## What changes were proposed in this pull request?
NA date values are serialized as "NA" and NA time values are serialized as NaN from R. In the backend we did not have proper logic to deal with them. As a result we got an IllegalArgumentException for Date and wrong value for time. This PR adds support for deserializing NA as Date and Time.
## How was this patch tested?
* [x] TODO
Author: Hossein <hossein@databricks.com>
Closes#15421 from falaki/SPARK-17811.
## What changes were proposed in this pull request?
Add crossJoin and do not default to cross join if joinExpr is left out
## How was this patch tested?
unit test
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#15559 from felixcheung/rcrossjoin.
## What changes were proposed in this pull request?
testthat library we are using for testing R is redirecting warning (and disabling `options("warn" = 2)`), we need to have a way to detect any new warning and fail
## How was this patch tested?
manual testing, Jenkins
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#15576 from felixcheung/rtestwarning.
## What changes were proposed in this pull request?
Fix for a bunch of test warnings that were added recently.
We need to investigate why warnings are not turning into errors.
```
Warnings -----------------------------------------------------------------------
1. createDataFrame uses files for large objects (test_sparkSQL.R#215) - Use Sepal_Length instead of Sepal.Length as column name
2. createDataFrame uses files for large objects (test_sparkSQL.R#215) - Use Sepal_Width instead of Sepal.Width as column name
3. createDataFrame uses files for large objects (test_sparkSQL.R#215) - Use Petal_Length instead of Petal.Length as column name
4. createDataFrame uses files for large objects (test_sparkSQL.R#215) - Use Petal_Width instead of Petal.Width as column name
Consider adding
importFrom("utils", "object.size")
to your NAMESPACE file.
```
## How was this patch tested?
unit tests
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#15560 from felixcheung/rwarnings.
## What changes were proposed in this pull request?
If the R data structure that is being parallelized is larger than `INT_MAX` we use files to transfer data to JVM. The serialization protocol mimics Python pickling. This allows us to simply call `PythonRDD.readRDDFromFile` to create the RDD.
I tested this on my MacBook. Following code works with this patch:
```R
intMax <- .Machine$integer.max
largeVec <- 1:intMax
rdd <- SparkR:::parallelize(sc, largeVec, 2)
```
## How was this patch tested?
* [x] Unit tests
Author: Hossein <hossein@databricks.com>
Closes#15375 from falaki/SPARK-17790.
## What changes were proposed in this pull request?
SQLConf is session-scoped and mutable. However, we do have the requirement for a static SQL conf, which is global and immutable, e.g. the `schemaStringThreshold` in `HiveExternalCatalog`, the flag to enable/disable hive support, the global temp view database in https://github.com/apache/spark/pull/14897.
Actually we've already implemented static SQL conf implicitly via `SparkConf`, this PR just make it explicit and expose it to users, so that they can see the config value via SQL command or `SparkSession.conf`, and forbid users to set/unset static SQL conf.
## How was this patch tested?
new tests in SQLConfSuite
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15295 from cloud-fan/global-conf.
## What changes were proposed in this pull request?
Fix SparkR ```spark.naiveBayes``` error when response variable of dataset is numeric type.
See details and how to reproduce this bug at [SPARK-15153](https://issues.apache.org/jira/browse/SPARK-15153).
## How was this patch tested?
Add unit test.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#15431 from yanboliang/spark-15153-2.
## What changes were proposed in this pull request?
This PR includes the changes below:
- Support `mode`/`options` in `read.parquet`, `write.parquet`, `read.orc`, `write.orc`, `read.text`, `write.text`, `read.json` and `write.json` APIs
- Support other types (logical, numeric and string) as options for `write.df`, `read.df`, `read.parquet`, `write.parquet`, `read.orc`, `write.orc`, `read.text`, `write.text`, `read.json` and `write.json`
## How was this patch tested?
Unit tests in `test_sparkSQL.R`/ `utils.R`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#15239 from HyukjinKwon/SPARK-17665.
## What changes were proposed in this pull request?
`write.df`/`read.df` API require path which is not actually always necessary in Spark. Currently, it only affects the datasources implementing `CreatableRelationProvider`. Currently, Spark currently does not have internal data sources implementing this but it'd affect other external datasources.
In addition we'd be able to use this way in Spark's JDBC datasource after https://github.com/apache/spark/pull/12601 is merged.
**Before**
- `read.df`
```r
> read.df(source = "json")
Error in dispatchFunc("read.df(path = NULL, source = NULL, schema = NULL, ...)", :
argument "x" is missing with no default
```
```r
> read.df(path = c(1, 2))
Error in dispatchFunc("read.df(path = NULL, source = NULL, schema = NULL, ...)", :
argument "x" is missing with no default
```
```r
> read.df(c(1, 2))
Error in invokeJava(isStatic = TRUE, className, methodName, ...) :
java.lang.ClassCastException: java.lang.Double cannot be cast to java.lang.String
at org.apache.spark.sql.execution.datasources.DataSource.hasMetadata(DataSource.scala:300)
at
...
In if (is.na(object)) { :
...
```
- `write.df`
```r
> write.df(df, source = "json")
Error in (function (classes, fdef, mtable) :
unable to find an inherited method for function ‘write.df’ for signature ‘"function", "missing"’
```
```r
> write.df(df, source = c(1, 2))
Error in (function (classes, fdef, mtable) :
unable to find an inherited method for function ‘write.df’ for signature ‘"SparkDataFrame", "missing"’
```
```r
> write.df(df, mode = TRUE)
Error in (function (classes, fdef, mtable) :
unable to find an inherited method for function ‘write.df’ for signature ‘"SparkDataFrame", "missing"’
```
**After**
- `read.df`
```r
> read.df(source = "json")
Error in loadDF : analysis error - Unable to infer schema for JSON at . It must be specified manually;
```
```r
> read.df(path = c(1, 2))
Error in f(x, ...) : path should be charactor, null or omitted.
```
```r
> read.df(c(1, 2))
Error in f(x, ...) : path should be charactor, null or omitted.
```
- `write.df`
```r
> write.df(df, source = "json")
Error in save : illegal argument - 'path' is not specified
```
```r
> write.df(df, source = c(1, 2))
Error in .local(df, path, ...) :
source should be charactor, null or omitted. It is 'parquet' by default.
```
```r
> write.df(df, mode = TRUE)
Error in .local(df, path, ...) :
mode should be charactor or omitted. It is 'error' by default.
```
## How was this patch tested?
Unit tests in `test_sparkSQL.R`
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#15231 from HyukjinKwon/write-default-r.
## What changes were proposed in this pull request?
Some tests in `test_mllib.r` are as below:
```r
expect_error(spark.mlp(df, layers = NULL), "layers must be a integer vector with length > 1.")
expect_error(spark.mlp(df, layers = c()), "layers must be a integer vector with length > 1.")
```
The problem is, `is.na` is internally called via `na.omit` in `spark.mlp` which causes warnings as below:
```
Warnings -----------------------------------------------------------------------
1. spark.mlp (test_mllib.R#400) - is.na() applied to non-(list or vector) of type 'NULL'
2. spark.mlp (test_mllib.R#401) - is.na() applied to non-(list or vector) of type 'NULL'
```
## How was this patch tested?
Manually tested. Also, Jenkins tests and AppVeyor.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#15232 from HyukjinKwon/remove-warnnings.
## What changes were proposed in this pull request?
#15140 exposed ```JavaSparkContext.addFile(path: String, recursive: Boolean)``` to Python/R, then we can update SparkR ```spark.addFile``` to support adding directory recursively.
## How was this patch tested?
Added unit test.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#15216 from yanboliang/spark-17577-2.
## What changes were proposed in this pull request?
Spark will add sparkr.zip to archive only when it is yarn mode (SparkSubmit.scala).
```
if (args.isR && clusterManager == YARN) {
val sparkRPackagePath = RUtils.localSparkRPackagePath
if (sparkRPackagePath.isEmpty) {
printErrorAndExit("SPARK_HOME does not exist for R application in YARN mode.")
}
val sparkRPackageFile = new File(sparkRPackagePath.get, SPARKR_PACKAGE_ARCHIVE)
if (!sparkRPackageFile.exists()) {
printErrorAndExit(s"$SPARKR_PACKAGE_ARCHIVE does not exist for R application in YARN mode.")
