Over the past three years, containers have grown in popularity like wildfire. You would be hard-pressed to attend an IT conference without finding popular sessions on Docker or containers in general.

Docker lies at the heart of the mass adoption and the excitement in the container space. As Malcom McLean revolutionized the physical shipping world in the 1950s by creating a standardized shipping container, which is used today for everything from ice cube trays to automobiles (you can refer to more details about this in point 1 in the References section at the end of the chapter), Linux containers are revolutionizing the software development world by making application environments portable and consistent across the infrastructure landscape. As an organization, Docker has taken the existing container technology to a new level by making it easy to implement and replicate across environments and providers.

At the core of container technology are control groups (cgroups) and namespaces. Additionally, Docker uses union filesystems for added benefits to the container development process.

Cgroups work by allowing the host to share and also limit the resources each process or container can consume. This is important for both, resource utilization and security, as it prevents denial-of-service attacks on the host's hardware resources. Several containers can share CPU and memory while staying within the predefined constraints.

Namespaces offer another form of isolation for process interaction within operating systems. Namespaces limit the visibility a process has on other processes, networking, filesystems, and user ID components. Container processes are limited to see only what is in the same namespace. Processes from containers or the host processes are not directly accessible from within this container process. Additionally, Docker gives each container its own networking stack that protects the sockets and interfaces in a similar fashion.

Union filesystems are also a key advantage of using Docker containers. Containers run from an image. Much like an image in the VM or Cloud world, it represents state at a particular point in time. Container images snapshot the filesystem, but tend to be much smaller than a VM. The container shares the host kernel and generally runs a much smaller set of processes, so the filesystem and boot strap period tend to be much smaller. Though those constraints are not strictly enforced. Second, the union filesystem allows for efficient storage, download, and execution of these images.

The easiest way to understand union filesystems is to think of them like a layer cake with each layer baked independently. The Linux kernel is our base layer; then, we might add an OS such as Red Hat Linux or Ubuntu. Next, we might add an application such as Nginx or Apache. Every change creates a new layer. Finally, as you make changes and new layers are added, you'll always have a top layer (think frosting) that is a writable layer.

What makes this truly efficient is that Docker caches the layers the first time we build them. So, let's say that we have an image with Ubuntu and then add Apache and build the image. Next, we build MySQL with Ubuntu as the base. The second build will be much faster because the Ubuntu layer is already cached. Essentially, our chocolate and vanilla layers, from the preceding Layered filesystem figure, are already baked. We simply need to bake the pistachio (MySQL) layer, assemble, and add the icing (the writable layer).

Containers on their own are not a new technology and have in fact been around for many years. What truly sets Docker apart is the tooling and ease of use they have brought to the community. Modern development practices promote the use of Continuous Integration and Continuous Deployment. These techniques, when done right, can have a profound impact on your software product quality.

ThoughtWorks defines Continuous Integration as a development practice that requires developers to integrate code into a shared repository several times a day. By having a continuous process of building and deploying code, organizations are able to instill quality control and testing as part of the everyday work cycle. The result is that updates and bug fixes happen much faster and the overall quality improves.

However, there has always been a challenge in creating development environments that match that of testing and production. Often inconsistencies in these environments make it difficult to gain the full advantage of continuous delivery.

Using Docker, developers are now able to have truly portable deployments. Containers that are deployed on a developer's laptop are easily deployed on an in-house staging server. They are then easily transferred to the production server running in the cloud. This is because Docker builds containers up with build files that specify parent layers. One advantage of this is that it becomes very easy to ensure OS, package, and application versions are the same across development, staging, and production environments.

Because all the dependencies are packaged into the layer, the same host server can have multiple containers running a variety of OS or package versions. Further, we can have various languages and frameworks on the same host server without the typical dependency clashes we would get in a virtual machine (VM) with a single operating system.

The well-defined isolation and layer filesystem also make containers ideal for running systems with a very small footprint and domain-specific purposes. A streamlined deployment and release process means we can deploy quickly and often. As such, many companies have reduced their deployment time from weeks or months to days and hours in some cases. This development life cycle lends itself extremely well to small, targeted teams working on small chunks of a larger application.

As we break down an application into very specific domains, we need a uniform way to communicate between all the various pieces and domains. Web services have served this purpose for years, but the added isolation and granular focus that containers bring have paved a way for microservices.

