演讲摘要：This talk discusses the complexities of implementing distributed transactions with strong consistency, isolation, and availability guarantees, and how deterministic transaction processing engines can eliminate many complexities without reducing these guarantees, nor reducing performance. For example, deterministic processing engines can avoid two phase commit, physical logging, and reduce the window of time in which concurrent transactions must abort or wait upon a data conflict. We also discuss some techniques for implementing determinism in practice. We discuss some latency pitfalls that may arise, especially in the context of geo-distributed transactions across large physical distances, and some recent research results that help avoid these pitfalls.
讲者简介：Daniel Abadi is the Darnell-Kanal Professor of Computer Science at the University of Maryland, College Park. He performs research on database system architecture and implementation, especially at the intersection with scalable and distributed systems. He is best-known for the development of the storage and query execution engines of the C-Store (column-oriented database) prototype, which was commercialized by Vertica and eventually acquired by Hewlett-Packard in 2011, for his HadoopDB research on fault tolerant scalable analytical database systems which was commercialized by Hadapt and acquired by Teradata in 2014, and deterministic, scalable, transactional, distributed systems such as Calvin which is currently being commercialized by Fauna. Abadi is an ACM Fellow and has been a recipient of a Churchill Scholarship, a NSF CAREER Award, a Sloan Research Fellowship, a VLDB Best Paper Award, two VLDB Test of Time Awards (for the work on C-Store and HadoopDB), the 2008 SIGMOD Jim Gray Doctoral Dissertation Award, the 2013-2014 Yale Provost's Teaching Prize, and the 2013 VLDB Early Career Researcher Award. He was the PhD dissertation advisor of Alexander Thomson's and Jose Falerio's PhD dissertations, both of which won SIGMOD Jim Gray Doctoral Dissertation Awards (in 2015 and 2020 respectively).
第38届CCF中国数据库学术会议（NDBC 2021）
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