}
val sparkRPackageURI = Utils.resolveURI(sparkRPackageFile.getAbsolutePath).toString
// Distribute the SparkR package.
// Assigns a symbol link name "sparkr" to the shipped package.
args.archives = mergeFileLists(args.archives, sparkRPackageURI + "#sparkr")
// Distribute the R package archive containing all the built R packages.
if (!RUtils.rPackages.isEmpty) {
val rPackageFile =
RPackageUtils.zipRLibraries(new File(RUtils.rPackages.get), R_PACKAGE_ARCHIVE)
if (!rPackageFile.exists()) {
printErrorAndExit("Failed to zip all the built R packages.")
}
val rPackageURI = Utils.resolveURI(rPackageFile.getAbsolutePath).toString
// Assigns a symbol link name "rpkg" to the shipped package.
args.archives = mergeFileLists(args.archives, rPackageURI + "#rpkg")
}
}
```
So it is necessary to pass spark.master from R process to JVM. Otherwise sparkr.zip won't be distributed to executor. Besides that I also pass spark.yarn.keytab/spark.yarn.principal to spark side, because JVM process need them to access secured cluster.
## How was this patch tested?
Verify it manually in R Studio using the following code.
```
Sys.setenv(SPARK_HOME="/Users/jzhang/github/spark")
.libPaths(c(file.path(Sys.getenv(), "R", "lib"), .libPaths()))
library(SparkR)
sparkR.session(master="yarn-client", sparkConfig = list(spark.executor.instances="1"))
df <- as.DataFrame(mtcars)
head(df)
```
…
Author: Jeff Zhang <zjffdu@apache.org>
Closes#14784 from zjffdu/SPARK-17210.
## What changes were proposed in this pull request?
update `MultilayerPerceptronClassifierWrapper.fit` paramter type:
`layers: Array[Int]`
`seed: String`
update several default params in sparkR `spark.mlp`:
`tol` --> 1e-6
`stepSize` --> 0.03
`seed` --> NULL ( when seed == NULL, the scala-side wrapper regard it as a `null` value and the seed will use the default one )
r-side `seed` only support 32bit integer.
remove `layers` default value, and move it in front of those parameters with default value.
add `layers` parameter validation check.
## How was this patch tested?
tests added.
Author: WeichenXu <WeichenXu123@outlook.com>
Closes#15051 from WeichenXu123/update_py_mlp_default.
## What changes were proposed in this pull request?
When we build the docs separately we don't have the JAR files from the Spark build in
the same tree. As the SparkR vignettes need to launch a SparkContext to be built, we skip building them if JAR files don't exist
## How was this patch tested?
To test this we can run the following:
```
build/mvn -DskipTests -Psparkr clean
./R/create-docs.sh
```
You should see a line `Skipping R vignettes as Spark JARs not found` at the end
Author: Shivaram Venkataraman <shivaram@cs.berkeley.edu>
Closes#15200 from shivaram/sparkr-vignette-skip.
## What changes were proposed in this pull request?
#14881 added Kolmogorov-Smirnov Test wrapper to SparkR. I found that ```print.summary.KSTest``` was implemented inappropriately and result in no effect.
Running the following code for KSTest:
```Scala
data <- data.frame(test = c(0.1, 0.15, 0.2, 0.3, 0.25, -1, -0.5))
df <- createDataFrame(data)
testResult <- spark.kstest(df, "test", "norm")
summary(testResult)
```
Before this PR:
After this PR:
The new implementation is similar with [```print.summary.GeneralizedLinearRegressionModel```](https://github.com/apache/spark/blob/master/R/pkg/R/mllib.R#L284) of SparkR and [```print.summary.glm```](https://svn.r-project.org/R/trunk/src/library/stats/R/glm.R) of native R.
BTW, I removed the comparison of ```print.summary.KSTest``` in unit test, since it's only wrappers of the summary output which has been checked. Another reason is that these comparison will output summary information to the test console, it will make the test output in a mess.
## How was this patch tested?
Existing test.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#15139 from yanboliang/spark-17315.
## What changes were proposed in this pull request?
Scala/Python users can add files to Spark job by submit options ```--files``` or ```SparkContext.addFile()```. Meanwhile, users can get the added file by ```SparkFiles.get(filename)```.
We should also support this function for SparkR users, since they also have the requirements for some shared dependency files. For example, SparkR users can download third party R packages to driver firstly, add these files to the Spark job as dependency by this API and then each executor can install these packages by ```install.packages```.
## How was this patch tested?
Add unit test.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#15131 from yanboliang/spark-17577.
## What changes were proposed in this pull request?
Clarify that slide and window duration are absolute, and not relative to a calendar.
## How was this patch tested?
Doc build (no functional change)
Author: Sean Owen <sowen@cloudera.com>
Closes#15142 from srowen/SPARK-17297.
## What changes were proposed in this pull request?
Point references to spark-packages.org to https://cwiki.apache.org/confluence/display/SPARK/Third+Party+Projects
This will be accompanied by a parallel change to the spark-website repo, and additional changes to this wiki.
## How was this patch tested?
Jenkins tests.
Author: Sean Owen <sowen@cloudera.com>
Closes#15075 from srowen/SPARK-17445.
## What changes were proposed in this pull request?
This PR tries to add a SparkR vignette, which works as a friendly guidance going through the functionality provided by SparkR.
## How was this patch tested?
Manual test.
Author: junyangq <qianjunyang@gmail.com>
Author: Shivaram Venkataraman <shivaram@cs.berkeley.edu>
Author: Junyang Qian <junyangq@databricks.com>
Closes#14980 from junyangq/SPARKR-vignette.
## What changes were proposed in this pull request?
Fix summary() method's `return` description for spark.mlp
## How was this patch tested?
Ran tests locally on my laptop.
Author: Xin Ren <iamshrek@126.com>
Closes#15015 from keypointt/SPARK-16445-2.
## What changes were proposed in this pull request?
SparkR ```spark.als``` arguments ```reg``` should be 0.1 by default, which need to be consistent with ML.
## How was this patch tested?
Existing tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#15021 from yanboliang/spark-17464.
## What changes were proposed in this pull request?
additional options were not passed down in write.df.
## How was this patch tested?
unit tests
falaki shivaram
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#15010 from felixcheung/testreadoptions.
## What changes were proposed in this pull request?
Fixed bug in `dapplyCollect` by changing the `compute` function of `worker.R` to explicitly handle raw (binary) vectors.
cc shivaram
## How was this patch tested?
Unit tests
Author: Clark Fitzgerald <clarkfitzg@gmail.com>
Closes#14783 from clarkfitzg/SPARK-16785.
## What changes were proposed in this pull request?
This PR tries to add Kolmogorov-Smirnov Test wrapper to SparkR. This wrapper implementation only supports one sample test against normal distribution.
## How was this patch tested?
R unit test.
Author: Junyang Qian <junyangq@databricks.com>
Closes#14881 from junyangq/SPARK-17315.
## What changes were proposed in this pull request?
This PR tries to add some more explanation to `sparkR.session`. It also modifies doc for `count` so when grouped in one doc, the description doesn't confuse users.
## How was this patch tested?
Manual test.
Author: Junyang Qian <junyangq@databricks.com>
Closes#14942 from junyangq/fixSparkRSessionDoc.
## What changes were proposed in this pull request?
Require the use of CROSS join syntax in SQL (and a new crossJoin
DataFrame API) to specify explicit cartesian products between relations.
By cartesian product we mean a join between relations R and S where
there is no join condition involving columns from both R and S.
If a cartesian product is detected in the absence of an explicit CROSS
join, an error must be thrown. Turning on the
"spark.sql.crossJoin.enabled" configuration flag will disable this check
and allow cartesian products without an explicit CROSS join.
The new crossJoin DataFrame API must be used to specify explicit cross
joins. The existing join(DataFrame) method will produce a INNER join
that will require a subsequent join condition.
That is df1.join(df2) is equivalent to select * from df1, df2.
## How was this patch tested?
Added cross-join.sql to the SQLQueryTestSuite to test the check for cartesian products. Added a couple of tests to the DataFrameJoinSuite to test the crossJoin API. Modified various other test suites to explicitly specify a cross join where an INNER join or a comma-separated list was previously used.
Author: Srinath Shankar <srinath@databricks.com>
Closes#14866 from srinathshankar/crossjoin.
