e8982ca7ad
## What changes were proposed in this pull request?
This PR targets to support Arrow optimization for conversion from R DataFrame to Spark DataFrame.
Like PySpark side, it falls back to non-optimization code path when it's unable to use Arrow optimization.
This can be tested as below:
```bash
$ ./bin/sparkR --conf spark.sql.execution.arrow.enabled=true
```
```r
collect(createDataFrame(mtcars))
```
### Requirements
- R 3.5.x
- Arrow package 0.12+
```bash
Rscript -e 'remotes::install_github("apache/arrowapache-arrow-0.12.0", subdir = "r")'
```
**Note:** currently, Arrow R package is not in CRAN. Please take a look at ARROW-3204.
**Note:** currently, Arrow R package seems not supporting Windows. Please take a look at ARROW-3204.
### Benchmarks
**Shall**
```bash
sync && sudo purge
./bin/sparkR --conf spark.sql.execution.arrow.enabled=false
```
```bash
sync && sudo purge
./bin/sparkR --conf spark.sql.execution.arrow.enabled=true
```
**R code**
```r
createDataFrame(mtcars) # Initializes
rdf <- read.csv("500000.csv")
test <- function() {
options(digits.secs = 6) # milliseconds
start.time <- Sys.time()
createDataFrame(rdf)
end.time <- Sys.time()
time.taken <- end.time - start.time
print(time.taken)
}
test()
```
**Data (350 MB):**
```r
object.size(read.csv("500000.csv"))
350379504 bytes
```
"500000 Records" http://eforexcel.com/wp/downloads-16-sample-csv-files-data-sets-for-testing/
**Results**
```
Time difference of 29.9468 secs
```
```
Time difference of 3.222129 secs
```
The performance improvement was around **950%**.
Actually, this PR improves around **1200%**+ because this PR includes a small optimization about regular R DataFrame -> Spark DatFrame. See https://github.com/apache/spark/pull/22954#discussion_r231847272
### Limitations:
For now, Arrow optimization with R does not support when the data is `raw`, and when user explicitly gives float type in the schema. They produce corrupt values.
In this case, we decide to fall back to non-optimization code path.
## How was this patch tested?
Small test was added.
I manually forced to set this optimization `true` for _all_ R tests and they were _all_ passed (with few of fallback warnings).
**TODOs:**
- [x] Draft codes
- [x] make the tests passed
- [x] make the CRAN check pass
- [x] Performance measurement
- [x] Supportability investigation (for instance types)
- [x] Wait for Arrow 0.12.0 release
- [x] Fix and match it to Arrow 0.12.0
Closes #22954 from HyukjinKwon/r-arrow-createdataframe.
Lead-authored-by: hyukjinkwon <gurwls223@apache.org>
Co-authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
|
||
|---|---|---|
| .. | ||
| 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