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Procella: Unifying serving and analytical data at YouTube
Biswapesh Chattopadhyay
Priyam Dutta
Weiran Liu
Andrew McCormick
Aniket Mokashi
Paul Harvey
Hector Gonzalez
David Lomax
Sagar Mittal
Nikita Mikhaylin
Hung-ching Lee
Xiaoyan Zhao
Guanzhong Xu
Luis Antonio Perez
Farhad Shahmohammadi
Tran Bui
Neil McKay
Vera Lychagina
PVLDB, 12(12) (2019), pp. 2022-2034
Preview abstract
Large organizations like YouTube are dealing with exploding data volume and increasing demand for data driven applications. Broadly, these can be categorized as: reporting and dashboarding, embedded statistics in pages, time-series monitoring, and ad-hoc analysis. Typically, organizations build specialized infrastructure for each of these use cases. This, however, creates silos of data and processing, and results in a complex, expensive, and harder to maintain infrastructure.
At YouTube, we solved this problem by building a new SQL query engine - Procella. Procella implements a super-set of capabilities required to address all of the four use cases above, with high scale and performance, in a single product. Today, Procella serves hundreds of billions of queries per day across all four workloads at YouTube and several other Google product areas.
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