---

name: strimzi compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont
• config description: '"Provides Strimzi in Kafka on Kubernetes - Apache Kafka for cloud-native environments"' license: MIT maturity: stable metadata: domain: cncf output-format: manifests role: reference scope: infrastructure triggers: apache, container orchestration, event streaming, k8s, kafka, message streaming, strimzi, kubernetes archetypes:
  • educational
  • strategic anti_triggers:
  • brainstorming
  • vague ideation
  • non-containerized architecture response_profile: verbosity: medium directive_strength: low abstraction_level: strategic version: "1.0.0"

---

related-skills: cncf-argo, cncf-artifact-hub, cncf-aws-eks, cncf-azure-aks

Strimzi in Cloud-Native Engineering

Category: Streaming & Messaging
Status: Active
Stars: 2,900
Last Updated: 2026-04-22
Primary Language: Java
Documentation: Kafka on Kubernetes - Apache Kafka for cloud-native environments

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Purpose and Use Cases

Strimzi is a core component of the cloud-native ecosystem, serving as cloud-native environments

What Problem Does It Solve?

Strimzi addresses the challenge of running Apache Kafka on Kubernetes with native integration. It provides Kafka-native on Kubernetes, automated management, and cloud-native scalability.

When to Use This Project

Use Strimzi when need Kafka on Kubernetes, want managed Kafka, or require Kafka at scale. Not ideal for simple deployments or when event-driven architecture, Kafka-native deployment, or Kafka scaling requirements.

Key Use Cases

• Kafka on Kubernetes Deployments
• Event-Driven Architecture
• Real-Time Data Streaming
• Kafka Multi-Tenancy
• Kafka Migration to Cloud

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Architecture Design Patterns

Core Components

• Cluster Operator: Manages Kafka clusters
• Kafka Node: Kafka broker instance
• ZooKeeper Node: ZooKeeper ensemble member
• Topic Operator: Manages topics
• User Operator: Manages users and ACLs

Component Interactions

1. Operator → Kafka: Operator manages Kafka cluster
2. Kafka → ZooKeeper: Kafka stores metadata in ZooKeeper
3. Topic Operator → Kafka: Topic operator manages topics
4. User Operator → Kafka: User operator manages users

Data Flow Patterns

1. Cluster Creation: Create Kafka CR → Operator creates cluster → Kafka ready
2. Topic Creation: Create Topic CR → Topic operator → Kafka topic
3. Message Flow: Producer → Kafka → Consumer
4. Configuration Sync: Config updates → Kafka pods

Design Principles

• Kafka Native: Full Kafka compatibility
• Automated Management: Full lifecycle management
• Kubernetes Native: Deep integration
• Operational Simplicity: Easy to operate

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Integration Approaches

Integration with Other CNCF Projects

• Kafka: Apache Kafka core
• ZooKeeper: Metadata storage
• Kubernetes: Platform integration
• Prometheus: Metrics collection

API Patterns

• Kafka CRD: Kafka cluster definition
• Topic CRD: Topic definition
• User CRD: User definition
• Kafka Connect CRD: Connect cluster definition

Configuration Patterns

• Kafka YAML: Cluster configuration
• Topic YAML: Topic configuration
• User YAML: User configuration
• Connect YAML: Connect configuration

Extension Mechanisms

• Custom Connectors: Add Kafka connectors
• Custom Metrics: Add custom metrics
• Custom Config: Custom Kafka config

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Common Pitfalls and How to Avoid Them

Misconfigurations

• Disk Space: Kafka log storage exhaustion
  • How to Avoid: Monitor disk space, configure retention, scale storage
• Network Issues: Network partitions
  • How to Avoid: Configure network isolation, monitor latency

Performance Issues

• Upgrade Issues: Kafka version upgrade problems
  • How to Avoid: Test upgrades, follow upgrade path, backup
• Schema Registry: Schema registry issues
  • How to Avoid: Configure registry, monitor health

Operational Challenges

• TLS Issues: TLS configuration problems
  • How to Avoid: Verify certificates, check TLS settings
• Replication Issues: Replication lag
  • How to Avoid: Monitor replication, check network

Security Pitfalls

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Coding Practices

Idiomatic Configuration

• Declarative Configuration: Define Kafka in YAML
• Topic Management: Use KafkaTopic CRs
• Monitoring Integration: Integrate with Prometheus

API Usage Patterns

• kubectl apply: Apply Kafka configurations
• strimzi-cli: Strimzi-specific commands
• Kafka CLI: Kafka tools
• kubectl describe: Describe Kafka resources

Observability Best Practices

• Kafka Metrics: Monitor Kafka cluster metrics
• Operator Metrics: Monitor operator health
• Topic Metrics: Track topic statistics

Testing Strategies

• Integration Tests: Test Kafka functionality
• Failover Tests: Test cluster failover
• Performance Tests: Validate performance

Development Workflow

• Local Development: Use minikube or kind
• Debug Commands: Check Kafka and operator logs
• Test Environment: Set up test cluster
• CI/CD Integration: Automate testing
• Monitoring Setup: Configure observability
• Documentation: Maintain documentation

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Fundamentals

Essential Concepts

• Kafka Cluster: Kafka cluster definition
• Cluster Operator: Cluster management
• Kafka Broker: Kafka broker instance
• ZooKeeper: ZooKeeper ensemble
• Topic Operator: Topic management
• User Operator: User management
• Kafka Connect: Kafka Connect cluster
• Kafka Mirror Maker: Data replication

