### What changes were proposed in this pull request?
This PR makes `element_at` in PySpark able to take PySpark `Column` instances.
### Why are the changes needed?
To match with Scala side. Seems it was intended but not working correctly as a bug.
### Does this PR introduce any user-facing change?
Yes. See below:
```python
from pyspark.sql import functions as F
x = spark.createDataFrame([([1,2,3],1),([4,5,6],2),([7,8,9],3)],['list','num'])
x.withColumn('aa',F.element_at('list',x.num.cast('int'))).show()
```
Before:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/.../spark/python/pyspark/sql/functions.py", line 2059, in element_at
return Column(sc._jvm.functions.element_at(_to_java_column(col), extraction))
File "/.../spark/python/lib/py4j-0.10.8.1-src.zip/py4j/java_gateway.py", line 1277, in __call__
File "/.../spark/python/lib/py4j-0.10.8.1-src.zip/py4j/java_gateway.py", line 1241, in _build_args
File "/.../spark/python/lib/py4j-0.10.8.1-src.zip/py4j/java_gateway.py", line 1228, in _get_args
File "/.../forked/spark/python/lib/py4j-0.10.8.1-src.zip/py4j/java_collections.py", line 500, in convert
File "/.../spark/python/pyspark/sql/column.py", line 344, in __iter__
raise TypeError("Column is not iterable")
TypeError: Column is not iterable
```
After:
```
+---------+---+---+
| list|num| aa|
+---------+---+---+
|[1, 2, 3]| 1| 1|
|[4, 5, 6]| 2| 5|
|[7, 8, 9]| 3| 9|
+---------+---+---+
```
### How was this patch tested?
Manually tested against literal, Python native types, and PySpark column.
Closes#25950 from HyukjinKwon/SPARK-29240.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
1, expose `BinaryClassificationMetrics.numBins` in `BinaryClassificationEvaluator`
2, expose `RegressionMetrics.throughOrigin` in `RegressionEvaluator`
3, add metric `explainedVariance` in `RegressionEvaluator`
### Why are the changes needed?
existing function in mllib.metrics should also be exposed in ml
### Does this PR introduce any user-facing change?
yes, this PR add two expert params and one metric option
### How was this patch tested?
existing and added tests
Closes#25940 from zhengruifeng/evaluator_add_param.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
### What changes were proposed in this pull request?
Added "array indices start at 1" in annotation to make it clear for the usage of function slice, in R Scala Python component
### Why are the changes needed?
It will throw exception if the value stare is 0, but array indices start at 0 most of times in other scenarios.
### Does this PR introduce any user-facing change?
Yes, more info provided to user.
### How was this patch tested?
No tests added, only doc change.
Closes#25704 from sheepstop/master.
Authored-by: sheepstop <yangting617@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR allows non-ascii string as an exception message in Python 2 by explicitly en/decoding in case of `str` in Python 2.
### Why are the changes needed?
Previously PySpark will hang when the `UnicodeDecodeError` occurs and the real exception cannot be passed to the JVM side.
See the reproducer as below:
```python
def f():
raise Exception("中")
spark = SparkSession.builder.master('local').getOrCreate()
spark.sparkContext.parallelize([1]).map(lambda x: f()).count()
```
### Does this PR introduce any user-facing change?
User may not observe hanging for the similar cases.
### How was this patch tested?
Added a new test and manually checking.
This pr is based on #18324, credits should also go to dataknocker.
To make lint-python happy for python3, it also includes a followup fix for #25814Closes#25847 from advancedxy/python_exception_19926_and_21045.
Authored-by: Xianjin YE <advancedxy@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR allows Python toLocalIterator to prefetch the next partition while the first partition is being collected. The PR also adds a demo micro bench mark in the examples directory, we may wish to keep this or not.
### Why are the changes needed?
In https://issues.apache.org/jira/browse/SPARK-23961 / 5e79ae3b40 we changed PySpark to only pull one partition at a time. This is memory efficient, but if partitions take time to compute this can mean we're spending more time blocking.
### Does this PR introduce any user-facing change?
A new param is added to toLocalIterator
### How was this patch tested?
New unit test inside of `test_rdd.py` checks the time that the elements are evaluated at. Another test that the results remain the same are added to `test_dataframe.py`.
I also ran a micro benchmark in the examples directory `prefetch.py` which shows an improvement of ~40% in this specific use case.
>
> 19/08/16 17:11:36 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
> Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
> Setting default log level to "WARN".
> To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
> Running timers:
>
> [Stage 32:> (0 + 1) / 1]
> Results:
>
> Prefetch time:
>
> 100.228110831
>
>
> Regular time:
>
> 188.341721614
>
>
>
Closes#25515 from holdenk/SPARK-27659-allow-pyspark-tolocalitr-to-prefetch.
