SparkR is an R package that provides a light-weight frontend to use Spark from R.
Libraries of sparkR need to be created in
$SPARK_HOME/R/lib. This can be done by running the script
$SPARK_HOME/R/install-dev.sh. By default the above script uses the system wide installation of R. However, this can be changed to any user installed location of R by setting the environment variable
R_HOME the full path of the base directory where R is installed, before running install-dev.sh script. Example:
You can start using SparkR by launching the SparkR shell with
sparkR script automatically creates a SparkContext with Spark by default in local mode. To specify the Spark master of a cluster for the automatically created SparkContext, you can run
To set other options like driver memory, executor memory etc. you can pass in the spark-submit arguments to
If you wish to use SparkR from RStudio, please refer SparkR documentation.
The instructions for making contributions to Spark also apply to SparkR. If you only make R file changes (i.e. no Scala changes) then you can just re-install the R package using
R/install-dev.sh and test your changes. Once you have made your changes, please include unit tests for them and run existing unit tests using the
R/run-tests.sh script as described below.
The SparkR documentation (Rd files and HTML files) are not a part of the source repository. To generate them you can run the script
R/create-docs.sh. This script uses
knitr to generate the docs and these packages need to be installed on the machine before using the script. Also, you may need to install these prerequisites. See also,
SparkR comes with several sample programs in the
examples/src/main/r directory. To run one of them, use
./bin/spark-submit <filename> <args>. For example:
You can run R unit tests by following the instructions under Running R Tests.
./bin/spark-submit can also be used to submit jobs to YARN clusters. You will need to set YARN conf dir before doing so. For example on CDH you can run