## What changes were proposed in this pull request?
change since version in doc
## How was this patch tested?
manual
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#14939 from felixcheung/rsparkversion2.
## What changes were proposed in this pull request?
Doc change - see https://issues.apache.org/jira/browse/SPARK-16324
## How was this patch tested?
manual check
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#14934 from felixcheung/regexpextractdoc.
## What changes were proposed in this pull request?
Add sparkR.version() API.
```
> sparkR.version()
[1] "2.1.0-SNAPSHOT"
```
## How was this patch tested?
manual, unit tests
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#14935 from felixcheung/rsparksessionversion.
## What changes were proposed in this pull request?
(Please fill in changes proposed in this fix)
registerTempTable(createDataFrame(iris), "iris")
str(collect(sql("select cast('1' as double) as x, cast('2' as decimal) as y from iris limit 5")))
'data.frame': 5 obs. of 2 variables:
$ x: num 1 1 1 1 1
$ y:List of 5
..$ : num 2
..$ : num 2
..$ : num 2
..$ : num 2
..$ : num 2
The problem is that spark returns `decimal(10, 0)` col type, instead of `decimal`. Thus, `decimal(10, 0)` is not handled correctly. It should be handled as "double".
As discussed in JIRA thread, we can have two potential fixes:
1). Scala side fix to add a new case when writing the object back; However, I can't use spark.sql.types._ in Spark core due to dependency issues. I don't find a way of doing type case match;
2). SparkR side fix: Add a helper function to check special type like `"decimal(10, 0)"` and replace it with `double`, which is PRIMITIVE type. This special helper is generic for adding new types handling in the future.
I open this PR to discuss pros and cons of both approaches. If we want to do Scala side fix, we need to find a way to match the case of DecimalType and StructType in Spark Core.
## How was this patch tested?
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
Manual test:
> str(collect(sql("select cast('1' as double) as x, cast('2' as decimal) as y from iris limit 5")))
'data.frame': 5 obs. of 2 variables:
$ x: num 1 1 1 1 1
$ y: num 2 2 2 2 2
R Unit tests
Author: wm624@hotmail.com <wm624@hotmail.com>
Closes#14613 from wangmiao1981/type.
https://issues.apache.org/jira/browse/SPARK-17241
## What changes were proposed in this pull request?
Spark has configurable L2 regularization parameter for generalized linear regression. It is very important to have them in SparkR so that users can run ridge regression.
## How was this patch tested?
Test manually on local laptop.
Author: Xin Ren <iamshrek@126.com>
Closes#14856 from keypointt/SPARK-17241.
## What changes were proposed in this pull request?
The usage in the original example is incorrect. This PR fixes it.
## How was this patch tested?
Manual test.
Author: Junyang Qian <junyangq@databricks.com>
Closes#14903 from junyangq/SPARKR-FixWindowPartitionByDoc.
## What changes were proposed in this pull request?
Remove cleanup.jobj test. Use JVM wrapper API for other test cases.
## How was this patch tested?
Run R unit tests with testthat 1.0
Author: Shivaram Venkataraman <shivaram@cs.berkeley.edu>
Closes#14904 from shivaram/sparkr-jvm-tests-fix.
## What changes were proposed in this pull request?
Currently, `HiveContext` in SparkR is not being tested and always skipped.
This is because the initiation of `TestHiveContext` is being failed due to trying to load non-existing data paths (test tables).
This is introduced from https://github.com/apache/spark/pull/14005
This enables the tests with SparkR.
## How was this patch tested?
Manually,
**Before** (on Mac OS)
```
...
Skipped ------------------------------------------------------------------------
1. create DataFrame from RDD (test_sparkSQL.R#200) - Hive is not build with SparkSQL, skipped
2. test HiveContext (test_sparkSQL.R#1041) - Hive is not build with SparkSQL, skipped
3. read/write ORC files (test_sparkSQL.R#1748) - Hive is not build with SparkSQL, skipped
4. enableHiveSupport on SparkSession (test_sparkSQL.R#2480) - Hive is not build with SparkSQL, skipped
5. sparkJars tag in SparkContext (test_Windows.R#21) - This test is only for Windows, skipped
...
```
**After** (on Mac OS)
```
...
Skipped ------------------------------------------------------------------------
1. sparkJars tag in SparkContext (test_Windows.R#21) - This test is only for Windows, skipped
...
```
Please refer the tests below (on Windows)
- Before: https://ci.appveyor.com/project/HyukjinKwon/spark/build/45-test123
- After: https://ci.appveyor.com/project/HyukjinKwon/spark/build/46-test123
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#14889 from HyukjinKwon/SPARK-17326.
## What changes were proposed in this pull request?
This change exposes a public API in SparkR to create objects, call methods on the Spark driver JVM
## How was this patch tested?
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
Unit tests, CRAN checks
Author: Shivaram Venkataraman <shivaram@cs.berkeley.edu>
Closes#14775 from shivaram/sparkr-java-api.
## What changes were proposed in this pull request?
This PR tries to fix the name of the `SparkDataFrame` used in the example. Also, it gives a reference url of an example data file so that users can play with.
## How was this patch tested?
Manual test.
Author: Junyang Qian <junyangq@databricks.com>
Closes#14853 from junyangq/SPARKR-FixLDADoc.
## What changes were proposed in this pull request?
The original example doesn't work because the features are not categorical. This PR fixes this by changing to another dataset.
## How was this patch tested?
Manual test.
Author: Junyang Qian <junyangq@databricks.com>
Closes#14820 from junyangq/SPARK-FixNaiveBayes.
## What changes were proposed in this pull request?
This PR gives informative message to users when they try to connect to a remote master but don't have Spark package in their local machine.
As a clarification, for now, automatic installation will only happen if they start SparkR in R console (rather than from sparkr-shell) and connect to local master. In the remote master mode, local Spark package is still needed, but we will not trigger the install.spark function because the versions have to match those on the cluster, which involves more user input. Instead, we here try to provide detailed message that may help the users.
Some of the other messages have also been slightly changed.
## How was this patch tested?
Manual test.
Author: Junyang Qian <junyangq@databricks.com>
Closes#14761 from junyangq/SPARK-16579-V1.
## What changes were proposed in this pull request?
This PR adds more examples to window function docs to make them more accessible to the users.
It also fixes default value issues for `lag` and `lead`.
## How was this patch tested?
Manual test, R unit test.
Author: Junyang Qian <junyangq@databricks.com>
Closes#14779 from junyangq/SPARKR-FixWindowFunctionDocs.
## What changes were proposed in this pull request?
Fixed several misplaced param tag - they should be on the spark.* method generics
## How was this patch tested?
run knitr
junyangq
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#14792 from felixcheung/rdocmllib.
https://issues.apache.org/jira/browse/SPARK-16445
## What changes were proposed in this pull request?
Create Multilayer Perceptron Classifier wrapper in SparkR
## How was this patch tested?
Tested manually on local machine
Author: Xin Ren <iamshrek@126.com>
Closes#14447 from keypointt/SPARK-16445.
## What changes were proposed in this pull request?
The original doc of `show` put methods for multiple classes together but the text only talks about `SparkDataFrame`. This PR tries to fix this problem.
## How was this patch tested?
Manual test.
Author: Junyang Qian <junyangq@databricks.com>
Closes#14776 from junyangq/SPARK-FixShowDoc.
## What changes were proposed in this pull request?
The PR removes reference link in the doc for environment variables for common Windows folders. The cran check gave code 503: service unavailable on the original link.
## How was this patch tested?
Manual check.
Author: Junyang Qian <junyangq@databricks.com>
Closes#14767 from junyangq/SPARKR-RemoveLink.
## What changes were proposed in this pull request?
Update DESCRIPTION
## How was this patch tested?
Run install and CRAN tests
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#14764 from felixcheung/rpackagedescription.
## What changes were proposed in this pull request?
(Please fill in changes proposed in this fix)
## How was this patch tested?
This change adds CRAN documentation checks to be run as a part of `R/run-tests.sh` . As this script is also used by Jenkins this means that we will get documentation checks on every PR going forward.
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Author: Shivaram Venkataraman <shivaram@cs.berkeley.edu>
Closes#14759 from shivaram/sparkr-cran-jenkins.
## What changes were proposed in this pull request?
replace ``` ` ``` in code doc with `\code{thing}`
remove added `...` for drop(DataFrame)
fix remaining CRAN check warnings
## How was this patch tested?
create doc with knitr
junyangq
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#14734 from felixcheung/rdoccleanup.
## What changes were proposed in this pull request?
This change adds Xiangrui Meng and Felix Cheung to the maintainers field in the package description.
## How was this patch tested?
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Author: Shivaram Venkataraman <shivaram@cs.berkeley.edu>
Closes#14758 from shivaram/sparkr-maintainers.
## What changes were proposed in this pull request?
refactor, cleanup, reformat, fix deprecation in test
## How was this patch tested?
unit tests, manual tests
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#14735 from felixcheung/rmllibutil.
## What changes were proposed in this pull request?
This PR tries to fix the scheme of local cache folder in Windows. The name of the environment variable should be `LOCALAPPDATA` rather than `%LOCALAPPDATA%`.
## How was this patch tested?
Manual test in Windows 7.
Author: Junyang Qian <junyangq@databricks.com>
Closes#14743 from junyangq/SPARKR-FixWindowsInstall.
## What changes were proposed in this pull request?
Ignore temp files generated by `check-cran.sh`.
Author: Xiangrui Meng <meng@databricks.com>
Closes#14740 from mengxr/R-gitignore.
## What changes were proposed in this pull request?
#14551 fixed off-by-one bug in ```randomizeInPlace``` and some test failure caused by this fix.
But for SparkR ```spark.gaussianMixture``` test case, the fix is inappropriate. It only changed the output result of native R which should be compared by SparkR, however, it did not change the R code in annotation which is used for reproducing the result in native R. It will confuse users who can not reproduce the same result in native R. This PR sends a more robust test case which can produce same result between SparkR and native R.
## How was this patch tested?
Unit test update.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#14730 from yanboliang/spark-16961-followup.
## What changes were proposed in this pull request?
This PR tries to fix all the remaining "undocumented/duplicated arguments" warnings given by CRAN-check.
One left is doc for R `stats::glm` exported in SparkR. To mute that warning, we have to also provide document for all arguments of that non-SparkR function.
Some previous conversation is in #14558.
## How was this patch tested?
R unit test and `check-cran.sh` script (with no-test).
Author: Junyang Qian <junyangq@databricks.com>
Closes#14705 from junyangq/SPARK-16508-master.
JIRA issue link:
https://issues.apache.org/jira/browse/SPARK-16961
Changed one line of Utils.randomizeInPlace to allow elements to stay in place.
Created a unit test that runs a Pearson's chi squared test to determine whether the output diverges significantly from a uniform distribution.
Author: Nick Lavers <nick.lavers@videoamp.com>
Closes#14551 from nicklavers/SPARK-16961-randomizeInPlace.
## What changes were proposed in this pull request?
Add LDA Wrapper in SparkR with the following interfaces:
- spark.lda(data, ...)
- spark.posterior(object, newData, ...)
- spark.perplexity(object, ...)
- summary(object)
- write.ml(object)
- read.ml(path)
## How was this patch tested?
Test with SparkR unit test.
Author: Xusen Yin <yinxusen@gmail.com>
Closes#14229 from yinxusen/SPARK-16447.
## What changes were proposed in this pull request?
Gaussian Mixture Model wrapper in SparkR, similarly to R's ```mvnormalmixEM```.
## How was this patch tested?
Unit test.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#14392 from yanboliang/spark-16446.
## What changes were proposed in this pull request?
(Please fill in changes proposed in this fix)
Add Isotonic Regression wrapper in SparkR
Wrappers in R and Scala are added.
Unit tests
Documentation
## How was this patch tested?
Manually tested with sudo ./R/run-tests.sh
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
Author: wm624@hotmail.com <wm624@hotmail.com>
Closes#14182 from wangmiao1981/isoR.
## What changes were proposed in this pull request?
Rename RDD functions for now to avoid CRAN check warnings.
Some RDD functions are sharing generics with DataFrame functions (hence the problem) so after the renames we need to add new generics, for now.
## How was this patch tested?
unit tests
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#14626 from felixcheung/rrddfunctions.
## What changes were proposed in this pull request?
Fix the issue that ```spark.glm``` ```weightCol``` should in the signature.
## How was this patch tested?
Existing tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#14641 from yanboliang/weightCol.
## What changes were proposed in this pull request?
Add an install_spark function to the SparkR package. User can run `install_spark()` to install Spark to a local directory within R.
Updates:
Several changes have been made:
- `install.spark()`
- check existence of tar file in the cache folder, and download only if not found
- trial priority of mirror_url look-up: user-provided -> preferred mirror site from apache website -> hardcoded backup option
- use 2.0.0
- `sparkR.session()`
- can install spark when not found in `SPARK_HOME`
## How was this patch tested?
Manual tests, running the check-cran.sh script added in #14173.
Author: Junyang Qian <junyangq@databricks.com>
Closes#14258 from junyangq/SPARK-16579.
## What changes were proposed in this pull request?
Training GLMs on weighted dataset is very important use cases, but it is not supported by SparkR currently. Users can pass argument ```weights``` to specify the weights vector in native R. For ```spark.glm```, we can pass in the ```weightCol``` which is consistent with MLlib.
## How was this patch tested?
Unit test.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#14346 from yanboliang/spark-16710.
## What changes were proposed in this pull request?
This change moves the include jar test from R to SparkSubmitSuite and uses a dynamically compiled jar. This helps us remove the binary jar from the R package and solves both the CRAN warnings and the lack of source being available for this jar.
## How was this patch tested?
SparkR unit tests, SparkSubmitSuite, check-cran.sh
Author: Shivaram Venkataraman <shivaram@cs.berkeley.edu>
Closes#14243 from shivaram/sparkr-jar-move.
## What changes were proposed in this pull request?
https://issues.apache.org/jira/browse/SPARK-16055
sparkPackages - argument is passed and we detect that we are in the R script mode, we should print some warning like --packages flag should be used with with spark-submit
## How was this patch tested?
In my system locally
Author: krishnakalyan3 <krishnakalyan3@gmail.com>
Closes#14179 from krishnakalyan3/spark-pkg.
## What changes were proposed in this pull request?
Fix R SparkSession init/stop, and warnings of reusing existing Spark Context
## How was this patch tested?
unit tests
shivaram
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#14177 from felixcheung/rsessiontest.
## What changes were proposed in this pull request?
Add a check-cran.sh script that runs `R CMD check` as CRAN. Also fixes a number of issues pointed out by the check. These include
- Updating `DESCRIPTION` to be appropriate
- Adding a .Rbuildignore to ignore lintr, src-native, html that are non-standard files / dirs
- Adding aliases to all S4 methods in DataFrame, Column, GroupedData etc. This is required as stated in https://cran.r-project.org/doc/manuals/r-release/R-exts.html#Documenting-S4-classes-and-methods
- Other minor fixes
## How was this patch tested?
SparkR unit tests, running the above mentioned script
Author: Shivaram Venkataraman <shivaram@cs.berkeley.edu>
Closes#14173 from shivaram/sparkr-cran-changes.
## What changes were proposed in this pull request?
More tests
I don't think this is critical for Spark 2.0.0 RC, maybe Spark 2.0.1 or 2.1.0.
## How was this patch tested?
unit tests
shivaram dongjoon-hyun
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#14206 from felixcheung/rroutetests.
## What changes were proposed in this pull request?
Fix function routing to work with and without namespace operator `SparkR::createDataFrame`
## How was this patch tested?
manual, unit tests
shivaram
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#14195 from felixcheung/rroutedefault.
## What changes were proposed in this pull request?
Rename window.partitionBy and window.orderBy to windowPartitionBy and windowOrderBy to pass CRAN package check.
## How was this patch tested?
SparkR unit tests.
Author: Sun Rui <sunrui2016@gmail.com>
Closes#14192 from sun-rui/SPARK-16509.
## What changes were proposed in this pull request?
Minor documentation update for code example, code style, and missed reference to "sparkR.init"
## How was this patch tested?
manual
shivaram
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#14178 from felixcheung/rcsvprogrammingguide.
## What changes were proposed in this pull request?
Minor example updates
## How was this patch tested?
manual
shivaram
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#14171 from felixcheung/rexample.
