kafka-kubernetes

name	kafka-kubernetes
description	Kubernetes deployment expert for Apache Kafka. Guides K8s deployments using Helm charts, operators (Strimzi, Confluent), StatefulSets, and production best practices. Activates for kubernetes, k8s, helm, kafka on kubernetes, strimzi, confluent operator, kafka operator, statefulset, kafka helm chart, k8s deployment, kubernetes kafka, deploy kafka to k8s.

Kafka on Kubernetes Deployment

Expert guidance for deploying Apache Kafka on Kubernetes using industry-standard tools.

When to Use This Skill

I activate when you need help with:

• Kubernetes deployments: "Deploy Kafka on Kubernetes", "run Kafka in K8s", "Kafka Helm chart"
• Operator selection: "Strimzi vs Confluent Operator", "which Kafka operator to use"
• StatefulSet patterns: "Kafka StatefulSet best practices", "persistent volumes for Kafka"
• Production K8s: "Production-ready Kafka on K8s", "Kafka high availability in Kubernetes"

What I Know

Deployment Options Comparison

Approach	Difficulty	Production-Ready	Best For
Strimzi Operator	Easy	✅ Yes	Self-managed Kafka on K8s, CNCF project
Confluent Operator	Medium	✅ Yes	Enterprise features, Confluent ecosystem
Bitnami Helm Chart	Easy	⚠️ Mostly	Quick dev/staging environments
Custom StatefulSet	Hard	⚠️ Requires expertise	Full control, custom requirements

Recommendation: Strimzi Operator for most production use cases (CNCF project, active community, KRaft support)

Deployment Approach 1: Strimzi Operator (Recommended)

Strimzi is a CNCF Sandbox project providing Kubernetes operators for Apache Kafka.

Features

• ✅ KRaft mode support (Kafka 3.6+, no ZooKeeper)
• ✅ Declarative Kafka management (CRDs)
• ✅ Automatic rolling upgrades
• ✅ Built-in monitoring (Prometheus metrics)
• ✅ Mirror Maker 2 for replication
• ✅ Kafka Connect integration
• ✅ User and topic management via CRDs

Installation (Helm)

# 1. Add Strimzi Helm repository
helm repo add strimzi https://strimzi.io/charts/
helm repo update

# 2. Create namespace
kubectl create namespace kafka

# 3. Install Strimzi Operator
helm install strimzi-kafka-operator strimzi/strimzi-kafka-operator \
  --namespace kafka \
  --set watchNamespaces="{kafka}" \
  --version 0.39.0

# 4. Verify operator is running
kubectl get pods -n kafka
# Output: strimzi-cluster-operator-... Running

Deploy Kafka Cluster (KRaft Mode)

# kafka-cluster.yaml
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaNodePool
metadata:
  name: kafka-pool
  namespace: kafka
  labels:
    strimzi.io/cluster: my-kafka-cluster
spec:
  replicas: 3
  roles:
    - controller
    - broker
  storage:
    type: jbod
    volumes:
      - id: 0
        type: persistent-claim
        size: 100Gi
        class: fast-ssd
        deleteClaim: false
---
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
  name: my-kafka-cluster
  namespace: kafka
  annotations:
    strimzi.io/kraft: enabled
    strimzi.io/node-pools: enabled
spec:
  kafka:
    version: 3.7.0
    metadataVersion: 3.7-IV4
    replicas: 3

    listeners:
      - name: plain
        port: 9092
        type: internal
        tls: false
      - name: tls
        port: 9093
        type: internal
        tls: true
        authentication:
          type: tls
      - name: external
        port: 9094
        type: loadbalancer
        tls: true
        authentication:
          type: tls

    config:
      default.replication.factor: 3
      min.insync.replicas: 2
      offsets.topic.replication.factor: 3
      transaction.state.log.replication.factor: 3
      transaction.state.log.min.isr: 2
      auto.create.topics.enable: false
      log.retention.hours: 168
      log.segment.bytes: 1073741824
      compression.type: lz4

    resources:
      requests:
        memory: 4Gi
        cpu: "2"
      limits:
        memory: 8Gi
        cpu: "4"

    jvmOptions:
      -Xms: 2048m
      -Xmx: 4096m

    metricsConfig:
      type: jmxPrometheusExporter
      valueFrom:
        configMapKeyRef:
          name: kafka-metrics
          key: kafka-metrics-config.yml

