In our last blog post on kubernetes from scratch, we created a pseudo cluster to show how Kubernetes works inside. Today we are going to add a second node and make sure the cluster utilizes it.
Creating second node
What about having more than one node? What if we would like to schedule pods on 2 nodes? It is as simple as running kubelet on another node and making sure it connects to our API Server.
First of all, we assume that we have the first node from our previous blog post, with 10.135.53.41 internal IP, 46.101.177.76 external IP, running API Server, etcd and nginx deployment.
Now let’s create a second one, as before, replace --ssh-keys with your SSH key. After that, copy kubeletconfig file from the master node.
$ doctl compute droplet create k8s-worker --region fra1 --size 2gb --image ubuntu-18-04-x64 --enable-private-networking --ssh-keys 79:29:54:77:13:2f:9c:b8:06:3e:8b:fe:8d:c0:d7:ba
ID Name Public IPv4 Private IPv4 Public IPv6 Memory VCPUs Disk Region Image Status Tags
63460608 k8s-worker 46.101.98.124 2048 2 40 fra1 Ubuntu 18.04.3 x64 new
$ scp -3 root@46.101.177.76:/var/lib/kubelet/config.yaml root@46.101.98.124:~/
$ ssh root@46.101.98.124
In the next step, you have to install essential prerequisites - Docker and Kubernetes node binaries. Install Dockerfollowing instructions from the previous part, and then install Kubernetes with:
Wait, can you see that pods have duplicated IP addresses? This is because we don’t have a way to manage IP address for pods among all nodes, this also means that there is no communication between pods located on different nodes. To fix that we have to introduce another cluster component – network fabric.
Kubernetes networking using Flannel
Kubernetes makes specific assumptions about networking in the cluster:
pods can communicate with each other by using unique pod’s IP address
nodes can communicate with pods using unique pod’s IP address
the IP that a container sees itself as is the same IP that others see it as
Kubernetes assumes that each pod and service in a cluster has a unique IP address and can communicate with other pods using their IP addresses. To achieve that we need a way to assign subnet of IP address for each node and ask Docker to use it when spawning containers, then we have to establish a non-NAT communication between these IP address. There is a lot of ways to do that, here we are going to focus on Flannel.
Flannel is one of the easiest ways to achieve these assumptions. Basically, Flannel runs as an agent on each node and is responsible for allocating a subnet for that node out of configured address space. That subnet is used by docker to obtain IP addresses for pods. Each subnet together with node’s IP address is stored in etcd and is readable by all agents. This allows flannel to obtain node location for given pod’s IP and forward traffic to that node.
This is how our networking will look like, it also shows how flannel works in the big picture.
Applying what we’ve just learned, now we can run flannel on our nodes. The tricky part is to configure docker to use flannel. This is what we are going to do:
create CNI configuration file and send it to both nodes with scp
insert initial flannel configuration to etcd using etcdctl
run flannel on both nodes pointing it to our etcd
rerun kubelet on both nodes with --network-plugin=cni option. That will use previously created config to allocate new pods to the subnet created by flannel
and now we can check pods’ IP addresses and try to ping them on different nodes:
root@k8s-master:~$ kubectl get pods -owide
NAME READY STATUS RESTARTS AGE IP NODE
nginx-86c57db685-6brvp 1/1 Running 0 26h 10.11.112.7 k8s-master
nginx-86c57db685-lphqr 1/1 Running 0 26h 10.15.176.4 k8s-worker
nginx-86c57db685-qc687 1/1 Running 0 26h 10.11.112.8 k8s-master
nginx-86c57db685-s6gx7 1/1 Running 0 26h 10.15.176.3 k8s-worker
nginx-86c57db685-zqtxx 1/1 Running 0 26h 10.15.176.2 k8s-worker
root@k8s-master:~$ kubectl run --generator=run-pod/v1 -it curl --image=ulamlabs/curlping --command -- bash
root@curl:/$ ping 10.11.112.7 -c 1 && ping 10.15.176.4 -c 1
PING 10.11.112.7 (10.11.112.7): 56 data bytes
64 bytes from 10.11.112.7: icmp_seq=0 ttl=64 time=0.051 ms
--- 10.11.112.7 ping statistics ---
1 packets transmitted, 1 packets received, 0% packet loss
round-trip min/avg/max/stddev = 0.051/0.051/0.051/0.000 ms
PING 10.15.176.4 (10.15.176.4): 56 data bytes
64 bytes from 10.15.176.4: icmp_seq=0 ttl=62 time=1.999 ms
--- 10.15.176.4 ping statistics ---
1 packets transmitted, 1 packets received, 0% packet loss
round-trip min/avg/max/stddev = 1.999/1.999/1.999/0.000 ms
Sweet, we have a pod to pod communication and this is how our nodes look like now:
Load balancing between nodes
So now we have both nodes fully capable of running pods, what about receiving traffic? Currently, we accept traffic only on the first node, it will be forwarded to pods on the second node (by flannel) but this is not high availability solution – the first node is single a point of failure. To solve that we should install Kube Proxy on all worker nodes, after doing that we can add an icing on our cake – DigitalOcean Load Balancer and balance ingress between nodes.
