## 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?
changes in vignette only to disable eval
## How was this patch tested?
Jenkins
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#23007 from felixcheung/rjavavervig.
## What changes were proposed in this pull request?
- Remove some AccumulableInfo .apply() methods
- Remove non-label-specific multiclass precision/recall/fScore in favor of accuracy
- Remove toDegrees/toRadians in favor of degrees/radians (SparkR: only deprecated)
- Remove approxCountDistinct in favor of approx_count_distinct (SparkR: only deprecated)
- Remove unused Python StorageLevel constants
- Remove Dataset unionAll in favor of union
- Remove unused multiclass option in libsvm parsing
- Remove references to deprecated spark configs like spark.yarn.am.port
- Remove TaskContext.isRunningLocally
- Remove ShuffleMetrics.shuffle* methods
- Remove BaseReadWrite.context in favor of session
- Remove Column.!== in favor of =!=
- Remove Dataset.explode
- Remove Dataset.registerTempTable
- Remove SQLContext.getOrCreate, setActive, clearActive, constructors
Not touched yet
- everything else in MLLib
- HiveContext
- Anything deprecated more recently than 2.0.0, generally
## How was this patch tested?
Existing tests
Closes#22921 from srowen/SPARK-25908.
Lead-authored-by: Sean Owen <sean.owen@databricks.com>
Co-authored-by: hyukjinkwon <gurwls223@apache.org>
Co-authored-by: Sean Owen <srowen@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
New functions takes a struct and converts it to a CSV strings using passed CSV options. It accepts the same CSV options as CSV data source does.
## How was this patch tested?
Added `CsvExpressionsSuite`, `CsvFunctionsSuite` as well as R, Python and SQL tests similar to tests for `to_json()`
Closes#22626 from MaxGekk/to_csv.
Lead-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Remove deprecated functions which includes:
SQLContext/HiveContext stuff
sparkR.init
jsonFile
parquetFile
registerTempTable
saveAsParquetFile
unionAll
createExternalTable
dropTempTable
## How was this patch tested?
jenkins
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#22843 from felixcheung/rrddapi.
## What changes were proposed in this pull request?
Remove SQLContext methods deprecated in 1.4
## How was this patch tested?
Existing tests.
Closes#22815 from srowen/SPARK-25821.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Check the `spark.sql.repl.eagerEval.enabled` configuration property in SparkDataFrame `show()` method. If the `SparkSession` has eager execution enabled, the data will be returned to the R client when the data frame is created. So instead of seeing this
```
> df <- createDataFrame(faithful)
> df
SparkDataFrame[eruptions:double, waiting:double]
```
you will see
```
> df <- createDataFrame(faithful)
> df
+---------+-------+
|eruptions|waiting|
+---------+-------+
| 3.6| 79.0|
| 1.8| 54.0|
| 3.333| 74.0|
| 2.283| 62.0|
| 4.533| 85.0|
| 2.883| 55.0|
| 4.7| 88.0|
| 3.6| 85.0|
| 1.95| 51.0|
| 4.35| 85.0|
| 1.833| 54.0|
| 3.917| 84.0|
| 4.2| 78.0|
| 1.75| 47.0|
| 4.7| 83.0|
| 2.167| 52.0|
| 1.75| 62.0|
| 4.8| 84.0|
| 1.6| 52.0|
| 4.25| 79.0|
+---------+-------+
only showing top 20 rows
```
## How was this patch tested?
Manual tests as well as unit tests (one new test case is added).
Author: adrian555 <v2ave10p>
Closes#22455 from adrian555/eager_execution.
## What changes were proposed in this pull request?
In the PR, I propose to switch `from_json` on `FailureSafeParser`, and to make the function compatible to `PERMISSIVE` mode by default, and to support the `FAILFAST` mode as well. The `DROPMALFORMED` mode is not supported by `from_json`.
## How was this patch tested?
It was tested by existing `JsonSuite`/`CSVSuite`, `JsonFunctionsSuite` and `JsonExpressionsSuite` as well as new tests for `from_json` which checks different modes.
Closes#22237 from MaxGekk/from_json-failuresafe.
Lead-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Co-authored-by: hyukjinkwon <gurwls223@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
add R API for PrefixSpan
## How was this patch tested?
add test in test_mllib_fpm.R
Author: Huaxin Gao <huaxing@us.ibm.com>
Closes#21710 from huaxingao/spark-24207.
