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Ankur Agiwal

Ankur Agiwal

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    Napa: Powering Scalable Data Warehousing with Robust Query Performance at Google
    Kevin Lai
    Min Chen
    Jim Chen
    Ming Dai
    Thanh Do
    Haoyu Gao
    Haoyan Geng
    Raman Grover
    Bo Huang
    Yanlai Huang
    Adam Li
    Jianyi Liang
    Tao Lin
    Li Liu
    Yao Liu
    Xi Mao
    Maya Meng
    Prashant Mishra
    Jay Patel
    Vijayshankar Raman
    Sourashis Roy
    Mayank Singh Shishodia
    Tianhang Sun
    Justin Tang
    Junichi Tatemura
    Sagar Trehan
    Ramkumar Vadali
    Prasanna Venkatasubramanian
    Joey Zhang
    Kefei Zhang
    Yupu Zhang
    Zeleng Zhuang
    Divyakanth Agrawal
    Jeff Naughton
    Sujata Sunil Kosalge
    Hakan Hacıgümüş
    Proceedings of the VLDB Endowment (PVLDB), vol. 14 (12) (2021), pp. 2986-2998
    Preview abstract There are numerous Google services that continuously generate vast amounts of log data that are used to provide valuable insights to internal and external business users. We need to store and serve these planet-scale data sets under extremely demanding requirements of scalability, sub-second query response times, availability even in the case of entire data center failures, strong consistency guarantees, ingesting a massive stream of updates coming from the applications used around the globe. We have developed and deployed in production an analytical data management system, called Napa, to meet these requirements. Napa is the backend for multiple internal and external clients in Google so there is a strong expectation of variance-free robust query performance. At its core, Napa’s principal technologies for robust query performance include the aggressive use of materialized views that are maintained consistently as new data is ingested across multiple data centers. Our clients also demand flexibility in being able to adjust their query performance, data freshness, and costs to suit their unique needs. Robust query processing and flexible configuration of client databases are the hallmark of Napa design. Most of the related work in this area takes advantage of full flexibility to design the whole system without the need to support a diverse set of preexisting use cases, whereas Napa needs to deal with the hard constraints of applications that differ on which characteristics of the system are most important to optimize. Those constraints led us to make particular design decisions and also devise new techniques to meet the challenges. In this paper, we share our experiences in designing, implementing, deploying, and running Napa in production with some of Google’s most demanding applications. View details
    Mesa: Geo-Replicated, Near Real-Time, Scalable Data Warehousing
    Fan Yang
    Jason Govig
    Adam Kirsch
    Kelvin Chan
    Kevin Lai
    Shuo Wu
    Sandeep Dhoot
    Abhilash Kumar
    Mingsheng Hong
    Jamie Cameron
    Masood Siddiqi
    David Jones
    Andrey Gubarev
    Shivakumar Venkataraman
    Divyakant Agrawal
    VLDB (2014)
    Preview abstract Mesa is a highly scalable analytic data warehousing system that stores critical measurement data related to Google's Internet advertising business. Mesa is designed to satisfy a complex and challenging set of user and systems requirements, including near real-time data ingestion and queryability, as well as high availability, reliability, fault tolerance, and scalability for large data and query volumes. Specifically, Mesa handles petabytes of data, processes millions of row updates per second, and serves billions of queries that fetch trillions of rows per day. Mesa is geo-replicated across multiple datacenters and provides consistent and repeatable query answers at low latency, even when an entire datacenter fails. This paper presents the Mesa system and reports the performance and scale that it achieves. View details
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