Terminology Glossary

• Cluster Operator: Manages Kafka clusters
• Kafka Broker: Kafka server instance
• Topic Operator: Manages topics
• User Operator: Manages users
• Mirror Maker: Data replication

Data Models and Types

• Kafka: Kafka cluster definition
• KafkaTopic: Topic definition
• KafkaUser: User definition
• KafkaConnect: Connect cluster

Lifecycle Management

• Cluster Creation: Create Kafka CR → Operator creates → Cluster ready
• Topic Creation: Create Topic CR → Operator creates → Topic exists
• Upgrade Process: Update Kafka version → Rolling restart → New version
• Failure Recovery: Detect failure → Heal → Restore

State Management

• Cluster State: Ready, error, or scaling
• Broker State: Running, stopped, or starting
• Topic State: Created, updating, or deleted
• Operator State: Running or error

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Scaling and Deployment Patterns

Horizontal Scaling

• Broker Scaling: Add/remove brokers
• Consumer Scaling: Scale consumer groups
• Topic Scaling: Replication factor changes
• Cluster Scaling: Add nodes to cluster

High Availability

• Broker HA: Multiple brokers per partition
• Replication HA: ISR configuration
• Operator HA: Multiple operator instances
• ZooKeeper HA: ZooKeeper ensemble

Production Deployments

• Cluster Setup: Deploy Kafka cluster
• Network Configuration: Configure network
• Security Setup: Enable TLS, SASL, RBAC
• Monitoring Setup: Configure metrics
• Logging Setup: Centralize logs
• Backup Strategy: Configure backups
• Resource Quotas: Set resource limits
• Performance Tuning: Optimize Kafka settings

Upgrade Strategies

• Kafka Upgrade: Upgrade Kafka version
• Operator Upgrade: Upgrade operator
• Broker Upgrade: Rolling broker upgrade
• Testing: Verify functionality

Resource Management

• CPU Resources: Broker CPU limits
• Memory Resources: Broker memory limits
• Storage Resources: Log storage configuration
• Network Resources: Network configuration

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Additional Resources

• Official Documentation: https://strimzi.io/docs/
• GitHub Repository: Check the project's official documentation for repository link
• CNCF Project Page: cncf.io/projects/cncf-strimzi/
• Community: Check the official documentation for community channels
• Versioning: Refer to project's release notes for version-specific features

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Troubleshooting

Common Issues

1. Deployment Failures

   • Check pod logs for errors
   • Verify configuration values
   • Ensure network connectivity

2. Performance Issues

   • Monitor resource usage
   • Adjust resource limits
   • Check for bottlenecks

3. Configuration Errors

   • Validate YAML syntax
   • Check required fields
   • Verify environment-specific settings

4. Integration Problems

   • Verify API compatibility
   • Check dependency versions
   • Review integration documentation

Getting Help

• Check official documentation
• Search GitHub issues
• Join community channels
• Review logs and metrics Content generated automatically. Verify against official documentation before production use.

Examples

Basic Configuration

# Basic configuration example
apiVersion: v1
kind: ConfigMap
metadata:
  name: {{project_name}}-config
  namespace: default
data:
  # Configuration goes here
  config.yaml: |
    # Base configuration
    # Add your settings here

Kubernetes Deployment

# Kubernetes deployment for {{project_name}}
apiVersion: apps/v1
kind: Deployment
metadata:
  name: {{project_name}}
  namespace: default
spec:
  replicas: 1
  selector:
    matchLabels:
      app: {{project_name}}
  template:
    metadata:
      labels:
        app: {{project_name}}
    spec:
      containers:
      - name: {{project_name}}
        image: {{project_name}}:latest
        ports:
        - containerPort: 8080
        resources:
          limits:
            memory: "128Mi"
            cpu: "500m"

Kubernetes Service

# Kubernetes service for {{project_name}}
apiVersion: v1
kind: Service
metadata:
  name: {{project_name}}
  namespace: default
spec:
  selector:
    app: {{project_name}}
  ports:
  - protocol: TCP
    port: 80
    targetPort: 8080
  type: ClusterIP

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When to Use

Use this skill when:

• Integrating a CNCF project into Kubernetes infrastructure — You need to configure, deploy, or troubleshoot a cloud-native tool within a cluster
• Designing cloud-native architecture — You are selecting and integrating CNCF tools to solve specific infrastructure challenges
• Resolving operational issues — A CNCF component is misbehaving, underperforming, or needs configuration changes

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Core Workflow

1. Assess Requirements — Understand the use case, scale, integration needs, and existing infrastructure. Checkpoint: Document requirements, constraints, and success criteria.

2. Design Architecture — Plan component interactions, data flow, and deployment strategy using cloud-native best practices. Checkpoint: Verify the architecture addresses all requirements and follows CNCF conventions.

3. Implement & Configure — Create manifests, configurations, and deployment scripts. Include resource limits, health checks, and observability hooks. Checkpoint: Validate all YAML against schema and test in a staging environment.

4. Deploy & Monitor — Apply manifests to the cluster, verify component health, and confirm observability is working. Checkpoint: Confirm all pods/services are running, probes passing, and metrics/alerts configured.

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Constraints

MUST DO

• Include at least one complete working YAML manifest example
• Note when content is auto-generated vs. manually verified
• Reference relevant CNCF project documentation

MUST NOT DO

• Deploy manifests without testing in a staging environment first
• Use deprecated API versions (e.g., apps/v1beta1)
• Omit resource limits and requests in Kubernetes manifests