The definition for microservices can be a bit nebulous, but a definition from Martin Fowler, a respected author and speaker on software development, says this (you can refer to more details about this in point 2 in the References section at the end of the chapter):

As the pivot to containerization and as microservices evolve in an organization, they will soon need a strategy to maintain many containers and microservices. Some organizations will have hundreds or even thousands of containers running in the years ahead.

Life cycle processes alone are an important piece of operations and management. How will we automatically recover when a container fails? Which upstream services are affected by such an outage? How will we patch our applications with minimal downtime? How will we scale up our containers and services as our traffic grows?

Networking and processing are also important concerns. Some processes are part of the same service and may benefit from the proximity to the network. Databases, for example, may send large amounts of data to a particular microservice for processing. How will we place containers near each other in our cluster? Is there common data that needs to be accessed? How will new services be discovered and made available to other systems?

Resource utilization is also a key. The small footprint of containers means that we can optimize our infrastructure for greater utilization. Extending the savings started in the elastic cloud will take us even further toward minimizing wasted hardware. How will we schedule workloads most efficiently? How will we ensure that our important applications always have the right resources? How can we run less important workloads on spare capacity?

Finally, portability is a key factor in moving many organizations to containerization. Docker makes it very easy to deploy a standard container across various operating systems, cloud providers, and on-premise hardware or even developer laptops. However, we still need tooling to move containers around. How will we move containers between different nodes on our cluster? How will we roll out updates with minimal disruption? What process do we use to perform blue-green deployments or canary releases?

Whether you are starting to build out individual microservices and separating concerns into isolated containers or if you simply want to take full advantage of the portability and immutability in your application development, the need for management and orchestration becomes clear. This is where orchestration tools such as Kubernetes offer the biggest value.

Kubernetes (K8s) is an open source project that was released by Google in June, 2014. Google released the project as part of an effort to share their own infrastructure and technology advantage with the community at large.

Google launches 2 billion containers a week in their infrastructure and has been using container technology for over a decade. Originally, they were building a system named Borg, now called Omega, to schedule their vast quantities of workloads across their ever-expanding data center footprint. They took many of the lessons they learned over the years and rewrote their existing data center management tool for wide adoption by the rest of the world. The result was the Kubernetes open-source project (you can refer to more details about this in point 3 in the References section at the end of the chapter).

Since its initial release in 2014, K8s has undergone rapid development with contributions all across the open-source community, including Red Hat, VMware, and Canonical. The 1.0 release of Kubernetes went live in July, 2015. Since then, it's been a fast-paced evolution of the project with wide support from one of the largest open-source communities on GitHub today. We'll be covering version 1.5 throughout the book. K8s gives organizations a tool to deal with some of the major operations and management concerns. We will explore how Kubernetes helps deal with resource utilization, high availability, updates, patching, networking, service discovery, monitoring, and logging.

Kubernetes is supported on a variety of platforms and OSes. For the examples in this book, I used an Ubuntu 16.04 Linux VirtualBox for my client and Google Compute Engine (GCE) with Debian for the cluster itself. We will also take a brief look at a cluster running on Amazon Web Services (AWS) with Ubuntu.

First, let's make sure that our environment is properly set up before we install Kubernetes. Start by updating packages:

$ sudo apt-get update

Install Python and curl if they are not present:

$ sudo apt-get install python
$ sudo apt-get install curl

Install the gcloud SDK:

$ curl https://sdk.cloud.google.com | bash

Configure your Google Cloud Platform (GCP) account information. This should automatically open a browser from where we can log in to our Google Cloud account and authorize the SDK:

$ gcloud auth login

A default project should be set, but we can verify this with the following command:

$ gcloud config list project

We can modify this and set a new default project with this command. Make sure to use project ID and not project name, as follows:

$ gcloud config set project <PROJECT ID>

Now that we have our environment set up, installing the latest Kubernetes version is done in a single step, as follows:

$ curl -sS https://get.k8s.io | bash

It may take a minute or two to download Kubernetes depending on your connection speed. Earlier versions would automatically call the kube-up.sh script and start building our cluster. In version 1.5, we will need to call the kube-up.sh script ourselves to launch the cluster. By default, it will use the Google Cloud and GCE:

$ kubernetes/cluster/kube-up.sh

After you run the kube-up.sh script, we will see quite a few lines roll past. Let's take a look at them one section at a time:

The preceding image, GCE prerequisite check, shows the checks for prerequisites as well as making sure that all components are up to date. This is specific to each provider. In the case of GCE, it will verify that the SDK is installed and that all components are up to date. If not, you will see a prompt at this point to install or update:

Now the script is turning up the cluster. Again, this is specific to the provider. For GCE, it first checks to make sure that the SDK is configured for a default project and zone. If they are set, you'll see those in the output.

