scalability

Horizontal scaling distributes traffic across multiple stateless service instances, while load balancers route requests efficiently. Adding distributed caches and asynchronous message queues prevents bottlenecks, allowing the API to handle traffic spikes and maintain consistent latency.

Horizontal scaling adds more service instances to handle increased load, unlike vertical scaling which upgrades a single machine. You need it in distributed systems when managing traffic spikes, supporting millions of users, or ensuring high availability across microservices.

Caching improves resilience in microservices by storing frequently accessed data in distributed caches, reducing direct database load. This separation of read and write operations prevents bottlenecks and maintains performance during sudden traffic spikes.

Yes, you can use asynchronous processing with message queues to handle traffic spikes in event-driven systems. Asynchronous pipelines decouple services, allowing background processing of heavy tasks without blocking the main application flow.

The best way to separate read and write operations is by implementing read/write separation patterns with distributed caches and asynchronous pipelines. This isolates database write bottlenecks from high-volume read requests, ensuring consistent latency across microservices.

Load balancing matters because it distributes incoming network traffic across multiple stateless services, preventing any single server from becoming a bottleneck. It is essential for auto-scaling and ensuring resilient operation during unexpected demand surges.