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onlineBeratung-k8s-config Template

The configuration template repository with default environment settings serving required data for k8s helm chart. It already provides the accoring k8s resources within a submodule pointing to kubernetes repository.

Prerequisits

  1. Up and running kubernetes cluster (e.g. minikube)
  2. kubectl
  3. helm
  4. Lens (optional but recommended for having a view on k8s cluster)

Installation

  1. If you install the chart on a local cluster and use the default configuration make sure you have the following local domain mappings in /etc/hosts (Linux) or C:\Windows\System32\drivers\etc\hosts (Windows)
  • onlineberatung.localhost -> 127.0.0.1
  • video.onlineberatung.localhost -> 127.0.0.1
  1. In order to be able to pull images from docker repository you create a Kubernetes secret named registry-secret
    kubectl create secret docker-registry registry-secret --docker-server=ghcr.io --docker-username=<your-github-username> --docker-password=<your-github-password> --docker-email=<your-email>
  2. Install the nginx Ingress Controller for Kubernetes
    helm upgrade --install ingress-nginx ingress-nginx --repo https://kubernetes.github.io/ingress-nginx --namespace ingress-nginx --create-namespace
  3. Navigate to the customization folder and execute the script: for non helm managed resources
    sh setup_customization_configmaps.sh <namespace> <release_name>
  4. Install the helm chart
    helm install <release_name> k8s --wait-for-jobs -f values.yaml -f values-secrets.yaml

Local setup with Minikube

For local setup on a minicube cluster, two additional steps are required after installation:

  1. Use minikube tunnel on the host to access the cluster
  2. Userservice, messageservice, and uploadservice run sidecars to set up master keys. On minikube, use kubectl port-forward -n ingress-nginx service/ingress-nginx-controller 80:80 to forward nginx port 80 to make this work

If you are experiencing problems, we have found that a minikube with too little memory can cause various problems during installation. For this reason we recommend allocating at least 10gb of RAM.

Upgrading an existing helm release based on a branch

  1. Make sure you have the latest changes of git submodule if you work on a branch
    cd k8s
    git fetch
    git pull
  2. Execute helm upgrade
    helm upgrade <release_name> k8s -f values.yaml -f values-secrets.yaml

Upgrading an existing helm release based on released tag

  1. Make sure you have the release tag you want to deploy checked out for
    cd k8s
    git fetch
    git checkout tags/<release_tag>
  2. Execute helm upgrade
    helm upgrade <release_name> k8s -f values.yaml -f values-secrets.yaml

Custom steps needed for Apple M1 chip users

If you use computers with apple m1 chips, you'll need to build your keycloak and consulting type service image locally and use these versions.

  1. git clone https://github.com/Onlineberatung/onlineberatung-keycloak-otp.git
  2. cd onlineberatung-keycloak-otp
  3. git checkout develop
  4. git pull origin develop
  5. mvn clean install
  6. In Dockerfile of the project add target folder prefix for the jar (target/) in the COPY line
  7. docker build . -t keycloak:dockerImage.develop.m1

Go to onlineBeratung-k8s-config/k8s/charts/keycloak/values.yaml and change the value to your custom built image version.

Repeat similar steps for consultingtype service:

  1. git clone https://github.com/Onlineberatung/onlineBeratung-consultingTypeService.git
  2. cd consultingTypeService
  3. git checkout develop
  4. git pull origin develop
  5. mvn clean install
  6. In Dockerfile of the project add target folder prefix for the jar (target/) in the COPY line
  7. docker build . -t consultingtype-service:dockerImage.develop.m1

Go to values.yaml and change the value of consulting type service to your locally built m1 version. Perform helm upgrade as described in the previous steps.