Disclaimer: This is not a tutorial per se, instead, this is me recording my observations as I setup a Kafka cluster for the first time on a Kubernetes platform using Strimzi.
Contents
- Configure the AWS CLI
- Create the EKS cluster
- Enter Kubernetes
- Install and configure Helm
- Install the Strimzi Kafka Operator
- Deploying the Kafka cluster
- Analysis
- Test the Kafka cluster with Node.js clients
- Clean up!
Let's get right into it, then!
We will be using eksctl, the official CLI for Amazon EKS, to spin up our K8s cluster.
Configure the AWS CLI
Ensure that the AWS CLI is configured. To view your configuration:
$ aws configure list
Name Value Type Location
---- ----- ---- --------
profile <not set> None None
access_key ****************7ONG shared-credentials-file
secret_key ****************lbQg shared-credentials-file
region ap-south-1 config-file ~/.aws/config
Note: The aws CLI config and credentials details are usually stored at ~/.aws/config and ~/.aws/credentials respectively.
Create the EKS cluster
$ eksctl create cluster --name=kafka-eks-cluster --nodes=4 --region=ap-south-1
[ℹ] using region ap-south-1
[ℹ] setting availability zones to [ap-south-1b ap-south-1a ap-south-1c]
[ℹ] subnets for ap-south-1b - public:192.168.0.0/19 private:192.168.96.0/19
[ℹ] subnets for ap-south-1a - public:192.168.32.0/19 private:192.168.128.0/19
[ℹ] subnets for ap-south-1c - public:192.168.64.0/19 private:192.168.160.0/19
[ℹ] nodegroup "ng-9f3cbfc7" will use "ami-09c3eb35bb3be46a4" [AmazonLinux2/1.12]
[ℹ] creating EKS cluster "kafka-eks-cluster" in "ap-south-1" region
[ℹ] will create 2 separate CloudFormation stacks for cluster itself and the initial nodegroup
[ℹ] if you encounter any issues, check CloudFormation console or try 'eksctl utils describe-stacks --region=ap-south-1 --name=kafka-eks-cluster'
[ℹ] 2 sequential tasks: { create cluster control plane "kafka-eks-cluster", create nodegroup "ng-9f3cbfc7" }
[ℹ] building cluster stack "eksctl-kafka-eks-cluster-cluster"
[ℹ] deploying stack "eksctl-kafka-eks-cluster-cluster"
[ℹ] building nodegroup stack "eksctl-kafka-eks-cluster-nodegroup-ng-9f3cbfc7"
[ℹ] --nodes-min=4 was set automatically for nodegroup ng-9f3cbfc7
[ℹ] --nodes-max=4 was set automatically for nodegroup ng-9f3cbfc7
[ℹ] deploying stack "eksctl-kafka-eks-cluster-nodegroup-ng-9f3cbfc7"
[✔] all EKS cluster resource for "kafka-eks-cluster" had been created
[✔] saved kubeconfig as "/Users/Bensooraj/.kube/config"
[ℹ] adding role "arn:aws:iam::account_numer:role/eksctl-kafka-eks-cluster-nodegrou-NodeInstanceRole-IG63RKPE03YQ" to auth ConfigMap
[ℹ] nodegroup "ng-9f3cbfc7" has 0 node(s)
[ℹ] waiting for at least 4 node(s) to become ready in "ng-9f3cbfc7"
[ℹ] nodegroup "ng-9f3cbfc7" has 4 node(s)
[ℹ] node "ip-192-168-25-34.ap-south-1.compute.internal" is ready
[ℹ] node "ip-192-168-50-249.ap-south-1.compute.internal" is ready
[ℹ] node "ip-192-168-62-231.ap-south-1.compute.internal" is ready
[ℹ] node "ip-192-168-69-95.ap-south-1.compute.internal" is ready
[ℹ] kubectl command should work with "/Users/Bensooraj/.kube/config", try 'kubectl get nodes'
[✔] EKS cluster "kafka-eks-cluster" in "ap-south-1" region is ready
A k8s cluster by the name kafka-eks-cluster will be created with 4 nodes (instance type: m5.large) in the Mumbai region (ap-south-1). You can view these in the AWS Console UI as well,
EKS:
CloudFormation UI:
Also, after the cluster is created, the appropriate kubernetes configuration will be added to your kubeconfig file (defaults to ~/.kube/config). The path to the kubeconfig file can be overridden using the --kubeconfig flag.
