Autoscaling Workflow Enhancement - Part 2 #94
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Jeffwan
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Aug 26, 2024
| } else if currentReplicas > pa.Spec.MaxReplicas { | ||
| desiredReplicas = pa.Spec.MaxReplicas | ||
| } else if currentReplicas < minReplicas { | ||
| desiredReplicas = minReplicas | ||
| } else { | ||
| // TODO: fix the compute replicas interface. | ||
| kpaScaler, err := scaler.NewKpaAutoscaler( |
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should we move the initialization earlier? for example in reconciler new, here is the reconcile workflow and we should use the reference directly.
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Fix it on newest commit 562c5d1
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besides the initialization part, rest looks good to me |
1. Relocate NewKPAAutoscaler from reconcileKPA to newReconciler. 2. remove attribute podCounter from KpaAutoscaler; dynamically fetch up-to-date readyPodCount in reconceilKPA and pass to Scale Algorithm.
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@kr11 It looks good to me and feel free to merge it |
gangmuk
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Jan 25, 2025
* add autoscaler pipeline part 2 * refactor the KPA initialization: 1. Relocate NewKPAAutoscaler from reconcileKPA to newReconciler. 2. remove attribute podCounter from KpaAutoscaler; dynamically fetch up-to-date readyPodCount in reconceilKPA and pass to Scale Algorithm.
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Pull Request Description
Major Updates:
appsv1.Deploymentinstead of the generalizedautoscalingv1.Scale. For a detailed discussion, refer to [issue AutoScaler Workflow Part 2: Areas for Optimization and Completion #95].NewDefaultDeciderKpaSpec()function to apply default values.PodAutoscalerReconciler::Reconcile: fix RBAC error about Event, add event viar.EventRecorder.Eventfto confirm the normal running of KPA Reconciler. Relevant discussion in [issue Add make docker-build in Integration Testing #93].Additional Updates:
readyPodsCountand a pre-constructedDeciderKpaSpecfor initializing a new KPA autoscaler.DeciderSpechas been renamed toDeciderKpaSpec, indicating that this specification is exclusively used by KPA.Open Issues and Enhancements:
For unresolved issues and potential enhancements, please see the discussion in [issue #95].
This update aims to enhance the scalability and reliability of our autoscaling processes, ensuring better performance and maintainability. Your feedback and contributions to these changes are welcome.
Related Issues
Resolves: [issue #95]
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