ADR-002: Microservices vs Monolith Architecture

Status

Accepted

Context

We need to decide on the overall application architecture for the GISE methodology platform. The decision between a monolithic architecture and microservices architecture will significantly impact development velocity, operational complexity, scalability, and team organization.

Current Situation

• Team size: 8-12 developers across backend, frontend, and DevOps
• Expected load: 10,000+ concurrent users at peak
• Feature scope: User management, project templates, code generation, collaboration tools
• Deployment frequency: Multiple deployments per week desired
• Development stage: Early product development with rapidly evolving requirements

Key Considerations

• Development Speed: Need to move quickly in early stage development
• Team Coordination: Relatively small team that can coordinate effectively
• Technology Consistency: Benefits of standardized technology stack
• Operational Complexity: Limited DevOps resources initially
• Future Scalability: Need to support growth to 100,000+ users
• Feature Isolation: Some features may need independent scaling

Decision

We will start with a Modular Monolith architecture and evolve to microservices as the product and team matures.

Rationale

Phase 1: Modular Monolith (Months 1-12)

Architecture Overview:

┌─────────────────────────────────────────────────────┐
│                GISE Platform API                    │
├─────────────────────────────────────────────────────┤
│  ┌─────────────┐ ┌─────────────┐ ┌─────────────┐    │
│  │    User     │ │   Project   │ │  Template   │    │
│  │   Module    │ │   Module    │ │   Module    │    │
│  └─────────────┘ └─────────────┘ └─────────────┘    │
├─────────────────────────────────────────────────────┤
│  ┌─────────────┐ ┌─────────────┐ ┌─────────────┐    │
│  │    Auth     │ │   Search    │ │  Analytics  │    │
│  │   Module    │ │   Module    │ │   Module    │    │
│  └─────────────┘ └─────────────┘ └─────────────┘    │
├─────────────────────────────────────────────────────┤
│              Shared Infrastructure                  │
│         (Database, Cache, Logging)                  │
└─────────────────────────────────────────────────────┘

Module Boundaries:

// Clear module interfaces prevent tight coupling
interface UserModule {
  createUser(userData: CreateUserRequest): Promise<User>;
  authenticateUser(credentials: Credentials): Promise<AuthResult>;
  updateUser(userId: string, updates: UserUpdates): Promise<User>;
}

interface ProjectModule {
  createProject(projectData: CreateProjectRequest): Promise<Project>;
  generateCode(projectId: string, template: Template): Promise<GeneratedCode>;
  shareProject(projectId: string, permissions: Permissions): Promise<void>;
}

interface TemplateModule {
  searchTemplates(query: SearchQuery): Promise<Template[]>;
  createTemplate(templateData: CreateTemplateRequest): Promise<Template>;
  validateTemplate(template: Template): Promise<ValidationResult>;
}

Benefits of Starting with Modular Monolith

Development Velocity:

• Single codebase simplifies development setup and debugging
• Shared code and utilities reduce duplication
• Atomic transactions across modules simplify data consistency
• Integrated testing environment with all components

Team Efficiency:

• Easier coordination with small team
• Simplified deployment and rollback procedures
• Single technology stack reduces context switching
• Unified monitoring and logging

Early Stage Advantages:

• Faster iteration on product-market fit
• Easier to refactor boundaries as requirements evolve
• Lower initial operational overhead
• Simpler CI/CD pipeline

Module Design Principles

Clear Boundaries:

// Each module has well-defined interfaces
export class ProjectService {
  constructor(
    private userService: UserService,    // Dependency injection
    private templateService: TemplateService,
    private database: Database
  ) {}

  async createProject(userId: string, projectData: CreateProjectRequest): Promise<Project> {
    // Validate user permissions
    await this.userService.validateUser(userId);
    
    // Use templates for validation
    const template = await this.templateService.getTemplate(projectData.templateId);
    
    // Create project with transaction
    return this.database.transaction(async (tx) => {
      return tx.projects.create({
        ownerId: userId,
        ...projectData,
        templateId: template.id
      });
    });
  }
}

Database Organization:

-- Logical separation within single database
-- User Module Tables
CREATE SCHEMA user_management;
CREATE TABLE user_management.users (...);
CREATE TABLE user_management.user_sessions (...);

-- Project Module Tables  
CREATE SCHEMA project_management;
CREATE TABLE project_management.projects (...);
CREATE TABLE project_management.project_collaborators (...);

