3493162c78
### What changes were proposed in this pull request? In Spark version 2.4 and earlier, datetime parsing, formatting and conversion are performed by using the hybrid calendar (Julian + Gregorian). Since the Proleptic Gregorian calendar is de-facto calendar worldwide, as well as the chosen one in ANSI SQL standard, Spark 3.0 switches to it by using Java 8 API classes (the java.time packages that are based on ISO chronology ). The switching job is completed in SPARK-26651. But after the switching, there are some patterns not compatible between Java 8 and Java 7, Spark needs its own definition on the patterns rather than depends on Java API. In this PR, we achieve this by writing the document and shadow the incompatible letters. See more details in [SPARK-31030](https://issues.apache.org/jira/browse/SPARK-31030) ### Why are the changes needed? For backward compatibility. ### Does this PR introduce any user-facing change? No. After we define our own datetime parsing and formatting patterns, it's same to old Spark version. ### How was this patch tested? Existing and new added UT. Locally document test:  Closes #27830 from xuanyuanking/SPARK-31030. Authored-by: Yuanjian Li <xyliyuanjian@gmail.com> Signed-off-by: Wenchen Fan <wenchen@databricks.com> |
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| .. | ||
| pkg | ||
| .gitignore | ||
| check-cran.sh | ||
| CRAN_RELEASE.md | ||
| create-docs.sh | ||
| create-rd.sh | ||
| DOCUMENTATION.md | ||
| find-r.sh | ||
| install-dev.bat | ||
| install-dev.sh | ||
| install-source-package.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.
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