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* [SPARK-16051][R] Add `read.orc/write.orc` to SparkRDongjoon Hyun2016-06-201-0/+21
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This issue adds `read.orc/write.orc` to SparkR for API parity. ## How was this patch tested? Pass the Jenkins tests (with new testcases). Author: Dongjoon Hyun <dongjoon@apache.org> Closes #13763 from dongjoon-hyun/SPARK-16051.
* [SPARK-16029][SPARKR] SparkR add dropTempView and deprecate dropTempTableFelix Cheung2016-06-201-7/+7
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Add dropTempView and deprecate dropTempTable ## How was this patch tested? unit tests shivaram liancheng Author: Felix Cheung <felixcheung_m@hotmail.com> Closes #13753 from felixcheung/rdroptempview.
* [SPARK-16059][R] Add `monotonically_increasing_id` function in SparkRDongjoon Hyun2016-06-201-1/+1
| | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR adds `monotonically_increasing_id` column function in SparkR for API parity. After this PR, SparkR supports the followings. ```r > df <- read.json("examples/src/main/resources/people.json") > collect(select(df, monotonically_increasing_id(), df$name, df$age)) monotonically_increasing_id() name age 1 0 Michael NA 2 1 Andy 30 3 2 Justin 19 ``` ## How was this patch tested? Pass the Jenkins tests (with added testcase). Author: Dongjoon Hyun <dongjoon@apache.org> Closes #13774 from dongjoon-hyun/SPARK-16059.
* [SPARK-15159][SPARKR] SparkR SparkSession APIFelix Cheung2016-06-1716-61/+138
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR introduces the new SparkSession API for SparkR. `sparkR.session.getOrCreate()` and `sparkR.session.stop()` "getOrCreate" is a bit unusual in R but it's important to name this clearly. SparkR implementation should - SparkSession is the main entrypoint (vs SparkContext; due to limited functionality supported with SparkContext in SparkR) - SparkSession replaces SQLContext and HiveContext (both a wrapper around SparkSession, and because of API changes, supporting all 3 would be a lot more work) - Changes to SparkSession is mostly transparent to users due to SPARK-10903 - Full backward compatibility is expected - users should be able to initialize everything just in Spark 1.6.1 (`sparkR.init()`), but with deprecation warning - Mostly cosmetic changes to parameter list - users should be able to move to `sparkR.session.getOrCreate()` easily - An advanced syntax with named parameters (aka varargs aka "...") is supported; that should be closer to the Builder syntax that is in Scala/Python (which unfortunately does not work in R because it will look like this: `enableHiveSupport(config(config(master(appName(builder(), "foo"), "local"), "first", "value"), "next, "value"))` - Updating config on an existing SparkSession is supported, the behavior is the same as Python, in which config is applied to both SparkContext and SparkSession - Some SparkSession changes are not matched in SparkR, mostly because it would be breaking API change: `catalog` object, `createOrReplaceTempView` - Other SQLContext workarounds are replicated in SparkR, eg. `tables`, `tableNames` - `sparkR` shell is updated to use the SparkSession entrypoint (`sqlContext` is removed, just like with Scale/Python) - All tests are updated to use the SparkSession entrypoint - A bug in `read.jdbc` is fixed TODO - [x] Add more tests - [ ] Separate PR - update all roxygen2 doc coding example - [ ] Separate PR - update SparkR programming guide ## How was this patch tested? unit tests, manual tests shivaram sun-rui rxin Author: Felix Cheung <felixcheung_m@hotmail.com> Author: felixcheung <felixcheung_m@hotmail.com> Closes #13635 from felixcheung/rsparksession.
* [SPARK-16005][R] Add `randomSplit` to SparkRDongjoon Hyun2016-06-171-0/+18
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR adds `randomSplit` to SparkR for API parity. ## How was this patch tested? Pass the Jenkins tests (with new testcase.) Author: Dongjoon Hyun <dongjoon@apache.org> Closes #13721 from dongjoon-hyun/SPARK-16005.
* [SPARK-15925][SPARKR] R DataFrame add back registerTempTable, add testsFelix Cheung2016-06-171-11/+19
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Add registerTempTable to DataFrame with Deprecate ## How was this patch tested? unit tests shivaram liancheng Author: Felix Cheung <felixcheung_m@hotmail.com> Closes #13722 from felixcheung/rregistertemptable.
