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Cloud Native DevOps with Kubernetes by Justin Domingus, John Arundel

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Chapter 4. Working with Kubernetes Objects

I can’t understand why people are frightened of new ideas. I’m frightened of the old ones.

John Cage

In Chapter 2, you built and deployed an application to Kubernetes. In this chapter, you’ll learn about the fundamental Kubernetes objects involved in that process: Pods, Deployments, and Services. You’ll also find out how to use the essential Helm tool to manage application in Kubernetes.

After working through the example in “Running the Demo App”, you should have a container image running in the Kubernetes cluster, but how does that actually work? Under the hood, the kubectl run command creates a Kubernetes resource called a Deployment. So what’s that? And how does a Deployment actually run your container image?

Deployments

Think back to how you ran the demo app with Docker. The docker container run command started the container, and it ran until you killed it with docker stop.

But suppose that the container exits for some other reason; maybe the program crashed, or there was a system error, or your machine ran out of disk space, or a cosmic ray hit your CPU at the wrong moment (unlikely, but it does happen). Assuming this is a production application, that means you now have unhappy users, until someone can get to a terminal and type docker container run to start the container again.

That’s an unsatisfactory arrangement. What you really want is a kind of supervisor program, which continually checks that the container is running, and if it ever stops, starts it again immediately. On traditional servers, you can use a tool like systemd, runit, or supervisord to do this; Docker has something similar, and you won’t be surprised to know that Kubernetes has a supervisor feature too: the Deployment.

Supervising and Scheduling

For each program that Kubernetes has to supervise, it creates a corresponding Deployment object, which records some information about the program: the name of the container image, the number of replicas you want to run, and whatever else it needs to know to start the container.

Working together with the Deployment resource is a kind of Kubernetes object called a controller. Controllers watch the resources they’re responsible for, making sure they’re present and working. If a given Deployment isn’t running enough replicas, for whatever reason, the controller will create some new ones. (If there were too many replicas for some reason, the controller would shut down the excess ones. Either way, the controller makes sure that the real state matches the desired state.)

Actually, a Deployment doesn’t manage replicas directly: instead, it automatically creates an associated object called a ReplicaSet, which handles that. We’ll talk more about ReplicaSets in a moment in “ReplicaSets”, but since you generally interact only with Deployments, let’s get more familiar with them first.

Restarting Containers

At first sight, the way Deployments behave might be a little surprising. If your container finishes its work and exits, the Deployment will restart it. If it crashes, or if you kill it with a signal, or terminate it with kubectl, the Deployment will restart it. (This is how you should think about it conceptually; the reality is a little more complicated, as we’ll see.)

Most Kubernetes applications are designed to be long-running and reliable, so this behavior makes sense: containers can exit for all sorts of reasons, and in most cases all a human operator would do is restart them, so that’s what Kubernetes does by default.

It’s possible to change this policy for an individual container: for example, to never restart it, or to restart it only on failure, not if it exited normally (see “Restart Policies”). However, the default behavior (restart always) is usually what you want.

A Deployment’s job is to watch its associated containers and make sure that the specified number of them is always running. If there are fewer, it will start more. If there are too many, it will terminate some. This is much more powerful and flexible than a traditional supervisor-type program.

Querying Deployments

You can see all the Deployments active in your current namespace (see “Using Namespaces”) by running the following command:

kubectl get deployments
NAME      DESIRED   CURRENT   UP-TO-DATE   AVAILABLE   AGE
demo      1         1         1            1           21h

To get more detailed information on this specific Deployment, run the following command:

kubectl describe deployments/demo
Name:                   demo
Namespace:              default
CreationTimestamp:      Tue, 08 May 2018 12:20:50 +0100
...

As you can see, there’s a lot of information here, most of which isn’t important for now. Let’s look more closely at the Pod Template section, though:

Pod Template:
  Labels:  app=demo
  Containers:
   demo:
    Image:        cloudnatived/demo:hello
    Port:         8888/TCP
    Host Port:    0/TCP
    Environment:  <none>
    Mounts:       <none>
  Volumes:        <none>

You know that a Deployment contains the information Kubernetes needs to run the container, and here it is. But what’s a Pod Template? Actually, before we answer that, what’s a Pod?

