Mind the gap: bridging multi-domain query workloads with EmptyHeaded

Executing domain specific workloads from a relational data warehouse is an increasingly popular task. Unfortunately, classic relational database management systems (RDBMS) are suboptimal in many domains (e.g., graph and linear algebra queries), and it is challenging to transfer data from an RDBMS to a domain specific toolkit in an efficient manner. This demonstration showcases the EmptyHeaded engine: an interactive query processing engine that leverages a novel query architecture to support efficient execution in multiple domains. To enable a unified design, the EmptyHeaded architecture is built around recent theoretical advancements in join processing and automated in-query data transformations. This demonstration highlights the strengths and weaknesses of this novel type of query processing architecture while showcasing its flexibility in multiple domains. In particular, attendees will use EmptyHeaded's Jupyter notebook front-end to interactively learn the theoretical advantages of this new (and largely unknown) approach and directly observe its performance impact in multiple domains.