3697232b7d
I've updated default values in comments, documentation, and in the command line builder to be 1g based on comments in the JIRA. I've also updated most usages to point at a single variable defined in the Utils.scala and JavaUtils.java files. This wasn't possible in all cases (R, shell scripts etc.) but usage in most code is now pointing at the same place. Please let me know if I've missed anything. Will the spark-shell use the value within the command line builder during instantiation? Author: Ilya Ganelin <ilya.ganelin@capitalone.com> Closes #7132 from ilganeli/SPARK-3071 and squashes the following commits: 4074164 [Ilya Ganelin] String fix 271610b [Ilya Ganelin] Merge branch 'SPARK-3071' of github.com:ilganeli/spark into SPARK-3071 273b6e9 [Ilya Ganelin] Test fix fd67721 [Ilya Ganelin] Update JavaUtils.java 26cc177 [Ilya Ganelin] test fix e5db35d [Ilya Ganelin] Fixed test failure 39732a1 [Ilya Ganelin] merge fix a6f7deb [Ilya Ganelin] Created default value for DRIVER MEM in Utils that's now used in almost all locations instead of setting manually in each 09ad698 [Ilya Ganelin] Update SubmitRestProtocolSuite.scala 19b6f25 [Ilya Ganelin] Missed one doc update 2698a3d [Ilya Ganelin] Updated default value for driver memory |
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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