-- Template Module Tables
CREATE SCHEMA template_system;
CREATE TABLE template_system.templates (...);
CREATE TABLE template_system.template_categories (...);

Phase 2: Selective Microservices (Months 6-18)

As the system matures, extract specific modules that benefit from independent scaling:

Candidates for Microservice Extraction:

1. Code Generation Service

   • CPU-intensive workloads
   • Independent scaling needs
   • Potential for different technology stack (Python for AI/ML)

2. Search Service

   • High read volume
   • Different performance characteristics
   • Potential integration with specialized search engines

3. Analytics Service

   • Different data patterns (write-heavy, batch processing)
   • Separate teams may own analytics functionality
   • Integration with external analytics tools

┌─────────────────┐    ┌──────────────────┐
│   Web Client    │    │   Mobile App     │
└─────────────────┘    └──────────────────┘
         │                        │
         └────────────┬───────────┘
                      │
         ┌────────────────────────┐
         │    API Gateway         │
         │   (Authentication,     │
         │   Rate Limiting)       │
         └────────────────────────┘
                      │
    ┌─────────────────┼─────────────────┐
    │                 │                 │
┌───▼────┐    ┌──────▼──────┐    ┌─────▼────┐
│  Core  │    │Code Generation│    │ Search   │
│Monolith│    │  Service      │    │ Service  │
│        │    │               │    │          │
└────────┘    └───────────────┘    └──────────┘

Implementation Plan

Phase 1: Modular Monolith Setup (Weeks 1-8)

Week 1-2: Foundation

// Project structure
src/
├── modules/
│   ├── user/
│   │   ├── user.service.ts
│   │   ├── user.controller.ts
│   │   ├── user.model.ts
│   │   └── user.types.ts
│   ├── project/
│   │   ├── project.service.ts
│   │   ├── project.controller.ts
│   │   └── ...
│   └── template/
├── shared/
│   ├── database/
│   ├── logging/
│   ├── validation/
│   └── types/
├── infrastructure/
│   ├── server.ts
│   ├── database.ts
│   └── middleware/
└── tests/

Week 3-4: Core Modules

• Implement User module with authentication
• Implement Project module with basic CRUD
• Implement Template module with search

Week 5-6: Integration

• Module integration and API composition
• Shared infrastructure (database, caching, logging)
• Error handling and validation

Week 7-8: Testing and Deployment

• Comprehensive test suite
• CI/CD pipeline setup
• Production deployment and monitoring

Phase 2: Monitoring and Metrics (Weeks 9-12)

Module Performance Tracking:

// Module performance decorators
@TrackPerformance('user-module')
export class UserService {
  @MetricsCollection({ operation: 'create-user' })
  async createUser(userData: CreateUserRequest): Promise<User> {
    // Implementation
  }
}

// Monitoring configuration
const moduleMetrics = {
  'user-module': {
    responseTime: { threshold: 100, percentile: 95 },
    errorRate: { threshold: 1 }, // 1% error rate
    throughput: { min: 100 } // requests per second
  },
  'project-module': {
    responseTime: { threshold: 200, percentile: 95 },
    errorRate: { threshold: 2 },
    throughput: { min: 50 }
  }
};

Phase 3: Extraction Decision Points (Months 6-9)

Microservice Extraction Criteria:

const extractionDecisionMatrix = {
  codeGeneration: {
    independentScaling: 9,    // High CPU usage, different scaling needs
    teamOwnership: 7,         // AI/ML team may own this
    technology: 8,            // Python may be better for AI/ML
    dataIsolation: 6,         // Some shared data needed
    deploymentFrequency: 8,   // Frequent algorithm updates
    score: 38 // Candidate for extraction
  },
  
  userManagement: {
    independentScaling: 4,    // Scales with overall system
    teamOwnership: 3,         // Core platform team
    technology: 2,            // No technology benefit
    dataIsolation: 3,         // Highly coupled data
    deploymentFrequency: 4,   // Standard deployment cycle
    score: 16 // Keep in monolith
  },
  
  search: {
    independentScaling: 8,    // High read volume
    teamOwnership: 6,         // Could have dedicated team
    technology: 7,            // Elasticsearch integration
    dataIsolation: 8,         # Mostly read-only data
    deploymentFrequency: 5,   // Standard updates
    score: 34 // Candidate for extraction
  }
};

// Extract services scoring > 30

Consequences

Positive Consequences

Development Benefits:

