Rolling updates with Kubernetes: Replication Controllers vs Deployments

A rolling update is the process of updating an application — whether it is a new version or just updated configuration — in a serial fashion. By updating one instance at a time, you are able to keep the application up and running. If you were to just update all instances at the same time, your application would likely experience downtime. In addition, performing a rolling update allows you to catch errors during the process so that you can rollback before it affects all of your users.

Historically, rolling updates could be performed using Kubernetes Replication Controllers and the kubectl rolling-update command. In Kubernetes 1.2, the Deployment object API was released in beta. Deployments operate at a higher level than Replication Controllers and are the preferred mechanism going forward.

We’ll look at how to do rolling updates with Kubernetes. First, we’ll go through the process using Replication Controllers. Afterwards, we’ll deploy and update the same application using the newer Deployment API, so that we can see the advantages that it provides.

Rolling Updates with a Replication Controller

Let’s go over how to do a rolling update using a Replication Controller. To do so, it is actually necessary to create a new Replication Controller with the updated configuration. The rolling update process coordinates the increase of the replica count for the new Replication Controller, while decreasing the number of replicas for the old Replication Controller. This continues until the number of replicas in the old Replication Controller reaches 0, and the desired number of pods are running with the new configuration defined in the new Replication Controller. Finally, the old replication is deleted from the system.

Let’s look at an example. I have a simple Golang service that returns a json response that includes a timestamp, hostname, the version of the app, and a value that is retrieved via an environment variable.

The source code for our application and all the Kubernetes specs discussed in this post can be found in the k8s-deployment-demo repository on github.

Here is an example of the response:

{
  "Environment": "production",
  "Hostname": "radon.local",
  "Time": "2016-06-10T08:50:24.377415946-04:00",
  "Version": "0.1"
}

We are going to use this response to monitor the changes to our application when we update it in Kubernetes. First, we’ll deploy the initial version. Below is our Replication Controller spec (demo-rc-v0.1.yml):

apiVersion: v1
kind: ReplicationController
metadata:
  name: k8s-deployment-demo-controller-v1
spec:
  replicas: 4
  selector:
    app: k8s-deployment-demo
    version: v0.1
  template:
    metadata:
      labels:
        app: k8s-deployment-demo
        version: v0.1
    spec:
      containers:
        - name: k8s-deployment-demo
          image: ryane/k8s-deployment-demo:0.1
          imagePullPolicy: Always
          ports:
            - containerPort: 8081
              protocol: TCP
          env:
            - name: DEMO_ENV
              value: staging

This is a pretty straightforward Replication Controller spec. There are just a few things to note:

  • The name of the Replication Controller is: k8s-deployment-demo-controller-v1.
  • We are deploying 4 instances (replicas: 4).
  • In the selector, we are using a version label set to v0.1 which matches the label set in the pod template.
  • We are deploying the 0.1 tag of the ryane/k8s-deployment-demo docker image.
  • We are setting the value of the DEMO_ENV environment variable to staging.

Let’s deploy it!

kubectl create -f demo-rc-v0.1.yml

After a few moments, you should see that the Replication Controller has been created and there are 4 pods running:

$ kubectl get rc
NAME                                DESIRED   CURRENT   AGE
k8s-deployment-demo-controller-v1   4         4         1m

$ kubectl get po
NAME                                      READY     STATUS    RESTARTS   AGE
k8s-deployment-demo-controller-v1-0w3ep   1/1       Running   0          30s
k8s-deployment-demo-controller-v1-6nkxi   1/1       Running   0          30s
k8s-deployment-demo-controller-v1-sl5ds   1/1       Running   0          30s
k8s-deployment-demo-controller-v1-z3qsr   1/1       Running   0          30s

Next, we are going to create a Service, so that we can access our application while we do the rolling update.