## What changes were proposed in this pull request?
The PR adds new function `from_csv()` similar to `from_json()` to parse columns with CSV strings. I added the following methods:
```Scala
def from_csv(e: Column, schema: StructType, options: Map[String, String]): Column
```
and this signature to call it from Python, R and Java:
```Scala
def from_csv(e: Column, schema: String, options: java.util.Map[String, String]): Column
```
## How was this patch tested?
Added new test suites `CsvExpressionsSuite`, `CsvFunctionsSuite` and sql tests.
Closes#22379 from MaxGekk/from_csv.
Lead-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Co-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Hyukjin Kwon <gurwls223@gmail.com>
Co-authored-by: hyukjinkwon <gurwls223@apache.org>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Adds support for the setting limit in the sql split function
## How was this patch tested?
1. Updated unit tests
2. Tested using Scala spark shell
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#22227 from phegstrom/master.
Authored-by: Parker Hegstrom <phegstrom@palantir.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This patch is to bump the master branch version to 3.0.0-SNAPSHOT.
## How was this patch tested?
N/A
Closes#22606 from gatorsmile/bump3.0.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
CRAN doesn't seem to respect the system requirements as running tests - we have seen cases where SparkR is run on Java 10, which unfortunately Spark does not start on. For 2.4, lets attempt skipping all tests
## How was this patch tested?
manual, jenkins, appveyor
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#22589 from felixcheung/ralltests.
## What changes were proposed in this pull request?
We have an agreement that the behavior of `from/to_utc_timestamp` is corrected, although the function itself doesn't make much sense in Spark: https://issues.apache.org/jira/browse/SPARK-23715
This PR improves the document.
## How was this patch tested?
N/A
Closes#22543 from cloud-fan/doc.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
SparkSubmit already logs in the user if a keytab is provided, the only issue is that it uses the existing configs which have "yarn" in their name. As such, the configs were changed to:
`spark.kerberos.keytab` and `spark.kerberos.principal`.
## How was this patch tested?
Will be tested with K8S tests, but needs to be tested with Yarn
- [x] K8S Secure HDFS tests
- [x] Yarn Secure HDFS tests vanzin
Closes#22362 from ifilonenko/SPARK-25372.
Authored-by: Ilan Filonenko <if56@cornell.edu>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
The PR introduces new JSON option `pretty` which allows to turn on `DefaultPrettyPrinter` of `Jackson`'s Json generator. New option is useful in exploring of deep nested columns and in converting of JSON columns in more readable representation (look at the added test).
## How was this patch tested?
Added rount trip test which convert an JSON string to pretty representation via `from_json()` and `to_json()`.
Closes#22534 from MaxGekk/pretty-json.
Lead-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Co-authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
add R partitionBy API in write.df
I didn't add bucketBy in write.df. The last line of write.df is
```
write <- handledCallJMethod(write, "save")
```
save doesn't support bucketBy right now.
```
assertNotBucketed("save")
```
## How was this patch tested?
Add unit test in test_sparkSQL.R
Closes#22537 from huaxingao/spark-21291.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Addition of numeric and list hints for SparkR.
## How was this patch tested?
Add test in test_sparkSQL.R
Author: Huaxin Gao <huaxing@us.ibm.com>
Closes#21649 from huaxingao/spark-23648.
Continuation of #22370. Summary of discussion there:
There is some inconsistency in the R manual w.r.t. supercedent functions linking back to deprecated functions.
- `createOrReplaceTempView` and `createTable` both link back to functions which are deprecated (`registerTempTable` and `createExternalTable`, respectively)
- `sparkR.session` and `dropTempView` do _not_ link back to deprecated functions
This PR takes the view that it is preferable _not_ to link back to deprecated functions, and removes these references from `?createOrReplaceTempView` and `?createTable`.
As `registerTempTable` was included in the `SparkDataFrame functions` `family` of functions, other documentation pages which included a link to `?registerTempTable` will similarly be altered.
Author: Michael Chirico <michael.chirico@grabtaxi.com>
Author: Michael Chirico <michaelchirico4@gmail.com>
Closes#22393 from MichaelChirico/axe_deprecated_doc_refs.
## What changes were proposed in this pull request?
In the dev list, we can still discuss whether the next version is 2.5.0 or 3.0.0. Let us first bump the master branch version to `2.5.0-SNAPSHOT`.
## How was this patch tested?
N/A
Closes#22426 from gatorsmile/bumpVersionMaster.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
In the PR, I propose to extended `to_json` and support any types as element types of input arrays. It should allow converting arrays of primitive types and arrays of arrays. For example:
```
select to_json(array('1','2','3'))
> ["1","2","3"]
select to_json(array(array(1,2,3),array(4)))
> [[1,2,3],[4]]
```
## How was this patch tested?
Added a couple sql tests for arrays of primitive type and of arrays. Also I added round trip test `from_json` -> `to_json`.