* [SPARK-15908][R] Add varargs-type dropDuplicates() function in SparkRDongjoon Hyun2016-06-161-0/+8
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR adds varargs-type `dropDuplicates` function to SparkR for API parity. Refer to https://issues.apache.org/jira/browse/SPARK-15807, too. ## How was this patch tested? Pass the Jenkins tests with new testcases. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #13684 from dongjoon-hyun/SPARK-15908.
* [SPARK-12922][SPARKR][WIP] Implement gapply() on DataFrame in SparkRNarine Kokhlikyan2016-06-151-0/+65
| | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? gapply() applies an R function on groups grouped by one or more columns of a DataFrame, and returns a DataFrame. It is like GroupedDataSet.flatMapGroups() in the Dataset API. Please, let me know what do you think and if you have any ideas to improve it. Thank you! ## How was this patch tested? Unit tests. 1. Primitive test with different column types 2. Add a boolean column 3. Compute average by a group Author: Narine Kokhlikyan <narine.kokhlikyan@gmail.com> Author: NarineK <narine.kokhlikyan@us.ibm.com> Closes #12836 from NarineK/gapply2.
* [SPARK-15637][SPARK-15931][SPARKR] Fix R masked functions checksFelix Cheung2016-06-151-9/+18
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Because of the fix in SPARK-15684, this exclusion is no longer necessary. ## How was this patch tested? unit tests shivaram Author: Felix Cheung <felixcheung_m@hotmail.com> Closes #13636 from felixcheung/rendswith.
* [SPARK-15925][SQL][SPARKR] Replaces registerTempTable with ↵Cheng Lian2016-06-131-7/+8
| | | | | | | | | | | | | | | | createOrReplaceTempView ## What changes were proposed in this pull request? This PR replaces `registerTempTable` with `createOrReplaceTempView` as a follow-up task of #12945. ## How was this patch tested? Existing SparkR tests. Author: Cheng Lian <lian@databricks.com> Closes #13644 from liancheng/spark-15925-temp-view-for-r.
* [SPARK-15684][SPARKR] Not mask startsWith and endsWith in Rwm624@hotmail.com2016-06-071-0/+7
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? In R 3.3.0, startsWith and endsWith are added. In this PR, I make the two work in SparkR. 1. Remove signature in generic.R 2. Add setMethod in column.R 3. Add unit tests ## How was this patch tested? Manually test it through SparkR shell for both column data and string data, which are added into the unit test file. Author: wm624@hotmail.com <wm624@hotmail.com> Closes #13476 from wangmiao1981/start.
* [SPARK-15637][SPARKR] fix R tests on R 3.2.2felixcheung2016-05-281-1/+1
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Change version check in R tests ## How was this patch tested? R tests shivaram Author: felixcheung <felixcheung_m@hotmail.com> Closes #13369 from felixcheung/rversioncheck.
* [SPARK-8603][SPARKR] Use shell() instead of system2() for SparkR on Windowshyukjinkwon2016-05-263-7/+32
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR corrects SparkR to use `shell()` instead of `system2()` on Windows. Using `system2(...)` on Windows does not process windows file separator `\`. `shell(tralsate = TRUE, ...)` can treat this problem. So, this was changed to be chosen according to OS. Existing tests were failed on Windows due to this problem. For example, those were failed. ``` 8. Failure: sparkJars tag in SparkContext (test_includeJAR.R#34) 9. Failure: sparkJars tag in SparkContext (test_includeJAR.R#36) ``` The cases above were due to using of `system2`. In addition, this PR also fixes some tests failed on Windows. ``` 5. Failure: sparkJars sparkPackages as comma-separated strings (test_context.R#128) 6. Failure: sparkJars sparkPackages as comma-separated strings (test_context.R#131) 7. Failure: sparkJars sparkPackages as comma-separated strings (test_context.R#134) ``` The cases above were due to a weird behaviour of `normalizePath()`. On Linux, if the path does not exist, it just prints out the input but it prints out including the current path on Windows. ```r # On Linus path <- normalizePath("aa") print(path) [1] "aa" # On Windows path <- normalizePath("aa") print(path) [1] "C:\\Users\\aa" ``` ## How was this patch tested? Jenkins tests and manually tested in a Window machine as below: Here is the [stdout](https://gist.github.com/HyukjinKwon/4bf35184f3a30f3bce987a58ec2bbbab) of testing. Closes #7025 Author: hyukjinkwon <gurwls223@gmail.com> Author: Hyukjin Kwon <gurwls223@gmail.com> Author: Prakash PC <prakash.chinnu@gmail.com> Closes #13165 from HyukjinKwon/pr/7025.
