SFU Computing Science researchers receive 2022 ACM SIGMOD Research Highlight Award.
By Deborah Acheampong
Computing science researchers Tianzheng Wang, Jianqiu Zhang, and Kaisong Huang have received a 2022 ACM SIGMOD Research Highlight Award for their paper: Skeena: Efficient and Consistent Cross-Engine Transactions.
“The SIGMOD Research Highlights showcase a set of research projects that exemplify core database research”, according to the awards committee on their website. “These projects address an important problem, represent a definitive milestone in solving the problem, and have the potential of significant impact. SIGMOD Research Highlights also aim to make the selected works widely known to the database community, our industry partners, and the broader ACM community”.
About the award-winning paper:
Database systems are becoming increasingly multi-engine. In particular, a main-memory database engine may coexist with a traditional storage-centric engine in a system to support various applications. It is desirable to allow applications to access data in both engines using cross-engine transactions. But existing systems are either only designed for single-engine accesses or impose many restrictions by limiting cross-engine transactions to certain isolation levels and table operations. The result is inadequate cross-engine support in terms of correctness, performance, and programmability.
This paper describes Skeena as a holistic approach to cross-engine transactions. The research proposes a lightweight snapshot tracking structure and an atomic commit protocol to efficiently ensure correctness and support various isolation levels. Evaluation results show that Skeena maintains high performance for single-engine transactions and enables cross-engine transactions, which can improve output by up to thirty times by judiciously placing tables in different engines.
Discussing the industrial impact of the paper, professor Wang asserts that researchers in the field have been leveraging Skeena’s results for similar problems in a related sub-area like lake house analytical engines. The work enables new uses of ultra-fast memory-optimized database engines, which has been a long-term issue industry is trying to figure out.
“Receiving this award is a great honor. It means a lot to me as a core data systems researcher and encourages me to continue to focus on the systems side of databases,” says professor Wang. “There are many exciting developments in different aspects that impact database engine designs, for example, programming languages, storage, and new interconnections. We’ll continue exploring the intersection of these areas and databases to make impacts beyond academia”.
Before winning the 2022 ACM SIGMOD Research Highlight Award, professor Wang and student Xiangpeng Hao received the 2021 ACM SIGMOD Research Highlight Award for their paper: Dash: Scalable Hashing on Persistent Memory. The Dash project explores how we make data access more efficient by designing novel indexing methods.
Dash is already in use in the industry, with companies like Dragonfly DB (https://dragonflydb.io) using it as its core dash table data structure.
Click below to learn more about these two award-winning papers: