## What changes were proposed in this pull request?
The names method fails to check for validity of the assignment values. This can be fixed by calling colnames within names.
## How was this patch tested?
new tests.
Author: actuaryzhang <actuaryzhang10@gmail.com>
Closes#16794 from actuaryzhang/sparkRNames.
## What changes were proposed in this pull request?
Current version has error in vignettes:
```
model <- spark.bisectingKmeans(df, Sepal_Length ~ Sepal_Width, k = 4)
summary(kmeansModel)
```
`kmeansModel` does not exist...
felixcheung wangmiao1981
Author: actuaryzhang <actuaryzhang10@gmail.com>
Closes#16799 from actuaryzhang/sparkRVignettes.
## What changes were proposed in this pull request?
Update programming guide, example and vignette with Bisecting k-means.
Author: krishnakalyan3 <krishnakalyan3@gmail.com>
Closes#16767 from krishnakalyan3/bisecting-kmeans.
## What changes were proposed in this pull request
When Kmeans using initMode = "random" and some random seed, it is possible the actual cluster size doesn't equal to the configured `k`.
In this case, summary(model) returns error due to the number of cols of coefficient matrix doesn't equal to k.
Example:
> col1 <- c(1, 2, 3, 4, 0, 1, 2, 3, 4, 0)
> col2 <- c(1, 2, 3, 4, 0, 1, 2, 3, 4, 0)
> col3 <- c(1, 2, 3, 4, 0, 1, 2, 3, 4, 0)
> cols <- as.data.frame(cbind(col1, col2, col3))
> df <- createDataFrame(cols)
>
> model2 <- spark.kmeans(data = df, ~ ., k = 5, maxIter = 10, initMode = "random", seed = 22222, tol = 1E-5)
>
> summary(model2)
Error in `colnames<-`(`*tmp*`, value = c("col1", "col2", "col3")) :
length of 'dimnames' [2] not equal to array extent
In addition: Warning message:
In matrix(coefficients, ncol = k) :
data length [9] is not a sub-multiple or multiple of the number of rows [2]
Fix: Get the actual cluster size in the summary and use it to build the coefficient matrix.
## How was this patch tested?
Add unit tests.
Author: wm624@hotmail.com <wm624@hotmail.com>
Closes#16666 from wangmiao1981/kmeans.
## What changes were proposed in this pull request?
The `coefficients` component in model summary should be 'matrix' but the underlying structure is indeed list. This affects several models except for 'AFTSurvivalRegressionModel' which has the correct implementation. The fix is to first `unlist` the coefficients returned from the `callJMethod` before converting to matrix. An example illustrates the issues:
```
data(iris)
df <- createDataFrame(iris)
model <- spark.glm(df, Sepal_Length ~ Sepal_Width, family = "gaussian")
s <- summary(model)
> str(s$coefficients)
List of 8
$ : num 6.53
$ : num -0.223
$ : num 0.479
$ : num 0.155
$ : num 13.6
$ : num -1.44
$ : num 0
$ : num 0.152
- attr(*, "dim")= int [1:2] 2 4
- attr(*, "dimnames")=List of 2
..$ : chr [1:2] "(Intercept)" "Sepal_Width"
..$ : chr [1:4] "Estimate" "Std. Error" "t value" "Pr(>|t|)"
> s$coefficients[, 2]
$`(Intercept)`
[1] 0.4788963
$Sepal_Width
[1] 0.1550809
```
This shows that the underlying structure of coefficients is still `list`.
felixcheung wangmiao1981
Author: actuaryzhang <actuaryzhang10@gmail.com>
Closes#16730 from actuaryzhang/sparkRCoef.
## What changes were proposed in this pull request?
With extract `[[` or replace `[[<-`, the parameter `i` is a column index, that needs to be corrected in doc. Also a few minor updates: examples, links.
## How was this patch tested?
manual
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#16721 from felixcheung/rsubsetdoc.
## What changes were proposed in this pull request?
This affects mostly running job from the driver in client mode when results are expected to be through stdout (which should be somewhat rare, but possible)
Before:
```
> a <- as.DataFrame(cars)
> b <- group_by(a, "dist")
> c <- count(b)
> sparkR.callJMethod(c$countjc, "explain", TRUE)
NULL
```
After:
```
> a <- as.DataFrame(cars)
> b <- group_by(a, "dist")
> c <- count(b)
> sparkR.callJMethod(c$countjc, "explain", TRUE)
count#11L
NULL
```
Now, `column.explain()` doesn't seem very useful (we can get more extensive output with `DataFrame.explain()`) but there are other more complex examples with calls of `println` in Scala/JVM side, that are getting dropped.
## How was this patch tested?
manual
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#16670 from felixcheung/rjvmstdout.
## What changes were proposed in this pull request?
add header
## How was this patch tested?
Manual run to check vignettes html is created properly
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#16709 from felixcheung/rfilelicense.
## What changes were proposed in this pull request?
With doc to say this would convert DF into RDD
## How was this patch tested?
unit tests, manual tests
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#16668 from felixcheung/rgetnumpartitions.
## What changes were proposed in this pull request?
Add R wrapper for bisecting Kmeans.
As JIRA is down, I will update title to link with corresponding JIRA later.
## How was this patch tested?
Add new unit tests.
Author: wm624@hotmail.com <wm624@hotmail.com>
Closes#16566 from wangmiao1981/bk.
## What changes were proposed in this pull request?
Support for
```
df[[myname]] <- 1
df[[2]] <- df$eruptions
```
## How was this patch tested?
manual tests, unit tests
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#16663 from felixcheung/rcolset.
## What changes were proposed in this pull request?
```spark.gaussianMixture``` supports output total log-likelihood for the model like R ```mvnormalmixEM```.
## How was this patch tested?
R unit test.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#16646 from yanboliang/spark-19291.
## What changes were proposed in this pull request?
When R is starting as a package and it needs to download the Spark release distribution we need to handle error for download and untar, and clean up, otherwise it will get stuck.
## How was this patch tested?
manually
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#16589 from felixcheung/rtarreturncode.
## What changes were proposed in this pull request?
Refactored script to remove duplications and clearer purpose for each script
## How was this patch tested?
manually
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#16249 from felixcheung/rscripts.
## What changes were proposed in this pull request?
Windows seems to be the only place with appauthor in the path, for which we should say "Apache" (and case sensitive)
Current path of `AppData\Local\spark\spark\Cache` is a bit odd.
## How was this patch tested?
manual.
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#16590 from felixcheung/rcachedir.
