You're building a dashboard with real-time updates, complex filtering, and must support 1000+ concurrent users. Describe your architecture.

You're building a dashboard with real-time updates, complex filtering, and must support 1000+ concurrent users. Describe your architecture.

Answer:

Frontend Architecture:

1. Framework & State Management:

   • React with TypeScript for type safety
   • Redux Toolkit + RTK Query for state management and caching
   • React Query for server state synchronization
   • Zustand for lightweight local state

2. Real-time Updates:

   // WebSocket connection with reconnection logic
   const useWebSocket = (url) => {
     const [socket, setSocket] = useState(null);
     const [connectionStatus, setConnectionStatus] = useState('disconnected');
     
     useEffect(() => {
       const ws = new WebSocket(url);
       
       ws.onopen = () => setConnectionStatus('connected');
       ws.onclose = () => {
         setConnectionStatus('disconnected');
         // Auto-reconnect with exponential backoff
         setTimeout(() => setSocket(new WebSocket(url)), 1000);
       };
       
       setSocket(ws);
       return () => ws.close();
     }, [url]);
     
     return { socket, connectionStatus };
   };
   

3. Performance Optimizations:

   • Virtual scrolling for large datasets (react-window)
   • Debounced search and filtering
   • Memoized components with React.memo
   • Code splitting with React.lazy
   • Service Worker for offline capabilities

Backend Architecture:

1. API Design:

   • RESTful APIs with GraphQL for complex queries
   • WebSocket server for real-time updates
   • Rate limiting and authentication middleware
   • API versioning strategy

2. Database Strategy:

   -- Optimized queries with proper indexing
   CREATE INDEX idx_dashboard_data_timestamp ON dashboard_data(timestamp);
   CREATE INDEX idx_dashboard_data_user_id ON dashboard_data(user_id);
   CREATE INDEX idx_dashboard_data_category ON dashboard_data(category);
   

3. Caching Layer:

   • Redis for session management and real-time data
   • CDN for static assets
   • Application-level caching with TTL
   • Database query result caching

Scalability Considerations:

1. Load Balancing:

   • Horizontal scaling with multiple server instances
   • Load balancer with sticky sessions for WebSocket connections
   • Database read replicas for query distribution

2. Microservices Architecture:

   ┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
   │   Auth Service  │    │  Data Service   │    │  Real-time      │
   │                 │    │                 │    │  Service        │
   └─────────────────┘    └─────────────────┘    └─────────────────┘
           │                       │                       │
           └───────────────────────┼───────────────────────┘
                                   │
                    ┌─────────────────┐
                    │   API Gateway   │
                    │   (Rate Limit,  │
                    │   Auth, Routing)│
                    └─────────────────┘
   

3. Monitoring & Observability:

   • Application Performance Monitoring (APM)
   • Real-time metrics with Prometheus + Grafana
   • Error tracking with Sentry
   • Log aggregation with ELK stack

Security & Performance:

1. Authentication:

   • JWT tokens with refresh mechanism
   • Role-based access control (RBAC)
   • API key management for external integrations

2. Data Flow:

   // Optimized data fetching with caching
   const useDashboardData = (filters) => {
     return useQuery({
       queryKey: ['dashboard', filters],
       queryFn: () => fetchDashboardData(filters),
       staleTime: 30000, // 30 seconds
       cacheTime: 300000, // 5 minutes
       refetchOnWindowFocus: false,
     });
   };
   

3. Real-time Data Pipeline:

   • WebSocket connections with connection pooling
   • Message queuing (Redis Pub/Sub or RabbitMQ)
   • Data streaming with Apache Kafka for high-volume updates
   • Client-side data synchronization with conflict resolution