CodePark Architecture Guide

System Overview
Architecture Layers
Component Details
Data Flow
Deployment Architecture
Scalability & Performance
Security Architecture
Monitoring & Observability

System Overview

CodePark follows a layered microservices architecture with edge computing capabilities, real-time communication, and AI/ML integration. The system is designed for high performance, scalability, and developer experience.

High-Level Architecture

┌─────────────────────────────────────────────────────────────┐
│                     Edge Layer (Cloudflare)                  │
│                    WebAssembly • Workers                     │
└──────────────────────────┬──────────────────────────────────┘
                           │
┌──────────────────────────▼──────────────────────────────────┐
│                      API Gateway Layer                       │
│         Express • GraphQL (Apollo) • REST • WebSocket       │
└──────────────┬──────────────────────────┬───────────────────┘
               │                          │
    ┌──────────▼─────────┐    ┌──────────▼──────────┐
    │  Business Logic    │    │   Real-Time Layer   │
    │  • AI/ML (TF.js)   │    │   • Socket.io       │
    │  • NLP Processing  │    │   • Yjs CRDT        │
    │  • Job Queue       │    │   • Live Cursors    │
    └──────────┬─────────┘    └──────────┬──────────┘
               │                          │
    ┌──────────▼──────────────────────────▼──────────┐
    │              Data Layer                         │
    │  MongoDB • Redis • Prisma ORM • Kafka          │
    └──────────┬────────────────────────────────────┘
               │
    ┌──────────▼──────────────────────────────────┐
    │         Observability Layer                  │
    │  OpenTelemetry • Prometheus • Sentry        │
    └─────────────────────────────────────────────┘

Key Design Principles

Scalability First: Horizontal scaling at every layer
Real-Time by Default: WebSocket and CRDT for instant updates
AI-Enhanced: Machine learning integrated throughout
Edge-Optimized: Static assets and compute at the edge
Observable: Comprehensive metrics, tracing, and logging
Secure: Defense in depth with multiple security layers

Architecture Layers

1. Edge Layer

Purpose: Reduce latency by serving content from locations closest to users.

Technologies:

Cloudflare Workers (serverless edge compute)
WebAssembly (WASM) modules for high-performance operations
Edge caching (static assets, API responses)

Responsibilities:

Static asset delivery (CDN)
Edge computing for lightweight operations
DDoS protection and rate limiting
SSL/TLS termination
Geographic routing

Benefits:

<50ms latency for edge operations
Global distribution without infrastructure management
Automatic scaling based on demand
Built-in security features

2. API Gateway Layer

Purpose: Unified entry point for all client requests.

Technologies:

Express.js (HTTP server)
Apollo Server v4 (GraphQL)
Socket.IO (WebSocket)
Express-GraphQL (hybrid endpoints)

Responsibilities:

Request routing and load balancing
Authentication and authorization
Rate limiting and throttling
Request/response transformation
API versioning
Protocol translation (REST ↔ GraphQL)

API Types:

REST API

GET    /api/v1/projects          # List projects
POST   /api/v1/projects          # Create project
GET    /api/v1/projects/:id      # Get project
PUT    /api/v1/projects/:id      # Update project
DELETE /api/v1/projects/:id      # Delete project

GraphQL API

query GetProjects {
  projects {
    id
    name
    collaborators {
      id
      name
    }
    aiInsights {
      complexity
      suggestions
    }
  }
}

WebSocket Events

// Client → Server
socket.emit("code-change", { projectId, content });
socket.emit("cursor-move", { position, userId });

// Server → Client
socket.on("code-update", (data) => {
  /* ... */
});
socket.on("user-joined", (user) => {
  /* ... */
});

3. Business Logic Layer

Purpose: Core application logic and AI/ML processing.

Components:

AI/ML Module

TensorFlow.js: Model inference (code completion, bug prediction)
Natural: NLP for text analysis
Compromise: Language understanding
Sentiment: Code review sentiment analysis

Use Cases:

Code completion suggestions
Bug prediction from commit history
Code quality scoring
Documentation generation
Automated code review

Job Queue (BullMQ)

Asynchronous task processing
Background jobs (email, notifications)
Scheduled tasks (data cleanup, reports)
Retry logic with exponential backoff

Queue Types:

// High priority queue (user-facing)
const criticalQueue = new Queue("critical", {
  defaultJobOptions: { priority: 1 },
});

// Low priority queue (background tasks)
const backgroundQueue = new Queue("background", {
  defaultJobOptions: { priority: 10 },
});

Event Streaming (Kafka)

Real-time event processing
Audit logging
Analytics pipeline
Inter-service communication

4. Real-Time Layer

Purpose: Enable collaborative features and live updates.

Technologies:

Socket.IO: Bidirectional WebSocket communication
Yjs: Conflict-free Replicated Data Types (CRDT)
y-websocket: WebSocket provider for Yjs

Features:

Live Collaboration

const Y = require("yjs");
const ydoc = new Y.Doc();
const ytext = ydoc.getText("shared-code");

// Multiple users editing simultaneously
ytext.insert(0, "function hello() {");
// No conflicts, automatic merging

Presence System

Online/offline status
Active cursors
User locations in documents
Typing indicators

Real-Time Notifications

Instant alerts for mentions
Code review updates
Build status changes
System announcements

5. Data Layer

Purpose: Persistent and temporary data storage.

Technologies:

MongoDB (Primary Database)

Use Case: Document storage for projects, users, files
Schema: Flexible JSON documents
Scaling: Replica sets and sharding
Indexing: Compound indexes for performance

Example Schema:

{
  _id: ObjectId,
  name: String,
  owner: ObjectId,
  collaborators: [ObjectId],
  files: [{
    path: String,
    content: String,
    version: Number
  }],
  aiInsights: {
    complexity: Number,
    suggestions: [String]
  },
  createdAt: Date,
  updatedAt: Date
}

Redis (Cache & Session Store)

Use Case: Session management, caching, pub/sub
Data Structures: Strings, Hashes, Lists, Sets, Sorted Sets
Persistence: RDB snapshots + AOF logs

Caching Strategy:

// Cache-aside pattern
const data = await redis.get(key);
if (!data) {
  data = await database.find(query);
  await redis.setex(key, 3600, JSON.stringify(data));
}

Prisma ORM

Type-safe database access
Auto-generated TypeScript types
Migration management
Connection pooling

Example Model:

model Project {
  id            String   @id @default(cuid())
  name          String
  owner         User     @relation(fields: [ownerId], references: [id])
  ownerId       String
  collaborators User[]   @relation("ProjectCollaborators")
  createdAt     DateTime @default(now())
  updatedAt     DateTime @updatedAt
}

Apache Kafka

Event streaming platform
High-throughput message processing
Event sourcing and CQRS patterns

6. Observability Layer

Purpose: Monitor, trace, and debug the system.

Technologies:

OpenTelemetry

Distributed tracing across services
Context propagation
Automatic instrumentation

Trace Example:

const tracer = trace.getTracer("codepark");

async function processRequest(req) {
  const span = tracer.startSpan("process-request");
  span.setAttribute("user.id", req.user.id);

  try {
    // ... processing ...
    span.setStatus({ code: SpanStatusCode.OK });
  } catch (error) {
    span.setStatus({
      code: SpanStatusCode.ERROR,
      message: error.message,
    });
  } finally {
    span.end();
  }
}

Prometheus

Time-series metrics collection
Custom business metrics
Alerting rules

Metrics:

const httpRequestDuration = new Histogram({
  name: "http_request_duration_seconds",
  help: "Duration of HTTP requests",
  labelNames: ["method", "route", "status_code"],
  buckets: [0.1, 0.5, 1, 2, 5],
});

Sentry

Error tracking and reporting
Performance monitoring
Release tracking
User feedback

Component Details

Authentication & Authorization

Flow:

1. User Login
   ↓
2. Validate Credentials (Argon2)
   ↓
3. Check 2FA (if enabled)
   ↓
4. Generate JWT (Access + Refresh)
   ↓
5. Return Tokens

JWT Structure:

{
  "access_token": {
    "sub": "user_id",
    "email": "user@example.com",
    "roles": ["user", "admin"],
    "exp": 1234567890,
    "iat": 1234567000
  },
  "refresh_token": {
    "sub": "user_id",
    "type": "refresh",
    "exp": 1234577890
  }
}