## What changes were proposed in this pull request?
* Update SparkR ML section to make them consistent with SparkR API docs.
* Since #13972 adds labelling support for the ```include_example``` Jekyll plugin, so that we can split the single ```ml.R``` example file into multiple line blocks with different labels, and include them in different algorithms/models in the generated HTML page.
## How was this patch tested?
Only docs update, manually check the generated docs.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#14011 from yanboliang/r-user-guide-update.
## What changes were proposed in this pull request?
This PR prevents ERRORs when `summary(df)` is called for `SparkDataFrame` with not-numeric columns. This failure happens only in `SparkR`.
**Before**
```r
> df <- createDataFrame(faithful)
> df <- withColumn(df, "boolean", df$waiting==79)
> summary(df)
16/07/07 14:15:16 ERROR RBackendHandler: describe on 34 failed
Error in invokeJava(isStatic = FALSE, objId$id, methodName, ...) :
org.apache.spark.sql.AnalysisException: cannot resolve 'avg(`boolean`)' due to data type mismatch: function average requires numeric types, not BooleanType;
```
**After**
```r
> df <- createDataFrame(faithful)
> df <- withColumn(df, "boolean", df$waiting==79)
> summary(df)
SparkDataFrame[summary:string, eruptions:string, waiting:string]
```
## How was this patch tested?
Pass the Jenkins with a updated testcase.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#14096 from dongjoon-hyun/SPARK-16425.
## What changes were proposed in this pull request?
Apply default "NA" as null string for R, like R read.csv na.string parameter.
https://stat.ethz.ch/R-manual/R-devel/library/utils/html/read.table.html
na.strings = "NA"
An user passing a csv file with NA value should get the same behavior with SparkR read.df(... source = "csv")
(couldn't open JIRA, will do that later)
## How was this patch tested?
unit tests
shivaram
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#13984 from felixcheung/rcsvnastring.
## What changes were proposed in this pull request?
ORC test should be enabled only when HiveContext is available.
## How was this patch tested?
Manual.
```
$ R/run-tests.sh
...
1. create DataFrame from RDD (test_sparkSQL.R#200) - Hive is not build with SparkSQL, skipped
2. test HiveContext (test_sparkSQL.R#1021) - Hive is not build with SparkSQL, skipped
3. read/write ORC files (test_sparkSQL.R#1728) - Hive is not build with SparkSQL, skipped
4. enableHiveSupport on SparkSession (test_sparkSQL.R#2448) - Hive is not build with SparkSQL, skipped
5. sparkJars tag in SparkContext (test_Windows.R#21) - This test is only for Windows, skipped
DONE ===========================================================================
Tests passed.
```
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#14019 from dongjoon-hyun/SPARK-16233.
## What changes were proposed in this pull request?
Capture errors from R workers in daemon.R to avoid deletion of R session temporary directory. See detailed description at https://issues.apache.org/jira/browse/SPARK-16299
## How was this patch tested?
SparkR unit tests.
Author: Sun Rui <sunrui2016@gmail.com>
Closes#13975 from sun-rui/SPARK-16299.
## What changes were proposed in this pull request?
gapplyCollect() does gapply() on a SparkDataFrame and collect the result back to R. Compared to gapply() + collect(), gapplyCollect() offers performance optimization as well as programming convenience, as no schema is needed to be provided.
This is similar to dapplyCollect().
## How was this patch tested?
Added test cases for gapplyCollect similar to dapplyCollect
Author: Narine Kokhlikyan <narine@slice.com>
Closes#13760 from NarineK/gapplyCollect.
## What changes were proposed in this pull request?
This PR implements `posexplode` table generating function. Currently, master branch raises the following exception for `map` argument. It's different from Hive.
**Before**
```scala
scala> sql("select posexplode(map('a', 1, 'b', 2))").show
org.apache.spark.sql.AnalysisException: No handler for Hive UDF ... posexplode() takes an array as a parameter; line 1 pos 7
```
**After**
```scala
scala> sql("select posexplode(map('a', 1, 'b', 2))").show
+---+---+-----+
|pos|key|value|
+---+---+-----+
| 0| a| 1|
| 1| b| 2|
+---+---+-----+
```
For `array` argument, `after` is the same with `before`.
```
scala> sql("select posexplode(array(1, 2, 3))").show
+---+---+
|pos|col|
+---+---+
| 0| 1|
| 1| 2|
| 2| 3|
+---+---+
```
## How was this patch tested?
Pass the Jenkins tests with newly added testcases.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13971 from dongjoon-hyun/SPARK-16289.
https://issues.apache.org/jira/browse/SPARK-16140
## What changes were proposed in this pull request?
Group the R doc of spark.kmeans, predict(KM), summary(KM), read/write.ml(KM) under Rd spark.kmeans. The example code was updated.
## How was this patch tested?
Tested on my local machine
And on my laptop `jekyll build` is failing to build API docs, so here I can only show you the html I manually generated from Rd files, with no CSS applied, but the doc content should be there.
Author: Xin Ren <iamshrek@126.com>
Closes#13921 from keypointt/SPARK-16140.
## What changes were proposed in this pull request?
Add unit tests for csv data for SPARKR
## How was this patch tested?
unit tests
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#13904 from felixcheung/rcsv.
## What changes were proposed in this pull request?
update sparkR DataFrame.R comment
SQLContext ==> SparkSession
## How was this patch tested?
N/A
Author: WeichenXu <WeichenXu123@outlook.com>
Closes#13946 from WeichenXu123/sparkR_comment_update_sparkSession.
## What changes were proposed in this pull request?
Allowing truncate to a specific number of character is convenient at times, especially while operating from the REPL. Sometimes those last few characters make all the difference, and showing everything brings in whole lot of noise.
## How was this patch tested?
Existing tests. + 1 new test in DataFrameSuite.
For SparkR and pyspark, existing tests and manual testing.
Author: Prashant Sharma <prashsh1@in.ibm.com>
Author: Prashant Sharma <prashant@apache.org>
Closes#13839 from ScrapCodes/add_truncateTo_DF.show.
## What changes were proposed in this pull request?
Add `conf` method to get Runtime Config from SparkSession
## How was this patch tested?
unit tests, manual tests
This is how it works in sparkR shell:
```
SparkSession available as 'spark'.
> conf()
$hive.metastore.warehouse.dir
[1] "file:/opt/spark-2.0.0-bin-hadoop2.6/R/spark-warehouse"
$spark.app.id
[1] "local-1466749575523"
$spark.app.name
[1] "SparkR"
$spark.driver.host
[1] "10.0.2.1"
$spark.driver.port
[1] "45629"
$spark.executorEnv.LD_LIBRARY_PATH
[1] "$LD_LIBRARY_PATH:/usr/lib/R/lib:/usr/lib/x86_64-linux-gnu:/usr/lib/jvm/default-java/jre/lib/amd64/server"
$spark.executor.id
[1] "driver"
$spark.home
[1] "/opt/spark-2.0.0-bin-hadoop2.6"
$spark.master
[1] "local[*]"
$spark.sql.catalogImplementation
[1] "hive"
$spark.submit.deployMode
[1] "client"
> conf("spark.master")
$spark.master
[1] "local[*]"
```
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#13885 from felixcheung/rconf.
## What changes were proposed in this pull request?
Updated setJobGroup, cancelJobGroup, clearJobGroup to not require sc/SparkContext as parameter.
Also updated roxygen2 doc and R programming guide on deprecations.
## How was this patch tested?
unit tests
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#13838 from felixcheung/rjobgroup.
## What changes were proposed in this pull request?
Guide for
- UDFs with dapply, dapplyCollect
- spark.lapply for running parallel R functions
## How was this patch tested?
build locally
<img width="654" alt="screen shot 2016-06-14 at 03 12 56" src="https://cloud.githubusercontent.com/assets/3419881/16039344/12a3b6a0-31de-11e6-8d77-fe23308075c0.png">
Author: Kai Jiang <jiangkai@gmail.com>
Closes#13660 from vectorijk/spark-15672-R-guide-update.
## What changes were proposed in this pull request?
add union and deprecate unionAll, separate roxygen2 doc for rbind (since their usage and parameter lists are quite different)
`explode` is also deprecated - but seems like replacement is a combination of calls; not sure if we should deprecate it in SparkR, yet.