Next, it uploads the server binaries to Google Cloud storage, as seen in the Creating gs:... lines:

It then checks for any pieces of a cluster already running. Then, we finally start creating the cluster. In the output in the preceding figure Master creation, we see it creating the master server, IP address, and appropriate firewall configurations for the cluster:

Finally, it creates the minions or nodes for our cluster. This is where our container workloads will actually run. It will continually loop and wait while all the minions start up. By default, the cluster will have four nodes (minions), but K8s supports having more than 1000 (and soon beyond). We will come back to scaling the nodes later on in the book.

Now that everything is created, the cluster is initialized and started. Assuming that everything goes well, we will get an IP address for the master. Also, note that configuration along with the cluster management credentials are stored in home/<Username>/.kube/config:

Then, the script will validate the cluster. At this point, we are no longer running provider-specific code. The validation script will query the cluster via the kubectl.sh script. This is the central script for managing our cluster. In this case, it checks the number of minions found, registered, and in a ready state. It loops through giving the cluster up to 10 minutes to finish initialization.

After a successful startup, a summary of the minions and the cluster component health is printed on the screen:

Finally, a kubectl cluster-info command is run, which outputs the URL for the master services including DNS, UI, and monitoring. Let's take a look at some of these components.

Open a browser and run the following code:

https://<your master ip>/ui/

The certificate is self-signed by default, so you'll need to ignore the warnings in your browser before proceeding. After this, we will see a login dialog. This is where we use the credentials listed during the K8s installation. We can find them at any time by simply using the config command:

$ kubectl config view

Now that we have credentials for login, use those, and we should see a dashboard like the following image:

The main dashboard takes us to a page with not much display at first. There is a link to deploy a containerized app that will take you to a GUI for deployment. This GUI can be a very easy way to get started deploying apps without worrying about the YAML syntax for Kubernetes. However, as your use of containers matures, it's good practice to use the YAML definitions that are checked in to source control.

If you click on the Nodes link on the left-hand side menu, you will see some metrics on the current cluster nodes:

At the top, we see an aggregate of the CPU and memory usages followed by a listing of our cluster nodes. Clicking on one of the nodes will take us to a page with detailed information about that node, its health, and various metrics.

The Kubernetes UI has a lot of other views that will become more useful as we start launching real applications and adding configurations to the cluster.

Another service installed by default is Grafana. This tool will give us a dashboard to view metrics on the cluster nodes. We can access it using the following syntax in a browser: https://<your master ip>/api/v1/proxy/namespaces/kube-system/services/monitoring-grafana

From the main page, click on the Home dropdown and select Cluster. Here, Kubernetes is actually running a number of services. Heapster is used to collect the resource usage on the pods and nodes and stores the information in InfluxDB. The results, such as CPU and memory usage, are what we see in the Grafana UI. We will explore this in depth in Chapter 8, Monitoring and Logging.

The kubectl script has commands to explore our cluster and the workloads running on it. You can find it in the /kubernetes/client/bin folder. We will be using this command throughout the book, so let's take a second to set up our environment. We can do so by putting the binaries folder on our PATH, in the following manner:

$ export PATH=$PATH:/<Path where you downloaded K8s>/kubernetes/client/bin
$ chmod +x /<Path where you downloaded K8s>/kubernetes/client/bin

Now that we have kubectl on our path, we can start working with it. It has quite a few commands. Since we have not spun up any applications yet, most of these commands will not be very interesting. However, we can explore with two commands right away.

First, we have already seen the cluster-info command during initialization, but we can run it again at any time with the following command:

$ kubectl cluster-info

Another useful command is get. It can be used to see currently running services, pods, replication controllers, and a lot more. Here are the three examples that are useful right out of the gate:

• List the nodes in our cluster:

    $ kubectl get nodes

• List cluster events:

    $ kubectl get events

• Finally, we can see any services that are running in the cluster, as follows:

    $ kubectl get services

To start with, we will only see one service, named kubernetes. This service is the core API server for the cluster.

