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[SPARK-8452] [SPARKR] expose jobGroup API in SparkR
Hossein authored 
This pull request adds following methods to SparkR:

```R
setJobGroup()
cancelJobGroup()
clearJobGroup()
```
For each method, the spark context is passed as the first argument. There does not seem to be a good way to test these in R.

cc shivaram and davies

Author: Hossein <hossein@databricks.com>

Closes #6889 from falaki/SPARK-8452 and squashes the following commits:

9ce9f1e [Hossein] Added basic tests to verify methods can be called and won't throw errors
c706af9 [Hossein] Added examples
a2c19af [Hossein] taking spark context as first argument
343ca77 [Hossein] Added setJobGroup, cancelJobGroup and clearJobGroup to SparkR
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Name Last commit Last update
..
pkg
.gitignore
DOCUMENTATION.md
README.md
WINDOWS.md
create-docs.sh
install-dev.bat
install-dev.sh
log4j.properties
run-tests.sh
README.md

R on Spark

SparkR is an R package that provides a light-weight frontend to use Spark from R.

SparkR development

Build Spark

Build Spark with Maven and include the -PsparkR profile to build the R package. For example to use the default Hadoop versions you can run

  build/mvn -DskipTests -Psparkr package

Running sparkR

You can start using SparkR by launching the SparkR shell with

./bin/sparkR

The 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

./bin/sparkR --master "local[2]"

To set other options like driver memory, executor memory etc. you can pass in the spark-submit arguments to ./bin/sparkR

Using SparkR from RStudio

If you wish to use SparkR from RStudio or other R frontends you will need to set some environment variables which point SparkR to your Spark installation. For example

# Set this to where Spark is installed
Sys.setenv(SPARK_HOME="/Users/shivaram/spark")
# This line loads SparkR from the installed directory
.libPaths(c(file.path(Sys.getenv("SPARK_HOME"), "R", "lib"), .libPaths()))
library(SparkR)
sc <- sparkR.init(master="local")

Making changes to SparkR

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 run-tests.sh script as described below.

Generating documentation

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 devtools and knitr to generate the docs and these packages need to be installed on the machine before using the script.

Examples, Unit tests

SparkR comes with several sample programs in the examples/src/main/r directory. To run one of them, use ./bin/sparkR <filename> <args>. For example:

./bin/sparkR examples/src/main/r/dataframe.R

You can also run the unit-tests for SparkR by running (you need to install the testthat package first):

R -e 'install.packages("testthat", repos="http://cran.us.r-project.org")'
./R/run-tests.sh

Running on YARN

The ./bin/spark-submit and ./bin/sparkR 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

export YARN_CONF_DIR=/etc/hadoop/conf
./bin/spark-submit --master yarn examples/src/main/r/dataframe.R