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Design Phase 4DDR Template

This template is optimized for decisions made during the Design phase of GISE methodology, focusing on system architecture, technical design patterns, and solution approaches.

Design 4DDR Template​

---
id: 4DDR-NNNN
status: proposed
phase: design
decision: "Clear statement of architectural or design choice made"
context: "Technical constraints, requirements, or problems this design addresses"
consequences: "Impact on maintainability, performance, scalability, and team productivity"
alternatives: ["Alternative architecture 1", "Alternative pattern 2", "Alternative approach 3"]
stakeholders: ["Tech Lead", "Senior Developers", "System Architect", "DevOps"]
author: "Architecture Team"
date: "YYYY-MM-DD"
tags: ["architecture", "design-pattern", "technical-decision"]
technical-debt: "Low/Medium/High - anticipated technical debt from this decision"
maintainability-impact: "How this affects long-term code maintenance"
performance-impact: "Expected performance implications"
scalability-considerations: "How this scales with growth"
---

# 4DDR-NNNN: [Design Decision Title]

## Technical Context

What technical challenge, constraint, or requirement drives this design decision?

### System Requirements
- **Functional Requirements**: What the system must do
- **Non-Functional Requirements**: Performance, security, scalability needs
- **Constraints**: Technology, time, budget, or team skill limitations
- **Dependencies**: External systems, libraries, or services involved

## Design Decision

State the specific architectural pattern, technology choice, or design approach selected.

### Architecture Overview
Provide a high-level view of how this decision fits into the overall system:

```mermaid
graph TD
    A[User Interface] --> B[API Gateway]
    B --> C[Service Layer]
    C --> D[Data Layer]
    
    style C fill:#e1f5fe
    style C stroke:#01579b

Design Rationale​

Why This Approach?​

  • Technical Benefits: Performance, maintainability, scalability advantages
  • Team Alignment: Fits team skills and preferences
  • Ecosystem Fit: Works well with existing technology stack
  • Future Flexibility: Enables future enhancements or changes

Trade-offs Accepted​

  • Complexity: Added complexity vs. benefits gained
  • Performance: Performance implications vs. other benefits
  • Cost: Development, infrastructure, or maintenance costs
  • Risk: Technical risks vs. potential rewards

Implementation Strategy​

Technical Specifications​

  • Interfaces: API contracts, data schemas, or service boundaries
  • Patterns: Specific design patterns or architectural styles used
  • Technologies: Frameworks, libraries, or tools required
  • Standards: Coding standards, conventions, or guidelines

Development Approach​

  • Phases: How implementation will be rolled out
  • Dependencies: What must be completed first
  • Testing Strategy: How the design will be validated
  • Migration Plan: If replacing existing systems

Quality Attributes​

Performance Characteristics​

  • Response Time: Expected latency or throughput
  • Resource Usage: Memory, CPU, or storage requirements
  • Concurrency: How system handles concurrent users/requests
  • Caching Strategy: Data caching and invalidation approach

Maintainability Factors​

  • Code Organization: Structure and modularity
  • Documentation: Required technical documentation
  • Testing: Unit, integration, and system testing approach
  • Monitoring: Observability and debugging capabilities

Security Considerations​

  • Authentication: User identity verification approach
  • Authorization: Access control and permissions model
  • Data Protection: Encryption, masking, or anonymization
  • Audit Trail: Logging and tracking requirements

Risk Assessment​

Technical Risks​

  • Complexity: Risk of over-engineering or under-engineering
  • Performance: Potential performance bottlenecks or issues
  • Scalability: Limitations on system growth or usage
  • Integration: Challenges with external systems or services

Mitigation Strategies​

  • Prototyping: Proof-of-concept or spike solutions
  • Monitoring: Early warning systems for issues
  • Rollback Plans: How to revert if problems arise
  • Alternative Approaches: Backup plans if primary approach fails

Success Metrics​

How will we measure the success of this design decision?

Technical Metrics​

  • Performance: Response times, throughput, error rates
  • Reliability: Uptime, failure rates, recovery times
  • Maintainability: Code quality metrics, bug rates, development velocity
  • Scalability: System capacity, resource efficiency

Team Metrics​

  • Developer Experience: Ease of development, debugging, testing
  • Knowledge Transfer: How quickly new team members can contribute
  • Development Velocity: Impact on feature delivery speed
  • Technical Debt: Accumulation rate and management effectiveness

Documentation Requirements​

Architecture Documentation​

  • System architecture diagrams
  • Component interaction diagrams
  • Data flow diagrams
  • Deployment architecture

Development Documentation​

  • API specifications or contracts
  • Database schema documentation
  • Configuration and setup guides
  • Testing and debugging guides

Review and Evolution​

Review Schedule​

  • Initial Review: 2 weeks after implementation
  • Performance Review: 1 month after production deployment
  • Architecture Review: Quarterly assessment of design decisions

Evolution Triggers​

When should this decision be reconsidered?

  • Performance Issues: If metrics fall below acceptable thresholds
  • Scalability Limits: When system approaches capacity constraints
  • Maintainability Problems: If development velocity significantly decreases
  • Technology Changes: When new technologies offer significant advantages

Link to related design decisions or dependencies:

  • 4DDR-XXXX: Related Architecture Decision
  • 4DDR-YYYY: Technology Stack Decision
  • External Architecture Document (link to external documentation)

## Design Phase Decision Types

### Architecture Decisions

**Common Examples**:
- System architecture patterns (monolith vs microservices)
- Data architecture (database choice, caching strategy)
- Integration patterns (API design, messaging systems)
- Security architecture (authentication, authorization)

**Template Focus**:
- Emphasize system-wide impact and technical trade-offs
- Include performance and scalability considerations
- Detail integration points and dependencies
- Specify quality attributes and success metrics

### Technology Stack Decisions

**Common Examples**:
- Programming language or framework selection
- Database technology choice
- Cloud platform or hosting decisions
- Third-party service integrations

**Template Customization**:
```yaml
# Additional fields for technology decisions
technology-category: ["language", "framework", "database", "platform", "service"]
learning-curve: ["low", "medium", "high"]
community-support: ["excellent", "good", "limited"]
vendor-lock-in: ["none", "low", "medium", "high"]
license-type: ["open-source", "commercial", "enterprise"]
total-cost-ownership: "$X,XXX annually"