Let's dig a little bit deeper into our new cluster and its core services. By default, machines are named with the kubernetes- prefix. We can modify this using $KUBE_GCE_INSTANCE_PREFIX before a cluster is spun up. For the cluster we just started, the master should be named kubernetes-master. We can use the gcloud command-line utility to SSH into the machine. The following command will start an SSH session with the master node. Be sure to substitute your project ID and zone to match your environment. Also, note that you can launch SSH from the Google Cloud console using the following syntax:

$ gcloud compute ssh --zone "<your gce zone>" "kubernetes-master"

Once we are logged in, we should get a standard shell prompt. Let's run the docker command that filters for Image and Status:

$ sudo docker ps --format 'table {{.Image}}t{{.Status}}' 

Even though we have not deployed any applications on Kubernetes yet, we note that there are several containers already running. The following is a brief description of each container:

• fluentd-gcp: This container collects and sends the cluster logs file to the Google Cloud Logging service.
• node-problem-detector: This container is a daemon that runs on every node and currently detects issues at the hardware and kernel layer.
• rescheduler: This is another add-on container that makes sure critical components are always running. In cases of low resources availability, it may even remove less critical pods to make room.
• glbc: This is another Kubernetes add-on container that provides Google Cloud Layer 7 load balancing using the new Ingress capability.
• kube-addon-manager: This component is core to the extension of Kubernetes through various add-ons. It also periodically applies any changes to  the /etc/kubernetes/addons directory.
• etcd-empty-dir-cleanup: A utility to cleanup empty keys in etcd.
• kube-controller-manager: This is a controller manager that controls a variety of cluster functions, ensuring accurate and up-to-date replication is one of its vital roles. Additionally, it monitors, manages, and discovers new nodes. Finally, it manages and updates service endpoints.
• kube-apiserver: This container runs the API server. As we explored in the Swagger interface, this RESTful API allows us to create, query, update, and remove various components of our Kubernetes cluster.
• kube-scheduler: This scheduler takes unscheduled pods and binds them to nodes based on the current scheduling algorithm.
• etcd: This runs the etcd software built by CoreOS, and it is a distributed and consistent key-value store. This is where the Kubernetes cluster state is stored, updated, and retrieved by various components of K8s.
• pause: This container is often referred to as the pod infrastructure container and is used to set up and hold the networking namespace and resource limits for each pod.

To exit the SSH session, simply type exit at the prompt.

We could SSH to one of the minions, but since Kubernetes schedules workloads across the cluster, we would not see all the containers on a single minion. However, we can look at the pods running on all the minions using the kubectl command:

$ kubectl get pods

Since we have not started any applications on the cluster yet, we don't see any pods. However, there are actually several system pods running pieces of the Kubernetes infrastructure. We can see these pods by specifying the kube-system namespace. We will explore namespaces and their significance later, but for now, the --namespace=kube-system command can be used to look at these K8s system resources, as follows:

$ kubectl get pods --namespace=kube-system

We should see something similar to the following:

etcd-empty-dir-cleanup-kubernetes-master 
etcd-server-events-kubernetes-master 
etcd-server-kubernetes-master 
fluentd-cloud-logging-kubernetes-master 
fluentd-cloud-logging-kubernetes-minion-group-xxxx
heapster-v1.2.0-xxxx 
kube-addon-manager-kubernetes-master 
kube-apiserver-kubernetes-master 
kube-controller-manager-kubernetes-master 
kube-dns-xxxx 
kube-dns-autoscaler-xxxx 
kube-proxy-kubernetes-minion-group-xxxx 
kube-scheduler-kubernetes-master 
kubernetes-dashboard-xxxx 
l7-default-backend-xxxx 
l7-lb-controller-v0.8.0-kubernetes-master 
monitoring-influxdb-grafana-xxxx 
node-problem-detector-v0.1-xxxx 
rescheduler-v0.2.1-kubernetes-master

The first six lines should look familiar. Some of these are the services we saw running on the master and will see pieces of these on the nodes. There are a few additional services we have not seen yet. The kube-dns option provides the DNS and service discovery plumbing, kubernetes-dashboard-xxxx is the user interface for Kubernetes, l7-default-backend-xxxx provides the default load balancing backend for the new Layer-7 load balancing capability, and heapster-v1.2.0-xxxx and monitoring-influx-grafana provide the Heapster database and user interface to monitor resource usage across the cluster. Finally, kube-proxy-kubernetes-minion-group-xxxx  is the proxy which directs traffic to the proper backing services and pods running on our cluster.

