Distributed Latency Profiling through Critical Path Tracing

Q3 Computer Science
Queue Pub Date : 2022-02-28 DOI:10.1145/3526967
Brian Eaton, Jeff Sterart, Jon Tedesco, N. Tas
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引用次数: 5

Abstract

Low latency is an important feature for many Google applications such as Search, and latency-analysis tools play a critical role in sustaining low latency at scale. For complex distributed systems that include services that constantly evolve in functionality and data, keeping overall latency to a minimum is a challenging task. In large, real-world distributed systems, existing tools such as RPC telemetry, CPU profiling, and distributed tracing are valuable to understand the subcomponents of the overall system, but are insufficient to perform end-to-end latency analyses in practice. Scalable and accurate fine-grain tracing has made Critical Path Tracing the standard approach for distributed latency analysis for many Google applications, including Google Search.
通过关键路径跟踪的分布式延迟分析
低延迟是许多谷歌应用程序(如Search)的一个重要特性,延迟分析工具在维持大规模低延迟方面发挥着关键作用。对于包含功能和数据不断发展的服务的复杂分布式系统,将总体延迟降至最低是一项具有挑战性的任务。在大型、真实的分布式系统中,现有的工具,如RPC遥测、CPU分析和分布式跟踪,对于理解整个系统的子组件是有价值的,但在实践中不足以执行端到端延迟分析。可伸缩和精确的细粒度跟踪使得关键路径跟踪成为许多谷歌应用程序(包括谷歌Search)的分布式延迟分析的标准方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Queue
Queue Computer Science-Computer Science (all)
CiteScore
1.80
自引率
0.00%
发文量
23
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