This tutorial describes how to set up an Apache Kafka® cluster on Enterprise Pivotal Container Service (Enterprise PKS) using Confluent Operator, which allows you to deploy and run Confluent Platform at scale on virtually any Kubernetes platform, including Pivotal Container Service (PKS). With Enterprise PKS, you can deploy, scale, patch, and upgrade all the Kubernetes clusters in your system without downtime.
You’ll start by creating a Kafka cluster in Enterprise PKS using Confluent Operator. Then, you’ll configure it to expose external endpoints so the cluster will be available for use outside of the Enterprise PKS environment. This is useful in cases where you are deploying a Pivotal Application Service (PAS) that produces and/or consumes to Kafka running in Enterprise PKS.
bin/directory for the external verification section of this tutorial.)
Run all the following command line tasks in a terminal unless explicitly noted otherwise.
pks login -a https://api.pks.example.cf-app.com:9021 -u confluent -p confluent-password -kusing the URL, username, and password from the first requirement in the section above.
pks get-credentials confluent-cluster.
kubectl config use-context confluent-clusterto point
kubectlto the Enterprise PKS cluster by default.
pks.yamlHelm Chart file ( in the
helm/providersdirectory wherever your Confluent Operator distribution was expanded. For this example, you can expand the Confluent Operator tarball to
~/dev/confluent-operator-20190726-v0.65.0and create a file in
helm/directory in your terminal, run the command below:
helm install \ -f ./providers/pks.yaml \ --name operator \ --namespace operator \ --set operator.enabled=true \ ./confluent-operator
kubectl get pods -n operator. You should see something similar to the following after issuing this command:
NAME READY STATUS RESTARTS AGE cc-manager-7bf99846cc-qx2hb 1/1 ContainerCreating 1 6m40s cc-operator-798b87b77-lx962 1/1 ContainerCreating 0 6m40s
Wait until the status changes from
helm install \ -f ./providers/pks.yaml \ --name zookeeper \ --namespace operator \ --set zookeeper.enabled=true \ ./confluent-operator
kubectl get pods -n operatorsample output:
kubectl get pods -n operator NAME READY STATUS RESTARTS AGE cc-manager-7bf99846cc-qx2hb 1/1 Running 1 6m40s cc-operator-798b87b77-lx962 1/1 Running 0 6m40s zookeeper-0 0/1 ContainerCreating 0 15s zookeeper-1 0/1 ContainerCreating 0 15s zookeeper-2 0/1 Pending 0 15s
Wait until the ZooKeeper pods are in
Running status before proceeding to the next step. It takes approximately one minute.
helm install \ -f ./providers/pks.yaml \ --name kafka \ --namespace operator \ --set kafka.enabled=true \ ./confluent-operator
kubectl get pods -n operator and wait for the status of the
kafka-2 pods to be
At this point, the setup is complete and you are ready to verify that the installation is successful.
Before beginning verification, some of you may be wondering about the rest of the Confluent Platform, such as Schema Registry, KSQL, Control Center, etc. Based on the steps you just went through, I suspect you already know the answer. Nevertheless, here’s an example of deploying Schema Registry:
helm install -f ./providers/pks.yaml --name schemaregistry --namespace operator --set schemaregistry.enabled=true ./confluent-operator
In this tutorial, verification involves both internal and external verification of tasks because you configured your
pks.yaml file for exposing external endpoints.
Internal verification involves connecting to one of the nodes in the cluster and ensuring there is communication amongst the nodes by executing various Kafka scripts.
kubectl -n operator exec -it kafka-0 bash
cat << EOF > kafka.properties sasl.mechanism=PLAIN sasl.jaas.config=org.apache.kafka.common.security.plain.PlainLoginModule required username=test password=test123; bootstrap.servers=kafka:9071 security.protocol=SASL_PLAINTEXT EOF
kafka-broker-api-versions --command-config kafka.properties --bootstrap-server kafka:9071
This is what running through these three steps looks like:
For external validation, you can interact with your Kafka cluster from the outside, such as performing these steps on your laptop. Make sure to have Confluent Platform downloaded and extracted, and have the scripts available in the
kubectl get services -n operator“EXTERNAL-IP” column:
NAME TYPE CLUSTER-IP EXTERNAL-IP kafka ClusterIP None kafka-0-internal ClusterIP 10.100.200.223 kafka-0-lb LoadBalancer 10.100.200.231 126.96.36.199 kafka-1-internal ClusterIP 10.100.200.213 kafka-1-lb LoadBalancer 10.100.200.200 188.8.131.52 kafka-2-internal ClusterIP 10.100.200.130 kafka-2-lb LoadBalancer 10.100.200.224 184.108.40.206 kafka-bootstrap-lb LoadBalancer 10.100.200.6 220.127.116.11
/etc/hostsfile. As an example, here are some specific entries you can make to your local
18.104.22.168 b0.supergloo.com b0 22.214.171.124 b1.supergloo.com b1 126.96.36.199 b2.supergloo.com b2 188.8.131.52 kafka.supergloo.com kafka
It is critical to map
b2 hosts to their corresponding
kafka-2 external IPs. The same goes for the bootstrap mapping. (Note: the domain supergloo.com was configured in the
kafka.propertiesfile with the following content:
sasl.mechanism=PLAIN sasl.jaas.config=org.apache.kafka.common.security.plain.PlainLoginModule required username=test password=test123; bootstrap.servers=kafka.supergloo.com:9071 security.protocol=SASL_PLAINTEXT
bin/kafka-topics --create --command-config kafka.properties --zookeeper localhost:2181/kafka-operator --replication-factor 3 --partitions 1 --topic example
bin/kafka-topics --list --command-config kafka.properties --bootstrap-server kafka.supergloo.com:9092
You’ve completed the tutorial and deployed a Kafka cluster to Enterprise PKS using Confluent Operator!
Next up in part 2, we’ll walk through how to deploy a sample Spring Boot application to PAS and configure it to produce and consume for the Kafka cluster created in this tutorial.
For more, check out Kafka Tutorials and find full code examples using Kafka, Kafka Streams, and KSQL.
Todd McGrath is a partner solution engineer at Confluent where he assists partners who are designing, developing, and embedding the Confluent Platform in their customer solutions. Todd has held a variety of roles and responsibilities over many years in software, including hands-on development, entrepreneurship, business development, engineering management, and pre-sales.