Krallistic Kafka Operator Save Abandoned

A Kafka Operator for Kubernetes

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kafka-operator - A Kafka Operator for Kubernetes

A Kubernetes Operator for Apache Kafka, which deploys, configures and manages your kafka cluster through its lifecycle. Features:

  • Fixed deployment of a Cluster, Services and PersistentVolumes
  • Upscaling of Cluster (eg adding a Broker)
  • Downscaling a Cluster, without dataloss (removes partition of broker first, under development)

Upcoming Features/Ideas:

  • [] Vertical Pod Autoscaling
  • [] Managed Topics and hot partition detection/shuffling
  • [] Advanced Partition Shuffling (based on rate/size of incomming msg)

Currently the Operator is under development. If you want to run Kafka in kubernetes a better option would be to look at the Helm Chart alternative this:

How to use it:

1.) Deploy the Operator

First we deploy the Operator inside our cluster:

# kubectl apply -f example/kafka-operator.yaml
deployment "kafka-operator" created

The Operator then creates a custom resource definition(CRD) "KafkaCluster" inside Kubernetes, which behaves like a normal k8s Object. The only difference is that no k8s internal components reacts to it, only our operator has a watch on it.

2) Deploy Zookeeper

Currently you need to deploy zookeeper by yourself (since managing zookeeper is a not a easy to topic, this is out of scope for now). As a starter you can find a example under example/manual-zookeeper.yaml for a single node zookeeper.

# kubectl apply -f example/manual-zookeeper.yaml
service "zk-headless" created
configmap "zk-config" created
statefulset "zk" created

3) Create a KafkaCluster spec and deploy

To deploy a kafka cluster we create spec (example/kafkaObj.yaml):

apiVersion: ""
kind: "Kafkacluster"
  name: test-cluster-1
    brokerCount: 3
      - name: "test1"
        replicationFactor: 1
        partitions: 1
      - name: "test2"
        replicationFactor: 2
        partitions: 2
       logRetentionHours: 24
       autoCreateTopics: false
       compressionType: "gzip"
    zookeeperConnect: zk-headless.default.svc.cluster.local
    image: confluentinc/cp-kafka:latest
    leaderImbalanceRatio: 0.1
    leaderImbalanceInterval: 600
    storageClass: emptyDir
    minimumGracePeriod: 1200
    jmxSidecar: false
      cpu: "1"
      memory: "1Gi"
      diskSpace: "50G"

We can then just deploy this yaml via kubectl:

# kubectl apply -f example/kafka-cluster.yaml
kafkacluster "test-cluster-1" created

into kubernetes. This creates a kafkacluster object inside the api server. We can check this with:

# kubectl get kafkacluster
NAME             KIND

The operators then picks up the newly created object and creates the actual pods which are needed for the spezified Kafka cluster. Create the whole cluster can take a while but after a bit you should see every broker running and services created to either access direclty or all broker load-balanced:

# kubectl get pods,service
NAME                                                      READY     STATUS    RESTARTS   AGE
po/kafka-offset-checker-test-cluster-1-3029848613-z8rtd   1/1       Running   3          1m
po/kafka-operator-767603131-zcnt0                         1/1       Running   0          1m
po/test-cluster-1-0                                       1/1       Running   0          1m
po/test-cluster-1-1                                       1/1       Running   0          54s
po/test-cluster-1-2                                       1/1       Running   0          40s
po/zk-0                                                   1/1       Running   0          1m

NAME                          CLUSTER-IP     EXTERNAL-IP   PORT(S)             AGE
svc/kubernetes           <none>        443/TCP             5h
svc/test-cluster-1            None           <none>        9092/TCP            1m
svc/test-cluster-1-broker-0    <nodes>       9092:31545/TCP      1m
svc/test-cluster-1-broker-1   <nodes>       9092:31850/TCP      1m
svc/test-cluster-1-broker-2   <nodes>       9092:32653/TCP      1m
svc/zk-headless               None           <none>        2888/TCP,3888/TCP   1m

3) Resize the cluster

If we want to upscale the cluster we can just change the brokerCount value. After we changed it (for example to 5) we do a kubectl apply -f example/kafka-cluster.yaml. The operators then should pick up the change and start upsizing the cluster:

# kubectl apply -f example/kafka-cluster.yaml
kafkacluster "test-cluster-1" configured
kubectl get pods
NAME                                                   READY     STATUS    RESTARTS   AGE
kafka-offset-checker-test-cluster-1-3029848613-z8rtd   1/1       Running   3          4m
kafka-operator-767603131-zcnt0                         1/1       Running   0          4m
test-cluster-1-0                                       1/1       Running   0          4m
test-cluster-1-1                                       1/1       Running   0          4m
test-cluster-1-2                                       1/1       Running   0          3m
test-cluster-1-3                                       0/1       Pending   0          35s
zk-0                                                   1/1       Running   0          4m

NOTE: Currently the operator does not automaticly rebalance topics with the new broker

3.b) Downscaling:

While downscaling the cluster is possible and simple a simple rebalancing is done to prevent data-loss, this is currently heavy under development and considered unstable.

4) Delete the cluster

When we are done, we can do a

# kubectl delete -f example/kafka-cluster.yaml
kafkacluster "test-cluster-1" deleted

to delete the kafkaCluster object. The operator then detects the deletion and shuts down all running components:

# kubectl get pods
NAME                             READY     STATUS        RESTARTS   AGE
kafka-operator-767603131-tv3ck   1/1       Running       0          1m
test-cluster-1-0                 0/1       Terminating   0          8m
zk-0                             1/1       Running       0          9m

Known Issues / Open Tasks

There are a couple of open Task/Issues, this is mainly just for me tracking progress:

  • Resisze Clusters (without Data Rebalancing)
  • Delete Cluster
  • Dokumentation, Vendoring and Testing
  • Use Ressource (K8s and kafka Options)
  • Monitoring with JMX Sidecar
  • Automaticly Rebalacing
  • Investigate Datagravity


To get Kafka Running a Zookeeper is needed. A simple one Node zK example is provided in the example Folder. But for any usage beyond testing/developing a proper Zookeeper setup should be used. A good example is the Zookeeper Chart in the offical Helm Repo.


Differences vs Helm Chart

While a Helm is a great tool, and the provided kafka chart is also pretty dope, Helm only managees deployment of a cluster. But since kafka is a statefull application its needs goes beyond the normal capabilities you can do with vanilla kubernetes. For example a downsizing/upsizing of the cluster requires moving partitions/replicas off/onto brokers. To automate that the operator is used. It looks at the current cluster state and takes neccesary actions to


Currently the supported image is are the offical images from confluent. ( While its possible to specify other images, due to instrumentation most other images wont work.


Dependency Managment

dep is used for dependecy managment.

Under e2e-test/hack are a couple of

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