## What changes were proposed in this pull request?
spark.lda passes the optimizer "em" or "online" as a string to the backend. However, LDAWrapper doesn't set optimizer based on the value from R. Therefore, for optimizer "em", the `isDistributed` field is FALSE, which should be TRUE based on scala code.
In addition, the `summary` method should bring back the results related to `DistributedLDAModel`.
## How was this patch tested?
Manual tests by comparing with scala example.
Modified the current unit test: fix the incorrect unit test and add necessary tests for `summary` method.
Author: wm624@hotmail.com <wm624@hotmail.com>
Closes#16464 from wangmiao1981/new.
## What changes were proposed in this pull request?
To allow specifying number of partitions when the DataFrame is created
## How was this patch tested?
manual, unit tests
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#16512 from felixcheung/rnumpart.
## What changes were proposed in this pull request?
spark.kmeans doesn't have interface to set initSteps, seed and tol. As Spark Kmeans algorithm doesn't take the same set of parameters as R kmeans, we should maintain a different interface in spark.kmeans.
Add missing parameters and corresponding document.
Modified existing unit tests to take additional parameters.
Author: wm624@hotmail.com <wm624@hotmail.com>
Closes#16523 from wangmiao1981/kmeans.
## What changes were proposed in this pull request?
```
df$foo <- 1
```
instead of
```
df$foo <- lit(1)
```
## How was this patch tested?
unit tests
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#16510 from felixcheung/rlitcol.
## What changes were proposed in this pull request?
R family is a longer list than what Spark supports.
## How was this patch tested?
manual
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#16511 from felixcheung/rdocglmfamily.
## What changes were proposed in this pull request?
- [X] Make sure all join types are clearly mentioned
- [X] Make join labeling/style consistent
- [X] Make join label ordering docs the same
- [X] Improve join documentation according to above for Scala
- [X] Improve join documentation according to above for Python
- [X] Improve join documentation according to above for R
## How was this patch tested?
No tests b/c docs.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: anabranch <wac.chambers@gmail.com>
Closes#16504 from anabranch/SPARK-19126.
## What changes were proposed in this pull request?
- [X] Fix inconsistencies in function reference for dense rank and dense
- [X] Make all languages equivalent in their reference to `dense_rank` and `rank`.
## How was this patch tested?
N/A for docs.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: anabranch <wac.chambers@gmail.com>
Closes#16505 from anabranch/SPARK-19127.
## What changes were proposed in this pull request?
SparkR ```mllib.R``` is getting bigger as we add more ML wrappers, I'd like to split it into multiple files to make us easy to maintain:
* mllib_classification.R
* mllib_clustering.R
* mllib_recommendation.R
* mllib_regression.R
* mllib_stat.R
* mllib_tree.R
* mllib_utils.R
Note: Only reorg, no actual code change.
## How was this patch tested?
Existing tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#16312 from yanboliang/spark-18862.
## What changes were proposed in this pull request?
#16126 bumps master branch version to 2.2.0-SNAPSHOT, but it seems R version was omitted.
## How was this patch tested?
N/A
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#16488 from yanboliang/r-version.
## What changes were proposed in this pull request?
It would make it easier to integrate with other component expecting row-based JSON format.
This replaces the non-public toJSON RDD API.
## How was this patch tested?
manual, unit tests
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#16368 from felixcheung/rJSON.
## What changes were proposed in this pull request?
API for SparkUI URL from SparkContext
## How was this patch tested?
manual, unit tests
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#16367 from felixcheung/rwebui.
## What changes were proposed in this pull request?
SparkR tests, `R/run-tests.sh`, succeeds only once because `test_sparkSQL.R` does not clean up the test table, `people`.
As a result, the rows in `people` table are accumulated at every run and the test cases fail.
The following is the failure result for the second run.
```r
Failed -------------------------------------------------------------------------
1. Failure: create DataFrame from RDD (test_sparkSQL.R#204) -------------------
collect(sql("SELECT age from people WHERE name = 'Bob'"))$age not equal to c(16).
Lengths differ: 2 vs 1
2. Failure: create DataFrame from RDD (test_sparkSQL.R#206) -------------------
collect(sql("SELECT height from people WHERE name ='Bob'"))$height not equal to c(176.5).
Lengths differ: 2 vs 1
```
## How was this patch tested?
Manual. Run `run-tests.sh` twice and check if it passes without failures.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#16310 from dongjoon-hyun/SPARK-18897.
## What changes were proposed in this pull request?
doc cleanup
## How was this patch tested?
~~vignettes is not building for me. I'm going to kick off a full clean build and try again and attach output here for review.~~
Output html here: https://felixcheung.github.io/sparkr-vignettes.html
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#16286 from felixcheung/rvignettespass.
## What changes were proposed in this pull request?
When do the QA work, I found that the following issues:
1). `spark.mlp` doesn't include an example;
2). `spark.mlp` and `spark.lda` have redundant parameter explanations;
3). `spark.lda` document misses default values for some parameters.
I also changed the `spark.logit` regParam in the examples, as we discussed in #16222.
## How was this patch tested?
Manual test
Author: wm624@hotmail.com <wm624@hotmail.com>
Closes#16284 from wangmiao1981/ks.
## What changes were proposed in this pull request?
Added short section for KSTest.
Also added logreg model to list of ML models in vignette. (This will be reorganized under SPARK-18849)
## How was this patch tested?
Manually tested example locally.
Built vignettes locally.
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#16283 from jkbradley/ksTest-vignette.
## What changes were proposed in this pull request?
While adding vignettes for kstest, I found some errors in the example:
1. There is a typo of kstest;
2. print.summary.KStest doesn't work with the example;
Fix the example errors;
Add a new unit test for print.summary.KStest;
## How was this patch tested?
Manual test;
Add new unit test;
Author: wm624@hotmail.com <wm624@hotmail.com>
Closes#16259 from wangmiao1981/ks.
## What changes were proposed in this pull request?
Mention `spark.randomForest` and `spark.gbt` in vignettes. Keep the content minimal since users can type `?spark.randomForest` to see the full doc.
cc: jkbradley
Author: Xiangrui Meng <meng@databricks.com>
Closes#16264 from mengxr/SPARK-18793.
## What changes were proposed in this pull request?
Support overriding the download url (include version directory) in an environment variable, `SPARKR_RELEASE_DOWNLOAD_URL`
## How was this patch tested?
unit test, manually testing
- snapshot build url
- download when spark jar not cached
- when spark jar is cached
- RC build url
- download when spark jar not cached
- when spark jar is cached
- multiple cached spark versions
- starting with sparkR shell
To use this,
```
SPARKR_RELEASE_DOWNLOAD_URL=http://this_is_the_url_to_spark_release_tgz R
```
then in R,
```
library(SparkR) # or specify lib.loc
sparkR.session()
```
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#16248 from felixcheung/rinstallurl.
