k8s-app-demo

This repository contains a sample container app (utilizes FastAPI framework), let's call it "tooling", that connects to a PostgreSQL database. Repository also contains a Helm Chart to release it to Kubernetes Cluster for demo/learning purposes.

To get familiar with some of the Kubernetes objects, please read this.

Requirements:

The following applications are utilized in this project, and thus, are required:

• minikube
• kubectl
• helm
• docker
• docker-compose

Cloning Repository:

Using HTTPS protocol:

git clone https://github.com/darkwizard242/k8s-app-demo.git k8s-app-demo

Using SSH protocol:

git clone [email protected]:darkwizard242/k8s-app-demo.git k8s-app-demo

Tutorial (docker/docker-compose)

This project already contains a Dockerfile and docker-compose. Using these, you can run the FastAPI app and Postgresql database.

1. Build container image:

Run the following to build the container image:

docker-compose build

2. Run container image:

Run the following to provision the container(s) in a detached mode (if you prefer non-detached mode, execute docker-compose up):

docker-compose up -d

Tutorial (Kubernetes/minikube)

1. Getting Minikube ready:

Following command starts a local kubernetes cluster with Kubernetes version 1.25.3 while utilizing docker as the driver.

minikube start --driver=docker --kubernetes-version=1.25.3

Validate the output of Cluster.

kubectl cluster-info

Retrieve Nodes information:

kubectl get nodes -o wide

Validate all pods in kube-system namespace are READY:

kubectl -n kube-system get pods

2. Install metrics-server:

Apply YAML manifest for metrics-server:

kubectl apply -f metrics-server.yaml

Ensure metrics-server Pod is in READY state:

kubectl get pods -l k8s-app=metrics-server -n kube-system

If POD is in a state where image PULL has failed, attempt to PULL using the following:

minikube ssh docker pull k8s.gcr.io/metrics-server/metrics-server:v0.6.2

3. Utilize minikube's docker daemon and build image:

To build and run container (in this case docker images) in minikube's environment, execute the following to configure the appropriate environment variables:

eval $(minikube docker-env)

Build the container image:

docker build -t local/tooling:v1 .

4. Deploy and validate:

For the purpose of this tutorial, we will release the helm chart against a specific namespace called `demo'. Let's get started by creating the namespace:

kubectl create ns demo

Pull dependent chart to disk:

helm dependency build helmchart/

Deploy the Kubernetes Objects defined in the Helm Chart using helm to the previously created demo namespace while naming the Helm Release as tooling:

helm upgrade -i tooling helmchart/ --namespace demo

Once deployed, validate that all Pods are demo namespace are in READY state. NOTE that it may take a few minutes for the Pods to be in READY state as a postgresql image is pulled down and rolled out, and is required for the app to successfully initialize).

kubectl get pods -n demo

Helm chart test hooks are part of the chart, run the following to let helm perform those tests for you:

helm test tooling -n demo

To tail the app logs, run the following:

kubectl logs -f -l app.kubernetes.io/name=tooling -n demo

5. Access Application Endpoints:

By default, ClusterIP is used in this helm chart, which technically means that the Service endpoint is only accessible within the cluster.

In order to access the application endpoints, kubectl port-forward can be used. It will allow connection/traffic to a local port to be forwarded to the port of the Service in the cluster for navigation/access purposes.

The command below will setup port forwarding (<HOST_PORT_80>:<SVC_PORT_80>) on a local host port 80 to be forwarded to the Service port, which also is running on port 80.

kubectl port-forward svc/tooling 80:80 -n demo

Please note that kubectl port-forward process runs interactively (i.e. as a foreground process | use CTRL+C or CMD+C keys to end the process), so you may need to open another terminal session for any other activities.

The following command intializes a Pod that makes calls to the Service Endpoint with the hostname path to retrieve hostname of the Pods traffic is routed to:

kubectl run -n demo -i --tty load-generator --rm --image=busybox --restart=Never -- sh -c "while sleep 0.01; do wget -q -O- http://tooling.demo.svc.cluster.local/private/hostname && echo; done"

6. Load testing (optional):

Following command will run a pod based on Apache Bench that will load test the Service Endpoint:

kubectl run -i --tty apache-bench --rm --image=jordi/ab --restart=Never -- -k -c 100 -t 300s http://tooling.demo.svc.cluster.local/private/hostname

In the mean time, you can monitor the HorizontalPodAutoscaler to view scale-in and scale-out activities:

kubectl get hpa tooling -n demo -w

7. Cleanup:

Uninstall the helm chart:

helm uninstall tooling -n demo

Stop minikube cluster:

minikube stop

Delete minikube cluster:

minikube delete

Endpoint Requests (optional)

GET Request (/):

curl -i -H "Content-Type: text/html" -X GET http://localhost/

GET Request (/private/hostname):

curl -i -H "Content-Type: application/json" -X GET http://localhost/private/hostname

GET Request (/health/liveness):

curl -i -H "Content-Type: application/json" -X GET http://localhost/health/liveness

GET Request (/health/readiness):

curl -i -H "Content-Type: application/json" -X GET http://localhost/health/readiness

GET Request (/secrets):

curl -i -H "Content-Type: application/json" -X GET http://localhost/secrets

POST Request (/publisher):

curl -i -d '{"text":"HTTP POST method testing"}' -H "Content-Type: application/json" -X POST http://localhost/publisher

License

MIT

Author Information

This project was developed by Ali Muhammad.