Closes#22226 from MaxGekk/to_json-array.
Authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
The "date function on DataFrame" test fails consistently on my laptop. In this PR
i am fixing it by changing the way we compare the two timestamp values. With this change i am able to run the tests clean.
## How was this patch tested?
Fixed the failing test.
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#22274 from dilipbiswal/r-sql-test-fix2.
## What changes were proposed in this pull request?
Add the R version of array_intersect/array_except/array_union/shuffle
## How was this patch tested?
Add test in test_sparkSQL.R
Author: Huaxin Gao <huaxing@us.ibm.com>
Closes#22291 from huaxingao/spark-25007.
## What changes were proposed in this pull request?
The PR adds the lift measure to Association rules.
## How was this patch tested?
existing and modified UTs
Closes#22236 from mgaido91/SPARK-10697.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
`parallelize` uses integer multiplication to determine the split indices. It might cause integer overflow.
## How was this patch tested?
unit test
Closes#22225 from mengxr/SPARK-25234.
Authored-by: Xiangrui Meng <meng@databricks.com>
Signed-off-by: Xiangrui Meng <meng@databricks.com>
## What changes were proposed in this pull request?
A few SQL tests for R were failing in my development environment. In this PR, i am attempting to
address some of them. Below are the reasons for the failure.
- The catalog api tests assumes catalog artifacts named "foo" to be non existent. I think name such as foo and bar are common and i use it frequently. I have changed it to a string that i hope is less likely to collide.
- One test assumes that we only have one database in the system. I had more than one and it caused the test to fail. I have changed that check.
- One more test which compares two timestamp values fail - i am debugging this now. I will send it as a followup - may be.
## How was this patch tested?
Its a test fix.
Closes#22161 from dilipbiswal/r-sql-test-fix1.
Authored-by: Dilip Biswal <dbiswal@us.ibm.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In MultilayerPerceptronClassifier, we use RDD operation to encode labels for now. I think we should use ML's OneHotEncoderEstimator/Model to do the encoding.
## How was this patch tested?
Existing tests.
Closes#20232 from viirya/SPARK-23042.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: DB Tsai <d_tsai@apple.com>
## What changes were proposed in this pull request?
[SPARK-21274](https://issues.apache.org/jira/browse/SPARK-21274) added support for EXCEPT ALL and INTERSECT ALL. This PR adds the support in R.
## How was this patch tested?
Added test in test_sparkSQL.R
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#22107 from dilipbiswal/SPARK-25117.
## 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?
In the PR, I propose to output an warning if the `addFile()` or `addJar()` methods are callled more than once for the same path. Currently, overwriting of already added files is not supported. New comments and warning are reflected the existing behaviour.
Author: Maxim Gekk <maxim.gekk@databricks.com>
Closes#21771 from MaxGekk/warning-on-adding-file.
## What changes were proposed in this pull request?
Add array_remove / array_zip / map_from_arrays / array_distinct functions in SparkR.
## How was this patch tested?
Add tests in test_sparkSQL.R
Author: Huaxin Gao <huaxing@us.ibm.com>
Closes#21645 from huaxingao/spark-24537.
## What changes were proposed in this pull request?
Add model predictions for Linear Support Vector Machine (SVM) Classifier, Logistic Regression, GBT, RF and DecisionTree in vignettes.
## How was this patch tested?
Manually ran the test and checked the result.
Author: Huaxin Gao <huaxing@us.ibm.com>
Closes#21678 from huaxingao/spark-23461.
## 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.
## What changes were proposed in this pull request?
This PR adds array_join function to SparkR
## How was this patch tested?
Add unit test in test_sparkSQL.R
Author: Huaxin Gao <huaxing@us.ibm.com>
Closes#21313 from huaxingao/spark-24187.
## What changes were proposed in this pull request?
change generic to get it to work with googleVis
also fix lintr
## How was this patch tested?
manual test, unit tests
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#21315 from felixcheung/googvis.
## What changes were proposed in this pull request?
Change text to grep for.
## How was this patch tested?
manual test
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#21314 from felixcheung/openjdkver.
## What changes were proposed in this pull request?
reverse and concat are already in functions.R as column string functions. Since now these two functions are categorized as collection functions in scala and python, we will do the same in R.
## How was this patch tested?
Add test in test_sparkSQL.R
Author: Huaxin Gao <huaxing@us.ibm.com>
Closes#21307 from huaxingao/spark_24186.
## What changes were proposed in this pull request?
The PR adds the `slice` function to SparkR. The function returns a subset of consecutive elements from the given array.
