How do you analyze cloud-native application logs and metrics in real-time?

Cloud-native applications log event streams, and metrics reflect system performance data. Real-time analysis is crucial for rapid troubleshooting, performance optimization, and ensuring service SLAs, serving as a core requirement for运维 in dynamic microservice and containerized environments.

Core solutions include:

1. Unified collection layer: Using Fluentd/Filebeat to collect container logs; Prometheus Operator to scrape application/node metrics

2. Stream processing pipeline: Transmitting data via Kafka/Pulsar, and performing real-time filtering and aggregation with Flink/Samza

3. Storage and computing: Storing logs in Elasticsearch/Loki, inputting metrics into Prometheus/Thanos; supporting real-time queries

Key technical features: Declarative collection configuration, low-latency stream processing, and correlation analysis capabilities (e.g., linkage between Jaeger distributed tracing and metrics).

Implementation steps:

1. Deploy log/metric collectors (DaemonSet or Sidecar mode)

2. Establish Kafka message queues to buffer data streams

3. Configure real-time computing rules (e.g., anomaly detection thresholds)

4. Integrate visualization tools (Grafana+Prometheus/ELK)

5. Set up alert notifications (Alertmanager/Slack)

Business value: Minute-level fault localization, real-time optimization of resource utilization (e.g., HPA auto-scaling), and visual dashboards for business health.