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Mesa: Geo-Replicated, Near Real-Time, Scalable Data Warehousing

Ashish Gupta
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)

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.