Minikube
Minikube runs a single-node Kubernetes cluster inside a Virtual Machine (VM) on your laptop for users looking to try out Kubernetes or develop with it day-to-day. For more information on minikube, see the Minikube documentation.
This document explains on how to deploy pega using minikube as a provider.
Quick Start
- For installing minikube - https://kubernetes.io/docs/tasks/tools/install-minikube/
- Minikube Documentation - https://minikube.sigs.k8s.io/docs/overview/
Basic Commands for Minikube
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Start a cluster by running:
minikube start
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Access the Kubernetes Dashboard running within the minikube cluster:
minikube dashboard
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Stop your local minikube cluster:
minikube stop
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Delete your local cluster:
minikube delete
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To start minikube with different version of kubernetes
minikube start --kubernetes-version v1.15.0
FAQ’s
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How to increase the memory limit of a running minikube
There is no direct way to increase the memory limit of a running minikube.
minikube stop
minikube delete
minikube start --cpus 4 --memory 12288
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How to start minikube with custom CPU/memory limits
minikube start --cpus 4 --memory 10240
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How to set default memory which is considered on each minikube start
minikube config set memory 5000
followed byminikube start
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How to access Pega Designer Studio after deployment
<minikube ip>:<Pega service nodePort>/prweb
minikube ip can be fetched using command - minikube ip
and Pega service Nodeport can be fetched using below command
kubectl get service -o go-template='Port to access: ' <service-name> --namespace <namespace name>
Recommended Memory Limits
Start minikube with at least 4 CPU’s and 10GB memory for complete pega deployment. As per the need increase the limits of minikube.
Note
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Use “values-minimal.yaml” to deploy pega which is available in the pega chart directory.
Example helm command to deploy
helm install . -n mypega --namespace myproject --values ./values-minimal.yaml
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As this runs on the personal laptop for a day-to-day project with minimal memory and CPU limits, minikube supports only “install”, “deploy” and “install-deploy” actions. It is advisable to use this kind of cluster configuration for simple activities on Pega as it might spike with CPU and memory.