When you apply the manifest, you will see output like the following.

$ kubectl apply -f zookeeper\_mini.yaml

service "zk-hs" created

service "zk-cs" created

poddisruptionbudget "zk-pdb" created

statefulset "zk" created

The manifest creates an ensemble of three ZooKeeper servers using a StatefulSet, zk; a Headless Service, zk-hs, to control the domain of the ensemble; a Service, zk-cs, that clients can use to connect to the ready ZooKeeper instances; and a PodDisruptionBugdet, zk-pdb, that allows for one planned disruption. (Note that while this ensemble is suitable for demonstration purposes, it isn’t sized correctly for production use.)

If you watch Pod creation, you will notice that, like the ZooKeeper ensemble created above, the Kafka cluster uses the Parallel podManagementPolicy.

$ kubectl get po -lapp=kafka -w

NAME           READY         STATUS        RESTARTS     AGE


kafka-0     0/1             Pending      0                   0s


kafka-0     0/1             Pending      0                  0s


kafka-1     0/1             Pending      0                  0s


kafka-1     0/1             Pending      0                  0s


kafka-2     0/1             Pending      0                  0s


kafka-0     0/1             ContainerCreating     0                  0s


kafka-2     0/1             Pending      0                  0s


kafka-1     0/1             ContainerCreating     0                  0s


kafka-1     0/1             Running     0                  11s


kafka-0     0/1             Running     0                  19s


kafka-1     1/1             Running     0                  23s


kafka-0     1/1             Running     0                  32s

Producing and consuming data

You can use kubectl run to execute the kafka-topics.sh script to create a topic named test.

$ kubectl run -ti --image=gcr.io/google\_containers/kubernetes-kafka:1.0-10.2.1 createtopic --restart=Never --rm -- kafka-topics.sh --create \

\> --topic test \

\> --zookeeper zk-cs.default.svc.cluster.local:2181 \

\> --partitions 1 \

\> --replication-factor 3

Now you can use kubectl run to execute the kafka-console-consumer.sh command to listen for messages.

$ kubectl run -ti --image=gcr.io/google\_containers/kubnetes-kafka:1.0-10.2.1 consume --restart=Never --rm -- kafka-console-consumer.sh --topic test --bootstrap-server kafka-0.kafka-hs.default.svc.cluster.local:9093

In another terminal, you can run the kafka-console-producer.sh command.

$kubectl run -ti --image=gcr.io/google\_containers/kubernetes-kafka:1.0-10.2.1 produce --restart=Never --rm \

\>   -- kafka-console-producer.sh --topic test --broker-list kafka-0.kafka-hs.default.svc.cluster.local:9093,kafka-1.kafka-hs.default.svc.cluster.local:9093,kafka-2.kafka-hs.default.svc.cluster.local:9093

Output from the second terminal appears in the first terminal. If you continue to produce and consume messages while updating the cluster, you will notice that no messages are lost. You may see error messages as the leader for the partition changes when individual brokers are updated, but the client retries until the message is committed. This is due to the ordered, sequential nature of StatefulSet rolling updates which we will explore further in the next section.

Updating the Kafka cluster

StatefulSet updates are like DaemonSet updates in that they are both configured by setting the spec.updateStrategy of the corresponding API object. When the update strategy is set to OnDelete, the respective controllers will only create new Pods when a Pod in the StatefulSet or DaemonSet has been deleted. When the update strategy is set to RollingUpdate, the controllers will delete and recreate Pods when a modification is made to the spec.template field of a DaemonSet or StatefulSet. You can use rolling updates to change the configuration (via environment variables or command line parameters), resource requests, resource limits, container images, labels, and/or annotations of the Pods in a StatefulSet or DaemonSet. Note that all updates are destructive, always requiring that each Pod in the DaemonSet or StatefulSet be destroyed and recreated. StatefulSet rolling updates differ from DaemonSet rolling updates in that Pod termination and creation is ordered and sequential.

You can patch the kafka StatefulSet to reduce the CPU resource request to 250m.

$ kubectl patch sts kafka --type='json' -p='[{"op": "replace", "path": "/spec/template/spec/containers/0/resources/requests/cpu", "value":"250m"}]'

statefulset "kafka" patched

If you watch the status of the Pods in the StatefulSet, you will see that each Pod is deleted and recreated in reverse ordinal order (starting with the Pod with the largest ordinal and progressing to the smallest). The controller waits for each updated Pod to be running and ready before updating the subsequent Pod.