If we did SSH into a random minion, we would see several containers that run across a few of these pods. A sample might look like this image:

Again, we saw a similar line up of services on the master. The services we did not see on the master include the following:

• kubedns: This container monitors the service and endpoint resources in Kubernetes and synchronizes any changes to DNS lookups.
• kube-dnsmasq: This is another container that provides DNS caching.
• dnsmasq-metrics: This provides metric reporting for DNS services in cluster.
• l7-defaultbackend: This is the default backend for handling the GCE L7 load balancer and Ingress.
• kube-proxy: This is the network and service proxy for your cluster. This component makes sure service traffic is directed to wherever your workloads are running on the cluster. We will explore this in more depth later in the book.
• heapster: This container is for monitoring and analytics.
• addon-resizer: This cluster utility is for scaling containers.
• heapster_grafana: This does resource usage and monitoring.
• heapster_influxdb: This time-series database is for Heapster data.
• cluster-proportional-autoscaler: This cluster utility is for scaling containers in proportion to the cluster size.
• exechealthz: This performs health checks on the pods.

Alright, this is our first cluster on GCE, but let's explore some other providers. To keep things simple, we need to remove the one we just created on GCE. We can tear down the cluster with one simple command:

$ kube-down.sh

By default, Kubernetes uses the GCE provider for Google Cloud. We can override this default by setting the KUBERNETES_PROVIDER environment variable. The following providers are supported with values listed in this table:

Let's try setting up the cluster on AWS. As a prerequisite, we need to have AWS Command Line Interface (CLI) installed and configured for our account. The AWS CLI installation and configuration documentation can be found at the following links:

• Installation documentation: http://docs.aws.amazon.com/cli/latest/userguide/installing.html#install-bundle-other-os
• Configuration documentation: http://docs.aws.amazon.com/cli/latest/userguide/cli-chap-getting-started.html

Then, it is a simple environment variable setting, as follows:

$ export KUBERNETES_PROVIDER=aws

Again, we can use the kube-up.sh command to spin up the cluster, as follows:

$ kube-up.sh

As with GCE, the setup activity will take a few minutes. It will stage files in S3 and create the appropriate instances, Virtual Private Cloud (VPC), security groups, and so on in our AWS account. Then, the Kubernetes cluster will be set up and started. Once everything is finished and started, we should see the cluster validation at the end of the output:

Note that the region where the cluster is spun up is determined by the KUBE_AWS_ZONE environment variable. By default, this is set to us-west-2a (the region is derived from this Availability Zone). Even if you have a region set in your AWS CLI, it will use the region defined in KUBE_AWS_ZONE.

Once again, we will SSH into master. This time, we can use the native SSH client. We'll find the key files in /home/<username>/.ssh:

$ ssh -v -i /home/<username>/.ssh/kube_aws_rsa ubuntu@<Your master IP>

We'll use sudo docker ps --format 'table {{.Image}}t{{.Status}}' to explore the running containers. We should see something like the following:

We see some of the same containers as our GCE cluster had. However, there are several missing. We see the core Kubernetes components, but the fluentd-gcp service is missing as well as some of the newer utilities such as node-problem-detector , rescheduler , glbc , kube-addon-manager , and etcd-empty-dir-cleanup. This reflects some of the subtle differences in the kube-up script between various Public Cloud providers. This is ultimately decided by the efforts of the large Kubernetes open-source community, but GCP often has many of the latest features first.

On the AWS provider, Elasticsearch and Kibana are set up for us. We can find the Kibana UI using the following syntax as URL:

https://<your master ip>/api/v1/proxy/namespaces/kube-system/services/kibana-logging

As in the case of the UI, you will be prompted for admin credentials, which can be obtained using the config command, as shown here:

$ kubectl config view

On the first visit, you'll need to set up your index. You can leave the defaults and choose @timestamp for the Time-field name. Then, click on Create and you'll be taken to the index settings page. From there, click on the Discover tab at the top and you can explore the log dashboards:

You just had a little taste of running the cluster on AWS. For the remainder of the book, I will be basing my examples on a GCE cluster. For the best experience following along, you can get back to a GCE cluster easily.

Simply tear down the AWS cluster, as follows:

$ kube-down.sh

Then, create a GCE cluster again using the following:

$ export KUBERNETES_PROVIDER=gce
$ kube-up.sh

It's worth getting to know the parameters used for the kube-up.sh script. Each provider under the kubernetes/cluster/ folder has its own su  folder which containers a config-default.sh  script.

For example, kubernetes/cluster/aws/config-default.sh has the default settings for using kube-up.sh with AWS. At the start of this script, you will see many of these values defined as well as environment variables that can be used to overrides the defaults.

