Decibel: the relational dataset branching system
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Madden_decibel-paper.pdf
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Author(s)
Elmore, Aaron J.
Parameswaran, Aditya
Deshpande, Amol
Goehring, David G.
Maddox, Michael A
Madden, Samuel R
Date Issued
May 2016
Journal
Proceedings of the VLDB Endowment
Publisher
Association for Computing Machinery (ACM)
Citation
Maddox, Michael, David Goehring, Aaron J. Elmore, Samuel Madden, Aditya Parameswaran, and Amol Deshpande. “Decibel.” Proceedings of the VLDB Endowment 9, no. 9 (May 1, 2016): 624–635.
Version
Author's final manuscript
Abstract
As scientific endeavors and data analysis become increasingly collaborative, there is a need for data management systems that natively support the versioning or branching of datasets to enable concurrent analysis, cleaning, integration, manipulation, or curation of data across teams of individuals. Common practice for sharing and collaborating on datasets involves creating or storing multiple copies of the dataset, one for each stage of analysis, with no provenance information tracking the relationships between these datasets. This results not only in wasted storage, but also makes it challenging to track and integrate modifications made by different users to the same dataset. In this paper, we introduce the Relational Dataset Branching System, Decibel, a new relational storage system with built-in version control designed to address these short-comings. We present our initial design for Decibel and provide a thorough evaluation of three versioned storage engine designs that focus on efficient query processing with minimal storage overhead. We also develop an exhaustive benchmark to enable the rigorous testing of these and future versioned storage engine designs.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Mathematics
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.14778/2947618.2947619