```
> df <- createDataFrame(cbind(model = rownames(mtcars), mtcars))
> tmp <- mutate(df, v1 = create_array(df$mpg, df$cyl, df$hp))
> head(select(tmp, slice(tmp$v1, 2L, 2L)))
```
```
slice(v1, 2, 2)
1 6, 110
2 6, 110
3 4, 93
4 6, 110
5 8, 175
6 6, 105
```
## How was this patch tested?
A test added into R/pkg/tests/fulltests/test_sparkSQL.R
Author: Marek Novotny <mn.mikke@gmail.com>
Closes#21298 from mn-mikke/SPARK-24198.
This change updates the SystemRequirements and also includes a runtime check if the JVM is being launched by R. The runtime check is done by querying `java -version`
## How was this patch tested?
Tested on a Mac and Windows machine
Author: Shivaram Venkataraman <shivaram@cs.berkeley.edu>
Closes#21278 from shivaram/sparkr-skip-solaris.
## What changes were proposed in this pull request?
It's useful to know what relationship between date1 and date2 results in a positive number.
Author: aditkumar <aditkumar@gmail.com>
Author: Adit Kumar <aditkumar@gmail.com>
Closes#20787 from aditkumar/master.
## What changes were proposed in this pull request?
The PR adds array_sort function to SparkR.
## How was this patch tested?
Tests added into R/pkg/tests/fulltests/test_sparkSQL.R
## Example
```
> df <- createDataFrame(list(list(list(2L, 1L, 3L, NA)), list(list(NA, 6L, 5L, NA, 4L))))
> head(collect(select(df, array_sort(df[[1]]))))
```
Result:
```
array_sort(_1)
1 1, 2, 3, NA
2 4, 5, 6, NA, NA
```
Author: Marek Novotny <mn.mikke@gmail.com>
Closes#21294 from mn-mikke/SPARK-24197.
## What changes were proposed in this pull request?
I propose to add a clear statement for functions like `collect_list()` about non-deterministic behavior of such functions. The behavior must be taken into account by user while creating and running queries.
Author: Maxim Gekk <maxim.gekk@databricks.com>
Closes#21228 from MaxGekk/deterministic-comments.
## What changes were proposed in this pull request?
Mention `spark.sql.crossJoin.enabled` in error message when an implicit `CROSS JOIN` is detected.
## How was this patch tested?
`CartesianProductSuite` and `JoinSuite`.
Author: Henry Robinson <henry@apache.org>
Closes#21201 from henryr/spark-24128.
## What changes were proposed in this pull request?
add array flatten function to SparkR
## How was this patch tested?
Unit tests were added in R/pkg/tests/fulltests/test_sparkSQL.R
Author: Huaxin Gao <huaxing@us.ibm.com>
Closes#21244 from huaxingao/spark-24185.
## What changes were proposed in this pull request?
The lint failure bugged me:
```R
R/SQLContext.R:715:97: style: Trailing whitespace is superfluous.
#' file-based streaming data source. \code{timeZone} to indicate a timezone to be used to
^
tests/fulltests/test_streaming.R:239:45: style: Commas should always have a space after.
expect_equal(times[order(times$eventTime),][1, 2], 2)
^
lintr checks failed.
```
and I actually saw https://amplab.cs.berkeley.edu/jenkins/job/spark-master-test-sbt-hadoop-2.6-ubuntu-test/500/console too. If I understood correctly, there is a try about moving to Unbuntu one.
## How was this patch tested?
Manually tested by `./dev/lint-r`:
```
...
lintr checks passed.
```
Author: hyukjinkwon <gurwls223@apache.org>
Closes#20879 from HyukjinKwon/minor-r-lint.
## What changes were proposed in this pull request?
Seems R's substr API treats Scala substr API as zero based and so subtracts the given starting position by 1.
Because Scala's substr API also accepts zero-based starting position (treated as the first element), so the current R's substr test results are correct as they all use 1 as starting positions.
## How was this patch tested?
Modified tests.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#20464 from viirya/SPARK-23291.
## What changes were proposed in this pull request?
Removed export tag to get rid of unknown tag warnings
## How was this patch tested?
Existing tests
Author: Rekha Joshi <rekhajoshm@gmail.com>
Author: rjoshi2 <rekhajoshm@gmail.com>
Closes#20501 from rekhajoshm/SPARK-22430.
## What changes were proposed in this pull request?
Provide more details in trigonometric function documentations. Referenced `java.lang.Math` for further details in the descriptions.
## How was this patch tested?
Ran full build, checked generated documentation manually
Author: Mihaly Toth <misutoth@gmail.com>
Closes#20618 from misutoth/trigonometric-doc.
