VLDB2021
Napa: Powering Scalable Data Warehousing with Robust Query Performance at Google
Ankur Agiwal, Kevin Lai, Gokul Nath Babu Manoharan, Indrajit Roy, Jagan Sankaranarayanan, Hao Zhang, Tao Zou, Jim Chen, Min 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, Rajesh Sr, Vijayshankar Raman, Sourashis Roy, Mayank Singh Shishodia, Tianhang Sun, Justin Tang, Jun'ichi Tatemura, Sagar Trehan, Ramkumar Vadali, Prasanna Venkatasubramanian, Joey Zhang, Kefei Zhang, Yupu Zhang, Zeleng Zhuang, Goetz Graefe, Divy Agrawal, Jeffrey F. Naughton, Sujata Kosalge, Hakan Hacigümüs
被引用 26 次
摘要
Google services continuously generate vast amounts of application data. This data provides valuable insights to business users. We need to store and serve these planet-scale data sets under the extremely demanding requirements of scalability, sub-second query response times, availability, and strong consistency; all this while ingesting a massive stream of updates from applications used around the globe. We have developed and deployed in production an analytical data management system, Napa, to meet these requirements. Napa is the backend for numerous clients in Google. These clients have 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, which are maintained consistently as new data is ingested across multiple data centers. Our clients also demand � exibility in being able to adjust their query performance, data freshness, and costs to suit their unique needs. Robust query processing and � exible con � guration of client databases are the hallmark of Napa design.