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Autoscaling the Compute Resource of a Kafka Topology Cluster
This guide will show you how to use KubeDB to autoscale compute resources i.e. cpu and memory of a Kafka topology cluster.
Before You Begin
At first, you need to have a Kubernetes cluster, and the
kubectlcommand-line tool must be configured to communicate with your cluster.Install
KubeDBProvisioner, Ops-manager and Autoscaler operator in your cluster following the steps here.Install
Metrics Serverfrom hereYou should be familiar with the following
KubeDBconcepts:
To keep everything isolated, we are going to use a separate namespace called demo throughout this tutorial.
$ kubectl create ns demo
namespace/demo created
Note: YAML files used in this tutorial are stored in docs/examples/kafka directory of kubedb/docs repository.
Autoscaling of Topology Cluster
Here, we are going to deploy a Kafka Topology Cluster using a supported version by KubeDB operator. Then we are going to apply KafkaAutoscaler to set up autoscaling.
Deploy Kafka Topology Cluster
In this section, we are going to deploy a Kafka Topology cluster with version 3.6.1. Then, in the next section we will set up autoscaling for this database using KafkaAutoscaler CRD. Below is the YAML of the Kafka CR that we are going to create,
apiVersion: kubedb.com/v1
kind: Kafka
metadata:
name: kafka-prod
namespace: demo
spec:
version: 3.6.1
topology:
broker:
replicas: 2
podTemplate:
spec:
containers:
- name: kafka
resources:
limits:
memory: 1Gi
requests:
cpu: 500m
memory: 1Gi
storage:
accessModes:
- ReadWriteOnce
resources:
requests:
storage: 1Gi
storageClassName: standard
controller:
replicas: 2
podTemplate:
spec:
containers:
- name: kafka
resources:
limits:
memory: 1Gi
requests:
cpu: 500m
memory: 1Gi
storage:
accessModes:
- ReadWriteOnce
resources:
requests:
storage: 1Gi
storageClassName: standard
storageType: Durable
deletionPolicy: WipeOut
Let’s create the Kafka CRO we have shown above,
$ kubectl create -f https://github.com/kubedb/docs/raw/v2024.9.30/docs/examples/kafka/autoscaler/kafka-topology.yaml
kafka.kubedb.com/kafka-prod created
Now, wait until kafka-prod has status Ready. i.e,
$ kubectl get kf -n demo -w
NAME TYPE VERSION STATUS AGE
kafka-prod kubedb.com/v1 3.6.1 Provisioning 0s
kafka-prod kubedb.com/v1 3.6.1 Provisioning 24s
.
.
kafka-prod kubedb.com/v1 3.6.1 Ready 118s
Kafka Topology Autoscaler(Broker)
Let’s check the Broker Pod containers resources,
$ kubectl get pod -n demo kafka-prod-broker-0 -o json | jq '.spec.containers[].resources'
{
"limits": {
"memory": "1Gi"
},
"requests": {
"cpu": "500m",
"memory": "1Gi"
}
}
Let’s check the Kafka resources for broker,
$ kubectl get kafka -n demo kafka-prod -o json | jq '.spec.topology.broker.podTemplate.spec.containers[].resources'
{
"limits": {
"memory": "1Gi"
},
"requests": {
"cpu": "500m",
"memory": "1Gi"
}
}
You can see from the above outputs that the resources for broker are same as the one we have assigned while deploying the kafka.
We are now ready to apply the KafkaAutoscaler CRO to set up autoscaling for this broker nodes.
Compute Resource Autoscaling
Here, we are going to set up compute resource autoscaling using a KafkaAutoscaler Object.