In the following example, the ZONE variable is set for the script and it uses the value from the environment variable named KUBE_AWS_ZONE. If this variable is not set, it will use the default us-west-2a:

ZONE=${KUBE_AWS_ZONE:-us-west-2a}

Understanding these parameters will help you get a lot more mileage out of your kube-up.sh script.

The kube-up.sh script is still a pretty handy way to get started using Kubernetes on your platform of choice. However, it's not without flaws and can sometimes run aground when conditions are not just so.

Luckily, since K8's inception, a number of alternative methods for creating clusters have emerged. Two such GitHub projects are KOPs and kube-aws. While the later is tied to AWS, they both provide an alternative method to easily spinning up your new cluster:

• https://github.com/kubernetes/kops
• https://github.com/coreos/kube-aws

Additionally, a number of managed services have arisen including Google Container Engine (GKE) and Microsoft Azure Container Service (ACS), which provide an automated install and some managed cluster operations. We will look at a brief demo of these in Chapter 12, Towards Production Ready.

Finally, there is the option to start from scratch. Luckily, starting in 1.4, the Kubernetes team has put a major focus in easing the cluster setup process. To that end they have introduced kubeadm for Ubuntu 16.04, CentOS 7, and HypriotOS v1.0.1+.

Let's take a quick look at spinning up a cluster on AWS from scratch using the kubeadm tool.

We will need to provision our cluster master and nodes beforehand. For the moment, we are limited to the operating systems and version listed earlier. Additionally, it is recommended that you have at least 1 GB of RAM and all the nodes must have network connectivity to one another.

For this walk through, we will need one t2.medium (master node) and three t2.mirco (nodes) sized instances on AWS. These instance have burstable CPU and come with the minimum 1 GB of RAM needed. We will need to create one master and three worker nodes.

We will also need to create some security groups for the cluster. The following ports are needed for the master:

The next table shows the ports node security groups:

Once you have these SGs, go ahead and spin up four instances (one t2.medium and three t2.mircos) using Ubuntu 16.04. If you are new to AWS, refer to the documentation on spinning up EC2 instances at the following URL:

http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/LaunchingAndUsingInstances.html

Be sure to identify the t2.medium instance as the master and associate the master security group. Name the other three as nodes and associate the node security group with those.

Next we will need to SSH into all four of the instances and install the Kubernetes components.

As root, perform the following steps on all four instances:

1. Update packages and install the apt-transport-https package so we can download from sources   that use HTTPS:

   $ apt-get update 
   $ apt-get install -y apt-transport-https

2. Install the Google Cloud public key:

   $ curl -s https://packages.cloud.google.com/apt/doc/apt-key.gpg |
   apt-key add -    

3. Next, create a source list for the Kubernetes package downloads with your favorite editor:

   $ vi /etc/apt/sources.list.d/kubernetes.list

4. Use the following as contents for this file and save:

   deb http://apt.kubernetes.io/ kubernetes-xenial main

Listing 1-1. /etc/apt/sources.list.d/kubernetes.list

5. Update your sources once more:

   $ apt-get update

6. Install Docker and the core Kubernetes components:

   $ apt-get install -y docker.io 
   $ apt-get install -y kubelet kubeadm kubectl kubernetes-cni

On the instance you have previously chosen as master, we will run master initialization. Again, as root run the following command:

$ kubeadm init

Note that initialization can only be run once, so if you run into problems you'll kubeadm reset.

After a successful initialization, you will get a join command that can be used by the nodes. Copy this down for the join process later on. It should look similar to this:

$ kubeadm join --token=<some token> <master ip address>

The token is used to authenticate cluster nodes, so make sure to store it somewhere securely for future use.

Our cluster will need a networking layer for the pods to communicate on. Note that kubeadm requires a CNI compatible network fabric. The list of plugins currently available can be found here:

http://kubernetes.io/docs/admin/addons/

For our example, we will use calico. We will need to create the calico components on our cluster using the following yaml. For convenience you can download it here:

http://docs.projectcalico.org/v1.6/getting-started/kubernetes/installation/hosted/kubeadm/calico.yaml

Once you have this file on your master, create the components with the following command:

$ kubectl apply -f calico.yaml

Give this a minute to run setup and then list the kube-system nodes to check:

$ kubectl get pods --namespace=kube-system

You should get a listing similar to the following one with three new calico pods and one completed job that is not shown:

Now we need to run the join command we copied earlier, on each of our node instances:

$ kubeadm join --token=<some token> <master ip address>

Once you've finished that, you should be able to see all nodes from the master by running this command:

$ kubectl get nodes

If all went well, this will show three nodes and one master, as shown here: