### What changes were proposed in this pull request?
Like [Hive UDFType](https://hive.apache.org/javadocs/r2.0.1/api/org/apache/hadoop/hive/ql/udf/UDFType.html), we should allow users to add the extra flags for ScalaUDF and JavaUDF too. _stateful_/_impliesOrder_ are not applicable to our Scala UDF. Thus, we only add the following two flags.
- deterministic: Certain optimizations should not be applied if UDF is not deterministic. Deterministic UDF returns same result each time it is invoked with a particular input. This determinism just needs to hold within the context of a query.
When the deterministic flag is not correctly set, the results could be wrong.
For ScalaUDF in Dataset APIs, users can call the following extra APIs for `UserDefinedFunction` to make the corresponding changes.
- `nonDeterministic`: Updates UserDefinedFunction to non-deterministic.
Also fixed the Java UDF name loss issue.
Will submit a separate PR for `distinctLike` for UDAF
### How was this patch tested?
Added test cases for both ScalaUDF
Author: gatorsmile <gatorsmile@gmail.com>
Author: Wenchen Fan <cloud0fan@gmail.com>
Closes#17848 from gatorsmile/udfRegister.
## What changes were proposed in this pull request?
After SPARK-12661, I guess we officially dropped Python 2.6 support. It looks there are few places missing this notes.
I grepped "Python 2.6" and "python 2.6" and the results were below:
```
./core/src/main/scala/org/apache/spark/api/python/SerDeUtil.scala: // Unpickle array.array generated by Python 2.6
./docs/index.md:Note that support for Java 7, Python 2.6 and old Hadoop versions before 2.6.5 were removed as of Spark 2.2.0.
./docs/rdd-programming-guide.md:Spark {{site.SPARK_VERSION}} works with Python 2.6+ or Python 3.4+. It can use the standard CPython interpreter,
./docs/rdd-programming-guide.md:Note that support for Python 2.6 is deprecated as of Spark 2.0.0, and may be removed in Spark 2.2.0.
./python/pyspark/context.py: warnings.warn("Support for Python 2.6 is deprecated as of Spark 2.0.0")
./python/pyspark/ml/tests.py: sys.stderr.write('Please install unittest2 to test with Python 2.6 or earlier')
./python/pyspark/mllib/tests.py: sys.stderr.write('Please install unittest2 to test with Python 2.6 or earlier')
./python/pyspark/serializers.py: # On Python 2.6, we can't write bytearrays to streams, so we need to convert them
./python/pyspark/sql/tests.py: sys.stderr.write('Please install unittest2 to test with Python 2.6 or earlier')
./python/pyspark/streaming/tests.py: sys.stderr.write('Please install unittest2 to test with Python 2.6 or earlier')
./python/pyspark/tests.py: sys.stderr.write('Please install unittest2 to test with Python 2.6 or earlier')
./python/pyspark/tests.py: # NOTE: dict is used instead of collections.Counter for Python 2.6
./python/pyspark/tests.py: # NOTE: dict is used instead of collections.Counter for Python 2.6
```
This PR only proposes to change visible changes as below:
```
./docs/rdd-programming-guide.md:Spark {{site.SPARK_VERSION}} works with Python 2.6+ or Python 3.4+. It can use the standard CPython interpreter,
./docs/rdd-programming-guide.md:Note that support for Python 2.6 is deprecated as of Spark 2.0.0, and may be removed in Spark 2.2.0.
./python/pyspark/context.py: warnings.warn("Support for Python 2.6 is deprecated as of Spark 2.0.0")
```
This one is already correct:
```
./docs/index.md:Note that support for Java 7, Python 2.6 and old Hadoop versions before 2.6.5 were removed as of Spark 2.2.0.
```
## How was this patch tested?
```bash
grep -r "Python 2.6" .
grep -r "python 2.6" .
```
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#18682 from HyukjinKwon/minor-python.26.
## What changes were proposed in this pull request?
This is the reopen of https://github.com/apache/spark/pull/14198, with merge conflicts resolved.
ueshin Could you please take a look at my code?
Fix bugs about types that result an array of null when creating DataFrame using python.
Python's array.array have richer type than python itself, e.g. we can have `array('f',[1,2,3])` and `array('d',[1,2,3])`. Codes in spark-sql and pyspark didn't take this into consideration which might cause a problem that you get an array of null values when you have `array('f')` in your rows.