* [SPARK-10903][SPARKR] R - Simplify SQLContext method signatures and use a ↵felixcheung2016-05-263-203/+221
| | | | | | | | | | | | | | singleton Eliminate the need to pass sqlContext to method since it is a singleton - and we don't want to support multiple contexts in a R session. Changes are done in a back compat way with deprecation warning added. Method signature for S3 methods are added in a concise, clean approach such that in the next release the deprecated signature can be taken out easily/cleanly (just delete a few lines per method). Custom method dispatch is implemented to allow for multiple JVM reference types that are all 'jobj' in R and to avoid having to add 30 new exports. Author: felixcheung <felixcheung_m@hotmail.com> Closes #9192 from felixcheung/rsqlcontext.
* [SPARK-15439][SPARKR] Failed to run unit test in SparkRwm624@hotmail.com2016-05-251-1/+5
| | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? (Please fill in changes proposed in this fix) There are some failures when running SparkR unit tests. In this PR, I fixed two of these failures in test_context.R and test_sparkSQL.R The first one is due to different masked name. I added missed names in the expected arrays. The second one is because one PR removed the logic of a previous fix of missing subset method. The file privilege issue is still there. I am debugging it. SparkR shell can run the test case successfully. test_that("pipeRDD() on RDDs", { actual <- collect(pipeRDD(rdd, "more")) When using run-test script, it complains no such directories as below: cannot open file '/tmp/Rtmp4FQbah/filee2273f9d47f7': No such file or directory ## How was this patch tested? (Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests) Manually test it Author: wm624@hotmail.com <wm624@hotmail.com> Closes #13284 from wangmiao1981/R.
* [SPARK-15397][SQL] fix string udf locate as hiveDaoyuan Wang2016-05-231-1/+1
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? in hive, `locate("aa", "aaa", 0)` would yield 0, `locate("aa", "aaa", 1)` would yield 1 and `locate("aa", "aaa", 2)` would yield 2, while in Spark, `locate("aa", "aaa", 0)` would yield 1, `locate("aa", "aaa", 1)` would yield 2 and `locate("aa", "aaa", 2)` would yield 0. This results from the different understanding of the third parameter in udf `locate`. It means the starting index and starts from 1, so when we use 0, the return would always be 0. ## How was this patch tested? tested with modified `StringExpressionsSuite` and `StringFunctionsSuite` Author: Daoyuan Wang <daoyuan.wang@intel.com> Closes #13186 from adrian-wang/locate.
* [SPARK-15202][SPARKR] add dapplyCollect() method for DataFrame in SparkR.Sun Rui2016-05-121-1/+20
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? dapplyCollect() applies an R function on each partition of a SparkDataFrame and collects the result back to R as a data.frame. ``` dapplyCollect(df, function(ldf) {...}) ``` ## How was this patch tested? SparkR unit tests. Author: Sun Rui <sunrui2016@gmail.com> Closes #12989 from sun-rui/SPARK-15202.
* [MINOR] [SPARKR] Update data-manipulation.R to use native csv readerYanbo Liang2016-05-091-4/+2
| | | | | | | | | | | | | ## What changes were proposed in this pull request? * Since Spark has supported native csv reader, it does not necessary to use the third party ```spark-csv``` in ```examples/src/main/r/data-manipulation.R```. Meanwhile, remove all ```spark-csv``` usage in SparkR. * Running R applications through ```sparkR``` is not supported as of Spark 2.0, so we change to use ```./bin/spark-submit``` to run the example. ## How was this patch tested? Offline test. Author: Yanbo Liang <ybliang8@gmail.com> Closes #13005 from yanboliang/r-df-examples.
* [SPARK-12479][SPARKR] sparkR collect on GroupedData throws R error "missing ↵Sun Rui2016-05-081-0/+4
| | | | | | | | | | | | | | | | | | value where TRUE/FALSE needed" ## What changes were proposed in this pull request? This PR is a workaround for NA handling in hash code computation. This PR is on behalf of paulomagalhaes whose PR is https://github.com/apache/spark/pull/10436 ## How was this patch tested? SparkR unit tests. Author: Sun Rui <sunrui2016@gmail.com> Author: ray <ray@rays-MacBook-Air.local> Closes #12976 from sun-rui/SPARK-12479.
* [SPARK-11395][SPARKR] Support over and window specification in SparkR.Sun Rui2016-05-051-0/+36
| | | | | | | | | | | | This PR: 1. Implement WindowSpec S4 class. 2. Implement Window.partitionBy() and Window.orderBy() as utility functions to create WindowSpec objects. 3. Implement over() of Column class. Author: Sun Rui <rui.sun@intel.com> Author: Sun Rui <sunrui2016@gmail.com> Closes #10094 from sun-rui/SPARK-11395.
