Since its introduction in Spark 1.4, SparkR has received contributions from both the Spark community and the R community. In this talk, we will summarize recent community efforts on extending SparkR for scalable advanced analytics. We start with the computation of summary statistics on distributed datasets, including single-pass approximate algorithms. Then we demonstrate MLlib machine learning algorithms that have been ported to SparkR and compare them with existing solutions on R, e.g., generalized linear models, classification and clustering algorithms. We also show how to integrate existing R packages with SparkR to accelerate existing R workflows.
Xiangrui Meng is an Apache Spark PMC member and a software engineer at Databricks. His main interests center around developing and implementing scalable algorithms for scientific applications. He has been actively involved in the development and maintenance of Spark MLlib since he joined Databricks. Before Databricks, he worked as an applied research engineer at LinkedIn, where he was the main developer of an offline machine learning framework in Hadoop MapReduce. His Ph.D. work at Stanford is on randomized algorithms for large-scale linear regression problems.