A simple code to reproduce this bug is:
```
from pyspark import SparkContext
from pyspark.sql import SQLContext,Row,DataFrame
from array import array
sc = SparkContext()
sqlContext = SQLContext(sc)
row1 = Row(floatarray=array('f',[1,2,3]), doublearray=array('d',[1,2,3]))
rows = sc.parallelize([ row1 ])
df = sqlContext.createDataFrame(rows)
df.show()
```
which have output
```
+---------------+------------------+
| doublearray| floatarray|
+---------------+------------------+
|[1.0, 2.0, 3.0]|[null, null, null]|
+---------------+------------------+
```
## How was this patch tested?
New test case added
Author: Xiang Gao <qasdfgtyuiop@gmail.com>
Author: Gao, Xiang <qasdfgtyuiop@gmail.com>
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#18444 from zasdfgbnm/fix_array_infer.
## What changes were proposed in this pull request?
Added functionality for CrossValidator and TrainValidationSplit to persist nested estimators such as OneVsRest. Also added CrossValidator and TrainValidation split persistence to pyspark.
## How was this patch tested?
Performed both cross validation and train validation split with a one vs. rest estimator and tested read/write functionality of the estimator parameter maps required by these meta-algorithms.
Author: Ajay Saini <ajays725@gmail.com>
Closes#18428 from ajaysaini725/MetaAlgorithmPersistNestedEstimators.
## What changes were proposed in this pull request?
This PR proposes to avoid `__name__` in the tuple naming the attributes assigned directly from the wrapped function to the wrapper function, and use `self._name` (`func.__name__` or `obj.__class__.name__`).
After SPARK-19161, we happened to break callable objects as UDFs in Python as below:
```python
from pyspark.sql import functions
class F(object):
def __call__(self, x):
return x
foo = F()
udf = functions.udf(foo)
```
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".../spark/python/pyspark/sql/functions.py", line 2142, in udf
return _udf(f=f, returnType=returnType)
File ".../spark/python/pyspark/sql/functions.py", line 2133, in _udf
return udf_obj._wrapped()
File ".../spark/python/pyspark/sql/functions.py", line 2090, in _wrapped
functools.wraps(self.func)
File "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/functools.py", line 33, in update_wrapper
setattr(wrapper, attr, getattr(wrapped, attr))
AttributeError: F instance has no attribute '__name__'
```
This worked in Spark 2.1:
```python
from pyspark.sql import functions
class F(object):
def __call__(self, x):
return x
foo = F()
udf = functions.udf(foo)
spark.range(1).select(udf("id")).show()
```
```
+-----+
|F(id)|
+-----+
| 0|
+-----+
```
**After**
```python
from pyspark.sql import functions
class F(object):
def __call__(self, x):
return x
foo = F()
udf = functions.udf(foo)
spark.range(1).select(udf("id")).show()
```
```
+-----+
|F(id)|
+-----+
| 0|
+-----+
```
_In addition, we also happened to break partial functions as below_:
```python
from pyspark.sql import functions
from functools import partial
partial_func = partial(lambda x: x, x=1)
udf = functions.udf(partial_func)
```
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".../spark/python/pyspark/sql/functions.py", line 2154, in udf
return _udf(f=f, returnType=returnType)
File ".../spark/python/pyspark/sql/functions.py", line 2145, in _udf
return udf_obj._wrapped()
File ".../spark/python/pyspark/sql/functions.py", line 2099, in _wrapped
functools.wraps(self.func, assigned=assignments)
File "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/functools.py", line 33, in update_wrapper
setattr(wrapper, attr, getattr(wrapped, attr))
AttributeError: 'functools.partial' object has no attribute '__module__'
```
This worked in Spark 2.1:
```python
from pyspark.sql import functions
from functools import partial
partial_func = partial(lambda x: x, x=1)
udf = functions.udf(partial_func)
spark.range(1).select(udf()).show()
```
```
+---------+
|partial()|
+---------+
| 1|
+---------+
```
**After**
```python
from pyspark.sql import functions
from functools import partial
partial_func = partial(lambda x: x, x=1)
udf = functions.udf(partial_func)
spark.range(1).select(udf()).show()
```
```
+---------+
|partial()|
+---------+
| 1|
+---------+
```
## How was this patch tested?
Unit tests in `python/pyspark/sql/tests.py` and manual tests.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#18615 from HyukjinKwon/callable-object.
## What changes were proposed in this pull request?
```RFormula``` should handle invalid for both features and label column.
#18496 only handle invalid values in features column. This PR add handling invalid values for label column and test cases.
## How was this patch tested?
Add test cases.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#18613 from yanboliang/spark-20307.
## What changes were proposed in this pull request?
1, HasHandleInvaild support override
2, Make QuantileDiscretizer/Bucketizer/StringIndexer/RFormula inherit from HasHandleInvalid
## How was this patch tested?
existing tests
[JIRA](https://issues.apache.org/jira/browse/SPARK-18619)
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#18582 from zhengruifeng/heritate_HasHandleInvalid.
