spark-instrumented-optimizer/R
HyukjinKwon b5bdbf2ebc [SPARK-30682][R][SQL][FOLLOW-UP] Keep the name similar with Scala side in higher order functions
### What changes were proposed in this pull request?

This PR is a followup of #27433. It fixes the naming to match with Scala side, and this is similar with https://github.com/apache/spark/pull/31062.

Note that:

- there are a bit of inconsistency already e.g.) `x`, `y` in SparkR and they are documented together for doc deduplication. This part I did not change but the name `zero` vs `initialValue` looks unnecessary.
- such naming matching seems already pretty common in SparkR.

### Why are the changes needed?

To make the usage similar with Scala side, and for consistency.

### Does this PR introduce _any_ user-facing change?

No, this is not released yet.

### How was this patch tested?

GitHub Actions and Jenkins build will test it out.

Also, I manually tested:

```r
> df <- select(createDataFrame(data.frame(id = 1)),expr("CAST(array(1.0, 2.0, -3.0, -4.0) AS array<double>) xs"))
> collect(select(df, array_aggregate("xs", initialValue = lit(0.0), merge = function(x, y) otherwise(when(x > y, x), y))))
  aggregate(xs, 0.0, lambdafunction(CASE WHEN (x > y) THEN x ELSE y END, x, y), lambdafunction(id, id))
1                                                                                                     2
```

Closes #31226 from HyukjinKwon/SPARK-30682.

Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2021-01-18 14:19:14 +09:00
..
pkg [SPARK-30682][R][SQL][FOLLOW-UP] Keep the name similar with Scala side in higher order functions 2021-01-18 14:19:14 +09:00
.gitignore [MINOR][R] add SparkR.Rcheck/ and SparkR_*.tar.gz to R/.gitignore 2016-08-21 10:31:25 -07:00
check-cran.sh [SPARK-29339][R] Support Arrow 0.14 in vectoried dapply and gapply (test it in AppVeyor build) 2019-10-04 08:56:45 +09:00
CRAN_RELEASE.md Spelling r common dev mlib external project streaming resource managers python 2020-11-27 10:22:45 -06:00
create-docs.sh [MINOR][R] small tidying of sh scripts for R 2020-04-30 16:58:05 -07:00
create-rd.sh [MINOR][R] small tidying of sh scripts for R 2020-04-30 16:58:05 -07:00
DOCUMENTATION.md [SPARK-26918][DOCS] All .md should have ASF license header 2019-03-30 19:49:45 -05:00
find-r.sh [SPARK-18828][SPARKR] Refactor scripts for R 2017-01-16 13:49:12 -08:00
install-dev.bat Spelling r common dev mlib external project streaming resource managers python 2020-11-27 10:22:45 -06:00
install-dev.sh [SPARK-22167][R][BUILD] sparkr packaging issue allow zinc 2017-10-02 11:46:51 -07:00
install-source-package.sh [SPARK-20123][BUILD] SPARK_HOME variable might have spaces in it(e.g. $SPARK… 2017-04-02 15:31:13 +01:00
log4j.properties [SPARK-8350] [R] Log R unit test output to "unit-tests.log" 2015-06-15 08:16:22 -07:00
README.md [SPARK-28473][DOC] Stylistic consistency of build command in README 2019-07-23 16:29:46 -07:00
run-tests.sh [SPARK-33304][R][SQL] Add from_avro and to_avro functions to SparkR 2020-11-19 09:52:29 +09:00
WINDOWS.md [SPARK-32073][R] Drop R < 3.5 support 2020-06-24 11:05:27 +09:00

R on Spark

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

Installing sparkR

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:

# where /home/username/R is where R is installed and /home/username/R/bin contains the files R and RScript
export R_HOME=/home/username/R
./install-dev.sh

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, please refer SparkR documentation.

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 R/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. Also, you may need to install these prerequisites. See also, R/DOCUMENTATION.md

Examples, Unit tests

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:

./bin/spark-submit examples/src/main/r/dataframe.R

You can run R unit tests by following the instructions under Running R Tests.

Running on YARN

The ./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

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