### What changes were proposed in this pull request?
Spark 3.0 accidentally dropped R < 3.5. It is built by R 3.6.3 which not support R < 3.5:
```
Error in readRDS(pfile) : cannot read workspace version 3 written by R 3.6.3; need R 3.5.0 or newer version.
```
In fact, with SPARK-31918, we will have to drop R < 3.5 entirely to support R 4.0.0. This is inevitable to release on CRAN because they require to make the tests pass with the latest R.
### Why are the changes needed?
To show the supported versions correctly, and support R 4.0.0 to unblock the releases.
### Does this PR introduce _any_ user-facing change?
In fact, no because Spark 3.0.0 already does not work with R < 3.5.
Compared to Spark 2.4, yes. R < 3.5 would not work.
### How was this patch tested?
Jenkins should test it out.
Closes#28908 from HyukjinKwon/SPARK-32073.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR fixes SparkR lint errors and adds `lint-r` GitHub Action to protect the branch.
### Why are the changes needed?
It turns out that we currently don't run it. It's recovered yesterday. However, after that, our Jenkins linter jobs (`master`/`branch-2.4`) has been broken on `lint-r` tasks.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Pass the GitHub Action on this PR in addition to Jenkins R and AppVeyor R.
Closes#26564 from dongjoon-hyun/SPARK-29936.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR proposes:
1. Use `is.data.frame` to check if it is a DataFrame.
2. to install Arrow and test Arrow optimization in AppVeyor build. We're currently not testing this in CI.
### Why are the changes needed?
1. To support SparkR with Arrow 0.14
2. To check if there's any regression and if it works correctly.
### Does this PR introduce any user-facing change?
```r
df <- createDataFrame(mtcars)
collect(dapply(df, function(rdf) { data.frame(rdf$gear + 1) }, structType("gear double")))
```
**Before:**
```
Error in readBin(con, raw(), as.integer(dataLen), endian = "big") :
invalid 'n' argument
```
**After:**
```
gear
1 5
2 5
3 5
4 4
5 4
6 4
7 4
8 5
9 5
...
```
### How was this patch tested?
AppVeyor
Closes#25993 from HyukjinKwon/arrow-r-appveyor.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR targets to add vectorized `dapply()` in R, Arrow optimization.
This can be tested as below:
```bash
$ ./bin/sparkR --conf spark.sql.execution.arrow.enabled=true
```
```r
df <- createDataFrame(mtcars)
collect(dapply(df, function(rdf) { data.frame(rdf$gear + 1) }, structType("gear double")))
```
### Requirements
- R 3.5.x
- Arrow package 0.12+
```bash
Rscript -e 'remotes::install_github("apache/arrowapache-arrow-0.12.0", subdir = "r")'
```
**Note:** currently, Arrow R package is not in CRAN. Please take a look at ARROW-3204.
**Note:** currently, Arrow R package seems not supporting Windows. Please take a look at ARROW-3204.
### Benchmarks
**Shall**
```bash
sync && sudo purge
./bin/sparkR --conf spark.sql.execution.arrow.enabled=false --driver-memory 4g
```
```bash
sync && sudo purge
./bin/sparkR --conf spark.sql.execution.arrow.enabled=true --driver-memory 4g
```
**R code**
```r
rdf <- read.csv("500000.csv")
df <- cache(createDataFrame(rdf))
count(df)
test <- function() {
options(digits.secs = 6) # milliseconds
start.time <- Sys.time()
count(cache(dapply(df, function(rdf) { rdf }, schema(df))))
end.time <- Sys.time()
time.taken <- end.time - start.time
print(time.taken)
}
test()
```
**Data (350 MB):**
```r
object.size(read.csv("500000.csv"))
350379504 bytes
```
"500000 Records" http://eforexcel.com/wp/downloads-16-sample-csv-files-data-sets-for-testing/
**Results**
```
Time difference of 13.42037 mins
```
```
Time difference of 30.64156 secs
```
The performance improvement was around **2627%**.
### Limitations
- For now, Arrow optimization with R does not support when the data is `raw`, and when user explicitly gives float type in the schema. They produce corrupt values.
- Due to ARROW-4512, it cannot send and receive batch by batch. It has to send all batches in Arrow stream format at once. It needs improvement later.
## How was this patch tested?
Unit tests were added, and manually tested.
Closes#23787 from HyukjinKwon/SPARK-26830-1.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR targets to add vectorized `gapply()` in R, Arrow optimization.
This can be tested as below:
```bash
$ ./bin/sparkR --conf spark.sql.execution.arrow.enabled=true
```
```r
df <- createDataFrame(mtcars)
collect(gapply(df,
"gear",
function(key, group) {
data.frame(gear = key[[1]], disp = mean(group$disp) > group$disp)
},
structType("gear double, disp boolean")))
```
### Requirements
- R 3.5.x
- Arrow package 0.12+
```bash
Rscript -e 'remotes::install_github("apache/arrowapache-arrow-0.12.0", subdir = "r")'
```
**Note:** currently, Arrow R package is not in CRAN. Please take a look at ARROW-3204.
**Note:** currently, Arrow R package seems not supporting Windows. Please take a look at ARROW-3204.
### Benchmarks
**Shall**
```bash
sync && sudo purge
./bin/sparkR --conf spark.sql.execution.arrow.enabled=false
```
```bash
sync && sudo purge
./bin/sparkR --conf spark.sql.execution.arrow.enabled=true
```
**R code**
```r
rdf <- read.csv("500000.csv")
rdf <- rdf[, c("Month.of.Joining", "Weight.in.Kgs.")] # We're only interested in the key and values to calculate.
