How do you perform root cause analysis using observability data?

Observability data includes logs, metrics, and trace data, which are used to capture system behavior and status in real time. Its importance lies in supporting proactive fault diagnosis and improving system reliability. Application scenarios include monitoring Kubernetes clusters in cloud-native environments to quickly respond to performance bottlenecks or service outages.

The core components include logs (recording event details), metrics (quantifying performance parameters), and traces (mapping request lifecycles), providing a comprehensive view of the system based on multi-dimensional aggregation. The principle is to reveal abnormal patterns through associated data to achieve root cause location. The practical impact is optimizing operational efficiency, reducing MTTR (Mean Time to Recovery), and promoting AIOps automated responses.

Implementation steps: 1) Integrate tools such as Prometheus and Jaeger to collect data; 2) Set thresholds to detect anomalies; 3) Correlate logs, metrics, and traces to identify fault sources; 4) Analyze context to infer root causes; 5) Verify fixes and optimize strategies. Typical scenarios handle service delays or crashes. Business values include accelerating problem resolution, enhancing SLA compliance rates, and reducing operational costs.