Taurus: lightweight parallel logging for in-memory database management systems
Name
2010.06760.pdf
Description
Accepted version
Size
1.51 MB
Format
Adobe PDF
Checksum (MD5)
966fd057a03ab80e5bf16f769a3f5321
Author(s)
Xia, Yu
Yu, Xiangyao
Pavlo, Andrew
Devadas, Srinivas
Date Issued
2020
Journal
Proceedings of the VLDB Endowment
Publisher
VLDB Endowment
Citation
Xia, Yu, Yu, Xiangyao, Pavlo, Andrew and Devadas, Srinivas. 2020. "Taurus: lightweight parallel logging for in-memory database management systems." Proceedings of the VLDB Endowment, 14 (2).
Version
Author's final manuscript
Abstract
Existing single-stream logging schemes are unsuitable for in-memory database management systems (DBMSs) as the single log is often a performance bottleneck. To overcome this problem, we present Taurus, an efficient parallel logging scheme that uses multiple log streams, and is compatible with both data and command logging. Taurus tracks and encodes transaction dependencies using a vector of log sequence numbers (LSNs). These vectors ensure that the dependencies are fully captured in logging and correctly enforced in recovery. Our experimental evaluation with an in-memory DBMS shows that Taurus’s parallel logging achieves up to 9.9× and 2.9× speedups over single-streamed data logging and command logging, respectively. It also enables the DBMS to recover up to 22.9× and 75.6× faster than these baselines for data and command logging, respectively. We also compare Taurus with two state-of-the-art parallel logging schemes and show that the DBMS achieves up to 2.8× better performance on NVMe drives and 9.2× on HDDs.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.14778/3425879.3425889
