Log management in cloud-native deployments has several key characteristics:
Scalability: Logs are generated from distributed microservices, requiring systems to handle high volumes dynamically. For example, a Kubernetes cluster with auto-scaling pods produces logs that must scale with workload changes. Tencent Cloud's CLS (Cloud Log Service) supports horizontal scaling to process massive log data efficiently.
Real-time Processing: Cloud-native apps demand near-instant log analysis for monitoring and troubleshooting. Tools like Tencent CLS enable real-time log ingestion and querying, helping detect anomalies promptly.
Decentralized Collection: Logs are scattered across containers, nodes, and services. Solutions like Tencent CLS integrate with Fluentd or Filebeat to aggregate logs from diverse sources into a unified platform.
Structured Logging: JSON or key-value formats improve searchability and analysis. For instance, a microservice might log structured data like {"user_id": 123, "action": "login"}, which Tencent CLS can index and query efficiently.
Temporary Storage: Logs in ephemeral containers vanish when pods restart. Tencent CLS provides persistent storage, ensuring logs are retained for compliance or auditing.
Integration with Observability Tools: Logs are often correlated with metrics and traces. Tencent CLS works with monitoring services like Tencent Cloud Monitor to provide holistic insights.
Security and Compliance: Logs must be encrypted and access-controlled. Tencent CLS offers role-based access and TLS encryption for data in transit and at rest.
Example: An e-commerce app on Kubernetes uses Tencent CLS to collect logs from API gateways, payment services, and databases, enabling real-time error tracking and traffic analysis.