Details

Autor(en) / Beteiligte

• Kaldor, Jonathan
• Mace, Jonathan
• Bejda, Michał
• Gao, Edison
• Kuropatwa, Wiktor
• O'Neill, Joe
• Ong, Kian Win
• Schaller, Bill
• Shan, Pingjia
• Viscomi, Brendan
• Venkataraman, Vinod
• Veeraraghavan, Kaushik
• Song, Yee Jiun

Titel

Canopy: An End-to-End Performance Tracing And Analysis System

Ist Teil von

• Proceedings of the 26th Symposium on Operating Systems Principles, 2017, p.34-50

Ort / Verlag

New York, NY, USA: ACM

Erscheinungsjahr

2017

Quelle

ACM Digital Library Complete

Beschreibungen/Notizen

• This paper presents Canopy, Facebook's end-to-end performance tracing infrastructure. Canopy records causally related performance data across the end-to-end execution path of requests, including from browsers, mobile applications, and backend services. Canopy processes traces in near real-time, derives user-specified features, and outputs to performance datasets that aggregate across billions of requests. Using Canopy, Facebook engineers can query and analyze performance data in real-time. Canopy addresses three challenges we have encountered in scaling performance analysis: supporting the range of execution and performance models used by different components of the Facebook stack; supporting interactive ad-hoc analysis of performance data; and enabling deep customization by users, from sampling traces to extracting and visualizing features. Canopy currently records and processes over 1 billion traces per day. We discuss how Canopy has evolved to apply to a wide range of scenarios, and present case studies of its use in solving various performance challenges.