Create KafkaAutoscaler Object
In order to set up compute resource autoscaling for this topology cluster, we have to create a KafkaAutoscaler CRO with our desired configuration. Below is the YAML of the KafkaAutoscaler object that we are going to create,
apiVersion: autoscaling.kubedb.com/v1alpha1
kind: KafkaAutoscaler
metadata:
name: kf-broker-autoscaler
namespace: demo
spec:
databaseRef:
name: kafka-prod
opsRequestOptions:
timeout: 5m
apply: IfReady
compute:
broker:
trigger: "On"
podLifeTimeThreshold: 5m
resourceDiffPercentage: 20
minAllowed:
cpu: 600m
memory: 1.5Gi
maxAllowed:
cpu: 1
memory: 2Gi
controlledResources: ["cpu", "memory"]
containerControlledValues: "RequestsAndLimits"
Here,
spec.databaseRef.namespecifies that we are performing compute resource scaling operation onkafka-prodcluster.spec.compute.broker.triggerspecifies that compute autoscaling is enabled for this node.spec.compute.broker.podLifeTimeThresholdspecifies the minimum lifetime for at least one of the pod to initiate a vertical scaling.spec.compute.broker.resourceDiffPercentagespecifies the minimum resource difference in percentage. The default is 10%. If the difference between current & recommended resource is less than ResourceDiffPercentage, Autoscaler Operator will ignore the updating.spec.compute.broker.minAllowedspecifies the minimum allowed resources for the cluster.spec.compute.broker.maxAllowedspecifies the maximum allowed resources for the cluster.spec.compute.broker.controlledResourcesspecifies the resources that are controlled by the autoscaler.spec.compute.broker.containerControlledValuesspecifies which resource values should be controlled. The default is “RequestsAndLimits”.spec.opsRequestOptionscontains the options to pass to the created OpsRequest. It has 2 fields.timeoutspecifies the timeout for the OpsRequest.applyspecifies when the OpsRequest should be applied. The default is “IfReady”.
Let’s create the KafkaAutoscaler CR we have shown above,
$ kubectl apply -f https://github.com/kubedb/docs/raw/v2024.9.30/docs/examples/kafka/autoscaler/compute/kafka-broker-autoscaler.yaml
kafkaautoscaler.autoscaling.kubedb.com/kf-broker-autoscaler created
Verify Autoscaling is set up successfully
Let’s check that the kafkaautoscaler resource is created successfully,
$ kubectl describe kafkaautoscaler kf-broker-autoscaler -n demo
$ kubectl describe kafkaautoscaler kf-broker-autoscaler -n demo
Name: kf-broker-autoscaler
Namespace: demo
Labels: <none>
Annotations: <none>
API Version: autoscaling.kubedb.com/v1alpha1
Kind: KafkaAutoscaler
Metadata:
Creation Timestamp: 2024-08-27T06:17:07Z
Generation: 1
Owner References:
API Version: kubedb.com/v1
Block Owner Deletion: true
Controller: true
Kind: Kafka
Name: kafka-prod
UID: 7cee41e0-259c-4a5e-856a-e8ca90056120
Resource Version: 1113275
UID: 7e3be99f-cd4d-440a-a477-8e8994840ebb
Spec:
Compute:
Broker:
Container Controlled Values: RequestsAndLimits
Controlled Resources:
cpu
memory
Max Allowed:
Cpu: 1
Memory: 2Gi
Min Allowed:
Cpu: 600m
Memory: 1536Mi
Pod Life Time Threshold: 5m0s
Resource Diff Percentage: 20
Trigger: On
Database Ref:
Name: kafka-prod
Ops Request Options:
Apply: IfReady
Timeout: 5m0s
Status:
Checkpoints:
Cpu Histogram:
Bucket Weights:
Index: 1
Weight: 10000
Index: 2
Weight: 2485
Index: 30
Weight: 1923
Reference Timestamp: 2024-08-27T06:20:00Z
Total Weight: 0.8587070656303101
First Sample Start: 2024-08-27T06:20:45Z
Last Sample Start: 2024-08-27T06:23:53Z
Last Update Time: 2024-08-27T06:24:10Z
Memory Histogram:
Bucket Weights:
Index: 20
Weight: 9682
Index: 21
Weight: 10000
Reference Timestamp: 2024-08-27T06:25:00Z
Total Weight: 1.9636285054518687
Ref:
Container Name: kafka
Vpa Object Name: kafka-prod-broker
Total Samples Count: 6
Version: v3
Conditions:
Last Transition Time: 2024-08-27T06:21:32Z
Message: Successfully created kafkaOpsRequest demo/kfops-kafka-prod-broker-f6qbth
Observed Generation: 1
Reason: CreateOpsRequest
Status: True
Type: CreateOpsRequest
Vpas:
Conditions:
Last Transition Time: 2024-08-27T06:21:10Z
Status: True
Type: RecommendationProvided
Recommendation:
Container Recommendations:
Container Name: kafka
Lower Bound:
Cpu: 600m
Memory: 1536Mi
Target:
Cpu: 813m
Memory: 1536Mi
Uncapped Target:
Cpu: 813m
Memory: 442809964
Upper Bound:
Cpu: 1
Memory: 2Gi
Vpa Name: kafka-prod-broker
Events: <none>
So, the kafkaautoscaler resource is created successfully.
