Many modern data-oriented applications are built on top of distributed OLTP databases for both scalability and high availability. However, when running transactions that span several partitions of the database, significant performance degradation is observed in existing distributed OLTP databases. In this thesis, we develop three systems — (1) STAR, (2) COCO, and (3) Aria — to address the inefficiency and limitations of existing distributed OLTP databases while using different mechanisms and bearing various tradeoffs. STAR eliminates two-phase commit and network communication through asymmetric replication. COCO eliminates two-phase commit and reduces the cost of replication through epoch-based commit and replication. Aria eliminates two-phase commit and the cost of replication through deterministic execution. Our experiments on two popular benchmarks (YCSB and TPC-C) show that these three systems outperform conventional designs by a large margin. We also characterize the tradeoffs in these systems and the settings in which they are most appropriate.
BIO: Yi Lu is a fifth-year PhD student at MIT Database Group, working with Prof. Sam Madden. He received his Bachelor degree from Harbin Institute of Technology in 2013 and Master degree from the Chinese University of Hong Kong in 2015. His research focuses on transaction processing and data warehousing. He is also a recipient of the Facebook Fellowship (2018-2020).
COMMITTEE: Sam Madden (CSAIL, MIT), Robert Morris (CSAIL, MIT), Tim Kraska, (CSAIL, MIT)