@inproceedings{kaldor2017canopy,
author = {Kaldor, Jonathan and Mace, Jonathan and Bejda, Michał and Gao, Edison and Kuropatwa, Wiktor and O'Neill, Joe and Win Ong, Kian and Schaller, Bill and Shan, Pingjia and Viscomi, Brendan and Venkataraman, Vinod and Veeraraghavan, Kaushik and Jiun Song, Yee},
title = {Canopy: An End-to-End Performance Tracing And Analysis System},
booktitle = {2017 Symposium on Operating Systems Principles},
year = {2017},
month = {October},
abstract = {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.},
publisher = {ACM},
url = {http://approjects.co.za/?big=en-us/research/publication/canopy-an-end-to-end-performance-tracing-and-analysis-system/},
pages = {34-50},
}