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* [SPARK-19464][BUILD][HOTFIX] run-tests should use hadoop2.6Dongjoon Hyun2017-02-081-3/+0
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? After SPARK-19464, **SparkPullRequestBuilder** fails because it still tries to use hadoop2.3. **BEFORE** https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/72595/console ``` ======================================================================== Building Spark ======================================================================== [error] Could not find hadoop2.3 in the list. Valid options are ['hadoop2.6', 'hadoop2.7'] Attempting to post to Github... > Post successful. ``` **AFTER** https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/72595/console ``` ======================================================================== Building Spark ======================================================================== [info] Building Spark (w/Hive 1.2.1) using SBT with these arguments: -Phadoop-2.6 -Pmesos -Pkinesis-asl -Pyarn -Phive-thriftserver -Phive test:package streaming-kafka-0-8-assembly/assembly streaming-flume-assembly/assembly streaming-kinesis-asl-assembly/assembly Using /usr/java/jdk1.8.0_60 as default JAVA_HOME. Note, this will be overridden by -java-home if it is set. ``` ## How was this patch tested? Pass the existing test. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #16858 from dongjoon-hyun/hotfix_run-tests.
* [SPARK-18810][SPARKR] SparkR install.spark does not work for RCs, snapshotsFelix Cheung2016-12-121-0/+11
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## 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.
* [SPARK-17838][SPARKR] Check named arguments for options and use formatted R ↵hyukjinkwon2016-11-011-0/+2
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | friendly message from JVM exception message ## 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.
* [SPARK-17665][SPARKR] Support options/mode all for read/write APIs and ↵hyukjinkwon2016-10-071-0/+9
| | | | | | | | | | | | | | | | | | | | options in other types ## 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.
* [SPARK-17658][SPARKR] read.df/write.df API taking path optionally in SparkRhyukjinkwon2016-10-041-0/+10
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## 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.
* [SPARK-16785] R dapply doesn't return array or raw columnsClark Fitzgerald2016-09-061-0/+24
| | | | | | | | | | | | | | | | ## 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.
* [SPARK-16519][SPARKR] Handle SparkR RDD generics that create warnings in R ↵Felix Cheung2016-08-161-3/+3
| | | | | | | | | | | | | | | | | CMD check ## What changes were proposed in this pull request? Rename RDD functions for now to avoid CRAN check warnings. Some RDD functions are sharing generics with DataFrame functions (hence the problem) so after the renames we need to add new generics, for now. ## How was this patch tested? unit tests Author: Felix Cheung <felixcheung_m@hotmail.com> Closes #14626 from felixcheung/rrddfunctions.
* [SPARK-16027][SPARKR] Fix R tests SparkSession init/stopFelix Cheung2016-07-171-1/+3
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Fix R SparkSession init/stop, and warnings of reusing existing Spark Context ## How was this patch tested? unit tests shivaram Author: Felix Cheung <felixcheung_m@hotmail.com> Closes #14177 from felixcheung/rsessiontest.
* [SPARK-15159][SPARKR] SparkR SparkSession APIFelix Cheung2016-06-171-1/+15
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## 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-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-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-13812][SPARKR] Fix SparkR lint-r test errors.Sun Rui2016-03-131-4/+4
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR fixes all newly captured SparkR lint-r errors after the lintr package is updated from github. ## How was this patch tested? dev/lint-r SparkR unit tests Author: Sun Rui <rui.sun@intel.com> Closes #11652 from sun-rui/SPARK-13812.
* [SPARK-12327][SPARKR] fix code for lintr warning for commented codefelixcheung2016-01-031-0/+2
| | | | | | | | shivaram Author: felixcheung <felixcheung_m@hotmail.com> Closes #10408 from felixcheung/rcodecomment.
* [SPARK-12034][SPARKR] Eliminate warnings in SparkR test cases.Sun Rui2015-12-071-0/+140
This PR: 1. Suppress all known warnings. 2. Cleanup test cases and fix some errors in test cases. 3. Fix errors in HiveContext related test cases. These test cases are actually not run previously due to a bug of creating TestHiveContext. 4. Support 'testthat' package version 0.11.0 which prefers that test cases be under 'tests/testthat' 5. Make sure the default Hadoop file system is local when running test cases. 6. Turn on warnings into errors. Author: Sun Rui <rui.sun@intel.com> Closes #10030 from sun-rui/SPARK-12034.
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