• Faster initial development and iteration
• Simplified debugging with single codebase
• Atomic transactions ensure data consistency
• Reduced operational complexity initially
• Easier to refactor module boundaries

Team Benefits:

• Single deployment process for entire team
• Shared knowledge across codebase
• Simplified local development setup
• Easier code sharing and reuse

Technical Benefits:

• Lower latency between modules
• Simplified error handling and monitoring
• Single technology stack
• Easier integration testing

Negative Consequences

Scalability Limitations:

• All modules scale together (may be inefficient)
• Single point of failure for entire system
• Technology constraints apply to all modules
• Resource allocation less flexible

Development Constraints:

• Larger codebase may slow development over time
• All developers need understanding of entire system
• Coordinated deployments required
• Technology choices affect entire system

Future Migration Costs:

• Breaking apart monolith requires significant refactoring
• Data migration complexity when extracting services
• Need to implement service communication patterns
• Testing complexity increases with distribution

Alternatives Considered

Pure Microservices Architecture

Pros:

• Independent scaling of services
• Technology diversity possible
• Team autonomy and ownership
• Fault isolation between services

Cons:

• High operational complexity for small team
• Distributed system complexity (network, consistency)
• Service discovery and communication overhead
• Debugging across services challenging
• Higher infrastructure costs

Decision: Rejected for initial implementation due to team size and operational complexity.

Traditional Monolith

Pros:

• Simplest possible architecture
• Fastest initial development
• Easiest deployment and operations
• Single technology stack

Cons:

• No clear boundaries or isolation
• Difficult to extract services later
• All code changes affect entire system
• Technology and scaling limitations

Decision: Rejected in favor of modular approach that enables future evolution.

Event-Driven Architecture

Pros:

• Loose coupling between components
• High scalability potential
• Good for complex workflows
• Natural fit for some business processes

Cons:

• Complex debugging and monitoring
• Eventual consistency challenges
• Event schema evolution complexity
• Requires significant messaging infrastructure

Decision: Rejected for initial implementation; may be adopted later for specific workflows.

Migration Strategy

Metrics-Driven Decision Making

Module Extraction Triggers:

extraction_criteria:
  performance:
    - response_time_p99 > 500ms consistently
    - cpu_usage > 80% due to single module
    - memory_usage > 70% due to single module
    
  team:
    - module_commits > 60% by single team
    - release_frequency different from main app
    - technology_requirements divergent
    
  business:
    - independent_scaling_value > $50k/year
    - compliance_requirements divergent
    - customer_sla_requirements different

Pre-Extraction Checklist:

## Service Extraction Checklist

### Technical Readiness
- [ ] Module has well-defined interfaces
- [ ] Data dependencies mapped and minimized
- [ ] Service communication patterns designed
- [ ] Monitoring and logging strategy defined
- [ ] Testing strategy for distributed system

### Team Readiness  
- [ ] Clear ownership model established
- [ ] Service-level SLAs defined
- [ ] Incident response procedures updated
- [ ] Documentation and runbooks prepared
- [ ] Team has microservices expertise

### Infrastructure Readiness
- [ ] Service discovery mechanism implemented
- [ ] Load balancing and routing configured
- [ ] Deployment pipeline automated
- [ ] Monitoring and alerting configured
- [ ] Security and authentication updated

Success Metrics

Phase 1 Success Criteria (Modular Monolith)

• Development velocity: Features delivered per sprint maintained or improved
• System reliability: 99.9% uptime maintained
• Response time: 95% of requests < 200ms
• Team satisfaction: Developer experience survey > 4.0/5.0
• Code quality: Test coverage > 80%, code review approval rate > 95%

Phase 2 Success Criteria (Selective Microservices)

• Extracted services meet independent SLAs
• Overall system complexity manageable (developer survey)
• Operational overhead < 20% of development time
• Service-to-service communication < 10ms p95
• Zero data inconsistency issues between services

Review Schedule

Quarterly Reviews: Assess extraction candidates based on:

• Performance metrics and bottlenecks
• Team organization and ownership changes
• Technology requirements evolution
• Business scaling needs

Annual Architecture Review:

• Overall architecture satisfaction survey
• Cost-benefit analysis of current approach
• Industry best practices evolution
• Technology landscape changes

Next Review: March 2025 (3 months post-implementation)

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Decision Date: December 19, 2024
Participants: Architecture Team, Development Teams, DevOps
Status: Implementation in progress