Pinterest的自主可观察性（第1部分，共2部分）

At Pinterest, inspiration isn’t just for our users — it shapes how we build and care for our platform. Until recently, our own observability (o11y) tools told a fragmented story: logs over here, traces over there, and metrics somewhere else. We’ve always excelled at collecting signals: time-series metrics, traces, logs, and change related events. But without the seamless context and unity now promised by open standards like OpenTelemetry (OTel), we were missing out on the “big picture”: the full narrative behind every anomaly and alert. While modern observability standards like OpenTelemetry (OTel) promise a unified world of correlated data, the reality for many mature, large-scale infrastructures is far more fragmented. These systems, often predating the widespread adoption of such standards, are composed of powerful but disconnected data silos. We solved the problem with a pragmatic solution to this common challenge by leveraging AI agents through a centralized Model Context Protocol (MCP) server to bridge these gaps without mandating a complete infrastructure overhaul.

在 Pinterest，灵感不仅仅是为了我们的用户——它塑造了我们构建和关心我们平台的方式。直到最近，我们自己的可观察性（o11y）工具讲述了一个碎片化的故事：日志在这里，追踪在那里，指标在别处。我们一直擅长收集信号：时间序列指标、追踪、日志和变更相关事件。但没有现在由 OpenTelemetry（OTel）等开放标准所承诺的无缝上下文和统一性，我们错过了“全局视图”：每个异常和警报背后的完整叙述。虽然像 OpenTelemetry（OTel）这样的现代可观察性标准承诺一个统一的相关数据世界，但对于许多成熟的大规模基础设施而言，现实却更加碎片化。这些系统往往在广泛采用此类标准之前就已存在，由强大但不相连的数据孤岛组成。我们通过利用 AI 代理通过集中模型上下文协议（MCP）服务器来弥合这些差距，提供了一种务实的解决方案来应对这一共同挑战，而无需强制进行全面的基础设施改造。

The Pinterest Observability team is charting a new course that meets the moment. We’re working both left and right: “shift-left” practices to bake better logging and instrumentation into the heart of our code, a...