### What changes were proposed in this pull request?
This PR fixes the same issue as https://github.com/apache/spark/pull/32424
```r
df <- sql("SELECT array(1, 2, 3) as numbers, array('a', 'b', 'c') as letters")
collect(select(
df,
array_transform("numbers", function(number) {
array_transform("letters", function(latter) {
struct(alias(number, "n"), alias(latter, "l"))
})
})
))
```
**Before:**
```
... a, a, b, b, c, c, a, a, b, b, c, c, a, a, b, b, c, c
```
**After:**
```
... 1, a, 1, b, 1, c, 2, a, 2, b, 2, c, 3, a, 3, b, 3, c
```
### Why are the changes needed?
To produce the correct results.
### Does this PR introduce _any_ user-facing change?
Yes, it fixes the results to be correct as mentioned above.
### How was this patch tested?
Manually tested as above, and unit test was added.
Closes#32517 from HyukjinKwon/SPARK-35381.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
1, remove existing agg, and use a new agg supporting virtual centering
2, add related testsuites
### Why are the changes needed?
centering vectors should accelerate convergence, and generate solution more close to R
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
updated testsuites and added testsuites
Closes#32124 from zhengruifeng/svc_agg_refactor.
Authored-by: Ruifeng Zheng <ruifengz@foxmail.com>
Signed-off-by: Ruifeng Zheng <ruifengz@foxmail.com>
### What changes were proposed in this pull request?
This PR proposes a change that allows us to build SparkR with SBT.
### Why are the changes needed?
In the current master, SparkR can be built only with Maven.
It's helpful if we can built it with SBT.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
I confirmed that I can build SparkR on Ubuntu 20.04 with the following command.
```
build/sbt -Psparkr package
```
Closes#32285 from sarutak/sbt-sparkr.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Declare the markdown package as a dependency of the SparkR package
### Why are the changes needed?
If we didn't install pandoc locally, running make-distribution.sh will fail with the following message:
```
— re-building ‘sparkr-vignettes.Rmd’ using rmarkdown
Warning in engine$weave(file, quiet = quiet, encoding = enc) :
Pandoc (>= 1.12.3) not available. Falling back to R Markdown v1.
Error: processing vignette 'sparkr-vignettes.Rmd' failed with diagnostics:
The 'markdown' package should be declared as a dependency of the 'SparkR' package (e.g., in the 'Suggests' field of DESCRIPTION), because the latter contains vignette(s) built with the 'markdown' package. Please see https://github.com/yihui/knitr/issues/1864 for more information.
— failed re-building ‘sparkr-vignettes.Rmd’
```
### Does this PR introduce _any_ user-facing change?
Yes. Workaround for R packaging.
### How was this patch tested?
Manually test. After the fix, the command `sh dev/make-distribution.sh -Psparkr -Phadoop-2.7 -Phive -Phive-thriftserver -Pyarn` in the environment without pandoc will pass.
Closes#32270 from xuanyuanking/SPARK-35171.
Authored-by: Yuanjian Li <yuanjian.li@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR fixes the URL links to use CRAN URL in canonical form.
CRAN package submission was failed as below:
```
Found the following (possibly) invalid URLs:
URL: https://cran.r-project.org/web/packages/e1071/index.html
From: man/spark.naiveBayes.Rd
Status: 200
Message: OK
CRAN URL not in canonical form
URL: https://cran.r-project.org/web/packages/mixtools/index.html
From: man/spark.gaussianMixture.Rd
Status: 200
Message: OK
CRAN URL not in canonical form
URL: https://cran.r-project.org/web/packages/survival/index.html
From: man/spark.survreg.Rd
Status: 200
Message: OK
CRAN URL not in canonical form
URL: https://cran.r-project.org/web/packages/topicmodels/index.html
From: man/spark.lda.Rd
Status: 200
Message: OK
CRAN URL not in canonical form
The canonical URL of the CRAN page for a package is
https://CRAN.R-project.org/package=pkgname
```
### Why are the changes needed?
To fix CRAN package submission
### Does this PR introduce _any_ user-facing change?
It exposes a canoncal form of URLs to end users.
### How was this patch tested?
I manually clicked each links.
Closes#31759 from HyukjinKwon/minor-doc-fixes.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### Why is this change being proposed?
This patch adds support for a new "product" aggregation function in `sql.functions` which multiplies-together all values in an aggregation group.
This is likely to be useful in statistical applications which involve combining probabilities, or financial applications that involve combining cumulative interest rates, but is also a versatile mathematical operation of similar status to `sum` or `stddev`. Other users [have noted](https://stackoverflow.com/questions/52991640/cumulative-product-in-spark) the absence of such a function in current releases of Spark.
This function is both much more concise than an expression of the form `exp(sum(log(...)))`, and avoids awkward edge-cases associated with some values being zero or negative, as well as being less computationally costly.
### Does this PR introduce _any_ user-facing change?
No - only adds new function.
### How was this patch tested?
Built-in tests have been added for the new `catalyst.expressions.aggregate.Product` class and its invocation via the (scala) `sql.functions.product` function. The latter, and the PySpark wrapper have also been manually tested in spark-shell and pyspark sessions. The SparkR wrapper is currently untested, and may need separate validation (I'm not an "R" user myself).
An illustration of the new functionality, within PySpark is as follows:
```
import pyspark.sql.functions as pf, pyspark.sql.window as pw
df = sqlContext.range(1, 17).toDF("x")
win = pw.Window.partitionBy(pf.lit(1)).orderBy(pf.col("x"))
df.withColumn("factorial", pf.product("x").over(win)).show(20, False)
+---+---------------+
|x |factorial |
+---+---------------+
|1 |1.0 |
|2 |2.0 |
|3 |6.0 |
|4 |24.0 |
|5 |120.0 |
|6 |720.0 |
|7 |5040.0 |
|8 |40320.0 |
|9 |362880.0 |
|10 |3628800.0 |
|11 |3.99168E7 |
|12 |4.790016E8 |
|13 |6.2270208E9 |
|14 |8.71782912E10 |
|15 |1.307674368E12 |
|16 |2.0922789888E13|
+---+---------------+
```
Closes#30745 from rwpenney/feature/agg-product.
Lead-authored-by: Richard Penney <rwp@rwpenney.uk>
Co-authored-by: Richard Penney <rwpenney@users.noreply.github.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR completes snake_case rule at functions APIs across the languages, see also SPARK-10621.
In more details, this PR:
- Adds `count_distinct` in Scala Python, and R, and document that `count_distinct` is encouraged. This was not deprecated because `countDistinct` is pretty commonly used. We could deprecate in the future releases.
- (Scala-specific) adds `typedlit` but doesn't deprecate `typedLit` which is arguably commonly used. Likewise, we could deprecate in the future releases.
- Deprecates and renames:
- `sumDistinct` -> `sum_distinct`
- `bitwiseNOT` -> `bitwise_not`
- `shiftLeft` -> `shiftleft` (matched with SQL name in `FunctionRegistry`)
- `shiftRight` -> `shiftright` (matched with SQL name in `FunctionRegistry`)
- `shiftRightUnsigned` -> `shiftrightunsigned` (matched with SQL name in `FunctionRegistry`)
- (Scala-specific) `callUDF` -> `call_udf`
### Why are the changes needed?
To keep the consistent naming in APIs.
### Does this PR introduce _any_ user-facing change?
Yes, it deprecates some APIs and add new renamed APIs as described above.
### How was this patch tested?
Unittests were added.
Closes#31408 from HyukjinKwon/SPARK-34306.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Replace v1 exec node `AlterTableRecoverPartitionsCommand` by the logical node `AlterTableRecoverPartitions` in `CatalogImpl.recoverPartitions()`.
### Why are the changes needed?
1. Print user friendly error message for views:
```
my_temp_table is a temp view. 'recoverPartitions()' expects a table
```
Before the changes:
```
Table or view 'my_temp_table' not found in database 'default'
```
2. To not bind to v1 `ALTER TABLE .. RECOVER PARTITIONS`, and to support v2 tables potentially as well.
### Does this PR introduce _any_ user-facing change?
Yes, it can.
### How was this patch tested?
By running new test in `CatalogSuite`:
```
$ build/sbt -Phive -Phive-thriftserver "test:testOnly org.apache.spark.sql.internal.CatalogSuite"
```
Closes#31403 from MaxGekk/catalogimpl-recoverPartitions.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR is a followup of #27433. It fixes the naming to match with Scala side, and this is similar with https://github.com/apache/spark/pull/31062.
Note that:
- there are a bit of inconsistency already e.g.) `x`, `y` in SparkR and they are documented together for doc deduplication. This part I did not change but the name `zero` vs `initialValue` looks unnecessary.
- such naming matching seems already pretty common in SparkR.
### Why are the changes needed?
To make the usage similar with Scala side, and for consistency.
### Does this PR introduce _any_ user-facing change?
No, this is not released yet.
### How was this patch tested?
GitHub Actions and Jenkins build will test it out.
