Manages scaling for GKE workloads using HPA and VPA. Use when configuring Horizontal Pod Autoscaler (HPA), configuring Vertical Pod Autoscaler (VPA), or applying best practices for GKE workload autoscaling. Do not use for cluster-level autoscaling (Cluster Autoscaler), static cluster sizing, or configuring node-level machine styles directly.
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Manages scaling for GKE workloads using HPA and VPA. Use when configuring Horizontal Pod Autoscaler (HPA), configuring Vertical Pod Autoscaler (VPA), or applying best practices for GKE workload autoscaling. Do not use for cluster-level autoscaling (Cluster Autoscaler), static cluster sizing, or configuring node-level machine styles directly.
This skill provides workflows and best practices for scaling applications on Google Kubernetes Engine (GKE). It covers manual scaling, Horizontal Pod Autoscaling (HPA), and Vertical Pod Autoscaling (VPA).
Scale a deployment to a fixed number of replicas. Useful for immediate manual intervention or testing.
Command:
Version history
kubectl scale deployment {deployment_name} --replicas={number} -n {namespace}
# Verify the scale event
kubectl get deployment {deployment_name} -n {namespace}
Automatically scale the number of pods based on observed CPU utilization, memory utilization, or custom metrics.
Prerequisites:
Quick Command:
kubectl autoscale deployment {deployment_name} --cpu-percent=50 --min=1 --max=10
Manifest Approach (Recommended): Use a YAML manifest for version-controlled configuration. See assets/hpa-example.yaml for a template.
kubectl apply -f assets/hpa-example.yaml
# Verify HPA is created and fetching metrics
kubectl get hpa
Custom Metrics & External Metrics: For GKE, the modern and recommended approach for scaling based on Cloud Monitoring metrics (e.g., Pub/Sub queue length) is to use the External metric type, which is natively supported by the GKE control plane without requiring the Custom Metrics Adapter. For application-specific metrics exposed via Prometheus, you can use Google Cloud Managed Service for Prometheus or the Prometheus Adapter.
Automatically adjust the CPU and memory reservations for your pods to match actual usage. This is critical for right-sizing workloads.
Prerequisites:
Enable VPA on Standard Cluster:
gcloud container clusters update {cluster_name} --enable-vertical-pod-autoscaling --zone {zone}
Update Modes:
Off: Calculates recommendations but does not apply them. Good for "dry
run" analysis.Initial: Assigns resources only at pod creation time.Auto: Updates running pods by restarting them if recommendations differ
significantly from requests.InPlaceOrRecreate: Attempts to update Pod resources without recreating the
Pod. If in-place update is not possible, it reverts to Auto mode (requires
GKE 1.34+).Example: See assets/vpa-example.yaml for a configuration template.
minReplicas in PodUpdatePolicy.Off mode for 24+ hourskubectl describe vpa {deployment_name}-vpa -n {namespace}target values against current requestsnew_request = target * 1.2| Condition | Recommendation | Risk |
|---|---|---|
| CPU request >5x P95 actual | Reduce to P95 * 1.2 | Medium |
| Memory request >3x P95 actual | Reduce to P95 * 1.2 | Medium |
| CPU request >2x P95 actual | Rightsizing with 20% buffer | Low |
| No resource limits set | Add limits to prevent noisy-neighbor | Low |
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