Integrating the R Language Runtime System with a Data Stream Warehouse

Computing mathematical functions or machine learning models on data streams is difficult: a popular approach is to use the R language. Unfortunately, R has important limitations: a dynamic runtime system incompatible with a DBMS, limited by available RAM and no data management capabilities. On the other hand, SQL is well established to write queries and manage data, but it is inadequate to perform mathematical computations. With that motivation in mind, we present a system that enables analysis in R on a time window, where the DBMS continuously inserts new records and propagates updates to materialized views. We explain the low-level integration enabling fast data transfer in RAM between the DBMS query process and the R runtime. Our system enables analytic calls in both directions: (1) R calling SQL to evaluate streaming queries; transferring output streaming tables and analyzing them with R operators and functions in the R runtime, (2) SQL calling R, to exploit R mathematical operators and mathematical models, computed in a streaming fashion inside the DBMS. We discuss analytic examples, illustrating analytic calls in both directions. We experimentally show our system achieves streaming speed to transfer data.