Also, I manually tested:
```r
> df <- select(createDataFrame(data.frame(id = 1)),expr("CAST(array(1.0, 2.0, -3.0, -4.0) AS array<double>) xs"))
> collect(select(df, array_aggregate("xs", initialValue = lit(0.0), merge = function(x, y) otherwise(when(x > y, x), y))))
aggregate(xs, 0.0, lambdafunction(CASE WHEN (x > y) THEN x ELSE y END, x, y), lambdafunction(id, id))
1 2
```
Closes#31226 from HyukjinKwon/SPARK-30682.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
3.0.1 CRAN submission was failed as the reason below:
```
Found the following (possibly) invalid URLs:
URL: http://jsonlines.org/ (moved to https://jsonlines.org/)
From: man/read.json.Rd
man/write.json.Rd
Status: 200
Message: OK
URL: https://dl.acm.org/citation.cfm?id=1608614 (moved to
https://dl.acm.org/doi/10.1109/MC.2009.263)
From: inst/doc/sparkr-vignettes.html
Status: 200
Message: OK
```
The links were being redirected now. This PR checked all hyperlinks in the docs such as `href{...}` and `url{...}`, and fixed all in SparkR:
- Fix two problems above.
- Fix http to https
- Fix `https://www.apache.org/https://spark.apache.org/` -> `https://www.apache.orghttps://spark.apache.org`.
### Why are the changes needed?
For CRAN submission.
### Does this PR introduce _any_ user-facing change?
Virtually no because it's just cleanup that CRAN requires.
### How was this patch tested?
Manually tested by clicking the links
Closes#31058 from HyukjinKwon/SPARK-34021.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
When sparkR is run at log level INFO, a summary of how the worker spent its time processing the partition is printed. There is a logic error where it is over-reporting the time inputting rows.
In detail: the variable inputElap in a wider context is used to mark the end of reading rows, but in the part changed here it was used as a local variable for measuring the beginning of compute time in a loop over the groups in the partition. Thus, the error is not observable if there is only one group per partition, which is what you get in unit tests.
For our application, here's what a log entry looks like before these changes were applied:
`20/10/09 04:08:58 INFO RRunner: Times: boot = 0.013 s, init = 0.005 s, broadcast = 0.000 s, read-input = 529.471 s, compute = 492.037 s, write-output = 0.020 s, total = 1021.546 s`
this indicates that we're spending more time reading rows than operating on the rows.
After these changes, it looks like this:
`20/12/15 06:43:29 INFO RRunner: Times: boot = 0.013 s, init = 0.010 s, broadcast = 0.000 s, read-input = 120.275 s, compute = 1680.161 s, write-output = 0.045 s, total = 1812.553 s
`
### Why are the changes needed?
Metrics shouldn't mislead?
### Does this PR introduce _any_ user-facing change?
Aside from no longer misleading, no
### How was this patch tested?
unit tests passed. Field test results seem plausible
Closes#31021 from WamBamBoozle/input_timing.
Authored-by: Tom.Howland <Tom.Howland@target.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Change the strategy for how the varargs are handled in the default `mutate` method
### Why are the changes needed?
Bugfix -- `deparse` + `sapply` not working as intended due to `width.cutoff`
### Does this PR introduce any user-facing change?
Yes, bugfix. Shouldn't change any working code.
### How was this patch tested?
None! yet.
Closes#28386 from MichaelChirico/r-mutate-deparse.
Lead-authored-by: Michael Chirico <michael.chirico@grabtaxi.com>
Co-authored-by: Michael Chirico <michaelchirico4@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR aims to update `master` branch version to 3.2.0-SNAPSHOT.
### Why are the changes needed?
Start to prepare Apache Spark 3.2.0.
### Does this PR introduce _any_ user-facing change?
N/A.
### How was this patch tested?
Pass the CIs.
Closes#30606 from dongjoon-hyun/SPARK-3.2.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR adds `array_to_vector` to R API.
### Why are the changes needed?
Feature parity.
### Does this PR introduce _any_ user-facing change?
New function exposed in the public API.
### How was this patch tested?
New unit test.
Manual verification of the documentation examples.
Closes#30561 from zero323/SPARK-33622.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR intends to fix typos in the sub-modules:
* `R`
* `common`
* `dev`
* `mlib`
* `external`
* `project`
* `streaming`
* `resource-managers`
* `python`
Split per srowen https://github.com/apache/spark/pull/30323#issuecomment-728981618
NOTE: The misspellings have been reported at 706a726f87 (commitcomment-44064356)
### Why are the changes needed?
Misspelled words make it harder to read / understand content.
### Does this PR introduce _any_ user-facing change?
There are various fixes to documentation, etc...
