Manage Autoscaling

Kf supports two primary autoscaling modes:

• Built-in autosacling similar to Cloud Foundry.
• Advanced autoscaling through the Kubernetes Horizontal Pod Autoscaler (HPA).

Built-in autoscaling

Kf Apps can be automatically scaled based on CPU usage. You can configure autoscaling limits for your Apps and the target CPU usage for each App instance. Kf automatically scales your Apps up and down in response to demand.

By default, autoscaling is disabled. Follow the steps below to enable autoscaling.

View Apps

You can view the autoscaling status for an App using the kf apps command. If autoscaling is enabled for an App, Instances includes the autoscaling status.

$ kf apps

Name   Instances              Memory  Disk  CPU
app1   4 (autoscaled 4 to 5)  256Mi   1Gi   100m
app2   1                      256Mi   1Gi   100m

Autoscaling is enabled for app1 with min-instances set to 4 and max-instances set to 5. Autoscaling is disabled for app2.

Update autoscaling limits

You can update the instance limits using the kf update-autoscaling-limits command.

kf update-autoscaling-limits app-name min-instances max-instances

Create autoscaling rule

You can create autoscaling rules using the kf create-autoscaling-rule command.

kf create-autoscaling-rule app-name CPU min-threshold max-threshold

Delete autoscaling rules

You can delete all autoscaling rules with the kf delete-autoscaling-rule command. Kf only supports one autoscaling rule.

kf delete-autoscaling-rules app-name

Enable and disable autoscaling

Autoscaling can be enabled by using enable-autoscaling and disabled by using disable-autoscaling. When it is disabled, the configurations, including limits and rules, are preserved.

kf enable-autoscaling app-name

kf disable-autoscaling app-name

Advanced autoscaling

Kf Apps support the Kubernetes Horizontal Pod Autoscaler interface and will therefore work with HPAs created using kubectl.

Kubernetes HPA policies are less restrictive than Kf’s built-in support for autoscaling.

They include support for:

• Scaling on memory, CPU, or disk usage.
• Scaling based on custom metrics, such as traffic load or queue length.
• Scaling on multiple metrics.
• The ability to tune reactivity to smooth out rapid scaling.

Using custom HPAs with apps

You can follow the Kubernetes HPA walkthrough to learn how to set up autoscalers.

When you create the HPA, make sure to set the scaleTargetRef to be your application:

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: app-scaler
  namespace: SPACE_NAME
spec:
  scaleTargetRef:
    apiVersion: kf.dev/v1alpha1
    kind: App
    name: APP_NAME
  minReplicas: 3
  maxReplicas: 10
  metrics:
  - type: Resource
    resource:
      name: memory
      target:
        type: Utilization
        averageUtilization: 60

Caveats

• You shouldn’t use Kf autoscaling with an HPA.
• When you use an HPA, kf apps will show the current number of instances, it won’t show that the App is being autoscaled.