## What changes were proposed in this pull request?
Several SparkR API calling into JVM methods that have void return values are getting printed out, especially when running in a REPL or IDE.
example:
```
> setLogLevel("WARN")
NULL
```
We should fix this to make the result more clear.
Also found a small change to return value of dropTempView in 2.1 - adding doc and test for it.
## How was this patch tested?
manually - I didn't find a expect_*() method in testthat for this
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#16237 from felixcheung/rinvis.
## What changes were proposed in this pull request?
In this PR, the document of `summary` method is improved in the format:
returns summary information of the fitted model, which is a list. The list includes .......
Since `summary` in R is mainly about the model, which is not the same as `summary` object on scala side, if there is one, the scala API doc is not pointed here.
In current document, some `return` have `.` and some don't have. `.` is added to missed ones.
Since spark.logit `summary` has a big refactoring, this PR doesn't include this one. It will be changed when the `spark.logit` PR is merged.
## How was this patch tested?
Manual build.
Author: wm624@hotmail.com <wm624@hotmail.com>
Closes#16150 from wangmiao1981/audit2.
## What changes were proposed in this pull request?
This PR has 2 key changes. One, we are building source package (aka bundle package) for SparkR which could be released on CRAN. Two, we should include in the official Spark binary distributions SparkR installed from this source package instead (which would have help/vignettes rds needed for those to work when the SparkR package is loaded in R, whereas earlier approach with devtools does not)
But, because of various differences in how R performs different tasks, this PR is a fair bit more complicated. More details below.
This PR also includes a few minor fixes.
### more details
These are the additional steps in make-distribution; please see [here](https://github.com/apache/spark/blob/master/R/CRAN_RELEASE.md) on what's going to a CRAN release, which is now run during make-distribution.sh.
1. package needs to be installed because the first code block in vignettes is `library(SparkR)` without lib path
2. `R CMD build` will build vignettes (this process runs Spark/SparkR code and captures outputs into pdf documentation)
3. `R CMD check` on the source package will install package and build vignettes again (this time from source packaged) - this is a key step required to release R package on CRAN
(will skip tests here but tests will need to pass for CRAN release process to success - ideally, during release signoff we should install from the R source package and run tests)
4. `R CMD Install` on the source package (this is the only way to generate doc/vignettes rds files correctly, not in step # 1)
(the output of this step is what we package into Spark dist and sparkr.zip)
Alternatively,
R CMD build should already be installing the package in a temp directory though it might just be finding this location and set it to lib.loc parameter; another approach is perhaps we could try calling `R CMD INSTALL --build pkg` instead.
But in any case, despite installing the package multiple times this is relatively fast.
Building vignettes takes a while though.
## How was this patch tested?
Manually, CI.
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#16014 from felixcheung/rdist.
## What changes were proposed in this pull request?
Reviewing SparkR ML wrappers API for 2.1 release, mainly two issues:
* Remove ```probabilityCol``` from the argument list of ```spark.logit``` and ```spark.randomForest```. Since it was used when making prediction and should be an argument of ```predict```, and we will work on this at [SPARK-18618](https://issues.apache.org/jira/browse/SPARK-18618) in the next release cycle.
* Fix ```spark.als``` params to make it consistent with MLlib.
## How was this patch tested?
Existing tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#16169 from yanboliang/spark-18326.
## What changes were proposed in this pull request?
Fix reservoir sampling bias for small k. An off-by-one error meant that the probability of replacement was slightly too high -- k/(l-1) after l element instead of k/l, which matters for small k.
## How was this patch tested?
Existing test plus new test case.
Author: Sean Owen <sowen@cloudera.com>
Closes#16129 from srowen/SPARK-18678.
## What changes were proposed in this pull request?
Several cleanup and improvements for ```spark.logit```:
* ```summary``` should return coefficients matrix, and should output labels for each class if the model is multinomial logistic regression model.
* ```summary``` should not return ```areaUnderROC, roc, pr, ...```, since most of them are DataFrame which are less important for R users. Meanwhile, these metrics ignore instance weights (setting all to 1.0) which will be changed in later Spark version. In case it will introduce breaking changes, we do not expose them currently.
* SparkR test improvement: comparing the training result with native R glmnet.
* Remove argument ```aggregationDepth``` from ```spark.logit```, since it's an expert Param(related with Spark architecture and job execution) that would be used rarely by R users.
## How was this patch tested?
Unit tests.
The ```summary``` output after this change:
multinomial logistic regression:
```
> df <- suppressWarnings(createDataFrame(iris))
> model <- spark.logit(df, Species ~ ., regParam = 0.5)
> summary(model)
$coefficients
versicolor virginica setosa
(Intercept) 1.514031 -2.609108 1.095077
Sepal_Length 0.02511006 0.2649821 -0.2900921
Sepal_Width -0.5291215 -0.02016446 0.549286
Petal_Length 0.03647411 0.1544119 -0.190886
Petal_Width 0.000236092 0.4195804 -0.4198165
```
binomial logistic regression:
```
> df <- suppressWarnings(createDataFrame(iris))
> training <- df[df$Species %in% c("versicolor", "virginica"), ]
> model <- spark.logit(training, Species ~ ., regParam = 0.5)
> summary(model)
$coefficients
Estimate
(Intercept) -6.053815
Sepal_Length 0.2449379
Sepal_Width 0.1648321
Petal_Length 0.4730718
Petal_Width 1.031947
```
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#16117 from yanboliang/spark-18686.
## What changes were proposed in this pull request?
If SparkR is running as a package and it has previously downloaded Spark Jar it should be able to run as before without having to set SPARK_HOME. Basically with this bug the auto install Spark will only work in the first session.
This seems to be a regression on the earlier behavior.
Fix is to always try to install or check for the cached Spark if running in an interactive session.
As discussed before, we should probably only install Spark iff running in an interactive session (R shell, RStudio etc)
## How was this patch tested?
Manually
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#16077 from felixcheung/rsessioninteractive.
## What changes were proposed in this pull request?
It's better we can fix this issue by providing an option ```type``` for users to change the ```predict``` output schema, then they could output probabilities, log-space predictions, or original labels. In order to not involve breaking API change for 2.1, so revert this change firstly and will add it back after [SPARK-18618](https://issues.apache.org/jira/browse/SPARK-18618) resolved.
## How was this patch tested?
Existing unit tests.
This reverts commit daa975f4bf.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#16118 from yanboliang/spark-18291-revert.
