Cherry Syte

Sweet little exercise in bundling up Docker based apps to run on K8s 🍒🍒.

Building the App image

cd <repo_directory>
docker build -t cherry-syte .

Running the App locally

docker run -p 5000:5000 cherry-syte

Deploy Kubernetes cluster in AWS

NOTE: This will create a VPC that includes subnets and EKS cluster

Install pre-requisites to run following commands - Mac

brew install terraform awscli wget kubernetes-cli aws-iam-authenticator

Run Terraform

NOTE: AWS credentials must exist locally with permissions to create VPC, EC2 and EKS AWS resources

cd terraform
terraform init -upgrade
terraform plan
terraform apply  (enter yes)

Basic Kubernetes configuration

aws eks --region $(terraform output -raw region) update-kubeconfig --name $(terraform output -raw cluster_name)
wget -O v0.3.6.tar.gz https://codeload.github.com/kubernetes-sigs/metrics-server/tar.gz/v0.3.6 && tar -xzf v0.3.6.tar.gz
kubectl apply -f metrics-server-0.3.6/deploy/1.8+/
kubectl get deployment metrics-server -n kube-system
kubectl apply -f https://raw.githubusercontent.com/kubernetes/dashboard/v2.0.0-beta8/aio/deploy/recommended.yaml
kubectl proxy

Accessing k8s Dashboard

In seperate terminal run

kubectl apply -f https://raw.githubusercontent.com/hashicorp/learn-terraform-provision-eks-cluster/master/kubernetes-dashboard-admin.rbac.yaml
kubectl -n kube-system describe secret $(kubectl -n kube-system get secret | grep service-controller-token | awk '{print $1}')

Now copy and past token from last step into the Dashboard UI
UI can then be accessed at this link

Deploy App to Kubernetes

cd <REPOSITORY_ROOT>
kubectl apply -f k8s
kubectl port-forward service/app 5000

Then you can access the application on localhost:5000

App Functionality

To get the server IP running the service and echo string back
Access the link http://localhost:5000?my_string=MY_STRING in a browser, or use CURL command

    curl http://localhost:5000?my_string=MY_STRING

To get index.html from html directory:
Access the link http://localhost:5000/html/index.html in a browser, or use CURL command

    curl http://localhost:5000/html/index.html

NOTE: any file added to html directory in the code can be accessed using the filename like so:

    curl http://localhost:5000/html/<MY_FILE>

J00MZ/syte-cherry