Two-Factor Authentication:

const speakeasy = require("speakeasy");

// Setup
const secret = speakeasy.generateSecret();
const qrCode = await QRCode.toDataURL(secret.otpauth_url);

// Verification
const verified = speakeasy.totp.verify({
  secret: user.mfaSecret,
  encoding: "base32",
  token: userProvidedToken,
  window: 1,
});

Rate Limiting Strategy

Tier-based Limits:

Tier	Requests/Min	Burst	WebSocket Connections
Anonymous	60	10	5
Free User	300	30	20
Pro User	1,200	100	100
Enterprise	Unlimited	500	1,000

Implementation:

const rateLimiter = new RateLimiterService({
  windowMs: 60 * 1000, // 1 minute
  max: (req) => getTierLimit(req.user),
  keyGenerator: (req) => req.user?.id || req.ip,
});

Caching Strategy

Cache Levels:

Edge Cache (Cloudflare): Static assets (1 week TTL)
Application Cache (Redis): API responses (5-60 min TTL)
Database Cache: Query result cache (Prisma)

Cache Invalidation:

// Write-through cache
async function updateProject(id, data) {
  await database.update(id, data);
  await cache.del(`project:${id}`);
  await cache.del("projects:list");
}

// Cache-aside (lazy loading)
async function getProject(id) {
  const cached = await cache.get(`project:${id}`);
  if (cached) return cached;

  const project = await database.findById(id);
  await cache.setex(`project:${id}`, 300, project);
  return project;
}

Data Flow

Typical Request Flow

1. Client Request
   ↓
2. Edge Layer (Cloudflare)
   ├─ Static Assets → Return from CDN
   └─ API Request → Continue
   ↓
3. API Gateway
   ├─ Authentication (JWT validation)
   ├─ Rate Limiting (check limits)
   └─ Route to handler
   ↓
4. Business Logic
   ├─ Check Cache (Redis)
   ├─ Database Query (if cache miss)
   ├─ AI Processing (if needed)
   └─ Format Response
   ↓
5. Response
   ├─ Update Cache
   ├─ Log Metrics (OpenTelemetry)
   └─ Return to Client

Real-Time Collaboration Flow

1. User Types in Editor
   ↓
2. Yjs Updates Local Document
   ↓
3. Yjs Generates Update Message
   ↓
4. WebSocket Sends to Server
   ↓
5. Server Broadcasts to Room
   ↓
6. Other Clients Receive Update
   ↓
7. Yjs Applies Updates Locally
   ↓
8. UI Updates (conflict-free)

AI/ML Processing Flow

1. Code Submitted
   ↓
2. Tokenize & Vectorize
   ↓
3. Load TensorFlow Model
   ↓
4. Run Inference
   ↓
5. Post-process Results
   ↓
6. Return Suggestions

Deployment Architecture

Production Deployment

┌──────────────────────────────────────────────┐
│         Load Balancer (HAProxy/Nginx)        │
└──────────────┬───────────────────────────────┘
               │
       ┌───────┴────────┐
       │                │
   ┌───▼────┐      ┌───▼────┐
   │ Node 1 │      │ Node 2 │  (PM2 Cluster)
   └───┬────┘      └───┬────┘
       │                │
       └───────┬────────┘
               │
    ┌──────────▼──────────┐
    │   Redis Cluster     │
    │   (Master+Replicas) │
    └─────────────────────┘
               │
    ┌──────────▼──────────┐
    │  MongoDB Replica Set│
    │  (Primary+Secondary)│
    └─────────────────────┘

Container Orchestration (Kubernetes)

apiVersion: apps/v1
kind: Deployment
metadata:
  name: codepark-api
spec:
  replicas: 3
  selector:
    matchLabels:
      app: codepark-api
  template:
    metadata:
      labels:
        app: codepark-api
    spec:
      containers:
        - name: api
          image: codepark/api:latest
          ports:
            - containerPort: 3000
          env:
            - name: NODE_ENV
              value: "production"
          resources:
            requests:
              memory: "512Mi"
              cpu: "500m"
            limits:
              memory: "1Gi"
              cpu: "1000m"