## What changes were proposed in this pull request?
This PR deals with four points as below:
- Reuse existing DDL parser APIs rather than reimplementing within PySpark
- Support DDL formatted string, `field type, field type`.
- Support case-insensitivity for parsing.
- Support nested data types as below:
**Before**
```
>>> spark.createDataFrame([[[1]]], "struct<a: struct<b: int>>").show()
...
ValueError: The strcut field string format is: 'field_name:field_type', but got: a: struct<b: int>
```
```
>>> spark.createDataFrame([[[1]]], "a: struct<b: int>").show()
...
ValueError: The strcut field string format is: 'field_name:field_type', but got: a: struct<b: int>
```
```
>>> spark.createDataFrame([[1]], "a int").show()
...
ValueError: Could not parse datatype: a int
```
**After**
```
>>> spark.createDataFrame([[[1]]], "struct<a: struct<b: int>>").show()
+---+
| a|
+---+
|[1]|
+---+
```
```
>>> spark.createDataFrame([[[1]]], "a: struct<b: int>").show()
+---+
| a|
+---+
|[1]|
+---+
```
```
>>> spark.createDataFrame([[1]], "a int").show()
+---+
| a|
+---+
| 1|
+---+
```
## How was this patch tested?
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#18590 from HyukjinKwon/deduplicate-python-ddl.
## What changes were proposed in this pull request?
This PR proposes to simply ignore the results in examples that are timezone-dependent in `unix_timestamp` and `from_unixtime`.
```
Failed example:
time_df.select(unix_timestamp('dt', 'yyyy-MM-dd').alias('unix_time')).collect()
Expected:
[Row(unix_time=1428476400)]
Got:unix_timestamp
[Row(unix_time=1428418800)]
```
```
Failed example:
time_df.select(from_unixtime('unix_time').alias('ts')).collect()
Expected:
[Row(ts=u'2015-04-08 00:00:00')]
Got:
[Row(ts=u'2015-04-08 16:00:00')]
```
## How was this patch tested?
Manually tested and `./run-tests --modules pyspark-sql`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#18597 from HyukjinKwon/SPARK-20456.
## What changes were proposed in this pull request?
At example of repartitionAndSortWithinPartitions at rdd.py, third argument should be True or False.
I proposed fix of example code.
## How was this patch tested?
* I rename test_repartitionAndSortWithinPartitions to test_repartitionAndSortWIthinPartitions_asc to specify boolean argument.
* I added test_repartitionAndSortWithinPartitions_desc to test False pattern at third argument.
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: chie8842 <chie8842@gmail.com>
Closes#18586 from chie8842/SPARK-21358.
## What changes were proposed in this pull request?
Integrate Apache Arrow with Spark to increase performance of `DataFrame.toPandas`. This has been done by using Arrow to convert data partitions on the executor JVM to Arrow payload byte arrays where they are then served to the Python process. The Python DataFrame can then collect the Arrow payloads where they are combined and converted to a Pandas DataFrame. Data types except complex, date, timestamp, and decimal are currently supported, otherwise an `UnsupportedOperation` exception is thrown.
Additions to Spark include a Scala package private method `Dataset.toArrowPayload` that will convert data partitions in the executor JVM to `ArrowPayload`s as byte arrays so they can be easily served. A package private class/object `ArrowConverters` that provide data type mappings and conversion routines. In Python, a private method `DataFrame._collectAsArrow` is added to collect Arrow payloads and a SQLConf "spark.sql.execution.arrow.enable" can be used in `toPandas()` to enable using Arrow (uses the old conversion by default).
## How was this patch tested?
Added a new test suite `ArrowConvertersSuite` that will run tests on conversion of Datasets to Arrow payloads for supported types. The suite will generate a Dataset and matching Arrow JSON data, then the dataset is converted to an Arrow payload and finally validated against the JSON data. This will ensure that the schema and data has been converted correctly.
Added PySpark tests to verify the `toPandas` method is producing equal DataFrames with and without pyarrow. A roundtrip test to ensure the pandas DataFrame produced by pyspark is equal to a one made directly with pandas.
Author: Bryan Cutler <cutlerb@gmail.com>
Author: Li Jin <ice.xelloss@gmail.com>
Author: Li Jin <li.jin@twosigma.com>
Author: Wes McKinney <wes.mckinney@twosigma.com>
Closes#18459 from BryanCutler/toPandas_with_arrow-SPARK-13534.
