Zamanian, Erfan ; Shun, Julian ; Binnig, Carsten ; Kraska, Tim (2020)
Chiller: Contention-Centric Transaction Execution and Data Partitioning for Modern Networks.
SIGMOD/PODS '20: International Conference on Management of Data. virtual Conference (14.-19.06.2020)
doi: 10.1145/3318464.3389724
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Distributed transactions on high-overhead TCP/IP-based networks were conventionally considered to be prohibitively expensive and thus were avoided at all costs. To that end, the primary goal of almost any existing partitioning scheme is to minimize the number of cross-partition transactions. However, with the new generation of fast RDMA-enabled networks, this assumption is no longer valid. In fact, recent work has shown that distributed databases can scale even when the majority of transactions are cross-partition. In this paper, we first make the case that the new bottleneck which hinders truly scalable transaction processing in modern RDMA-enabled databases is data contention, and that optimizing for data contention leads to different partitioning layouts than optimizing for the number of distributed transactions. We then present Chiller, a new approach to data partitioning and transaction execution, which aims to minimize data contention for both local and distributed transactions. Finally, we evaluate Chiller using various workloads, and show that our partitioning and execution strategy outperforms traditional partitioning techniques which try to avoid distributed transactions, by up to a factor of 2.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2020 |
| Autor(en): | Zamanian, Erfan ; Shun, Julian ; Binnig, Carsten ; Kraska, Tim |
| Art des Eintrags: | Bibliographie |
| Titel: | Chiller: Contention-Centric Transaction Execution and Data Partitioning for Modern Networks |
| Sprache: | Deutsch |
| Publikationsjahr: | 2020 |
| Verlag: | ACM |
| Buchtitel: | Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data |
| Veranstaltungstitel: | SIGMOD/PODS '20: International Conference on Management of Data |
| Veranstaltungsort: | virtual Conference |
| Veranstaltungsdatum: | 14.-19.06.2020 |
| DOI: | 10.1145/3318464.3389724 |
| URL / URN: | https://doi.org/10.1145/3318464.3389724 |
| Kurzbeschreibung (Abstract): | Distributed transactions on high-overhead TCP/IP-based networks were conventionally considered to be prohibitively expensive and thus were avoided at all costs. To that end, the primary goal of almost any existing partitioning scheme is to minimize the number of cross-partition transactions. However, with the new generation of fast RDMA-enabled networks, this assumption is no longer valid. In fact, recent work has shown that distributed databases can scale even when the majority of transactions are cross-partition. In this paper, we first make the case that the new bottleneck which hinders truly scalable transaction processing in modern RDMA-enabled databases is data contention, and that optimizing for data contention leads to different partitioning layouts than optimizing for the number of distributed transactions. We then present Chiller, a new approach to data partitioning and transaction execution, which aims to minimize data contention for both local and distributed transactions. Finally, we evaluate Chiller using various workloads, and show that our partitioning and execution strategy outperforms traditional partitioning techniques which try to avoid distributed transactions, by up to a factor of 2. |
| Freie Schlagworte: | dm |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Data Management (2022 umbenannt in Data and AI Systems) |
| Hinterlegungsdatum: | 14 Dez 2020 09:28 |
| Letzte Änderung: | 21 Apr 2022 09:05 |
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