* [SPARK-15110] [SPARKR] Implement repartitionByColumn for SparkR DataFramesNarineK2016-05-051-0/+36
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Implement repartitionByColumn on DataFrame. This will allow us to run R functions on each partition identified by column groups with dapply() method. ## How was this patch tested? Unit tests Author: NarineK <narine.kokhlikyan@us.ibm.com> Closes #12887 from NarineK/repartitionByColumns.
* [SPARK-15091][SPARKR] Fix warnings and a failure in SparkR test cases with ↵Sun Rui2016-05-034-10/+9
| | | | | | | | | | | | | | testthat version 1.0.1 ## What changes were proposed in this pull request? Fix warnings and a failure in SparkR test cases with testthat version 1.0.1 ## How was this patch tested? SparkR unit test cases. Author: Sun Rui <sunrui2016@gmail.com> Closes #12867 from sun-rui/SPARK-15091.
* [SPARK-15030][ML][SPARKR] Support formula in spark.kmeans in SparkRYanbo Liang2016-04-301-6/+6
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? * ```RFormula``` supports empty response variable like ```~ x + y```. * Support formula in ```spark.kmeans``` in SparkR. * Fix some outdated docs for SparkR. ## How was this patch tested? Unit tests. Author: Yanbo Liang <ybliang8@gmail.com> Closes #12813 from yanboliang/spark-15030.
* [SPARK-14831][.2][ML][R] rename ml.save/ml.load to write.ml/read.mlXiangrui Meng2016-04-301-20/+20
| | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Continue the work of #12789 to rename ml.asve/ml.load to write.ml/read.ml, which are more consistent with read.df/write.df and other methods in SparkR. I didn't rename `data` to `df` because we still use `predict` for prediction, which uses `newData` to match the signature in R. ## How was this patch tested? Existing unit tests. cc: yanboliang thunterdb Author: Xiangrui Meng <meng@databricks.com> Closes #12807 from mengxr/SPARK-14831.
* [SPARK-14831][SPARKR] Make the SparkR MLlib API more consistent with SparkTimothy Hunter2016-04-291-5/+136
| | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR splits the MLlib algorithms into two flavors: - the R flavor, which tries to mimic the existing R API for these algorithms (and works as an S4 specialization for Spark dataframes) - the Spark flavor, which follows the same API and naming conventions as the rest of the MLlib algorithms in the other languages In practice, the former calls the latter. ## How was this patch tested? The tests for the various algorithms were adapted to be run against both interfaces. Author: Timothy Hunter <timhunter@databricks.com> Closes #12789 from thunterdb/14831.
* [SPARK-12919][SPARKR] Implement dapply() on DataFrame in SparkR.Sun Rui2016-04-291-0/+40
| | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? dapply() applies an R function on each partition of a DataFrame and returns a new DataFrame. The function signature is: dapply(df, function(localDF) {}, schema = NULL) R function input: local data.frame from the partition on local node R function output: local data.frame Schema specifies the Row format of the resulting DataFrame. It must match the R function's output. If schema is not specified, each partition of the result DataFrame will be serialized in R into a single byte array. Such resulting DataFrame can be processed by successive calls to dapply(). ## How was this patch tested? SparkR unit tests. Author: Sun Rui <rui.sun@intel.com> Author: Sun Rui <sunrui2016@gmail.com> Closes #12493 from sun-rui/SPARK-12919.
* [SPARK-14314][SPARK-14315][ML][SPARKR] Model persistence in SparkR (glm & ↵Yanbo Liang2016-04-291-0/+41
| | | | | | | | | | | | | | | kmeans) SparkR ```glm``` and ```kmeans``` model persistence. Unit tests. Author: Yanbo Liang <ybliang8@gmail.com> Author: Gayathri Murali <gayathri.m.softie@gmail.com> Closes #12778 from yanboliang/spark-14311. Closes #12680 Closes #12683
* [SPARK-7264][ML] Parallel lapply for sparkRTimothy Hunter2016-04-281-0/+6
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR adds a new function in SparkR called `sparkLapply(list, function)`. This function implements a distributed version of `lapply` using Spark as a backend. TODO: - [x] check documentation - [ ] check tests Trivial example in SparkR: ```R sparkLapply(1:5, function(x) { 2 * x }) ``` Output: ``` [[1]] [1] 2 [[2]] [1] 4 [[3]] [1] 6 [[4]] [1] 8 [[5]] [1] 10 ``` Here is a slightly more complex example to perform distributed training of multiple models. Under the hood, Spark broadcasts the dataset. ```R library("MASS") data(menarche) families <- c("gaussian", "poisson") train <- function(family){glm(Menarche ~ Age , family=family, data=menarche)} results <- sparkLapply(families, train) ``` ## How was this patch tested? This PR was tested in SparkR. I am unfamiliar with R and SparkR, so any feedback on style, testing, etc. will be much appreciated. cc falaki davies Author: Timothy Hunter <timhunter@databricks.com> Closes #12426 from thunterdb/7264.