## What changes were proposed in this pull request?
Similar to SPARK-18401, as a classification algorithm, logistic regression should support output original label instead of supporting index label.
In this PR, original label output is supported and test cases are modified and added. Document is also modified.
## How was this patch tested?
Unit tests.
Author: wm624@hotmail.com <wm624@hotmail.com>
Closes#15910 from wangmiao1981/audit.
## What changes were proposed in this pull request?
### The Issue
If I specify my schema when doing
```scala
spark.read
.schema(someSchemaWherePartitionColumnsAreStrings)
```
but if the partition inference can infer it as IntegerType or I assume LongType or DoubleType (basically fixed size types), then once UnsafeRows are generated, your data will be corrupted.
### Proposed solution
The partition handling code path is kind of a mess. In my fix I'm probably adding to the mess, but at least trying to standardize the code path.
The real issue is that a user that uses the `spark.read` code path can never clearly specify what the partition columns are. If you try to specify the fields in `schema`, we practically ignore what the user provides, and fall back to our inferred data types. What happens in the end is data corruption.
My solution tries to fix this by always trying to infer partition columns the first time you specify the table. Once we find what the partition columns are, we try to find them in the user specified schema and use the dataType provided there, or fall back to the smallest common data type.
We will ALWAYS append partition columns to the user's schema, even if they didn't ask for it. We will only use the data type they provided if they specified it. While this is confusing, this has been the behavior since Spark 1.6, and I didn't want to change this behavior in the QA period of Spark 2.1. We may revisit this decision later.
A side effect of this PR is that we won't need https://github.com/apache/spark/pull/15942 if this PR goes in.
## How was this patch tested?
Regression tests
Author: Burak Yavuz <brkyvz@gmail.com>
Closes#15951 from brkyvz/partition-corruption.
## What changes were proposed in this pull request?
Updates links to the wiki to links to the new location of content on spark.apache.org.
## How was this patch tested?
Doc builds
Author: Sean Owen <sowen@cloudera.com>
Closes#15967 from srowen/SPARK-18073.1.
## What changes were proposed in this pull request?
* Fix SparkR ```spark.glm``` errors when fitting on collinear data, since ```standard error of coefficients, t value and p value``` are not available in this condition.
* Scala/Python GLM summary should throw exception if users get ```standard error of coefficients, t value and p value``` but the underlying WLS was solved by local "l-bfgs".
## How was this patch tested?
Add unit tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#15930 from yanboliang/spark-18501.
## What changes were proposed in this pull request?
When running SparkR job in yarn-cluster mode, it will download Spark package from apache website which is not necessary.
```
./bin/spark-submit --master yarn-cluster ./examples/src/main/r/dataframe.R
```
The following is output:
```
Attaching package: ‘SparkR’
The following objects are masked from ‘package:stats’:
cov, filter, lag, na.omit, predict, sd, var, window
The following objects are masked from ‘package:base’:
as.data.frame, colnames, colnames<-, drop, endsWith, intersect,
rank, rbind, sample, startsWith, subset, summary, transform, union
Spark not found in SPARK_HOME:
Spark not found in the cache directory. Installation will start.
MirrorUrl not provided.
Looking for preferred site from apache website...
......
```
There's no ```SPARK_HOME``` in yarn-cluster mode since the R process is in a remote host of the yarn cluster rather than in the client host. The JVM comes up first and the R process then connects to it. So in such cases we should never have to download Spark as Spark is already running.
## How was this patch tested?
Offline test.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#15888 from yanboliang/spark-18444.
## What changes were proposed in this pull request?
I found the documentation for the sample method to be confusing, this adds more clarification across all languages.
- [x] Scala
- [x] Python
- [x] R
- [x] RDD Scala
- [ ] RDD Python with SEED
- [X] RDD Java
- [x] RDD Java with SEED
- [x] RDD Python
## How was this patch tested?
NA
Please review https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark before opening a pull request.
Author: anabranch <wac.chambers@gmail.com>
Author: Bill Chambers <bill@databricks.com>
Closes#15815 from anabranch/SPARK-18365.
## What changes were proposed in this pull request?
```spark.mlp``` should support ```RFormula``` like other ML algorithm wrappers.
BTW, I did some cleanup and improvement for ```spark.mlp```.
## How was this patch tested?
Unit tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#15883 from yanboliang/spark-18438.
## What changes were proposed in this pull request?
* Fix the following exceptions which throws when ```spark.randomForest```(classification), ```spark.gbt```(classification), ```spark.naiveBayes``` and ```spark.glm```(binomial family) were fitted on libsvm data.
```
java.lang.IllegalArgumentException: requirement failed: If label column already exists, forceIndexLabel can not be set with true.
```
See [SPARK-18412](https://issues.apache.org/jira/browse/SPARK-18412) for more detail about how to reproduce this bug.
* Refactor out ```getFeaturesAndLabels``` to RWrapperUtils, since lots of ML algorithm wrappers use this function.
* Drop some unwanted columns when making prediction.
## How was this patch tested?
Add unit test.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#15851 from yanboliang/spark-18412.
## What changes were proposed in this pull request?
SparkR ```spark.randomForest``` classification prediction should output original label rather than the indexed label. This issue is very similar with [SPARK-18291](https://issues.apache.org/jira/browse/SPARK-18291).
## How was this patch tested?
Add unit tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#15842 from yanboliang/spark-18401.
## What changes were proposed in this pull request?
Gradient Boosted Tree in R.
With a few minor improvements to RandomForest in R.
Since this is relatively isolated I'd like to target this for branch-2.1
## How was this patch tested?
manual tests, unit tests
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#15746 from felixcheung/rgbt.
## What changes were proposed in this pull request?
minor doc update that should go to master & branch-2.1
## How was this patch tested?
manual
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#15747 from felixcheung/pySPARK-14393.
## What changes were proposed in this pull request?
In test_mllib.R, there are two unnecessary suppressWarnings. This PR just removes them.
## How was this patch tested?
Existing unit tests.
Author: wm624@hotmail.com <wm624@hotmail.com>
Closes#15697 from wangmiao1981/rtest.
## What changes were proposed in this pull request?
Due to a limitation of hive metastore(table location must be directory path, not file path), we always store `path` for data source table in storage properties, instead of the `locationUri` field. However, we should not expose this difference to `CatalogTable` level, but just treat it as a hack in `HiveExternalCatalog`, like we store table schema of data source table in table properties.
This PR unifies `path` and `locationUri` outside of `HiveExternalCatalog`, both data source table and hive serde table should use the `locationUri` field.
