Abstract
Serializable snapshot isolation (SSI) is a promising technique to exploit parallelism for multi-core databases. However, SSI suffers from excessive transaction aborts. Existing remedies have three drawbacks: 1) tracking prohibitively transitive dependencies; 2) aborting on every write-write conflict; and 3) requiring manual annotation on transaction programs.
In this paper, we propose to suppress transaction aborts by reassigning timestamps. We combine static analysis with augmented query plan. In this way, we save both aborts caused by read-write and write-write conflicts, without tracking transitive dependency and annotating transaction programs. As such, our approach does not exhibit drawbacks of existing methods. Extensive experiments demonstrate the effectiveness and practicality of our approach.
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Acknowledgements
This work was partially supported by the National Key R&D Program of China (2018YFB1004401), and the National Natural Science Foundation of China Key Project (Grant No. 61732014).
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Ningnan Zhou is currently a PhD student at the School of Information, Renmin University of China. He received his BE degree in Computer Science from Renmin University of China in 2012. His research interests include database system and data mining.
Xiao Zhang is an associated professor at the School of Information, Renmin University of China. He received his PhD degree from Institute of Computing Technology, Chinese Academy of Science, China. His research interests include database system and big data management.
Shan Wang is a professor at the School of Information, Renmin University of China. Her research interests include RDBMS (XDB, KingbaseES), parallel and distributed DBMS(PBase) and main memory DBMS, data warehouse, DB-IR and big data management. She has extensive publications including SIGMOD, VLDB, ICDE, SIGKDD, TKDE and CACM. She was the co-chair of VLDB’02 and honorary chair of SIGMOD’07.
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Zhou, N., Zhang, X. & Wang, S. Timestamp reassignment: taming transaction abort for serializable snapshot isolation. Front. Comput. Sci. 13, 1282–1295 (2019). https://doi.org/10.1007/s11704-018-7018-z
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DOI: https://doi.org/10.1007/s11704-018-7018-z