df <- cache(createDataFrame(rdf))
count(df)
test <- function() {
options(digits.secs = 6) # milliseconds
start.time <- Sys.time()
count(gapply(df,
"Month_of_Joining",
function(key, group) {
data.frame(Month_of_Joining = key[[1]], Weight_in_Kgs_ = mean(group$Weight_in_Kgs_) > group$Weight_in_Kgs_)
},
structType("Month_of_Joining integer, Weight_in_Kgs_ boolean")))
end.time <- Sys.time()
time.taken <- end.time - start.time
print(time.taken)
}
test()
```
**Data (350 MB):**
```r
object.size(read.csv("500000.csv"))
350379504 bytes
```
"500000 Records" http://eforexcel.com/wp/downloads-16-sample-csv-files-data-sets-for-testing/
**Results**
```
Time difference of 35.67459 secs
```
```
Time difference of 4.301399 secs
```
The performance improvement was around **829%**.
**Note that** I am 100% sure this PR improves more then 829% because I gave up testing it with non-Arrow optimization because it took super super super long when the data size becomes bigger.
### Limitations
- For now, Arrow optimization with R does not support when the data is `raw`, and when user explicitly gives float type in the schema. They produce corrupt values.
- Due to ARROW-4512, it cannot send and receive batch by batch. It has to send all batches in Arrow stream format at once. It needs improvement later.
## How was this patch tested?
Unit tests were added
**TODOs:**
- [x] Draft codes
- [x] make the tests passed
- [x] make the CRAN check pass
- [x] Performance measurement
- [x] Supportability investigation (for instance types)
Closes#23746 from HyukjinKwon/SPARK-26759.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
1. Removed empty space at the beginning of welcome message lines of sparkR to be consistent with welcome message of `pyspark` and `spark-shell`
2. Setting indent of logo message lines to 3 to be consistent with welcome message of `pyspark` and `spark-shell`
Output of `pyspark`:
```
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/__ / .__/\_,_/_/ /_/\_\ version 2.4.0
/_/
Using Python version 3.6.6 (default, Jun 28 2018 11:07:29)
SparkSession available as 'spark'.
```
Output of `spark-shell`:
```
Spark session available as 'spark'.
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 2.4.0
/_/
Using Scala version 2.11.12 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_161)
Type in expressions to have them evaluated.
Type :help for more information.
```
## How was this patch tested?
Before:
Output of `sparkR`:
```
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 2.4.0
/_/
SparkSession available as 'spark'.
```
After:
```
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 2.4.0
/_/
SparkSession available as 'spark'.
```
Closes#23293 from AzureQ/master.
Authored-by: Qi Shao <qi.shao.nyu@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR proposes to bump up the minimum versions of R from 3.1 to 3.4.
R version. 3.1.x is too old. It's released 4.5 years ago. R 3.4.0 is released 1.5 years ago. Considering the timing for Spark 3.0, deprecating lower versions, bumping up R to 3.4 might be reasonable option.
It should be good to deprecate and drop < R 3.4 support.
## How was this patch tested?
Jenkins tests.
Closes#23012 from HyukjinKwon/SPARK-26014.
Authored-by: hyukjinkwon <gurwls223@apache.org>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Fixing typos is sometimes very hard. It's not so easy to visually review them. Recently, I discovered a very useful tool for it, [misspell](https://github.com/client9/misspell).
This pull request fixes minor typos detected by [misspell](https://github.com/client9/misspell) except for the false positives. If you would like me to work on other files as well, let me know.