Authored-by: Holden Karau <hkarau@apple.com>
Signed-off-by: Holden Karau <hkarau@apple.com>
### What changes were proposed in this pull request?
Add some common classes in Python to make it have the same structure as Scala
1. Scala has ClassifierParams/Classifier/ClassificationModel:
```
trait ClassifierParams
extends PredictorParams with HasRawPredictionCol
abstract class Classifier
extends Predictor with ClassifierParams {
def setRawPredictionCol
}
abstract class ClassificationModel
extends PredictionModel with ClassifierParams {
def setRawPredictionCol
}
```
This PR makes Python has the following:
```
class JavaClassifierParams(HasRawPredictionCol, JavaPredictorParams):
pass
class JavaClassifier(JavaPredictor, JavaClassifierParams):
def setRawPredictionCol
class JavaClassificationModel(JavaPredictionModel, JavaClassifierParams):
def setRawPredictionCol
```
2. Scala has ProbabilisticClassifierParams/ProbabilisticClassifier/ProbabilisticClassificationModel:
```
trait ProbabilisticClassifierParams
extends ClassifierParams with HasProbabilityCol with HasThresholds
abstract class ProbabilisticClassifier
extends Classifier with ProbabilisticClassifierParams {
def setProbabilityCol
def setThresholds
}
abstract class ProbabilisticClassificationModel
extends ClassificationModel with ProbabilisticClassifierParams {
def setProbabilityCol
def setThresholds
}
```
This PR makes Python have the following:
```
class JavaProbabilisticClassifierParams(HasProbabilityCol, HasThresholds, JavaClassifierParams):
pass
class JavaProbabilisticClassifier(JavaClassifier, JavaProbabilisticClassifierParams):
def setProbabilityCol
def setThresholds
class JavaProbabilisticClassificationModel(JavaClassificationModel, JavaProbabilisticClassifierParams):
def setProbabilityCol
def setThresholds
```
3. Scala has PredictorParams/Predictor/PredictionModel:
```
trait PredictorParams extends Params
with HasLabelCol with HasFeaturesCol with HasPredictionCol
abstract class Predictor
extends Estimator with PredictorParams {
def setLabelCol
def setFeaturesCol
def setPredictionCol
}
abstract class PredictionModel
extends Model with PredictorParams {
def setFeaturesCol
def setPredictionCol
def numFeatures
def predict
}
```
This PR makes Python have the following:
```
class JavaPredictorParams(HasLabelCol, HasFeaturesCol, HasPredictionCol):
pass
class JavaPredictor(JavaEstimator, JavaPredictorParams):
def setLabelCol
def setFeaturesCol
def setPredictionCol
class JavaPredictionModel(JavaModel, JavaPredictorParams):
def setFeaturesCol
def setPredictionCol
def numFeatures
def predict
```
### Why are the changes needed?
Have parity between Python and Scala ML
### Does this PR introduce any user-facing change?
Yes. Add the following changes:
```
LinearSVCModel
- get/setFeatureCol
- get/setPredictionCol
- get/setLabelCol
- get/setRawPredictionCol
- predict
```
```
LogisticRegressionModel
DecisionTreeClassificationModel
RandomForestClassificationModel
GBTClassificationModel
NaiveBayesModel
MultilayerPerceptronClassificationModel
- get/setFeatureCol
- get/setPredictionCol
- get/setLabelCol
- get/setRawPredictionCol
- get/setProbabilityCol
- predict
```
```
LinearRegressionModel
IsotonicRegressionModel
DecisionTreeRegressionModel
RandomForestRegressionModel
GBTRegressionModel
AFTSurvivalRegressionModel
GeneralizedLinearRegressionModel
- get/setFeatureCol
- get/setPredictionCol
- get/setLabelCol
- predict
```
### How was this patch tested?
Add a few doc tests.
Closes#25776 from huaxingao/spark-28985.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
Add multiple columns support to PySpark QuantileDiscretizer
### Why are the changes needed?
Multiple columns support for QuantileDiscretizer was in scala side a while ago. We need to add multiple columns support to python too.
### Does this PR introduce any user-facing change?
Yes. New Python is added
### How was this patch tested?
Add doctest
Closes#25812 from huaxingao/spark-22796.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Liang-Chi Hsieh <liangchi@uber.com>
### What changes were proposed in this pull request?
The str of `CapaturedException` is now returned by str(self.desc) rather than repr(self.desc), which is more user-friendly. It also handles unicode under python2 specially.
### Why are the changes needed?
This is an improvement, and makes exception more human readable in python side.
### Does this PR introduce any user-facing change?
Before this pr, select `中文字段` throws exception something likes below:
```
Traceback (most recent call last):
File "/Users/advancedxy/code_workspace/github/spark/python/pyspark/sql/tests/test_utils.py", line 34, in test_capture_user_friendly_exception
raise e
AnalysisException: u"cannot resolve '`\u4e2d\u6587\u5b57\u6bb5`' given input columns: []; line 1 pos 7;\n'Project ['\u4e2d\u6587\u5b57\u6bb5]\n+- OneRowRelation\n"
```
after this pr:
```
Traceback (most recent call last):
File "/Users/advancedxy/code_workspace/github/spark/python/pyspark/sql/tests/test_utils.py", line 34, in test_capture_user_friendly_exception
raise e
AnalysisException: cannot resolve '`中文字段`' given input columns: []; line 1 pos 7;
'Project ['中文字段]
+- OneRowRelation
```
### How was this patch
Add a new test to verify unicode are correctly converted and manual checks for thrown exceptions.
This pr's credits should go to uncleGen and is based on https://github.com/apache/spark/pull/17267Closes#25814 from advancedxy/python_exception_19926_and_21045.
Authored-by: Xianjin YE <advancedxy@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Fitting ALS model can be failed due to nondeterministic input data. Currently the failure is thrown by an ArrayIndexOutOfBoundsException which is not explainable for end users what is wrong in fitting.
This patch catches this exception and rethrows a more explainable one, when the input data is nondeterministic.
Because we may not exactly know the output deterministic level of RDDs produced by user code, this patch also adds a note to Scala/Python/R ALS document about the training data deterministic level.
### Why are the changes needed?
ArrayIndexOutOfBoundsException was observed during fitting ALS model. It was caused by mismatching between in/out user/item blocks during computing ratings.
If the training RDD output is nondeterministic, when fetch failure is happened, rerun part of training RDD can produce inconsistent user/item blocks.
This patch is needed to notify users ALS fitting on nondeterministic input.
### Does this PR introduce any user-facing change?
Yes. When fitting ALS model on nondeterministic input data, previously if rerun happens, users would see ArrayIndexOutOfBoundsException caused by mismatch between In/Out user/item blocks.
After this patch, a SparkException with more clear message will be thrown, and original ArrayIndexOutOfBoundsException is wrapped.
### How was this patch tested?
Tested on development cluster.
Closes#25789 from viirya/als-indeterminate-input.
Lead-authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Co-authored-by: Liang-Chi Hsieh <liangchi@uber.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
Bucketizer support multi-column in the python side
### Why are the changes needed?
Bucketizer should support multi-column like the scala side.
### Does this PR introduce any user-facing change?
yes, this PR add new Python API
### How was this patch tested?
added testsuites
Closes#25801 from zhengruifeng/20542_py.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
### What changes were proposed in this pull request?
Follow the scala ```OneVsRestParams``` implementation, move ```setClassifier``` from ```OneVsRestParams``` to ```OneVsRest``` in Pyspark
### Why are the changes needed?
1. Maintain the parity between scala and python code.
2. ```Classifier``` can only be set in the estimator.
### Does this PR introduce any user-facing change?
Yes.
Previous behavior: ```OneVsRestModel``` has method ```setClassifier```
Current behavior: ```setClassifier``` is removed from ```OneVsRestModel```. ```classifier``` can only be set in ```OneVsRest```.
### How was this patch tested?
Use existing tests
Closes#25715 from huaxingao/spark-28969.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
reorganize the packages of DS v2 interfaces/classes:
1. `org.spark.sql.connector.catalog`: put `TableCatalog`, `Table` and other related interfaces/classes
2. `org.spark.sql.connector.expression`: put `Expression`, `Transform` and other related interfaces/classes
3. `org.spark.sql.connector.read`: put `ScanBuilder`, `Scan` and other related interfaces/classes
4. `org.spark.sql.connector.write`: put `WriteBuilder`, `BatchWrite` and other related interfaces/classes
### Why are the changes needed?
Data Source V2 has evolved a lot. It's a bit weird that `Expression` is in `org.spark.sql.catalog.v2` and `Table` is in `org.spark.sql.sources.v2`.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
existing tests
Closes#25700 from cloud-fan/package.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
JIRA :https://issues.apache.org/jira/browse/SPARK-29050
'a hdfs' change into 'an hdfs'
'an unique' change into 'a unique'
'an url' change into 'a url'
'a error' change into 'an error'
Closes#25756 from dengziming/feature_fix_typos.
Authored-by: dengziming <dengziming@growingio.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR proposes to allow `bytes` as an acceptable type for binary type for `createDataFrame`.
### Why are the changes needed?
`bytes` is a standard type for binary in Python. This should be respected in PySpark side.
### Does this PR introduce any user-facing change?
Yes, _when specified type is binary_, we will allow `bytes` as a binary type. Previously this was not allowed in both Python 2 and Python 3 as below:
```python
spark.createDataFrame([[b"abcd"]], "col binary")
```
in Python 3
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/.../spark/python/pyspark/sql/session.py", line 787, in createDataFrame
rdd, schema = self._createFromLocal(map(prepare, data), schema)
File "/.../spark/python/pyspark/sql/session.py", line 442, in _createFromLocal
data = list(data)
File "/.../spark/python/pyspark/sql/session.py", line 769, in prepare
verify_func(obj)
File "/.../forked/spark/python/pyspark/sql/types.py", line 1403, in verify
verify_value(obj)
File "/.../spark/python/pyspark/sql/types.py", line 1384, in verify_struct
verifier(v)
File "/.../spark/python/pyspark/sql/types.py", line 1403, in verify
verify_value(obj)
File "/.../spark/python/pyspark/sql/types.py", line 1397, in verify_default
verify_acceptable_types(obj)
File "/.../spark/python/pyspark/sql/types.py", line 1282, in verify_acceptable_types
% (dataType, obj, type(obj))))
TypeError: field col: BinaryType can not accept object b'abcd' in type <class 'bytes'>
```
in Python 2:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/.../spark/python/pyspark/sql/session.py", line 787, in createDataFrame
rdd, schema = self._createFromLocal(map(prepare, data), schema)
File "/.../spark/python/pyspark/sql/session.py", line 442, in _createFromLocal
data = list(data)
File "/.../spark/python/pyspark/sql/session.py", line 769, in prepare
verify_func(obj)
File "/.../spark/python/pyspark/sql/types.py", line 1403, in verify
verify_value(obj)
File "/.../spark/python/pyspark/sql/types.py", line 1384, in verify_struct
verifier(v)
File "/.../spark/python/pyspark/sql/types.py", line 1403, in verify
verify_value(obj)
File "/.../spark/python/pyspark/sql/types.py", line 1397, in verify_default
verify_acceptable_types(obj)
File "/.../spark/python/pyspark/sql/types.py", line 1282, in verify_acceptable_types
% (dataType, obj, type(obj))))
TypeError: field col: BinaryType can not accept object 'abcd' in type <type 'str'>
```
So, it won't break anything.
### How was this patch tested?
Unittests were added and also manually tested as below.
```bash
./run-tests --python-executables=python2,python3 --testnames "pyspark.sql.tests.test_serde"
```
Closes#25749 from HyukjinKwon/SPARK-29041.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
- Remove SQLContext.createExternalTable and Catalog.createExternalTable, deprecated in favor of createTable since 2.2.0, plus tests of deprecated methods
- Remove HiveContext, deprecated in 2.0.0, in favor of `SparkSession.builder.enableHiveSupport`
- Remove deprecated KinesisUtils.createStream methods, plus tests of deprecated methods, deprecate in 2.2.0
- Remove deprecated MLlib (not Spark ML) linear method support, mostly utility constructors and 'train' methods, and associated docs. This includes methods in LinearRegression, LogisticRegression, Lasso, RidgeRegression. These have been deprecated since 2.0.0
- Remove deprecated Pyspark MLlib linear method support, including LogisticRegressionWithSGD, LinearRegressionWithSGD, LassoWithSGD
- Remove 'runs' argument in KMeans.train() method, which has been a no-op since 2.0.0
- Remove deprecated ChiSqSelector isSorted protected method
- Remove deprecated 'yarn-cluster' and 'yarn-client' master argument in favor of 'yarn' and deploy mode 'cluster', etc
Notes:
- I was not able to remove deprecated DataFrameReader.json(RDD) in favor of DataFrameReader.json(Dataset); the former was deprecated in 2.2.0, but, it is still needed to support Pyspark's .json() method, which can't use a Dataset.
- Looks like SQLContext.createExternalTable was not actually deprecated in Pyspark, but, almost certainly was meant to be? Catalog.createExternalTable was.
- I afterwards noted that the toDegrees, toRadians functions were almost removed fully in SPARK-25908, but Felix suggested keeping just the R version as they hadn't been technically deprecated. I'd like to revisit that. Do we really want the inconsistency? I'm not against reverting it again, but then that implies leaving SQLContext.createExternalTable just in Pyspark too, which seems weird.
- I *kept* LogisticRegressionWithSGD, LinearRegressionWithSGD, LassoWithSGD, RidgeRegressionWithSGD in Pyspark, though deprecated, as it is hard to remove them (still used by StreamingLogisticRegressionWithSGD?) and they are not fully removed in Scala. Maybe should not have been deprecated.
### Why are the changes needed?
Deprecated items are easiest to remove in a major release, so we should do so as much as possible for Spark 3. This does not target items deprecated 'recently' as of Spark 2.3, which is still 18 months old.
### Does this PR introduce any user-facing change?
Yes, in that deprecated items are removed from some public APIs.
### How was this patch tested?
Existing tests.
Closes#25684 from srowen/SPARK-28980.
Lead-authored-by: Sean Owen <sean.owen@databricks.com>
Co-authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
Add HasNumFeatures in the scala side, with `1<<18` as the default value
### Why are the changes needed?
HasNumFeatures is already added in the py side, it is reasonable to keep them in sync.
I don't find other similar place.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Existing testsuites
Closes#25671 from zhengruifeng/add_HasNumFeatures.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
### What changes were proposed in this pull request?
`DataFrameReader.json()` accepts a partition column that is of numeric, date or timestamp type, according to the implementation in `JDBCRelation.scala`. Update the scaladoc accordingly, to match the documentation in `sql-data-sources-jdbc.md` too.
### Why are the changes needed?
scaladoc is incorrect.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
N/A
Closes#25687 from srowen/SPARK-28977.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
The Experimental and Evolving annotations are both (like Unstable) used to express that a an API may change. However there are many things in the code that have been marked that way since even Spark 1.x. Per the dev thread, anything introduced at or before Spark 2.3.0 is pretty much 'stable' in that it would not change without a deprecation cycle. Therefore I'd like to remove most of these annotations. And, remove the `:: Experimental ::` scaladoc tag too. And likewise for Python, R.
The changes below can be summarized as:
- Generally, anything introduced at or before Spark 2.3.0 has been unmarked as neither Evolving nor Experimental
- Obviously experimental items like DSv2, Barrier mode, ExperimentalMethods are untouched
- I _did_ unmark a few MLlib classes introduced in 2.4, as I am quite confident they're not going to change (e.g. KolmogorovSmirnovTest, PowerIterationClustering)
It's a big change to review, so I'd suggest scanning the list of _files_ changed to see if any area seems like it should remain partly experimental and examine those.
### Why are the changes needed?
Many of these annotations are incorrect; the APIs are de facto stable. Leaving them also makes legitimate usages of the annotations less meaningful.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#25558 from srowen/SPARK-28855.
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?
This PR proposes to match the test with branch-2.4. See https://github.com/apache/spark/pull/25593#discussion_r318109047
Seems using `SparkSession.builder` with Spark conf possibly affects other tests.
### Why are the changes needed?
To match with branch-2.4 and to make easier to backport.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Test was fixed.
Closes#25603 from HyukjinKwon/SPARK-28881-followup.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Make `spark.sql.crossJoin.enabled` default value true
### Why are the changes needed?
For implicit cross join, we can set up a watchdog to cancel it if running for a long time.
When "spark.sql.crossJoin.enabled" is false, because `CheckCartesianProducts` is implemented in logical plan stage, it may generate some mismatching error which may confuse end user:
* it's done in logical phase, so we may fail queries that can be executed via broadcast join, which is very fast.