### How was this patch tested?
No testing was performed
Closes#30402 from jsoref/spelling-R_common_dev_mlib_external_project_streaming_resource-managers_python.
Authored-by: Josh Soref <jsoref@users.noreply.github.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
This PR adds the following functions (introduced in Scala API with SPARK-33061):
- `acosh`
- `asinh`
- `atanh`
to Python and R.
### Why are the changes needed?
Feature parity.
### Does this PR introduce _any_ user-facing change?
New functions.
### How was this patch tested?
New unit tests.
Closes#30501 from zero323/SPARK-33563.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Adds `from_avro` and `to_avro` functions to SparkR.
### Why are the changes needed?
Feature parity.
### Does this PR introduce _any_ user-facing change?
New functions exposed in SparkR API.
### How was this patch tested?
New unit tests.
Closes#30216 from zero323/SPARK-33304.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
add prompt information about current applicationId, current URL and master info when pyspark / sparkR starts.
### Why are the changes needed?
The information printed when pyspark/sparkR starts does not prompt the basic information of current application, and it is not convenient when used pyspark/sparkR in dos.
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
manual test result shows below:
Closes#30266 from akiyamaneko/pyspark-hint-info.
Authored-by: neko <echohlne@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR modifies `R/pkg/tests/run-all.R` by:
- Removing `testthat` 1.x support, as Jenkins has been upgraded to 2.x with SPARK-30637 and this code is no longer relevant.
- Add `testthat` 3.x support to avoid AppVeyor failures.
### Why are the changes needed?
Currently used internal API has been removed in the latest `testthat` release.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Tests executed against `testthat == 2.3.2` and `testthat == 3.0.0`
Closes#30219 from zero323/SPARK-33313.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Return schema in SQL format instead of Catalog string from the SchemaOfCsv expression.
### Why are the changes needed?
To unify output of the `schema_of_json()` and `schema_of_csv()`.
### Does this PR introduce _any_ user-facing change?
Yes, they can but `schema_of_csv()` is usually used in combination with `from_csv()`, so, the format of schema shouldn't be much matter.
Before:
```
> SELECT schema_of_csv('1,abc');
struct<_c0:int,_c1:string>
```
After:
```
> SELECT schema_of_csv('1,abc');
STRUCT<`_c0`: INT, `_c1`: STRING>
```
### How was this patch tested?
By existing test suites `CsvFunctionsSuite` and `CsvExpressionsSuite`.
Closes#30180 from MaxGekk/schema_of_csv-sql-schema.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Return schema in SQL format instead of Catalog string from the `SchemaOfJson` expression.
### Why are the changes needed?
In some cases, `from_json()` cannot parse schemas returned by `schema_of_json`, for instance, when JSON fields have spaces (gaps). Such fields will be quoted after the changes, and can be parsed by `from_json()`.
Here is the example:
```scala
val in = Seq("""{"a b": 1}""").toDS()
in.select(from_json('value, schema_of_json("""{"a b": 100}""")) as "parsed")
```
raises the exception:
```
== SQL ==
struct<a b:bigint>
------^^^
at org.apache.spark.sql.catalyst.parser.ParseException.withCommand(ParseDriver.scala:263)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parse(ParseDriver.scala:130)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parseTableSchema(ParseDriver.scala:76)
at org.apache.spark.sql.types.DataType$.fromDDL(DataType.scala:131)
at org.apache.spark.sql.catalyst.expressions.ExprUtils$.evalTypeExpr(ExprUtils.scala:33)
at org.apache.spark.sql.catalyst.expressions.JsonToStructs.<init>(jsonExpressions.scala:537)
at org.apache.spark.sql.functions$.from_json(functions.scala:4141)
```
### Does this PR introduce _any_ user-facing change?
Yes. For example, `schema_of_json` for the input `{"col":0}`.
Before: `struct<col:bigint>`
After: `STRUCT<`col`: BIGINT>`
### How was this patch tested?
By existing test suites `JsonFunctionsSuite` and `JsonExpressionsSuite`.
Closes#30172 from MaxGekk/schema_of_json-sql-schema.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR adds the following `Column` methods to R API:
- asc_nulls_first
- asc_nulls_last
- desc_nulls_first
- desc_nulls_last
### Why are the changes needed?
Feature parity.
### Does this PR introduce _any_ user-facing change?
No, new methods.
### How was this patch tested?
New unit tests.
Closes#30159 from zero323/SPARK-33258.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
As [SPARK-13860](https://issues.apache.org/jira/browse/SPARK-13860) stated, TPCDS Query 39 returns wrong results using SparkSQL. The root cause is that when stddev_samp is applied to a single element set, with TPCDS answer, it return null; as in SparkSQL, it return Double.NaN which caused the wrong result.