Enter Kubernetes
Fetching all k8s controllers lists the default kubernetes service. This confirms that kubectl is properly configured to point to the cluster that we just created.
$ kubectl get all
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
service/kubernetes ClusterIP 10.100.0.1 <none> 443/TCP 19m
Install and configure Helm
Helm is a package manager and application management tool for Kubernetes that packages multiple Kubernetes resources into a single logical deployment unit called Chart.
I use Homebrew, so the installation was pretty straightforward: brew install kubernetes-helm.
Alternatively, to install helm, run the following:
$ cd ~/eks-kafka-strimzi
$ curl https://raw.githubusercontent.com/kubernetes/helm/master/scripts/get > get_helm.sh
$ chmod +x get_helm.sh
$ ./get_helm.sh
Read through their installation guide, if you are looking for more options.
Do not run helm init yet.
Helm relies on a service called tiller that requires special permission on the kubernetes cluster, so we need to build a Service Account (RBAC access) for tiller to use.
The rbac.yaml file would look like the following:
---
apiVersion: v1
kind: ServiceAccount
metadata:
name: tiller
namespace: kube-system
---
apiVersion: rbac.authorization.k8s.io/v1beta1
kind: ClusterRoleBinding
metadata:
name: tiller
roleRef:
apiGroup: rbac.authorization.k8s.io
kind: ClusterRole
name: cluster-admin
subjects:
- kind: ServiceAccount
name: tiller
namespace: kube-system
Apply this to the kafka-eks-cluster cluster:
$ kubectl apply -f rbac.yaml
serviceaccount/tiller created
clusterrolebinding.rbac.authorization.k8s.io/tiller created
# Verify (listing only the relevant ones)
$ kubectl get sa,clusterrolebindings --namespace=kube-system
NAME SECRETS AGE
.
serviceaccount/tiller 1 5m22s
.
NAME AGE
.
clusterrolebinding.rbac.authorization.k8s.io/tiller 5m23s
.
Now, run helm init using the service account we setup. This will install tiller into the cluster which gives it access to manage resources in your cluster.
$ helm init --service-account=tiller
$HELM_HOME has been configured at /Users/Bensooraj/.helm.
Tiller (the Helm server-side component) has been installed into your Kubernetes Cluster.
Please note: by default, Tiller is deployed with an insecure 'allow unauthenticated users' policy.
To prevent this, run `helm init` with the --tiller-tls-verify flag.
For more information on securing your installation see: https://docs.helm.sh/using_helm/#securing-your-helm-installation
Install the Strimzi Kafka Operator
Add the Strimzi repository and install the Strimzi Helm Chart:
# Add the repo
$ helm repo add strimzi http://strimzi.io/charts/
"strimzi" has been added to your repositories
# Search for all Strimzi charts
$ helm search strim
NAME CHART VERSION APP VERSION DESCRIPTION
strimzi/strimzi-kafka-operator 0.14.0 0.14.0 Strimzi: Kafka as a Service
# Install the kafka operator
$ helm install strimzi/strimzi-kafka-operator
NAME: bulging-gnat
LAST DEPLOYED: Wed Oct 2 15:23:45 2019
NAMESPACE: default
STATUS: DEPLOYED
RESOURCES:
==> v1/ClusterRole
NAME AGE
strimzi-cluster-operator-global 0s
strimzi-cluster-operator-namespaced 0s
strimzi-entity-operator 0s
strimzi-kafka-broker 0s
strimzi-topic-operator 0s
==> v1/ClusterRoleBinding
NAME AGE
strimzi-cluster-operator 0s
strimzi-cluster-operator-kafka-broker-delegation 0s
==> v1/Deployment
NAME READY UP-TO-DATE AVAILABLE AGE
strimzi-cluster-operator 0/1 1 0 0s
==> v1/Pod(related)
NAME READY STATUS RESTARTS AGE
strimzi-cluster-operator-6667fbc5f8-cqvdv 0/1 ContainerCreating 0 0s
==> v1/RoleBinding
NAME AGE
strimzi-cluster-operator 0s
strimzi-cluster-operator-entity-operator-delegation 0s
strimzi-cluster-operator-topic-operator-delegation 0s
==> v1/ServiceAccount
NAME SECRETS AGE
strimzi-cluster-operator 1 0s
==> v1beta1/CustomResourceDefinition
NAME AGE
kafkabridges.kafka.strimzi.io 0s
kafkaconnects.kafka.strimzi.io 0s
kafkaconnects2is.kafka.strimzi.io 0s
kafkamirrormakers.kafka.strimzi.io 0s
kafkas.kafka.strimzi.io 1s
kafkatopics.kafka.strimzi.io 1s
kafkausers.kafka.strimzi.io 1s
NOTES:
Thank you for installing strimzi-kafka-operator-0.14.0
To create a Kafka cluster refer to the following documentation.