Pods

A Pod is the Kubernetes object that represents a group of one or more containers (pod is also the name for a group of whales, which fits in with the vaguely seafaring flavor of Kubernetes metaphors).

Why doesn’t a Deployment just manage an individual container directly? The answer is that sometimes a set of containers needs to be scheduled together, running on the same node, and communicating locally, perhaps sharing storage.

For example, a blog application might have one container that syncs content with a Git repository, and an Nginx web server container that serves the blog content to users. Since they share data, the two containers need to be scheduled together in a Pod. In practice, though, many Pods only have one container, as in this case. (See “What Belongs in a Pod?” for more about this.)

So a Pod specification (spec for short) has a list of Containers, and in our example there is only one container, demo:

demo:
 Image:        cloudnatived/demo:hello
 Port:         8888/TCP
 Host Port:    0/TCP
 Environment:  <none>
 Mounts:       <none>

The Image spec will be, in your case, YOUR_DOCKER_ID/myhello, and together with the port number, that’s all the information the Deployment needs to start the Pod and keep it running.

And that’s an important point. The kubectl run command didn’t actually create the Pod directly. Instead it created a Deployment, and then the Deployment started the Pod. The Deployment is a declaration of your desired state: “A Pod should be running with the myhello container inside it.”

ReplicaSets

We said that Deployments start Pods, but there’s a little more to it than that. In fact, Deployments don’t manage Pods directly. That’s the job of the ReplicaSet object.

A ReplicaSet is responsible for a group of identical Pods, or replicas. If there are too few (or too many) Pods, compared to the specification, the ReplicaSet controller will start (or stop) some Pods to rectify the situation.

Deployments, in turn, manage ReplicaSets, and control how the replicas behave when you update them—by rolling out a new version of your application, for example (see “Deployment Strategies”). When you update the Deployment, a new ReplicaSet is created to manage the new Pods, and when the update is completed, the old ReplicaSet and its Pods are terminated.

In Figure 4-1, each ReplicaSet (V1, V2, V3) represents a different version of the application, with its corresponding Pods.

Diagram of a Deployment managing ReplicaSets
Figure 4-1. Deployments, ReplicaSets, and Pods

Usually, you won’t interact with ReplicaSets directly, since Deployments do the work for you—but it’s useful to know what they are.

Maintaining Desired State

Kubernetes controllers continually check the desired state specified by each resource against the actual state of the cluster, and make any necessary adjustments to keep them in sync. This process is called the reconciliation loop, because it loops forever, trying to reconcile the actual state with the desired state.

For example, when you first create the demo Deployment, there is no demo Pod running. So Kubernetes will start the required Pod immediately. If it were ever to stop, Kubernetes will start it again, so long as the Deployment still exists.

Let’s verify that right now by stopping the Pod manually. First, check that the Pod is indeed running:

kubectl get pods --selector app=demo
NAME                    READY     STATUS    RESTARTS   AGE
demo-54df94b7b7-qgtc6   1/1       Running   1          22h

Now, run the following command to stop the Pod:

kubectl delete pods --selector app=demo
pod "demo-54df94b7b7-qgtc6" deleted

List the Pods again:

kubectl get pods --selector app=demo
NAME                    READY     STATUS        RESTARTS   AGE
demo-54df94b7b7-hrspp   1/1       Running       0          5s
demo-54df94b7b7-qgtc6   0/1       Terminating   1          22h

You can see the original Pod shutting down (its status is Terminating), but it’s already been replaced by a new Pod, which is only five seconds old. That’s the reconciliation loop at work.

You told Kubernetes, by means of the Deployment you created, that the demo Pod must always be running. It takes you at your word, and even if you delete the Pod yourself, Kubernetes assumes you must have made a mistake, and helpfully starts a new Pod to replace it for you.

Once you’ve finished experimenting with the Deployment, shut it down and clean up using the following command:

kubectl delete all --selector app=demo
pod "demo-54df94b7b7-hrspp" deleted
service "demo" deleted
deployment.apps "demo" deleted

The Kubernetes Scheduler

We’ve said things like the Deployment will create Pods and Kubernetes will start the required Pod, without really explaining how that happens.

