Publication Type
Journal Article
Version
submittedVersion
Publication Date
11-2013
Abstract
The TPC benchmarks have helped users evaluate database system performance at different scales. Although each benchmark is domain-specific, it is not equally relevant to different applications in the same domain. The present proliferation of applications also leaves many of them uncovered by the very limited number of current TPC benchmarks. There is therefore a need to develop tools for application-specific database benchmarking. This paper presents UpSizeR, a software that addresses the Dataset Scaling Problem: Given an empirical set of relational tables D and a scale factor s, generate a database state e D that is similar to D but s times its size. Such a tool can be useful for scaling up D for scalability testing (s > 1), scaling down for application testing (s < 1), or anonymization (s = 1). Experiments with Flickr show that query results and response times on UpSizeR output match those on crawled data. They also accurately predict throughput degradation for a scale out test. The UpSizeR version in this paper focuses on extracting and replicating the correlation induced by the primary and foreign keys. There are many other forms of correlation involving nonkey values. It is a large task to develop UpSizeR into a tool that can extract and replicate all important correlation, so community effort is required. The current UpSizeR code has therefore been released for open-source development. The ultimate objective is to replace TPC with UpSizeR, so database owners can generate benchmarks that are relevant to their applications.
Keywords
application-specific benchmarking, synthetic data generation, scale factor, empirical dataset, attribute value correlation, social networks
Discipline
Databases and Information Systems
Publication
Information Systems
Volume
38
Issue
8
First Page
1168
Last Page
1183
ISSN
0306-4379
Identifier
10.1016/j.is.2013.07.004
Publisher
Elsevier
Embargo Period
2-19-2014
Citation
TAY, Y. C.; DAI, Bing Tian; WANG, Daniel T.; SUN, Eldora Y.; LIN, Yong; and LIN, Yuting.
UpSizeR: Synthetically scaling an empirical relational database. (2013). Information Systems. 38, (8), 1168-1183.
Available at: https://ink.library.smu.edu.sg/sis_research/2048
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://dx.doi.org/10.1016/j.is.2013.07.004