Observability helps ensure the reliability and maintainability of cloud-native applications. As software architectures become increasingly distributed and subject to change, it becomes a greater challenge to diagnose system issues effectively, often having to deal with fragmented observability and more difficult root cause analysis. This paper builds upon our previous work and introduces three design patterns that address key challenges in monitoring cloud-native applications. Distributed Tracing improves visibility into request flows across services, aiding in latency analysis and root cause detection, Application Metrics provides a structured approach to instrumenting applications with meaningful performance indicators, enabling real-time monitoring and anomaly detection, and Infrastructure Metrics focuses on monitoring the environment in which the system is operated, helping teams assess resource utilization, scalability, and operational health. These patterns are derived from industry practices and observability frameworks and aim to offer guidance for software practitioners.

翻译：可观测性有助于确保云原生应用的可靠性与可维护性。随着软件架构日益分布式化且频繁变更，有效诊断系统问题变得更具挑战性，往往需要应对碎片化的可观测性及更复杂的根因分析。本文基于我们先前的研究，提出了三种应对云原生应用监控关键挑战的设计模式。分布式追踪提升了跨服务请求流的可见性，有助于延迟分析与根因定位；应用度量提供了通过关键性能指标对应用进行结构化埋点的方案，支持实时监控与异常检测；基础设施度量则聚焦于监控系统运行环境，帮助团队评估资源利用率、可扩展性与运行状态。这些模式源于行业实践与可观测性框架，旨在为软件从业者提供实践指导。