you can see in the Status.VPAs.Recommendation section, that recommendation has been generated for our database. Our autoscaler operator continuously watches the recommendation generated and creates an kafkaopsrequest based on the recommendations, if the database pods resources are needed to scaled up or down.
Let’s watch the kafkaopsrequest in the demo namespace to see if any kafkaopsrequest object is created. After some time you’ll see that a kafkaopsrequest will be created based on the recommendation.
$ watch kubectl get kafkaopsrequest -n demo
Every 2.0s: kubectl get kafkaopsrequest -n demo
NAME TYPE STATUS AGE
kfops-kafka-prod-broker-f6qbth VerticalScaling Progressing 10s
Let’s wait for the ops request to become successful.
$ kubectl get kafkaopsrequest -n demo
NAME TYPE STATUS AGE
kfops-kafka-prod-broker-f6qbth VerticalScaling Successful 3m2s
We can see from the above output that the KafkaOpsRequest has succeeded. If we describe the KafkaOpsRequest we will get an overview of the steps that were followed to scale the cluster.
$ kubectl describe kafkaopsrequests -n demo kfops-kafka-prod-broker-f6qbth
Name: kfops-kafka-prod-broker-f6qbth
Namespace: demo
Labels: app.kubernetes.io/component=database
app.kubernetes.io/instance=kafka-prod
app.kubernetes.io/managed-by=kubedb.com
app.kubernetes.io/name=kafkas.kubedb.com
Annotations: <none>
API Version: ops.kubedb.com/v1alpha1
Kind: KafkaOpsRequest
Metadata:
Creation Timestamp: 2024-08-27T06:21:32Z
Generation: 1
Owner References:
API Version: autoscaling.kubedb.com/v1alpha1
Block Owner Deletion: true
Controller: true
Kind: KafkaAutoscaler
Name: kf-broker-autoscaler
UID: 7e3be99f-cd4d-440a-a477-8e8994840ebb
Resource Version: 1113011
UID: a040a45b-135c-454a-8ddd-d4bd5000ffba
Spec:
Apply: IfReady
Database Ref:
Name: kafka-prod
Timeout: 5m0s
Type: VerticalScaling
Vertical Scaling:
Broker:
Resources:
Limits:
Memory: 1536Mi
Requests:
Cpu: 813m
Memory: 1536Mi
Status:
Conditions:
Last Transition Time: 2024-08-27T06:21:32Z
Message: Kafka ops-request has started to vertically scaling the kafka nodes
Observed Generation: 1
Reason: VerticalScaling
Status: True
Type: VerticalScaling
Last Transition Time: 2024-08-27T06:21:35Z
Message: Successfully updated PetSets Resources
Observed Generation: 1
Reason: UpdatePetSets
Status: True
Type: UpdatePetSets
Last Transition Time: 2024-08-27T06:21:40Z
Message: get pod; ConditionStatus:True; PodName:kafka-prod-broker-0
Observed Generation: 1
Status: True
Type: GetPod--kafka-prod-broker-0
Last Transition Time: 2024-08-27T06:21:41Z
Message: evict pod; ConditionStatus:True; PodName:kafka-prod-broker-0
Observed Generation: 1
Status: True
Type: EvictPod--kafka-prod-broker-0
Last Transition Time: 2024-08-27T06:21:55Z
Message: check pod running; ConditionStatus:True; PodName:kafka-prod-broker-0
Observed Generation: 1
Status: True
Type: CheckPodRunning--kafka-prod-broker-0
Last Transition Time: 2024-08-27T06:22:00Z
Message: get pod; ConditionStatus:True; PodName:kafka-prod-broker-1
Observed Generation: 1
Status: True
Type: GetPod--kafka-prod-broker-1
Last Transition Time: 2024-08-27T06:22:01Z