* [SPARK-12235][SPARKR] Enhance mutate() to support replace existing columns.Sun Rui2016-04-281-0/+18
| | | | | | | | | | Make the behavior of mutate more consistent with that in dplyr, besides support for replacing existing columns. 1. Throw error message when there are duplicated column names in the DataFrame being mutated. 2. when there are duplicated column names in specified columns by arguments, the last column of the same name takes effect. Author: Sun Rui <rui.sun@intel.com> Closes #10220 from sun-rui/SPARK-12235.
* [SPARK-13436][SPARKR] Added parameter drop to subsetting operator [Oscar D. Lara Yejas2016-04-271-8/+16
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Added parameter drop to subsetting operator [. This is useful to get a Column from a DataFrame, given its name. R supports it. In R: ``` > name <- "Sepal_Length" > class(iris[, name]) [1] "numeric" ``` Currently, in SparkR: ``` > name <- "Sepal_Length" > class(irisDF[, name]) [1] "DataFrame" ``` Previous code returns a DataFrame, which is inconsistent with R's behavior. SparkR should return a Column instead. Currently, in order for the user to return a Column given a column name as a character variable would be through `eval(parse(x))`, where x is the string `"irisDF$Sepal_Length"`. That itself is pretty hacky. `SparkR:::getColumn() `is another choice, but I don't see why this method should be externalized. Instead, following R's way to do things, the proposed implementation allows this: ``` > name <- "Sepal_Length" > class(irisDF[, name, drop=T]) [1] "Column" > class(irisDF[, name, drop=F]) [1] "DataFrame" ``` This is consistent with R: ``` > name <- "Sepal_Length" > class(iris[, name]) [1] "numeric" > class(iris[, name, drop=F]) [1] "data.frame" ``` Author: Oscar D. Lara Yejas <odlaraye@oscars-mbp.usca.ibm.com> Author: Oscar D. Lara Yejas <odlaraye@oscars-mbp.attlocal.net> Closes #11318 from olarayej/SPARK-13436.
* [SPARK-13734][SPARKR] Added histogram functionOscar D. Lara Yejas2016-04-261-0/+45
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Added method histogram() to compute the histogram of a Column Usage: ``` ## Create a DataFrame from the Iris dataset irisDF <- createDataFrame(sqlContext, iris) ## Render a histogram for the Sepal_Length column histogram(irisDF, "Sepal_Length", nbins=12) ``` ![histogram](https://cloud.githubusercontent.com/assets/13985649/13588486/e1e751c6-e484-11e5-85db-2fc2115c4bb2.png) Note: Usage will change once SPARK-9325 is figured out so that histogram() only takes a Column as a parameter, as opposed to a DataFrame and a name ## How was this patch tested? All unit tests pass. I added specific unit cases for different scenarios. Author: Oscar D. Lara Yejas <odlaraye@oscars-mbp.usca.ibm.com> Author: Oscar D. Lara Yejas <odlaraye@oscars-mbp.attlocal.net> Closes #11569 from olarayej/SPARK-13734.
* [SPARK-14313][ML][SPARKR] AFTSurvivalRegression model persistence in SparkRYanbo Liang2016-04-261-0/+13
| | | | | | | | | | | | ## What changes were proposed in this pull request? ```AFTSurvivalRegressionModel``` supports ```save/load``` in SparkR. ## How was this patch tested? Unit tests. Author: Yanbo Liang <ybliang8@gmail.com> Closes #12685 from yanboliang/spark-14313.
* [SPARK-14312][ML][SPARKR] NaiveBayes model persistence in SparkRYanbo Liang2016-04-251-0/+12
| | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? SparkR ```NaiveBayesModel``` supports ```save/load``` by the following API: ``` df <- createDataFrame(sqlContext, infert) model <- naiveBayes(education ~ ., df, laplace = 0) ml.save(model, path) model2 <- ml.load(path) ``` ## How was this patch tested? Add unit tests. cc mengxr Author: Yanbo Liang <ybliang8@gmail.com> Closes #12573 from yanboliang/spark-14312.
