Design a scalable real-time notification system for a social media platform. Discuss the components, architecture patterns, and trade-offs related to concurrency and parallelism.

A scalable real-time notification system requires an event-driven architecture with decoupled components. Use a message broker (e.g., Kafka or RabbitMQ) to handle event streaming, a push server (e.g., WebSockets or Firebase Cloud Messaging) for client communication, and a distributed database (e.g., Redis) for caching. Implement load balancing and horizontal scaling for high concurrency. Trade-offs include latency vs. consistency, memory usage vs. throughput, and complexity vs. fault tolerance. Prioritize asynchronous processing and backpressure handling to manage spikes in traffic while ensuring reliability through idempotency and retries.

The system uses an event-driven architecture with microservices. User actions (e.g., likes, comments) are captured by event producers and published to a message broker (Kafka) for decoupling. Notification processors consume events, generate payloads, and store them in Redis for low-latency access. A push server (e.g., WebSocket-based) subscribes to Redis and broadcasts notifications to clients. For scalability, Kafka partitions events, and Redis clusters handle data sharding. Load balancers route traffic to multiple push servers. Trade-offs include eventual consistency (Redis may lag behind Kafka) and increased complexity from managing distributed state. To handle concurrency, use connection pooling and backpressure mechanisms in the push server. Horizontal scaling of notification processors and push servers ensures fault tolerance, while caching reduces database load. However, maintaining session state across WebSocket connections adds overhead, requiring trade-offs between stateful vs. stateless designs.