Message: evict pod; ConditionStatus:True; PodName:kafka-prod-broker-1
Observed Generation: 1
Status: True
Type: EvictPod--kafka-prod-broker-1
Last Transition Time: 2024-08-27T06:22:21Z
Message: check pod running; ConditionStatus:True; PodName:kafka-prod-broker-1
Observed Generation: 1
Status: True
Type: CheckPodRunning--kafka-prod-broker-1
Last Transition Time: 2024-08-27T06:22:25Z
Message: Successfully Restarted Pods With Resources
Observed Generation: 1
Reason: RestartPods
Status: True
Type: RestartPods
Last Transition Time: 2024-08-27T06:22:26Z
Message: Successfully completed the vertical scaling for kafka
Observed Generation: 1
Reason: Successful
Status: True
Type: Successful
Observed Generation: 1
Phase: Successful
Events:
Type Reason Age From Message
---- ------ ---- ---- -------
Normal Starting 4m55s KubeDB Ops-manager Operator Start processing for KafkaOpsRequest: demo/kfops-kafka-prod-broker-f6qbth
Normal Starting 4m55s KubeDB Ops-manager Operator Pausing Kafka databse: demo/kafka-prod
Normal Successful 4m55s KubeDB Ops-manager Operator Successfully paused Kafka database: demo/kafka-prod for KafkaOpsRequest: kfops-kafka-prod-broker-f6qbth
Normal UpdatePetSets 4m52s KubeDB Ops-manager Operator Successfully updated PetSets Resources
Warning get pod; ConditionStatus:True; PodName:kafka-prod-broker-0 4m47s KubeDB Ops-manager Operator get pod; ConditionStatus:True; PodName:kafka-prod-broker-0
Warning evict pod; ConditionStatus:True; PodName:kafka-prod-broker-0 4m46s KubeDB Ops-manager Operator evict pod; ConditionStatus:True; PodName:kafka-prod-broker-0
Warning check pod running; ConditionStatus:False; PodName:kafka-prod-broker-0 4m42s KubeDB Ops-manager Operator check pod running; ConditionStatus:False; PodName:kafka-prod-broker-0
Warning check pod running; ConditionStatus:True; PodName:kafka-prod-broker-0 4m32s KubeDB Ops-manager Operator check pod running; ConditionStatus:True; PodName:kafka-prod-broker-0
Warning get pod; ConditionStatus:True; PodName:kafka-prod-broker-1 4m27s KubeDB Ops-manager Operator get pod; ConditionStatus:True; PodName:kafka-prod-broker-1
Warning evict pod; ConditionStatus:True; PodName:kafka-prod-broker-1 4m26s KubeDB Ops-manager Operator evict pod; ConditionStatus:True; PodName:kafka-prod-broker-1
Warning check pod running; ConditionStatus:False; PodName:kafka-prod-broker-1 4m22s KubeDB Ops-manager Operator check pod running; ConditionStatus:False; PodName:kafka-prod-broker-1
Warning check pod running; ConditionStatus:True; PodName:kafka-prod-broker-1 4m7s KubeDB Ops-manager Operator check pod running; ConditionStatus:True; PodName:kafka-prod-broker-1
Normal RestartPods 4m2s KubeDB Ops-manager Operator Successfully Restarted Pods With Resources
Normal Starting 4m1s KubeDB Ops-manager Operator Resuming Kafka database: demo/kafka-prod
Normal Successful 4m1s KubeDB Ops-manager Operator Successfully resumed Kafka database: demo/kafka-prod for KafkaOpsRequest: kfops-kafka-prod-broker-f6qbth
Now, we are going to verify from the Pod, and the Kafka yaml whether the resources of the broker node has updated to meet up the desired state, Let’s check,
