Dessine Moi Un Cluster

Instructions to build a Kubernetes control plane one piece at a time, for learning purposes.
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Dessine-moi un cluster

In december 2018, there were 4000 Certified Kubernetes Administrators. I'm now one of them (yay!) and while preparing the certification, I wanted to understand better how the Kubernetes control plane works. I tinkered a bit with Kubernetes The Hard Way, but I also wanted to get a simpler, easier setup. This is what I came up with.

This will NOT tell you how to set up a "production-ready" Kubernetes cluster. This will show you how to set up a very simplified Kubernetes cluster, taking many shortcuts in the process (for instance, the first iteration gets you a 1-node cluster!) but giving a lot of space for further experimentation.

TL,DR: this is for learning and educational purposes only!

I gave a talk and a workshop based on these instructions in October 2019 at the LISA19 conference in Portland, Oregon. The video for that talk is available.


First things first: we need a bunch of binaries. Specifically:

  • etcd
  • Kubernetes
  • Docker (or some other container engine)

If you are already on the machine where you want to build your cluster, I suggest placing all these binaries in /usr/local/bin/. If you are going to do it on another machine, I suggest downloading all the binaries to a bin/ directory, then later copying that directory to the target machine.


Get binaries from the etcd release page. Pick the tarball for Linux amd64. In that tarball, we just need etcd and (just in case) etcdctl.

This is a fancy one-liner to download the tarball and extract just what we need:

curl -L | 
  tar --strip-components=1 --wildcards -zx '*/etcd' '*/etcdctl'


Then, get binaries from the kubernetes release page. We want the "server" bundle for amd64 Linux.

In that tarball, we just need one file: hyperkube.

It is a kind of meta-binary that contains all other binaries (API server, scheduler, kubelet, kubectl...), a bit like busybox, if you will.

This is a fancy one-liner to download the bundle and extract hyperkube:

curl -L | 
  tar --strip-components=3 -zx kubernetes/server/bin/hyperkube

For convenience, create a handful of symlinks. This is not strictly necessary, but if we don't, we will have to prefix every command with hyperkube, for instance hyperkube kubectl get nodes.

for BINARY in kubectl kube-apiserver kube-scheduler kube-controller-manager kubelet kube-proxy;
  ln -s hyperkube $BINARY


And then, we need Docker (or another runtime). Let's get one more tarball:

curl -L |
  tar --strip-components=1 -zx


Let's get this cluster started.

Root of all evil

We'll do everything as root for now.

Yes, it's ugly! But our goal is to set things up one at a time.

Get root, fire up tmux. We are going to use it as a crude process manager and log monitor. Yes, it's ugly! But ... etc.

Start etcd:


That's it, we have a one-node etcd cluster.

Create a new pane in tmux (Ctrl-b c).

Start the API server:

kube-apiserver --etcd-servers http://localhost:2379

Congratulations, we now have a zero-node Kubernetes cluster! (Kind of.)

Let's take a moment to reflect on the output of these commands:

kubectl get all
kubectl get nodes
kubectl get componentstatuses

Alright, maybe we could try to run a Deployment?

kubectl create deployment web --image=nginx

If we check with kubectl get all, the Deployment has been created, but nothing else happens. Because the code responsible for managing deployments (and creating replica sets and pods etc.) is not running yet.

Let's start it!

kube-controller-manager --master http://localhost:8080

Create a new tmux pane (Ctrl-b c again), and look at resources and events (with kubectl get events). We see a problem related to service account "default".

We didn't indicate that we wanted a service account, but it has been automatically added by the ServiceAccount admission controller.

We have two options:

  1. Restart the API server with --disable-admission-plugins=ServiceAccount
  2. Edit our Deployment spec to add automountServiceAccountToken: false

After doing one or the other, kubectl get all will show you that a pod has been created, but it is still Pending. Why?

Because we don't have a scheduler yet. And, most importantly... we don't even have a node!

So let's start the Docker Engine.


That's it! Then, create a new tmux pane. (Ctrl-b c one more time.)

If you want, test that Docker really works:

docker run alpine echo hello

Now we can start kubelet. If we start kubelet "as is," it will work, but it won't connect to the API server and it won't join our cluster. This will be a bit more complicated than for the controller manager (we can't just pass a --master flag to Kubelet). We need to give it a kubeconfig file.

This kubeconfig file has exactly the same format as the one we use when connecting to a Kubernetes API server. We can create it with kubectl config commands:

kubectl --kubeconfig kubeconfig.kubelet config set-cluster localhost --server http://localhost:8080
kubectl --kubeconfig kubeconfig.kubelet config set-context localhost --cluster localhost
kubectl --kubeconfig kubeconfig.kubelet config use-context localhost

Now we can really start kubelet, passing it this kubeconfig file.

kubelet --kubeconfig kubeconfig.kubelet

If we create a new tmux pane (do you remember how? ☺) and run kubectl get nodes, our node shows up. Great!

