03 September 2019
By Yann Albou.
This blog post was originally published on Medium
Photo by Jonathan Hoxmark on Unsplash
UPDATE: A new blog post is available with k3d v3.x
K3d is a wrapper to easily launch a Kubernetes cluster using the very lightweight Rancher k3s distribution..
It fits particularly well in a development environnement when you want to test your application with the k8s manifests in real condition or as an administrator to validate behaviours or evaluate new k8s features.
In this blog post I will explain how to install it, how to create a full Kubernetes cluster with worker nodes and how it works.
First a bit of explanation on k3s. As mentioned in the Rancher web site the idea behind k3s is to get a very efficient and lightweight fully compliant Kubernetes distribution.
The Rancher team did a great job by reducing the binary to less than 40 mb removing all unnecessary components (Legacy, alpha, non-default features, …)
K3s used the following components:
K3s can be install either through a simple script that will download and configure a linux binary (less than 40Mb) plus a ‘k3s’ cli.
Or, which is my prefer way, through a docker image or with a pre-configured docker-compose. and this is where ‘k3d’ comes in.
k3d is a utility designed to easily run k3s in Docker, it provides a simple CLI to create, run, delete a full compliance Kubernetes cluster with 0 to n worker nodes.
First the installation (of course you need to have docker installed and the kubectl cli).
Launch the following install script that will detect your processor architecture (386, amd64) and your OS (linux, darwin, windows) and then install the cli tool :
wget -q -O - https://raw.githubusercontent.com/rancher/k3d/master/install.sh | bash
Create your first cluster:
k3d create --name dev --api-port 6551 --publish 8081:80
‘dev’ is the name of your Kubernetes that expose the api server port to 6551 and publish k3s node ports to the host on port 8081
Once created you can check the cluster status:
K3d list + — — — + — — — — — — — — — — — — — — — + — — — — -+ — — — — -+ | NAME | IMAGE | STATUS | WORKERS | + — — — + — — — — — — — — — — — — — — — + — — — — -+ — — — — -+ | dev | docker.io/rancher/k3s:v0.7.0 | running | 0/0 | + — — — + — — — — — — — — — — — — — — — + — — — — -+ — — — — -+
and to connect to it (the kubeconfig is stored in your user directory but it can easily be retrieved with a simple k3d command):
export KUBECONFIG="$(k3d get-kubeconfig --name='dev')" kubectl cluster-info kubectl get nodes NAME STATUS ROLES AGE VERSION k3d-dev-server Ready master 6d1h v1.14.4-k3s.1
One missing piece is the metric server which is a cluster-wide aggregator of resource usage data. It collects metrics like CPU or memory consumption for containers or nodes, exposed by Kubelet on each node.
So, if you want to use k8s’ features like horizontal pod autoscaler or even to be able to use kubectl top command you need to use the metrics-server (which replace Heapster that was marked as deprecated with Kubernetes version 1.11 and retired in 1.13)
To install it :
git clone https://github.com/kubernetes-incubator/metrics-server.git kubectl apply -f metrics-server/deploy/1.8+/
Wait 1 or 2 minutes and then you can now use the following commands:
kubectl top node kubectl top pod --all-namespaces
you can now deploy a simple nginx server using a deployment, a service and an Ingress manifest.
--- apiVersion: apps/v1 kind: Deployment metadata: labels: app: nginx name: nginx spec: replicas: 1 selector: matchLabels: app: nginx template: metadata: labels: app: nginx spec: containers: - image: nginx name: nginx ports: - containerPort: 80 protocol: TCP --- apiVersion: v1 kind: Service metadata: name: nginx spec: ports: - port: 80 protocol: TCP targetPort: 80 selector: app: nginx sessionAffinity: None type: ClusterIP --- apiVersion: extensions/v1beta1 kind: Ingress metadata: name: nginx annotations: ingress.kubernetes.io/ssl-redirect: "false" spec: rules: - http: paths: - path: / backend: serviceName: nginx servicePort: 80
I used the declarative versus imperative approach (see my previous blog on the declarative approach with the desire state)
kubectl apply -f https://raw.githubusercontent.com/myannou/k3d-demo/master/nginx.yaml
Once pods are running (kubectl get pods) you can access to nginx using localhost and the k3d publish port (8081 in our case):
Now repeat those steps creating 2 others k8s clusters (one with 1 worker node and the other with 2 worker nodes:
k3d create --name stag --api-port 6552 --publish 8082:80 --workers 1 k3d create --name prod --api-port 6553 --publish 8083:80 --workers 2
and then for instance you can scale up to 3 pods in the « prod » cluster:
kubectl scale --replicas=3 deployment/nginx
I am running 3 kubernetes clusters on my local macbook pro with respectively 1 master, 1 master with 1 worker node and 1 master with 2 worker nodes !