$ kubectl get pod -n demo kafka-prod-broker-0 -o json | jq '.spec.containers[].resources'
{
"limits": {
"memory": "1536Mi"
},
"requests": {
"cpu": "600m",
"memory": "1536Mi"
}
}
$ kubectl get kafka -n demo kafka-prod -o json | jq '.spec.topology.broker.podTemplate.spec.containers[].resources'
{
"limits": {
"memory": "1536Mi"
},
"requests": {
"cpu": "600m",
"memory": "1536Mi"
}
}
Kafka Topology Autoscaler(Controller)
Let’s check the Controller Pod containers resources,
$ kubectl get pod -n demo kafka-prod-controller-0 -o json | jq '.spec.containers[].resources'
{
"limits": {
"memory": "1Gi"
},
"requests": {
"cpu": "500m",
"memory": "1Gi"
}
}
Let’s check the Kafka resources for broker,
$ kubectl get kafka -n demo kafka-prod -o json | jq '.spec.topology.controller.podTemplate.spec.containers[].resources'
{
"limits": {
"memory": "1Gi"
},
"requests": {
"cpu": "500m",
"memory": "1Gi"
}
}
You can see from the above outputs that the resources for controller are same as the one we have assigned while deploying the kafka.
We are now ready to apply the KafkaAutoscaler CRO to set up autoscaling for this broker nodes.
Compute Resource Autoscaling
Here, we are going to set up compute resource autoscaling using a KafkaAutoscaler Object.
Create KafkaAutoscaler Object
In order to set up compute resource autoscaling for this topology cluster, we have to create a KafkaAutoscaler CRO with our desired configuration. Below is the YAML of the KafkaAutoscaler object that we are going to create,
apiVersion: autoscaling.kubedb.com/v1alpha1
kind: KafkaAutoscaler
metadata:
name: kf-controller-autoscaler
namespace: demo
spec:
databaseRef:
name: kafka-prod
opsRequestOptions:
timeout: 5m
apply: IfReady
compute:
controller:
trigger: "On"
podLifeTimeThreshold: 5m
resourceDiffPercentage: 20
minAllowed:
cpu: 600m
memory: 1.5Gi
maxAllowed:
cpu: 1
memory: 2Gi
controlledResources: ["cpu", "memory"]
containerControlledValues: "RequestsAndLimits"
Here,
spec.databaseRef.namespecifies that we are performing compute resource scaling operation onkafka-prodcluster.spec.compute.controller.triggerspecifies that compute autoscaling is enabled for this node.spec.compute.controller.podLifeTimeThresholdspecifies the minimum lifetime for at least one of the pod to initiate a vertical scaling.spec.compute.controller.resourceDiffPercentagespecifies the minimum resource difference in percentage. The default is 10%. If the difference between current & recommended resource is less than ResourceDiffPercentage, Autoscaler Operator will ignore the updating.spec.compute.controller.minAllowedspecifies the minimum allowed resources for the cluster.spec.compute.controller.maxAllowedspecifies the maximum allowed resources for the cluster.spec.compute.controller.controlledResourcesspecifies the resources that are controlled by the autoscaler.spec.compute.controller.containerControlledValuesspecifies which resource values should be controlled. The default is “RequestsAndLimits”.spec.opsRequestOptionscontains the options to pass to the created OpsRequest. It has 2 fields.timeoutspecifies the timeout for the OpsRequest.applyspecifies when the OpsRequest should be applied. The default is “IfReady”.
