A Simple Kubernetes Cluster on AWS - Tutorial

iRonin IT Team - Experts in software development
aws, clusters, devops, kubernetes

It’s DevOps tutorial time again! This time around we take a closer look at Kubernetes, the container orchestration package, and how we can use it to deploy and load balance clusters on AWS with Kubespray.

Kubernetes is a rising star in the DevOps world. This clever container orchestration package is making waves due to its management and configuration options - which make it ideal for load balancing. In this continuation of our DevOps tutorials, we will show you how to setup a simple Kubernetes cluster on AWS using Kubespray.

You might ask, “Why Kubespray?”, since there are other more popular solutions like Cobs. Kubespray is architecture agnostic - it’s a community driven combination of Ansible playbooks that allows us to create a Kubernetes cluster on AWS EC2, DigitalOcean, VPS - or even bare metal.

AWS Setup

First we need to configure networking for our Kubernetes cluster.


Let’s create a kubernetes-ironin-vpc VPC with as CIDR and the DNS resolution and DNS hostnames enabled:


Let’s add 2 new subnets for the VPC we just created:

  1. kubernates-ironin-1 with CIDR set to in eu-central-1a availability zone
  2. kubernates-ironin-2 with CIDR set to in eu-central-1b availability zone

Routing table

We will also need a routing table for our VPC with 2 subnets. Let’s name it kubernetes-ironin-routetable.

Internet gateway

Finally, we need to create an internet gateway (named kubernetes-ironin-internetgw) that will allow us to connect to our VPC from the outside world.

Security groups

Since we have a basic network setup we can create proper security groups for connecting to our instances.

Here is the minimal set of rules that should keep us going:

They will allow us to create a cluster using internal IPs, as well as connect to the dashboard (https://master.node.external.ip:6443/ui) from our personal machine (source my.personal.static.ip/32).

Now we can create our EC2 instances (we suggest at least 2x t2.small instances running Ubuntu 16.04) in eu-central-1a and eu-central-1b availability zones, assigned to the kubernetes security group we created before.

After successful creation, we can connect to all instances via SSH and run sudo apt-get update to update the packages so that new packages can be correctly installed from the playbooks.

Creating an inventory

Note: Before cloning Kubespray, make sure that Python3 with pip is installed.

  1. Clone https://github.com/kubernetes-incubator/kubespray to your local drive
  2. Install python dependencies:
pip3 install -r requirements.txt
  1. Create an inventory/inventory.cfg file similar to the one below:
ip-10-1-1-2.eu-centeral-1.computer.internal ansible_host=ip1 ip= ansible_user=ubuntu ansible_python_interpreter=/usr/bin/python3
ip-10-1-1-3.eu-centeral-1.computer.internal ansible_host=ip2 ip= ansible_user=ubuntu ansible_python_interpreter=/usr/bin/python3





Provide instance’s external ips under ansible_host attribute so Ansible know how to connect to your instances.

Setting up the cluster

Once you are ready run the following command to provision your servers:

ansible-playbook -i inventory/inventory.cfg -b -v cluster.yml --private-key=~/.ssh/your_key

After provision is successful you can login to the master instance and check if nodes are connected correctly using kubectl get nodes command. It should give you similar output:

NAME            STATUS    ROLES     AGE       VERSION
ip-10-1-1-184   Ready     node      3m        v1.9.1+coreos.0
ip-10-1-1-216   Ready     master    3m        v1.9.1+coreos.0

Deploying an app

In this tutorial we will deploy simple Node.js hello world app. Let’s login to the master instance and create a new deployment file:

# template.yml
apiVersion: apps/v1
kind: Deployment
  name: app-deployment
    app: node-app
  replicas: 2
      app: node-app
        app: node-app
      - name: node-app
        image: lmironin/hello-docker:latest
        - containerPort: 8080
apiVersion: v1
kind: Service
  name: app-service
    app: node-app
  type: NodePort
  - port: 80
    targetPort: 8080
    nodePort: 30080
    protocol: TCP

This template defines Deployment and a Service. Deployment (a rule for running the pods on the cluster). In our case, we have 2 replicas running lmironin/hello-docker app which exposes 8080 port.

A Service with the NodePort type basically creates a service on all Nodes, so that we can access our pods using NodeIp:NodePort - even though the pods change (when you delete a pod and deployment creates a new one, it will have a different IP address).

Let’s create our deployment and service:

$ kubectl create -f template.yml
> deployment "app-deployment" created
> service "app-service" created

You can check if pods are running with the kubectl get pods command. This should give you output similar to below:

$ kubectl get pods
> NAME                              READY     STATUS    RESTARTS   AGE
> app-deployment-85c868cc55-44s5m   1/1       Running   0          36m
> app-deployment-85c868cc55-b6kjz   1/1       Running   0          36m

If you remove one of the pods, deployment will automatically spin up a new one to preserve the number of running replicas. You can try this yourself:

  1. Remove a pod by running: kubectl delete pod pod's id (you can take pod’s id from the command above)
  2. Check running pods again: kubectl get pods. It should give you output similar to this:
$ kubectl get pods
> NAME                              READY     STATUS        RESTARTS   AGE
> app-deployment-85c868cc55-44s5m   1/1       Running       0          39m
> app-deployment-85c868cc55-b6kjz   1/1       Terminating   0          39m
> app-deployment-85c868cc55-bdfkf   1/1       Running       0          7s

You can run the following command to check the pod’s internal IP:

$ kubectl describe pod app-deployment-85c868cc55-44s5m | grep IP
> IP:   

By having our app-service running, we can access our pods running on a specific instance using the instance’s IP and nodePort. This way, we can create an ElasticLoadBalancer and register all the Kubernetes nodes as registered targets, so the load balancer will automatically balance the traffic between our services running on different instances.

This is not the best approach - as it could lead to unbalanced traffic - but it’s more than enough for the purposes of this tutorial. A better option would be to use a service with the LoadBalancer type, but it would require us to provide the necessary AWS configuration to the Kubespray setup so it could correctly manage AWS resources.

Now if you open the Elastic Load Balancer address in your browser, you should see a response from the app running in our pods:

That’s all for now. Keep in mind that the process we showed you is very manual and not designed for production environments. In production environments you probably want to use CI for deployments and services with LoadBalancer for registering nodes in the ELB automatically, as well as namespaces for separating Kubernetes environments. However the tutorial should give you a better picture of how the Kubernetes pieces work together and what they are capable of. Keep in mind this is just tip of the iceberg - people are already running huge and complex clusters with Kubernetes!

If you would like assistance in setting up Kubernetes in your production environment and would like a hand, then turn to us at iRonin. We have people on the team experienced in complex Kubernetes setups that would be happy to help out.

Author's Bio
iRonin IT Team

Experts in software development

We are a 100% remote team of software development experts, providing web & mobile application development and DevOps services for international clients.

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