📜 Diagram for Lesson 7: Workflow Input/Output
Visualizing Complex Data Flow Patterns
Complex data flow patterns for workflow input/output handling with rich data structures and validation
Order Processing Data Flow
flowchart TD
A[Client Request] --> B[OrderRequest Data Structure]
subgraph "Rich Input Data Model"
B --> C[Order ID & Customer ID]
B --> D[List of OrderItems]
B --> E[Shipping Address]
B --> F[Payment Method]
B --> G[Priority & Metadata]
D --> H[Product ID, Quantity, Price]
E --> I[Street, City, State, ZIP]
F --> J[Type, Card Info, Account]
end
B --> K[OrderProcessingWorkflow.processOrder]
style A fill:#e1f5fe
style B fill:#f3e5f5
style K fill:#e8f5e8
Workflow Processing Pipeline
flowchart TD
subgraph "Workflow Processing Steps"
K[OrderProcessingWorkflow] --> L[Step 1: Input Validation]
L --> M[Step 2: Order Processing]
M --> N[Step 3: Result Aggregation]
L --> O[Validate Order Items]
L --> P[Validate Address Format]
L --> Q[Validate Payment Method]
M --> R[Calculate Totals]
M --> S[Process Each Item]
M --> T[Generate Tracking Info]
end
N --> U[OrderResult Data Structure]
style L fill:#fff3e0
style M fill:#e8f5e8
style N fill:#f3e5f5
style U fill:#e3f2fd
Rich Output Data Model
flowchart TD
subgraph "Rich Output Data Model"
U[OrderResult] --> V[Order Metadata]
U --> W[Processing Steps History]
U --> X[Tracking Information]
U --> Y[Final Status]
V --> Z[Order ID, Customer, Timestamps]
W --> AA[Step Status, Duration, Errors]
X --> BB[Tracking Number, Carrier, ETA]
Y --> CC[SUCCESS, PARTIAL, FAILED]
end
U --> LL[Client Response]
style V fill:#e8f5e8
style W fill:#fff3e0
style X fill:#f3e5f5
style Y fill:#ffebee
style LL fill:#e1f5fe
Data Design Patterns
graph TB
subgraph "Data Validation Patterns"
DD[Input Validation]
EE[Business Rule Validation]
FF[Output Enrichment]
GG[Error Aggregation]
end
subgraph "Serialization Considerations"
HH[JSON Serializable]
II[Version Compatible]
JJ[Size Optimized]
KK[Type Safe]
end
DD --> HH
EE --> II
FF --> JJ
GG --> KK
style DD fill:#fff3e0
style HH fill:#f3e5f5
style EE fill:#e8f5e8
style II fill:#e3f2fd
💡 Key Data Flow Insights
Input Design Patterns:
- ✅ Rich data structures with clear relationships
- ✅ Nested objects for complex business domains
- ✅ Type safety through data classes and enums
- ✅ Validation boundaries at workflow entry points
Processing Patterns:
- ✅ Step-by-step validation with clear error reporting
- ✅ Business logic separation between validation and processing
- ✅ Tracking and audit of all processing steps
Output Design Best Practices
Comprehensive Results:
- ✅ Status information for success/failure determination
- ✅ Processing history for audit and debugging
- ✅ Rich metadata for downstream system integration
- ✅ Error context for meaningful failure responses
Serialization Requirements:
- ✅ JSON compatible for API integration
- ✅ Version safe for schema evolution
- ✅ Size optimized for performance
- ✅ Type safe for compile-time validation
🚀 Production Benefits
This data flow pattern provides:
- ✅ Type safety throughout the entire workflow lifecycle
- ✅ Rich observability through comprehensive result tracking
- ✅ Easy debugging with detailed processing history
- ✅ API compatibility through structured input/output
- ✅ Schema evolution support for changing requirements
Building robust, maintainable data flows! 🎉