The Kubernetes scheduler is the component responsible for this part of the process. When a Deployment (via its associated ReplicaSet) decides that a new replica is needed, it creates a Pod resource in the Kubernetes database. Simultaneously, this Pod is added to a queue, which is like the scheduler’s inbox.

The scheduler’s job is to watch its queue of unscheduled Pods, grab the next Pod from it, and find a node to run it on. It will use a few different criteria, including the Pod’s resource requests, to choose a suitable node, assuming there is one available (we’ll talk more about this process in Chapter 5).

Once the Pod has been scheduled on a node, the kubelet running on that node picks it up and takes care of actually starting its containers (see “Node Components”).

When you deleted a Pod in “Maintaining Desired State”, it was the node’s kubelet that spotted this and started a replacement. It knows that a demo Pod should be running on its node, and if it doesn’t find one, it will start one. (What would happen if you shut the node down altogether? Its Pods would become unscheduled and go back into the scheduler’s queue, to be reassigned to other nodes.)

Stripe engineer Julia Evans has written a delightfully clear explanation of how scheduling works in Kubernetes.

Resource Manifests in YAML Format

Now that you know how to run an application in Kubernetes, is that it? Are you done? Not quite. Using the kubectl run command to create a Deployment is useful, but limited. Suppose you want to change something about the Deployment spec: the image name or version, say. You could delete the existing Deployment (using kubectl delete) and create a new one with the right fields. But let’s see if we can do better.

Because Kubernetes is inherently a declarative system, continuously reconciling actual state with desired state, all you need to do is change the desired state—the Deployment spec—and Kubernetes will do the rest. How do you do that?

Resources Are Data

All Kubernetes resources, such as Deployments or Pods, are represented by records in its internal database. The reconciliation loop watches the database for any changes to those records, and takes the appropriate action. In fact, all the kubectl run command does is add a new record in the database corresponding to a Deployment, and Kubernetes does the rest.

But you don’t need to use kubectl run in order to interact with Kubernetes. You can also create and edit the resource manifest (the specification for the desired state of the resource) directly. You can keep the manifest file in a version control system, and instead of running imperative commands to make on-the-fly changes, you can change your manifest files and then tell Kubernetes to read the updated data.

Deployment Manifests

The usual format for Kubernetes manifest files is YAML, although it can also understand the JSON format. So what does the YAML manifest for a Deployment look like?

Have a look at our example for the demo application (hello-k8s/k8s/deployment.yaml):

apiVersion: extensions/v1beta1
kind: Deployment
metadata:
  name: demo
  labels:
    app: demo
spec:
  replicas: 1
  selector:
    matchLabels:
      app: demo
  template:
    metadata:
      labels:
        app: demo
    spec:
      containers:
        - name: demo
          image: cloudnatived/demo:hello
          ports:
          - containerPort: 8888

At first glance, this looks complicated, but it’s mostly boilerplate. The only interesting parts are the same information that you’ve already seen in various forms: the container image name and port. When you gave this information to kubectl run earlier, it created the equivalent of this YAML manifest behind the scenes and submitted it to Kubernetes.

Using kubectl apply

To use the full power of Kubernetes as a declarative infrastructure as code system, submit YAML manifests to the cluster yourself, using the kubectl apply command.

Try it with our example Deployment manifest, hello-k8s/k8s/deployment.yaml.1

Run the following commands in your copy of the demo repo:

cd hello-k8s
kubectl apply -f k8s/deployment.yaml
deployment.apps "demo" created

After a few seconds, a demo Pod should be running:

kubectl get pods --selector app=demo
NAME                    READY     STATUS    RESTARTS   AGE
demo-6d99bf474d-z9zv6   1/1       Running   0          2m

We’re not quite done, though, because in order to connect to the demo Pod with a web browser, we’re going to create a Service, which is a Kubernetes resource that lets you connect to your deployed Pods (more on this in a moment).

First, let’s explore what a Service is, and why we need one.

Service Resources

Suppose you want to make a network connection to a Pod (such as our example application). How do you do that? You could find out the Pod’s IP address and connect directly to that address and the app’s port number. But the IP address may change when the Pod is restarted, so you’ll have to keep looking it up to make sure it’s up to date.

