## 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?
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?
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?
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?
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?
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?
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?
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.
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?
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?
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.
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?
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.
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?
In order to support running SQL directly on files, we added some code in ResolveRelations to catch the exception thrown by catalog.lookupRelation and ignore it. This unfortunately masks all the exceptions. This patch changes the logic to simply test the table's existence.
## How was this patch tested?
I manually hacked some bugs into Spark and made sure the exceptions were being propagated up.
Author: Reynold Xin <rxin@databricks.com>
Closes#12634 from rxin/SPARK-14869.
## What changes were proposed in this pull request?
Changed class name defined in R from "DataFrame" to "SparkDataFrame". A popular package, S4Vector already defines "DataFrame" - this change is to avoid conflict.
Aside from class name and API/roxygen2 references, SparkR APIs like `createDataFrame`, `as.DataFrame` are not changed (S4Vector does not define a "as.DataFrame").
Since in R, one would rarely reference type/class, this change should have minimal/almost-no impact to a SparkR user in terms of back compat.
## How was this patch tested?
SparkR tests, manually loading S4Vector then SparkR package
Author: felixcheung <felixcheung_m@hotmail.com>
Closes#12621 from felixcheung/rdataframe.
## What changes were proposed in this pull request?
This issue aims to expose Scala `bround` function in Python/R API.
`bround` function is implemented in SPARK-14614 by extending current `round` function.
We used the following semantics from Hive.
```java
public static double bround(double input, int scale) {
if (Double.isNaN(input) || Double.isInfinite(input)) {
return input;
}
return BigDecimal.valueOf(input).setScale(scale, RoundingMode.HALF_EVEN).doubleValue();
}
```
After this PR, `pyspark` and `sparkR` also support `bround` function.
**PySpark**
```python
>>> from pyspark.sql.functions import bround
>>> sqlContext.createDataFrame([(2.5,)], ['a']).select(bround('a', 0).alias('r')).collect()
[Row(r=2.0)]
```
**SparkR**
```r
> df = createDataFrame(sqlContext, data.frame(x = c(2.5, 3.5)))
> head(collect(select(df, bround(df$x, 0))))
bround(x, 0)
1 2
2 4
```
## How was this patch tested?
Pass the Jenkins tests (including new testcases).
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#12509 from dongjoon-hyun/SPARK-14639.
## What changes were proposed in this pull request?
Change the signature of as.data.frame() to be consistent with that in the R base package to meet R user's convention.
## How was this patch tested?
dev/lint-r
SparkR unit tests
Author: Sun Rui <rui.sun@intel.com>
Closes#11811 from sun-rui/SPARK-13905.
#### What changes were proposed in this pull request?
This PR is to address the comment: https://github.com/apache/spark/pull/12146#discussion-diff-59092238. It removes the function `isViewSupported` from `SessionCatalog`. After the removal, we still can capture the user errors if users try to drop a table using `DROP VIEW`.
#### How was this patch tested?
Modified the existing test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes#12284 from gatorsmile/followupDropTable.
## What changes were proposed in this pull request?
The `window` function was added to Dataset with [this PR](https://github.com/apache/spark/pull/12008).
This PR adds the R API for this function.
With this PR, SQL, Java, and Scala will share the same APIs as in users can use:
- `window(timeColumn, windowDuration)`
- `window(timeColumn, windowDuration, slideDuration)`
- `window(timeColumn, windowDuration, slideDuration, startTime)`
In Python and R, users can access all APIs above, but in addition they can do
- In R:
`window(timeColumn, windowDuration, startTime=...)`
that is, they can provide the startTime without providing the `slideDuration`. In this case, we will generate tumbling windows.
## How was this patch tested?
Unit tests + manual tests
Author: Burak Yavuz <brkyvz@gmail.com>
Closes#12141 from brkyvz/R-windows.
## What changes were proposed in this pull request?
This reopens#11836, which was merged but promptly reverted because it introduced flaky Hive tests.
## How was this patch tested?
See `CatalogTestCases`, `SessionCatalogSuite` and `HiveContextSuite`.
Author: Andrew Or <andrew@databricks.com>
Closes#11938 from andrewor14/session-catalog-again.
