835a79d78e
The basic idea is that: The archive of the SparkR package itself, that is sparkr.zip, is created during build process and is contained in the Spark binary distribution. No change to it after the distribution is installed as the directory it resides ($SPARK_HOME/R/lib) may not be writable. When there is R source code contained in jars or Spark packages specified with "--jars" or "--packages" command line option, a temporary directory is created by calling Utils.createTempDir() where the R packages built from the R source code will be installed. The temporary directory is writable, and won't interfere with each other when there are multiple SparkR sessions, and will be deleted when this SparkR session ends. The R binary packages installed in the temporary directory then are packed into an archive named rpkg.zip. sparkr.zip and rpkg.zip are distributed to the cluster in YARN modes. The distribution of rpkg.zip in Standalone modes is not supported in this PR, and will be address in another PR. Various R files are updated to accept multiple lib paths (one is for SparkR package, the other is for other R packages) so that these package can be accessed in R. Author: Sun Rui <rui.sun@intel.com> Closes #9390 from sun-rui/SPARK-10500. |
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pkg | ||
.gitignore | ||
create-docs.sh | ||
DOCUMENTATION.md | ||
install-dev.bat | ||
install-dev.sh | ||
log4j.properties | ||
README.md | ||
run-tests.sh | ||
WINDOWS.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