## What changes were proposed in this pull request?
This PR supports schema in a DDL formatted string for `from_json` in R/Python and `dapply` and `gapply` in R, which are commonly used and/or consistent with Scala APIs.
Additionally, this PR exposes `structType` in R to allow working around in other possible corner cases.
**Python**
`from_json`
```python
from pyspark.sql.functions import from_json
data = [(1, '''{"a": 1}''')]
df = spark.createDataFrame(data, ("key", "value"))
df.select(from_json(df.value, "a INT").alias("json")).show()
```
**R**
`from_json`
```R
df <- sql("SELECT named_struct('name', 'Bob') as people")
df <- mutate(df, people_json = to_json(df$people))
head(select(df, from_json(df$people_json, "name STRING")))
```
`structType.character`
```R
structType("a STRING, b INT")
```
`dapply`
```R
dapply(createDataFrame(list(list(1.0)), "a"), function(x) {x}, "a DOUBLE")
```
`gapply`
```R
gapply(createDataFrame(list(list(1.0)), "a"), "a", function(key, x) { x }, "a DOUBLE")
```
## How was this patch tested?
Doc tests for `from_json` in Python and unit tests `test_sparkSQL.R` in R.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#18498 from HyukjinKwon/SPARK-21266.
## What changes were proposed in this pull request?
This adds documentation to many functions in pyspark.sql.functions.py:
`upper`, `lower`, `reverse`, `unix_timestamp`, `from_unixtime`, `rand`, `randn`, `collect_list`, `collect_set`, `lit`
Add units to the trigonometry functions.
Renames columns in datetime examples to be more informative.
Adds links between some functions.
## How was this patch tested?
`./dev/lint-python`
`python python/pyspark/sql/functions.py`
`./python/run-tests.py --module pyspark-sql`
Author: Michael Patterson <map222@gmail.com>
Closes#17865 from map222/spark-20456.
## What changes were proposed in this pull request?
Currently `ArrayConstructor` handles an array of typecode `'l'` as `int` when converting Python object in Python 2 into Java object, so if the value is larger than `Integer.MAX_VALUE` or smaller than `Integer.MIN_VALUE` then the overflow occurs.
```python
import array
data = [Row(longarray=array.array('l', [-9223372036854775808, 0, 9223372036854775807]))]
df = spark.createDataFrame(data)
df.show(truncate=False)
```
```
+----------+
|longarray |
+----------+
|[0, 0, -1]|
+----------+
```
This should be:
```
+----------------------------------------------+
|longarray |
+----------------------------------------------+
|[-9223372036854775808, 0, 9223372036854775807]|
+----------------------------------------------+
```
## How was this patch tested?
Added a test and existing tests.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#18553 from ueshin/issues/SPARK-21327.
## What changes were proposed in this pull request?
Support register Java UDAFs in PySpark so that user can use Java UDAF in PySpark. Besides that I also add api in `UDFRegistration`
## How was this patch tested?
Unit test is added
Author: Jeff Zhang <zjffdu@apache.org>
Closes#17222 from zjffdu/SPARK-19439.
## What changes were proposed in this pull request?
Add offset to PySpark in GLM as in #16699.
## How was this patch tested?
Python test
Author: actuaryzhang <actuaryzhang10@gmail.com>
Closes#18534 from actuaryzhang/pythonOffset.
## What changes were proposed in this pull request?
**Context**
While reviewing https://github.com/apache/spark/pull/17227, I realised here we type-dispatch per record. The PR itself is fine in terms of performance as is but this prints a prefix, `"obj"` in exception message as below:
```
from pyspark.sql.types import *
schema = StructType([StructField('s', IntegerType(), nullable=False)])
spark.createDataFrame([["1"]], schema)
...
TypeError: obj.s: IntegerType can not accept object '1' in type <type 'str'>
```
I suggested to get rid of this but during investigating this, I realised my approach might bring a performance regression as it is a hot path.
Only for SPARK-19507 and https://github.com/apache/spark/pull/17227, It needs more changes to cleanly get rid of the prefix and I rather decided to fix both issues together.
**Propersal**
This PR tried to
- get rid of per-record type dispatch as we do in many code paths in Scala so that it improves the performance (roughly ~25% improvement) - SPARK-21296
This was tested with a simple code `spark.createDataFrame(range(1000000), "int")`. However, I am quite sure the actual improvement in practice is larger than this, in particular, when the schema is complicated.
- improve error message in exception describing field information as prose - SPARK-19507
## How was this patch tested?
Manually tested and unit tests were added in `python/pyspark/sql/tests.py`.
