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pega-helm-charts

Orchestrate a Pega Platform™ deployment by using Docker, Kubernetes, and Helm to take advantage of Pega Platform Cloud Choice flexibility.

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

  1. For installing minikube - https://kubernetes.io/docs/tasks/tools/install-minikube/
  2. Minikube Documentation - https://minikube.sigs.k8s.io/docs/overview/

Basic Commands for Minikube

FAQ’s

  1. 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

  2. How to start minikube with custom CPU/memory limits

    minikube start --cpus 4 --memory 10240

  3. How to set default memory which is considered on each minikube start

    minikube config set memory 5000 followed by minikube start

  4. 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

  1. 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

  2. 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.