Sanjay Bhansali
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Mesa: Geo-Replicated, Near Real-Time, Scalable Data Warehousing
Shuo Wu
Fan Yang
Sandeep Dhoot
Adam Kirsch
David Jones
Jason Govig
Kevin Lai
Masood Siddiqi
Jamie Cameron
Kelvin Chan
Divyakant Agrawal
Abhilash Kumar
Mingsheng Hong
Andrey Gubarev
Shivakumar Venkataraman
VLDB (2014)
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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.
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Searching for Build Debt: Experiences Managing Technical Debt at Google
Misha Gridnev
J. David Morgenthaler
Proceedings of the Third International Workshop on Managing Technical Debt, IEEE (2012), pp. 1-6
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With a large and rapidly changing codebase, Google software engineers are constantly paying interest on various forms of technical debt. Google engineers also make efforts to pay down that debt, whether through special Fixit days, or via dedicated teams, variously known as janitors, cultivators,
or demolition experts. We describe several related efforts to measure and pay down technical debt found in Google's BUILD files and associated dead code. We address debt found in dependency specifications, unbuildable targets, and
unnecessary command line flags. These efforts often expose other forms of technical debt that must first be managed.
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