* if we move the check to the physical phase, then a query may success at the beginning, and begin to fail when the table size gets larger (other people insert data to the table). This can be quite confusing.
* the CROSS JOIN syntax doesn't work well if join reorder happens.
* some non-equi-join will generate plan using cartesian product, but `CheckCartesianProducts` do not detect it and raise error.
So that in order to address this in simpler way, we can turn off showing this cross-join error by default.
For reference, I list some cases raising mismatching error here:
Providing:
```
spark.range(2).createOrReplaceTempView("sm1") // can be broadcast
spark.range(50000000).createOrReplaceTempView("bg1") // cannot be broadcast
spark.range(60000000).createOrReplaceTempView("bg2") // cannot be broadcast
```
1) Some join could be convert to broadcast nested loop join, but CheckCartesianProducts raise error. e.g.
```
select sm1.id, bg1.id from bg1 join sm1 where sm1.id < bg1.id
```
2) Some join will run by CartesianJoin but CheckCartesianProducts DO NOT raise error. e.g.
```
select bg1.id, bg2.id from bg1 join bg2 where bg1.id < bg2.id
```
### Does this PR introduce any user-facing change?
### How was this patch tested?
Closes#25520 from WeichenXu123/SPARK-28621.
Authored-by: WeichenXu <weichen.xu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR proposes to add a test case for:
```bash
./bin/pyspark --conf spark.driver.maxResultSize=1m
spark.conf.set("spark.sql.execution.arrow.enabled",True)
```
```python
spark.range(10000000).toPandas()
```
```
Empty DataFrame
Columns: [id]
Index: []
```
which can result in partial results (see https://github.com/apache/spark/pull/25593#issuecomment-525153808). This regression was found between Spark 2.3 and Spark 2.4, and accidentally fixed.
### Why are the changes needed?
To prevent the same regression in the future.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Test was added.
Closes#25594 from HyukjinKwon/SPARK-28881.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
expose the newly added tree-based transformation in the py side
### Why are the changes needed?
function parity
### Does this PR introduce any user-facing change?
yes, add `setLeafCol` & `getLeafCol` in the py side
### How was this patch tested?
added tests & local tests
Closes#25566 from zhengruifeng/py_tree_path.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
<!--
Thanks for sending a pull request! Here are some tips for you:
1. If this is your first time, please read our contributor guidelines: https://spark.apache.org/contributing.html
2. Ensure you have added or run the appropriate tests for your PR: https://spark.apache.org/developer-tools.html
3. If the PR is unfinished, add '[WIP]' in your PR title, e.g., '[WIP][SPARK-XXXX] Your PR title ...'.
4. Be sure to keep the PR description updated to reflect all changes.
5. Please write your PR title to summarize what this PR proposes.
6. If possible, provide a concise example to reproduce the issue for a faster review.
-->
### What changes were proposed in this pull request?
SparkML writer gets hadoop conf from session state, instead of the spark context.
<!--
Please clarify what changes you are proposing. The purpose of this section is to outline the changes and how this PR fixes the issue.
If possible, please consider writing useful notes for better and faster reviews in your PR. See the examples below.
1. If you refactor some codes with changing classes, showing the class hierarchy will help reviewers.
2. If you fix some SQL features, you can provide some references of other DBMSes.
3. If there is design documentation, please add the link.
4. If there is a discussion in the mailing list, please add the link.
-->
### Why are the changes needed?
Allow for multiple sessions in the same context that have different hadoop configurations.
<!--
Please clarify why the changes are needed. For instance,
1. If you propose a new API, clarify the use case for a new API.
2. If you fix a bug, you can clarify why it is a bug.
-->
### Does this PR introduce any user-facing change?
<!--
If yes, please clarify the previous behavior and the change this PR proposes - provide the console output, description and/or an example to show the behavior difference if possible.
If no, write 'No'.
-->
No
### How was this patch tested?
<!--
If tests were added, say they were added here. Please make sure to add some test cases that check the changes thoroughly including negative and positive cases if possible.
If it was tested in a way different from regular unit tests, please clarify how you tested step by step, ideally copy and paste-able, so that other reviewers can test and check, and descendants can verify in the future.
If tests were not added, please describe why they were not added and/or why it was difficult to add.
-->
Tested in pyspark.ml.tests.test_persistence.PersistenceTest test_default_read_write
Closes#25505 from helenyugithub/SPARK-28776.
Authored-by: heleny <heleny@palantir.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
The parameters doc string of the function format_string was changed from _col_, _d_ to _format_, _cols_ which is what the actual function declaration states
### Why are the changes needed?
The parameters stated by the documentation was inaccurate
### Does this PR introduce any user-facing change?
Yes.
**BEFORE**
**AFTER**
### How was this patch tested?
N/A: documentation only
<!--
If tests were added, say they were added here. Please make sure to add some test cases that check the changes thoroughly including negative and positive cases if possible.
If it was tested in a way different from regular unit tests, please clarify how you tested step by step, ideally copy and paste-able, so that other reviewers can test and check, and descendants can verify in the future.
If tests were not added, please describe why they were not added and/or why it was difficult to add.
-->
Closes#25506 from darrentirto/SPARK-28777.