Worse, there may be multiple replicas of the Pod, each with different addresses. Every other application that needs to contact the Pod would have to maintain a list of those addresses, which doesn’t sound like a great idea.

Fortunately, there’s a better way: a Service resource gives you a single, unchanging IP address or DNS name that will be automatically routed to any matching Pod. Later on in “Ingress Resources” we will talk about the Ingress resource, which allows for more advanced routing and using TLS certificates.

But for now, let’s take a closer look at how a Kubernetes Service works.

You can think of a Service as being like a web proxy or a load balancer, forwarding requests to a set of backend Pods (Figure 4-2). However, it isn’t restricted to web ports: a Service can forward traffic from any port to any other port, as detailed in the ports part of the spec.

Diagram showing a Service forwarding traffic to Pods
Figure 4-2. A Service provides a persistent endpoint for a group of Pods

Here’s the YAML manifest of the Service for our demo app:

apiVersion: v1
kind: Service
metadata:
  name: demo
  labels:
    app: demo
spec:
  ports:
  - port: 9999
    protocol: TCP
    targetPort: 8888
  selector:
    app: demo
  type: ClusterIP

You can see that it looks somewhat similar to the Deployment resource we showed earlier. However, the kind is Service, instead of Deployment, and the spec just includes a list of ports, plus a selector and a type.

If you zoom in a little, you can see that the Service is forwarding its port 9999 to the Pod’s port 8888:

...
ports:
- port: 9999
  protocol: TCP
  targetPort: 8888

The selector is the part that tells the Service how to route requests to particular Pods. Requests will be forwarded to any Pods matching the specified set of labels; in this case, just app: demo (see “Labels”). In our example, there’s only one Pod that matches, but if there were multiple Pods, the Service would send each request to a randomly selected one.2

In this respect, a Kubernetes Service is a little like a traditional load balancer, and, in fact, both Services and Ingresses can automatically create cloud load balancers (see “Ingress Resources”).

For now, the main thing to remember is that a Deployment manages a set of Pods for your application, and a Service gives you a single entry point for requests to those Pods.

Go ahead and apply the manifest now, to create the Service:

kubectl apply -f k8s/service.yaml
service "demo" created

kubectl port-forward service/demo 9999:8888
Forwarding from 127.0.0.1:9999 -> 8888
Forwarding from [::1]:9999 -> 8888

As before, kubectl port-forward will connect the demo service to a port on your local machine, so that you can connect to http://localhost:9999/ with your web browser.

Once you’re satisfied that everything is working correctly, run the following command to clean up before moving on to the next section:

kubectl delete -f k8s/
Tip

You can use kubectl delete with a label selector, as we did earlier on, to delete all resources that match the selector (see “Labels”). Alternatively, you can use kubectl delete -f, as here, with a directory of manifests. All the resources described by the manifest files will be deleted.

Querying the Cluster with kubectl

The kubectl tool is the Swiss Army knife of Kubernetes: it applies configuration, creates, modifies, and destroys resources, and can also query the cluster for information about the resources that exist, as well as their status.

We’ve already seen how to use kubectl get to query Pods and Deployments. You can also use it to see what nodes exist in your cluster:

kubectl get nodes
NAME                 STATUS    ROLES     AGE       VERSION
docker-for-desktop   Ready     master    1d        v1.10.0

If you want to see resources of all types, use kubectl get all. (In fact, this doesn’t show literally all resources, just the most common types, but we won’t quibble about that for now.)

To see comprehensive information about an individual Pod (or any other resource), use kubectl describe:

kubectl describe pod/demo-dev-6c96484c48-69vss
Name:           demo-dev-6c96484c48-69vss
Namespace:      default
Node:           docker-for-desktop/10.0.2.15
Start Time:     Wed, 06 Jun 2018 10:48:50 +0100
...
Containers:
  demo:
    Container ID:   docker://646aaf7c4baf6d...
    Image:          cloudnatived/demo:hello
...
Conditions:
  Type           Status
  Initialized    True
  Ready          True
  PodScheduled   True
...
Events:
  Type    Reason     Age   From               Message
  ----    ------     ----  ----               -------
  Normal  Scheduled  1d    default-scheduler  Successfully assigned demo-dev...
  Normal  Pulling    1d    kubelet            pulling image "cloudnatived/demo...
...