* [SPARK-14869][SQL] Don't mask exceptions in ResolveRelationsReynold Xin2016-04-231-1/+1
| | | | | | | | | | | | ## What changes were proposed in this pull request? In order to support running SQL directly on files, we added some code in ResolveRelations to catch the exception thrown by catalog.lookupRelation and ignore it. This unfortunately masks all the exceptions. This patch changes the logic to simply test the table's existence. ## How was this patch tested? I manually hacked some bugs into Spark and made sure the exceptions were being propagated up. Author: Reynold Xin <rxin@databricks.com> Closes #12634 from rxin/SPARK-14869.
* [SPARK-12148][SPARKR] SparkR: rename DataFrame to SparkDataFramefelixcheung2016-04-231-51/+51
| | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Changed class name defined in R from "DataFrame" to "SparkDataFrame". A popular package, S4Vector already defines "DataFrame" - this change is to avoid conflict. Aside from class name and API/roxygen2 references, SparkR APIs like `createDataFrame`, `as.DataFrame` are not changed (S4Vector does not define a "as.DataFrame"). Since in R, one would rarely reference type/class, this change should have minimal/almost-no impact to a SparkR user in terms of back compat. ## How was this patch tested? SparkR tests, manually loading S4Vector then SparkR package Author: felixcheung <felixcheung_m@hotmail.com> Closes #12621 from felixcheung/rdataframe.
* [SPARK-13178] RRDD faces with concurrency issue in case of rdd.zip(rdd).count().Sun Rui2016-04-221-2/+0
| | | | | | | | | | | | | ## What changes were proposed in this pull request? The concurrency issue reported in SPARK-13178 was fixed by the PR https://github.com/apache/spark/pull/10947 for SPARK-12792. This PR just removes a workaround not needed anymore. ## How was this patch tested? SparkR unit tests. Author: Sun Rui <rui.sun@intel.com> Closes #12606 from sun-rui/SPARK-13178.
* [SPARK-14780] [R] Add `setLogLevel` to SparkRDongjoon Hyun2016-04-211-0/+5
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR aims to add `setLogLevel` function to SparkR shell. **Spark Shell** ```scala scala> sc.setLogLevel("ERROR") ``` **PySpark** ```python >>> sc.setLogLevel("ERROR") ``` **SparkR (this PR)** ```r > setLogLevel(sc, "ERROR") NULL ``` ## How was this patch tested? Pass the Jenkins tests including a new R testcase. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #12547 from dongjoon-hyun/SPARK-14780.
* [SPARK-14639] [PYTHON] [R] Add `bround` function in Python/R.Dongjoon Hyun2016-04-191-0/+5
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This issue aims to expose Scala `bround` function in Python/R API. `bround` function is implemented in SPARK-14614 by extending current `round` function. We used the following semantics from Hive. ```java public static double bround(double input, int scale) { if (Double.isNaN(input) || Double.isInfinite(input)) { return input; } return BigDecimal.valueOf(input).setScale(scale, RoundingMode.HALF_EVEN).doubleValue(); } ``` After this PR, `pyspark` and `sparkR` also support `bround` function. **PySpark** ```python >>> from pyspark.sql.functions import bround >>> sqlContext.createDataFrame([(2.5,)], ['a']).select(bround('a', 0).alias('r')).collect() [Row(r=2.0)] ``` **SparkR** ```r > df = createDataFrame(sqlContext, data.frame(x = c(2.5, 3.5))) > head(collect(select(df, bround(df$x, 0)))) bround(x, 0) 1 2 2 4 ``` ## How was this patch tested? Pass the Jenkins tests (including new testcases). Author: Dongjoon Hyun <dongjoon@apache.org> Closes #12509 from dongjoon-hyun/SPARK-14639.
* [SPARK-13905][SPARKR] Change signature of as.data.frame() to be consistent ↵Sun Rui2016-04-192-1/+4
| | | | | | | | | | | | | | | | with the R base package. ## What changes were proposed in this pull request? Change the signature of as.data.frame() to be consistent with that in the R base package to meet R user's convention. ## How was this patch tested? dev/lint-r SparkR unit tests Author: Sun Rui <rui.sun@intel.com> Closes #11811 from sun-rui/SPARK-13905.
