This article is part 2 of a 3 part series of my job interview experience as a DevOps Engineer.
In the last part I introduced in the exercises and talked about the complications I had with building a Dockerimage with Chrome inside on an
arm64 platform. Also the used NodeJS app and its’
Dockerfile was presented. This part will be more about Kubernetes, setting up the Cluster and deploying the NodeJS app.
Setting up a Kubernetes Cluster
After creating the
Dockerfile it was about setting up a Kubernetes cluster. Which program I use for this was up to me. In my DevOps with Kubernetes course we use k3d, this implements K3s in Docker. During the course I had not experienced any problems with this solution, so I was sure to use k3d to solve the assignments.
Installing k3d on macOS is easy via Homebrew with
brew install k3d. The cluster can then be created with
k3d cluster create --port '8082:30080@agent' -p 8081:80@loadbalancer --agents 2. The
8081:80@loadbalancer will allow our apps to be accessible to us via
Deploying the app
With a running Kubernetes cluster, deployment was now on the agenda. I decided from the beginning to logically separate the app from the rest of the cluster and created a separate namespace. This is possible with
kubectl create namespace exercise-ns in Kubernetes. The basic structure for deployment can be found in the Kubernetes documentation (Deployments | Kubernetes). So I created a
deployment.yaml in my
manifests folder with the following content:
apiVersion: apps/v1 kind: Deployment metadata: name: exercise-app namespace: exercise-ns labels: app: exercise-app spec: replicas: 1 selector: matchLabels: app: exercise-app template: metadata: labels: app: exercise-app spec: containers: - name: exercise-app image: niklasmtj/exercise-app:v1
To make the deployment discoverable I also created a service. The Kubernetes documentation for services (Deployments | Kubernetes) again provides the basic structure. The service definition can be found under
apiVersion: v1 kind: Service metadata: name: exercise-svc namespace: exercise-ns spec: type: ClusterIP selector: app: exercise-app ports: - protocol: TCP port: 80 targetPort: 3000
It is important to note here that the
targetPort corresponds to the port exposed by the Docker container.
After preparing the deployment, the task was now to be able to access it. Thus, the connection via an Ingress came into play. By default, K3s uses the Traefik Ingress Controller for Ingress routing, which I also used. The ingress configuration is quite simple. In an
ingress.yaml file I used the following configuration:
apiVersion: extensions/v1beta1 kind: Ingress metadata: name: exercise-ingress namespace: exercise-ns spec: rules: - http: paths: - path: / backend: serviceName: exercise-svc servicePort: 80
This way the
/ path is directly forwarded to the
exercise service we defined in
service.yaml on port 80.
Another task I had to solve with an Ingress was to limit the IP range that is accepted at all. Unfortunately, due to my previous knowledge, I did not had the possibility to implement the whole thing at that time. Nevertheless I read up on how I would implement it and documented it in my readme. During the task I took a closer look at the NGINX Ingress Controller, because I found the most results about it during my research. There it seems to work by a simple annotation in the
metadata: annotations: ingress.kubernetes.io/whitelist-source-range: "192.168.0.0/16"
This will make sure that only IPs from the
192.168.0.0/16 net is able to connect to the apps. So every IP starting at
22.214.171.124 are eligible to connect. NGINX will drop every request not coming from this IP range.
Also, attempts with Traefik as the Ingress controller, which is used by k3s by default, failed. This is an area I need to look at in more detail in the near future to understand exactly how Ingress works and at what level you can block IPs. Additionally, I need to look at whether it makes a difference that my k3s cluster is running inside Docker and how that affects incoming IPs.
Thank you for reading,
The code from this post can also be found on GitHub: niklasmtj/kubernetes-exercise.
Additionally, I created an
arm64 as well as an
amd64 docker image for
niklasmtj/exercise-app:v1. So the example app should be usable on other devices as well.