https://strimzi.io/docs/0.14.0/#kafka-cluster-str
List all the kubernetes objects created again:
$ kubectl get all
NAME READY STATUS RESTARTS AGE
pod/strimzi-cluster-operator-6667fbc5f8-cqvdv 1/1 Running 0 9m25s
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
service/kubernetes ClusterIP 10.100.0.1 <none> 443/TCP 90m
NAME DESIRED CURRENT UP-TO-DATE AVAILABLE AGE
deployment.apps/strimzi-cluster-operator 1 1 1 1 9m25s
NAME DESIRED CURRENT READY AGE
replicaset.apps/strimzi-cluster-operator-6667fbc5f8 1 1 1 9m26s
Deploying the Kafka cluster
We will now create a Kafka cluster with 3 brokers. The YAML file (kafka-cluster.Kafka.yaml) for creating the Kafka cluster would like the following:
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
name: kafka-cluster
spec:
kafka:
version: 2.3.0 # Kafka version
replicas: 3 # Replicas specifies the number of broker nodes.
listeners: # Listeners configure how clients connect to the Kafka cluster
plain: {} # 9092
tls: {} # 9093
config:
offsets.topic.replication.factor: 3
transaction.state.log.replication.factor: 3
transaction.state.log.min.isr: 2
log.message.format.version: "2.3"
delete.topic.enable: "true"
storage:
type: persistent-claim
size: 10Gi
deleteClaim: false
zookeeper:
replicas: 3
storage:
type: persistent-claim # Persistent storage backed by AWS EBS
size: 10Gi
deleteClaim: false
entityOperator:
topicOperator: {} # Operator for topic administration
userOperator: {}
Apply the above YAML file:
$ kubectl apply -f kafka-cluster.Kafka.yaml
Analysis
This is where things get interesting. We will now analyse some of the k8s resources which the strimzi kafka operator has created for us under the hood.
$ kubectl get statefulsets.apps,pod,deployments,svc
NAME DESIRED CURRENT AGE
statefulset.apps/kafka-cluster-kafka 3 3 78m
statefulset.apps/kafka-cluster-zookeeper 3 3 79m
NAME READY STATUS RESTARTS AGE
pod/kafka-cluster-entity-operator-54cb77fd9d-9zbcx 3/3 Running 0 77m
pod/kafka-cluster-kafka-0 2/2 Running 0 78m
pod/kafka-cluster-kafka-1 2/2 Running 0 78m
pod/kafka-cluster-kafka-2 2/2 Running 0 78m
pod/kafka-cluster-zookeeper-0 2/2 Running 0 79m
pod/kafka-cluster-zookeeper-1 2/2 Running 0 79m
pod/kafka-cluster-zookeeper-2 2/2 Running 0 79m
pod/strimzi-cluster-operator-6667fbc5f8-cqvdv 1/1 Running 0 172m
NAME DESIRED CURRENT UP-TO-DATE AVAILABLE AGE
deployment.extensions/kafka-cluster-entity-operator 1 1 1 1 77m
deployment.extensions/strimzi-cluster-operator 1 1 1 1 172m
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
service/kafka-cluster-kafka-bootstrap ClusterIP 10.100.177.177 <none> 9091/TCP,9092/TCP,9093/TCP 78m
service/kafka-cluster-kafka-brokers ClusterIP None <none> 9091/TCP,9092/TCP,9093/TCP 78m
service/kafka-cluster-zookeeper-client ClusterIP 10.100.199.128 <none> 2181/TCP 79m
service/kafka-cluster-zookeeper-nodes ClusterIP None <none> 2181/TCP,2888/TCP,3888/TCP 79m
service/kubernetes ClusterIP 10.100.0.1 <none> 443/TCP 4h13m
Points to note:
- The StatefulSet
kafka-cluster-zookeeperhas created 3 pods -kafka-cluster-zookeeper-0,kafka-cluster-zookeeper-1andkafka-cluster-zookeeper-2. The headless servicekafka-cluster-zookeeper-nodesfacilitates network identity of these 3 pods (the 3 Zookeeper nodes). - The StatefulSet
kafka-cluster-kafkahas created 3 pods -kafka-cluster-kafka-0,kafka-cluster-kafka-1andkafka-cluster-kafka-2. The headless servicekafka-cluster-kafka-brokersfacilitates network identity of these 3 pods (the 3 Kafka brokers).
