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Architecture Overview

This document provides a comprehensive overview of the Solverhood platform architecture, including system design, technology choices, and key architectural decisions.

🏗️ System Architecture​

High-Level Architecture​

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   Frontend      │    │   API Gateway   │    │   Backend       │
│   (React/TS)    │◄──►│   (GraphQL)     │◄──►│   (Go)          │
└─────────────────┘    └─────────────────┘    └─────────────────┘
                                │                       │
                                ▼                       ▼
                       ┌─────────────────┐    ┌─────────────────┐
                       │   PostgreSQL    │    │   ClickHouse    │
                       │   (OLTP)        │    │   (OLAP)        │
                       └─────────────────┘    └─────────────────┘
                                │                       │
                                ▼                       ▼
                       ┌─────────────────┐    ┌─────────────────┐
                       │   Redis Cache   │    │   Message Queue │
                       │                 │    │   (SQS/NATS)    │
                       └─────────────────┘    └─────────────────┘

🎯 Design Principles​

1. Microservices Architecture​

  • Service Independence: Each service can be developed, deployed, and scaled independently
  • Technology Diversity: Different services can use the most appropriate technology stack
  • Fault Isolation: Failures in one service don't cascade to others

2. Event-Driven Design​

  • Loose Coupling: Services communicate through events rather than direct calls
  • Scalability: Easy to add new services that react to existing events
  • Reliability: Event persistence ensures no data loss

3. Data-First Approach​

  • OLTP for Transactions: PostgreSQL handles real-time transactional data
  • OLAP for Analytics: ClickHouse processes analytical queries efficiently
  • Caching Strategy: Redis provides fast access to frequently accessed data

4. API-First Design​

  • GraphQL Interface: Single endpoint for all data queries and mutations
  • Type Safety: Generated types ensure consistency across frontend and backend
  • Real-time Updates: Subscriptions for live data updates

🏛️ Core Components​

Frontend Layer​

Technology Stack​

  • Framework: React 18+ with TypeScript
  • State Management: Zustand (lightweight) or Redux Toolkit (complex state)
  • Styling: Tailwind CSS with component libraries
  • Build Tool: Vite for fast development and optimized builds

Architecture Patterns​

  • Component-Based: Reusable, composable UI components
  • Container/Presenter: Separation of business logic and presentation
  • Custom Hooks: Shared logic across components
  • Error Boundaries: Graceful error handling

API Layer​

GraphQL Server (GQLGen)​

// Example GraphQL schema
type Query {
  users: [User!]!
  user(id: ID!): User
  analytics: AnalyticsData
}

type Mutation {
  createUser(input: CreateUserInput!): User!
  updateUser(id: ID!, input: UpdateUserInput!): User!
}

type Subscription {
  userUpdated(id: ID!): User!
}

Key Features​

  • Type Safety: Generated Go types from GraphQL schema
  • Resolvers: Clean separation of business logic
  • Middleware: Authentication, logging, and error handling
  • Subscriptions: Real-time updates via WebSocket

Backend Services​

Core Services​

  1. User Service: Authentication, authorization, user management
  2. Data Service: CRUD operations, data validation
  3. Analytics Service: Data processing, reporting, insights
  4. Notification Service: Email, push notifications, messaging
  5. File Service: File upload, storage, CDN integration

Service Communication​

// Example service interface
type UserService interface {
    CreateUser(ctx context.Context, input CreateUserInput) (*User, error)
    GetUser(ctx context.Context, id string) (*User, error)
    UpdateUser(ctx context.Context, id string, input UpdateUserInput) (*User, error)
    DeleteUser(ctx context.Context, id string) error
}

Data Layer​

PostgreSQL (OLTP Database)​

Purpose: Transactional data, user data, business logic Schema Design:

-- Example user table
CREATE TABLE users (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    email VARCHAR(255) UNIQUE NOT NULL,
    name VARCHAR(255) NOT NULL,
    created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
    updated_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
);

-- Indexes for performance
CREATE INDEX idx_users_email ON users(email);
CREATE INDEX idx_users_created_at ON users(created_at);

ClickHouse (OLAP Database)​

Purpose: Analytics, reporting, data warehousing Schema Design:

-- Example analytics table
CREATE TABLE user_events (
    event_id UUID,
    user_id UUID,
    event_type String,
    event_data JSON,
    timestamp DateTime64(3),
    created_at DateTime64(3) DEFAULT now64()
) ENGINE = MergeTree()
ORDER BY (timestamp, user_id, event_type);

Redis (Caching Layer)​

Purpose: Session storage, API response caching, rate limiting Usage Patterns:

// Example caching implementation
func (s *UserService) GetUser(ctx context.Context, id string) (*User, error) {
    // Check cache first
    if user, found := s.cache.Get(id); found {
        return user.(*User), nil
    }

    // Fetch from database
    user, err := s.repo.GetByID(ctx, id)
    if err != nil {
        return nil, err
    }