## How was this patch tested?
### before
```
$ misspell . | grep -v '.js'
R/pkg/R/SQLContext.R:354:43: "definiton" is a misspelling of "definition"
R/pkg/R/SQLContext.R:424:43: "definiton" is a misspelling of "definition"
R/pkg/R/SQLContext.R:445:43: "definiton" is a misspelling of "definition"
R/pkg/R/SQLContext.R:495:43: "definiton" is a misspelling of "definition"
NOTICE-binary:454:16: "containd" is a misspelling of "contained"
R/pkg/R/context.R:46:43: "definiton" is a misspelling of "definition"
R/pkg/R/context.R:74:43: "definiton" is a misspelling of "definition"
R/pkg/R/DataFrame.R:591:48: "persistance" is a misspelling of "persistence"
R/pkg/R/streaming.R:166:44: "occured" is a misspelling of "occurred"
R/pkg/inst/worker/worker.R:65:22: "ouput" is a misspelling of "output"
R/pkg/tests/fulltests/test_utils.R:106:25: "environemnt" is a misspelling of "environment"
common/kvstore/src/test/java/org/apache/spark/util/kvstore/InMemoryStoreSuite.java:38:39: "existant" is a misspelling of "existent"
common/kvstore/src/test/java/org/apache/spark/util/kvstore/LevelDBSuite.java:83:39: "existant" is a misspelling of "existent"
common/network-common/src/main/java/org/apache/spark/network/crypto/TransportCipher.java:243:46: "transfered" is a misspelling of "transferred"
common/network-common/src/main/java/org/apache/spark/network/sasl/SaslEncryption.java:234:19: "transfered" is a misspelling of "transferred"
common/network-common/src/main/java/org/apache/spark/network/sasl/SaslEncryption.java:238:63: "transfered" is a misspelling of "transferred"
common/network-common/src/main/java/org/apache/spark/network/sasl/SaslEncryption.java:244:46: "transfered" is a misspelling of "transferred"
common/network-common/src/main/java/org/apache/spark/network/sasl/SaslEncryption.java:276:39: "transfered" is a misspelling of "transferred"
common/network-common/src/main/java/org/apache/spark/network/util/AbstractFileRegion.java:27:20: "transfered" is a misspelling of "transferred"
common/unsafe/src/test/scala/org/apache/spark/unsafe/types/UTF8StringPropertyCheckSuite.scala:195:15: "orgin" is a misspelling of "origin"
core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala:621:39: "gauranteed" is a misspelling of "guaranteed"
core/src/main/scala/org/apache/spark/status/storeTypes.scala:113:29: "ect" is a misspelling of "etc"
core/src/main/scala/org/apache/spark/storage/DiskStore.scala:282:18: "transfered" is a misspelling of "transferred"
core/src/main/scala/org/apache/spark/util/ListenerBus.scala:64:17: "overriden" is a misspelling of "overridden"
core/src/test/scala/org/apache/spark/ShuffleSuite.scala:211:7: "substracted" is a misspelling of "subtracted"
core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala:1922:49: "agriculteur" is a misspelling of "agriculture"
core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala:2468:84: "truely" is a misspelling of "truly"
core/src/test/scala/org/apache/spark/storage/FlatmapIteratorSuite.scala:25:18: "persistance" is a misspelling of "persistence"
core/src/test/scala/org/apache/spark/storage/FlatmapIteratorSuite.scala:26:69: "persistance" is a misspelling of "persistence"
data/streaming/AFINN-111.txt:1219:0: "humerous" is a misspelling of "humorous"
dev/run-pip-tests:55:28: "enviroments" is a misspelling of "environments"
dev/run-pip-tests:91:37: "virutal" is a misspelling of "virtual"
dev/merge_spark_pr.py:377:72: "accross" is a misspelling of "across"
dev/merge_spark_pr.py:378:66: "accross" is a misspelling of "across"
dev/run-pip-tests:126:25: "enviroments" is a misspelling of "environments"
docs/configuration.md:1830:82: "overriden" is a misspelling of "overridden"
docs/structured-streaming-programming-guide.md:525:45: "processs" is a misspelling of "processes"
docs/structured-streaming-programming-guide.md:1165:61: "BETWEN" is a misspelling of "BETWEEN"
docs/sql-programming-guide.md:1891:810: "behaivor" is a misspelling of "behavior"
examples/src/main/python/sql/arrow.py:98:8: "substract" is a misspelling of "subtract"
examples/src/main/python/sql/arrow.py:103:27: "substract" is a misspelling of "subtract"
licenses/LICENSE-heapq.txt:5:63: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:6:2: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:262:29: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:262:39: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:269:49: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:269:59: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:274:2: "STICHTING" is a misspelling of "STITCHING"
licenses/LICENSE-heapq.txt:274:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses/LICENSE-heapq.txt:276:29: "STICHTING" is a misspelling of "STITCHING"
licenses/LICENSE-heapq.txt:276:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses-binary/LICENSE-heapq.txt:5:63: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:6:2: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:262:29: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:262:39: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:269:49: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:269:59: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:274:2: "STICHTING" is a misspelling of "STITCHING"
licenses-binary/LICENSE-heapq.txt:274:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses-binary/LICENSE-heapq.txt:276:29: "STICHTING" is a misspelling of "STITCHING"
licenses-binary/LICENSE-heapq.txt:276:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
mllib/src/main/resources/org/apache/spark/ml/feature/stopwords/hungarian.txt:170:0: "teh" is a misspelling of "the"
mllib/src/main/resources/org/apache/spark/ml/feature/stopwords/portuguese.txt:53:0: "eles" is a misspelling of "eels"
mllib/src/main/scala/org/apache/spark/ml/stat/Summarizer.scala:99:20: "Euclidian" is a misspelling of "Euclidean"
mllib/src/main/scala/org/apache/spark/ml/stat/Summarizer.scala:539:11: "Euclidian" is a misspelling of "Euclidean"
mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala:77:36: "Teh" is a misspelling of "The"
mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala:230:24: "inital" is a misspelling of "initial"
mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala:276:9: "Euclidian" is a misspelling of "Euclidean"
mllib/src/test/scala/org/apache/spark/ml/clustering/KMeansSuite.scala:237:26: "descripiton" is a misspelling of "descriptions"
python/pyspark/find_spark_home.py:30:13: "enviroment" is a misspelling of "environment"
python/pyspark/context.py:937:12: "supress" is a misspelling of "suppress"
python/pyspark/context.py:938:12: "supress" is a misspelling of "suppress"
python/pyspark/context.py:939:12: "supress" is a misspelling of "suppress"
python/pyspark/context.py:940:12: "supress" is a misspelling of "suppress"
python/pyspark/heapq3.py:6:63: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:7:2: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:263:29: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:263:39: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:270:49: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:270:59: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:275:2: "STICHTING" is a misspelling of "STITCHING"
python/pyspark/heapq3.py:275:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
python/pyspark/heapq3.py:277:29: "STICHTING" is a misspelling of "STITCHING"
python/pyspark/heapq3.py:277:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
python/pyspark/heapq3.py:713:8: "probabilty" is a misspelling of "probability"
python/pyspark/ml/clustering.py:1038:8: "Currenlty" is a misspelling of "Currently"
python/pyspark/ml/stat.py:339:23: "Euclidian" is a misspelling of "Euclidean"
python/pyspark/ml/regression.py:1378:20: "paramter" is a misspelling of "parameter"
python/pyspark/mllib/stat/_statistics.py:262:8: "probabilty" is a misspelling of "probability"
python/pyspark/rdd.py:1363:32: "paramter" is a misspelling of "parameter"
python/pyspark/streaming/tests.py:825:42: "retuns" is a misspelling of "returns"
python/pyspark/sql/tests.py:768:29: "initalization" is a misspelling of "initialization"
python/pyspark/sql/tests.py:3616:31: "initalize" is a misspelling of "initialize"
resource-managers/mesos/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendUtil.scala:120:39: "arbitary" is a misspelling of "arbitrary"
resource-managers/mesos/src/test/scala/org/apache/spark/deploy/mesos/MesosClusterDispatcherArgumentsSuite.scala:26:45: "sucessfully" is a misspelling of "successfully"
resource-managers/mesos/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerUtils.scala:358:27: "constaints" is a misspelling of "constraints"
resource-managers/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala:111:24: "senstive" is a misspelling of "sensitive"
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/catalog/SessionCatalog.scala:1063:5: "overwirte" is a misspelling of "overwrite"
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeExpressions.scala:1348:17: "compatability" is a misspelling of "compatibility"
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicLogicalOperators.scala:77:36: "paramter" is a misspelling of "parameter"
sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala:1374:22: "precendence" is a misspelling of "precedence"
sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala:238:27: "unnecassary" is a misspelling of "unnecessary"
sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ConditionalExpressionSuite.scala:212:17: "whn" is a misspelling of "when"
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamingSymmetricHashJoinHelper.scala:147:60: "timestmap" is a misspelling of "timestamp"
sql/core/src/test/scala/org/apache/spark/sql/TPCDSQuerySuite.scala:150:45: "precentage" is a misspelling of "percentage"
sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/csv/CSVInferSchemaSuite.scala:135:29: "infered" is a misspelling of "inferred"
sql/hive/src/test/resources/golden/udf_instr-1-2e76f819563dbaba4beb51e3a130b922:1:52: "occurance" is a misspelling of "occurrence"
sql/hive/src/test/resources/golden/udf_instr-2-32da357fc754badd6e3898dcc8989182:1:52: "occurance" is a misspelling of "occurrence"
sql/hive/src/test/resources/golden/udf_locate-1-6e41693c9c6dceea4d7fab4c02884e4e:1:63: "occurance" is a misspelling of "occurrence"
sql/hive/src/test/resources/golden/udf_locate-2-d9b5934457931447874d6bb7c13de478:1:63: "occurance" is a misspelling of "occurrence"
sql/hive/src/test/resources/golden/udf_translate-2-f7aa38a33ca0df73b7a1e6b6da4b7fe8:9:79: "occurence" is a misspelling of "occurrence"
sql/hive/src/test/resources/golden/udf_translate-2-f7aa38a33ca0df73b7a1e6b6da4b7fe8:13:110: "occurence" is a misspelling of "occurrence"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/annotate_stats_join.q:46:105: "distint" is a misspelling of "distinct"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/auto_sortmerge_join_11.q:29:3: "Currenly" is a misspelling of "Currently"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/avro_partitioned.q:72:15: "existant" is a misspelling of "existent"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/decimal_udf.q:25:3: "substraction" is a misspelling of "subtraction"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/groupby2_map_multi_distinct.q:16:51: "funtion" is a misspelling of "function"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/groupby_sort_8.q:15:30: "issueing" is a misspelling of "issuing"
sql/hive/src/test/scala/org/apache/spark/sql/sources/HadoopFsRelationTest.scala:669:52: "wiht" is a misspelling of "with"
sql/hive-thriftserver/src/main/java/org/apache/hive/service/cli/session/HiveSessionImpl.java:474:9: "Refering" is a misspelling of "Referring"
```
### after
```
$ misspell . | grep -v '.js'
common/network-common/src/main/java/org/apache/spark/network/util/AbstractFileRegion.java:27:20: "transfered" is a misspelling of "transferred"
core/src/main/scala/org/apache/spark/status/storeTypes.scala:113:29: "ect" is a misspelling of "etc"
core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala:1922:49: "agriculteur" is a misspelling of "agriculture"
data/streaming/AFINN-111.txt:1219:0: "humerous" is a misspelling of "humorous"