$ kubectl expose rc k8s-deployment-demo-controller-v1 --name=k8s-deployment-demo-svc --port=80 --target-port=8081 --selector="app=k8s-deployment-demo"
service "k8s-deployment-demo-svc" exposed

After running the above command, there will be a k8s-deployment-demo-svc Service object with an endpoint for each pod in our Replication Controller:

$ kc describe svc k8s-deployment-demo-svc
Name:                   k8s-deployment-demo-svc
Namespace:              default
Labels:                 app=k8s-deployment-demo,version=v0.1
Selector:               app=k8s-deployment-demo,version=v0.1
Type:                   ClusterIP
IP:                     10.0.115.6
Port:                   <unset> 80/TCP
Endpoints:              10.244.0.7:8081,10.244.1.7:8081,10.244.2.8:8081 + 1 more...
Session Affinity:       None
No events.

Since we have not exposed our service externally (the ClusterIP service type only exposes the service internally), we need to access it from within the cluster. We’ll launch a pod with an interactive shell prompt so that we can interact with the application.

$ kubectl run curl --image=radial/busyboxplus:curl -i --tty --restart=Never
Waiting for pod default/curl-6aeta to be running, status is Pending, pod ready: false

Hit enter for command prompt
                            /bin/sh: shopt: not found

[ root@curl-6aeta:/ ]$ curl -s k8s-deployment-demo-svc.default.svc.cluster.local
{"Version":"0.1","Hostname":"k8s-deployment-demo-controller-v1-krn3r","Environment":"staging","Time":"2016-06-12T13:05:23.947906828Z"}
[ root@curl-6aeta:/ ]$ curl -s k8s-deployment-demo-svc.default.svc.cluster.local
{"Version":"0.1","Hostname":"k8s-deployment-demo-controller-v1-06ifw","Environment":"staging","Time":"2016-06-12T13:05:25.564241985Z"}
[ root@curl-6aeta:/ ]$ curl -s k8s-deployment-demo-svc.default.svc.cluster.local
{"Version":"0.1","Hostname":"k8s-deployment-demo-controller-v1-k63c2","Environment":"staging","Time":"2016-06-12T13:05:28.3038308Z"}

This launches a job running the radial/busyboxplus:curl docker image. We are using the curl utility to make a request against the DNS name for the service we created in the previous step. In the above example, you can see our application is returning the version, the environment setting, a timestamp, and the hostname of the pod is running on. You should see the hostname changing as the DNS name for the service resolves to the different pods in the Replication Controller. If you’re following along, it will be useful to keep this interactive shell running so that you can interact with it while we continue.

Now we’ve decided this application needs to be updated to version 0.2. Let’s see how to do that. Our new Replication Controller spec looks like this:

apiVersion: v1
kind: ReplicationController
metadata:
  name: k8s-deployment-demo-controller-v2
spec:
  replicas: 4
  selector:
    app: k8s-deployment-demo
    version: v0.2
  template:
    metadata:
      labels:
        app: k8s-deployment-demo
        version: v0.2
    spec:
      containers:
        - name: k8s-deployment-demo
          image: ryane/k8s-deployment-demo:0.2
          imagePullPolicy: Always
          ports:
            - containerPort: 8081
              protocol: TCP
          env:
            - name: DEMO_ENV
              value: production

This is almost identical to the v1 version of the spec. The only differences are:

  • We’ve changed the name to k8s-deployment-demo-controller-v2.
  • The version label in the pod template has been updated to v0.2 and the selector for the Replication Controller has also been updated with that label.
  • The tag for the docker image has been updated to 0.2.
  • The value for the DEMO_ENV environment variable has been updated to “production”. This is done for the purpose of this demo so that you can easily see the value change during the update.

To see all the changes, check out this diff.

Let’s perform the update! We will use kubectl rolling-update to specify that we want to update our running k8s-deployment-demo-controller-v1 Replication Controller to k8s-deployment-demo-controller-v2 (the above yaml is saved in a file called demo-rc-v02.yml).

$ kubectl rolling-update k8s-deployment-demo-controller-v1 --update-period=10s -f demo-rc-v0.2.yml

We are using most of the default options, but we are specifying an update period of 10 seconds. This is the amount of time to wait between updating each pod in the Replication Controller. The default is 1 minute, but we want it to run a bit faster for this demo application.

