Composite Key Generation on a Shared-Nothing Architecture
Generating synthetic data sets is integral to benchmarking, debugging, and simulating future scenarios. As data sets become larger, real data characteristics thereby become necessary for the success of new algorithms. Recently introduced software systems allow for synthetic data generation that is truly parallel. These systems use fast pseudorandom number generators and can handle complex schemas and uniqueness constraints on single attributes. Uniqueness is essential for forming keys, which identify single entries in a database instance. The uniqueness property is usually guaranteed by sampling from a uniform distribution and adjusting the sample size to the output size of the table such that there are no collisions. However, when it comes to real composite keys, where only the combination of the key attribute has the uniqueness property, a different strategy needs to be employed. In this paper, we present a novel approach on how to generate composite keys within a parallel data generation framework. We compute a joint probability distribution that incorporates the distributions of the key attributes and use the unique sequence positions of entries to address distinct values in the key domain.
KeywordsHash Function Pseudorandom Number Generator Attribute Domain Output Domain Joint Histogram
We thank the anonymous reviewers for their input that helped to improve the quality of the paper. Furthermore, the first author would like to thank Christian Lessig for his valuable assistance in editing.
- 2.Bruno, N., Chaudhuri, S.: Flexible database generators. In: Proceedings of the 31st International Conference on Very Large Data Bases, VLDB 2005, pp. 1097–1107. VLDB Endowment (2005)Google Scholar
- 6.Gray, J., Sundaresan, P., Englert, S., Baclawski, K., Weinberger, P.J.: Quickly generating billion-record synthetic databases. In: ACM SIGMOD Record, vol. 23, pp. 243–252. ACM (1994)Google Scholar
- 7.Hoag, J.E.: Synthetic Data Generation: Theory, Techniques and Applications. PhD thesis, University of Arkansas (2007)Google Scholar
- 9.Marsaglia, G.: Xorshift rngs. J. Stat. Softw. 8(14), 1–6, 7 (2003)Google Scholar
- 11.Rabl, T., Poess, M.: Parallel data generation for performance analysis of large, complex RDBMS. DBTest, pp. 1–6 (2011)Google Scholar