licenses/LICENSE-heapq.txt:5:63: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:6:2: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:262:29: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:262:39: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:269:49: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:269:59: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:274:2: "STICHTING" is a misspelling of "STITCHING"
licenses/LICENSE-heapq.txt:274:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses/LICENSE-heapq.txt:276:29: "STICHTING" is a misspelling of "STITCHING"
licenses/LICENSE-heapq.txt:276:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses-binary/LICENSE-heapq.txt:5:63: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:6:2: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:262:29: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:262:39: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:269:49: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:269:59: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:274:2: "STICHTING" is a misspelling of "STITCHING"
licenses-binary/LICENSE-heapq.txt:274:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses-binary/LICENSE-heapq.txt:276:29: "STICHTING" is a misspelling of "STITCHING"
licenses-binary/LICENSE-heapq.txt:276:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
mllib/src/main/resources/org/apache/spark/ml/feature/stopwords/hungarian.txt:170:0: "teh" is a misspelling of "the"
mllib/src/main/resources/org/apache/spark/ml/feature/stopwords/portuguese.txt:53:0: "eles" is a misspelling of "eels"
mllib/src/main/scala/org/apache/spark/ml/stat/Summarizer.scala:99:20: "Euclidian" is a misspelling of "Euclidean"
mllib/src/main/scala/org/apache/spark/ml/stat/Summarizer.scala:539:11: "Euclidian" is a misspelling of "Euclidean"
mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala:77:36: "Teh" is a misspelling of "The"
mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala:276:9: "Euclidian" is a misspelling of "Euclidean"
python/pyspark/heapq3.py:6:63: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:7:2: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:263:29: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:263:39: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:270:49: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:270:59: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:275:2: "STICHTING" is a misspelling of "STITCHING"
python/pyspark/heapq3.py:275:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
python/pyspark/heapq3.py:277:29: "STICHTING" is a misspelling of "STITCHING"
python/pyspark/heapq3.py:277:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
python/pyspark/ml/stat.py:339:23: "Euclidian" is a misspelling of "Euclidean"
```
Closes#22070 from seratch/fix-typo.
Authored-by: Kazuhiro Sera <seratch@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
## What changes were proposed in this pull request?
during my travails in porting spark builds to run on our centos worker, i managed to recreate (as best i could) the centos environment on our new ubuntu-testing machine.
while running my initial builds, lintr was crashing on some extraneous spaces in test_basic.R (see: https://amplab.cs.berkeley.edu/jenkins/job/spark-master-test-sbt-hadoop-2.6-ubuntu-test/862/console)
after removing those spaces, the ubuntu build happily passed the lintr tests.
## How was this patch tested?
i then tested this against a modified spark-master-test-sbt-hadoop-2.6 build (see https://amplab.cs.berkeley.edu/jenkins/view/RISELab%20Infra/job/testing-spark-master-test-with-updated-R-crap/4/), which scp'ed a copy of test_basic.R in to the repo after the git clone. everything seems to be working happily.
Author: shane knapp <incomplete@gmail.com>
Closes#21864 from shaneknapp/fixing-R-lint-spacing.
## What changes were proposed in this pull request?
change to skip tests if
- couldn't determine java version
fix problem on windows
## How was this patch tested?
unit test, manual, win-builder
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#21666 from felixcheung/rjavaskip.
This PR sets the java.io.tmpdir for CRAN checks and also disables the hsperfdata for the JVM when running CRAN checks. Together this prevents files from being left behind in `/tmp`
## How was this patch tested?
Tested manually on a clean EC2 machine
Author: Shivaram Venkataraman <shivaram@cs.berkeley.edu>
Closes#19589 from shivaram/sparkr-tmpdir-clean.
## What changes were proposed in this pull request?
Currently, we set lintr to jimhester/lintra769c0b (see [this](7d1175011c) and [SPARK-14074](https://issues.apache.org/jira/browse/SPARK-14074)).
I first tested and checked lintr-1.0.1 but it looks many important fixes are missing (for example, checking 100 length). So, I instead tried the latest commit, 5431140ffe, in my local and fixed the check failures.
It looks it has fixed many bugs and now finds many instances that I have observed and thought should be caught time to time, here I filed [the results](https://gist.github.com/HyukjinKwon/4f59ddcc7b6487a02da81800baca533c).
The downside looks it now takes about 7ish mins, (it was 2ish mins before) in my local.
## How was this patch tested?
Manually, `./dev/lint-r` after manually updating the lintr package.
Author: hyukjinkwon <gurwls223@gmail.com>
Author: zuotingbing <zuo.tingbing9@zte.com.cn>
Closes#19290 from HyukjinKwon/upgrade-r-lint.
## What changes were proposed in this pull request?
This is a retry for #18320. This PR was reverted due to unexpected test failures with -10 error code.
I was unable to reproduce in MacOS, CentOS and Ubuntu but only in Jenkins. So, the tests proceeded to verify this and revert the past try here - https://github.com/apache/spark/pull/18456
This new approach was tested in https://github.com/apache/spark/pull/18463.
**Test results**:
- With the part of suspicious change in the past try (466325d3fd)
Tests ran 4 times and 2 times passed and 2 time failed.
- Without the part of suspicious change in the past try (466325d3fd)
Tests ran 5 times and they all passed.