While the update is running, you can use the curl job prompt we started above to monitor the process. Here is a sample of what the requests looked like on my system:

[ root@curl-6aeta:/ ]$ curl -s k8s-deployment-demo-svc.default.svc.cluster.local
{"Version":"0.1","Hostname":"k8s-deployment-demo-controller-v1-apw5d","Environment":"staging","Time":"2016-06-12T13:40:35.975460052Z"}
[ root@curl-6aeta:/ ]$ curl -s k8s-deployment-demo-svc.default.svc.cluster.local
{"Version":"0.1","Hostname":"k8s-deployment-demo-controller-v1-9vqyp","Environment":"staging","Time":"2016-06-12T13:40:39.826921421Z"}
[ root@curl-6aeta:/ ]$ curl -s k8s-deployment-demo-svc.default.svc.cluster.local
{"Version":"0.2","Hostname":"k8s-deployment-demo-controller-v2-t3zsa","Environment":"production","Time":"2016-06-12T13:40:40.780995539Z"}
[ root@curl-6aeta:/ ]$ curl -s k8s-deployment-demo-svc.default.svc.cluster.local
{"Version":"0.2","Hostname":"k8s-deployment-demo-controller-v2-t3zsa","Environment":"production","Time":"2016-06-12T13:40:41.931620697Z"}
[ root@curl-6aeta:/ ]$ curl -s k8s-deployment-demo-svc.default.svc.cluster.local
{"Version":"0.1","Hostname":"k8s-deployment-demo-controller-v1-keolo","Environment":"staging","Time":"2016-06-12T13:40:43.426662617Z"}

Note how the Version and Environment fields are bouncing between the v0.1 and v0.2 releases. From the end user perspective, the application stays up and running the entire time but, while the update is running, both versions may end up serving requests. By the time the update is complete, you will only have v0.2 pods running.

The kubectl rolling-update output explains what is going on during the update:

Created k8s-deployment-demo-controller-v2
Scaling up k8s-deployment-demo-controller-v2 from 0 to 4, scaling down k8s-deployment-demo-controller-v1 from 4 to 0 (keep 4 pods available, don't exceed 5 pods)
Scaling k8s-deployment-demo-controller-v2 up to 1
Scaling k8s-deployment-demo-controller-v1 down to 3
Scaling k8s-deployment-demo-controller-v2 up to 2
Scaling k8s-deployment-demo-controller-v1 down to 2
Scaling k8s-deployment-demo-controller-v2 up to 3
Scaling k8s-deployment-demo-controller-v1 down to 1
Scaling k8s-deployment-demo-controller-v2 up to 4
Scaling k8s-deployment-demo-controller-v1 down to 0
Update succeeded. Deleting k8s-deployment-demo-controller-v1
replicationcontroller "k8s-deployment-demo-controller-v1" rolling updated to "k8s-deployment-demo-controller-v2"

That’s it! Now 4 instances of version 0.2 of our application are running in our cluster. The upgrade process completed without any interruption of service to the consumers of the application. This is a powerful capability.

However, there are some drawbacks to this approach. As mentioned above, to perform the update it is necessary to create a new Replication Controller with a new name. If you store your Kubernetes manifests in source control, you need to bounce between at least two manifests to coordinate between releases. Also, the rolling update occurs on the client side via kubectl. This means the rolling update is more vulnerable to network interruptions. Also, rolling back is not as simple as it could be. Rollbacks require running another rolling update back to another Replication Controller with the previous configuration. And, finally, there is no audit trail with this approach — the deployment history is not tracked anywhere within Kubernetes.

There is a bit of a shortcut if you are running a single container pod and are only updating the docker image of that container. Instead of creating a new Replication Controller manifest, you can simply run a command like this:

kubectl rolling-update k8s-deployment-demo-controller-v1 --image=ryane/k8s-deployment-demo:0.2

Behind the scenes, this will create a new Replication Controller with a temporary name. Once the update is complete, it will delete the original Replication Controller and update the new Replication Controller with the name of the original one. This certainly simplifies the process, but it is often not usable for many applications since you will often be running pods with multiple containers or will need to update more than just the docker image — configuration settings, secrets, or volumes, for example.

