12e1583093
### What changes were proposed in this pull request? Fitting ALS model can be failed due to nondeterministic input data. Currently the failure is thrown by an ArrayIndexOutOfBoundsException which is not explainable for end users what is wrong in fitting. This patch catches this exception and rethrows a more explainable one, when the input data is nondeterministic. Because we may not exactly know the output deterministic level of RDDs produced by user code, this patch also adds a note to Scala/Python/R ALS document about the training data deterministic level. ### Why are the changes needed? ArrayIndexOutOfBoundsException was observed during fitting ALS model. It was caused by mismatching between in/out user/item blocks during computing ratings. If the training RDD output is nondeterministic, when fetch failure is happened, rerun part of training RDD can produce inconsistent user/item blocks. This patch is needed to notify users ALS fitting on nondeterministic input. ### Does this PR introduce any user-facing change? Yes. When fitting ALS model on nondeterministic input data, previously if rerun happens, users would see ArrayIndexOutOfBoundsException caused by mismatch between In/Out user/item blocks. After this patch, a SparkException with more clear message will be thrown, and original ArrayIndexOutOfBoundsException is wrapped. ### How was this patch tested? Tested on development cluster. Closes #25789 from viirya/als-indeterminate-input. Lead-authored-by: Liang-Chi Hsieh <viirya@gmail.com> Co-authored-by: Liang-Chi Hsieh <liangchi@uber.com> Signed-off-by: Sean Owen <sean.owen@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