Benchmark - codes: https://gist.github.com/HyukjinKwon/c3397469c56cb26c2d7dd521ed0bc5a3
Error message - codes: https://gist.github.com/HyukjinKwon/b1b2c7f65865444c4a8836435100e398
**Before**
Benchmark:
- Results: https://gist.github.com/HyukjinKwon/4a291dab45542106301a0c1abcdca924
Error message
- Results: https://gist.github.com/HyukjinKwon/57b1916395794ce924faa32b14a3fe19
**After**
Benchmark
- Results: https://gist.github.com/HyukjinKwon/21496feecc4a920e50c4e455f836266e
Error message
- Results: https://gist.github.com/HyukjinKwon/7a494e4557fe32a652ce1236e504a395Closes#17227
Author: hyukjinkwon <gurwls223@gmail.com>
Author: David Gingrich <david@textio.com>
Closes#18521 from HyukjinKwon/python-type-dispatch.
## What changes were proposed in this pull request?
Currently, it throws a NPE when missing columns but join type is speicified in join at PySpark as below:
```python
spark.conf.set("spark.sql.crossJoin.enabled", "false")
spark.range(1).join(spark.range(1), how="inner").show()
```
```
Traceback (most recent call last):
...
py4j.protocol.Py4JJavaError: An error occurred while calling o66.join.
: java.lang.NullPointerException
at org.apache.spark.sql.Dataset.join(Dataset.scala:931)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
...
```
```python
spark.conf.set("spark.sql.crossJoin.enabled", "true")
spark.range(1).join(spark.range(1), how="inner").show()
```
```
...
py4j.protocol.Py4JJavaError: An error occurred while calling o84.join.
: java.lang.NullPointerException
at org.apache.spark.sql.Dataset.join(Dataset.scala:931)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
...
```
This PR suggests to follow Scala's one as below:
```scala
scala> spark.conf.set("spark.sql.crossJoin.enabled", "false")
scala> spark.range(1).join(spark.range(1), Seq.empty[String], "inner").show()
```
```
org.apache.spark.sql.AnalysisException: Detected cartesian product for INNER join between logical plans
Range (0, 1, step=1, splits=Some(8))
and
Range (0, 1, step=1, splits=Some(8))
Join condition is missing or trivial.
Use the CROSS JOIN syntax to allow cartesian products between these relations.;
...
```
```scala
scala> spark.conf.set("spark.sql.crossJoin.enabled", "true")
scala> spark.range(1).join(spark.range(1), Seq.empty[String], "inner").show()
```
```
+---+---+
| id| id|
+---+---+
| 0| 0|
+---+---+
```
**After**
```python
spark.conf.set("spark.sql.crossJoin.enabled", "false")
spark.range(1).join(spark.range(1), how="inner").show()
```
```
Traceback (most recent call last):
...
pyspark.sql.utils.AnalysisException: u'Detected cartesian product for INNER join between logical plans\nRange (0, 1, step=1, splits=Some(8))\nand\nRange (0, 1, step=1, splits=Some(8))\nJoin condition is missing or trivial.\nUse the CROSS JOIN syntax to allow cartesian products between these relations.;'
```
```python
spark.conf.set("spark.sql.crossJoin.enabled", "true")
spark.range(1).join(spark.range(1), how="inner").show()
```
```
+---+---+
| id| id|
+---+---+
| 0| 0|
+---+---+
```
## How was this patch tested?
Added tests in `python/pyspark/sql/tests.py`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#18484 from HyukjinKwon/SPARK-21264.
## What changes were proposed in this pull request?
This PR is to maintain API parity with changes made in SPARK-17498 to support a new option
'keep' in StringIndexer to handle unseen labels or NULL values with PySpark.
Note: This is updated version of #17237 , the primary author of this PR is VinceShieh .
## How was this patch tested?
Unit tests.
Author: VinceShieh <vincent.xie@intel.com>
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#18453 from yanboliang/spark-19852.
## What changes were proposed in this pull request?
1, make param support non-final with `finalFields` option
2, generate `HasSolver` with `finalFields = false`
3, override `solver` in LiR, GLR, and make MLPC inherit `HasSolver`
## How was this patch tested?
existing tests
Author: Ruifeng Zheng <ruifengz@foxmail.com>
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#16028 from zhengruifeng/param_non_final.
## What changes were proposed in this pull request?
This pr supported a DDL-formatted string in `DataStreamReader.schema`.
This fix could make users easily define a schema without importing the type classes.
For example,
```scala
scala> spark.readStream.schema("col0 INT, col1 DOUBLE").load("/tmp/abc").printSchema()
root
|-- col0: integer (nullable = true)
|-- col1: double (nullable = true)
```
## How was this patch tested?
Added tests in `DataStreamReaderWriterSuite`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#18373 from HyukjinKwon/SPARK-20431.