Now, let’s take a look at using the newer Kubernetes Deployment object to perform the same task.

Rolling Updates with a Deployment

Before we get started, let’s tear down the resources we created above:

$ kubectl delete rc k8s-deployment-demo-controller-v2
$ kubectl delete svc k8s-deployment-demo-svc

Now that we have a clean slate, we can use a Deployment to run our application. Here is our deployment manifest (demo-deployment-v1.yml):

apiVersion: extensions/v1beta1
kind: Deployment
metadata:
  name: k8s-deployment-demo-deployment
spec:
  replicas: 4
  selector:
    matchLabels:
      app: k8s-deployment-demo
  minReadySeconds: 10
  template:
    metadata:
      labels:
        app: k8s-deployment-demo
        version: v0.1
    spec:
      containers:
        - name: k8s-deployment-demo
          image: ryane/k8s-deployment-demo:0.1
          imagePullPolicy: Always
          ports:
            - containerPort: 8081
              protocol: TCP
          env:
            - name: DEMO_ENV
              value: staging

This actually looks quite similar to our Replication Controller manifest. There are just a few differences:

  • The kind is Deployment instead of ReplicationController.
  • The selector uses matchLabels as the Deployment object supports Set-based label requirements.
  • Note that we are excluding the version label in the selector. As we will see, the same Deployment object is going to support multiple versions of our application.
  • We are setting minReadySeconds so that there is a 10 second grace period after a pod is started and before it is considered ready. This can be used in conjunction with a ReadinessProbe that will let your application distinguish between when it is just up and running and actually ready to serve requests (perhaps there is an initialization or bootstrapping phase for your application). In this case, we don’t have a ReadinessProbe defined for our pod; we are just using the delay so that we can monitor the process of our update. Otherwise, it just happens too fast for us to see what is going on!

Now we can run our deployment. As with the Replication Controller, we use kubectl create for this. In this example, we are also using the --record option. This saves the command along with the resource in the Kubernetes API server. This will be useful when we look at the deployment history later.

$ kubectl create -f demo-deployment-v1.yml --record
deployment "k8s-deployment-demo-deployment" created

As with the Replication Controller example, we should see that 4 pods will be running our application and that our Deployment object is created (it may take a few moments for Kubernetes to reach the desired state).

$ kubectl get pods
NAME                                              READY     STATUS    RESTARTS   AGE
k8s-deployment-demo-deployment-3774590724-2scro   1/1       Running   0          10s
k8s-deployment-demo-deployment-3774590724-cdtsh   1/1       Running   0          10s
k8s-deployment-demo-deployment-3774590724-dokm9   1/1       Running   0          10s
k8s-deployment-demo-deployment-3774590724-m58pe   1/1       Running   0          10s

$ kubectl get deployment
NAME                             DESIRED   CURRENT   UP-TO-DATE   AVAILABLE   AGE
k8s-deployment-demo-deployment   4         4         4            4           5s

Under the hood, we also can see that a new object that was created. The ReplicaSet.

$ kubectl get replicaset
NAME                                        DESIRED   CURRENT   AGE
k8s-deployment-demo-deployment-3774590724   4         4         3m

At this point, ReplicaSets are virtually identical to Replication Controllers. The major difference is that they support the newer set-based label selectors as mentioned above. You won’t typically need to work with ReplicaSets directly as the Deployment will manage them for you behind the scenes.

Just like before, we are going to create a Service so that we can access our application.