Authored-by: darrentirto <darrentirto@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
<!--
Thanks for sending a pull request! Here are some tips for you:
1. If this is your first time, please read our contributor guidelines: https://spark.apache.org/contributing.html
2. Ensure you have added or run the appropriate tests for your PR: https://spark.apache.org/developer-tools.html
3. If the PR is unfinished, add '[WIP]' in your PR title, e.g., '[WIP][SPARK-XXXX] Your PR title ...'.
4. Be sure to keep the PR description updated to reflect all changes.
5. Please write your PR title to summarize what this PR proposes.
6. If possible, provide a concise example to reproduce the issue for a faster review.
-->
### What changes were proposed in this pull request?
<!--
Please clarify what changes you are proposing. The purpose of this section is to outline the changes and how this PR fixes the issue.
If possible, please consider writing useful notes for better and faster reviews in your PR. See the examples below.
1. If you refactor some codes with changing classes, showing the class hierarchy will help reviewers.
2. If you fix some SQL features, you can provide some references of other DBMSes.
3. If there is design documentation, please add the link.
4. If there is a discussion in the mailing list, please add the link.
-->
This PR proposes to fix both tests below:
```
======================================================================
FAIL: test_raw_and_probability_prediction (pyspark.ml.tests.test_algorithms.MultilayerPerceptronClassifierTest)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/Users/dongjoon/APACHE/spark-master/python/pyspark/ml/tests/test_algorithms.py", line 89, in test_raw_and_probability_prediction
self.assertTrue(np.allclose(result.rawPrediction, expected_rawPrediction, atol=1E-4))
AssertionError: False is not true
```
```
File "/Users/dongjoon/APACHE/spark-master/python/pyspark/mllib/clustering.py", line 386, in __main__.GaussianMixtureModel
Failed example:
abs(softPredicted[0] - 1.0) < 0.001
Expected:
True
Got:
False
**********************************************************************
File "/Users/dongjoon/APACHE/spark-master/python/pyspark/mllib/clustering.py", line 388, in __main__.GaussianMixtureModel
Failed example:
abs(softPredicted[1] - 0.0) < 0.001
Expected:
True
Got:
False
```
to pass in JDK 11.
The root cause seems to be different float values being understood via Py4J. This issue also was found in https://github.com/apache/spark/pull/25132 before.
When floats are transferred from Python to JVM, the values are sent as are. Python floats are not "precise" due to its own limitation - https://docs.python.org/3/tutorial/floatingpoint.html.
For some reasons, the floats from Python on JDK 8 and JDK 11 are different, which is already explicitly not guaranteed.
This seems why only some tests in PySpark with floats are being failed.
So, this PR fixes it by increasing tolerance in identified test cases in PySpark.
### Why are the changes needed?
<!--
Please clarify why the changes are needed. For instance,
1. If you propose a new API, clarify the use case for a new API.
2. If you fix a bug, you can clarify why it is a bug.
-->
To fully support JDK 11. See, for instance, https://github.com/apache/spark/pull/25443 and https://github.com/apache/spark/pull/25423 for ongoing efforts.
### Does this PR introduce any user-facing change?
<!--
If yes, please clarify the previous behavior and the change this PR proposes - provide the console output, description and/or an example to show the behavior difference if possible.
If no, write 'No'.
-->
No.
### How was this patch tested?
<!--
If tests were added, say they were added here. Please make sure to add some test cases that check the changes thoroughly including negative and positive cases if possible.
If it was tested in a way different from regular unit tests, please clarify how you tested step by step, ideally copy and paste-able, so that other reviewers can test and check, and descendants can verify in the future.
If tests were not added, please describe why they were not added and/or why it was difficult to add.
-->
Manually tested as described in JIRAs:
```
$ build/sbt -Phadoop-3.2 test:package
$ python/run-tests --testnames 'pyspark.ml.tests.test_algorithms' --python-executables python
```
```
$ build/sbt -Phadoop-3.2 test:package
$ python/run-tests --testnames 'pyspark.mllib.clustering' --python-executables python
```
Closes#25475 from HyukjinKwon/SPARK-28735.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Leave ```shared.py``` untouched. Move Python ```DecisionTreeParams``` to ```regression.py```
## How was this patch tested?
Use existing tests
Closes#25406 from huaxingao/spark-28243.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
A GROUPED_AGG pandas python udf can't work, if without group by clause, like `select udf(id) from table`.
This doesn't match with aggregate function like sum, count..., and also dataset API like `df.agg(udf(df['id']))`.
When we parse a udf (or an aggregate function) like that from SQL syntax, it is known as a function in a project. `GlobalAggregates` rule in analysis makes such project as aggregate, by looking for aggregate expressions. At the moment, we should also look for GROUPED_AGG pandas python udf.
## How was this patch tested?
Added tests.
Closes#25352 from viirya/SPARK-28422.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
The mapping of Spark schema to Avro schema is many-to-many. (See https://spark.apache.org/docs/latest/sql-data-sources-avro.html#supported-types-for-spark-sql---avro-conversion)
The default schema mapping might not be exactly what users want. For example, by default, a "string" column is always written as "string" Avro type, but users might want to output the column as "enum" Avro type.
With PR https://github.com/apache/spark/pull/21847, Spark supports user-specified schema in the batch writer.
For the function `to_avro`, we should support user-specified output schema as well.
## How was this patch tested?
Unit test.
Closes#25419 from gengliangwang/to_avro.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
In this PR, we implements a complete process of GPU-aware resources scheduling
in Standalone. The whole process looks like: Worker sets up isolated resources
when it starts up and registers to master along with its resources. And, Master
picks up usable workers according to driver/executor's resource requirements to
launch driver/executor on them. Then, Worker launches the driver/executor after
preparing resources file, which is created under driver/executor's working directory,
with specified resource addresses(told by master). When driver/executor finished,
their resources could be recycled to worker. Finally, if a worker stops, it
should always release its resources firstly.