Let’s create the KafkaAutoscaler CR we have shown above,
$ kubectl apply -f https://github.com/kubedb/docs/raw/v2024.9.30/docs/examples/kafka/autoscaler/compute/kafka-controller-autoscaler.yaml
kafkaautoscaler.autoscaling.kubedb.com/kf-controller-autoscaler created
Verify Autoscaling is set up successfully
Let’s check that the kafkaautoscaler resource is created successfully,
$ kubectl describe kafkaautoscaler kf-controller-autoscaler -n demo
Name: kf-controller-autoscaler
Namespace: demo
Labels: <none>
Annotations: <none>
API Version: autoscaling.kubedb.com/v1alpha1
Kind: KafkaAutoscaler
Metadata:
Creation Timestamp: 2024-08-27T06:29:45Z
Generation: 1
Owner References:
API Version: kubedb.com/v1
Block Owner Deletion: true
Controller: true
Kind: Kafka
Name: kafka-prod
UID: 7cee41e0-259c-4a5e-856a-e8ca90056120
Resource Version: 1116548
UID: 49461872-3628-4bc2-8692-f147bc55aa49
Spec:
Compute:
Controller:
Container Controlled Values: RequestsAndLimits
Controlled Resources:
cpu
memory
Max Allowed:
Cpu: 1
Memory: 2Gi
Min Allowed:
Cpu: 600m
Memory: 1536Mi
Pod Life Time Threshold: 5m0s
Resource Diff Percentage: 20
Trigger: On
Database Ref:
Name: kafka-prod
Ops Request Options:
Apply: IfReady
Timeout: 5m0s
Status:
Checkpoints:
Cpu Histogram:
Bucket Weights:
Index: 1
Weight: 10000
Index: 3
Weight: 4666
Reference Timestamp: 2024-08-27T06:30:00Z
Total Weight: 0.3085514112801626
First Sample Start: 2024-08-27T06:29:52Z
Last Sample Start: 2024-08-27T06:30:49Z
Last Update Time: 2024-08-27T06:31:11Z
Memory Histogram:
Reference Timestamp: 2024-08-27T06:35:00Z
Ref:
Container Name: kafka
Vpa Object Name: kafka-prod-controller
Total Samples Count: 3
Version: v3
Conditions:
Last Transition Time: 2024-08-27T06:30:32Z
Message: Successfully created kafkaOpsRequest demo/kfops-kafka-prod-controller-3vlvzr
Observed Generation: 1
Reason: CreateOpsRequest
Status: True
Type: CreateOpsRequest
Vpas:
Conditions:
Last Transition Time: 2024-08-27T06:30:11Z
Status: True
Type: RecommendationProvided
Recommendation:
Container Recommendations:
Container Name: kafka
Lower Bound:
Cpu: 600m
Memory: 1536Mi
Target:
Cpu: 600m
Memory: 1536Mi
Uncapped Target:
Cpu: 100m
Memory: 297164212
Upper Bound:
Cpu: 1
Memory: 2Gi
Vpa Name: kafka-prod-controller
Events: <none>
So, the kafkaautoscaler resource is created successfully.
you can see in the Status.VPAs.Recommendation section, that recommendation has been generated for our controller cluster. Our autoscaler operator continuously watches the recommendation generated and creates an kafkaopsrequest based on the recommendations, if the controller node pods resources are needed to scaled up or down.
Let’s watch the kafkaopsrequest in the demo namespace to see if any kafkaopsrequest object is created. After some time you’ll see that a kafkaopsrequest will be created based on the recommendation.
$ watch kubectl get kafkaopsrequest -n demo
Every 2.0s: kubectl get kafkaopsrequest -n demo
NAME TYPE STATUS AGE
kfops-kafka-prod-controller-3vlvzr VerticalScaling Progressing 10s
Let’s wait for the ops request to become successful.
$ kubectl get kafkaopsrequest -n demo
NAME TYPE STATUS AGE
kfops-kafka-prod-controller-3vlvzr VerticalScaling Successful 3m2s
We can see from the above output that the KafkaOpsRequest has succeeded. If we describe the KafkaOpsRequest we will get an overview of the steps that were followed to scale the cluster.