This PR also unifies the way we handle default table location for managed table. Previously, the default table location of hive serde managed table is set by external catalog, but the one of data source table is set by command. After this PR, we follow the hive way and the default table location is always set by external catalog.
For managed non-file-based tables, we will assign a default table location and create an empty directory for it, the table location will be removed when the table is dropped. This is reasonable as metastore doesn't care about whether a table is file-based or not, and an empty table directory has no harm.
For external non-file-based tables, ideally we can omit the table location, but due to a hive metastore issue, we will assign a random location to it, and remove it right after the table is created. See SPARK-15269 for more details. This is fine as it's well isolated in `HiveExternalCatalog`.
To keep the existing behaviour of the `path` option, in this PR we always add the `locationUri` to storage properties using key `path`, before passing storage properties to `DataSource` as data source options.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15024 from cloud-fan/path.
## What changes were proposed in this pull request?
Simplify struct creation, especially the aspect of `CleanupAliases` which missed some aliases when handling trees created by `CreateStruct`.
This PR includes:
1. A failing test (create struct with nested aliases, some of the aliases survive `CleanupAliases`).
2. A fix that transforms `CreateStruct` into a `CreateNamedStruct` constructor, effectively eliminating `CreateStruct` from all expression trees.
3. A `NamePlaceHolder` used by `CreateStruct` when column names cannot be extracted from unresolved `NamedExpression`.
4. A new Analyzer rule that resolves `NamePlaceHolder` into a string literal once the `NamedExpression` is resolved.
5. `CleanupAliases` code was simplified as it no longer has to deal with `CreateStruct`'s top level columns.
## How was this patch tested?
Running all tests-suits in package org.apache.spark.sql, especially including the analysis suite, making sure added test initially fails, after applying suggested fix rerun the entire analysis package successfully.
Modified few tests that expected `CreateStruct` which is now transformed into `CreateNamedStruct`.
Author: eyal farago <eyal farago>
Author: Herman van Hovell <hvanhovell@databricks.com>
Author: eyal farago <eyal.farago@gmail.com>
Author: Eyal Farago <eyal.farago@actimize.com>
Author: Hyukjin Kwon <gurwls223@gmail.com>
Author: eyalfa <eyal.farago@gmail.com>
Closes#15718 from hvanhovell/SPARK-16839-2.
## What changes were proposed in this pull request?
This PR proposes to
- improve the R-friendly error messages rather than raw JVM exception one.
As `read.json`, `read.text`, `read.orc`, `read.parquet` and `read.jdbc` are executed in the same path with `read.df`, and `write.json`, `write.text`, `write.orc`, `write.parquet` and `write.jdbc` shares the same path with `write.df`, it seems it is safe to call `handledCallJMethod` to handle
JVM messages.
- prevent `zero-length variable name` and prints the ignored options as an warning message.
**Before**
``` r
> read.json("path", a = 1, 2, 3, "a")
Error in env[[name]] <- value :
zero-length variable name
```
``` r
> read.json("arbitrary_path")
Error in invokeJava(isStatic = FALSE, objId$id, methodName, ...) :
org.apache.spark.sql.AnalysisException: Path does not exist: file:/...;
at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$12.apply(DataSource.scala:398)
...
> read.orc("arbitrary_path")
Error in invokeJava(isStatic = FALSE, objId$id, methodName, ...) :
org.apache.spark.sql.AnalysisException: Path does not exist: file:/...;
at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$12.apply(DataSource.scala:398)
...
> read.text("arbitrary_path")
Error in invokeJava(isStatic = FALSE, objId$id, methodName, ...) :
org.apache.spark.sql.AnalysisException: Path does not exist: file:/...;
at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$12.apply(DataSource.scala:398)
...
> read.parquet("arbitrary_path")
Error in invokeJava(isStatic = FALSE, objId$id, methodName, ...) :
org.apache.spark.sql.AnalysisException: Path does not exist: file:/...;
at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$12.apply(DataSource.scala:398)
...
```
``` r
> write.json(df, "existing_path")
Error in invokeJava(isStatic = FALSE, objId$id, methodName, ...) :
org.apache.spark.sql.AnalysisException: path file:/... already exists.;
at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand.run(InsertIntoHadoopFsRelationCommand.scala:68)
> write.orc(df, "existing_path")
Error in invokeJava(isStatic = FALSE, objId$id, methodName, ...) :
org.apache.spark.sql.AnalysisException: path file:/... already exists.;
at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand.run(InsertIntoHadoopFsRelationCommand.scala:68)
> write.text(df, "existing_path")
Error in invokeJava(isStatic = FALSE, objId$id, methodName, ...) :
org.apache.spark.sql.AnalysisException: path file:/... already exists.;
at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand.run(InsertIntoHadoopFsRelationCommand.scala:68)
> write.parquet(df, "existing_path")
Error in invokeJava(isStatic = FALSE, objId$id, methodName, ...) :
org.apache.spark.sql.AnalysisException: path file:/... already exists.;
at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand.run(InsertIntoHadoopFsRelationCommand.scala:68)
```
**After**
``` r
read.json("arbitrary_path", a = 1, 2, 3, "a")
Unnamed arguments ignored: 2, 3, a.
```
``` r
> read.json("arbitrary_path")
Error in json : analysis error - Path does not exist: file:/...
> read.orc("arbitrary_path")
Error in orc : analysis error - Path does not exist: file:/...
> read.text("arbitrary_path")
Error in text : analysis error - Path does not exist: file:/...
> read.parquet("arbitrary_path")
Error in parquet : analysis error - Path does not exist: file:/...
```
``` r
> write.json(df, "existing_path")
Error in json : analysis error - path file:/... already exists.;
> write.orc(df, "existing_path")
Error in orc : analysis error - path file:/... already exists.;
> write.text(df, "existing_path")
Error in text : analysis error - path file:/... already exists.;
> write.parquet(df, "existing_path")
Error in parquet : analysis error - path file:/... already exists.;
```
## How was this patch tested?
Unit tests in `test_utils.R` and `test_sparkSQL.R`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#15608 from HyukjinKwon/SPARK-17838.
## What changes were proposed in this pull request?
Simplify struct creation, especially the aspect of `CleanupAliases` which missed some aliases when handling trees created by `CreateStruct`.
This PR includes:
1. A failing test (create struct with nested aliases, some of the aliases survive `CleanupAliases`).
2. A fix that transforms `CreateStruct` into a `CreateNamedStruct` constructor, effectively eliminating `CreateStruct` from all expression trees.
