712f5b7a9a
This PR adds synonyms for ```merge``` and ```summary``` in SparkR DataFrame API. cc shivaram Author: Hossein <hossein@databricks.com> Closes #7806 from falaki/SPARK-9320 and squashes the following commits: 72600f7 [Hossein] Updated docs 92a6e75 [Hossein] Fixed merge generic signature issue 4c2b051 [Hossein] Fixing naming with mllib summary 0f3a64c [Hossein] Added ... to generic for merge 30fbaf8 [Hossein] Merged master ae1a4cf [Hossein] Merge branch 'master' into SPARK-9320 e8eb86f [Hossein] Add a generic for merge fc01f2d [Hossein] Added unit test 8d92012 [Hossein] Added merge as an alias for join 5b8bedc [Hossein] Added unit test 632693d [Hossein] Added summary as an alias for describe for DataFrame |
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pkg | ||
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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