gke-workload-scaling

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.