UpSizeR: Synthetically scaling an empirical relational database

作者：Tay Y C^*; Dai Bing Tian; Wang Daniel T; Sun Eldora Y; Lin Yong; Lin Yuting

来源：Information Systems, 2013, 38(8): 1168-1183.

DOI：10.1016/j.is.2013.07.004

• Application-specific benchmarking
• Synthetic data generation
• Scale factor
• Empirical dataset
• Attribute value correlation
• Social networks

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 (D) over tilde, that is similar to D but s times its size. Such a tool can be useful for scaling up 7, 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 non-key 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.

• 出版日期2013-11