Camunda 8 Kubernetes Deployment Explained

Deploying Camunda 8 in Kubernetes is the recommended approach for production environments. Since Camunda 8 is designed as a cloud-native distributed system, Kubernetes provides the ideal orchestration layer for scaling, resilience, and fault tolerance.

In this guide, we’ll break down:

  • Camunda 8 architecture in Kubernetes

  • Core components

  • Helm-based deployment

  • Scaling strategy

  • Common pitfalls

  • Production best practices

1️⃣ Why Kubernetes for Camunda 8?

Camunda 8 uses:

  • Zeebe (distributed workflow engine)

  • Operate

  • Tasklist

  • Optimize

  • Connectors

  • Elasticsearch

  • Identity

  • Gateway

These are microservices.

Kubernetes provides:

✅ Horizontal scaling
✅ Self-healing pods
✅ Rolling updates
✅ Resource management
✅ Production-grade orchestration

Camunda 8 is NOT designed like Camunda 7’s embedded engine — it needs orchestration.

2️⃣ Camunda 8 Architecture in Kubernetes

Typical Deployment Layout:

Kubernetes Cluster
│
├── Zeebe Brokers (StatefulSet)
│   ├── Partition 1
│   ├── Partition 2
│   └── Partition 3
│
├── Zeebe Gateway (Deployment)
│
├── Operate
├── Tasklist
├── Optimize
├── Connectors
├── Identity
│
└── Elasticsearch Cluster

Key Concepts:

🔹 Zeebe Brokers

  • Run as StatefulSet

  • Store workflow state

  • Partitioned for scalability

🔹 Gateway

  • Entry point for clients

  • Stateless

  • Can scale independently

🔹 Elasticsearch

  • Required for Operate & Optimize

  • Must be production-sized

3️⃣ Official Deployment Method – Helm Charts

Camunda provides official Helm charts.

Step 1: Add Helm Repository

helm repo add camunda https://helm.camunda.io
helm repo update

Step 2: Install Camunda 8

helm install camunda camunda/camunda-platform

This installs:

  • Zeebe

  • Gateway

  • Operate

  • Tasklist

  • Optimize

  • Identity

  • Elasticsearch

4️⃣ Production Configuration (Important)

Default Helm install is NOT production-ready.

You must configure:

🔹 Zeebe Partitioning

Example:

zeebe:
  clusterSize: 3
  partitionCount: 3
  replicationFactor: 3

Best Practice:

  • 3 brokers minimum

  • Replication factor ≥ 3

🔹 Persistent Volumes

Zeebe is stateful.

Never deploy without:

persistence:
  enabled: true
  size: 32Gi

Use:

  • SSD-backed storage

  • Fast IOPS

🔹 Resource Allocation

Example:

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

Under-sizing causes:

  • Backpressure

  • Incidents

  • Slow processing

5️⃣ Scaling Strategy

Horizontal Scaling

Scale Zeebe brokers:

kubectl scale statefulset zeebe --replicas=5

Scale Gateway:

kubectl scale deployment zeebe-gateway --replicas=3

Scale Workers independently.

6️⃣ Common Mistakes

❌ Running with single broker in production
❌ Not configuring persistent volumes
❌ Using default Elasticsearch settings
❌ Ignoring resource requests
❌ No monitoring

7️⃣ Monitoring & Observability

Use:

  • Prometheus

  • Grafana

  • Kubernetes metrics

  • Camunda Operate

Monitor:

  • Backpressure

  • Partition health

  • Incident rate

  • Gateway latency

8️⃣ Security Best Practices

Enable:

  • TLS

  • Ingress with HTTPS

  • Role-based access

  • OAuth for Identity

  • Network policies

Never expose Zeebe broker directly.

9️⃣ When NOT to Use Kubernetes

  • Small PoC

  • Single-node local setup

  • Development machines

Use Docker Compose for local only.

🔟 Final Thoughts

Camunda 8 was built for distributed, cloud-native environments. Kubernetes allows you to fully leverage:

  • Elastic scaling

  • Fault tolerance

  • Rolling updates

  • High availability

However, production deployment requires:

  • Proper partitioning

  • Resource tuning

  • Persistent storage

  • Monitoring strategy

If you're moving from Camunda 7, remember:
You’re shifting from a monolithic engine to a distributed platform.

💼 Professional Support Available

If you are facing issues in real projects related to enterprise backend development or workflow automation, I provide paid consulting, production debugging, project support, and focused trainings.

Technologies covered include Java, Spring Boot, PL/SQL, Azure,CMS and workflow automation (jBPM, Camunda BPM, RHPAM).


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