在 Meta 保障部署安全：面向持续变更安全的健康检查
Making Deployments Safe at Meta: Health Checks for Continuous Change-Safety
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中文总结 AI 辅助
Meta 介绍其用于平衡持续部署速度与可靠性的 Service Health Checker 健康检查基础设施，阐述其架构、集成方式、解决的运营问题及未来 AI 辅助调优方向。
中文摘要 AI 辅助
向大规模生产系统持续部署会在发布速度与可靠性之间产生矛盾：每次变更都可能引发可靠性事故，而每次延迟又会错失机会。本文介绍 Meta 用于调解这一矛盾、覆盖数千种异构服务的部署时健康检查基础设施，总结名为 Service Health Checker 的预防型分布式系统服务架构，说明检查作者如何组合模板化指标查询、阈值与工作流谓词，以及该系统如何与分层、分阶段发布集成，以便在出现退化时触发自动回滚。随后阐述大规模部署中出现的运营问题，如噪声、警报疲劳、漂移及未覆盖的退化，以及为解决这些问题而实施的测量、工具和改进默认值方案。最后总结 Meta 多年运营部署健康检查的经验教训，并介绍正在探索的方向，包括 AI 辅助的健康检查调优。
英文摘要
Continuous deployment to large scale production systems creates a tension between release velocity and reliability. Every change is a potential reliability incident, yet every delay is a missed opportunity. This paper describes the deployment time health check infrastructure that Meta uses to mediate this tension across thousands of heterogeneous services. We summarize the architecture of this prevention based distributed system's service called Service Health Checker, explain how check authors compose templated metric queries, thresholds, and workflow predicates; and discuss how the system is integrated with tiered and phased rollouts so that regressions trigger automatic rollback. We then describe the operational problems that emerged at scale, such as noise, alert fatigue, drift, and uncovered regressions, and the program of measurement, tooling, and improved defaults we deployed to address them. We close with lessons learned from years of operating deployment health checks at Meta, and the directions we are exploring next, including AI assisted health check tuning. Index Terms: deployment safety, continuous deployment, monitoring, software reliability, release engineering, software reliability engineering, AIOps, anomaly detection
发表机构
- Meta Platforms, Inc.（元平台公司）
机构由 AI 辅助整理，请以论文原文为准。