* [SPARK-12224][SPARKR] R support for JDBC sourcefelixcheung2016-04-191-0/+24
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Add R API for `read.jdbc`, `write.jdbc`. Tested this quite a bit manually with different combinations of parameters. It's not clear if we could have automated tests in R for this - Scala `JDBCSuite` depends on Java H2 in-memory database. Refactored some code into util so they could be tested. Core's R SerDe code needs to be updated to allow access to java.util.Properties as `jobj` handle which is required by DataFrameReader/Writer's `jdbc` method. It would be possible, though more code to add a `sql/r/SQLUtils` helper function. Tested: ``` # with postgresql ../bin/sparkR --driver-class-path /usr/share/java/postgresql-9.4.1207.jre7.jar # read.jdbc df <- read.jdbc(sqlContext, "jdbc:postgresql://localhost/db", "films2", user = "user", password = "12345") df <- read.jdbc(sqlContext, "jdbc:postgresql://localhost/db", "films2", user = "user", password = 12345) # partitionColumn and numPartitions test df <- read.jdbc(sqlContext, "jdbc:postgresql://localhost/db", "films2", partitionColumn = "did", lowerBound = 0, upperBound = 200, numPartitions = 4, user = "user", password = 12345) a <- SparkR:::toRDD(df) SparkR:::getNumPartitions(a) [1] 4 SparkR:::collectPartition(a, 2L) # defaultParallelism test df <- read.jdbc(sqlContext, "jdbc:postgresql://localhost/db", "films2", partitionColumn = "did", lowerBound = 0, upperBound = 200, user = "user", password = 12345) SparkR:::getNumPartitions(a) [1] 2 # predicates test df <- read.jdbc(sqlContext, "jdbc:postgresql://localhost/db", "films2", predicates = list("did<=105"), user = "user", password = 12345) count(df) == 1 # write.jdbc, default save mode "error" irisDf <- as.DataFrame(sqlContext, iris) write.jdbc(irisDf, "jdbc:postgresql://localhost/db", "films2", user = "user", password = "12345") "error, already exists" write.jdbc(irisDf, "jdbc:postgresql://localhost/db", "iris", user = "user", password = "12345") ``` Author: felixcheung <felixcheung_m@hotmail.com> Closes #10480 from felixcheung/rreadjdbc.
* [SPARK-13925][ML][SPARKR] Expose R-like summary statistics in SparkR::glm ↵Yanbo Liang2016-04-151-0/+49
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | for more family and link functions ## What changes were proposed in this pull request? Expose R-like summary statistics in SparkR::glm for more family and link functions. Note: Not all values in R [summary.glm](http://stat.ethz.ch/R-manual/R-patched/library/stats/html/summary.glm.html) are exposed, we only provide the most commonly used statistics in this PR. More statistics can be added in the followup work. ## How was this patch tested? Unit tests. SparkR Output: ``` Deviance Residuals: (Note: These are approximate quantiles with relative error <= 0.01) Min 1Q Median 3Q Max -0.95096 -0.16585 -0.00232 0.17410 0.72918 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.6765 0.23536 7.1231 4.4561e-11 Sepal_Length 0.34988 0.046301 7.5566 4.1873e-12 Species_versicolor -0.98339 0.072075 -13.644 0 Species_virginica -1.0075 0.093306 -10.798 0 (Dispersion parameter for gaussian family taken to be 0.08351462) Null deviance: 28.307 on 149 degrees of freedom Residual deviance: 12.193 on 146 degrees of freedom AIC: 59.22 Number of Fisher Scoring iterations: 1 ``` R output: ``` Deviance Residuals: Min 1Q Median 3Q Max -0.95096 -0.16522 0.00171 0.18416 0.72918 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.67650 0.23536 7.123 4.46e-11 *** Sepal.Length 0.34988 0.04630 7.557 4.19e-12 *** Speciesversicolor -0.98339 0.07207 -13.644 < 2e-16 *** Speciesvirginica -1.00751 0.09331 -10.798 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for gaussian family taken to be 0.08351462) Null deviance: 28.307 on 149 degrees of freedom Residual deviance: 12.193 on 146 degrees of freedom AIC: 59.217 Number of Fisher Scoring iterations: 2 ``` cc mengxr Author: Yanbo Liang <ybliang8@gmail.com> Closes #12393 from yanboliang/spark-13925.