# Apply Kafka cluster
kubectl apply -f kafka-cluster.yaml

# Wait for cluster to be ready (5-10 minutes)
kubectl wait kafka/my-kafka-cluster --for=condition=Ready --timeout=600s -n kafka

# Check status
kubectl get kafka -n kafka
# Output: my-kafka-cluster   3.7.0   3         True

Create Topics (Declaratively)

# kafka-topics.yaml
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaTopic
metadata:
  name: user-events
  namespace: kafka
  labels:
    strimzi.io/cluster: my-kafka-cluster
spec:
  partitions: 12
  replicas: 3
  config:
    retention.ms: 604800000  # 7 days
    segment.bytes: 1073741824
    compression.type: lz4
---
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaTopic
metadata:
  name: order-events
  namespace: kafka
  labels:
    strimzi.io/cluster: my-kafka-cluster
spec:
  partitions: 6
  replicas: 3
  config:
    retention.ms: 2592000000  # 30 days
    min.insync.replicas: 2

# Apply topics
kubectl apply -f kafka-topics.yaml

# Verify topics created
kubectl get kafkatopics -n kafka

Create Users (Declaratively)

# kafka-users.yaml
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaUser
metadata:
  name: my-producer
  namespace: kafka
  labels:
    strimzi.io/cluster: my-kafka-cluster
spec:
  authentication:
    type: tls
  authorization:
    type: simple
    acls:
      - resource:
          type: topic
          name: user-events
          patternType: literal
        operations: [Write, Describe]
      - resource:
          type: topic
          name: order-events
          patternType: literal
        operations: [Write, Describe]
---
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaUser
metadata:
  name: my-consumer
  namespace: kafka
  labels:
    strimzi.io/cluster: my-kafka-cluster
spec:
  authentication:
    type: tls
  authorization:
    type: simple
    acls:
      - resource:
          type: topic
          name: user-events
          patternType: literal
        operations: [Read, Describe]
      - resource:
          type: group
          name: my-consumer-group
          patternType: literal
        operations: [Read]

# Apply users
kubectl apply -f kafka-users.yaml

# Get user credentials (TLS certificates)
kubectl get secret my-producer -n kafka -o jsonpath='{.data.user\.crt}' | base64 -d > producer.crt
kubectl get secret my-producer -n kafka -o jsonpath='{.data.user\.key}' | base64 -d > producer.key
kubectl get secret my-kafka-cluster-cluster-ca-cert -n kafka -o jsonpath='{.data.ca\.crt}' | base64 -d > ca.crt

Deployment Approach 2: Confluent Operator

Confluent for Kubernetes (CFK) provides enterprise-grade Kafka management.

Features

• ✅ Full Confluent Platform (Kafka, Schema Registry, ksqlDB, Connect)
• ✅ Hybrid deployments (K8s + on-prem)
• ✅ Rolling upgrades with zero downtime
• ✅ Multi-region replication
• ✅ Advanced security (RBAC, encryption)
• ⚠️ Requires Confluent Platform license (paid)

Installation

# 1. Add Confluent Helm repository
helm repo add confluentinc https://packages.confluent.io/helm
helm repo update

# 2. Create namespace
kubectl create namespace confluent

# 3. Install Confluent Operator
helm install confluent-operator confluentinc/confluent-for-kubernetes \
  --namespace confluent \
  --version 0.921.11

# 4. Verify
kubectl get pods -n confluent

Deploy Kafka Cluster

# kafka-cluster-confluent.yaml
apiVersion: platform.confluent.io/v1beta1
kind: Kafka
metadata:
  name: kafka
  namespace: confluent
spec:
  replicas: 3
  image:
    application: confluentinc/cp-server:7.6.0
    init: confluentinc/confluent-init-container:2.7.0

  dataVolumeCapacity: 100Gi
  storageClass:
    name: fast-ssd

  metricReporter:
    enabled: true

  listeners:
    internal:
      authentication:
        type: plain
      tls:
        enabled: true
    external:
      authentication:
        type: plain
      tls:
        enabled: true

  dependencies:
    zookeeper:
      endpoint: zookeeper.confluent.svc.cluster.local:2181

  podTemplate:
    resources:
      requests:
        memory: 4Gi
        cpu: 2
      limits:
        memory: 8Gi
        cpu: 4

# Apply Kafka cluster
kubectl apply -f kafka-cluster-confluent.yaml

# Wait for cluster
kubectl wait kafka/kafka --for=condition=Ready --timeout=600s -n confluent

Deployment Approach 3: Bitnami Helm Chart (Dev/Staging)

Bitnami Helm Chart is simple but less suitable for production.