Design Pattern Decisions​

Common Examples:

  • API design patterns (REST, GraphQL, gRPC)
  • Data access patterns (Repository, Active Record, Data Mapper)
  • UI/UX patterns (Component libraries, design systems)
  • Error handling and logging patterns

Template Customization:

# Additional fields for pattern decisions
pattern-type: ["behavioral", "structural", "creational", "architectural"]
complexity-level: ["beginner", "intermediate", "advanced"]
team-familiarity: ["high", "medium", "low"]
reusability: ["project-specific", "team-wide", "organization-wide"]

Design Phase Examples​

Example 1: Database Architecture Decision​

---
id: 4DDR-0015
status: accepted
phase: design
decision: "Use PostgreSQL with read replicas for high-traffic e-commerce platform"
context: "Need ACID compliance for transactions, expect 10K+ concurrent users, complex reporting needs"
consequences: "Strong consistency and reliability, but increased infrastructure complexity and cost"
alternatives: ["MongoDB for flexibility", "MySQL for familiarity", "DynamoDB for AWS integration"]
stakeholders: ["Database Architect", "Backend Team", "DevOps Engineer", "Data Analyst"]
author: "Data Architecture Team"
date: "2025-07-22"
tags: ["database", "postgresql", "scalability", "architecture"]
technical-debt: "Low"
maintainability-impact: "High - well-known technology with good tooling"
performance-impact: "High - optimized for complex queries and transactions"
scalability-considerations: "Horizontal scaling via read replicas, vertical scaling for writes"
---