Persistent volumes are dynamically provisioned:
$ kubectl get pv,pvc
NAME CAPACITY ACCESS MODES RECLAIM POLICY STATUS CLAIM STORAGECLASS REASON AGE
persistentvolume/pvc-7ff2909f-e507-11e9-91df-0a1e73fdd786 10Gi RWO Delete Bound default/data-kafka-cluster-zookeeper-1 gp2 11h
persistentvolume/pvc-7ff290c4-e507-11e9-91df-0a1e73fdd786 10Gi RWO Delete Bound default/data-kafka-cluster-zookeeper-2 gp2 11h
persistentvolume/pvc-7ffd1d22-e507-11e9-a775-029ce0835b96 10Gi RWO Delete Bound default/data-kafka-cluster-zookeeper-0 gp2 11h
persistentvolume/pvc-a5997b77-e507-11e9-91df-0a1e73fdd786 10Gi RWO Delete Bound default/data-kafka-cluster-kafka-0 gp2 11h
persistentvolume/pvc-a599e52b-e507-11e9-91df-0a1e73fdd786 10Gi RWO Delete Bound default/data-kafka-cluster-kafka-1 gp2 11h
persistentvolume/pvc-a59c6cd2-e507-11e9-91df-0a1e73fdd786 10Gi RWO Delete Bound default/data-kafka-cluster-kafka-2 gp2 11h
NAME STATUS VOLUME CAPACITY ACCESS MODES STORAGECLASS AGE
persistentvolumeclaim/data-kafka-cluster-kafka-0 Bound pvc-a5997b77-e507-11e9-91df-0a1e73fdd786 10Gi RWO gp2 11h
persistentvolumeclaim/data-kafka-cluster-kafka-1 Bound pvc-a599e52b-e507-11e9-91df-0a1e73fdd786 10Gi RWO gp2 11h
persistentvolumeclaim/data-kafka-cluster-kafka-2 Bound pvc-a59c6cd2-e507-11e9-91df-0a1e73fdd786 10Gi RWO gp2 11h
persistentvolumeclaim/data-kafka-cluster-zookeeper-0 Bound pvc-7ffd1d22-e507-11e9-a775-029ce0835b96 10Gi RWO gp2 11h
persistentvolumeclaim/data-kafka-cluster-zookeeper-1 Bound pvc-7ff2909f-e507-11e9-91df-0a1e73fdd786 10Gi RWO gp2 11h
persistentvolumeclaim/data-kafka-cluster-zookeeper-2 Bound pvc-7ff290c4-e507-11e9-91df-0a1e73fdd786 10Gi RWO gp2 11h
You can view the provisioned AWS EBS volumes in the UI as well:
Create topics
Before we get started with clients we need to create a topic (with 3 partitions and a replication factor of 3), over which our producer and the consumer and produce messages and consume messages on respectively.
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaTopic
metadata:
name: test-topic
labels:
strimzi.io/cluster: kafka-cluster
spec:
partitions: 3
replicas: 3
Apply the YAML to the k8s cluster:
$ kubectl apply -f create-topics.yaml
kafkatopic.kafka.strimzi.io/test-topic created
Test the Kafka cluster with Node.js clients
The multi-broker Kafka cluster that we deployed is backed by statefulsets and their corresponding headless services.
Since each Pod (Kafka broker) now has a network identity, clients can connect to the Kafka brokers via a combination of the pod name and service name: $(podname).$(governing service domain). In our case, these would be the following URLs:
kafka-cluster-kafka-0.kafka-cluster-kafka-brokerskafka-cluster-kafka-1.kafka-cluster-kafka-brokerskafka-cluster-kafka-2.kafka-cluster-kafka-brokers
Note:
- If the Kafka cluster is deployed in a different namespace, you will have to expand it a little further:
$(podname).$(service name).$(namespace).svc.cluster.local. - Alternatively, the clients can connect to the Kafka cluster using the Service
kafka-cluster-kafka-bootstrap:9092as well. It distributes the connection over the three broker specific endpoints I have listed above. As I no longer keep track of the individual broker endpoints, this method plays out well when I have to scale up or down the number of brokers in the Kafka cluster.
