dc.contributor.author | Lu, Yi | |
dc.contributor.author | Yu, Xiangyao | |
dc.contributor.author | Cao, Lei | |
dc.contributor.author | Madden, Samuel | |
dc.date.accessioned | 2021-10-27T19:57:30Z | |
dc.date.available | 2021-10-27T19:57:30Z | |
dc.date.issued | 2020 | |
dc.identifier.uri | https://hdl.handle.net/1721.1/133983 | |
dc.description.abstract | © 2020, VLDB Endowment. Deterministic databases are able to efficiently run transactions across different replicas without coordination. However, existing state-of-the-art deterministic databases require that transaction read/write sets are known before execution, making such systems impractical in many OLTP applications. In this paper, we present Aria, a new distributed and deterministic OLTP database that does not have this limitation. The key idea behind Aria is that it first executes a batch of transactions against the same database snapshot in an execution phase, and then deterministically (without communication between replicas) chooses those that should commit to ensure serializability in a commit phase. We also propose a novel deterministic reordering mechanism that allows Aria to order transactions in a way that reduces the number of con icts. Our experiments on a cluster of eight nodes show that Aria outperforms systems with conventional nondeterministic concurrency control algorithms and the state-of-the-art deterministic databases by up to a factor of two on two popular benchmarks (YCSB and TPC-C). | |
dc.language.iso | en | |
dc.publisher | VLDB Endowment | |
dc.relation.isversionof | 10.14778/3407790.3407808 | |
dc.rights | Creative Commons Attribution-NonCommercial-NoDerivs License | |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.source | VLDB Endowment | |
dc.title | Aria: a fast and practical deterministic OLTP database | |
dc.type | Article | |
dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | |
dc.contributor.department | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory | |
dc.relation.journal | Proceedings of the VLDB Endowment | |
dc.eprint.version | Final published version | |
dc.type.uri | http://purl.org/eprint/type/ConferencePaper | |
eprint.status | http://purl.org/eprint/status/NonPeerReviewed | |
dc.date.updated | 2021-01-29T18:14:37Z | |
dspace.orderedauthors | Lu, Y; Yu, X; Cao, L; Madden, S | |
dspace.date.submission | 2021-01-29T18:14:41Z | |
mit.journal.volume | 13 | |
mit.journal.issue | 12 | |
mit.license | PUBLISHER_CC | |
mit.metadata.status | Authority Work and Publication Information Needed | |