## What changes were proposed in this pull request?
Integrate Apache Arrow with Spark to increase performance of `DataFrame.toPandas`. This has been done by using Arrow to convert data partitions on the executor JVM to Arrow payload byte arrays where they are then served to the Python process. The Python DataFrame can then collect the Arrow payloads where they are combined and converted to a Pandas DataFrame. All non-complex data types are currently supported, otherwise an `UnsupportedOperation` exception is thrown.
Additions to Spark include a Scala package private method `Dataset.toArrowPayloadBytes` that will convert data partitions in the executor JVM to `ArrowPayload`s as byte arrays so they can be easily served. A package private class/object `ArrowConverters` that provide data type mappings and conversion routines. In Python, a public method `DataFrame.collectAsArrow` is added to collect Arrow payloads and an optional flag in `toPandas(useArrow=False)` to enable using Arrow (uses the old conversion by default).
## How was this patch tested?
Added a new test suite `ArrowConvertersSuite` that will run tests on conversion of Datasets to Arrow payloads for supported types. The suite will generate a Dataset and matching Arrow JSON data, then the dataset is converted to an Arrow payload and finally validated against the JSON data. This will ensure that the schema and data has been converted correctly.
Added PySpark tests to verify the `toPandas` method is producing equal DataFrames with and without pyarrow. A roundtrip test to ensure the pandas DataFrame produced by pyspark is equal to a one made directly with pandas.
Author: Bryan Cutler <cutlerb@gmail.com>
Author: Li Jin <ice.xelloss@gmail.com>
Author: Li Jin <li.jin@twosigma.com>
Author: Wes McKinney <wes.mckinney@twosigma.com>
Closes#15821 from BryanCutler/wip-toPandas_with_arrow-SPARK-13534.
## What changes were proposed in this pull request?
Currently we convert a spark DataFrame to Pandas Dataframe by `pd.DataFrame.from_records`. It infers the data type from the data and doesn't respect the spark DataFrame Schema. This PR fixes it.
## How was this patch tested?
a new regression test
Author: hyukjinkwon <gurwls223@gmail.com>
Author: Wenchen Fan <wenchen@databricks.com>
Author: Wenchen Fan <cloud0fan@gmail.com>
Closes#18378 from cloud-fan/to_pandas.
## What changes were proposed in this pull request?
Add Python wrappers for `o.a.s.sql.functions.explode_outer` and `o.a.s.sql.functions.posexplode_outer`.
## How was this patch tested?
Unit tests, doctests.
Author: zero323 <zero323@users.noreply.github.com>
Closes#18049 from zero323/SPARK-20830.
## What changes were proposed in this pull request?
Extend setJobDescription to PySpark and JavaSpark APIs
SPARK-21125
## How was this patch tested?
Testing was done by running a local Spark shell on the built UI. I originally had added a unit test but the PySpark context cannot easily access the Scala Spark Context's private variable with the Job Description key so I omitted the test, due to the simplicity of this addition.
Also ran the existing tests.
# Misc
This contribution is my original work and that I license the work to the project under the project's open source license.
Author: sjarvie <sjarvie@uber.com>
Closes#18332 from sjarvie/add_python_set_job_description.
## What changes were proposed in this pull request?
LinearSVC should use its own threshold param, rather than the shared one, since it applies to rawPrediction instead of probability. This PR changes the param in the Scala, Python and R APIs.
## How was this patch tested?
New unit test to make sure the threshold can be set to any Double value.
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#18151 from jkbradley/ml-2.2-linearsvc-cleanup.
## What changes were proposed in this pull request?
Fix some typo of the document.
## How was this patch tested?
Existing tests.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Xianyang Liu <xianyang.liu@intel.com>
Closes#18350 from ConeyLiu/fixtypo.
## What changes were proposed in this pull request?
This fix tries to address the issue in SPARK-19975 where we
have `map_keys` and `map_values` functions in SQL yet there
is no Python equivalent functions.
This fix adds `map_keys` and `map_values` functions to Python.
## How was this patch tested?
This fix is tested manually (See Python docs for examples).
Author: Yong Tang <yong.tang.github@outlook.com>
Closes#17328 from yongtang/SPARK-19975.
### What changes were proposed in this pull request?
The current option name `wholeFile` is misleading for CSV users. Currently, it is not representing a record per file. Actually, one file could have multiple records. Thus, we should rename it. Now, the proposal is `multiLine`.
### How was this patch tested?
N/A
Author: Xiao Li <gatorsmile@gmail.com>
Closes#18202 from gatorsmile/renameCVSOption.