- With this new approach (0a7589c09f)
Tests ran 5 times and they all passed.
It looks the cause is as below (see 466325d3fd):
```diff
+ exitCode <- 1
...
+ data <- parallel:::readChild(child)
+ if (is.raw(data)) {
+ if (unserialize(data) == exitCode) {
...
+ }
+ }
...
- parallel:::mcexit(0L)
+ parallel:::mcexit(0L, send = exitCode)
```
Two possibilities I think
- `parallel:::mcexit(.. , send = exitCode)`
https://stat.ethz.ch/R-manual/R-devel/library/parallel/html/mcfork.html
> It sends send to the master (unless NULL) and then shuts down the child process.
However, it looks possible that the parent attemps to terminate the child right after getting our custom exit code. So, the child gets terminated between "send" and "shuts down", failing to exit properly.
- A bug between `parallel:::mcexit(..., send = ...)` and `parallel:::readChild`.
**Proposal**:
To resolve this, I simply decided to avoid both possibilities with this new approach here (9ff89a7859). To support this idea, I explained with some quotation of the documentation as below:
https://stat.ethz.ch/R-manual/R-devel/library/parallel/html/mcfork.html
> `readChild` and `readChildren` return a raw vector with a "pid" attribute if data were available, an integer vector of length one with the process ID if a child terminated or `NULL` if the child no longer exists (no children at all for `readChildren`).
`readChild` returns "an integer vector of length one with the process ID if a child terminated" so we can check if it is `integer` and the same selected "process ID". I believe this makes sure that the children are exited.
In case that children happen to send any data manually to parent (which is why we introduced the suspicious part of the change (466325d3fd)), this should be raw bytes and will be discarded (and then will try to read the next and check if it is `integer` in the next loop).
## How was this patch tested?
Manual tests and Jenkins tests.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#18465 from HyukjinKwon/SPARK-21093-retry-1.
## What changes were proposed in this pull request?
`mcfork` in R looks opening a pipe ahead but the existing logic does not properly close it when it is executed hot. This leads to the failure of more forking due to the limit for number of files open.
This hot execution looks particularly for `gapply`/`gapplyCollect`. For unknown reason, this happens more easily in CentOS and could be reproduced in Mac too.
All the details are described in https://issues.apache.org/jira/browse/SPARK-21093
This PR proposes simply to terminate R's worker processes in the parent of R's daemon to prevent a leak.
## How was this patch tested?
I ran the codes below on both CentOS and Mac with that configuration disabled/enabled.
```r
df <- createDataFrame(list(list(1L, 1, "1", 0.1)), c("a", "b", "c", "d"))
collect(gapply(df, "a", function(key, x) { x }, schema(df)))
collect(gapply(df, "a", function(key, x) { x }, schema(df)))
... # 30 times
```
Also, now it passes R tests on CentOS as below:
```
SparkSQL functions: Spark package found in SPARK_HOME: .../spark
..............................................................................................................................................................
..............................................................................................................................................................
..............................................................................................................................................................
..............................................................................................................................................................
..............................................................................................................................................................
....................................................................................................................................
```
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#18320 from HyukjinKwon/SPARK-21093.
## What changes were proposed in this pull request?
Move all existing tests to non-installed directory so that it will never run by installing SparkR package
For a follow-up PR:
- remove all skip_on_cran() calls in tests
- clean up test timer
- improve or change basic tests that do run on CRAN (if anyone has suggestion)
It looks like `R CMD build pkg` will still put pkg\tests (ie. the full tests) into the source package but `R CMD INSTALL` on such source package does not install these tests (and so `R CMD check` does not run them)
## How was this patch tested?
- [x] unit tests, Jenkins
- [x] AppVeyor
- [x] make a source package, install it, `R CMD check` it - verify the full tests are not installed or run
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#18264 from felixcheung/rtestset.
## What changes were proposed in this pull request?
to investigate how long they run
## How was this patch tested?
Jenkins, AppVeyor
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#18104 from felixcheung/rtimetest.
## What changes were proposed in this pull request?
Joint coefficients with intercept for SparkR linear SVM summary.
## How was this patch tested?
Existing tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#18035 from yanboliang/svm-r.
## What changes were proposed in this pull request?
This change skips tests that use the Hadoop libraries while running
on CRAN check with Windows as the operating system. This is to handle
cases where the Hadoop winutils binaries are missing on the target
system. The skipped tests consist of
1. Tests that save, load a model in MLlib
2. Tests that save, load CSV, JSON and Parquet files in SQL
3. Hive tests
## How was this patch tested?
Tested by running on a local windows VM with HADOOP_HOME unset. Also testing with https://win-builder.r-project.org
Author: Shivaram Venkataraman <shivaram@cs.berkeley.edu>
Closes#17966 from shivaram/sparkr-windows-cran.
## What changes were proposed in this pull request?
support decision tree in R
## How was this patch tested?
added tests
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#17981 from zhengruifeng/dt_r.
## What changes were proposed in this pull request?
- Adds R wrapper for `o.a.s.sql.functions.broadcast`.
- Renames `broadcast` to `broadcast_`.
## How was this patch tested?
Unit tests, check `check-cran.sh`.
Author: zero323 <zero323@users.noreply.github.com>
Closes#17965 from zero323/SPARK-20726.