$kubectl get po -lapp=kafka -w

NAME           READY         STATUS       RESTARTS     AGE


kafka-0     1/1             Running     0                   13m


kafka-1     1/1             Running     0                   13m


kafka-2     1/1             Running     0                   13m


kafka-2     1/1             Terminating     0                 14m


kafka-2     0/1             Terminating     0                 14m


kafka-2     0/1             Terminating     0                 14m


kafka-2     0/1             Terminating     0                 14m


kafka-2     0/1             Pending     0                 0s


kafka-2     0/1             Pending     0                 0s


kafka-2     0/1             ContainerCreating     0                 0s


kafka-2     0/1             Running     0                 10s


kafka-2     1/1             Running     0                 21s


kafka-1     1/1             Terminating     0                 14m


kafka-1     0/1             Terminating     0                 14m


kafka-1     0/1             Terminating     0                 14m


kafka-1     0/1             Terminating     0                 14m


kafka-1     0/1             Pending     0                 0s


kafka-1     0/1             Pending     0                 0s


kafka-1     0/1             ContainerCreating     0                 0s


kafka-1     0/1             Running     0                 11s


kafka-1     1/1             Running     0                 21s


kafka-0     1/1             Terminating     0                 14m


kafka-0     0/1             Terminating     0                 14m


kafka-0     0/1             Terminating     0                 14m


kafka-0     0/1             Terminating     0                 14m


kafka-0     0/1             Pending     0                 0s


kafka-0     0/1             Pending     0                 0s


kafka-0     0/1             ContainerCreating     0                 0s


kafka-0     0/1             Running     0                 10s


kafka-0     1/1             Running     0                 22s

Note that unplanned disruptions will not lead to unintentional updates during the update process. That is, the StatefulSet controller will always recreate the Pod at the correct version to ensure the ordering of the update is preserved. If a Pod is deleted, and if it has already been updated, it will be created from the updated version of the StatefulSet’s spec.template. If the Pod has not already been updated, it will be created from the previous version of the StatefulSet’s spec.template. We will explore this further in the following sections.

Staging an update

Depending on how your organization handles deployments and configuration modifications, you may want or need to stage updates to a StatefulSet prior to allowing the roll out to progress. You can accomplish this by setting a partition for the RollingUpdate. When the StatefulSet controller detects a partition in the updateStrategy of a StatefulSet, it will only apply the updated version of the StatefulSet’s spec.template to Pods whose ordinal is greater than or equal to the value of the partition.

You can patch the kafka StatefulSet to add a partition to the RollingUpdate update strategy. If you set the partition to a number greater than or equal to the StatefulSet’s spec.replicas (as below), any subsequent updates you perform to the StatefulSet’s spec.template will be staged for roll out, but the StatefulSet controller will not start a rolling update.

$ kubectl patch sts kafka -p '{"spec":{"updateStrategy":{"type":"RollingUpdate","rollingUpdate":{"partition":3}}}}'

statefulset "kafka" patched

If you patch the StatefulSet to set the requested CPU to 0.3, you will notice that none of the Pods are updated.

$ kubectl patch sts kafka --type='json' -p='[{"op": "replace", "path": "/spec/template/spec/containers/0/resources/requests/cpu", "value":"0.3"}]'

statefulset "kafka" patched

Even if you delete a Pod and wait for the StatefulSet controller to recreate it, you will notice that the Pod is recreated with current CPU request.

$   kubectl delete po kafka-1


pod "kafka-1" deleted


$ kubectl get po kafka-1 -w

NAME           READY         STATUS                           RESTARTS     AGE


kafka-1     0/1             ContainerCreating     0                   10s


kafka-1     0/1             Running     0                 19s


kafka-1     1/1             Running     0                 21s



$ kubectl get po kafka-1 -o yaml

apiVersion: v1

kind: Pod

metadata:

   ...


       resources:


           requests:


               cpu: 250m


               memory: 1Gi

Rolling out a canary

$   kubectl get po -lapp=kafka -w


NAME           READY         STATUS       RESTARTS     AGE


kafka-0     1/1             Running     0                   50m


kafka-1     1/1             Running     0                   10m


kafka-2     1/1             Running     0                   29s


kafka-2     1/1             Terminating     0                 34s


kafka-2     0/1             Terminating     0                 38s


kafka-2     0/1             Terminating     0                 39s


kafka-2     0/1             Terminating     0                 39s


kafka-2     0/1             Pending     0                 0s


kafka-2     0/1             Pending     0                 0s


kafka-2     0/1             Terminating     0                 20s


kafka-2     0/1             Terminating     0                 20s


kafka-2     0/1             Pending     0                 0s


kafka-2     0/1             Pending     0                 0s


kafka-2     0/1             ContainerCreating     0                 0s


kafka-2     0/1             Running     0                 19s


kafka-2     1/1             Running     0                 22s

Phased roll outs

Similar to rolling out a canary, you can roll out updates based on a phased progression (e.g. linear, geometric, or exponential roll outs).