Docker Compose (Development)

version: "3.8"
services:
  api:
    build: .
    ports:
      - "3000:3000"
    environment:
      - NODE_ENV=development
      - MONGODB_URI=mongodb://mongo:27017/codepark
      - REDIS_URL=redis://redis:6379
    depends_on:
      - mongo
      - redis

  mongo:
    image: mongo:latest
    ports:
      - "27017:27017"
    volumes:
      - mongo-data:/data/db

  redis:
    image: redis:latest
    ports:
      - "6379:6379"

volumes:
  mongo-data:

Scalability & Performance

Horizontal Scaling

Stateless Application Servers:

Multiple API server instances behind load balancer
Session stored in Redis (shared state)
No server affinity required

Database Scaling:

MongoDB sharding for data distribution
Read replicas for read-heavy workloads
Separate analytics database

Redis Scaling:

Redis Cluster for data partitioning
Separate Redis instances for different use cases:
- Session store
- Cache
- Job queue
- Pub/Sub

Performance Optimizations

Connection Pooling:

const pool = {
  min: 10,
  max: 100,
  acquireTimeoutMillis: 30000,
  idleTimeoutMillis: 30000,
};

Database Indexing:

// Compound indexes for common queries
db.projects.createIndex({ ownerId: 1, createdAt: -1 });
db.projects.createIndex({ "collaborators.userId": 1 });
db.projects.createIndex({ name: "text" });

Query Optimization:

// Use projection to limit fields
db.projects.find({ ownerId: userId }, { name: 1, createdAt: 1, _id: 1 });

// Use aggregation for complex queries
db.projects.aggregate([
  { $match: { ownerId: userId } },
  {
    $lookup: {
      from: "users",
      localField: "collaborators",
      foreignField: "_id",
      as: "collaboratorData",
    },
  },
  { $project: { name: 1, collaboratorCount: { $size: "$collaborators" } } },
]);

Security Architecture

Defense in Depth

Layer 1: Edge Protection

DDoS protection (Cloudflare)
WAF rules
Bot detection
Geographic filtering

Layer 2: API Gateway

Rate limiting
Input validation
JWT verification
CORS policies

Layer 3: Application

Authentication (JWT + 2FA)
Authorization (RBAC)
Data encryption (at rest and in transit)
Secure headers (Helmet.js)

Layer 4: Data

Encrypted connections (TLS 1.3)
Field-level encryption
Regular backups
Access audit logs

Security Headers

app.use(
  helmet({
    contentSecurityPolicy: {
      directives: {
        defaultSrc: ["'self'"],
        styleSrc: ["'self'", "'unsafe-inline'"],
        scriptSrc: ["'self'"],
        imgSrc: ["'self'", "data:", "https:"],
      },
    },
    hsts: {
      maxAge: 31536000,
      includeSubDomains: true,
      preload: true,
    },
  }),
);

Monitoring & Observability

Key Metrics

Application Metrics:

Request rate (req/sec)
Response time (p50, p95, p99)
Error rate (%)
Active WebSocket connections
AI inference time

Infrastructure Metrics:

CPU usage (%)
Memory usage (MB)
Disk I/O (IOPS)
Network bandwidth (Mbps)

Business Metrics:

Active users (DAU/MAU)
Projects created
Collaboration sessions
AI suggestions accepted

Alerting Rules

groups:
  - name: api_alerts
    rules:
      - alert: HighErrorRate
        expr: rate(http_requests_total{status=~"5.."}[5m]) > 0.05
        for: 5m
        labels:
          severity: critical
        annotations:
          summary: "High error rate detected"

      - alert: HighResponseTime
        expr: histogram_quantile(0.95, http_request_duration_seconds) > 2
        for: 10m
        labels:
          severity: warning
        annotations:
          summary: "P95 response time > 2s"

Logging Strategy

Log Levels:

ERROR: Application errors, exceptions
WARN: Potential issues, deprecated usage
INFO: Important events, state changes
DEBUG: Detailed debugging information
TRACE: Very detailed tracing information

Structured Logging:

const logger = require("pino")({
  level: process.env.LOG_LEVEL || "info",
  formatters: {
    level: (label) => ({ level: label }),
  },
});

logger.info({ userId, projectId, action: "create" }, "Project created");

