduling related configurations:
spark.kubernetes.job.queue: The name of the queue to which the job is submitted.spark.kubernetes.job.minCPU: The minimum CPU for running the job.spark.kubernetes.job.minMemory: The minimum memory for running the job, in MiB unless otherwise specified.spark.kubernetes.job.minMember: The minimum number of pods running in a job.spark.kubernetes.job.priorityClassName: The priority of the running job.These info will be stored in configuration and passed to specified feature step, such as it will be used by Yunikorn/Volcano feature step.
Why are the changes needed?
This PR help user integrate Spark with Volcano Scheduler to enable minResource/queue/priority support.
See also: SPARK-36057
Does this PR introduce any user-facing change?
Yes, configuration added.
How was this patch tested?
CI passed