For the case of Workers and Drivers in **client** mode run on the same host, we introduce
a config option named `spark.resources.coordinate.enable`(default true) to indicate
whether Spark should coordinate resources for user. If `spark.resources.coordinate.enable=false`, user should be responsible for configuring different resources for Workers and Drivers when use resourcesFile or discovery script. If true, Spark would help user to assign different resources for Workers and Drivers.
The solution for Spark to coordinate resources among Workers and Drivers is:
Generally, use a shared file named *____allocated_resources____.json* to sync allocated
resources info among Workers and Drivers on the same host.
After a Worker or Driver found all resources using the configured resourcesFile and/or
discovery script during launching, it should filter out available resources by excluding resources already allocated in *____allocated_resources____.json* and acquire resources from available resources according to its own requirement. After that, it should write its allocated resources along with its process id (pid) into *____allocated_resources____.json*. Pid (proposed by tgravescs) here used to check whether the allocated resources are still valid in case of Worker or Driver crashes and doesn't release resources properly. And when a Worker or Driver finished, normally, it would always clean up its own allocated resources in *____allocated_resources____.json*.
Note that we'll always get a file lock before any access to file *____allocated_resources____.json*
and release the lock finally.
Futhermore, we appended resources info in `WorkerSchedulerStateResponse` to work
around master change behaviour in HA mode.
## How was this patch tested?
Added unit tests in WorkerSuite, MasterSuite, SparkContextSuite.
Manually tested with client/cluster mode (e.g. multiple workers) in a single node Standalone.
Closes#25047 from Ngone51/SPARK-27371.
Authored-by: wuyi <ngone_5451@163.com>
Signed-off-by: Thomas Graves <tgraves@apache.org>
## What changes were proposed in this pull request?
Right now, batch DataFrame always changes the schema to nullable automatically (See this line: 325bc8e9c6/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSource.scala (L399)). But streaming file source is missing this.
This PR updates the streaming file source schema to force it be nullable. I also added a flag `spark.sql.streaming.fileSource.schema.forceNullable` to disable this change since some users may rely on the old behavior.
## How was this patch tested?
The new unit test.
Closes#25382 from zsxwing/SPARK-28651.
Authored-by: Shixiong Zhu <zsxwing@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Currently, PythonBroadcast may delete its data file while a python worker still needs it. This happens because PythonBroadcast overrides the `finalize()` method to delete its data file. So, when GC happens and no references on broadcast variable, it may trigger `finalize()` to delete
data file. That's also means, data under python Broadcast variable couldn't be deleted when `unpersist()`/`destroy()` called but relys on GC.
In this PR, we removed the `finalize()` method, and map the PythonBroadcast data file to a BroadcastBlock(which has the same broadcast id with the broadcast variable who wrapped this PythonBroadcast) when PythonBroadcast is deserializing. As a result, the data file could be deleted just like other pieces of the Broadcast variable when `unpersist()`/`destroy()` called and do not rely on GC any more.
## How was this patch tested?
Added a Python test, and tested manually(verified create/delete the broadcast block).
Closes#25262 from Ngone51/SPARK-28486.
Authored-by: wuyi <ngone_5451@163.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR picks up https://github.com/apache/spark/pull/25315 back after removing `Popen.wait` usage which exists in Python 3 only. I saw the last test results wrongly and thought it was passed.
Fix flaky test DaemonTests.do_termination_test which fail on Python 3.7. I add a sleep after the test connection to daemon.
## How was this patch tested?
Run test
```
python/run-tests --python-executables=python3.7 --testname "pyspark.tests.test_daemon DaemonTests"
```
**Before**
Fail on test "test_termination_sigterm". And we can see daemon process do not exit.
**After**
Test passed
Closes#25343 from HyukjinKwon/SPARK-28582.
Authored-by: WeichenXu <weichen.xu@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Update HashingTF to use new implementation of MurmurHash3
Make HashingTF use the old MurmurHash3 when a model from pre 3.0 is loaded
## How was this patch tested?
Change existing unit tests. Also add one unit test to make sure HashingTF use the old MurmurHash3 when a model from pre 3.0 is loaded
Closes#25303 from huaxingao/spark-23469.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Fix flaky test DaemonTests.do_termination_test which fail on Python 3.7. I add a sleep after the test connection to daemon.
## How was this patch tested?
Run test
```
python/run-tests --python-executables=python3.7 --testname "pyspark.tests.test_daemon DaemonTests"
```
**Before**
Fail on test "test_termination_sigterm". And we can see daemon process do not exit.
**After**
Test passed
Closes#25315 from WeichenXu123/fix_py37_daemon.
Authored-by: WeichenXu <weichen.xu@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR proposes to use `AtomicReference` so that parent and child threads can access to the same file block holder.
Python UDF expressions are turned to a plan and then it launches a separate thread to consume the input iterator. In the separate child thread, the iterator sets `InputFileBlockHolder.set` before the parent does which the parent thread is unable to read later.
1. In this separate child thread, if it happens to call `InputFileBlockHolder.set` first without initialization of the parent's thread local (which is done when the `ThreadLocal.get()` is first called), the child thread seems calling its own `initialValue` to initialize.