First, clone this repo:
# Create the configmap, which contains details such as the broker DNS names, topic name and consumer group ID
$ kubectl apply -f test/k8s/config.yaml
configmap/kafka-client-config created
# Create the producer deployment
$ kubectl apply -f test/k8s/producer.Deployment.yaml
deployment.apps/node-test-producer created
# Expose the producer deployment via a service of type LoadBalancer (backed by the AWS Elastic Load Balancer). This just makes it easy for me to curl from postman
$ kubectl apply -f test/k8s/producer.Service.yaml
service/node-test-producer created
# Finally, create the consumer deployment
$ kubectl apply -f test/k8s/consumer.Deployment.yaml
deployment.apps/node-test-consumer created
If you list the producer service that we created, you would notice a URL under EXTERNAL-IP:
$ kubectl get svc
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
.
.
node-test-producer LoadBalancer 10.100.145.203 ac5f3d0d1e55a11e9a775029ce0835b9-2040242746.ap-south-1.elb.amazonaws.com 80:31231/TCP 55m
The URL ac5f3d0d1e55a11e9a775029ce0835b9-2040242746.ap-south-1.elb.amazonaws.com is an AWS ELB backed public endpoint which we will be querying for producing messages to the Kafka cluster.
Also, you can see that there is 1 producer and 3 consumers (one for each partition of the topic test-topic):
$ kubectl get pod
NAME READY STATUS RESTARTS AGE
node-test-consumer-96b44cbcb-gs2km 1/1 Running 0 125m
node-test-consumer-96b44cbcb-ptvjd 1/1 Running 0 125m
node-test-consumer-96b44cbcb-xk75j 1/1 Running 0 125m
node-test-producer-846d9c5986-vcsf2 1/1 Running 0 125m
The producer app basically exposes 3 URLs:
/kafka-test/green/:message/kafka-test/blue/:message/kafka-test/cyan/:message
Where :message can be any valid string. Each of these URLs produce a message along with the colour information to the topic test-topic.
The consumer group (the 3 consumer pods that we spin-up) listening for any incoming messages from the topic test-topic, then receives these messages and prints them on to the console according to the colour instruction.
I curl each URL 3 times. From the following GIF you can see how message consumption is distributed across the 3 consumers in a round-robin manner:
Clean Up!
# Delete the test producer and consumer apps:
$ kubectl delete -f test/k8s/
configmap "kafka-client-config" deleted
deployment.apps "node-test-consumer" deleted
deployment.apps "node-test-producer" deleted
service "node-test-producer" deleted
# Delete the Kafka cluster
$ kubectl delete kafka kafka-cluster
kafka.kafka.strimzi.io "kafka-cluster" deleted
# Delete the Strimzi cluster operator
$ kubectl delete deployments. strimzi-cluster-operator
deployment.extensions "strimzi-cluster-operator" deleted
# Manually delete the persistent volumes
# Kafka
$ kubectl delete pvc data-kafka-cluster-kafka-0
$ kubectl delete pvc data-kafka-cluster-kafka-1
$ kubectl delete pvc data-kafka-cluster-kafka-2
# Zookeeper
$ kubectl delete pvc data-kafka-cluster-zookeeper-0
$ kubectl delete pvc data-kafka-cluster-zookeeper-1
$ kubectl delete pvc data-kafka-cluster-zookeeper-2
Finally, delete the EKS cluster:
$ eksctl delete cluster kafka-eks-cluster
[ℹ] using region ap-south-1
[ℹ] deleting EKS cluster "kafka-eks-cluster"
[✔] kubeconfig has been updated
[ℹ] 2 sequential tasks: { delete nodegroup "ng-9f3cbfc7", delete cluster control plane "kafka-eks-cluster" [async] }
[ℹ] will delete stack "eksctl-kafka-eks-cluster-nodegroup-ng-9f3cbfc7"
[ℹ] waiting for stack "eksctl-kafka-eks-cluster-nodegroup-ng-9f3cbfc7" to get deleted
[ℹ] will delete stack "eksctl-kafka-eks-cluster-cluster"
[✔] all cluster resources were deleted
Hope this helped!
Top comments (1)
kafka-cluster.Kafka.yaml --> FROM that section code is not working, would re-check code once and update this post , IT is very useful, Thank you