## What changes were proposed in this pull request?
Document Dataset.union is resolution by position, not by name, since this has been a confusing point for a lot of users.
## How was this patch tested?
N/A - doc only change.
Author: Reynold Xin <rxin@databricks.com>
Closes#18256 from rxin/SPARK-21042.
## What changes were proposed in this pull request?
Allow fill/replace of NAs with booleans, both in Python and Scala
## How was this patch tested?
Unit tests, doctests
This PR is original work from me and I license this work to the Spark project
Author: Ruben Berenguel Montoro <ruben@mostlymaths.net>
Author: Ruben Berenguel <ruben@mostlymaths.net>
Closes#18164 from rberenguel/SPARK-19732-fillna-bools.
### What changes were proposed in this pull request?
This PR does the following tasks:
- Added since
- Added the Python API
- Added test cases
### How was this patch tested?
Added test cases to both Scala and Python
Author: gatorsmile <gatorsmile@gmail.com>
Closes#18147 from gatorsmile/createOrReplaceGlobalTempView.
## What changes were proposed in this pull request?
PySpark supports stringIndexerOrderType in RFormula as in #17967.
## How was this patch tested?
docstring test
Author: actuaryzhang <actuaryzhang10@gmail.com>
Closes#18122 from actuaryzhang/PythonRFormula.
Now that Structured Streaming has been out for several Spark release and has large production use cases, the `Experimental` label is no longer appropriate. I've left `InterfaceStability.Evolving` however, as I think we may make a few changes to the pluggable Source & Sink API in Spark 2.3.
Author: Michael Armbrust <michael@databricks.com>
Closes#18065 from marmbrus/streamingGA.
## What changes were proposed in this pull request?
Expose numPartitions (expert) param of PySpark FPGrowth.
## How was this patch tested?
+ [x] Pass all unit tests.
Author: Yan Facai (颜发才) <facai.yan@gmail.com>
Closes#18058 from facaiy/ENH/pyspark_fpg_add_num_partition.
## What changes were proposed in this pull request?
Follow-up for #17218, some minor fix for PySpark ```FPGrowth```.
## How was this patch tested?
Existing UT.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#18089 from yanboliang/spark-19281.
## What changes were proposed in this pull request?
Fixed TypeError with python3 and numpy 1.12.1. Numpy's `reshape` no longer takes floats as arguments as of 1.12. Also, python3 uses float division for `/`, we should be using `//` to ensure that `_dataWithBiasSize` doesn't get set to a float.
## How was this patch tested?
Existing tests run using python3 and numpy 1.12.
Author: Bago Amirbekian <bago@databricks.com>
Closes#18081 from MrBago/BF-py3floatbug.
## What changes were proposed in this pull request?
- Fix incorrect tests for `_check_thresholds`.
- Move test to `ParamTests`.
## How was this patch tested?
Unit tests.
Author: zero323 <zero323@users.noreply.github.com>
Closes#18085 from zero323/SPARK-20631-FOLLOW-UP.
## What changes were proposed in this pull request?
Add test cases for PR-18062
## How was this patch tested?
The existing UT
Author: Peng <peng.meng@intel.com>
Closes#18068 from mpjlu/moreTest.
Changes:
pyspark.ml Estimators can take either a list of param maps or a dict of params. This change allows the CrossValidator and TrainValidationSplit Estimators to pass through lists of param maps to the underlying estimators so that those estimators can handle parallelization when appropriate (eg distributed hyper parameter tuning).
Testing:
Existing unit tests.
Author: Bago Amirbekian <bago@databricks.com>
Closes#18077 from MrBago/delegate_params.
## What changes were proposed in this pull request?
SPARK-20097 exposed degreesOfFreedom in LinearRegressionSummary and numInstances in GeneralizedLinearRegressionSummary. Python API should be updated to reflect these changes.
## How was this patch tested?
The existing UT
Author: Peng <peng.meng@intel.com>
Closes#18062 from mpjlu/spark-20764.
## What changes were proposed in this pull request?
PySpark StringIndexer supports StringOrderType added in #17879.
Author: Wayne Zhang <actuaryzhang@uber.com>
Closes#17978 from actuaryzhang/PythonStringIndexer.
## What changes were proposed in this pull request?
Review new Scala APIs introduced in 2.2.
## How was this patch tested?
Existing tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#17934 from yanboliang/spark-20501.
## What changes were proposed in this pull request?
Before 2.2, MLlib keep to remove APIs deprecated in last feature/minor release. But from Spark 2.2, we decide to remove deprecated APIs in a major release, so we need to change corresponding annotations to tell users those will be removed in 3.0.
