System Architecture

Overview

ImpactPulse follows a modern, scalable architecture optimized for high concurrency, rapid development, and comprehensive testing.

Architecture Principles

1. Backend-First Development: Business logic in API layer
2. Massive Parallelization: Support 1000+ concurrent operations
3. In-Memory for Development: Zero disk I/O during development
4. Test Everything: 100% backend coverage, E2E for critical paths
5. Clean Separation: Clear boundaries between layers

System Components

Backend (FastAPI + MongoDB)

Frontend (React + Vite)

Technology Stack

Backend Technologies

• Framework: FastAPI (async Python)
• Database: MongoDB with Motor (async driver)
• Cache: Redis for sessions and temporary data
• Auth: JWT with refresh tokens
• Validation: Pydantic models
• Testing: pytest with pytest-asyncio
• Task Queue: Celery with Redis broker
• Email: Resend API

Frontend Technologies

• Framework: React 19
• Build Tool: Vite
• Language: TypeScript
• Styling: Tailwind CSS
• State: React Query + Context
• Forms: React Hook Form
• Charts: Recharts/D3.js
• Testing: Vitest + React Testing Library

Infrastructure

• Container: Docker + Docker Compose
• CI/CD: GitHub Actions
• Monitoring: OpenTelemetry
• Logging: Structured JSON logs
• Deployment: Azure (App Service, Static Web Apps)

Data Flow

Request Flow

Authentication Flow

Session Management

Database Design

MongoDB Advantages

• Flexible schema for survey responses
• Native JSON storage
• Horizontal scaling capability
• Aggregation pipeline for analytics
• Change streams for real-time updates

Collection Relationships

API Design

RESTful Principles

• Resource-based URLs
• HTTP verbs for actions
• Consistent response format
• HATEOAS where applicable

Response Format

{
  "success": true,
  "data": {},
  "message": "Operation successful",
  "errors": [],
  "metadata": {
    "timestamp": "2024-01-15T10:00:00Z",
    "request_id": "uuid"
  }
}

Error Handling

{
  "success": false,
  "data": null,
  "message": "Validation failed",
  "errors": [
    {
      "field": "email",
      "message": "Invalid email format"
    }
  ]
}

Security Architecture

Authentication

• JWT tokens with short expiry (15 min)
• Refresh tokens with longer expiry (7 days)
• Organization context in token claims
• Role-based access control (RBAC)

Authorization

# Decorator-based authorization
@require_auth
@require_role("admin")
@require_organization
async def admin_endpoint():
    pass

Data Security

• Encryption at rest (MongoDB)
• TLS for all connections
• Input validation and sanitization
• XSS protection
• CSRF tokens

Scalability Patterns

Horizontal Scaling

• Stateless API servers
• MongoDB replica sets
• Redis cluster
• Load balancer distribution

Caching Strategy

• Redis for session data
• MongoDB query result caching
• Frontend API response caching
• CDN for static assets

Performance Optimizations

• Database indexing
• Query optimization
• Lazy loading
• Pagination
• Request batching

Background Jobs

Job Types

• Email reminders
• Report generation
• Data aggregation
• Cleanup tasks

Job Architecture

Testing Architecture

Testing Levels

1. Unit Tests (80%): Business logic
2. Integration Tests (15%): API endpoints
3. E2E Tests (5%): Critical user flows

Testing Strategy

# Parallel execution
pytest -n 32

# Test isolation
@pytest.fixture
async def test_db():
    # Create test database
    # Run migrations
    # Yield
    # Cleanup

Monitoring & Observability

Metrics

• Request latency
• Error rates
• Database performance
• Queue depth
• Email delivery rates

Logging

logger.info("Survey completed", extra={
    "user_id": user_id,
    "survey_id": survey_id,
    "attempt": attempt_number,
    "duration": duration
})

Tracing

• OpenTelemetry integration
• Distributed tracing
• Performance profiling

Development Environment

Local Setup

# Backend
docker-compose up mongodb redis
cd backend && uvicorn app.main:app --reload

# Frontend
cd frontend && npm run dev

# Tests
pytest tests/ -n auto

Environment Variables

# Development
USE_INMEMORY_DB=true
DEBUG=true
LOG_LEVEL=debug

# Production
USE_INMEMORY_DB=false
DEBUG=false
LOG_LEVEL=info

Deployment Architecture

Container Strategy

# Multi-stage build
FROM python:3.11 as builder
# Build dependencies

FROM python:3.11-slim
# Copy only necessary files
# Run application

Zero-Downtime Deployment

1. Build new container
2. Run migrations
3. Health check
4. Switch load balancer
5. Drain old containers

Future Considerations

Microservices (if needed)

• Auth service
• Email service
• Analytics service
• Survey service

Event-Driven Architecture

• Event sourcing for audit trail
• CQRS for read/write separation
• Message queue for async operations

Multi-Region

• Geographic distribution
• Data residency compliance
• Edge caching