argoyle
eefab471d6
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Even with the schema cache and startup warmup, residual CPU bursts (metrics-server occasionally samples a request mid-flight) were enough to trip a brief scale-up, after which the default 5min scaleDown stabilization pinned the deployment at maxReplicas long after the spike had subsided. Tune both directions: - scaleUp.stabilizationWindowSeconds: 120 — a transient spike must persist for two consecutive minutes before any pod is added. Brief metric anomalies no longer move replicas. - scaleUp policy: add at most 1 pod per 60s. Smooths reaction. - scaleDown.stabilizationWindowSeconds: 120 (default 300) — once the workload calms, return to minReplicas faster. - scaleDown policy: remove at most 1 pod per 60s. Avoids thundering-herd scale-down.
41 lines
1.1 KiB
YAML
41 lines
1.1 KiB
YAML
apiVersion: autoscaling/v2
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kind: HorizontalPodAutoscaler
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metadata:
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labels:
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app.kubernetes.io/name: schemas
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name: schemas
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spec:
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scaleTargetRef:
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apiVersion: apps/v1
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kind: Deployment
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name: schemas
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minReplicas: 2
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maxReplicas: 4
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metrics:
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- type: Resource
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resource:
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name: cpu
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target:
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type: Utilization
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averageUtilization: 60
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behavior:
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scaleUp:
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# Wait 2min of sustained high CPU before scaling up. Schemas is
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# event-driven and the per-request work is bursty even with the
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# cache + warmup, so single spikes shouldn't pull replicas up.
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stabilizationWindowSeconds: 120
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policies:
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- type: Pods
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value: 1
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periodSeconds: 60
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scaleDown:
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# Default 300s window kept pods pinned at maxReplicas long after
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# the triggering spike had subsided. 120s is long enough to avoid
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# flapping but lets the deployment return to minReplicas quickly
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# once the workload calms.
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stabilizationWindowSeconds: 120
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policies:
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- type: Pods
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value: 1
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periodSeconds: 60
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