Scaling

This page discusses horizontal scaling to meet increasing demands.

The page does not cover scaling services such as Kafka, Zookeeper, and PostgreSQL Metrics.

Horizontal Scaling of ESS

Horizontal scaling of ESS involves adding more worker nodes (VMs) to run additional instances of ESS services; for instance, having 2 instances of LDP service, each running on a separate server, instead of a single LDP service running on one server.

Scale Services Independently

With ESS, you can scale each service independently of each other. For example, you can have 3 instances of the LDP service and 1 instance of the Solid OpenID Connect service.

Stateless Services

All user-facing ESS services (LDP, etc.) are stateless. Using stateless services strongly supports horizontal scaling as a user’s requests to a given service do not need to route to the same instance of that service.

Scale a Deployment

You can use Kustomize Overlays to scale your deployment.

Example: Scale a Deployment Manually

1. Create an overlay structure as described in Customize ESS.

2. Add the customization overlay:

   #kustomization.yaml
   
   ...
   
   patches:
     - target:
         kind: Deployment
         name: ess-signup-static
       patch: |-
         -  op: add
            path: /spec/replicas
            value: 3
   

   By adding more Signup page pods, the customization helps with resiliency.

Example: Scale Using a Horizontal Pod Autoscaler

1. Create an overlay structure as described in Customize ESS.

2. Add the customization overlay:

   #kustomization.yaml
   
   ...
   
   resources:
     - ess-ldp-autoscaler.yaml
   

   #ess-ldp-autoscaler.yaml
   # ESS Autoscaling
   
   # This needs a Metrics server to be installed. See:
   # https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale/
   # The command to install a metrics server is:
   # `kubectl apply -f https://github.com/kubernetes-sigs/metrics-server/releases/download/v0.3.6/components.yaml`
   
   # When the average CPU load is below 40 percent, the autoscaler tries to reduce the number of instances
   # in the deployment, to a minimum of nine which means it uses more than one machine.
   # When the load is greater than 40 percent, the autoscaler
   # tries to increase the number of instances in the deployment, up to a maximum of twenty.
   ---
   apiVersion: autoscaling/v1
   kind: HorizontalPodAutoscaler
   metadata:
     name: ess-ldp
   spec:
     maxReplicas: 20
     minReplicas: 9
     scaleTargetRef:
       apiVersion: apps/v1
       kind: Deployment
       name: ess-ldp
     # Average CPU usage across all LDP instances in the cluster.
     targetCPUUtilizationPercentage: 40