In the example output, you can see that kubectl gives you some basic information about the container itself, including its image identifier and status, along with an ordered list of events that have happened to the container. (We’ll learn a lot more about the power of kubectl in Chapter 7.)

Taking Resources to the Next Level

You now know everything you need to know to deploy applications to Kubernetes clusters using declarative YAML manifests. But there’s a lot of repetition in these files: for example, you’ve repeated the name demo, the label selector app: demo, and the port 8888 several times.

Shouldn’t you be able to just specify those values once, and then reference them wherever they occur through the Kubernetes manifests?

For example, it would be great to be able to define variables called something like container.name and container.port, and then use them wherever they’re needed in the YAML files. Then, if you needed to change the name of the app or the port number it listens on, you’d only have to change them in one place, and all the manifests would be updated automatically.

Fortunately, there’s a tool for that, and in the final section of this chapter we’ll show you a little of what it can do.

Helm: A Kubernetes Package Manager

One popular package manager for Kubernetes is called Helm, and it works just the way we’ve described in the previous section. You can use the helm command-line tool to install and configure applications (your own or anyone else’s), and you can create packages called Helm charts, which completely specify the resources needed to run the application, its dependencies, and its configurable settings.

Helm is part of the Cloud Native Computing Foundation family of projects (see “Cloud Native”), which reflects its stability and widespread adoption.

Note

It’s important to realize that a Helm chart, unlike the binary software packages used by tools like APT or Yum, doesn’t actually include the container image itself. Instead, it simply contains metadata about where the image can be found, just as a Kubernetes Deployment does.

When you install the chart, Kubernetes itself will locate and download the binary container image from the place you specified. In fact, a Helm chart is really just a convenient wrapper around Kubernetes YAML manifests.

Installing Helm

Follow the Helm installation instructions for your operating system.

Once you have Helm installed, you will need to create some Kubernetes resources to authorize it for access to your cluster. There’s a suitable YAML file in the example repo, in the hello-helm directory, named helm-auth.yaml. To apply it, run the following commands:

cd ../hello-helm
kubectl apply -f helm-auth.yaml
serviceaccount "tiller" created
clusterrolebinding.rbac.authorization.k8s.io "tiller" created

Now that the necessary permissions are in place, you can initialize Helm for access to the cluster, using this command:

helm init --service-account tiller
$HELM_HOME has been configured at /Users/john/.helm.

It may take five minutes or so for Helm to finish initializing itself. To verify that Helm is up and working, run:

helm version
Client: &version.Version{SemVer:"v2.9.1",
GitCommit:"20adb27c7c5868466912eebdf6664e7390ebe710", GitTreeState:"clean"}
Server: &version.Version{SemVer:"v2.9.1",
GitCommit:"20adb27c7c5868466912eebdf6664e7390ebe710", GitTreeState:"clean"}

Once this command succeeds, you’re ready to start using Helm. If you see an Error: cannot connect to Tiller message, wait a few minutes and try again.

Installing a Helm Chart

What would the Helm chart for our demo application look like? In the hello-helm directory, you’ll see a k8s subdirectory, which in the previous example (hello-k8s) contained just the Kubernetes manifest files to deploy the application. Now it contains a Helm chart, in the demo directory:

ls k8s/demo
Chart.yaml             prod-values.yaml staging-values.yaml    templates
values.yaml

We’ll see what all these files are for in “What’s Inside a Helm Chart?”, but for now, let’s use Helm to install the demo application. First, clean up the resources from any previous deployments:

kubectl delete all --selector app=demo

Then run the following command:

helm install --name demo ./k8s/demo
NAME:   demo
LAST DEPLOYED: Wed Jun  6 10:48:50 2018
NAMESPACE: default
STATUS: DEPLOYED

RESOURCES:
==> v1/Service
NAME              TYPE       CLUSTER-IP     EXTERNAL-IP  PORT(S)   AGE
demo-service      ClusterIP  10.98.231.112  <none>       80/TCP  0s

==> v1/Deployment
NAME      DESIRED  CURRENT  UP-TO-DATE  AVAILABLE  AGE
demo      1        1        1           0          0s

==> v1/Pod(related)
NAME                       READY  STATUS             RESTARTS  AGE
demo-6c96484c48-69vss      0/1    ContainerCreating  0         0s

You can see that Helm has created a Deployment resource (which starts a Pod) and a Service, just as in the previous example. The helm install does this by creating a Kubernetes object called a Helm release.