Installation

# 1. Add Bitnami repository
helm repo add bitnami https://charts.bitnami.com/bitnami
helm repo update

# 2. Install Kafka (KRaft mode)
helm install kafka bitnami/kafka \
  --namespace kafka \
  --create-namespace \
  --set kraft.enabled=true \
  --set controller.replicaCount=3 \
  --set broker.replicaCount=3 \
  --set persistence.size=100Gi \
  --set persistence.storageClass=fast-ssd \
  --set metrics.kafka.enabled=true \
  --set metrics.jmx.enabled=true

# 3. Get bootstrap servers
export KAFKA_BOOTSTRAP=$(kubectl get svc kafka -n kafka -o jsonpath='{.status.loadBalancer.ingress[0].hostname}'):9092

Limitations:

• ⚠️ Less production-ready than Strimzi/Confluent
• ⚠️ Limited declarative topic/user management
• ⚠️ Fewer advanced features (no MirrorMaker 2, limited RBAC)

Production Best Practices

1. Storage Configuration

Use SSD-backed storage classes for Kafka logs:

apiVersion: storage.k8s.io/v1
kind: StorageClass
metadata:
  name: fast-ssd
provisioner: kubernetes.io/aws-ebs  # or pd.csi.storage.gke.io for GKE
parameters:
  type: gp3  # AWS EBS GP3 (or io2 for extreme performance)
  iopsPerGB: "50"
  throughput: "125"
allowVolumeExpansion: true
volumeBindingMode: WaitForFirstConsumer

Kafka storage requirements:

• Min IOPS: 3000+ per broker
• Min Throughput: 125 MB/s per broker
• Persistent: Use deleteClaim: false (don't delete data on pod deletion)

2. Resource Limits

resources:
  requests:
    memory: 4Gi
    cpu: "2"
  limits:
    memory: 8Gi
    cpu: "4"

jvmOptions:
  -Xms: 2048m  # Initial heap (50% of memory request)
  -Xmx: 4096m  # Max heap (50% of memory limit, leave room for OS cache)

Sizing guidelines:

• Small (dev): 2 CPU, 4Gi memory
• Medium (staging): 4 CPU, 8Gi memory
• Large (production): 8 CPU, 16Gi memory

3. Pod Disruption Budgets

Ensure high availability during K8s upgrades:

apiVersion: policy/v1
kind: PodDisruptionBudget
metadata:
  name: kafka-pdb
  namespace: kafka
spec:
  maxUnavailable: 1
  selector:
    matchLabels:
      app.kubernetes.io/name: kafka

4. Affinity Rules

Spread brokers across availability zones:

spec:
  kafka:
    template:
      pod:
        affinity:
          podAntiAffinity:
            requiredDuringSchedulingIgnoredDuringExecution:
              - labelSelector:
                  matchExpressions:
                    - key: strimzi.io/name
                      operator: In
                      values:
                        - my-kafka-cluster-kafka
                topologyKey: topology.kubernetes.io/zone

5. Network Policies

Restrict access to Kafka brokers:

apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: kafka-network-policy
  namespace: kafka
spec:
  podSelector:
    matchLabels:
      strimzi.io/name: my-kafka-cluster-kafka
  policyTypes:
    - Ingress
  ingress:
    - from:
      - podSelector:
          matchLabels:
            app: my-producer
      - podSelector:
          matchLabels:
            app: my-consumer
      ports:
      - protocol: TCP
        port: 9092
      - protocol: TCP
        port: 9093

Monitoring Integration

Prometheus + Grafana Setup

Strimzi provides built-in Prometheus metrics exporter:

# kafka-metrics-configmap.yaml
apiVersion: v1
kind: ConfigMap
metadata:
  name: kafka-metrics
  namespace: kafka
data:
  kafka-metrics-config.yml: |
    # Use JMX Exporter config from:
    # plugins/specweave-kafka/monitoring/prometheus/kafka-jmx-exporter.yml
    lowercaseOutputName: true
    lowercaseOutputLabelNames: true
    whitelistObjectNames:
      - "kafka.server:type=BrokerTopicMetrics,name=*"
      # ... (copy from kafka-jmx-exporter.yml)

