## 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?
#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?
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?
#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?
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?
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?
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?
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.
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?
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?
#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.
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?
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?
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?
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?
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?
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.
## 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?
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?
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.
