How do you handle traffic spikes in microservices environments?

Traffic peak refers to the sudden influx of high concurrent requests into the system, which is crucial in a microservices architecture as its distributed nature can easily trigger cascading service failures. Its importance lies in ensuring high availability and business continuity, commonly seen in scenarios such as e-commerce promotions, to guarantee timely response to user requests.

The core of handling traffic peaks is based on the principles of elasticity and scalability, involving components such as auto-scaling (dynamically adjusting Pods through Kubernetes HPA), load balancing (distributing requests via Ingress or service mesh), and service degradation (implementing circuit breaking and rate limiting such as Istio). In practical applications, cloud-native tools like Prometheus monitor and trigger scaling to achieve traffic balancing and resource optimization, thereby enhancing overall resilience.

Implementation steps: 1. Deploy auto-scaling policies to adjust the number of service instances. 2. Use API gateways or service meshes (such as Envoy) to limit and circuit break abnormal traffic. 3. Combine caching (such as Redis) to degrade non-core services. Typical scenarios include maintaining system stability during large promotions; the business value is to reduce downtime risks, improve user experience, and ensure revenue continuity.