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Roee Ebenstein

Roee Ebenstein

DSDQuery DSI - Querying Scientific Data Repositories with Structured Operators (IEEE BigData, 2015)
FluxQuery: An Execution Framework for Highly Interactive Query Workloads (SIGMOD, 2016)
DistriPlan: An Optimized Join Execution Framework for Geo-Distributed Scientific Data (SSDBM, 2017)
FDQ: Advance Analytics Over Real Scientific Array Datasets (e-science, 2018)

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    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, 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. View details
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