Charts, Repositories, and Releases

These are the three most important Helm terms you need to know:

  • A chart is a Helm package, containing all the resource definitions necessary to run an application in Kubernetes.

  • A repository is a place where charts can be collected and shared.

  • A release is a particular instance of a chart running in a Kubernetes cluster.

One chart can often be installed many times into the same cluster. For example, you might be running multiple copies of the Nginx web server chart, each serving a different site. Each separate instance of the chart is a distinct release.

Each release has a unique name, which you specify with the -name flag to helm install. (If you don’t specify a name, Helm will choose a random one for you. You probably won’t like it.)

Listing Helm Releases

To check what releases you have running at any time, run helm list:

helm list
NAME    REVISION UPDATED                  STATUS   CHART      NAMESPACE
demo    1        Wed Jun  6 10:48:50 2018 DEPLOYED demo-1.0.1 default

To see the exact status of a particular release, run helm status followed by the name of the release. You’ll see the same information that you did when you first deployed the release: a list of all the Kubernetes resources associated with the release, including Deployments, Pods, and Services.

Later in the book, we’ll show you how to build your own Helm charts for your applications (see “What’s Inside a Helm Chart?”). For now, just know that Helm is a handy way to install applications from public charts.

Tip

You can see the full list of public Helm charts on GitHub.

You can also get a list of available charts by running helm search with no arguments (or helm search redis to search for a Redis chart, for example).

Summary

This isn’t a book about Kubernetes internals (sorry, no refunds). Our aim is to show you what Kubernetes can do, and bring you quickly to the point where you can run real workloads in production. However, it’s useful to know at least some of the main pieces of machinery you’ll be working with, such as Pods and Deployments. In this chapter we’ve briefly introduced some of the most important ones.

As fascinating as the technology is to geeks like us, though, we’re also interested in getting stuff done. Therefore, we haven’t exhaustively covered every kind of resource Kubernetes provides, because there are a lot, and many of them you almost certainly won’t need (at least, not yet).

The key points we think you need to know right now:

  • The Pod is the fundamental unit of work in Kubernetes, specifying a single container or group of communicating containers that are scheduled together.

  • A Deployment is a high-level Kubernetes resource that declaratively manages Pods, deploying, scheduling, updating, and restarting them when necessary.

  • A Service is the Kubernetes equivalent of a load balancer or proxy, routing traffic to its matching Pods via a single, well-known, durable IP address or DNS name.

  • The Kubernetes scheduler watches for a Pod that isn’t yet running on any node, finds a suitable node for it, and instructs the kubelet on that node to run the Pod.

  • Resources like Deployments are represented by records in Kubernetes’s internal database. Externally, these resources can be represented by text files (known as manifests) in YAML format. The manifest is a declaration of the desired state of the resource.

  • kubectl is the main tool for interacting with Kubernetes, allowing you to apply manifests, query resources, make changes, delete resources, and do many other tasks.

  • Helm is a Kubernetes package manager. It simplifies configuring and deploying Kubernetes applications, allowing you to use a single set of values (such as the application name or listen port) and a set of templates to generate Kubernetes YAML files, instead of having to maintain the raw YAML files yourself.

1 k8s, pronounced kates, is a common abbreviation for Kubernetes, following the geeky pattern of abbreviating words as a numeronym: their first and last letters, plus the number of letters in between (k-8-s). See also i18n (internationalization), a11y (accessibility), and o11y (observability).

2 This is the default load balancing algorithm; Kubernetes versions 1.10+ support other algorithms too, such as least connection. See https://kubernetes.io/blog/2018/07/09/ipvs-based-in-cluster-load-balancing-deep-dive/.

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