Example 2: API Design Decision​

---
id: 4DDR-0028
status: accepted
phase: design
decision: "Implement GraphQL API with Apollo Server for client-facing endpoints"
context: "Multiple client applications need flexible data fetching, REST API becoming unwieldy"
consequences: "Improved client performance and developer experience, but increased backend complexity"
alternatives: ["Continue with REST API", "Implement gRPC", "Use tRPC for type safety"]
stakeholders: ["Frontend Team", "Backend Team", "Mobile Team", "API Product Manager"]
author: "API Architecture Team"
date: "2025-07-25"
tags: ["api", "graphql", "apollo", "client-server"]
technical-debt: "Medium"
maintainability-impact: "Medium - requires GraphQL expertise for optimal use"
performance-impact: "High - reduces over-fetching and under-fetching"
scalability-considerations: "Query complexity analysis needed, caching more complex"
---

Example 3: Frontend Architecture Decision​

---
id: 4DDR-0042
status: accepted
phase: design
decision: "Adopt micro-frontend architecture using Module Federation"
context: "Large application with multiple teams, need independent deployment and development"
consequences: "Team autonomy and faster deployment, but increased coordination overhead"
alternatives: ["Monolithic frontend", "Separate applications with shared components", "Monorepo approach"]
stakeholders: ["Frontend Architects", "Team Leads", "DevOps", "UX Designer"]
author: "Frontend Architecture Team"
date: "2025-07-28"
tags: ["frontend", "micro-frontend", "module-federation", "team-autonomy"]
technical-debt: "High"
maintainability-impact: "Medium - requires discipline in API design and shared state"
performance-impact: "Medium - runtime overhead but improved caching"
scalability-considerations: "Scales well with team size, complexity increases with micro-frontends"
---

Design Phase Quality Checklist​

Before finalizing a Design phase 4DDR:

Technical Soundness​

  • Architectural coherence - Does the design fit well with existing architecture?
  • Technology compatibility - Are all components compatible and well-integrated?
  • Performance viability - Can the design meet performance requirements?
  • Security adequacy - Are security requirements adequately addressed?

Implementation Feasibility​

  • Team capability - Does the team have skills to implement this design?
  • Timeline realism - Can this be implemented within project constraints?
  • Resource availability - Are necessary tools and infrastructure available?
  • Risk management - Are major risks identified with mitigation plans?

Long-term Viability​

  • Maintainability - Can the design be maintained and evolved over time?
  • Scalability - Will the design handle expected growth?
  • Flexibility - Can the design adapt to changing requirements?
  • Technical debt - Is technical debt at acceptable levels?

Integration with GISE Workflow​

Blueprint Integration​

When creating blueprints that reference design decisions:

## Architecture Context
Based on 4DDR-0015 (Database Architecture), our system will use:
- PostgreSQL primary database for ACID compliance
- Read replicas for query performance scaling
- Connection pooling for efficient resource usage

This architecture supports our requirements for...

Handoff to Develop Phase​

Design 4DDRs should provide:

  • Technical specifications clear enough for implementation
  • Interface contracts between system components
  • Quality standards for code and testing
  • Performance targets and monitoring requirements

Connect design decisions to:

  • Technical specifications and API documentation
  • Architecture diagrams and system models
  • Prototype code or proof-of-concept implementations
  • Performance and load testing plans

Common Design Phase Pitfalls​

Over-Architecture​

Problem: Creating overly complex designs for simple problems Solution: Include complexity assessment and justification in the 4DDR

Under-Specification​

Problem: Design decisions lack sufficient detail for implementation Solution: Include technical specifications and implementation guidance

Technology Bias​

Problem: Choosing technologies based on preferences rather than requirements Solution: Document evaluation criteria and objective technology assessment

Ignoring Non-Functional Requirements​

Problem: Focusing only on functional requirements while ignoring performance, security, etc. Solution: Explicitly address quality attributes and non-functional requirements

The Design phase 4DDR template ensures that architectural and design decisions are well-reasoned, technically sound, and provide clear guidance for the implementation team.