doppioDB 2.0: Hardware Techniques for Improved Integration of Machine Learning into Databases

Database engines are starting to incorporate machine learning (ML) functionality as part of their repertoire. Machine learning algorithms, however, have very different characteristics than those of relational operators. In this demonstration, we explore the challenges that arise when integrating generalized linear models into a database engine and how to incorporate hardware accelerators into the execution, a tool now widely used for ML workloads. The demo explores two complementary alternatives: (1) how to train models directly on compressed/encrypted column-stores using a specialized coordinate descent engine, and (2) how to use a bitwise weaving index for stochastic gradient descent on low precision input data. We present these techniques as implemented in our prototype database doppioDB 2.0 and show how the new functionality can be used from SQL. PVLDB Reference Format: Kaan Kara, Zeke Wang, Ce Zhang, Gustavo Alonso. doppioDB 2.0: Hardware Techniques for Improved Integration of Machine Learning into Databases. PVLDB, 12(12): 1818-1821, 2019. DOI: https://doi.org/10.14778/3352063.3352074