Machine learning with SparkR
SparkR is integrated with Spark's MLlib machine learning library so that algorithms can be parallelized seamlessly without specifying manually which part of the algorithm can be run in parallel. MLlib is one of the fastest-growing machine learning libraries; hence, the ability to use R with MLlib will create a huge number of contributions to MLlib from R users. As of Spark 1.6, there is support for generalized linear models (Gaussian and binomial) over DataFrames and as per Spark 2.0.2, the algorithms such as Naive Bayes and KMeans are available.
Getting ready
To step through this recipe, you will need a running Spark Cluster either in pseudo distributed mode or in one of the distributed modes, that is, standalone, YARN, or Mesos. Also, install RStudio. Please refer to Installing R recipe for details on the installation of R and the Creating SparkR DataFrames recipe to get acquainted with the creation of DataFrames from a variety of data sources.