Add an extra legacy config to fallback into the NaN logical, and return null by default to align with TPCDS standard.
### Why are the changes needed?
SQL correctness issue.
### Does this PR introduce any user-facing change?
Yes. See sql-migration-guide
In Spark 3.1, statistical aggregation function includes `std`, `stddev`, `stddev_samp`, `variance`, `var_samp`, `skewness`, `kurtosis`, `covar_samp`, `corr` will return `NULL` instead of `Double.NaN` when `DivideByZero` occurs during expression evaluation, for example, when `stddev_samp` applied on a single element set. In Spark version 3.0 and earlier, it will return `Double.NaN` in such case. To restore the behavior before Spark 3.1, you can set `spark.sql.legacy.statisticalAggregate` to `true`.
### How was this patch tested?
Updated DataFrameAggregateSuite/DataFrameWindowFunctionsSuite to test both default and legacy behavior.
Adjust DataFrameWindowFunctionsSuite/SQLQueryTestSuite and some R case to update to the default return null behavior.
Closes#29983 from leanken/leanken-SPARK-13860.
Authored-by: xuewei.linxuewei <xuewei.linxuewei@alibaba-inc.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
- Annotated return types of `assert_true` and `raise_error` as discussed [here](https://github.com/apache/spark/pull/29947#pullrequestreview-504495801).
- Add `assert_true` and `raise_error` to SparkR NAMESPACE.
- Validating message vector size in SparkR as discussed [here](https://github.com/apache/spark/pull/29947#pullrequestreview-504539004).
### Why are the changes needed?
As discussed in review for #29947.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
- Existing tests.
- Validation of annotations using MyPy
Closes#29978 from zero323/SPARK-32793-FOLLOW-UP.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Adds a SQL function `raise_error` which underlies the refactored `assert_true` function. `assert_true` now also (optionally) accepts a custom error message field.
`raise_error` is exposed in SQL, Python, Scala, and R.
`assert_true` was previously only exposed in SQL; it is now also exposed in Python, Scala, and R.
### Why are the changes needed?
Improves usability of `assert_true` by clarifying error messaging, and adds the useful helper function `raise_error`.
### Does this PR introduce _any_ user-facing change?
Yes:
- Adds `raise_error` function to the SQL, Python, Scala, and R APIs.
- Adds `assert_true` function to the SQL, Python and R APIs.
### How was this patch tested?
Adds unit tests in SQL, Python, Scala, and R for `assert_true` and `raise_error`.
Closes#29947 from karenfeng/spark-32793.
Lead-authored-by: Karen Feng <karen.feng@databricks.com>
Co-authored-by: Hyukjin Kwon <gurwls223@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR adds `dropFields` method to:
- PySpark `Column`
- SparkR `Column`
### Why are the changes needed?
Feature parity.
### Does this PR introduce _any_ user-facing change?
No, new API.
### How was this patch tested?
- New unit tests.
- Manual verification of examples / doctests.
- Manual run of MyPy tests
Closes#29967 from zero323/SPARK-32511-FOLLOW-UP-PYSPARK-SPARKR.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
- Reorder choices of `dtype` to match Scala defaults.
- Add example to ml_functions.
### Why are the changes needed?
As requested:
- https://github.com/apache/spark/pull/29917#pullrequestreview-501715344
- https://github.com/apache/spark/pull/29917#pullrequestreview-501716521
### Does this PR introduce _any_ user-facing change?
No (changes to newly added component).
### How was this patch tested?
Existing tests.
Closes#29944 from zero323/SPARK-33040-FOLLOW-UP.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Add SparkR wrapper for `o.a.s.ml.functions.vector_to_array`
### Why are the changes needed?
- Currently ML vectors, including predictions, are almost inaccessible to R users. That's is a serious loss of functionality.
- Feature parity.
### Does this PR introduce _any_ user-facing change?
Yes, new R function is added.
### How was this patch tested?
- New unit tests.
- Manual verification.
Closes#29917 from zero323/SPARK-33040.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Re-indent lines of SparkR `timestamp_seconds`.
### Why are the changes needed?
Current indentation is not aligned with the opening line.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#29940 from zero323/SPARK-32949-FOLLOW-UP.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Adds `nth_value` function to SparkR.
### Why are the changes needed?
Feature parity. The function has been already added to [Scala](https://issues.apache.org/jira/browse/SPARK-27951) and [Python](https://issues.apache.org/jira/browse/SPARK-33020).