## How was this patch tested?
unit tests, manual checks for r doc
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#13805 from felixcheung/runion.
## What changes were proposed in this pull request?
Found these issues while reviewing for SPARK-16090
## How was this patch tested?
roxygen2 doc gen, checked output html
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#13803 from felixcheung/rdocrd.
## What changes were proposed in this pull request?
This PR is a subset of #13023 by yanboliang to make SparkR model param names and default values consistent with MLlib. I tried to avoid other changes from #13023 to keep this PR minimal. I will send a follow-up PR to improve the documentation.
Main changes:
* `spark.glm`: epsilon -> tol, maxit -> maxIter
* `spark.kmeans`: default k -> 2, default maxIter -> 20, default initMode -> "k-means||"
* `spark.naiveBayes`: laplace -> smoothing, default 1.0
## How was this patch tested?
Existing unit tests.
Author: Xiangrui Meng <meng@databricks.com>
Closes#13801 from mengxr/SPARK-15177.1.
## What changes were proposed in this pull request?
I ran a full pass from A to Z and fixed the obvious duplications, improper grouping etc.
There are still more doc issues to be cleaned up.
## How was this patch tested?
manual tests
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#13798 from felixcheung/rdocseealso.
## What changes were proposed in this pull request?
This PR adds `pivot` function to SparkR for API parity. Since this PR is based on https://github.com/apache/spark/pull/13295 , mhnatiuk should be credited for the work he did.
## How was this patch tested?
Pass the Jenkins tests (including new testcase.)
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13786 from dongjoon-hyun/SPARK-15294.
## What changes were proposed in this pull request?
Removed unnecessary duplicated documentation in dapply and dapplyCollect.
In this pull request I created separate R docs for dapply and dapplyCollect - kept dapply's documentation separate from dapplyCollect's and referred from one to another via a link.
## How was this patch tested?
Existing test cases.
Author: Narine Kokhlikyan <narine@slice.com>
Closes#13790 from NarineK/dapply-docs-fix.
## What changes were proposed in this pull request?
This PR adds `since` tags to Roxygen documentation according to the previous documentation archive.
https://home.apache.org/~dongjoon/spark-2.0.0-docs/api/R/
## How was this patch tested?
Manual.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13734 from dongjoon-hyun/SPARK-14995.
## What changes were proposed in this pull request?
roxygen2 doc, programming guide, example updates
## How was this patch tested?
manual checks
shivaram
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#13751 from felixcheung/rsparksessiondoc.
## What changes were proposed in this pull request?
This PR adds `spark_partition_id` virtual column function in SparkR for API parity.
The following is just an example to illustrate a SparkR usage on a partitioned parquet table created by `spark.range(10).write.mode("overwrite").parquet("/tmp/t1")`.
```r
> collect(select(read.parquet('/tmp/t1'), c('id', spark_partition_id())))
id SPARK_PARTITION_ID()
1 3 0
2 4 0
3 8 1
4 9 1
5 0 2
6 1 3
7 2 4
8 5 5
9 6 6
10 7 7
```
## How was this patch tested?
Pass the Jenkins tests (including new testcase).
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13768 from dongjoon-hyun/SPARK-16053.
## What changes were proposed in this pull request?
fix code doc
## How was this patch tested?
manual
shivaram
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#13782 from felixcheung/rcountdoc.
## What changes were proposed in this pull request?
spark.lapply and setLogLevel
## How was this patch tested?
unit test
shivaram thunterdb
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#13752 from felixcheung/rlapply.
## What changes were proposed in this pull request?
This issue adds `read.orc/write.orc` to SparkR for API parity.
## How was this patch tested?
Pass the Jenkins tests (with new testcases).
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13763 from dongjoon-hyun/SPARK-16051.
## What changes were proposed in this pull request?
Add dropTempView and deprecate dropTempTable
## How was this patch tested?
unit tests
shivaram liancheng
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#13753 from felixcheung/rdroptempview.
## What changes were proposed in this pull request?
This PR adds `monotonically_increasing_id` column function in SparkR for API parity.
After this PR, SparkR supports the followings.
```r
> df <- read.json("examples/src/main/resources/people.json")
> collect(select(df, monotonically_increasing_id(), df$name, df$age))
monotonically_increasing_id() name age
1 0 Michael NA
2 1 Andy 30
3 2 Justin 19
```
## How was this patch tested?
Pass the Jenkins tests (with added testcase).
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13774 from dongjoon-hyun/SPARK-16059.
## What changes were proposed in this pull request?
This PR introduces the new SparkSession API for SparkR.
`sparkR.session.getOrCreate()` and `sparkR.session.stop()`
"getOrCreate" is a bit unusual in R but it's important to name this clearly.
SparkR implementation should
- SparkSession is the main entrypoint (vs SparkContext; due to limited functionality supported with SparkContext in SparkR)
- SparkSession replaces SQLContext and HiveContext (both a wrapper around SparkSession, and because of API changes, supporting all 3 would be a lot more work)
- Changes to SparkSession is mostly transparent to users due to SPARK-10903
- Full backward compatibility is expected - users should be able to initialize everything just in Spark 1.6.1 (`sparkR.init()`), but with deprecation warning
- Mostly cosmetic changes to parameter list - users should be able to move to `sparkR.session.getOrCreate()` easily
- An advanced syntax with named parameters (aka varargs aka "...") is supported; that should be closer to the Builder syntax that is in Scala/Python (which unfortunately does not work in R because it will look like this: `enableHiveSupport(config(config(master(appName(builder(), "foo"), "local"), "first", "value"), "next, "value"))`
- Updating config on an existing SparkSession is supported, the behavior is the same as Python, in which config is applied to both SparkContext and SparkSession
- Some SparkSession changes are not matched in SparkR, mostly because it would be breaking API change: `catalog` object, `createOrReplaceTempView`
- Other SQLContext workarounds are replicated in SparkR, eg. `tables`, `tableNames`
- `sparkR` shell is updated to use the SparkSession entrypoint (`sqlContext` is removed, just like with Scale/Python)
- All tests are updated to use the SparkSession entrypoint
- A bug in `read.jdbc` is fixed
TODO
- [x] Add more tests
- [ ] Separate PR - update all roxygen2 doc coding example
- [ ] Separate PR - update SparkR programming guide
## How was this patch tested?
unit tests, manual tests
shivaram sun-rui rxin
Author: Felix Cheung <felixcheung_m@hotmail.com>
Author: felixcheung <felixcheung_m@hotmail.com>
Closes#13635 from felixcheung/rsparksession.
## What changes were proposed in this pull request?
This PR adds `randomSplit` to SparkR for API parity.
## How was this patch tested?
Pass the Jenkins tests (with new testcase.)
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13721 from dongjoon-hyun/SPARK-16005.
## What changes were proposed in this pull request?
Add registerTempTable to DataFrame with Deprecate
## How was this patch tested?
unit tests
shivaram liancheng
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#13722 from felixcheung/rregistertemptable.
## What changes were proposed in this pull request?
This PR adds varargs-type `dropDuplicates` function to SparkR for API parity.
Refer to https://issues.apache.org/jira/browse/SPARK-15807, too.
## How was this patch tested?
Pass the Jenkins tests with new testcases.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13684 from dongjoon-hyun/SPARK-15908.
## What changes were proposed in this pull request?
R Docs changes
include typos, format, layout.
## How was this patch tested?
Test locally.
Author: Kai Jiang <jiangkai@gmail.com>
Closes#13394 from vectorijk/spark-15490.
## What changes were proposed in this pull request?
gapply() applies an R function on groups grouped by one or more columns of a DataFrame, and returns a DataFrame. It is like GroupedDataSet.flatMapGroups() in the Dataset API.
Please, let me know what do you think and if you have any ideas to improve it.
Thank you!
## How was this patch tested?
Unit tests.
1. Primitive test with different column types
2. Add a boolean column
3. Compute average by a group
Author: Narine Kokhlikyan <narine.kokhlikyan@gmail.com>
Author: NarineK <narine.kokhlikyan@us.ibm.com>
Closes#12836 from NarineK/gapply2.
## What changes were proposed in this pull request?
Because of the fix in SPARK-15684, this exclusion is no longer necessary.
## How was this patch tested?
unit tests
shivaram
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#13636 from felixcheung/rendswith.