3. A `NamePlaceHolder` used by `CreateStruct` when column names cannot be extracted from unresolved `NamedExpression`.
4. A new Analyzer rule that resolves `NamePlaceHolder` into a string literal once the `NamedExpression` is resolved.
5. `CleanupAliases` code was simplified as it no longer has to deal with `CreateStruct`'s top level columns.
## How was this patch tested?
running all tests-suits in package org.apache.spark.sql, especially including the analysis suite, making sure added test initially fails, after applying suggested fix rerun the entire analysis package successfully.
modified few tests that expected `CreateStruct` which is now transformed into `CreateNamedStruct`.
Credit goes to hvanhovell for assisting with this PR.
Author: eyal farago <eyal farago>
Author: eyal farago <eyal.farago@gmail.com>
Author: Herman van Hovell <hvanhovell@databricks.com>
Author: Eyal Farago <eyal.farago@actimize.com>
Author: Hyukjin Kwon <gurwls223@gmail.com>
Author: eyalfa <eyal.farago@gmail.com>
Closes#14444 from eyalfa/SPARK-16839_redundant_aliases_after_cleanupAliases.
## What changes were proposed in this pull request?
Random Forest Regression and Classification for R
Clean-up/reordering generics.R
## How was this patch tested?
manual tests, unit tests
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#15607 from felixcheung/rrandomforest.
## What changes were proposed in this pull request?
This patch makes RBackend connection timeout configurable by user.
## How was this patch tested?
N/A
Author: Hossein <hossein@databricks.com>
Closes#15471 from falaki/SPARK-17919.
## What changes were proposed in this pull request?
API and programming guide doc changes for Scala, Python and R.
## How was this patch tested?
manual test
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#15629 from felixcheung/jsondoc.
## What changes were proposed in this pull request?
a couple of small late finding fixes for doc
## How was this patch tested?
manually
wangmiao1981
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#15650 from felixcheung/logitfix.
## What changes were proposed in this pull request?
As we discussed in #14818, I added a separate R wrapper spark.logit for logistic regression.
This single interface supports both binary and multinomial logistic regression. It also has "predict" and "summary" for binary logistic regression.
## How was this patch tested?
New unit tests are added.
Author: wm624@hotmail.com <wm624@hotmail.com>
Closes#15365 from wangmiao1981/glm.
## What changes were proposed in this pull request?
Add storageLevel to DataFrame for SparkR.
This is similar to this RP: https://github.com/apache/spark/pull/13780
but in R I do not make a class for `StorageLevel`
but add a method `storageToString`
## How was this patch tested?
test added.
Author: WeichenXu <WeichenXu123@outlook.com>
Closes#15516 from WeichenXu123/storageLevel_df_r.
## What changes were proposed in this pull request?
update SparkR MLP, add initalWeights parameter.
## How was this patch tested?
test added.
Author: WeichenXu <WeichenXu123@outlook.com>
Closes#15552 from WeichenXu123/mlp_r_add_initialWeight_param.
## What changes were proposed in this pull request?
Fixes for R doc
## How was this patch tested?
N/A
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#15589 from felixcheung/rdocmergefix.
(cherry picked from commit 0e0d83a597)
Signed-off-by: Felix Cheung <felixcheung@apache.org>
## What changes were proposed in this pull request?
NA date values are serialized as "NA" and NA time values are serialized as NaN from R. In the backend we did not have proper logic to deal with them. As a result we got an IllegalArgumentException for Date and wrong value for time. This PR adds support for deserializing NA as Date and Time.
## How was this patch tested?
* [x] TODO
Author: Hossein <hossein@databricks.com>
Closes#15421 from falaki/SPARK-17811.
## What changes were proposed in this pull request?
Add crossJoin and do not default to cross join if joinExpr is left out
## How was this patch tested?
unit test
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#15559 from felixcheung/rcrossjoin.
## What changes were proposed in this pull request?
testthat library we are using for testing R is redirecting warning (and disabling `options("warn" = 2)`), we need to have a way to detect any new warning and fail
## How was this patch tested?
manual testing, Jenkins
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#15576 from felixcheung/rtestwarning.
## What changes were proposed in this pull request?
Fix for a bunch of test warnings that were added recently.
We need to investigate why warnings are not turning into errors.
```
Warnings -----------------------------------------------------------------------
1. createDataFrame uses files for large objects (test_sparkSQL.R#215) - Use Sepal_Length instead of Sepal.Length as column name
2. createDataFrame uses files for large objects (test_sparkSQL.R#215) - Use Sepal_Width instead of Sepal.Width as column name
3. createDataFrame uses files for large objects (test_sparkSQL.R#215) - Use Petal_Length instead of Petal.Length as column name
4. createDataFrame uses files for large objects (test_sparkSQL.R#215) - Use Petal_Width instead of Petal.Width as column name
Consider adding
importFrom("utils", "object.size")
to your NAMESPACE file.
```
## How was this patch tested?
unit tests
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#15560 from felixcheung/rwarnings.
## What changes were proposed in this pull request?
If the R data structure that is being parallelized is larger than `INT_MAX` we use files to transfer data to JVM. The serialization protocol mimics Python pickling. This allows us to simply call `PythonRDD.readRDDFromFile` to create the RDD.
I tested this on my MacBook. Following code works with this patch:
```R
intMax <- .Machine$integer.max
largeVec <- 1:intMax
rdd <- SparkR:::parallelize(sc, largeVec, 2)
```
## How was this patch tested?
* [x] Unit tests
Author: Hossein <hossein@databricks.com>
Closes#15375 from falaki/SPARK-17790.
## What changes were proposed in this pull request?
SQLConf is session-scoped and mutable. However, we do have the requirement for a static SQL conf, which is global and immutable, e.g. the `schemaStringThreshold` in `HiveExternalCatalog`, the flag to enable/disable hive support, the global temp view database in https://github.com/apache/spark/pull/14897.
Actually we've already implemented static SQL conf implicitly via `SparkConf`, this PR just make it explicit and expose it to users, so that they can see the config value via SQL command or `SparkSession.conf`, and forbid users to set/unset static SQL conf.
## How was this patch tested?
new tests in SQLConfSuite
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15295 from cloud-fan/global-conf.
## What changes were proposed in this pull request?
Fix SparkR ```spark.naiveBayes``` error when response variable of dataset is numeric type.
See details and how to reproduce this bug at [SPARK-15153](https://issues.apache.org/jira/browse/SPARK-15153).
## How was this patch tested?
Add unit test.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#15431 from yanboliang/spark-15153-2.
