7f605f5559
### What changes were proposed in this pull request?
Make `spark.sql.crossJoin.enabled` default value true
### Why are the changes needed?
For implicit cross join, we can set up a watchdog to cancel it if running for a long time.
When "spark.sql.crossJoin.enabled" is false, because `CheckCartesianProducts` is implemented in logical plan stage, it may generate some mismatching error which may confuse end user:
* it's done in logical phase, so we may fail queries that can be executed via broadcast join, which is very fast.
* if we move the check to the physical phase, then a query may success at the beginning, and begin to fail when the table size gets larger (other people insert data to the table). This can be quite confusing.
* the CROSS JOIN syntax doesn't work well if join reorder happens.
* some non-equi-join will generate plan using cartesian product, but `CheckCartesianProducts` do not detect it and raise error.
So that in order to address this in simpler way, we can turn off showing this cross-join error by default.
For reference, I list some cases raising mismatching error here:
Providing:
```
spark.range(2).createOrReplaceTempView("sm1") // can be broadcast
spark.range(50000000).createOrReplaceTempView("bg1") // cannot be broadcast
spark.range(60000000).createOrReplaceTempView("bg2") // cannot be broadcast
```
1) Some join could be convert to broadcast nested loop join, but CheckCartesianProducts raise error. e.g.
```
select sm1.id, bg1.id from bg1 join sm1 where sm1.id < bg1.id
```
2) Some join will run by CartesianJoin but CheckCartesianProducts DO NOT raise error. e.g.
```
select bg1.id, bg2.id from bg1 join bg2 where bg1.id < bg2.id
```
### Does this PR introduce any user-facing change?
### How was this patch tested?
Closes #25520 from WeichenXu123/SPARK-28621.
Authored-by: WeichenXu <weichen.xu@databricks.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