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Mesa: A Geo-Replicated Online Data Warehouse for Google's Advertising System.

Authors :
Gupta, Ashish
Fan Yang
Govig, Jason
Kirsch, Adam
Chan, Kelvin
Lai, Kevin
Shuo Wu
Dhoot, Sandeep
Kumar, Abhilash Rajesh
Agiwal, Ankur
Bhansali, Sanjay
Mingsheng Hong
Cameron, Jamie
Siddiqi, Masood
Jones, David
Shute, Jeff
Gubarev, Andrey
Venkataraman, Shivakumar
Agrawal, Divyakant
Source :
Communications of the ACM. Jul2016, Vol. 59 Issue 7, p117-125. 9p. 4 Diagrams, 2 Charts, 4 Graphs.
Publication Year :
2016

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 retrieval, 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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00010782
Volume :
59
Issue :
7
Database :
Academic Search Index
Journal :
Communications of the ACM
Publication Type :
Periodical
Accession number :
116599174
Full Text :
https://doi.org/10.1145/2936722