I also did the test with an old MacBook with less memory and I couldn’t run the third cluster but it was easy with the k3d command to stop the other clusters:
k3d stop --name=dev k3d stop --name=stag
and you can restart them later (k3d start –name=dev) retrieving the same state as before
You can even decide to create a cluster with a specific version of the k3s docker image which target a specific version of Kubernetes :
k3d create --name dev-0-8-1 --api-port 6554 --publish 8084:80 --version=0.8.1
it will target a 1.14.6 kubernetes version !
see the available k3s version: https://github.com/rancher/k3s/releases
A « docker ps » shows that the only started docker containers are the one from the master and the workers, you don’t see any docker container for your nginx images we previously started:
docker ps IMAGE COMMAND NAMES rancher/k3s:v0.7.0 "/bin/k3s agent" k3d-prod-worker-1 rancher/k3s:v0.7.0 "/bin/k3s agent" k3d-prod-worker-0 rancher/k3s:v0.7.0 "/bin/k3s server --h…" k3d-prod-server rancher/k3s:v0.7.0 "/bin/k3s agent" k3d-stag-worker-0 rancher/k3s:v0.7.0 "/bin/k3s server --h…" k3d-stag-server rancher/k3s:v0.7.0 "/bin/k3s server --h…" k3d-dev-server
So how it works to do docker in docker without mapping the docker socket ?
To understand execute the following commands in the k3d-dev-server docker container:
docker exec -it k3d-dev-server crictl imagesIMAGE TAG IMAGE ID SIZE docker.io/coredns/coredns 1.3.0 2ee68ed074c6e 12.3MB docker.io/library/nginx latest 5a3221f0137be 50.7MB docker.io/library/traefik 1.7.9 98768a8bf3fed 19.9MB docker.io/rancher/klipper-helm v0.1.5 c1e4f72eb6760 27.1MB docker.io/rancher/klipper-lb v0.1.1 4a065d8dfa588 2.71MB k8s.gcr.io/metrics-server-amd64 v0.3.3 c6b5d3e48b43d 10.5MB k8s.gcr.io/pause 3.1 da86e6ba6ca19 317kB docker exec -it k3d-dev-server crictl ps CONTAINER ID IMAGE STATE NAME 2796b478f1422 c6b5d3e48b43d Running metrics-server e524745ae7fc9 5a3221f0137be Running nginx d331d0f08e225 98768a8bf3fed Running traefik 50a618c636f6e 4a065d8dfa588 Running lb-port-443 44d89c5d598e2 2ee68ed074c6e Running coredns 444f2b203f128 4a065d8dfa588 Running lb-port-80
As previously mentioned k3s relies on the « Containerd » runtime container.
Docker, Containerd, and CRI-O are all container engines for Kubernetes and are all CRI (Container Runtime Interface) compatible
CRI was introduced in Kubernetes 1.5 and acts as a bridge between the kubelet and the container runtime
Like any API, CRI give you an abstraction layer that theoretically allows end users, Cloud providers and even Kubernetes distributions to switch from implementation. K3s allows to switch to docker although it is not recommended:
k3s includes and defaults to containerd. Why? Because it’s just plain better. If you want to run with Docker first stop and think, “Really? Do I really want more headache?” If still yes then you just need to run the agent with the –docker flag
Containerd was initially developed by Docker but was donated in 2017 to CNCF to serve as the industry standard for container management daemon. Docker is still using Containerd, but Containerd is independent now and doesn’t require docker at all (especially the Docker daemon).
We can interact with a CRI runtime directly using the « crictl » tool (like the docker cli)
Containerd adopters are Rancher, Google,… whereas Red Hat is investing heavily in CRI-O.
K3s relies on standards with CRI implemented through Containerd which makes it possible to run inside a Docker image without using ugly and non secured tricks.
The Rancher team did a great job with k3s and k3d making very easy, simple and efficient to run several instances of Kubernetes clusters on a single machine.
Usages are multiples and very adapted to Kubernetes development, testing and training.