    // Cache for 5 minutes
    s.cache.Set(id, user, 5*time.Minute)
    return user, nil
}

🔄 Data Flow​

Request Flow​

  1. Client Request: Frontend sends GraphQL query/mutation
  2. API Gateway: Routes request to appropriate service
  3. Authentication: Validates JWT token and user permissions
  4. Business Logic: Service processes request
  5. Data Access: Repository pattern for database operations
  6. Response: Returns structured data to client

Event Flow​

  1. Event Generation: Service emits domain events
  2. Event Publishing: Events sent to message queue (NATS/SQS)
  3. Event Processing: Other services consume and react to events
  4. Data Consistency: Event sourcing for audit trail

🔒 Security Architecture​

Authentication & Authorization​

  • JWT Tokens: Stateless authentication
  • Role-Based Access Control (RBAC): Fine-grained permissions
  • API Rate Limiting: Prevent abuse and ensure fair usage
  • Input Validation: Comprehensive validation at all layers

Data Protection​

  • Encryption at Rest: Database encryption
  • Encryption in Transit: TLS/SSL for all communications
  • Secrets Management: Environment variables and secure storage
  • Audit Logging: Complete audit trail for compliance

📊 Monitoring & Observability​

Logging Strategy​

  • Structured Logging: JSON format for easy parsing
  • Log Levels: DEBUG, INFO, WARN, ERROR, FATAL
  • Centralized Logging: Grafana Loki for log aggregation
  • Log Correlation: Request IDs for tracing

Metrics & Monitoring​

  • Application Metrics: Prometheus for metrics collection
  • Infrastructure Monitoring: System health and performance
  • Alerting: Proactive notification of issues
  • Dashboards: Grafana for visualization

Distributed Tracing​

  • Request Tracing: Track requests across services
  • Performance Analysis: Identify bottlenecks
  • Error Tracking: Detailed error context and stack traces

🚀 Deployment Architecture​

Container Strategy​

# Example multi-stage Dockerfile
FROM golang:1.21-alpine AS builder
WORKDIR /app
COPY go.mod go.sum ./
RUN go mod download
COPY . .
RUN go build -o main ./cmd/server

FROM alpine:latest
RUN apk --no-cache add ca-certificates
WORKDIR /root/
COPY --from=builder /app/main .
CMD ["./main"]

Orchestration​

  • Docker Compose: Local development
  • Kubernetes: Production deployment
  • Service Mesh: Istio for advanced traffic management
  • Auto-scaling: Horizontal pod autoscaling

CI/CD Pipeline​

  1. Code Commit: Trigger automated build
  2. Testing: Unit, integration, and E2E tests
  3. Security Scan: Vulnerability scanning
  4. Build: Create container images
  5. Deploy: Rolling deployment to staging/production

🔧 Configuration Management​

Environment Configuration​

// Example configuration structure
type Config struct {
    Server   ServerConfig   `pkl:"server"`
    Database DatabaseConfig `pkl:"database"`
    Redis    RedisConfig    `pkl:"redis"`
    AWS      AWSConfig      `pkl:"aws"`
}

type ServerConfig struct {
    Port    int    `pkl:"port"`
    Host    string `pkl:"host"`
    Timeout int    `pkl:"timeout"`
}

Feature Flags​

  • Runtime Configuration: Dynamic feature toggles
  • A/B Testing: Gradual feature rollouts
  • Environment-Specific: Different configs per environment

📈 Scalability Considerations​

Horizontal Scaling​

  • Stateless Services: Easy horizontal scaling
  • Load Balancing: Distribute traffic across instances
  • Database Sharding: Partition data for performance
  • CDN Integration: Global content delivery

Performance Optimization​

  • Caching Strategy: Multi-layer caching
  • Database Optimization: Query optimization and indexing
  • Connection Pooling: Efficient resource utilization
  • Async Processing: Background job processing

🔄 Migration Strategy​

Database Migrations​

  • Version Control: All schema changes tracked
  • Rollback Support: Safe migration rollbacks
  • Zero-Downtime: Blue-green deployments
  • Data Validation: Post-migration verification

API Versioning​

  • Backward Compatibility: Maintain API stability
  • Version Deprecation: Clear deprecation timelines
  • Migration Tools: Automated migration assistance

🎯 Future Considerations​

Planned Improvements​

  1. Service Mesh: Advanced traffic management
  2. Event Sourcing: Complete audit trail
  3. CQRS: Command Query Responsibility Segregation
  4. GraphQL Federation: Distributed GraphQL schema

Technology Evolution​

  • Go 1.22+: Latest language features
  • React 19: Concurrent features
  • Database Evolution: New database technologies
  • Cloud Native: Kubernetes-native development

This architecture provides a solid foundation for building scalable, maintainable, and high-performance applications. For detailed implementation guides, see our Development Overview.