## What changes were proposed in this pull request?
This PR replaces `registerTempTable` with `createOrReplaceTempView` as a follow-up task of #12945.
## How was this patch tested?
Existing SparkR tests.
Author: Cheng Lian <lian@databricks.com>
Closes#13644 from liancheng/spark-15925-temp-view-for-r.
## What changes were proposed in this pull request?
When reviewing SPARK-15545, we found that is.nan is not exported, which should be exported.
Add it to the NAMESPACE.
## How was this patch tested?
Manual tests.
Author: wm624@hotmail.com <wm624@hotmail.com>
Closes#13508 from wangmiao1981/unused.
## What changes were proposed in this pull request?
In R 3.3.0, startsWith and endsWith are added. In this PR, I make the two work in SparkR.
1. Remove signature in generic.R
2. Add setMethod in column.R
3. Add unit tests
## How was this patch tested?
Manually test it through SparkR shell for both column data and string data, which are added into the unit test file.
Author: wm624@hotmail.com <wm624@hotmail.com>
Closes#13476 from wangmiao1981/start.
## What changes were proposed in this pull request?
`an -> a`
Use cmds like `find . -name '*.R' | xargs -i sh -c "grep -in ' an [^aeiou]' {} && echo {}"` to generate candidates, and review them one by one.
## How was this patch tested?
manual tests
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#13515 from zhengruifeng/an_a.
## What changes were proposed in this pull request?
changes in R/README.md
- Make step of generating SparkR document more clear.
- link R/DOCUMENTATION.md from R/README.md
- turn on some code syntax highlight in R/README.md
## How was this patch tested?
local test
Author: Kai Jiang <jiangkai@gmail.com>
Closes#13488 from vectorijk/R-Readme.
## What changes were proposed in this pull request?
Change version check in R tests
## How was this patch tested?
R tests
shivaram
Author: felixcheung <felixcheung_m@hotmail.com>
Closes#13369 from felixcheung/rversioncheck.
## What changes were proposed in this pull request?
Follow up on the earlier PR - in here we are fixing up roxygen2 doc examples.
Also add to the programming guide migration section.
## How was this patch tested?
SparkR tests
Author: felixcheung <felixcheung_m@hotmail.com>
Closes#13340 from felixcheung/sqlcontextdoc.
## What changes were proposed in this pull request?
This PR corrects SparkR to use `shell()` instead of `system2()` on Windows.
Using `system2(...)` on Windows does not process windows file separator `\`. `shell(tralsate = TRUE, ...)` can treat this problem. So, this was changed to be chosen according to OS.
Existing tests were failed on Windows due to this problem. For example, those were failed.
```
8. Failure: sparkJars tag in SparkContext (test_includeJAR.R#34)
9. Failure: sparkJars tag in SparkContext (test_includeJAR.R#36)
```
The cases above were due to using of `system2`.
In addition, this PR also fixes some tests failed on Windows.
```
5. Failure: sparkJars sparkPackages as comma-separated strings (test_context.R#128)
6. Failure: sparkJars sparkPackages as comma-separated strings (test_context.R#131)
7. Failure: sparkJars sparkPackages as comma-separated strings (test_context.R#134)
```
The cases above were due to a weird behaviour of `normalizePath()`. On Linux, if the path does not exist, it just prints out the input but it prints out including the current path on Windows.
```r
# On Linus
path <- normalizePath("aa")
print(path)
[1] "aa"
# On Windows
path <- normalizePath("aa")
print(path)
[1] "C:\\Users\\aa"
```
## How was this patch tested?
Jenkins tests and manually tested in a Window machine as below:
Here is the [stdout](https://gist.github.com/HyukjinKwon/4bf35184f3a30f3bce987a58ec2bbbab) of testing.
Closes#7025
Author: hyukjinkwon <gurwls223@gmail.com>
Author: Hyukjin Kwon <gurwls223@gmail.com>
Author: Prakash PC <prakash.chinnu@gmail.com>
Closes#13165 from HyukjinKwon/pr/7025.
https://issues.apache.org/jira/browse/SPARK-15542
## What changes were proposed in this pull request?
When running`./R/install-dev.sh` in **Mac OS EI Captain** environment, I got
```
mbp185-xr:spark xin$ ./R/install-dev.sh
usage: dirname path
```
This message is very confusing to me, and then I found R is not properly configured on my Mac when this script is using `$(which R)` to get R home.
I tried similar situation on CentOS with R missing, and it's giving me very clear error message while MacOS is not.
on CentOS:
```
[rootip-xxx-31-9-xx spark]# which R
/usr/bin/which: no R in (/usr/local/sbin:/usr/local/bin:/sbin:/bin:/usr/sbin:/usr/bin:/usr/lib/jvm/java-1.7.0-openjdk.x86_64/bin:/root/bin)
```
but on Mac, if not found then nothing returned and this is causing the confusing message for R build failure and running R/install-dev.sh:
```
mbp185-xr:spark xin$ which R
mbp185-xr:spark xin$
```
Here I just added a clear message for this miss configuration for R when running `R/install-dev.sh`.
```
mbp185-xr:spark xin$ ./R/install-dev.sh
Cannot find R home by running 'which R', please make sure R is properly installed.
```
## How was this patch tested?
Manually tested on local machine.
Author: Xin Ren <iamshrek@126.com>
Closes#13308 from keypointt/SPARK-15542.
Eliminate the need to pass sqlContext to method since it is a singleton - and we don't want to support multiple contexts in a R session.
Changes are done in a back compat way with deprecation warning added. Method signature for S3 methods are added in a concise, clean approach such that in the next release the deprecated signature can be taken out easily/cleanly (just delete a few lines per method).
Custom method dispatch is implemented to allow for multiple JVM reference types that are all 'jobj' in R and to avoid having to add 30 new exports.
Author: felixcheung <felixcheung_m@hotmail.com>
Closes#9192 from felixcheung/rsqlcontext.
## What changes were proposed in this pull request?
(Please fill in changes proposed in this fix)
There are some failures when running SparkR unit tests.
In this PR, I fixed two of these failures in test_context.R and test_sparkSQL.R
The first one is due to different masked name. I added missed names in the expected arrays.
The second one is because one PR removed the logic of a previous fix of missing subset method.
The file privilege issue is still there. I am debugging it. SparkR shell can run the test case successfully.
test_that("pipeRDD() on RDDs", {
actual <- collect(pipeRDD(rdd, "more"))
When using run-test script, it complains no such directories as below:
cannot open file '/tmp/Rtmp4FQbah/filee2273f9d47f7': No such file or directory
## How was this patch tested?
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
Manually test it
Author: wm624@hotmail.com <wm624@hotmail.com>
Closes#13284 from wangmiao1981/R.
## What changes were proposed in this pull request?
in hive, `locate("aa", "aaa", 0)` would yield 0, `locate("aa", "aaa", 1)` would yield 1 and `locate("aa", "aaa", 2)` would yield 2, while in Spark, `locate("aa", "aaa", 0)` would yield 1, `locate("aa", "aaa", 1)` would yield 2 and `locate("aa", "aaa", 2)` would yield 0. This results from the different understanding of the third parameter in udf `locate`. It means the starting index and starts from 1, so when we use 0, the return would always be 0.
## How was this patch tested?
tested with modified `StringExpressionsSuite` and `StringFunctionsSuite`
Author: Daoyuan Wang <daoyuan.wang@intel.com>
Closes#13186 from adrian-wang/locate.
## What changes were proposed in this pull request?
This PR adds the description for running unit tests in Windows.
## How was this patch tested?
On a bare machine (Window 7, 32bits), this was manually built and tested.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#13217 from HyukjinKwon/minor-r-doc.
## What changes were proposed in this pull request?
This patch is a follow-up to https://github.com/apache/spark/pull/13104 and adds documentation to clarify the semantics of read.text with respect to partitioning.
## How was this patch tested?
N/A
Author: Reynold Xin <rxin@databricks.com>
Closes#13184 from rxin/SPARK-14463.
## What changes were proposed in this pull request?
dapplyCollect() applies an R function on each partition of a SparkDataFrame and collects the result back to R as a data.frame.
```
dapplyCollect(df, function(ldf) {...})
```
## How was this patch tested?
SparkR unit tests.
Author: Sun Rui <sunrui2016@gmail.com>
Closes#12989 from sun-rui/SPARK-15202.
