How do you scale cloud-native applications without impacting performance?

Cloud-native application scaling refers to dynamically adjusting the number of application instances in the cloud to adapt to load changes, with the core being maintaining high performance and high availability. Its importance lies in ensuring elastic resource utilization, applicable to scenarios such as e-commerce peak traffic or real-time data processing, preventing performance degradation and improving user experience.

Core components include auto-scalers (e.g., Kubernetes Horizontal Pod Autoscaler), microservice architecture, load balancers, and monitoring systems (e.g., Prometheus). The principle is to automatically increase or decrease instances based on metric thresholds (e.g., CPU usage). In practical applications, Kubernetes enables seamless scaling, and the independent design of microservices supports zero-downtime expansion, optimizing resource allocation and enhancing system resilience.

Implementation steps: 1. Configure auto-scaling policies (e.g., set HPA thresholds). 2. Adopt microservices to decouple components for independent scaling. 3. Monitor performance metrics and adjust in real-time. A typical scenario is handling sudden traffic. Business values include improved high availability (>99.9%), cost optimization (on-demand resources), and business scale flexibility.