## What changes were proposed in this pull request?
- [x] need to test by running R CMD check --as-cran
- [x] sanity check vignettes
## How was this patch tested?
Jenkins
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#17945 from felixcheung/rchangesforpackage.
## What changes were proposed in this pull request?
Change it to check for relative count like in this test https://github.com/apache/spark/blame/master/R/pkg/inst/tests/testthat/test_sparkSQL.R#L3355 for catalog APIs
## How was this patch tested?
unit tests, this needs to combine with another commit with SQL change to check
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#17905 from felixcheung/rtabletests.
## What changes were proposed in this pull request?
Cleaning existing temp tables before running tableNames tests
## How was this patch tested?
SparkR Unit tests
Author: Hossein <hossein@databricks.com>
Closes#17903 from falaki/SPARK-20661.
## What changes were proposed in this pull request?
set timezone on windows
## How was this patch tested?
unit test, AppVeyor
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#17892 from felixcheung/rtimestamptest.
## What changes were proposed in this pull request?
- Add SparkR wrapper for `Dataset.alias`.
- Adjust roxygen annotations for `functions.alias` (including example usage).
## How was this patch tested?
Unit tests, `check_cran.sh`.
Author: zero323 <zero323@users.noreply.github.com>
Closes#17825 from zero323/SPARK-20550.
## What changes were proposed in this pull request?
add environment
## How was this patch tested?
wait for appveyor run
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#17878 from felixcheung/appveyorrcran.
## What changes were proposed in this pull request?
Make tests more reliable by having it till processed.
Increasing timeout value might help but ultimately the flakiness from processing delay when Jenkins is hard to account for. This isn't an actual public API supported
## How was this patch tested?
unit tests
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#17857 from felixcheung/rsstestrelia.
## What changes were proposed in this pull request?
Adds wrapper for `o.a.s.sql.functions.input_file_name`
## How was this patch tested?
Existing unit tests, additional unit tests, `check-cran.sh`.
Author: zero323 <zero323@users.noreply.github.com>
Closes#17818 from zero323/SPARK-20544.
## What changes were proposed in this pull request?
Adds support for generic hints on `SparkDataFrame`
## How was this patch tested?
Unit tests, `check-cran.sh`
Author: zero323 <zero323@users.noreply.github.com>
Closes#17851 from zero323/SPARK-20585.
## What changes were proposed in this pull request?
General rule on skip or not:
skip if
- RDD tests
- tests could run long or complicated (streaming, hivecontext)
- tests on error conditions
- tests won't likely change/break
## How was this patch tested?
unit tests, `R CMD check --as-cran`, `R CMD check`
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#17817 from felixcheung/rskiptest.
## What changes were proposed in this pull request?
Adds R wrappers for:
- `o.a.s.sql.functions.grouping` as `o.a.s.sql.functions.is_grouping` (to avoid shading `base::grouping`
- `o.a.s.sql.functions.grouping_id`
## How was this patch tested?
Existing unit tests, additional unit tests. `check-cran.sh`.
Author: zero323 <zero323@users.noreply.github.com>
Closes#17807 from zero323/SPARK-20532.
## What changes were proposed in this pull request?
Add without param for timeout - will need this to submit a job that runs until stopped
Need this for 2.2
## How was this patch tested?
manually, unit test
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#17815 from felixcheung/rssawaitinfinite.
## What changes were proposed in this pull request?
- Add null-safe equality operator `%<=>%` (sames as `o.a.s.sql.Column.eqNullSafe`, `o.a.s.sql.Column.<=>`)
- Add boolean negation operator `!` and function `not `.
## How was this patch tested?
Existing unit tests, additional unit tests, `check-cran.sh`.
Author: zero323 <zero323@users.noreply.github.com>
Closes#17783 from zero323/SPARK-20490.
## What changes were proposed in this pull request?
Ad R wrappers for
- `o.a.s.sql.functions.explode_outer`
- `o.a.s.sql.functions.posexplode_outer`
## How was this patch tested?
Additional unit tests, manual testing.
Author: zero323 <zero323@users.noreply.github.com>
Closes#17809 from zero323/SPARK-20535.
## What changes were proposed in this pull request?
It seems we are using `SQLUtils.getSQLDataType` for type string in structField. It looks we can replace this with `CatalystSqlParser.parseDataType`.
They look similar DDL-like type definitions as below:
```scala
scala> Seq(Tuple1(Tuple1("a"))).toDF.show()
```
```
+---+
| _1|
+---+
|[a]|
+---+
```
```scala
scala> Seq(Tuple1(Tuple1("a"))).toDF.select($"_1".cast("struct<_1:string>")).show()
```
```
+---+
| _1|
+---+
|[a]|
+---+
```
Such type strings looks identical when R’s one as below:
```R
> write.df(sql("SELECT named_struct('_1', 'a') as struct"), "/tmp/aa", "parquet")
> collect(read.df("/tmp/aa", "parquet", structType(structField("struct", "struct<_1:string>"))))
struct
1 a
```
R’s one is stricter because we are checking the types via regular expressions in R side ahead.
Actual logics there look a bit different but as we check it ahead in R side, it looks replacing it would not introduce (I think) no behaviour changes. To make this sure, the tests dedicated for it were added in SPARK-20105. (It looks `structField` is the only place that calls this method).