### Does this PR introduce _any_ user-facing change?
Yes. New function is exposed to R users.
### How was this patch tested?
New unit tests.
Closes#29905 from zero323/SPARK-33030.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Explicitly document that `current_date` and `current_timestamp` are executed at the start of query evaluation. And all calls of `current_date`/`current_timestamp` within the same query return the same value
### Why are the changes needed?
Users could expect that `current_date` and `current_timestamp` return the current date/timestamp at the moment of query execution but in fact the functions are folded by the optimizer at the start of query evaluation:
0df8dd6073/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/finishAnalysis.scala (L71-L91)
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
by running `./dev/scalastyle`.
Closes#29892 from MaxGekk/doc-current_date.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
More precise description of the result of the `percentile_approx()` function and its synonym `approx_percentile()`. The proposed sentence clarifies that the function returns **one of elements** (or array of elements) from the input column.
### Why are the changes needed?
To improve Spark docs and avoid misunderstanding of the function behavior.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
`./dev/scalastyle`
Closes#29835 from MaxGekk/doc-percentile_approx.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
### What changes were proposed in this pull request?
This PR adds R wrapper for `timestamp_seconds` function.
### Why are the changes needed?
Feature parity.
### Does this PR introduce _any_ user-facing change?
Yes, it adds a new R function.
### How was this patch tested?
New unit tests.
Closes#29822 from zero323/SPARK-32949.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR adds `withColumn` function SparkR.
### Why are the changes needed?
### Does this PR introduce _any_ user-facing change?
Yes, new function, equivalent to Scala and PySpark equivalents, is exposed to the end user.
### How was this patch tested?
New unit tests added.
Closes#29814 from zero323/SPARK-32946.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Add optional `allowMissingColumns` argument to SparkR `unionByName`.
### Why are the changes needed?
Feature parity.
### Does this PR introduce _any_ user-facing change?
`unionByName` supports `allowMissingColumns`.
### How was this patch tested?
Existing unit tests. New unit tests targeting this feature.
Closes#29813 from zero323/SPARK-32799.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
Fix the R style issue which is not catched by the R style checker. Got error:
```
R/DataFrame.R:1244:17: style: Closing curly-braces should always be on their own line, unless it's followed by an else.
}, finally = {
^
lintr checks failed.
```
Closes#29574 from lu-wang-dl/fix-r-style.
Lead-authored-by: Lu WANG <lu.wang@databricks.com>
Co-authored-by: Lu Wang <38018689+lu-wang-dl@users.noreply.github.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR proposes to deduplicate configuration set/unset in `test_sparkSQL_arrow.R`.
Setting `spark.sql.execution.arrow.sparkr.enabled` can be globally done instead of doing it in each test case.
### Why are the changes needed?
To duduplicate the codes.
### Does this PR introduce _any_ user-facing change?
No, dev-only
### How was this patch tested?
Manually ran the tests.
Closes#29592 from HyukjinKwon/SPARK-32747.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
In the docs concerning the approx_count_distinct I have changed the description of the rsd parameter from **_maximum estimation error allowed_** to _**maximum relative standard deviation allowed**_
### Why are the changes needed?
Maximum estimation error allowed can be misleading. You can set the target relative standard deviation, which affects the estimation error, but on given runs the estimation error can still be above the rsd parameter.
### Does this PR introduce _any_ user-facing change?
This PR should make it easier for users reading the docs to understand that the rsd parameter in approx_count_distinct doesn't cap the estimation error, but just sets the target deviation instead,
### How was this patch tested?
No tests, as no code changes were made.
Closes#29424 from Comonut/fix-approx_count_distinct-rsd-param-description.
Authored-by: alexander-daskalov <alexander.daskalov@adevinta.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
This PR aims to add `StorageLevel.DISK_ONLY_3` as a built-in `StorageLevel`.
### Why are the changes needed?
In a YARN cluster, HDFS uaually provides storages with replication factor 3. So, we can save the result to HDFS to get `StorageLevel.DISK_ONLY_3` technically. However, disaggregate clusters or clusters without storage services are rising. Previously, in that situation, the users were able to use similar `MEMORY_AND_DISK_2` or a user-created `StorageLevel`. This PR aims to support those use cases officially for better UX.
### Does this PR introduce _any_ user-facing change?
Yes. This provides a new built-in option.
### How was this patch tested?
Pass the GitHub Action or Jenkins with the revised test cases.
Closes#29331 from dongjoon-hyun/SPARK-32517.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
SparkR increased the minimal version of Arrow R version to 1.0.0 at SPARK-32452, and Arrow R 0.14 dropped `as_tibble`. We can remove the usage in SparkR.