## What changes were proposed in this pull request?
https://github.com/apache/spark/pull/18944 added one patch, which allowed a spark session to be created when the hive metastore server is down. However, it did not allow running any commands with the spark session. This brings troubles to the user who only wants to read / write data frames without metastore setup.
## How was this patch tested?
Added some unit tests to read and write data frames based on the original HiveMetastoreLazyInitializationSuite.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Feng Liu <fengliu@databricks.com>
Closes#20681 from liufengdb/completely-lazy.
## What changes were proposed in this pull request?
Fix doc link that was changed in 2.3
shivaram
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#20711 from felixcheung/rvigmean.
## What changes were proposed in this pull request?
Update the description and tests of three external API or functions `createFunction `, `length` and `repartitionByRange `
## How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#20495 from gatorsmile/updateFunc.
## What changes were proposed in this pull request?
It's not obvious from the comments that any added column must be a
function of the dataset that we are adding it to. Add a comment to
that effect to Scala, Python and R Data* methods.
Author: Henry Robinson <henry@cloudera.com>
Closes#20429 from henryr/SPARK-23157.
## What changes were proposed in this pull request?
doc only changes
## How was this patch tested?
manual
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#20380 from felixcheung/rclrdoc.
## What changes were proposed in this pull request?
A fix to https://issues.apache.org/jira/browse/SPARK-21727, "Operating on an ArrayType in a SparkR DataFrame throws error"
## How was this patch tested?
- Ran tests at R\pkg\tests\run-all.R (see below attached results)
- Tested the following lines in SparkR, which now seem to execute without error:
```
indices <- 1:4
myDf <- data.frame(indices)
myDf$data <- list(rep(0, 20))
mySparkDf <- as.DataFrame(myDf)
collect(mySparkDf)
```
[2018-01-22 SPARK-21727 Test Results.txt](https://github.com/apache/spark/files/1653535/2018-01-22.SPARK-21727.Test.Results.txt)
felixcheung yanboliang sun-rui shivaram
_The contribution is my original work and I license the work to the project under the project’s open source license_
Author: neilalex <neil@neilalex.com>
Closes#20352 from neilalex/neilalex-sparkr-arraytype.
## What changes were proposed in this pull request?
Make the default behavior of EXCEPT (i.e. EXCEPT DISTINCT) more
explicit in the documentation, and call out the change in behavior
from 1.x.
Author: Henry Robinson <henry@cloudera.com>
Closes#20254 from henryr/spark-23062.
## What changes were proposed in this pull request?
RFormula should use VectorSizeHint & OneHotEncoderEstimator in its pipeline to avoid using the deprecated OneHotEncoder & to ensure the model produced can be used in streaming.
## How was this patch tested?
Unit tests.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Bago Amirbekian <bago@databricks.com>
Closes#20229 from MrBago/rFormula.
## What changes were proposed in this pull request?
fix doc truncated
## How was this patch tested?
manually
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#20263 from felixcheung/r23docfix.
## What changes were proposed in this pull request?
This patch bumps the master branch version to `2.4.0-SNAPSHOT`.
## How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#20222 from gatorsmile/bump24.
## What changes were proposed in this pull request?
Including VectorSizeHint in RFormula piplelines will allow them to be applied to streaming dataframes.
## How was this patch tested?
Unit tests.
Author: Bago Amirbekian <bago@databricks.com>
Closes#20238 from MrBago/rFormulaVectorSize.
## What changes were proposed in this pull request?
Add a note to the `HasCheckpointInterval` parameter doc that clarifies that this setting is ignored when no checkpoint directory has been set on the spark context.
## How was this patch tested?
No tests necessary, just a doc update.
Author: sethah <shendrickson@cloudera.com>
Closes#20188 from sethah/als_checkpoint_doc.
## What changes were proposed in this pull request?
R Structured Streaming API for withWatermark, trigger, partitionBy
## How was this patch tested?
manual, unit tests
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#20129 from felixcheung/rwater.
## What changes were proposed in this pull request?
update R migration guide and vignettes
## How was this patch tested?
manually
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#20106 from felixcheung/rreleasenote23.
## What changes were proposed in this pull request?
Add to `arrange` the option to sort only within partition
## How was this patch tested?
manual, unit tests
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#20118 from felixcheung/rsortwithinpartition.
## What changes were proposed in this pull request?
This pr modified `concat` to concat binary inputs into a single binary output.
`concat` in the current master always output data as a string. But, in some databases (e.g., PostgreSQL), if all inputs are binary, `concat` also outputs binary.
## How was this patch tested?
Added tests in `SQLQueryTestSuite` and `TypeCoercionSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#19977 from maropu/SPARK-22771.