## What changes were proposed in this pull request?
This PR includes the changes below:
- Support `mode`/`options` in `read.parquet`, `write.parquet`, `read.orc`, `write.orc`, `read.text`, `write.text`, `read.json` and `write.json` APIs
- Support other types (logical, numeric and string) as options for `write.df`, `read.df`, `read.parquet`, `write.parquet`, `read.orc`, `write.orc`, `read.text`, `write.text`, `read.json` and `write.json`
## How was this patch tested?
Unit tests in `test_sparkSQL.R`/ `utils.R`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#15239 from HyukjinKwon/SPARK-17665.
## What changes were proposed in this pull request?
`write.df`/`read.df` API require path which is not actually always necessary in Spark. Currently, it only affects the datasources implementing `CreatableRelationProvider`. Currently, Spark currently does not have internal data sources implementing this but it'd affect other external datasources.
In addition we'd be able to use this way in Spark's JDBC datasource after https://github.com/apache/spark/pull/12601 is merged.
**Before**
- `read.df`
```r
> read.df(source = "json")
Error in dispatchFunc("read.df(path = NULL, source = NULL, schema = NULL, ...)", :
argument "x" is missing with no default
```
```r
> read.df(path = c(1, 2))
Error in dispatchFunc("read.df(path = NULL, source = NULL, schema = NULL, ...)", :
argument "x" is missing with no default
```
```r
> read.df(c(1, 2))
Error in invokeJava(isStatic = TRUE, className, methodName, ...) :
java.lang.ClassCastException: java.lang.Double cannot be cast to java.lang.String
at org.apache.spark.sql.execution.datasources.DataSource.hasMetadata(DataSource.scala:300)
at
...
In if (is.na(object)) { :
...
```
- `write.df`
```r
> write.df(df, source = "json")
Error in (function (classes, fdef, mtable) :
unable to find an inherited method for function ‘write.df’ for signature ‘"function", "missing"’
```
```r
> write.df(df, source = c(1, 2))
Error in (function (classes, fdef, mtable) :
unable to find an inherited method for function ‘write.df’ for signature ‘"SparkDataFrame", "missing"’
```
```r
> write.df(df, mode = TRUE)
Error in (function (classes, fdef, mtable) :
unable to find an inherited method for function ‘write.df’ for signature ‘"SparkDataFrame", "missing"’
```
**After**
- `read.df`
```r
> read.df(source = "json")
Error in loadDF : analysis error - Unable to infer schema for JSON at . It must be specified manually;
```
```r
> read.df(path = c(1, 2))
Error in f(x, ...) : path should be charactor, null or omitted.
```
```r
> read.df(c(1, 2))
Error in f(x, ...) : path should be charactor, null or omitted.
```
- `write.df`
```r
> write.df(df, source = "json")
Error in save : illegal argument - 'path' is not specified
```
```r
> write.df(df, source = c(1, 2))
Error in .local(df, path, ...) :
source should be charactor, null or omitted. It is 'parquet' by default.
```
```r
> write.df(df, mode = TRUE)
Error in .local(df, path, ...) :
mode should be charactor or omitted. It is 'error' by default.
```
## How was this patch tested?
Unit tests in `test_sparkSQL.R`
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#15231 from HyukjinKwon/write-default-r.
## What changes were proposed in this pull request?
Some tests in `test_mllib.r` are as below:
```r
expect_error(spark.mlp(df, layers = NULL), "layers must be a integer vector with length > 1.")
expect_error(spark.mlp(df, layers = c()), "layers must be a integer vector with length > 1.")
```
The problem is, `is.na` is internally called via `na.omit` in `spark.mlp` which causes warnings as below:
```
Warnings -----------------------------------------------------------------------
1. spark.mlp (test_mllib.R#400) - is.na() applied to non-(list or vector) of type 'NULL'
2. spark.mlp (test_mllib.R#401) - is.na() applied to non-(list or vector) of type 'NULL'
```
## How was this patch tested?
Manually tested. Also, Jenkins tests and AppVeyor.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#15232 from HyukjinKwon/remove-warnnings.
## What changes were proposed in this pull request?
#15140 exposed ```JavaSparkContext.addFile(path: String, recursive: Boolean)``` to Python/R, then we can update SparkR ```spark.addFile``` to support adding directory recursively.
## How was this patch tested?
Added unit test.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#15216 from yanboliang/spark-17577-2.
## What changes were proposed in this pull request?
Spark will add sparkr.zip to archive only when it is yarn mode (SparkSubmit.scala).
```
if (args.isR && clusterManager == YARN) {
val sparkRPackagePath = RUtils.localSparkRPackagePath
if (sparkRPackagePath.isEmpty) {
printErrorAndExit("SPARK_HOME does not exist for R application in YARN mode.")
}
val sparkRPackageFile = new File(sparkRPackagePath.get, SPARKR_PACKAGE_ARCHIVE)
if (!sparkRPackageFile.exists()) {
printErrorAndExit(s"$SPARKR_PACKAGE_ARCHIVE does not exist for R application in YARN mode.")
}
val sparkRPackageURI = Utils.resolveURI(sparkRPackageFile.getAbsolutePath).toString
// Distribute the SparkR package.
// Assigns a symbol link name "sparkr" to the shipped package.
args.archives = mergeFileLists(args.archives, sparkRPackageURI + "#sparkr")
// Distribute the R package archive containing all the built R packages.
if (!RUtils.rPackages.isEmpty) {
val rPackageFile =
RPackageUtils.zipRLibraries(new File(RUtils.rPackages.get), R_PACKAGE_ARCHIVE)
if (!rPackageFile.exists()) {
printErrorAndExit("Failed to zip all the built R packages.")
}
val rPackageURI = Utils.resolveURI(rPackageFile.getAbsolutePath).toString
// Assigns a symbol link name "rpkg" to the shipped package.
args.archives = mergeFileLists(args.archives, rPackageURI + "#rpkg")
}
}
```
So it is necessary to pass spark.master from R process to JVM. Otherwise sparkr.zip won't be distributed to executor. Besides that I also pass spark.yarn.keytab/spark.yarn.principal to spark side, because JVM process need them to access secured cluster.
## How was this patch tested?
Verify it manually in R Studio using the following code.
```
Sys.setenv(SPARK_HOME="/Users/jzhang/github/spark")
.libPaths(c(file.path(Sys.getenv(), "R", "lib"), .libPaths()))
library(SparkR)
sparkR.session(master="yarn-client", sparkConfig = list(spark.executor.instances="1"))
df <- as.DataFrame(mtcars)
head(df)
```
…
Author: Jeff Zhang <zjffdu@apache.org>
Closes#14784 from zjffdu/SPARK-17210.
