How do you monitor cloud-native applications for performance issues?

Cloud-native applications are based on containerization and microservice architectures, deployed in cloud environments (such as Kubernetes). Performance monitoring is crucial for ensuring high availability and detecting bottlenecks, with application scenarios including production deployment and troubleshooting to optimize resource utilization and reduce downtime risks.

Core components include Metrics (such as CPU usage and request latency), Logs (application logs), and Traces (distributed tracing), using tools like Prometheus, Grafana, and Jaeger to implement data collection and analysis. Its principle is based on real-time metric aggregation and anomaly detection. In practical applications, it improves system observability, helps quickly diagnose errors, and enhances resilience, with a significant impact on the cloud-native ecosystem by improving operational efficiency.

Implementation steps: first, integrate monitoring tools (such as deploying Prometheus via Helm); define key performance indicators (e.g., response time and error rate); set up alert rules; and finally, visualize dashboards to analyze data. A typical scenario is real-time monitoring of Kubernetes cluster resource consumption. The business value lies in reducing Mean Time to Recovery (MTTR), lowering costs, and improving user satisfaction.