Scalable Linear Algebra on a Relational Database System. - Shangyu Luo; Gao, Zekai J.; Gubanov, Michael; Perez, Luis L.; Jankov, Dimitrije; Jermaine, Christopher (Communications of the ACM, 2020)
As data analytics has become an important application for modern data management systems, a new category of data management system has appeared recently: the scalable linear algebra system. We argue that a parallel or distributed database system is actually an excellent platform upon which to build such functionality. Most relational systems already have support for cost-based optimization--which is vital to scaling linear algebra computations--and it is well known how to make relational systems scalable. We show that by making just a few changes to a parallel/distributed relational database system, such a system can become a competitive platform for scalable linear algebra. Taken together, our results should at least raise the possibility that brand new systems designed from the ground up to support scalable linear algebra are not absolutely necessary, and that such systems could instead be built on top of existing relational technology.