2. After that, the parent calls its own `initialValue` to initializes at the first call of `ThreadLocal.get()`.
3. Both now have two different references. Updating at child isn't reflected to parent.
This PR fixes it via initializing parent's thread local with `AtomicReference` for file status so that they can be used in each task, and children thread's update is reflected.
I also tried to explain this a bit more at https://github.com/apache/spark/pull/24958#discussion_r297203041.
## How was this patch tested?
Manually tested and unittest was added.
Closes#24958 from HyukjinKwon/SPARK-28153.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
I remove the deprecate `ImageSchema.readImages`.
Move some useful methods from class `ImageSchema` into class `ImageFileFormat`.
In pyspark, I rename `ImageSchema` class to be `ImageUtils`, and keep some useful python methods in it.
## How was this patch tested?
UT.
Please review https://spark.apache.org/contributing.html before opening a pull request.
Closes#25245 from WeichenXu123/remove_image_schema.
Authored-by: WeichenXu <weichen.xu@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
PySpark worker daemon reads from stdin the worker PIDs to kill. 1bb60ab839/python/pyspark/daemon.py (L127)
However, the worker process is a forked process from the worker daemon process and we didn't close stdin on the child after fork. This means the child and user program can read stdin as well, which blocks daemon from receiving the PID to kill. This can cause issues because the task reaper might detect the task was not terminated and eventually kill the JVM.
This PR fix this by redirecting the standard input of the forked child to devnull.
## How was this patch tested?
Manually test.
In `pyspark`, run:
```
import subprocess
def task(_):
subprocess.check_output(["cat"])
sc.parallelize(range(1), 1).mapPartitions(task).count()
```
Before:
The job will get stuck and press Ctrl+C to exit the job but the python worker process do not exit.
After:
The job finish correctly. The "cat" print nothing (because the dummay stdin is "/dev/null").
The python worker process exit normally.
Please review https://spark.apache.org/contributing.html before opening a pull request.
Closes#25138 from WeichenXu123/SPARK-26175.
Authored-by: WeichenXu <weichen.xu@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Implement `RobustScaler`
Since the transformation is quite similar to `StandardScaler`, I refactor the transform function so that it can be reused in both scalers.
## How was this patch tested?
existing and added tests
Closes#25160 from zhengruifeng/robust_scaler.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
In the PR, I propose to use `uuuu` for years instead of `yyyy` in date/timestamp patterns without the era pattern `G` (https://docs.oracle.com/javase/8/docs/api/java/time/format/DateTimeFormatter.html). **Parsing/formatting of positive years (current era) will be the same.** The difference is in formatting negative years belong to previous era - BC (Before Christ).
I replaced the `yyyy` pattern by `uuuu` everywhere except:
1. Test, Suite & Benchmark. Existing tests must work as is.
2. `SimpleDateFormat` because it doesn't support the `uuuu` pattern.
3. Comments and examples (except comments related to already replaced patterns).
Before the changes, the year of common era `100` and the year of BC era `-99`, showed similarly as `100`. After the changes negative years will be formatted with the `-` sign.
Before:
```Scala
scala> Seq(java.time.LocalDate.of(-99, 1, 1)).toDF().show
+----------+
| value|
+----------+
|0100-01-01|
+----------+
```
After:
```Scala
scala> Seq(java.time.LocalDate.of(-99, 1, 1)).toDF().show
+-----------+
| value|
+-----------+
|-0099-01-01|
+-----------+
```
## How was this patch tested?
By existing test suites, and added tests for negative years to `DateFormatterSuite` and `TimestampFormatterSuite`.
Closes#25230 from MaxGekk/year-pattern-uuuu.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Add indexOf method for ml.feature.HashingTF.
## How was this patch tested?
Add Unit test.
Closes#25250 from huaxingao/spark-21481.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Replace duplicate code by function `_memory_limit`
## How was this patch tested?
Existing UTs
Closes#25273 from WangGuangxin/python_memory_limit.
Authored-by: wangguangxin.cn <wangguangxin.cn@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
remove deprecated ``` def context(self, sqlContext)``` from ```pyspark/ml/util.py```
## How was this patch tested?
test with existing ml PySpark test suites
Closes#25246 from huaxingao/spark-28507.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Remove deprecated setFeatureSubsetStrategy and setSubsamplingRate from Python TreeEnsembleParams
## How was this patch tested?
Use existing tests.
Closes#25046 from huaxingao/spark-28243.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This adds simple check for `count` argument:
- If it is a `Column` we apply `_to_java_column` before invoking JVM counterpart
- Otherwise we proceed as before.
## How was this patch tested?
Manual testing.
Closes#25193 from zero323/SPARK-28278.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
In the following python code
```
df.write.mode("overwrite").insertInto("table")
```
```insertInto``` ignores ```mode("overwrite")``` and appends by default.
## How was this patch tested?
Add Unit test.
Closes#25175 from huaxingao/spark-28411.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In the PR, I propose to convert options values to strings by using `to_str()` for the following functions: `from_csv()`, `to_csv()`, `from_json()`, `to_json()`, `schema_of_csv()` and `schema_of_json()`. This will make handling of function options consistent to option handling in `DataFrameReader`/`DataFrameWriter`.
For example:
```Python
df.select(from_csv(df.value, "s string", {'ignoreLeadingWhiteSpace': True})
```
## How was this patch tested?
Added an example for `from_csv()` which was tested by:
```Shell
./python/run-tests --testnames pyspark.sql.functions
```
Closes#25182 from MaxGekk/options_to_str.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