### Why are the changes needed?
To remove codes unused anymore.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
GitHub Actions will test them out.
Closes#29361 from HyukjinKwon/SPARK-32543.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR proposes to:
1. Fix the error message when the output schema is misbatched with R DataFrame from the given function. For example,
```R
df <- createDataFrame(list(list(a=1L, b="2")))
count(gapply(df, "a", function(key, group) { group }, structType("a int, b int")))
```
**Before:**
```
Error in handleErrors(returnStatus, conn) :
...
java.lang.UnsupportedOperationException
...
```
**After:**
```
Error in handleErrors(returnStatus, conn) :
...
java.lang.AssertionError: assertion failed: Invalid schema from gapply: expected IntegerType, IntegerType, got IntegerType, StringType
...
```
2. Update documentation about the schema matching for `gapply` and `dapply`.
### Why are the changes needed?
To show which schema is not matched, and let users know what's going on.
### Does this PR introduce _any_ user-facing change?
Yes, error message is updated as above, and documentation is updated.
### How was this patch tested?
Manually tested and unitttests were added.
Closes#29283 from HyukjinKwon/r-vectorized-error.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR proposes to set the minimum Arrow version as 1.0.0 to minimise the maintenance overhead and keep the minimal version up to date.
Other required changes to support 1.0.0 were already made in SPARK-32451.
### Why are the changes needed?
R side, people rather aggressively encourage people to use the latest version, and SparkR vectorization is very experimental that was added from Spark 3.0.
Also, we're technically not testing old Arrow versions in SparkR for now.
### Does this PR introduce _any_ user-facing change?
Yes, users wouldn't be able to use SparkR with old Arrow.
### How was this patch tested?
GitHub Actions and AppVeyor are already testing them.
Closes#29253 from HyukjinKwon/SPARK-32452.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR will remove references to these "blacklist" and "whitelist" terms besides the blacklisting feature as a whole, which can be handled in a separate JIRA/PR.
This touches quite a few files, but the changes are straightforward (variable/method/etc. name changes) and most quite self-contained.
### Why are the changes needed?
As per discussion on the Spark dev list, it will be beneficial to remove references to problematic language that can alienate potential community members. One such reference is "blacklist" and "whitelist". While it seems to me that there is some valid debate as to whether these terms have racist origins, the cultural connotations are inescapable in today's world.
### Does this PR introduce _any_ user-facing change?
In the test file `HiveQueryFileTest`, a developer has the ability to specify the system property `spark.hive.whitelist` to specify a list of Hive query files that should be tested. This system property has been renamed to `spark.hive.includelist`. The old property has been kept for compatibility, but will log a warning if used. I am open to feedback from others on whether keeping a deprecated property here is unnecessary given that this is just for developers running tests.
### How was this patch tested?
Existing tests should be suitable since no behavior changes are expected as a result of this PR.
Closes#28874 from xkrogen/xkrogen-SPARK-32036-rename-blacklists.
Authored-by: Erik Krogen <ekrogen@linkedin.com>
Signed-off-by: Thomas Graves <tgraves@apache.org>
### What changes were proposed in this pull request?
Adding support to Association Rules in Spark ml.fpm.
### Why are the changes needed?
Support is an indication of how frequently the itemset of an association rule appears in the database and suggests if the rules are generally applicable to the dateset. Refer to [wiki](https://en.wikipedia.org/wiki/Association_rule_learning#Support) for more details.
### Does this PR introduce _any_ user-facing change?
Yes. Associate Rules now have support measure
### How was this patch tested?
existing and new unit test
Closes#28903 from huaxingao/fpm.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
R version 4.0.2 was released, see https://cran.r-project.org/doc/manuals/r-release/NEWS.html. This PR targets to upgrade R version in AppVeyor CI environment.
### Why are the changes needed?
To test the latest R versions before the release, and see if there are any regressions.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
AppVeyor will test.
Closes#28909 from HyukjinKwon/SPARK-32074.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Spark 3.0 accidentally dropped R < 3.5. It is built by R 3.6.3 which not support R < 3.5:
```
Error in readRDS(pfile) : cannot read workspace version 3 written by R 3.6.3; need R 3.5.0 or newer version.
```
In fact, with SPARK-31918, we will have to drop R < 3.5 entirely to support R 4.0.0. This is inevitable to release on CRAN because they require to make the tests pass with the latest R.
### Why are the changes needed?
To show the supported versions correctly, and support R 4.0.0 to unblock the releases.
### Does this PR introduce _any_ user-facing change?
In fact, no because Spark 3.0.0 already does not work with R < 3.5.