root@k8s-worker:~$ kube-proxy --master=http://$MASTER_IP:8080 &> /tmp/proxy.log &
$ doctl compute load-balancer create --name lb --region fra1 --forwarding-rules entry_protocol:http,entry_port:80,target_protocol:http,target_port:30073 --health-check protocol:http,port:30073,path:/,check_interval_seconds:10,response_timeout_seconds:5,healthy_threshold:5,unhealthy_threshold:3
$ doctl compute droplet list "k8s*"
ID Name Public IPv4 Private IPv4 Public IPv6 Memory VCPUs Disk Region Image Status Tags
63370004 k8s-master 46.101.177.76 10.135.53.41 2048 2 40 fra1 Ubuntu 18.04.3 x64 active
63460608 k8s-worker 46.101.98.124 10.135.40.58 2048 2 40 fra1 Ubuntu 18.04.3 x64 active
$ doctl compute load-balancer add-droplets 58f02699-5717-43e6-bbfe-51ef4cc0a227 --droplet-ids 63370004,63460608
$ doctl compute load-balancer get 58f02699-5717-43e6-bbfe-51ef4cc0a227
ID IP Name Status Created At Algorithm Region Tag Droplet IDs SSL Sticky Sessions Health Check Forwarding Rules
58f02699-5717-43e6-bbfe-51ef4cc0a227 67.207.79.225 lb active 2017-09-27T09:40:56Z round_robin fra1 63370004,63460608 false type:none,cookie_name:,cookie_ttl_seconds:0 protocol:http,port:30073,path:/,check_interval_seconds:10,response_timeout_seconds:5,healthy_threshold:5,unhealthy_threshold:3 entry_protocol:http,entry_port:80,target_protocol:http,target_port:30073,certificate_id:,tls_passthrough:false
$ curl http://67.207.79.225
<!DOCTYPE html>
<html>
<head>
<title>Welcome to nginx!</title>
Everything works, we could even assume that we have production ready cluster 😀 but of course, we are far from that.
This is how our cluster looks now:
Setting up Kubernetes component - Ingress
Load balancer is a great tool to expose our cluster to the public, but still it's not the best what you can get.As your cluster will grow, possibly you'll end up in situation where you'll need more and more load balancers. Inproduction environment, usually you will want to replicate your control plane node for high availability, so youwill have to set up a load balancer balancing traffic between control plane nodes. Usually you will deploy a fewor lot of applications inside your cluster. That will lead you to setting up a load balancer of each application.The problem is, every load balancer costs money. Single load balancer does not cost a fortune, but as your kubernetes cluster grows, cost will become significant.You can solve this problem with another Kubernetes component - Ingress. Its task is to define routing rules to the services inside the cluster. For example, you can configure which service will be targeted depending on Host HTTP header.
First you will need an ingress controller which will be deployed as a NodePort service and previously createdload balancer will lead to it instead of nginx service. There is a variety of ingress controllers available, we will use NGINX controller here.
Let's start with creating nginx-ingress.yaml manifest file, don't forget to replace server IP with internal IP of your master node in nginx-ingress-kubeconfig:
Everything work as before, we can access the nginx service by the load balancer. Benefits from using Ingress rise when there are, let's say, 10 applications with different domains on your cluster. Instead of setting up a load balancer for each application, you'll need only one for the ingress controller, and ingress will do the rest later on.
Running Flannel as a DaemonSet
When you were setting up the second node, probably you have noticed that there is a lot of repetitive work - running and configuring kube-proxy and flannel. As the cluster grows, likely there will be more services that will be needed on each node.For this purpose there are another Kubernetes resource - DaemonSet. DaemonSet does exactly that - it runs a pod on each node in the kubernetes cluster.
At first, on both nodes kill flanneld process with pkill flanneld, next, on the master node create flannel manifest file, replace IP in --etcd-endpoints option with your master node internal IP:
root@k8s-master:~$ kubectl apply -f flannel.yaml
root@k8s-master:~$ kubectl get pods -n kube-system
NAME READY STATUS RESTARTS AGE
kube-flannel-mjk7w 1/1 Running 1 4m12s
kube-flannel-smk6t 1/1 Running 1 3m58s
root@k8s-master:~$ systemctl restart docker
root@k8s-worker:~$ systemctl restart docker
$ curl http://67.207.79.225
<!DOCTYPE html>
<html>
<head>
<title>Welcome to nginx!</title>
As you see, everything works as before, and now, if you add a node to the cluster, flannel will be set up automatically.You can now try setting up kube-proxy as DaemonSet yourself.
Wrapping up on Kubernetes deployment from scratch
In this blog post, we have learned how nodes in the kubernetes cluster communicate together and how pods are exposed to the outer world through services.Next we have learnt about two extremely useful Kubernetes resources - Ingresses and DaemonSets.There are more aspects of Kubernetes that we need to cover before we can say that our cluster is production ready. We are going to cover them in future blog posts, so stay tuned!
If you’d like to know more about our services for your business, don’t hesitate to get in touch.
Seasoned Lead Full Stack Python Developer and System Engineer
Konrad has 6 years of strong background in Embedded & Networking programming, as well as experience leading multiple teams for UK and US customers, which has laid the foundations for him to become a natural Technology leader. A man obsessed with continuous improvements, Konrad is never satisfied with his results.
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