## How was this patch tested?
Existing tests - https://github.com/apache/spark/blob/master/R/pkg/inst/tests/testthat/test_sparkSQL.R#L143-L194 should cover this.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#17785 from HyukjinKwon/SPARK-20493.
## What changes were proposed in this pull request?
Some PySpark & SparkR tests run with tiny dataset and tiny ```maxIter```, which means they are not converged. I don’t think checking intermediate result during iteration make sense, and these intermediate result may vulnerable and not stable, so we should switch to check the converged result. We hit this issue at #17746 when we upgrade breeze to 0.13.1.
## How was this patch tested?
Existing tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#17757 from yanboliang/flaky-test.
## What changes were proposed in this pull request?
- Add `rollup` and `cube` methods and corresponding generics.
- Add short description to the vignette.
## How was this patch tested?
- Existing unit tests.
- Additional unit tests covering new features.
- `check-cran.sh`.
Author: zero323 <zero323@users.noreply.github.com>
Closes#17728 from zero323/SPARK-20437.
## What changes were proposed in this pull request?
Upgrade breeze version to 0.13.1, which fixed some critical bugs of L-BFGS-B.
## How was this patch tested?
Existing unit tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#17746 from yanboliang/spark-20449.
## What changes were proposed in this pull request?
Add wrappers for `o.a.s.sql.functions`:
- `split` as `split_string`
- `repeat` as `repeat_string`
## How was this patch tested?
Existing tests, additional unit tests, `check-cran.sh`
Author: zero323 <zero323@users.noreply.github.com>
Closes#17729 from zero323/SPARK-20438.
## What changes were proposed in this pull request?
Adds wrappers for `collect_list` and `collect_set`.
## How was this patch tested?
Unit tests, `check-cran.sh`
Author: zero323 <zero323@users.noreply.github.com>
Closes#17672 from zero323/SPARK-20371.
## What changes were proposed in this pull request?
Adds wrappers for `o.a.s.sql.functions.array` and `o.a.s.sql.functions.map`
## How was this patch tested?
Unit tests, `check-cran.sh`
Author: zero323 <zero323@users.noreply.github.com>
Closes#17674 from zero323/SPARK-20375.
## What changes were proposed in this pull request?
Checking a source parameter is asynchronous. When the query is created, it's not guaranteed that source has been created. This PR just increases the timeout of awaitTermination to ensure the parsing error is thrown.
## How was this patch tested?
Jenkins
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#17687 from zsxwing/SPARK-20397.
## What changes were proposed in this pull request?
This was suggested to be `as.json.array` at the first place in the PR to SPARK-19828 but we could not do this as the lint check emits an error for multiple dots in the variable names.
After SPARK-20278, now we are able to use `multiple.dots.in.names`. `asJsonArray` in `from_json` function is still able to be changed as 2.2 is not released yet.
So, this PR proposes to rename `asJsonArray` to `as.json.array`.
## How was this patch tested?
Jenkins tests, local tests with `./R/run-tests.sh` and manual `./dev/lint-r`. Existing tests should cover this.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#17653 from HyukjinKwon/SPARK-19828-followup.
## What changes were proposed in this pull request?
Fixed spelling of "charactor"
## How was this patch tested?
Spelling change only
Author: Brendan Dwyer <brendan.dwyer@ibm.com>
Closes#17611 from bdwyer2/SPARK-20298.
## What changes were proposed in this pull request?
Following up on #17483, add createTable (which is new in 2.2.0) and deprecate createExternalTable, plus a number of minor fixes
## How was this patch tested?
manual, unit tests
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#17511 from felixcheung/rceatetable.
## What changes were proposed in this pull request?
Adds SparkR API for FPGrowth: [SPARK-19825](https://issues.apache.org/jira/browse/SPARK-19825):
- `spark.fpGrowth` -model training.
- `freqItemsets` and `associationRules` methods with new corresponding generics.
- Scala helper: `org.apache.spark.ml.r. FPGrowthWrapper`
- unit tests.
## How was this patch tested?
Feature specific unit tests.
Author: zero323 <zero323@users.noreply.github.com>
Closes#17170 from zero323/SPARK-19825.
## What changes were proposed in this pull request?
Add a set of catalog API in R
```
"currentDatabase",
"listColumns",
"listDatabases",
"listFunctions",
"listTables",
"recoverPartitions",
"refreshByPath",
"refreshTable",
"setCurrentDatabase",
```
https://github.com/apache/spark/pull/17483/files#diff-6929e6c5e59017ff954e110df20ed7ff
## How was this patch tested?
manual tests, unit tests
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#17483 from felixcheung/rcatalog.
## What changes were proposed in this pull request?
It seems `checkType` and the type string in `structField` are not being tested closely. This string format currently seems SparkR-specific (see d1f6c64c4b/sql/core/src/main/scala/org/apache/spark/sql/api/r/SQLUtils.scala (L93-L131)) but resembles SQL type definition.
Therefore, it seems nicer if we test positive/negative cases in R side.
## How was this patch tested?
Unit tests in `test_sparkSQL.R`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#17439 from HyukjinKwon/r-typestring-tests.