But if we look at our pod with kubectl get pod, it is still Pending.


Because there is no scheduler to decide where it should go. Sure, that might seem weird, since there is only one node anyway (and the pod has nowhere to go, nowhere to hide!), but keep in mind that the scheduler also checks for various constraints. We might very well have only one node, but the pod might not be allowed to run there, because the node is full, or doesn't satisfy some other constraint.

We have two options here.

  1. Manually assign the pod to our node.
  2. Start the scheduler.

Option 1 would require us to export the YAML definition of the pod, and recreate it after adding nodeName: XXX to its spec. (We cannot just kubectl edit the pod, because nodeName is not a mutable field.)

Option 2 is simpler. All we have to do is:

kube-scheduler --master http://localhost:8080

Note that we could also run kube-scheduler --kubeconfig kubeconfig.kubelet and it would have the same result, since (at this point) kubeconfig.kubelet contains information saying "the API server is at http://localhost:8080."

What's next?

Well, running NGINX is great, but connecting to it is better.

First, to make sure that we're in good shape, we can get the IP address of the NGINX pod with kubectl get pods -o wide, and curl that IP address. This should get us the "Welcome to NGINX" page.

Then, we are going to create a ClusterIP service to obtain a stable IP address (and load balancer) for our deployment.

kubectl expose deployment web --port=80

Get the service address that was allocated:

kubectl get svc web

And try to access it with curl. Unfortunately, it will time out.

To access service addresses, we need to run kube-proxy. It is similar to other cluster components that we started earlier.

kube-proxy --master http://localhost:8080

We can now open a new pane in tmux, and if we curl that service IP, it should get us to the NGINX page.

How did we get there? Ah, we can dive into iptables to get an idea.

iptables -t nat -L OUTPUT

This will show us that all traffic goes through a chain called KUBE-SERVICES. Let's have a look at it.

iptables -t nat -L KUBE-SERVICES

In that chain, there should be two more sub-chains called KUBE-SVC-XXX..., one for the kubernetes service (which corresponds to the API server itself) and another for our web service.

If we look at that chain with iptables -t nat -L KUBE-SVC-XXX..., it sends traffic to another sub-chain called KUBE-SEP-YYY.... SEP stands for "Service EndPoint". If we look at that chain, we will see an iptables rule that DNATs traffic to our container.

If you wonder how kube-proxy load balances traffic between pods, try the following experiment. First, scale up our Deployment.

kubectl scale deployment web --replicas=4

Wait until all the pods are running. Then, look at the KUBE-SVC-XXX... chain from earlier. It will look like this:

Chain KUBE-SVC-BIJGBSD4RZCCZX5R (1 references)
target     prot opt source               destination         
KUBE-SEP-ESTRSP6725AF5NCN  all  --  anywhere             anywhere             statistic mode random probability 0.25000000000
KUBE-SEP-VEPQL5BTFC5ANBYK  all  --  anywhere             anywhere             statistic mode random probability 0.33332999982
KUBE-SEP-7G72APUFO7T3E33L  all  --  anywhere             anywhere             statistic mode random probability 0.50000000000
KUBE-SEP-ZSGQYP5GSBQYQECF  all  --  anywhere             anywhere            

Each time a new connection is made to the service IP address, it goes through that chain, and each rule is examined in order. The first rule is using probabilistic matching, and will catch p=0.25 (in other words: 25%) of the connections. The second rule catches p=0.33 (so, 33%) of the remaining connections. The third rule catches p=0.50 (50%) of the connections that remain after that. The last rule catches everything that wasn't caught until then. That's it!

What now?

We have a one-node cluster, and it works, but:

  • if we want to add more nodes, we need to setup kubelet to use a network plugin (CNI or otherwise), because for now, we are using the internal Docker bridge;
  • as we add more nodes, we will need to make sure that the API server knows how to contact them, because by default it will try to use their names (which won't work unless you have a properly-set up local DNS server) and this problem will become apparent when using commands like kubectl logs or kubectl exec;
  • we have no security and this is BAD: we need to set up TLS certificates;
  • our control plane (etcd, API server, controller manager, scheduler) could be moved to containers and/or run as a set of non-root processes;
  • that control plane could be made highly available;
  • ideally, all these things should start automatically at boot (instead of manually in tmux!).

About that last item: it is possible to set up only kubelet (and the container engine) to start automatically at boot (e.g. with an appropriate systemd unit) and have everything else started by containers in "static pods".

To be continued!

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