Mesa: Georeplicated, near realtime, scalable data warehousing

Ashish Gupta, Fan Yang, Jason Govig, Adam Kirsch, Kelvin Chan, Kevin Lai, Shuo Wu, Sandeep Govind Dhoot, Abhilash Rajesh Kumar, Ankur Agiwal, Sanjay Bhansali, Mingsheng Hong, Jamie Cameron, Masood Siddiqi, David Jones, Jeff Shute, Andrey Gubarev, Shivakumar Venkataraman, Divyakant Agrawal

Research output: Contribution to journalArticle

46 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1259-1270
Number of pages12
JournalProceedings of the VLDB Endowment
Issue number12
Publication statusPublished - 2014


ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Computer Science(all)

Cite this

Gupta, A., Yang, F., Govig, J., Kirsch, A., Chan, K., Lai, K., Wu, S., Dhoot, S. G., Kumar, A. R., Agiwal, A., Bhansali, S., Hong, M., Cameron, J., Siddiqi, M., Jones, D., Shute, J., Gubarev, A., Venkataraman, S., & Agrawal, D. (2014). Mesa: Georeplicated, near realtime, scalable data warehousing. Proceedings of the VLDB Endowment, 7(12), 1259-1270.