$ kubectl describe kafkaopsrequests -n demo kfops-kafka-prod-controller-3vlvzr
Name: kfops-kafka-prod-controller-3vlvzr
Namespace: demo
Labels: app.kubernetes.io/component=database
app.kubernetes.io/instance=kafka-prod
app.kubernetes.io/managed-by=kubedb.com
app.kubernetes.io/name=kafkas.kubedb.com
Annotations: <none>
API Version: ops.kubedb.com/v1alpha1
Kind: KafkaOpsRequest
Metadata:
Creation Timestamp: 2024-08-27T06:30:32Z
Generation: 1
Owner References:
API Version: autoscaling.kubedb.com/v1alpha1
Block Owner Deletion: true
Controller: true
Kind: KafkaAutoscaler
Name: kf-controller-autoscaler
UID: 49461872-3628-4bc2-8692-f147bc55aa49
Resource Version: 1117285
UID: 22228813-bf11-4d8a-9bea-53a1995fe4d0
Spec:
Apply: IfReady
Database Ref:
Name: kafka-prod
Timeout: 5m0s
Type: VerticalScaling
Vertical Scaling:
Controller:
Resources:
Limits:
Memory: 1536Mi
Requests:
Cpu: 600m
Memory: 1536Mi
Status:
Conditions:
Last Transition Time: 2024-08-27T06:30:32Z
Message: Kafka ops-request has started to vertically scaling the kafka nodes
Observed Generation: 1
Reason: VerticalScaling
Status: True
Type: VerticalScaling
Last Transition Time: 2024-08-27T06:30:35Z
Message: Successfully updated PetSets Resources
Observed Generation: 1
Reason: UpdatePetSets
Status: True
Type: UpdatePetSets
Last Transition Time: 2024-08-27T06:30:40Z
Message: get pod; ConditionStatus:True; PodName:kafka-prod-controller-0
Observed Generation: 1
Status: True
Type: GetPod--kafka-prod-controller-0
Last Transition Time: 2024-08-27T06:30:40Z
Message: evict pod; ConditionStatus:True; PodName:kafka-prod-controller-0
Observed Generation: 1
Status: True
Type: EvictPod--kafka-prod-controller-0
Last Transition Time: 2024-08-27T06:31:11Z
Message: check pod running; ConditionStatus:True; PodName:kafka-prod-controller-0
Observed Generation: 1
Status: True
Type: CheckPodRunning--kafka-prod-controller-0
Last Transition Time: 2024-08-27T06:31:15Z
Message: get pod; ConditionStatus:True; PodName:kafka-prod-controller-1
Observed Generation: 1
Status: True
Type: GetPod--kafka-prod-controller-1
Last Transition Time: 2024-08-27T06:31:16Z
Message: evict pod; ConditionStatus:True; PodName:kafka-prod-controller-1
Observed Generation: 1
Status: True
Type: EvictPod--kafka-prod-controller-1
Last Transition Time: 2024-08-27T06:31:30Z
Message: check pod running; ConditionStatus:True; PodName:kafka-prod-controller-1
Observed Generation: 1
Status: True
Type: CheckPodRunning--kafka-prod-controller-1
Last Transition Time: 2024-08-27T06:31:35Z
Message: Successfully Restarted Pods With Resources
Observed Generation: 1
Reason: RestartPods
Status: True
Type: RestartPods
Last Transition Time: 2024-08-27T06:31:36Z
Message: Successfully completed the vertical scaling for kafka
Observed Generation: 1
Reason: Successful
Status: True
Type: Successful
Observed Generation: 1
Phase: Successful
Events:
Type Reason Age From Message
---- ------ ---- ---- -------
Normal Starting 2m33s KubeDB Ops-manager Operator Start processing for KafkaOpsRequest: demo/kfops-kafka-prod-controller-3vlvzr
Normal Starting 2m33s KubeDB Ops-manager Operator Pausing Kafka databse: demo/kafka-prod
Normal Successful 2m33s KubeDB Ops-manager Operator Successfully paused Kafka database: demo/kafka-prod for KafkaOpsRequest: kfops-kafka-prod-controller-3vlvzr