## What changes were proposed in this pull request?
* Since Spark has supported native csv reader, it does not necessary to use the third party ```spark-csv``` in ```examples/src/main/r/data-manipulation.R```. Meanwhile, remove all ```spark-csv``` usage in SparkR.
* Running R applications through ```sparkR``` is not supported as of Spark 2.0, so we change to use ```./bin/spark-submit``` to run the example.
## How was this patch tested?
Offline test.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#13005 from yanboliang/r-df-examples.
## What changes were proposed in this pull request?
This PR is a workaround for NA handling in hash code computation.
This PR is on behalf of paulomagalhaes whose PR is https://github.com/apache/spark/pull/10436
## How was this patch tested?
SparkR unit tests.
Author: Sun Rui <sunrui2016@gmail.com>
Author: ray <ray@rays-MacBook-Air.local>
Closes#12976 from sun-rui/SPARK-12479.
This PR:
1. Implement WindowSpec S4 class.
2. Implement Window.partitionBy() and Window.orderBy() as utility functions to create WindowSpec objects.
3. Implement over() of Column class.
Author: Sun Rui <rui.sun@intel.com>
Author: Sun Rui <sunrui2016@gmail.com>
Closes#10094 from sun-rui/SPARK-11395.
## What changes were proposed in this pull request?
Implement repartitionByColumn on DataFrame.
This will allow us to run R functions on each partition identified by column groups with dapply() method.
## How was this patch tested?
Unit tests
Author: NarineK <narine.kokhlikyan@us.ibm.com>
Closes#12887 from NarineK/repartitionByColumns.
## What changes were proposed in this pull request?
Fix warnings and a failure in SparkR test cases with testthat version 1.0.1
## How was this patch tested?
SparkR unit test cases.
Author: Sun Rui <sunrui2016@gmail.com>
Closes#12867 from sun-rui/SPARK-15091.
## What changes were proposed in this pull request?
* ```RFormula``` supports empty response variable like ```~ x + y```.
* Support formula in ```spark.kmeans``` in SparkR.
* Fix some outdated docs for SparkR.
## How was this patch tested?
Unit tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#12813 from yanboliang/spark-15030.
## What changes were proposed in this pull request?
Continue the work of #12789 to rename ml.asve/ml.load to write.ml/read.ml, which are more consistent with read.df/write.df and other methods in SparkR.
I didn't rename `data` to `df` because we still use `predict` for prediction, which uses `newData` to match the signature in R.
## How was this patch tested?
Existing unit tests.
cc: yanboliang thunterdb
Author: Xiangrui Meng <meng@databricks.com>
Closes#12807 from mengxr/SPARK-14831.
## What changes were proposed in this pull request?
This PR splits the MLlib algorithms into two flavors:
- the R flavor, which tries to mimic the existing R API for these algorithms (and works as an S4 specialization for Spark dataframes)
- the Spark flavor, which follows the same API and naming conventions as the rest of the MLlib algorithms in the other languages
In practice, the former calls the latter.
## How was this patch tested?
The tests for the various algorithms were adapted to be run against both interfaces.
Author: Timothy Hunter <timhunter@databricks.com>
Closes#12789 from thunterdb/14831.
## What changes were proposed in this pull request?
dapply() applies an R function on each partition of a DataFrame and returns a new DataFrame.
The function signature is:
dapply(df, function(localDF) {}, schema = NULL)
R function input: local data.frame from the partition on local node
R function output: local data.frame
Schema specifies the Row format of the resulting DataFrame. It must match the R function's output.
If schema is not specified, each partition of the result DataFrame will be serialized in R into a single byte array. Such resulting DataFrame can be processed by successive calls to dapply().
## How was this patch tested?
SparkR unit tests.
Author: Sun Rui <rui.sun@intel.com>
Author: Sun Rui <sunrui2016@gmail.com>
Closes#12493 from sun-rui/SPARK-12919.
SparkR ```glm``` and ```kmeans``` model persistence.
Unit tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Author: Gayathri Murali <gayathri.m.softie@gmail.com>
Closes#12778 from yanboliang/spark-14311.
Closes#12680Closes#12683
## What changes were proposed in this pull request?
This PR adds a new function in SparkR called `sparkLapply(list, function)`. This function implements a distributed version of `lapply` using Spark as a backend.
TODO:
- [x] check documentation
- [ ] check tests
Trivial example in SparkR:
```R
sparkLapply(1:5, function(x) { 2 * x })
```
Output:
```
[[1]]
[1] 2
[[2]]
[1] 4
[[3]]
[1] 6
[[4]]
[1] 8
[[5]]
[1] 10
```
Here is a slightly more complex example to perform distributed training of multiple models. Under the hood, Spark broadcasts the dataset.
```R
library("MASS")
data(menarche)
families <- c("gaussian", "poisson")
train <- function(family){glm(Menarche ~ Age , family=family, data=menarche)}
results <- sparkLapply(families, train)
```
## How was this patch tested?
This PR was tested in SparkR. I am unfamiliar with R and SparkR, so any feedback on style, testing, etc. will be much appreciated.
cc falaki davies
Author: Timothy Hunter <timhunter@databricks.com>
Closes#12426 from thunterdb/7264.
Make the behavior of mutate more consistent with that in dplyr, besides support for replacing existing columns.
1. Throw error message when there are duplicated column names in the DataFrame being mutated.
2. when there are duplicated column names in specified columns by arguments, the last column of the same name takes effect.
Author: Sun Rui <rui.sun@intel.com>
Closes#10220 from sun-rui/SPARK-12235.
Added parameter drop to subsetting operator [. This is useful to get a Column from a DataFrame, given its name. R supports it.
In R:
```
> name <- "Sepal_Length"
> class(iris[, name])
[1] "numeric"
```
Currently, in SparkR:
```
> name <- "Sepal_Length"
> class(irisDF[, name])
[1] "DataFrame"
```
Previous code returns a DataFrame, which is inconsistent with R's behavior. SparkR should return a Column instead. Currently, in order for the user to return a Column given a column name as a character variable would be through `eval(parse(x))`, where x is the string `"irisDF$Sepal_Length"`. That itself is pretty hacky. `SparkR:::getColumn() `is another choice, but I don't see why this method should be externalized. Instead, following R's way to do things, the proposed implementation allows this:
```
> name <- "Sepal_Length"
> class(irisDF[, name, drop=T])
[1] "Column"
> class(irisDF[, name, drop=F])
[1] "DataFrame"
```
This is consistent with R:
```
> name <- "Sepal_Length"
> class(iris[, name])
[1] "numeric"
> class(iris[, name, drop=F])
[1] "data.frame"
```
Author: Oscar D. Lara Yejas <odlaraye@oscars-mbp.usca.ibm.com>
Author: Oscar D. Lara Yejas <odlaraye@oscars-mbp.attlocal.net>
Closes#11318 from olarayej/SPARK-13436.
## What changes were proposed in this pull request?
Added method histogram() to compute the histogram of a Column
Usage:
```
## Create a DataFrame from the Iris dataset
irisDF <- createDataFrame(sqlContext, iris)
## Render a histogram for the Sepal_Length column
histogram(irisDF, "Sepal_Length", nbins=12)
```
Note: Usage will change once SPARK-9325 is figured out so that histogram() only takes a Column as a parameter, as opposed to a DataFrame and a name
## How was this patch tested?
All unit tests pass. I added specific unit cases for different scenarios.
Author: Oscar D. Lara Yejas <odlaraye@oscars-mbp.usca.ibm.com>
Author: Oscar D. Lara Yejas <odlaraye@oscars-mbp.attlocal.net>
Closes#11569 from olarayej/SPARK-13734.
## What changes were proposed in this pull request?
```AFTSurvivalRegressionModel``` supports ```save/load``` in SparkR.
## How was this patch tested?
Unit tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#12685 from yanboliang/spark-14313.
## What changes were proposed in this pull request?
SparkR ```NaiveBayesModel``` supports ```save/load``` by the following API:
```
df <- createDataFrame(sqlContext, infert)
model <- naiveBayes(education ~ ., df, laplace = 0)
ml.save(model, path)
model2 <- ml.load(path)
```
## How was this patch tested?
Add unit tests.
cc mengxr
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#12573 from yanboliang/spark-14312.
