Composite Key Generation on a Shared-Nothing Architecture

Abstract

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.

A Composite Keys

Listing 1.1 shows four SQL statements for creating simple schemas. For the sake of simplicity the statements only declare key columns. Table Simple has one column protein which is declared as primary key and is necessarily unique, i.e. protein makes up a simple key. Table Compound has two attributes, each making up a simple key in its own right, since they are declared as unique. Tables Composite1 and Composite2 are examples of composite key declarations. Composite1 has only one key attribute which makes up a simple key. Table Composite2 has even two attributes for which uniqueness exclusively holds for their combination. Possible instances of all four relations are shown below.