If you patch the kafka StatefulSet to set the partition to 1, the StatefulSet controller updates one more broker.

$ kubectl patch sts kafka -p '{"spec":{"updateStrategy":{"type":"RollingUpdate","rollingUpdate":{"partition":1}}}}'

statefulset "kafka" patched

If you set it to 0, the StatefulSet controller updates the final broker and completes the update.

$ kubectl patch sts kafka -p '{"spec":{"updateStrategy":{"type":"RollingUpdate","rollingUpdate":{"partition":0}}}}'

statefulset "kafka" patched

Note that you don’t have to decrement the partition by one. For a larger StatefulSet--for example, one with 100 replicas--you might use a progression more like 100, 99, 90, 50, 0. In this case, you would stage your update, deploy a canary, roll out to 10 instances, update fifty percent of the Pods, and then complete the update.

Cleaning up

To delete the API Objects created above, you can use kubectl delete on the two manifests you used to create the ZooKeeper ensemble and the Kafka cluster.

$ kubectl delete -f kafka\_mini.yaml

service "kafka-hs" deleted

poddisruptionbudget "kafka-pdb" deleted

Statefulset “kafka” deleted


$ kubectl delete -f zookeeper\_mini.yaml

service "zk-hs" deleted

service "zk-cs" deleted

poddisruptionbudget "zk-pdb" deleted

statefulset "zk" deleted

By design, the StatefulSet controller does not delete any persistent volume claims (PVCs): the PVCs created for the ZooKeeper ensemble and the Kafka cluster must be manually deleted. Depending on the storage reclamation policy of your cluster, you many also need to manually delete the backing PVs.

DaemonSet rolling update, history, and rollback

To follow along with this section of the blog, you need a working Kubernetes 1.7 cluster and kubectl version 1.7 or later. If you followed along with the first section, you can use the same cluster.

DaemonSet rolling upFirst, prepare the node exporter DaemonSet manifest to run a v0.13 Prometheus node exporter on every node in the cluster:

$ cat \>\> node-exporter-v0.13.yaml \<\<EOF

apiVersion: extensions/v1beta1  
kind: DaemonSet  
metadata:  
   name: node-exporter  

spec:  
   updateStrategy:  

       type: RollingUpdate  

   template:  

       metadata:  

           labels:  

               app: node-exporter  

           name: node-exporter  

       spec:  

           containers:  

           - image: prom/node-exporter:v0.13.0  

               name: node-exporter  

               ports:  

               - containerPort: 9100  

                   hostPort: 9100  

                   name: scrape  

           hostNetwork: true  

           hostPID: true


EOF

Note that you need to enable the DaemonSet rolling update feature by explicitly setting DaemonSet .spec.updateStrategy.type to RollingUpdate.

Apply the manifest to create the node exporter DaemonSet:

$ kubectl apply -f node-exporter-v0.13.yaml --record

daemonset "node-exporter" created

Wait for the first DaemonSet rollout to complete:

$ kubectl rollout status ds node-exporter  
daemon set "node-exporter" successfully rolled out

You should see each of your node runs one copy of the node exporter pod:

$ kubectl get pods -l app=node-exporter -o wide

To perform a rolling update on the node exporter DaemonSet, prepare a manifest that includes the v0.14 Prometheus node exporter:

$ cat node-exporter-v0.13.yaml ```  sed "s/v0.13.0/v0.14.0/g" \> node-exporter-v0.14.yaml

Then apply the v0.14 node exporter DaemonSet:

$ kubectl apply -f node-exporter-v0.14.yaml --record

daemonset "node-exporter" configured

Wait for the DaemonSet rolling update to complete:

$ kubectl rollout status ds node-exporter

...

