How do you monitor cloud-native applications for performance and errors?

Monitoring the performance and errors of cloud-native applications is crucial for ensuring high availability and enabling rapid troubleshooting. Cloud-native applications are characterized by their distributed, dynamic, and ephemeral nature, making traditional monitoring methods unsuitable. Instead, full-stack observability must be implemented across the application, infrastructure, and orchestration layers to ensure stable business operations and a good user experience.

The core lies in collecting and analyzing three types of telemetry data: Metrics for quantifying performance (such as request latency and CPU usage); Logs for recording event details to locate errors; and Traces for analyzing cross-service request flows to identify bottlenecks. It is necessary to use Prometheus for metric collection, Loki/Fluentd for log aggregation, Jaeger/Zipkin for implementing distributed tracing, and enhance monitoring with Kubernetes resource metric APIs and Service Mesh (e.g., Istio). Alert rules and runtime health checks (Liveness/Readiness probes) are the foundation for automatically detecting anomalies.

The implementation steps are:

1. Deploy monitoring agents/collectors (e.g., Prometheus Operator, OpenTelemetry agent) and configure target auto-discovery.

2. Integrate the log pipeline: container logs → Fluentd → Elasticsearch/Loki → Grafana.

3. Instrument tracing libraries and configure trace collection.

4. Define Kubernetes health checks and Service Level Objectives (SLOs).

5. Set up threshold-based intelligent alerts (e.g., Prometheus Alertmanager) to avoid false positives.

6. Create a unified dashboard (Grafana) to enable correlated analysis of metrics, logs, and traces.

This solution can achieve second-level anomaly detection, improve the efficiency of cross-service fault localization by more than 50%, and support automated recovery.