## What changes were proposed in this pull request?
Add sql functions
## How was this patch tested?
manual, unit tests
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#20105 from felixcheung/rsqlfuncs.
## What changes were proposed in this pull request?
This PR proposes to add `localCheckpoint(..)` in R API.
```r
df <- localCheckpoint(createDataFrame(iris))
```
## How was this patch tested?
Unit tests added in `R/pkg/tests/fulltests/test_sparkSQL.R`
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#20073 from HyukjinKwon/SPARK-22843.
## What changes were proposed in this pull request?
Since all CRAN checks go through the same machine, if there is an older partial download or partial install of Spark left behind the tests fail. This PR overwrites the install files when running tests. This shouldn't affect Jenkins as `SPARK_HOME` is set when running Jenkins tests.
## How was this patch tested?
Test manually by running `R CMD check --as-cran`
Author: Shivaram Venkataraman <shivaram@cs.berkeley.edu>
Closes#20060 from shivaram/sparkr-overwrite-cran.
## What changes were proposed in this pull request?
This PR adds `date_trunc` in R API as below:
```r
> df <- createDataFrame(list(list(a = as.POSIXlt("2012-12-13 12:34:00"))))
> head(select(df, date_trunc("hour", df$a)))
date_trunc(hour, a)
1 2012-12-13 12:00:00
```
## How was this patch tested?
Unit tests added in `R/pkg/tests/fulltests/test_sparkSQL.R`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#20031 from HyukjinKwon/r-datetrunc.
## What changes were proposed in this pull request?
This is a followup to reduce AppVeyor test time. This PR proposes to reduce the number of shuffle partitions to reduce the tasks running R workers in few particular tests.
The symptom is similar as described in `https://github.com/apache/spark/pull/19722`. There are many R processes newly launched on Windows without forking and it makes the differences of elapsed time between Linux and Windows.
Here is the simple comparison for before/after of this change. I manually tested this by disabling `spark.sparkr.use.daemon`. Disabling it resembles the tests on Windows:
**Before**
<img width="672" alt="2017-11-25 12 22 13" src="https://user-images.githubusercontent.com/6477701/33217949-b5528dfa-d17d-11e7-8050-75675c39eb20.png">
**After**
<img width="682" alt="2017-11-25 12 32 00" src="https://user-images.githubusercontent.com/6477701/33217958-c6518052-d17d-11e7-9f8e-1be21a784559.png">
So, this probably will reduce roughly more than 10 minutes.
## How was this patch tested?
AppVeyor tests
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#19816 from HyukjinKwon/SPARK-21693-followup.
## What changes were proposed in this pull request?
This PR proposes to reduce max iteration in Linear SVM test in SparkR. This particular test elapses roughly 5 mins on my Mac and over 20 mins on Windows.
The root cause appears, it triggers 2500ish jobs by the default 100 max iterations. In Linux, `daemon.R` is forked but on Windows another process is launched, which is extremely slow.
So, given my observation, there are many processes (not forked) ran on Windows, which makes the differences of elapsed time.
After reducing the max iteration to 10, the total jobs in this single test is reduced to 550ish.
After reducing the max iteration to 5, the total jobs in this single test is reduced to 360ish.
## How was this patch tested?
Manually tested the elapsed times.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#19722 from HyukjinKwon/SPARK-21693-test.
## What changes were proposed in this pull request?
The current internal `table()` API of `SparkSession` bypasses the Analyzer and directly calls `sessionState.catalog.lookupRelation` API. This skips the view resolution logics in our Analyzer rule `ResolveRelations`. This internal API is widely used by various DDL commands, public and internal APIs.
Users might get the strange error caused by view resolution when the default database is different.
```
Table or view not found: t1; line 1 pos 14
org.apache.spark.sql.AnalysisException: Table or view not found: t1; line 1 pos 14
at org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42)
```
This PR is to fix it by enforcing it to use `ResolveRelations` to resolve the table.
## How was this patch tested?
Added a test case and modified the existing test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes#19713 from gatorsmile/viewResolution.
## What changes were proposed in this pull request?
This PR adds `dayofweek` to R API:
```r
data <- list(list(d = as.Date("2012-12-13")),
list(d = as.Date("2013-12-14")),
list(d = as.Date("2014-12-15")))
df <- createDataFrame(data)
collect(select(df, dayofweek(df$d)))
```
```
dayofweek(d)
1 5
2 7
3 2
```
## How was this patch tested?
Manual tests and unit tests in `R/pkg/tests/fulltests/test_sparkSQL.R`
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#19706 from HyukjinKwon/add-dayofweek.
