720708ccdd
## What changes were proposed in this pull request? This PR proposes three things as below: - Use casting rules to a timestamp in `to_timestamp` by default (it was `yyyy-MM-dd HH:mm:ss`). - Support single argument for `to_timestamp` similarly with APIs in other languages. For example, the one below works ``` import org.apache.spark.sql.functions._ Seq("2016-12-31 00:12:00.00").toDF("a").select(to_timestamp(col("a"))).show() ``` prints ``` +----------------------------------------+ |to_timestamp(`a`, 'yyyy-MM-dd HH:mm:ss')| +----------------------------------------+ | 2016-12-31 00:12:00| +----------------------------------------+ ``` whereas this does not work in SQL. **Before** ``` spark-sql> SELECT to_timestamp('2016-12-31 00:12:00'); Error in query: Invalid number of arguments for function to_timestamp; line 1 pos 7 ``` **After** ``` spark-sql> SELECT to_timestamp('2016-12-31 00:12:00'); 2016-12-31 00:12:00 ``` - Related document improvement for SQL function descriptions and other API descriptions accordingly. **Before** ``` spark-sql> DESCRIBE FUNCTION extended to_date; ... Usage: to_date(date_str, fmt) - Parses the `left` expression with the `fmt` expression. Returns null with invalid input. Extended Usage: Examples: > SELECT to_date('2016-12-31', 'yyyy-MM-dd'); 2016-12-31 ``` ``` spark-sql> DESCRIBE FUNCTION extended to_timestamp; ... Usage: to_timestamp(timestamp, fmt) - Parses the `left` expression with the `format` expression to a timestamp. Returns null with invalid input. Extended Usage: Examples: > SELECT to_timestamp('2016-12-31', 'yyyy-MM-dd'); 2016-12-31 00:00:00.0 ``` **After** ``` spark-sql> DESCRIBE FUNCTION extended to_date; ... Usage: to_date(date_str[, fmt]) - Parses the `date_str` expression with the `fmt` expression to a date. Returns null with invalid input. By default, it follows casting rules to a date if the `fmt` is omitted. Extended Usage: Examples: > SELECT to_date('2009-07-30 04:17:52'); 2009-07-30 > SELECT to_date('2016-12-31', 'yyyy-MM-dd'); 2016-12-31 ``` ``` spark-sql> DESCRIBE FUNCTION extended to_timestamp; ... Usage: to_timestamp(timestamp[, fmt]) - Parses the `timestamp` expression with the `fmt` expression to a timestamp. Returns null with invalid input. By default, it follows casting rules to a timestamp if the `fmt` is omitted. Extended Usage: Examples: > SELECT to_timestamp('2016-12-31 00:12:00'); 2016-12-31 00:12:00 > SELECT to_timestamp('2016-12-31', 'yyyy-MM-dd'); 2016-12-31 00:00:00 ``` ## How was this patch tested? Added tests in `datetime.sql`. Author: hyukjinkwon <gurwls223@gmail.com> Closes #17901 from HyukjinKwon/to_timestamp_arg. |
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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 or other R frontends you will need to set some environment variables which point SparkR to your Spark installation. For example
# Set this to where Spark is installed
Sys.setenv(SPARK_HOME="/Users/username/spark")
# This line loads SparkR from the installed directory
.libPaths(c(file.path(Sys.getenv("SPARK_HOME"), "R", "lib"), .libPaths()))
library(SparkR)
sparkR.session()
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 also run the unit tests for SparkR by running. You need to install the testthat package first:
R -e 'install.packages("testthat", repos="http://cran.us.r-project.org")'
./R/run-tests.sh
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