Google Research

Procella: Unifying serving and analytical data at YouTube

  • Biswapesh Chattopadhyay
  • Priyam Dutta
  • Weiran Liu
  • Ott Tinn
  • Andrew McCormick
  • Aniket Mokashi
  • Paul Harvey
  • Hector Gonzalez
  • David Lomax
  • Sagar Mittal
  • Roee Aharon Ebenstein
  • Nikita Mikhaylin
  • Hung-ching Lee
  • Xiaoyan Zhao
  • Guanzhong Xu
  • Luis Antonio Perez
  • Farhad Shahmohammadi
  • Tran Bui
  • Neil McKay
  • Vera Lychagina
  • Brett Elliott
PVLDB, vol. 12(12) (2019), pp. 2022-2034


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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