## What changes were proposed in this pull request?
update `MultilayerPerceptronClassifierWrapper.fit` paramter type:
`layers: Array[Int]`
`seed: String`
update several default params in sparkR `spark.mlp`:
`tol` --> 1e-6
`stepSize` --> 0.03
`seed` --> NULL ( when seed == NULL, the scala-side wrapper regard it as a `null` value and the seed will use the default one )
r-side `seed` only support 32bit integer.
remove `layers` default value, and move it in front of those parameters with default value.
add `layers` parameter validation check.
## How was this patch tested?
tests added.
Author: WeichenXu <WeichenXu123@outlook.com>
Closes#15051 from WeichenXu123/update_py_mlp_default.
## What changes were proposed in this pull request?
When we build the docs separately we don't have the JAR files from the Spark build in
the same tree. As the SparkR vignettes need to launch a SparkContext to be built, we skip building them if JAR files don't exist
## How was this patch tested?
To test this we can run the following:
```
build/mvn -DskipTests -Psparkr clean
./R/create-docs.sh
```
You should see a line `Skipping R vignettes as Spark JARs not found` at the end
Author: Shivaram Venkataraman <shivaram@cs.berkeley.edu>
Closes#15200 from shivaram/sparkr-vignette-skip.
## What changes were proposed in this pull request?
#14881 added Kolmogorov-Smirnov Test wrapper to SparkR. I found that ```print.summary.KSTest``` was implemented inappropriately and result in no effect.
Running the following code for KSTest:
```Scala
data <- data.frame(test = c(0.1, 0.15, 0.2, 0.3, 0.25, -1, -0.5))
df <- createDataFrame(data)
testResult <- spark.kstest(df, "test", "norm")
summary(testResult)
```
Before this PR:
After this PR:
The new implementation is similar with [```print.summary.GeneralizedLinearRegressionModel```](https://github.com/apache/spark/blob/master/R/pkg/R/mllib.R#L284) of SparkR and [```print.summary.glm```](https://svn.r-project.org/R/trunk/src/library/stats/R/glm.R) of native R.
BTW, I removed the comparison of ```print.summary.KSTest``` in unit test, since it's only wrappers of the summary output which has been checked. Another reason is that these comparison will output summary information to the test console, it will make the test output in a mess.
## How was this patch tested?
Existing test.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#15139 from yanboliang/spark-17315.
## What changes were proposed in this pull request?
Scala/Python users can add files to Spark job by submit options ```--files``` or ```SparkContext.addFile()```. Meanwhile, users can get the added file by ```SparkFiles.get(filename)```.
We should also support this function for SparkR users, since they also have the requirements for some shared dependency files. For example, SparkR users can download third party R packages to driver firstly, add these files to the Spark job as dependency by this API and then each executor can install these packages by ```install.packages```.
## How was this patch tested?
Add unit test.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#15131 from yanboliang/spark-17577.
## What changes were proposed in this pull request?
Clarify that slide and window duration are absolute, and not relative to a calendar.
## How was this patch tested?
Doc build (no functional change)
Author: Sean Owen <sowen@cloudera.com>
Closes#15142 from srowen/SPARK-17297.
## What changes were proposed in this pull request?
Point references to spark-packages.org to https://cwiki.apache.org/confluence/display/SPARK/Third+Party+Projects
This will be accompanied by a parallel change to the spark-website repo, and additional changes to this wiki.
## How was this patch tested?
Jenkins tests.
Author: Sean Owen <sowen@cloudera.com>
Closes#15075 from srowen/SPARK-17445.
## What changes were proposed in this pull request?
This PR tries to add a SparkR vignette, which works as a friendly guidance going through the functionality provided by SparkR.
## How was this patch tested?
Manual test.
Author: junyangq <qianjunyang@gmail.com>
Author: Shivaram Venkataraman <shivaram@cs.berkeley.edu>
Author: Junyang Qian <junyangq@databricks.com>
Closes#14980 from junyangq/SPARKR-vignette.
## What changes were proposed in this pull request?
Fix summary() method's `return` description for spark.mlp
## How was this patch tested?
Ran tests locally on my laptop.
Author: Xin Ren <iamshrek@126.com>
Closes#15015 from keypointt/SPARK-16445-2.
## What changes were proposed in this pull request?
SparkR ```spark.als``` arguments ```reg``` should be 0.1 by default, which need to be consistent with ML.
## How was this patch tested?
Existing tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#15021 from yanboliang/spark-17464.
## What changes were proposed in this pull request?
additional options were not passed down in write.df.
## How was this patch tested?
unit tests
falaki shivaram
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#15010 from felixcheung/testreadoptions.
## What changes were proposed in this pull request?
Fixed bug in `dapplyCollect` by changing the `compute` function of `worker.R` to explicitly handle raw (binary) vectors.
cc shivaram
## How was this patch tested?
Unit tests
Author: Clark Fitzgerald <clarkfitzg@gmail.com>
Closes#14783 from clarkfitzg/SPARK-16785.
## What changes were proposed in this pull request?
This PR tries to add Kolmogorov-Smirnov Test wrapper to SparkR. This wrapper implementation only supports one sample test against normal distribution.
## How was this patch tested?
R unit test.
Author: Junyang Qian <junyangq@databricks.com>
Closes#14881 from junyangq/SPARK-17315.
## What changes were proposed in this pull request?
This PR tries to add some more explanation to `sparkR.session`. It also modifies doc for `count` so when grouped in one doc, the description doesn't confuse users.
## How was this patch tested?
Manual test.
Author: Junyang Qian <junyangq@databricks.com>
Closes#14942 from junyangq/fixSparkRSessionDoc.
## What changes were proposed in this pull request?
Require the use of CROSS join syntax in SQL (and a new crossJoin
DataFrame API) to specify explicit cartesian products between relations.
By cartesian product we mean a join between relations R and S where
there is no join condition involving columns from both R and S.
If a cartesian product is detected in the absence of an explicit CROSS
join, an error must be thrown. Turning on the
"spark.sql.crossJoin.enabled" configuration flag will disable this check
and allow cartesian products without an explicit CROSS join.
The new crossJoin DataFrame API must be used to specify explicit cross
joins. The existing join(DataFrame) method will produce a INNER join
that will require a subsequent join condition.
That is df1.join(df2) is equivalent to select * from df1, df2.
## How was this patch tested?
Added cross-join.sql to the SQLQueryTestSuite to test the check for cartesian products. Added a couple of tests to the DataFrameJoinSuite to test the crossJoin API. Modified various other test suites to explicitly specify a cross join where an INNER join or a comma-separated list was previously used.
Author: Srinath Shankar <srinath@databricks.com>
Closes#14866 from srinathshankar/crossjoin.
