Answering GPSJ queries in a polystore: A dataspace-based approach

The discipline of data science is steering analysts away from traditional data warehousing and towards a more flexible and lightweight approach to data analysis. The idea is to perform OLAP analyses in a pay-as-you-go manner across heterogeneous schemas and data models, where the integration is progressively carried out by the user as the available data is explored. In this paper, we propose an approach to support data analysis within a polystore supporting relational, document and column data models by automatically handling both data model and schema heterogeneity through a dataspace layer on top of the underlying databases. The expressiveness we enable corresponds to GPSJ queries, which are the most common class of queries in OLAP applications. We rely on Nested Relational Algebra to define a cross-database execution plan. The plan is composed of several local plans, to be executed on the distinct databases, and a global plan, which combines and possibly aggregates inter-database data. The system has been prototyped on Apache Spark.