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when pod scheduling faild, patch pod in parallel. #129794
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when pod scheduling faild, patch pod in parallel. #129794
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Adding the "do-not-merge/release-note-label-needed" label because no release-note block was detected, please follow our release note process to remove it. Instructions for interacting with me using PR comments are available here. If you have questions or suggestions related to my behavior, please file an issue against the kubernetes-sigs/prow repository. |
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This issue is currently awaiting triage. If a SIG or subproject determines this is a relevant issue, they will accept it by applying the The Instructions for interacting with me using PR comments are available here. If you have questions or suggestions related to my behavior, please file an issue against the kubernetes-sigs/prow repository. |
Hi @github-wyy. Thanks for your PR. I'm waiting for a kubernetes member to verify that this patch is reasonable to test. If it is, they should reply with Once the patch is verified, the new status will be reflected by the I understand the commands that are listed here. Instructions for interacting with me using PR comments are available here. If you have questions or suggestions related to my behavior, please file an issue against the kubernetes-sigs/prow repository. |
[APPROVALNOTIFIER] This PR is NOT APPROVED This pull-request has been approved by: github-wyy The full list of commands accepted by this bot can be found here.
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} | ||
if nnnNeedsUpdate { | ||
podStatusCopy.NominatedNodeName = nominatingInfo.NominatedNodeName | ||
} | ||
return util.PatchPodStatus(ctx, client, pod, podStatusCopy) | ||
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go util.PatchPodStatusWithoutErrorReturn(ctx, client, pod, podStatusCopy) |
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Why don't we need errors here? Are there any performance issues or other context that require this?
What type of PR is this?
/kind feature
when pod scheduling faild, patch pod in parallel.
What this PR does / why we need it:
when pod scheduling faild, patch pod in parallel. used to improve the scheduling speed of the scheduler.
This change has been running for 9 to 10 months across over 200 internal clusters without any issues, and it can effectively improve scheduling speed under specific conditions.