$ kubectl expose deployment k8s-deployment-demo-deployment --name=k8s-deployment-demo-svc --port=80 --target-port=8081 --selector="app=k8s-deployment-demo"

You can run the curl job — like we did above — to monitor the application while we perform the rolling update:

$ kubectl run curl --image=radial/busyboxplus:curl -i --tty --restart=Never
Waiting for pod default/curl-3x5ma to be running, status is Pending, pod ready: false

Hit enter for command prompt
                            /bin/sh: shopt: not found

[ root@curl-3x5ma:/ ]$ while true; do curl -s k8s-deployment-demo-svc.default.sv
c.cluster.local; sleep 1; done
{"Version":"0.1","Hostname":"k8s-deployment-demo-deployment-3774590724-dokm9","Environment":"staging","Time":"2016-06-14T12:54:01.663928214Z"}
{"Version":"0.1","Hostname":"k8s-deployment-demo-deployment-3774590724-m58pe","Environment":"staging","Time":"2016-06-14T12:54:02.677043164Z"}
{"Version":"0.1","Hostname":"k8s-deployment-demo-deployment-3774590724-dokm9","Environment":"staging","Time":"2016-06-14T12:54:03.686170751Z"}
{"Version":"0.1","Hostname":"k8s-deployment-demo-deployment-3774590724-2scro","Environment":"staging","Time":"2016-06-14T12:54:04.691405055Z"}

If you stopped your curl job from the previous example, you may need to delete the job first with kubectl delete job curl before you can run it again.

Let’s prepare our application for version 0.0.2. Below is an updated Deployment manifest (demo-deployment-v2.yml):

apiVersion: extensions/v1beta1
kind: Deployment
metadata:
  name: k8s-deployment-demo-deployment
spec:
  replicas: 4
  selector:
    matchLabels:
      app: k8s-deployment-demo
  minReadySeconds: 10
  template:
    metadata:
      labels:
        app: k8s-deployment-demo
        version: v0.2
    spec:
      containers:
        - name: k8s-deployment-demo
          image: ryane/k8s-deployment-demo:0.2
          imagePullPolicy: Always
          ports:
            - containerPort: 8081
              protocol: TCP
          env:
            - name: DEMO_ENV
              value: production

For the purposes of the demo, I am using two different yaml files for the v1 and v2 version of the Deployment. This is not necessary; instead, you could just update the single deployment manifest (ideally committing it into source control) and apply it.

The only differences between this and the v1 version of the deployments are the version label for the pod template, the change in the container image to use the 0.2 tag, and the value of the DEMO_ENV environment variable.

Here is the diff.

Now we can roll out our new version:

$ kubectl apply -f demo-deployment-v2.yml --record
deployment "k8s-deployment-demo-deployment" configured

You won’t get the output like you saw when you ran kubectl rolling-update, but you can monitor the status of the update using other kubectl commands. Here are few samples:

$ kc get deployment
NAME                             DESIRED   CURRENT   UP-TO-DATE   AVAILABLE   AGE
k8s-deployment-demo-deployment   4         5         4            3           1m

$ kubectl get pods
NAME                                              READY     STATUS        RESTARTS   AGE
k8s-deployment-demo-deployment-129478752-3i3zx    1/1       Running       0          51s
k8s-deployment-demo-deployment-129478752-cgp8k    1/1       Running       0          51s
k8s-deployment-demo-deployment-129478752-oqvqe    1/1       Running       0          19s
k8s-deployment-demo-deployment-129478752-vg5ha    1/1       Running       0          19s
k8s-deployment-demo-deployment-3774590724-1oyko   1/1       Terminating   0          1m
k8s-deployment-demo-deployment-3774590724-4a41o   1/1       Terminating   0          1m
k8s-deployment-demo-deployment-3774590724-ga7ew   1/1       Running       0          1m

