Due to R's popularity as a data-mining tool, many distributed systems expose an R-based API to users who need to build a distributed application in R. As a result, data scientists have to learn to use different interfaces such as RHadoop, SparkR, Revolution R's ScaleR, and HPE's Distributed R. Unfortunately, these interfaces are custom, non-standard, and difficult to learn. Not surprisingly, R applications written in one framework do not work in another, and each backend infrastructure has spent redundant effort in implementing distributed machine learning algorithms. Working with the members of R-core, we have created ddR (Distributed Data structures in R), a unified system that works across different distributed frameworks. In ddR, we introduce a novel programming primitive called dmapply that executes functions on distributed data structures. The dmapply primitive encapsulates different computation patterns: from function and data broadcast to pair-wise communication. We show that dmapply is powerful enough to express algorithms that fit the statistical query model, which includes many popular machine learning algorithms, as well as applications written in MapReduce. We have integrated ddR with many backends, such as R's single-node parallel framework, multi-node SNOW framework, Spark, and HPE Distributed R, with few or no modifications to any of these systems. We have also implemented multiple machine learning algorithms which are not only portable across different distributed systems, but also have performance comparable to the "native" implementations on the backends. We believe that ddR will standardize distributed computing in R, just like the SQL interface has standardized how relational data is manipulated.

A typical predictive analytics workflow will pre-process data in a database, transfer the resulting data to an external statistical tool such as R, create machine learning models in R, and then apply the model on newly arriving data. Today, this workflow is slow and cumbersome. Extracting data from databases, using ODBC connectors, can take hours on multi-gigabyte datasets. Building models on single-threaded R does not scale. Finally, it is nearly impossible to use R or other common tools, to apply models on terabytes of newly arriving data.