Waiting for rollout to finish: 3 out of 4 new pods have been updated...  
Waiting for rollout to finish: 3 of 4 updated pods are available...  
daemon set "node-exporter" successfully rolled out

We just triggered a DaemonSet rolling update by updating the DaemonSet template. By default, one old DaemonSet pod will be killed and one new DaemonSet pod will be created at a time.

Now we’ll cause a rollout to fail by updating the image to an invalid value:

$ cat node-exporter-v0.13.yaml | sed "s/v0.13.0/bad/g" \> node-exporter-bad.yaml


$ kubectl apply -f node-exporter-bad.yaml --record

daemonset "node-exporter" configured

Notice that the rollout never finishes:

$ kubectl rollout status ds node-exporter   
Waiting for rollout to finish: 0 out of 4 new pods have been updated...  
Waiting for rollout to finish: 1 out of 4 new pods have been updated…

# Use ^C to exit

This behavior is expected. We mentioned earlier that a DaemonSet rolling update kills and creates one pod at a time. Because the new pod never becomes available, the rollout is halted, preventing the invalid specification from propagating to more than one node. StatefulSet rolling updates implement the same behavior with respect to failed deployments. Unsuccessful updates are blocked until it corrected via roll back or by rolling forward with a specification.

$ kubectl get pods -l app=node-exporter

NAME                                   READY         STATUS                 RESTARTS     AGE


node-exporter-f2n14     0/1             ErrImagePull     0                   3m


...


# N = number of nodes

$ kubectl get ds node-exporter  
NAME                       DESIRED     CURRENT     READY         UP-TO-DATE     AVAILABLE     NODE SELECTOR     AGE  

node-exporter     N                 N                 N-1             1                       N                     \<none\>                   46m

DaemonSet history, rollbacks, and rolling forward

Next, perform a rollback. Take a look at the node exporter DaemonSet rollout history:

$ kubectl rollout history ds node-exporter   
daemonsets "node-exporter"  
REVISION               CHANGE-CAUSE  

1                             kubectl apply --filename=node-exporter-v0.13.yaml --record=true  

2                             kubectl apply --filename=node-exporter-v0.14.yaml --record=true


3                             kubectl apply --filename=node-exporter-bad.yaml --record=true

Check the details of the revision you want to roll back to:

$ kubectl rollout history ds node-exporter --revision=2  
daemonsets "node-exporter" with revision #2  
Pod Template:  
   Labels:             app=node-exporter  

   Containers:  

     node-exporter:  

       Image:           prom/node-exporter:v0.14.0  

       Port:             9100/TCP  

       Environment:               \<none\>  

       Mounts:         \<none\>  

   Volumes:           \<none\>

You can quickly roll back to any DaemonSet revision you found through kubectl rollout history:

# Roll back to the last revision

$ kubectl rollout undo ds node-exporter   
daemonset "node-exporter" rolled back


# Or use --to-revision to roll back to a specific revision

$ kubectl rollout undo ds node-exporter --to-revision=2  
daemonset "node-exporter" rolled back

A DaemonSet rollback is done by rolling forward. Therefore, after the rollback, DaemonSet revision 2 becomes revision 4 (current revision):

$ kubectl rollout history ds node-exporter   
daemonsets "node-exporter"  
REVISION               CHANGE-CAUSE  

1                             kubectl apply --filename=node-exporter-v0.13.yaml --record=true  

3                             kubectl apply --filename=node-exporter-bad.yaml --record=true  

4                             kubectl apply --filename=node-exporter-v0.14.yaml --record=true

The node exporter DaemonSet is now healthy again:

$ kubectl rollout status ds node-exporter  
daemon set "node-exporter" successfully rolled out


# N = number of nodes

$ kubectl get ds node-exporter

NAME                       DESIRED     CURRENT     READY         UP-TO-DATE     AVAILABLE     NODE SELECTOR     AGE  

node-exporter     N                 N                 N                 N                       N                     \<none\>                   46m

If current DaemonSet revision is specified while performing a rollback, the rollback is skipped:

$ kubectl rollout undo ds node-exporter --to-revision=4  
daemonset "node-exporter" skipped rollback (current template already matches revision 4)

You will see this complaint from kubectl if the DaemonSet revision is not found:

$ kubectl rollout undo ds node-exporter --to-revision=10  
error: unable to find specified revision 10 in history

Note that kubectl rollout history and kubectl rollout status support StatefulSets, too!

Cleaning up

$ kubectl delete ds node-exporter

What’s next for DaemonSet and StatefulSet