Compared to Spark 2.4, yes. R < 3.5 would not work.
### How was this patch tested?
Jenkins should test it out.
Closes#28908 from HyukjinKwon/SPARK-32073.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR proposes to ignore S4 generic methods under SparkR namespace in closure cleaning to support R 4.0.0+.
Currently, when you run the codes that runs R native codes, it fails as below with R 4.0.0:
```r
df <- createDataFrame(lapply(seq(100), function (e) list(value=e)))
count(dapply(df, function(x) as.data.frame(x[x$value < 50,]), schema(df)))
```
```
org.apache.spark.SparkException: R unexpectedly exited.
R worker produced errors: Error in lapply(part, FUN) : attempt to bind a variable to R_UnboundValue
```
The root cause seems to be related to when an S4 generic method is manually included into the closure's environment via `SparkR:::cleanClosure`. For example, when an RRDD is created via `createDataFrame` with calling `lapply` to convert, `lapply` itself:
f53d8c63e8/R/pkg/R/RDD.R (L484)
is added into the environment of the cleaned closure - because this is not an exposed namespace; however, this is broken in R 4.0.0+ for an unknown reason with an error message such as "attempt to bind a variable to R_UnboundValue".
Actually, we don't need to add the `lapply` into the environment of the closure because it is not supposed to be called in worker side. In fact, there is no private generic methods supposed to be called in worker side in SparkR at all from my understanding.
Therefore, this PR takes a simpler path to work around just by explicitly excluding the S4 generic methods under SparkR namespace to support R 4.0.0. in SparkR.
### Why are the changes needed?
To support R 4.0.0+ with SparkR, and unblock the releases on CRAN. CRAN requires the tests pass with the latest R.
### Does this PR introduce _any_ user-facing change?
Yes, it will support R 4.0.0 to end-users.
### How was this patch tested?
Manually tested. Both CRAN and tests with R 4.0.1:
```
══ testthat results ═══════════════════════════════════════════════════════════
[ OK: 13 | SKIPPED: 0 | WARNINGS: 0 | FAILED: 0 ]
✔ | OK F W S | Context
✔ | 11 | binary functions [2.5 s]
✔ | 4 | functions on binary files [2.1 s]
✔ | 2 | broadcast variables [0.5 s]
✔ | 5 | functions in client.R
✔ | 46 | test functions in sparkR.R [6.3 s]
✔ | 2 | include R packages [0.3 s]
✔ | 2 | JVM API [0.2 s]
✔ | 75 | MLlib classification algorithms, except for tree-based algorithms [86.3 s]
✔ | 70 | MLlib clustering algorithms [44.5 s]
✔ | 6 | MLlib frequent pattern mining [3.0 s]
✔ | 8 | MLlib recommendation algorithms [9.6 s]
✔ | 136 | MLlib regression algorithms, except for tree-based algorithms [76.0 s]
✔ | 8 | MLlib statistics algorithms [0.6 s]
✔ | 94 | MLlib tree-based algorithms [85.2 s]
✔ | 29 | parallelize() and collect() [0.5 s]
✔ | 428 | basic RDD functions [25.3 s]
✔ | 39 | SerDe functionality [2.2 s]
✔ | 20 | partitionBy, groupByKey, reduceByKey etc. [3.9 s]
✔ | 4 | functions in sparkR.R
✔ | 16 | SparkSQL Arrow optimization [19.2 s]
✔ | 6 | test show SparkDataFrame when eager execution is enabled. [1.1 s]
✔ | 1175 | SparkSQL functions [134.8 s]
✔ | 42 | Structured Streaming [478.2 s]
✔ | 16 | tests RDD function take() [1.1 s]
✔ | 14 | the textFile() function [2.9 s]
✔ | 46 | functions in utils.R [0.7 s]
✔ | 0 1 | Windows-specific tests
────────────────────────────────────────────────────────────────────────────────
test_Windows.R:22: skip: sparkJars tag in SparkContext
Reason: This test is only for Windows, skipped
────────────────────────────────────────────────────────────────────────────────
══ Results ═════════════════════════════════════════════════════════════════════
Duration: 987.3 s
OK: 2304
Failed: 0
Warnings: 0
Skipped: 1
...
Status: OK
+ popd
Tests passed.
```
Note that I tested to build SparkR in R 4.0.0, and run the tests with R 3.6.3. It all passed. See also [the comment in the JIRA](https://issues.apache.org/jira/browse/SPARK-31918?focusedCommentId=17142837&page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-17142837).
Closes#28907 from HyukjinKwon/SPARK-31918.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