## What changes were proposed in this pull request?
`SessionCatalog`, introduced in #11750, is a catalog that keeps track of temporary functions and tables, and delegates metastore operations to `ExternalCatalog`. This functionality overlaps a lot with the existing `analysis.Catalog`.
As of this commit, `SessionCatalog` and `ExternalCatalog` will no longer be dead code. There are still things that need to be done after this patch, namely:
- SPARK-14013: Properly implement temporary functions in `SessionCatalog`
- SPARK-13879: Decide which DDL/DML commands to support natively in Spark
- SPARK-?????: Implement the ones we do want to support through `SessionCatalog`.
- SPARK-?????: Merge SQL/HiveContext
## How was this patch tested?
This is largely a refactoring task so there are no new tests introduced. The particularly relevant tests are `SessionCatalogSuite` and `ExternalCatalogSuite`.
Author: Andrew Or <andrew@databricks.com>
Author: Yin Huai <yhuai@databricks.com>
Closes#11836 from andrewor14/use-session-catalog.
## What changes were proposed in this pull request?
This PR fixes all newly captured SparkR lint-r errors after the lintr package is updated from github.
## How was this patch tested?
dev/lint-r
SparkR unit tests
Author: Sun Rui <rui.sun@intel.com>
Closes#11652 from sun-rui/SPARK-13812.
## What changes were proposed in this pull request?
SparkR support first/last with ignore NAs
cc sun-rui felixcheung shivaram
## How was the this patch tested?
unit tests
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#11267 from yanboliang/spark-13389.
Author: Oscar D. Lara Yejas <odlaraye@oscars-mbp.attlocal.net>
Author: Oscar D. Lara Yejas <odlaraye@oscars-mbp.usca.ibm.com>
Closes#11220 from olarayej/SPARK-13312-3.
## What changes were proposed in this pull request?
Add ```approxQuantile``` for SparkR.
## How was this patch tested?
unit tests
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#11383 from yanboliang/spark-13504 and squashes the following commits:
4f17adb [Yanbo Liang] Add approxQuantile for SparkR
This PR introduces several major changes:
1. Replacing `Expression.prettyString` with `Expression.sql`
The `prettyString` method is mostly an internal, developer faced facility for debugging purposes, and shouldn't be exposed to users.
1. Using SQL-like representation as column names for selected fields that are not named expression (back-ticks and double quotes should be removed)
Before, we were using `prettyString` as column names when possible, and sometimes the result column names can be weird. Here are several examples:
Expression | `prettyString` | `sql` | Note
------------------ | -------------- | ---------- | ---------------
`a && b` | `a && b` | `a AND b` |
`a.getField("f")` | `a[f]` | `a.f` | `a` is a struct
1. Adding trait `NonSQLExpression` extending from `Expression` for expressions that don't have a SQL representation (e.g. Scala UDF/UDAF and Java/Scala object expressions used for encoders)
`NonSQLExpression.sql` may return an arbitrary user facing string representation of the expression.
Author: Cheng Lian <lian@databricks.com>
Closes#10757 from liancheng/spark-12799.simplify-expression-string-methods.
Add ```covar_samp``` and ```covar_pop``` for SparkR.
Should we also provide ```cov``` alias for ```covar_samp```? There is ```cov``` implementation at stats.R which masks ```stats::cov``` already, but may bring to breaking API change.
cc sun-rui felixcheung shivaram
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#10829 from yanboliang/spark-12903.
I've tried to solve some of the issues mentioned in: https://issues.apache.org/jira/browse/SPARK-12629
Please, let me know what do you think.
Thanks!
Author: Narine Kokhlikyan <narine.kokhlikyan@gmail.com>
Closes#10580 from NarineK/sparkrSavaAsRable.
The current parser turns a decimal literal, for example ```12.1```, into a Double. The problem with this approach is that we convert an exact literal into a non-exact ```Double```. The PR changes this behavior, a Decimal literal is now converted into an extact ```BigDecimal```.
The behavior for scientific decimals, for example ```12.1e01```, is unchanged. This will be converted into a Double.
This PR replaces the ```BigDecimal``` literal by a ```Double``` literal, because the ```BigDecimal``` is the default now. You can use the double literal by appending a 'D' to the value, for instance: ```3.141527D```
cc davies rxin
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#10796 from hvanhovell/SPARK-12848.
