UpSizeR

Synthetically Scaling up A Given Database State

 

About UpSizeR

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About UpSizeR

The rise of e-commerce and social networking has spawned many Internet-scale service providers. Several have phenomenal growth rates. These providers and the wannabes that they inspire must ensure that their systems are scalable. Engineering for rapid growth in this highly competitive setting requires intensive testing with scaled-up datasets. This motivates the Dataset Scaling Problem (DSP):

The Dataset Scaling Problem
Given a set of relational tables D and a scale factor s, generate a database state D' that is similar to D but s times its size.

UpSizeR, a tool for solving DSP, does this by extracting inter-column and inter-row information from D. Although UpSizeR was conceived for scalability testing (s>1), it can also be used by an enterprise to make a synthetic copy (s=1) of its proprietary dataset for a vendor, or scale down a production dataset (s<1) for non-production testing. Our wish is that UpSizeR can work for any relational database. However, given the diversity of applications, the complexities in real data and the pressing need for a scaling tool, UpSizeR development requires a community effort. We are therefore releasing UpSizeR, to encourage open-source collaboration to develop it.

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Video Presentation

UpSizeR at Google

 

UpSizeR at Microsoft Research

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Publications

Y.C. Tay. Data Generation for Application-Specific Benchmarking. Proc. VLDB 2011 (Challenges and Visions Track), 1470-1473 (Aug. 2011).[PDF Version]

Y.C. Tay, Bing Tian Dai, Daniel T. Wang, Eldora Y. Sun, Yong Lin and Yuting Lin. UpSizeR: Synthetically Scaling an Empirical Relational Database. Information Systems 38, 8(Nov. 2013), 1168--1183. [ PDF Version]

J.W. Zhang and Y.C. Tay. GSCALER: Synthetically Scaling A Given Graph. Proc. 19th Int. Conf. Extending Database Technology (EDBT), Bordeaux, France (March 2016), 53--64. PDF Version (Source Code)

J.W. Zhang and Y.C. Tay. DSCALER: Synthetically Scaling A Given Relational Database. PVLDB 9, 14 (Sept. 2016), 1671--1682. PDF Version (Source Code)

J.W. Zhang, A. Mal and Y.C. Tay. GscalerCloud: A Web-Based Graph Scaling Service ( Demo Website). Proc. 33rd IEEE Int. Conf. Data Engineering (ICDE), San Diego, USA (Apr. 2017),1391--1392. PDF Version

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UpSizeR - Centralized Version
[]UpSizeR - Hadoop Version

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Contact Us

Please contact us at
tayyc at comp dot nus dot edu dot sg

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