Workflow Architecture - How KubeOrch Deploys to Kubernetes

[mohit-nagaraj]

Executive Summary

KubeOrch implements a template-driven deployment system that transforms visual workflows into Kubernetes resources. Think of it as "Helm charts meets visual programming" - users design workflows visually, and we handle the complex Kubernetes operations.

Architecture Overview

How It Works

1. Visual Design Phase (UI)

Users create workflows by:

• Dragging deployment nodes onto canvas
• Configuring each node (image, replicas, ports)
• Connecting nodes to define dependencies
• Setting cluster targets

Example Node Data:

{
  "id": "node-1",
  "type": "deployment",
  "data": {
    "templateId": "core/deployment",
    "image": "nginx:latest",
    "replicas": 3,
    "port": 80
  }
}

2. Template System (Backend)

We use Go templates (like Helm) to transform configurations into Kubernetes manifests:

# templates/core/deployment/template.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: {{ .Name }}
spec:
  replicas: {{ .Replicas }}
  template:
    spec:
      containers:
      - name: {{ .Name }}
        image: {{ .Image }}
        ports:
        - containerPort: {{ .Port }}

3. Execution Flow

UI Action: "Run Workflow"
           ↓
1. Save workflow version (snapshot)
           ↓
2. Create workflow_run record
           ↓
3. For each deployment node:
   a. Extract parameters
   b. Validate inputs
   c. Render template → YAML
   d. Apply to Kubernetes
   e. Track status
           ↓
4. Update workflow_run with results

4. The Magic: Universal Manifest Applier

Instead of writing code for each Kubernetes resource type, we built a universal applier using Kubernetes dynamic client:

// This single function handles ANY Kubernetes resource
func (a *ManifestApplier) ApplyYAML(ctx context.Context, yamlContent []byte)

This means:

• ✅ Works with Deployments, Services, ConfigMaps, Secrets, etc.
• ✅ No code changes needed for new resource types
• ✅ Just add a new YAML template
• ✅ Handles create AND update (like kubectl apply)

Key Components

1. Template Engine (pkg/template/engine.go)

• Renders Go templates with user values
• Supports helper functions (default values, conditionals)
• Template inheritance ready

2. Manifest Applier (pkg/applier/manifest_applier.go)

• Uses Kubernetes dynamic client
• Applies any valid YAML to cluster
• Handles create/update automatically
• Returns detailed apply results

3. Workflow Executor (services/workflow_executor.go)

• Orchestrates the entire deployment
• Manages workflow run lifecycle
• Handles node execution sequentially (parallel coming soon)
• Updates MongoDB with progress

4. Resource Validator (pkg/validator/resource_validator.go)

• Validates parameters before deployment
• Resource-specific rules (ports, image formats, etc.)
• Extensible for new resource types

Why This Architecture?

1. Flexibility

• Add ANY Kubernetes resource without code changes
• Just create a new template file
• System automatically supports it

2. Familiar Patterns

• Similar to Helm (templates)
• Like ArgoCD (GitOps-ready)
• kubectl apply behavior

3. Production Ready

• Full audit trail in MongoDB
• Version control for workflows
• Rollback capabilities
• Multi-cluster support

4. Developer Friendly

• Clear separation of concerns
• Easy to extend
• Well-documented flow
• Standard Kubernetes patterns

Adding New Resources

To add a new Kubernetes resource (e.g., Service):

1. Create template:

vim templates/core/service/template.yaml

2. Add validation (optional):

// In resource_validator.go
case "service", "core/service":
    v.validateService(params, result)

3. That's it! Use from UI:

{
  "templateId": "core/service",
  "port": 80,
  "serviceType": "LoadBalancer"
}

Current Capabilities

✅ Supported:

• Deployments
• Services (template ready)
• Any Kubernetes resource (via templates)
• Multi-cluster deployments
• Bearer token authentication
• Version control
• Execution tracking

🚧 Coming Soon:

• Parallel node execution
• Conditional logic nodes
• Webhook triggers
• Scheduled runs
• Template marketplace

Security

• Authentication: JWT tokens for API
• Cluster Access: Encrypted credentials in MongoDB
• Validation: Input sanitization before template rendering
• Audit: Complete trail of who deployed what, when

Performance

• Template Rendering: < 10ms
• Kubernetes Apply: Network dependent (typically < 1s)
• MongoDB Operations: Indexed, < 50ms
• Concurrent Workflows: Supported

Best Practices

1. Always validate inputs before rendering templates
2. Use namespaces to isolate deployments
3. Set resource limits in templates
4. Version your templates alongside code
5. Monitor workflow runs for failures

Comparison with Other Tools

Feature	KubeOrch	Helm	ArgoCD	Flux
Visual Workflow	✅	❌	❌	❌
Template System	✅	✅	❌	❌
Dynamic Resources	✅	❌	✅	✅
Built-in UI	✅	❌	✅	❌
Version Control	✅	✅	✅	✅

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Summary

KubeOrch provides a visual, template-driven approach to Kubernetes deployments. By combining the simplicity of visual workflows with the power of Kubernetes templates, we make complex orchestration accessible to all team members while maintaining the flexibility that DevOps teams need.

The key insight: Don't write code for each resource type - use templates and a universal applier.

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For implementation details, see /docs/DEPLOYMENT_FLOW.md