How to Set Up and Run Kafka on K8s

Apache Kafka is a leading open-source distributed streaming platform first developed at LinkedIn. It consists of several APIs such as the Producer, the Consumer, the Connector and the Streams. Together, those systems act as high-throughput, low-latency platforms for handling real-time data. This is why Kafka is preferred among several of the top-tier tech companies such as Uber, Zalando and AirBnB.

Quite often, we would like to deploy a fully-fledged Kafka cluster in Kubernetes, just because we have a collection of microservices and we need a resilient message broker in the center. We also want to spread the Kafka instances across nodes, to minimize the impact of a failure.

In the following tutorial, the Platform9 technical team presents an example Kafka deployment within the Platform9 Free Tier Kubernetes platform, backed up by some DigitalOcean droplets.

Let’s get started.

Setting Up the Platform9 Free Tier Cluster

Below are the brief instructions to get you up and running with a working Kubernetes Cluster from Platform9:

1. Signup with Platform9
2. Click the Create Cluster button and inspect the instructions. We need a server to host the Cluster.
3. Create a few Droplets with at least 3gb Ram and 2vCPU’s. Follow instructions to install the pf9cli tool and prepping the nodes.

$ bash <(curl -sL http://pf9.io/get_cli)
$ pf9ctl cluster prep-node -i

1. Switch to the Platform9 UI and click the refresh button. You should see the new nodes in the list. Assign the first node as a master and the other ones as workers.
2. Leave the default values in the next steps. Then create the cluster.
3. Wait until the cluster becomes healthy. It will take at least 20 minutes to finish.

4. Click on the API Access tab and select to download the kubeconfig button:
   

5. Once downloaded, export the config and test the cluster health:
   

   
   
   $ export KUBECONFIG=/Users/itspare/Theo/Projects/platform9/example.yaml
   
   $ kubectl cluster-info
   
   
   Kubernetes master is running at https://134.122.106.235
   
   CoreDNS is running at https://134.122.106.235/api/v1/namespaces/kube-system/services/kube-dns:dns/proxy
   
   Metrics-server is running at https://134.122.106.235/api/v1/namespaces/kube-system/services/https:metrics-server:/proxy
   
   

   Creating Persistent Volumes

   Before we install Helm and the Kafka chart, we need to create some persistent volumes for storing Kafka replication message files.

   This step is crucial to be able to enable persistence in our cluster because without that, the topics and messages would disappear after we shutdown any of the servers, as they live in memory.

   In our example, we are going to use a local file system, Persistent Volume (PV), and we need one persistent volume for each Kafka instance; so if we plan to deploy three instances, we need three PV’s.

   Create and apply first the Kafka namespace and the PV specs:

   
   $ cat namespace.yml
   ---
   apiVersion: v1
   kind: Namespace
   metadata:
     name: kafka
   
   $ kubectl apply -f namespace.yml
   namespace/kafka created
   
   
   $ cat pv.yml
   ---
   apiVersion: v1
   kind: PersistentVolume
   metadata:
     name: kafka-pv-volume
     labels:
       type: local
   spec:
     storageClassName: manual
     capacity:
       storage: 10Gi
     accessModes:
       - ReadWriteOnce
     hostPath:
       path: "/mnt/data"
   ---
   apiVersion: v1
   kind: PersistentVolume
   metadata:
     name: kafka-pv-volume-2
     labels:
       type: local
   spec:
     storageClassName: manual
     capacity:
       storage: 10Gi
     accessModes:
       - ReadWriteOnce
     hostPath:
       path: "/mnt/data"
   ---
   apiVersion: v1
   kind: PersistentVolume
   metadata:
     name: kafka-pv-volume-3
     labels:
       type: local
   spec:
     storageClassName: manual
     capacity:
       storage: 10Gi
     accessModes:
       - ReadWriteOnce
     hostPath:
       path: "/mnt/data"
   
   $ kubectl apply -f pv.yml
   
   

   If you are using the Kubernetes UI, you should be able to see the PV volumes on standby:
   

   Installing Helm

   We begin by installing Helm on our computer and installing it in Kubernetes, as it’s not bundled by default.

   First, we download the install script:

   
   $ curl https://raw.githubusercontent.com/kubernetes/helm/master/scripts/get > install-helm.sh
   

   Make the script executable with chmod:

   
   $ chmod u+x install-helm.sh
   

   Create the tiller service account:

   
   $ kubectl -n kube-system create serviceaccount tiller
   

   Next, bind the tiller serviceaccount to the cluster-admin role:

   
   $ kubectl create clusterrolebinding tiller --clusterrole cluster-admin --serviceaccount=kube-system:tiller
   

   Now we can run helm init:

   
   $ helm init --service-account tiller
   

   Now we are ready to install the Kafka chart.

   Deploying the Helm Chart

   In the past, trying to deploy Kafka on Kubernetes was a good exercise. You had to deploy a working Zookeeper Cluster, role bindings, persistent volume claims and apply correct configuration.