* [SPARK-12566][SPARK-14324][ML] GLM model family, link function support in ↵Yanbo Liang2016-04-121-66/+29
| | | | | | | | | | | | | | | | | SparkR:::glm * SparkR glm supports families and link functions which match R's signature for family. * SparkR glm API refactor. The comparative standard of the new API is R glm, so I only expose the arguments that R glm supports: ```formula, family, data, epsilon and maxit```. * This PR is focus on glm() and predict(), summary statistics will be done in a separate PR after this get in. * This PR depends on #12287 which make GLMs support link prediction at Scala side. After that merged, I will add more tests for predict() to this PR. Unit tests. cc mengxr jkbradley hhbyyh Author: Yanbo Liang <ybliang8@gmail.com> Closes #12294 from yanboliang/spark-12566.
* [SPARK-14362][SPARK-14406][SQL][FOLLOW-UP] DDL Native Support: Drop View and ↵gatorsmile2016-04-101-1/+1
| | | | | | | | | | | | | | Drop Table #### What changes were proposed in this pull request? This PR is to address the comment: https://github.com/apache/spark/pull/12146#discussion-diff-59092238. It removes the function `isViewSupported` from `SessionCatalog`. After the removal, we still can capture the user errors if users try to drop a table using `DROP VIEW`. #### How was this patch tested? Modified the existing test cases Author: gatorsmile <gatorsmile@gmail.com> Closes #12284 from gatorsmile/followupDropTable.
* [SPARK-14353] Dataset Time Window `window` API for RBurak Yavuz2016-04-052-1/+37
| | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? The `window` function was added to Dataset with [this PR](https://github.com/apache/spark/pull/12008). This PR adds the R API for this function. With this PR, SQL, Java, and Scala will share the same APIs as in users can use: - `window(timeColumn, windowDuration)` - `window(timeColumn, windowDuration, slideDuration)` - `window(timeColumn, windowDuration, slideDuration, startTime)` In Python and R, users can access all APIs above, but in addition they can do - In R: `window(timeColumn, windowDuration, startTime=...)` that is, they can provide the startTime without providing the `slideDuration`. In this case, we will generate tumbling windows. ## How was this patch tested? Unit tests + manual tests Author: Burak Yavuz <brkyvz@gmail.com> Closes #12141 from brkyvz/R-windows.
* [SPARK-12792] [SPARKR] Refactor RRDD to support R UDF.Sun Rui2016-03-281-0/+8
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Refactor RRDD by separating the common logic interacting with the R worker to a new class RRunner, which can be used to evaluate R UDFs. Now RRDD relies on RRuner for RDD computation and RRDD could be reomved if we want to remove RDD API in SparkR later. ## How was this patch tested? dev/lint-r SparkR unit tests Author: Sun Rui <rui.sun@intel.com> Closes #12024 from sun-rui/SPARK-12792_new.
* Revert "[SPARK-12792] [SPARKR] Refactor RRDD to support R UDF."Davies Liu2016-03-281-8/+0
| | | | This reverts commit 40984f67065eeaea731940008e6677c2323dda3e.
* [SPARK-12792] [SPARKR] Refactor RRDD to support R UDF.Sun Rui2016-03-281-0/+8
| | | | | | | | | | Refactor RRDD by separating the common logic interacting with the R worker to a new class RRunner, which can be used to evaluate R UDFs. Now RRDD relies on RRuner for RDD computation and RRDD could be reomved if we want to remove RDD API in SparkR later. Author: Sun Rui <rui.sun@intel.com> Closes #10947 from sun-rui/SPARK-12792.
* [SPARK-14014][SQL] Integrate session catalog (attempt #2)Andrew Or2016-03-241-1/+2
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This reopens #11836, which was merged but promptly reverted because it introduced flaky Hive tests. ## How was this patch tested? See `CatalogTestCases`, `SessionCatalogSuite` and `HiveContextSuite`. Author: Andrew Or <andrew@databricks.com> Closes #11938 from andrewor14/session-catalog-again.
* [SPARK-13010][ML][SPARKR] Implement a simple wrapper of ↵Yanbo Liang2016-03-241-0/+49
| | | | | | | | | | | | | | | | | | AFTSurvivalRegression in SparkR ## What changes were proposed in this pull request? This PR continues the work in #11447, we implemented the wrapper of ```AFTSurvivalRegression``` named ```survreg``` in SparkR. ## How was this patch tested? Test against output from R package survival's survreg. cc mengxr felixcheung Close #11447 Author: Yanbo Liang <ybliang8@gmail.com> Closes #11932 from yanboliang/spark-13010-new.
* Revert "[SPARK-14014][SQL] Replace existing catalog with SessionCatalog"Andrew Or2016-03-231-2/+1
| | | | This reverts commit 5dfc01976bb0d72489620b4f32cc12d620bb6260.
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