Disaster Recovery

Backup Strategy

Database Backups:

Full backup: Daily at 2 AM UTC
Incremental backup: Every 6 hours
Retention: 30 days
Off-site storage: AWS S3 / Google Cloud Storage

Redis Backups:

RDB snapshots: Every hour
AOF logs: Continuous
Retention: 7 days

Recovery Time Objectives:

RTO (Recovery Time Objective): < 1 hour
RPO (Recovery Point Objective): < 15 minutes

High Availability

Application Tier:

Multi-zone deployment
Auto-scaling groups
Health checks and auto-recovery

Database Tier:

MongoDB replica set (3 nodes minimum)
Automatic failover
Read preference: primaryPreferred

Cache Tier:

Redis Cluster with sentinel
Automatic failover
Multi-AZ deployment

Migration Strategy

Database Migrations

# Create migration
npx prisma migrate dev --name add_ai_insights

# Apply migration (production)
npx prisma migrate deploy

# Rollback (manual)
mongo codepark --eval "db.migrations.deleteOne({version: '20231201120000'})"

Zero-Downtime Deployment

1. Deploy New Version (Blue)
   ↓
2. Run Health Checks
   ↓
3. Gradually Shift Traffic (10% → 50% → 100%)
   ↓
4. Monitor Error Rates
   ↓
5. Complete Cutover or Rollback
   ↓
6. Decommission Old Version (Green)

Future Enhancements

Planned Features

Service Mesh (Istio/Linkerd): Advanced traffic management
GraphQL Federation: Distributed schema composition
Event Sourcing: Complete audit trail with event replay
ML Model Versioning: A/B testing for AI models
Multi-Region Deployment: Global distribution
Blockchain Integration: Immutable audit logs

Technology Radar

Adopt:

Bun runtime (alternative to Node.js)
Deno for edge functions
SolidJS for frontend

Trial:

tRPC (type-safe APIs)
Temporal (workflow orchestration)
Vector databases (Pinecone, Weaviate)

Assess:

Web3 integration
Quantum-resistant cryptography
Federated learning

Appendix

Glossary

CRDT: Conflict-free Replicated Data Type
CQRS: Command Query Responsibility Segregation
DAU/MAU: Daily/Monthly Active Users
IOPS: Input/Output Operations Per Second
JWT: JSON Web Token
NLP: Natural Language Processing
ORM: Object-Relational Mapping
RBAC: Role-Based Access Control
RTO/RPO: Recovery Time/Point Objective
TOTP: Time-based One-Time Password
WASM: WebAssembly

References

Document Version: 1.0
Last Updated: December 2025
Maintained By: CodePark Architecture Team

FilesExpand file tree

ARCHITECTURE.md

Latest commit

History

ARCHITECTURE.md

File metadata and controls

CodePark Architecture Guide

Table of Contents

System Overview

High-Level Architecture

Key Design Principles

Architecture Layers

1. Edge Layer

2. API Gateway Layer

REST API

GraphQL API

WebSocket Events

3. Business Logic Layer

AI/ML Module

Job Queue (BullMQ)

Event Streaming (Kafka)

4. Real-Time Layer

Live Collaboration

Presence System

Real-Time Notifications

5. Data Layer

MongoDB (Primary Database)

Redis (Cache & Session Store)

Prisma ORM

Apache Kafka

6. Observability Layer

OpenTelemetry

Prometheus

Sentry

Component Details

Authentication & Authorization

Rate Limiting Strategy

Caching Strategy

Data Flow

Typical Request Flow

Real-Time Collaboration Flow

AI/ML Processing Flow

Deployment Architecture

Production Deployment

Container Orchestration (Kubernetes)

Docker Compose (Development)

Scalability & Performance

Horizontal Scaling

Performance Optimizations

Security Architecture

Defense in Depth

Security Headers

Monitoring & Observability

Key Metrics

Alerting Rules

Logging Strategy

Disaster Recovery

Backup Strategy

High Availability

Migration Strategy

Database Migrations

Zero-Downtime Deployment

Future Enhancements

Planned Features

Technology Radar

Appendix

Glossary

References