   Hopefully for us, with the use of the Kafka Incubator Chart, the whole process is mostly automated (with a few quirks here and there).

   We add the Helm chart:

   
   $ helm repo add incubator http://storage.googleapis.com/kubernetes-charts-incubator
   

   Export the chart values in a file:

   
   $ curl https://raw.githubusercontent.com/helm/charts/master/incubator/kafka/values.yaml > config.yml
   

   Carefully inspect the configuration values, particularly around the parts about persistence and about the number of Kafka stateful sets to deploy.

   Then install the chart:

   
   $ helm install --name kafka-demo --namespace kafka incubator/kafka -f values.yml --debug
   

   Check the status of the deployment

   
   $ helm status kafka-demo
   LAST DEPLOYED: Sun Apr 19 14:05:15 2020
   NAMESPACE: kafka
   STATUS: DEPLOYED
   
   RESOURCES:
   ==> v1/ConfigMap
   NAME                  DATA  AGE
   kafka-demo-zookeeper  3     5m29s
   
   ==> v1/Pod(related)
   NAME                    READY  STATUS   RESTARTS  AGE
   kafka-demo-zookeeper-0  1/1    Running  0         5m28s
   kafka-demo-zookeeper-1  1/1    Running  0         4m50s
   kafka-demo-zookeeper-2  1/1    Running  0         4m12s
   kafka-demo-zookeeper-0  1/1    Running  0         5m28s
   kafka-demo-zookeeper-1  1/1    Running  0         4m50s
   kafka-demo-zookeeper-2  1/1    Running  0         4m12s
   
   ==> v1/Service
   NAME                           TYPE       CLUSTER-IP     EXTERNAL-IP  PORT(S)                     AGE
   kafka-demo                     ClusterIP  10.21.255.214         9092/TCP                    5m29s
   kafka-demo-headless            ClusterIP  None                  9092/TCP                    5m29s
   kafka-demo-zookeeper           ClusterIP  10.21.13.232          2181/TCP                    5m29s
   kafka-demo-zookeeper-headless  ClusterIP  None                  2181/TCP,3888/TCP,2888/TCP  5m29s
   
   ==> v1/StatefulSet
   NAME                  READY  AGE
   kafka-demo            3/3    5m28s
   kafka-demo-zookeeper  3/3    5m28s
   
   ==> v1beta1/PodDisruptionBudget
   NAME                  MIN AVAILABLE  MAX UNAVAILABLE  ALLOWED DISRUPTIONS  AGE
   kafka-demo-zookeeper  N/A            1                1                    5m29s
   

   During this phase, you may want to navigate to the Kubernetes UI and inspect the dashboard for any issues. Once everything is complete, then the pods and Persistent Volume Claims should be bound and green.
   

   Now we can test the Kafka cluster.

   Testing the Kafka Cluster

   We are going to deploy a test client that will execute scripts against the Kafka cluster.

   Create and apply the following deployment:

   
   $ cat testclient.yml
   
   apiVersion: v1
   kind: Pod
   metadata:
     name: testclient
     namespace: kafka
   spec:
     containers:
     - name: kafka
       image: solsson/kafka:0.11.0.0
       command:
         - sh
         - -c
         - "exec tail -f /dev/null"
   
   $ kubectl apply -f testclient
   

   Then, using the testclient, we create the first topic, which we are going to use to post messages:

   
   $ kubectl -n kafka exec -ti testclient -- ./bin/kafka-topics.sh --zookeeper kafka-demo-zookeeper:2181 --topic messages --create --partitions 1 --replication-factor 1
   Created topic "messages".
   

   Here we need to use the correct hostname for zookeeper cluster and the topic configuration.

   Next, verify that the topic exists:

   
   $ kubectl -n kafka exec -ti testclient -- ./bin/kafka-topics.sh --zookeeper kafka-demo-zookeeper:2181 --list
   Messages
   

   Now we can create one consumer and one producer instance so that we can send and consume messages.

   First create one or two listeners, each on its own shell:

   
   $ kubectl -n kafka exec -ti testclient -- ./bin/kafka-console-consumer.sh --bootstrap-server kafka-demo:9092 --topic messages --from-beginning
   

   Then create the producer session and type some messages. You will be able to see them propagate to the consumer sessions:

   
   $ kubectl -n kafka exec -ti testclient -- ./bin/kafka-console-producer.sh --broker-list kafka-demo:9092 --topic messages
   >Hi
   >How are you?
   >Hope you're well
   >
   
   
   
   Hi
   How are you?
   Hope you're well
   

   Destroying the Helm Chart

   To clean up our resources, we just destroy the Helm Chart and delete the PVs we created earlier:

   
   $ helm delete kafka-demo --purge
   $ kubectl delete -f pv.yml -n kafka
   

   Next Steps

   Stay put for more tutorials showcasing common deployment scenarios within Platform9’s fully-managed Kubernetes platform.