doppioDB 1.0: Machine Learning inside a Relational Engine

Abstract

Advances in hardware are a challenge but also a new opportunity. In particular, devices like FPGAs and GPUs are a chance to extend and customize relational engines with new operations that would be difficult to support otherwise. Doing so would offer database users the possibility of conducting, e.g., complete data analyses involving machine learning inside the database instead of having to take the data out, process it in a different platform, and then store the results back in the database as it is often done today. In this paper we present doppioDB 1.0, an FPGA-enabled database engine incorporating FPGA-based machine learning operators into a main memory, columnar DBMS (MonetDB). This first version of doppioDB provides a platform for extending traditional relational processing with customizable hardware to support stochastic gradient descent and decision tree ensembles. Using these operators, we show examples of how they could be included into SQL and embedded as part of conventional components of a relational database engine. While these results are still a preliminary, exploratory step, they illustrate the challenges to be tackled and the advantages of using hardware accelerators as a way to extend database functionality in a non-disruptive manner