At the core of container technology are cGroups and namespaces. Additionally, Docker uses union file systems for added benefits to the container development process.

Control groups (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 in the way of processes. Processes are limited to see only the process ID in the same namespace. Namespaces from other system processes would not be accessible from a container process. For example, a network namespace would isolate access to the network interfaces and configuration, which allows the separation of network interfaces, routes, and firewall rules.

Figure 1.1. Composition of a container

Union file systems are also a key advantage to using Docker containers. The easiest way to understand union file systems 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 like Red Hat Linux or Ubuntu. Next, we might add an application like 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.

Figure 1.2. Layered file system

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 Figure 1.2, are already baked. We simply need to bake the pistachio (MySQL) layer, assemble, and add the icing (writable layer).

Wikipedia defines Continuous Integration as "the practice, in software engineering, of merging all developer working copies to a shared mainline 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 overall quality improves.

However, there has always been a challenge in setting development environments to 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.

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 proximity on 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 world even further towards minimizing wasted hardware. How will we schedule workloads most efficiently? How will we ensure that our important applications always have the 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.

Open a browser and try the following code:

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

Figure 1.10. Kubernetes UI dashboard

The main dashboard page gives us a summary of the minions (or slave nodes). We can also see the CPU, memory, and used disk space on each minion as well the IP address.

The UI has a number of built-in views listed under the Views dropdown menu on the top right of the screen. However, most of them will be empty by default. Once workloads and services are spun up, these views will become a lot more interesting.

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 by using the following syntax in a browser:

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

Figure 1.11. Kubernetes Grafana dashboard

Here, Kubernetes is actually running a number of services. Heapster is used to collect resource usage on the pods and nodes and stores the information in InfluxDB. The results, like CPU and memory usage, are what we see in the Grafana UI. We will explore this in depth in Chapter 6, Monitoring and Logging.

Swagger (http://swagger.io/) is a tool to add a higher level of interaction and easy discovery to an API.

Kubernetes has built a Swagger-enabled API, which can be accessed by using https://<your master ip>/swagger-ui/.

Figure 1.12. Kubernetes Swagger dashboard

Through this interface, you can learn a lot about the Kubernetes RESTful API. The bulk of the interesting endpoints are listed under v1. If we look at /api/v1/nodes, we can see the structure of the JSON response as well as details of possible parameters for the request. In this case, we see that the first parameter is pretty, which toggles whether the JSON is returned with pretty indentation for easier reading.

We can try this out by using https://<your master ip>/api/v1/nodes/.

By default, we'll see a JSON response with pretty indentation enabled. The response should have a list of all the nodes currently in our cluster.

Now, let's try tweaking the pretty request parameter you just learned about. Use https://<your master ip>/api/v1/nodes/?pretty=false.

Now we have the same response output, but with no indentation. This is a great resource for exploring the API and learning how to use various function calls to get more information and interact with your cluster programmatically.

The kubectl.sh script has commands to explore our cluster and the workloads running on it. 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 making the script executable and putting it on our PATH, in the following manner:

$ cd /home/<Username>/kubernetes/cluster
$ chmod +x kubectl.sh
$ export PATH=$PATH:/home/<Username>/kubernetes/cluster
$ ln -s kubectl.sh kubectl

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:

$ kubectl cluster-info

Another useful command is get. The get command 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:

To start with, we will only see one service, named kubernetes. This service is the core API server, monitoring and logging services for the pods and 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 --project "<Your project ID>" ssh --zone "<your gce zone>" "kubernetes-master"

Once we are logged in, we should get a standard shell prompt. Let's run the familiar sudo docker ps command.

Figure 1.13. Master container listing

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:

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-server
fluentd-cloud-logging
kube-apiserver
kube-controller-manager
kube-scheduler
kube-ui
kube-dns
monitoring-heapster
monitoring-influx-grafana

The first six should look familiar. These are additional pieces of the services we saw running on the master. The final three are services we have not seen yet. kube-dns provides the DNS and service discovery plumbing. monitoring-heapster is the system used to monitor resource usage across the cluster. monitoring-influx-grafana provides the database and user interface we saw earlier for monitoring the infrastructure.

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 the image here:

Figure 1.14. Minion container listing

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

OK, 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

That is 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 following:

$ export KUBERNETES_PROVIDER=gce
$ kube-up.sh

¹Malcom McLean entry on Wikipedia: https://en.wikipedia.org/wiki/Malcom_McLean

²Martin Fowler on microservices: http://martinfowler.com/articles/microservices.html

³Kubernetes GitHub project page: https://github.com/kubernetes/kubernetes