From Kubernetes to CI Pipeline: A Strategic Infrastructure Migration

Introduction

Sometimes the most important architectural decisions are the ones that go against conventional wisdom. This post chronicles a strategic decision to migrate from Kubernetes deployment to CI pipeline-based deployment, the reasoning behind it, and the lessons learned.

The Original Setup

Our wireless monitoring service initially followed standard microservice deployment patterns:

• Kubernetes pods across multiple datacenters (CH1, DC2)
• Traditional service mesh architecture
• Complex networking and service discovery
• Multi-replica deployments for high availability

The Inflection Point

During development, several factors made us reconsider our deployment strategy:

1. Service Classification Analysis

The wireless query exporter was classified as a non-critical service: - No direct customer impact if temporarily unavailable - Monitoring data, not core business logic - Recovery time measured in minutes, not seconds

2. Operational Complexity

Kubernetes deployment introduced unnecessary complexity: - Multiple datacenter coordination - Network policy management
- Service mesh overhead - Resource allocation across clusters

3. Resource Efficiency

Multi-replica deployment was overkill: - Single replica could handle the workload efficiently - Database queries were the bottleneck, not application processing - Minimal memory and CPU requirements

The Migration Strategy

Phase 1: Infrastructure Assessment

# Before: Multi-cluster deployment
meta-prod.yml:
 replicas: 2
 clusters: [ch1, dc2] # After: Single datacenter optimization 
meta-prod.yml:
 replicas: 1
 clusters: [ch1]

Phase 2: Deployment Pipeline Transition

The key commit that changed everything:

# Jenkinsfile modification
pipeline {
 agent any
 stages {
 stage('Deploy') {
 steps {
 // Switched from kubectl to CI pipeline deployment
 sh 'deploy-via-pipeline.sh'
 }
 }
 }
}

Phase 3: Secret Management Optimization

During migration, we also improved secret management:

# Before: Manual secret definitions
spec:
 containers:
 - name: app
 env:
 - name: DB_PASSWORD
 value: "hardcoded-value" # Bad practice # After: Vault integration
spec:
 containers:
 - name: app
 envFrom:
 - secretRef:
 name: wireless-secrets # Vault-managed

Technical Implementation Details

CI Pipeline Deployment

The new deployment strategy uses: - Jenkins pipeline for orchestration - Docker containers for consistency - Environment-specific configurations for flexibility - Health check integration for reliability

Configuration Management

Streamlined configuration management:

# main.py - Simplified health monitoring
class HealthQueryExporterScript(QueryExporterScript): async def on_application_startup(self, application: Application): application.router.add_get("/health", self._handle_health) await super().on_application_startup(application)

Monitoring Integration

Maintained monitoring capabilities: - Prometheus metrics endpoint - Health check endpoint - Integration with existing alerting systems

Results & Impact

1. Reduced Complexity

• 50% reduction in deployment configuration
• Simplified troubleshooting and debugging
• Faster deployment cycles

2. Improved Reliability

• Single point of failure, but faster recovery
• Simplified rollback procedures
• Clearer error diagnosis

3. Resource Efficiency

• 50% reduction in resource allocation
• Lower operational overhead
• Simplified capacity planning

4. Operational Benefits

• Faster deployments (minutes vs. hours)
• Simpler monitoring and alerting
• Reduced team cognitive load

When This Strategy Makes Sense

This migration strategy works well for:

✅ Good Candidates

• Non-critical services
• Low-traffic applications
• Monitoring and logging services
• Development and staging environments
• Services with simple scaling requirements

❌ Not Recommended For

• Customer-facing APIs
• High-availability requirements
• Services requiring auto-scaling
• Complex networking requirements
• Stateful applications requiring coordination

Lessons Learned

1. Right-Size Your Architecture

Not every service needs the full complexity of microservice architecture. Match your deployment strategy to your service requirements.

2. Consider Operational Overhead

Kubernetes is powerful but comes with operational complexity. For some services, simpler deployment strategies provide better ROI.

3. Security Doesn't Have to Suffer

We maintained security best practices (Vault integration, secret management) while simplifying deployment.

4. Monitoring Remains Critical

Regardless of deployment strategy, proper monitoring and health checks are essential.

Migration Checklist

If you're considering a similar migration:

• [ ] Assess service criticality - Is high availability required?
• [ ] Evaluate traffic patterns - Can a single instance handle the load?
• [ ] Review dependencies - Are there complex networking requirements?
• [ ] Consider team expertise - What's the operational complexity preference?
• [ ] Plan rollback strategy - How quickly can you revert if needed?
• [ ] Update monitoring - Ensure observability during transition
• [ ] Test thoroughly - Validate behavior in staging first

Alternative Approaches

Other options we considered:

1. Kubernetes with minimal resources - Stay in K8s but reduce replicas
2. Serverless deployment - Functions for periodic metric collection
3. Bare metal deployment - Direct server deployment
4. Hybrid approach - K8s for prod, CI pipeline for dev/staging

Future Considerations

This decision isn't permanent. We'll reassess if: - Service becomes business-critical - Traffic patterns change significantly - Team operational preferences evolve - Technology landscape shifts

Conclusion

The migration from Kubernetes to CI pipeline deployment demonstrates that the best architecture is the one that matches your actual requirements, not necessarily the most sophisticated one.

Key takeaways: - Match complexity to requirements - Don't over-engineer - Consider operational overhead - Simple can be better - Maintain observability - Monitoring remains crucial regardless of deployment strategy - Stay flexible - Architectural decisions can evolve

Sometimes stepping back from cutting-edge solutions leads to more maintainable, efficient systems. The goal isn't to use the newest technology—it's to solve business problems effectively.

This migration reduced deployment complexity by 50% while maintaining service reliability and improving team velocity. Sometimes the best path forward is intentionally simple.