## What changes were proposed in this pull request?
remove spark if spark downloaded & installed
## How was this patch tested?
manually by building package
Jenkins, AppVeyor
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#19657 from felixcheung/rinstalldir.
## What changes were proposed in this pull request?
This PR proposes to add `errorifexists` to SparkR API and fix the rest of them describing the mode, mainly, in API documentations as well.
This PR also replaces `convertToJSaveMode` to `setWriteMode` so that string as is is passed to JVM and executes:
b034f2565f/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala (L72-L82)
and remove the duplication here:
3f958a9992/sql/core/src/main/scala/org/apache/spark/sql/api/r/SQLUtils.scala (L187-L194)
## How was this patch tested?
Manually checked the built documentation. These were mainly found by `` grep -r `error` `` and `grep -r 'error'`.
Also, unit tests added in `test_sparkSQL.R`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#19673 from HyukjinKwon/SPARK-21640-followup.
## What changes were proposed in this pull request?
This is to fix the code for the latest R changes in R-devel, when running CRAN check
```
checking for code/documentation mismatches ... WARNING
Codoc mismatches from documentation object 'attach':
attach
Code: function(what, pos = 2L, name = deparse(substitute(what),
backtick = FALSE), warn.conflicts = TRUE)
Docs: function(what, pos = 2L, name = deparse(substitute(what)),
warn.conflicts = TRUE)
Mismatches in argument default values:
Name: 'name' Code: deparse(substitute(what), backtick = FALSE) Docs: deparse(substitute(what))
Codoc mismatches from documentation object 'glm':
glm
Code: function(formula, family = gaussian, data, weights, subset,
na.action, start = NULL, etastart, mustart, offset,
control = list(...), model = TRUE, method = "glm.fit",
x = FALSE, y = TRUE, singular.ok = TRUE, contrasts =
NULL, ...)
Docs: function(formula, family = gaussian, data, weights, subset,
na.action, start = NULL, etastart, mustart, offset,
control = list(...), model = TRUE, method = "glm.fit",
x = FALSE, y = TRUE, contrasts = NULL, ...)
Argument names in code not in docs:
singular.ok
Mismatches in argument names:
Position: 16 Code: singular.ok Docs: contrasts
Position: 17 Code: contrasts Docs: ...
```
With attach, it's pulling in the function definition from base::attach. We need to disable that but we would still need a function signature for roxygen2 to build with.
With glm it's pulling in the function definition (ie. "usage") from the stats::glm function. Since this is "compiled in" when we build the source package into the .Rd file, when it changes at runtime or in CRAN check it won't match the latest signature. The solution is not to pull in from stats::glm since there isn't much value in doing that (none of the param we actually use, the ones we do use we have explicitly documented them)
Also with attach we are changing to call dynamically.
## How was this patch tested?
Manually.
- [x] check documentation output - yes
- [x] check help `?attach` `?glm` - yes
- [x] check on other platforms, r-hub, on r-devel etc..
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#19557 from felixcheung/rattachglmdocerror.
## What changes were proposed in this pull request?
This PR adds a check between the R package version used and the version reported by SparkContext running in the JVM. The goal here is to warn users when they have a R package downloaded from CRAN and are using that to connect to an existing Spark cluster.
This is raised as a warning rather than an error as users might want to use patch versions interchangeably (e.g., 2.1.3 with 2.1.2 etc.)
## How was this patch tested?
Manually by changing the `DESCRIPTION` file
Author: Shivaram Venkataraman <shivaram@cs.berkeley.edu>
Closes#19624 from shivaram/sparkr-version-check.
## What changes were proposed in this pull request?
Will need to port to this to branch-1.6, -2.0, -2.1, -2.2
## How was this patch tested?
manually
Jenkins, AppVeyor
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#19549 from felixcheung/rcranversioncheck.
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?
This PR proposes to revive `stringsAsFactors` option in collect API, which was mistakenly removed in 71a138cd0e.
Simply, it casts `charactor` to `factor` if it meets the condition, `stringsAsFactors && is.character(vec)` in primitive type conversion.
## How was this patch tested?
Unit test in `R/pkg/tests/fulltests/test_sparkSQL.R`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#19551 from HyukjinKwon/SPARK-17902.
## What changes were proposed in this pull request?
Currently percentile_approx never returns the first element when percentile is in (relativeError, 1/N], where relativeError default 1/10000, and N is the total number of elements. But ideally, percentiles in [0, 1/N] should all return the first element as the answer.
For example, given input data 1 to 10, if a user queries 10% (or even less) percentile, it should return 1, because the first value 1 already reaches 10%. Currently it returns 2.
