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ADR-001: Architecture Style Selection for QR Contact Card Generator (Part 1 - Human Readable)

Document Specification Block​

Document: ADR-001-architecture-style-part1-human
Version: 1.0.0
Purpose: Define and justify the microservices architecture style for the QR Contact Card Generator platform
Audience: Technical stakeholders, architects, business leaders, developers
Date Created: 2025-10-03
Date Updated: 2025-10-03
Status: PROPOSED
Type: DUAL-PART (Part 1 of 2)
Related: ADR-001-architecture-style-part2-technical
Score Required: 100% (40/40 points)

Executive Summary​

We have chosen a microservices architecture with event-driven communication for the QR Contact Card Generator platform. This decision enables independent scaling of viral mechanics, supports our aggressive growth targets (100K users in 6 months), and allows autonomous agents to develop and maintain individual services independently.

Context and Problem Statement​

The QR Contact Card Generator must support:

  • Viral growth mechanics targeting K-factor 4.5+
  • 10,000+ concurrent users generating QR codes
  • Multi-channel sharing (email, SMS, WhatsApp, LinkedIn)
  • Real-time analytics for growth optimization
  • 99.95% uptime with <50ms global latency
  • Agent-based development where AI agents can autonomously build features

Traditional monolithic architectures would create bottlenecks in our viral email system and prevent independent scaling of compute-intensive QR generation.

Decision Outcome​

We will implement a microservices architecture with the following characteristics:

  1. Service Decomposition:

    • API Gateway Service (request routing)
    • Authentication Service (JWT-based auth)
    • Card Management Service (CRUD operations)
    • QR Generation Service (WASM-based generation)
    • Viral Engine Service (multi-channel sharing)
    • Analytics Service (K-factor calculation)

  2. Communication Pattern:

    • Synchronous REST for user-facing operations
    • Asynchronous events for viral mechanics
    • gRPC for inter-service communication

  3. Data Management:

    • Service-specific databases (bounded contexts)
    • Event sourcing for viral tracking
    • CQRS for read-heavy operations

Architectural Diagram​

Considered Alternatives​

Alternative 1: Monolithic Architecture​

Pros:

  • Simpler initial development
  • Single deployment unit
  • Lower operational complexity

Cons:

  • Cannot scale viral engine independently
  • Single point of failure
  • 30+ second email blocking would freeze entire app
  • Difficult for agents to work on isolated features

Verdict: Rejected due to viral scaling requirements

Alternative 2: Serverless Functions​

Pros:

  • Infinite scaling potential
  • Pay-per-execution model
  • No infrastructure management

Cons:

  • Cold start latency (2-5 seconds)
  • Difficult state management
  • Vendor lock-in
  • Complex debugging for agents

Verdict: Rejected due to cold start impact on user experience

Alternative 3: Service-Oriented Architecture (SOA)​

Pros:

  • Service isolation
  • Reusable components
  • Enterprise proven

Cons:

  • Heavy XML/SOAP overhead
  • Tight coupling through ESB
  • Not cloud-native
  • Poor fit for event-driven patterns

Verdict: Rejected as outdated for cloud-native development

Consequences​

Positive Consequences​

  1. Independent Scaling: Viral engine can scale to 1000 instances during campaigns
  2. Fault Isolation: Email service failure doesn't affect QR generation
  3. Technology Diversity: Can use Rust for performance, Node.js for I/O
  4. Agent Development: Each service has clear boundaries for AI development
  5. Cost Optimization: Scale only what's needed (QR service during events)

Negative Consequences​

  1. Operational Complexity: Multiple services to monitor and maintain
  2. Network Latency: Inter-service calls add 5-10ms overhead
  3. Data Consistency: Eventual consistency requires careful design
  4. Development Overhead: Service contracts and API versioning

Risk Mitigation​

  • Complexity: Kubernetes operators for automated management
  • Latency: Service mesh with circuit breakers
  • Consistency: Event sourcing with saga pattern
  • Development: OpenAPI contracts with code generation

Implementation Roadmap​

Phase 1: Core Services (Weeks 1-2)​

  • Authentication Service with JWT
  • Card Management Service with PostgreSQL
  • Basic API Gateway

Phase 2: Viral Engine (Weeks 3-4)​

  • Event-driven Viral Service
  • Multi-channel Share Service
  • Pub/Sub integration

Phase 3: Analytics & Optimization (Weeks 5-6)​

  • Analytics Service with BigQuery sink
  • Real-time K-factor calculation
  • A/B testing framework

Success Metrics​

MetricTargetMeasurement
Service Uptime99.95% per servicePrometheus/Grafana
API Latency<50ms p95Distributed tracing
Deployment FrequencyDaily per serviceCI/CD metrics
Viral Processing<100ms to queueEvent timestamp
Cost per User<$0.005/monthCloud billing
  • ADR-002: PostgreSQL for service databases
  • ADR-003: Cloud Pub/Sub for event bus
  • ADR-004: React with TypeScript for frontend
  • ADR-005: WASM for client-side QR generation

References​

  1. Martin Fowler - Microservices
  2. Sam Newman - Building Microservices
  3. Google Cloud - Microservices Architecture
  4. 12 Factor App Methodology

Review and Approval​

QA Review Block:
  Review Date: TBD
  Reviewer: TBD
  Part 1 Score: TBD/40
  Part 2 Score: TBD/40
  Status: AWAITING REVIEW
  Comments: |
    This ADR proposes microservices architecture optimized for:
    - Viral growth mechanics requiring independent scaling
    - Agent-based development with clear service boundaries
    - 99.95% uptime through fault isolation

Next Document: ADR-001 Part 2 - Technical Implementation Blueprint