gke-workload-scaling

name	gke-workload-scaling
description	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.
metadata	{"category":"Containers"}

GKE Workload Scaling

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).

Workflows

1. Manual Scaling

Scale a deployment to a fixed number of replicas. Useful for immediate manual intervention or testing.

Command:

kubectl scale deployment {deployment_name} --replicas={number} -n {namespace}

# Verify the scale event
kubectl get deployment {deployment_name} -n {namespace}

2. Horizontal Pod Autoscaling (HPA)

Automatically scale the number of pods based on observed CPU utilization, memory utilization, or custom metrics.

Prerequisites:

• Metrics Server must be running (enabled by default on GKE).
• Containers clearly define resource requests/limits.

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.

3. Vertical Pod Autoscaling (VPA)

Automatically adjust the CPU and memory reservations for your pods to match actual usage. This is critical for right-sizing workloads.

Prerequisites:

• VPA must be enabled on the cluster.
  • Autopilot: Enabled by default.
  • Standard: Must be enabled manually.

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.

Best Practices

1. Define Resource Requests: HPA and VPA rely on accurate resource requests. Always define them in your container specs.
2. Avoid Metric Conflicts: Do not configure HPA and VPA to use the same metric (e.g., both CPU). This causes thrashing.
   • Typical Pattern: HPA on CPU, VPA on Memory.
3. Pod Disruption Budgets (PDBs): Define PDBs to ensure application availability during scaling events or node upgrades.
4. HPA Lag: HPA has a stabilization window (default 5 mins) to prevent rapid fluctuation.
5. VPA "Auto" Mode Risks: In "Auto" mode, VPA restarts pods to change resources. Ensure your application handles restarts gracefully (e.g., handles SIGTERM).
   • Note: By default, VPA requires at least 2 replicas to perform evictions (to prevent a situation where the only running replica is evicted, causing downtime). In GKE 1.22+, you can override this by setting minReplicas in PodUpdatePolicy.

Rightsizing Workflow

1. Deploy VPA in Off mode for 24+ hours
2. Read recommendations: kubectl describe vpa {deployment_name}-vpa -n {namespace}
3. Compare target values against current requests
4. Apply with 20% buffer: new_request = target * 1.2
5. Use patch format or update deployment manifest to apply new resource requests

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