$ kubectl describe deployment k8s-deployment-demo-deployment
Name:                   k8s-deployment-demo-deployment
Namespace:              default
CreationTimestamp:      Tue, 14 Jun 2016 10:21:45 -0400
Labels:                 app=k8s-deployment-demo,version=v0.1
Selector:               app=k8s-deployment-demo
Replicas:               4 updated | 4 total | 3 available | 2 unavailable
StrategyType:           RollingUpdate
MinReadySeconds:        10
RollingUpdateStrategy:  1 max unavailable, 1 max surge
OldReplicaSets:         k8s-deployment-demo-deployment-3774590724 (1/1 replicas created)
NewReplicaSet:          k8s-deployment-demo-deployment-129478752 (4/4 replicas created)
Events:
  FirstSeen     LastSeen        Count   From                            SubobjectPath   Type            Reason                  Message
  ---------     --------        -----   ----                            -------------   --------        ------                  -------
  3m            3m              1       {deployment-controller }                        Normal          ScalingReplicaSet       Scaled up replica set k8s-deployment-demo-deployment-3774590724 to 4
  20s           20s             1       {deployment-controller }                        Normal          ScalingReplicaSet       Scaled up replica set k8s-deployment-demo-deployment-129478752 to 1
  20s           20s             1       {deployment-controller }                        Normal          ScalingReplicaSet       Scaled down replica set k8s-deployment-demo-deployment-3774590724 to 3
  20s           20s             1       {deployment-controller }                        Normal          ScalingReplicaSet       Scaled up replica set k8s-deployment-demo-deployment-129478752 to 2
  2s            2s              1       {deployment-controller }                        Normal          ScalingReplicaSet       Scaled down replica set k8s-deployment-demo-deployment-3774590724 to 1
  2s            2s              1       {deployment-controller }                        Normal          ScalingReplicaSet       Scaled up replica set k8s-deployment-demo-deployment-129478752 to 4

If you’re monitoring the output of the curl job, you should not see any disruption while the update is happening. Both the 0.1 and 0.2 version of the application will serve requests until the upgrade is completed.

{"Version":"0.2","Hostname":"k8s-deployment-demo-deployment-129478752-vg5ha","Environment":"production","Time":"2016-06-14T13:48:19.608435577Z"}
{"Version":"0.1","Hostname":"k8s-deployment-demo-deployment-3774590724-ga7ew","Environment":"staging","Time":"2016-06-14T13:48:20.623657093Z"}
{"Version":"0.1","Hostname":"k8s-deployment-demo-deployment-3774590724-ga7ew","Environment":"staging","Time":"2016-06-14T13:48:21.631715042Z"}
{"Version":"0.1","Hostname":"k8s-deployment-demo-deployment-3774590724-ga7ew","Environment":"staging","Time":"2016-06-14T13:48:22.63956071Z"}
{"Version":"0.2","Hostname":"k8s-deployment-demo-deployment-129478752-vg5ha","Environment":"production","Time":"2016-06-14T13:48:23.648553098Z"}
{"Version":"0.1","Hostname":"k8s-deployment-demo-deployment-3774590724-ga7ew","Environment":"staging","Time":"2016-06-14T13:48:24.655309069Z"}
{"Version":"0.2","Hostname":"k8s-deployment-demo-deployment-129478752-cgp8k","Environment":"production","Time":"2016-06-14T13:48:25.665096707Z"}

As mentioned, one of the benefits of using Deployments is the fact that the update history is stored in Kubernetes. We can use the kubectl rollout command to view the history:

$ kubectl rollout history deployment k8s-deployment-demo-deployment
deployments "k8s-deployment-demo-deployment":
REVISION        CHANGE-CAUSE
1               kubectl create -f demo-deployment-v1.yml --record
2               kubectl apply -f demo-deployment-v2.yml --record

You can also see that the there are now two ReplicaSets that are associated with our Deployment:

$ kubectl get replicaset
NAME                                        DESIRED   CURRENT   AGE
k8s-deployment-demo-deployment-129478752    4         4         6m
k8s-deployment-demo-deployment-3774590724   0         0         8m

With all of this information captured in Kubernetes, it is much simpler to perform a rollback. As an example, let’s rollback to version 0.1 of our application:

$ kubectl rollout undo deployment k8s-deployment-demo-deployment
deployment "k8s-deployment-demo-deployment" rolled back

You can use the various kubectl commands described above and the curl job to monitor the status of the rollback. In less than a minute or so, the application should be back to 4 pods running the 0.1 version.

As you can see, rolling updates is an important feature in Kubernetes and the capabilities continue to improve. The new Deployment feature in 1.2+ is an elegant way to manage your application deployments. I hope this was helpful. Thanks for reading!


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