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 this tutorial we are going to see an example Kafka deployment within Platform9 Free Tier Kubernetes platform, backed up by some DigitalOcean droplets.Let’s get started.
Below are the brief instructions to get you up and running with a working Kubernetes Cluster from Platform9:
$ bash <(curl -sL http://pf9.io/get_cli) $ pf9ctl cluster prep-node -i
$ export KUBECONFIG=/Users/itspare/Theo/Projects/platform9/example.yaml $ kubectl cluster-info Kubernetes master is running at https://22.214.171.124 CoreDNS is running at https://126.96.36.199/api/v1/namespaces/kube-system/services/kube-dns:dns/proxy Metrics-server is running at https://188.8.131.52/api/v1/namespaces/kube-system/services/https:metrics-server:/proxy
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:
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 are ready to install the Kafka 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.
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 > switching on each consumer you will see Hi How are you? Hope you're well
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
Stay put for more tutorials showcasing common deployment scenarios within Platform9’s fully- managed Kubernetes platform.