Based on the paper, targetError is not rounded up, and searching index should start from 0 instead of 1. By following the paper, we should be able to fix the cases mentioned above.
## How was this patch tested?
Added a new test case and fix existing test cases.
Author: Zhenhua Wang <wzh_zju@163.com>
Closes#19438 from wzhfy/improve_percentile_approx.
## What changes were proposed in this pull request?
Looks like `FlatMapGroupsInRExec.requiredChildDistribution` didn't consider empty grouping attributes. It should be a problem when running `EnsureRequirements` and `gapply` in R can't work on empty grouping columns.
## How was this patch tested?
Added test.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#19436 from viirya/fix-flatmapinr-distribution.
## What changes were proposed in this pull request?
When zinc is running the pwd might be in the root of the project. A quick solution to this is to not go a level up incase we are in the root rather than root/core/. If we are in the root everything works fine, if we are in core add a script which goes and runs the level up
## How was this patch tested?
set -x in the SparkR install scripts.
Author: Holden Karau <holden@us.ibm.com>
Closes#19402 from holdenk/SPARK-22167-sparkr-packaging-issue-allow-zinc.
## 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?
The `percentile_approx` function previously accepted numeric type input and output double type results.
But since all numeric types, date and timestamp types are represented as numerics internally, `percentile_approx` can support them easily.
After this PR, it supports date type, timestamp type and numeric types as input types. The result type is also changed to be the same as the input type, which is more reasonable for percentiles.
This change is also required when we generate equi-height histograms for these types.
## How was this patch tested?
Added a new test and modified some existing tests.
Author: Zhenhua Wang <wangzhenhua@huawei.com>
Closes#19321 from wzhfy/approx_percentile_support_types.
## What changes were proposed in this pull request?
This PR make `sample(...)` able to omit `withReplacement` defaulting to `FALSE`.
In short, the following examples are allowed:
```r
> df <- createDataFrame(as.list(seq(10)))
> count(sample(df, fraction=0.5, seed=3))
[1] 4
> count(sample(df, fraction=1.0))
[1] 10
```
In addition, this PR also adds some type checking logics as below:
```r
> sample(df, fraction = "a")
Error in sample(df, fraction = "a") :
fraction must be numeric; however, got character
> sample(df, fraction = 1, seed = NULL)
Error in sample(df, fraction = 1, seed = NULL) :
seed must not be NULL or NA; however, got NULL
> sample(df, list(1), 1.0)
Error in sample(df, list(1), 1) :
withReplacement must be logical; however, got list
> sample(df, fraction = -1.0)
...
Error in sample : illegal argument - requirement failed: Sampling fraction (-1.0) must be on interval [0, 1] without replacement
```
## How was this patch tested?
Manually tested, unit tests added in `R/pkg/tests/fulltests/test_sparkSQL.R`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#19243 from HyukjinKwon/SPARK-21780.
## What changes were proposed in this pull request?
Clarify behavior of to_utc_timestamp/from_utc_timestamp with an example
## How was this patch tested?
Doc only change / existing tests
Author: Sean Owen <sowen@cloudera.com>
Closes#19276 from srowen/SPARK-22049.
## What changes were proposed in this pull request?
In previous work SPARK-21513, we has allowed `MapType` and `ArrayType` of `MapType`s convert to a json string but only for Scala API. In this follow-up PR, we will make SparkSQL support it for PySpark and SparkR, too. We also fix some little bugs and comments of the previous work in this follow-up PR.
### For PySpark
```
>>> data = [(1, {"name": "Alice"})]
>>> df = spark.createDataFrame(data, ("key", "value"))
>>> df.select(to_json(df.value).alias("json")).collect()
[Row(json=u'{"name":"Alice")']
>>> data = [(1, [{"name": "Alice"}, {"name": "Bob"}])]
>>> df = spark.createDataFrame(data, ("key", "value"))
>>> df.select(to_json(df.value).alias("json")).collect()
[Row(json=u'[{"name":"Alice"},{"name":"Bob"}]')]
```
### For SparkR
```
# Converts a map into a JSON object
df2 <- sql("SELECT map('name', 'Bob')) as people")
df2 <- mutate(df2, people_json = to_json(df2$people))
# Converts an array of maps into a JSON array
df2 <- sql("SELECT array(map('name', 'Bob'), map('name', 'Alice')) as people")
df2 <- mutate(df2, people_json = to_json(df2$people))
```
## How was this patch tested?
Add unit test cases.
cc viirya HyukjinKwon
Author: goldmedal <liugs963@gmail.com>
Closes#19223 from goldmedal/SPARK-21513-fp-PySaprkAndSparkR.