Normal UpdatePetSets 2m30s KubeDB Ops-manager Operator Successfully updated PetSets Resources
Warning get pod; ConditionStatus:True; PodName:kafka-prod-controller-0 2m25s KubeDB Ops-manager Operator get pod; ConditionStatus:True; PodName:kafka-prod-controller-0
Warning evict pod; ConditionStatus:True; PodName:kafka-prod-controller-0 2m25s KubeDB Ops-manager Operator evict pod; ConditionStatus:True; PodName:kafka-prod-controller-0
Warning check pod running; ConditionStatus:False; PodName:kafka-prod-controller-0 2m20s KubeDB Ops-manager Operator check pod running; ConditionStatus:False; PodName:kafka-prod-controller-0
Warning check pod running; ConditionStatus:True; PodName:kafka-prod-controller-0 115s KubeDB Ops-manager Operator check pod running; ConditionStatus:True; PodName:kafka-prod-controller-0
Warning get pod; ConditionStatus:True; PodName:kafka-prod-controller-1 110s KubeDB Ops-manager Operator get pod; ConditionStatus:True; PodName:kafka-prod-controller-1
Warning evict pod; ConditionStatus:True; PodName:kafka-prod-controller-1 109s KubeDB Ops-manager Operator evict pod; ConditionStatus:True; PodName:kafka-prod-controller-1
Warning check pod running; ConditionStatus:False; PodName:kafka-prod-controller-1 105s KubeDB Ops-manager Operator check pod running; ConditionStatus:False; PodName:kafka-prod-controller-1
Warning check pod running; ConditionStatus:True; PodName:kafka-prod-controller-1 95s KubeDB Ops-manager Operator check pod running; ConditionStatus:True; PodName:kafka-prod-controller-1
Normal RestartPods 90s KubeDB Ops-manager Operator Successfully Restarted Pods With Resources
Normal Starting 90s KubeDB Ops-manager Operator Resuming Kafka database: demo/kafka-prod
Normal Successful 90s KubeDB Ops-manager Operator Successfully resumed Kafka database: demo/kafka-prod for KafkaOpsRequest: kfops-kafka-prod-controller-3vlvzr
Now, we are going to verify from the Pod, and the Kafka yaml whether the resources of the controller node has updated to meet up the desired state, Let’s check,
$ kubectl get pod -n demo kafka-prod-controller-0 -o json | jq '.spec.containers[].resources'
{
"limits": {
"memory": "1536Mi"
},
"requests": {
"cpu": "600m",
"memory": "1536Mi"
}
}
$ kubectl get kafka -n demo kafka-prod -o json | jq '.spec.topology.controller.podTemplate.spec.containers[].resources'
{
"limits": {
"memory": "1536Mi"
},
"requests": {
"cpu": "600m",
"memory": "1536Mi"
}
}
The above output verifies that we have successfully auto scaled the resources of the Kafka topology cluster for broker and controller. You can create a similar KafkaAutoscaler object with both broker and controller resources to auto scale the resources of the Kafka topology cluster.
Cleaning Up
To clean up the Kubernetes resources created by this tutorial, run:
kubectl delete kafkaopsrequest -n demo kfops-kafka-prod-broker-f6qbth kfops-kafka-prod-controller-3vlvzr
kubectl delete kafkaautoscaler -n demo kf-broker-autoscaler kf-controller-autoscaler
kubectl delete kf -n demo kafka-prod
kubectl delete ns demo
Next Steps
- Detail concepts of Kafka object.
- Different Kafka topology clustering modes here.
- Monitor your Kafka database with KubeDB using out-of-the-box Prometheus operator.
- Want to hack on KubeDB? Check our contribution guidelines.