# Apply metrics config
kubectl apply -f kafka-metrics-configmap.yaml

# Install Prometheus Operator (if not already installed)
helm install prometheus prometheus-community/kube-prometheus-stack \
  --namespace monitoring \
  --create-namespace

# Create PodMonitor for Kafka
kubectl apply -f - <<EOF
apiVersion: monitoring.coreos.com/v1
kind: PodMonitor
metadata:
  name: kafka-metrics
  namespace: kafka
spec:
  selector:
    matchLabels:
      strimzi.io/kind: Kafka
  podMetricsEndpoints:
    - port: tcp-prometheus
      interval: 30s
EOF

# Access Grafana dashboards (from kafka-observability skill)
kubectl port-forward -n monitoring svc/prometheus-grafana 3000:80
# Open: http://localhost:3000
# Dashboards: Kafka Cluster Overview, Broker Metrics, Consumer Lag, Topic Metrics, JVM Metrics

Troubleshooting

"Pods stuck in Pending state"

Cause: Insufficient resources or storage class not found Fix:

# Check events
kubectl describe pod kafka-my-kafka-cluster-0 -n kafka

# Check storage class exists
kubectl get storageclass

# If missing, create fast-ssd storage class (see Production Best Practices above)

"Kafka broker not ready after 10 minutes"

Cause: Slow storage provisioning or resource limits too low Fix:

# Check broker logs
kubectl logs kafka-my-kafka-cluster-0 -n kafka

# Common issues:
# 1. Low IOPS on storage → Use GP3 or better
# 2. Low memory → Increase resources.requests.memory
# 3. KRaft quorum not formed → Check all brokers are running

"Cannot connect to Kafka from outside K8s"

Cause: External listener not configured Fix:

# Add external listener (Strimzi)
spec:
  kafka:
    listeners:
      - name: external
        port: 9094
        type: loadbalancer
        tls: true
        authentication:
          type: tls

# Get external bootstrap server
kubectl get kafka my-kafka-cluster -n kafka -o jsonpath='{.status.listeners[?(@.name=="external")].bootstrapServers}'

Scaling Operations

Horizontal Scaling (Add Brokers)

# Strimzi: Update KafkaNodePool replicas
kubectl patch kafkanodepool kafka-pool -n kafka --type='json' \
  -p='[{"op": "replace", "path": "/spec/replicas", "value": 5}]'

# Confluent: Update Kafka CR
kubectl patch kafka kafka -n confluent --type='json' \
  -p='[{"op": "replace", "path": "/spec/replicas", "value": 5}]'

# Wait for new brokers
kubectl rollout status statefulset/kafka-my-kafka-cluster-kafka -n kafka

Vertical Scaling (Change Resources)

# Update resources in Kafka CR
kubectl patch kafka my-kafka-cluster -n kafka --type='json' \
  -p='[
    {"op": "replace", "path": "/spec/kafka/resources/requests/memory", "value": "8Gi"},
    {"op": "replace", "path": "/spec/kafka/resources/requests/cpu", "value": "4"}
  ]'

# Rolling restart will happen automatically

Integration with Other Skills

• kafka-iac-deployment: Alternative to K8s (use Terraform for cloud-managed Kafka)
• kafka-observability: Set up Prometheus + Grafana dashboards for K8s Kafka
• kafka-architecture: Cluster sizing and partitioning strategy
• kafka-cli-tools: Test K8s Kafka cluster with kcat

Quick Reference Commands

# Strimzi
kubectl get kafka -n kafka                    # List Kafka clusters
kubectl get kafkatopics -n kafka              # List topics
kubectl get kafkausers -n kafka               # List users
kubectl logs kafka-my-kafka-cluster-0 -n kafka  # Check broker logs

# Confluent
kubectl get kafka -n confluent                # List Kafka clusters
kubectl get schemaregistry -n confluent       # List Schema Registry
kubectl get ksqldb -n confluent               # List ksqlDB

# Port-forward for testing
kubectl port-forward -n kafka svc/my-kafka-cluster-kafka-bootstrap 9092:9092

---

Next Steps After K8s Deployment:

1. Use kafka-observability skill to verify Prometheus metrics and Grafana dashboards
2. Use kafka-cli-tools skill to test cluster with kcat
3. Deploy your producer/consumer applications to K8s
4. Set up GitOps for declarative topic/user management (ArgoCD, Flux)