Meanwhile, this fixed bugs in ML documents. The original ML docs can't show deprecated annotations in ```MLWriter``` and ```MLReader``` related class, we correct it in this PR.
Before:
After:
## How was this patch tested?
Existing tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#17946 from yanboliang/spark-20707.
## What changes were proposed in this pull request?
This PR proposes three things as below:
- Use casting rules to a timestamp in `to_timestamp` by default (it was `yyyy-MM-dd HH:mm:ss`).
- Support single argument for `to_timestamp` similarly with APIs in other languages.
For example, the one below works
```
import org.apache.spark.sql.functions._
Seq("2016-12-31 00:12:00.00").toDF("a").select(to_timestamp(col("a"))).show()
```
prints
```
+----------------------------------------+
|to_timestamp(`a`, 'yyyy-MM-dd HH:mm:ss')|
+----------------------------------------+
| 2016-12-31 00:12:00|
+----------------------------------------+
```
whereas this does not work in SQL.
**Before**
```
spark-sql> SELECT to_timestamp('2016-12-31 00:12:00');
Error in query: Invalid number of arguments for function to_timestamp; line 1 pos 7
```
**After**
```
spark-sql> SELECT to_timestamp('2016-12-31 00:12:00');
2016-12-31 00:12:00
```
- Related document improvement for SQL function descriptions and other API descriptions accordingly.
**Before**
```
spark-sql> DESCRIBE FUNCTION extended to_date;
...
Usage: to_date(date_str, fmt) - Parses the `left` expression with the `fmt` expression. Returns null with invalid input.
Extended Usage:
Examples:
> SELECT to_date('2016-12-31', 'yyyy-MM-dd');
2016-12-31
```
```
spark-sql> DESCRIBE FUNCTION extended to_timestamp;
...
Usage: to_timestamp(timestamp, fmt) - Parses the `left` expression with the `format` expression to a timestamp. Returns null with invalid input.
Extended Usage:
Examples:
> SELECT to_timestamp('2016-12-31', 'yyyy-MM-dd');
2016-12-31 00:00:00.0
```
**After**
```
spark-sql> DESCRIBE FUNCTION extended to_date;
...
Usage:
to_date(date_str[, fmt]) - Parses the `date_str` expression with the `fmt` expression to
a date. Returns null with invalid input. By default, it follows casting rules to a date if
the `fmt` is omitted.
Extended Usage:
Examples:
> SELECT to_date('2009-07-30 04:17:52');
2009-07-30
> SELECT to_date('2016-12-31', 'yyyy-MM-dd');
2016-12-31
```
```
spark-sql> DESCRIBE FUNCTION extended to_timestamp;
...
Usage:
to_timestamp(timestamp[, fmt]) - Parses the `timestamp` expression with the `fmt` expression to
a timestamp. Returns null with invalid input. By default, it follows casting rules to
a timestamp if the `fmt` is omitted.
Extended Usage:
Examples:
> SELECT to_timestamp('2016-12-31 00:12:00');
2016-12-31 00:12:00
> SELECT to_timestamp('2016-12-31', 'yyyy-MM-dd');
2016-12-31 00:00:00
```
## How was this patch tested?
Added tests in `datetime.sql`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#17901 from HyukjinKwon/to_timestamp_arg.
## What changes were proposed in this pull request?
This pr supported a DDL-formatted string in `DataFrameReader.schema`.
This fix could make users easily define a schema without importing `o.a.spark.sql.types._`.
## How was this patch tested?
Added tests in `DataFrameReaderWriterSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#17719 from maropu/SPARK-20431.
## What changes were proposed in this pull request?
There's a latent corner-case bug in PySpark UDF evaluation where executing a `BatchPythonEvaluation` with a single multi-argument UDF where _at least one argument value is repeated_ will crash at execution with a confusing error.
This problem was introduced in #12057: the code there has a fast path for handling a "batch UDF evaluation consisting of a single Python UDF", but that branch incorrectly assumes that a single UDF won't have repeated arguments and therefore skips the code for unpacking arguments from the input row (whose schema may not necessarily match the UDF inputs due to de-duplication of repeated arguments which occurred in the JVM before sending UDF inputs to Python).
This fix here is simply to remove this special-casing: it turns out that the code in the "multiple UDFs" branch just so happens to work for the single-UDF case because Python treats `(x)` as equivalent to `x`, not as a single-argument tuple.
## How was this patch tested?
New regression test in `pyspark.python.sql.tests` module (tested and confirmed that it fails before my fix).
Author: Josh Rosen <joshrosen@databricks.com>
Closes#17927 from JoshRosen/SPARK-20685.
## What changes were proposed in this pull request?
It turns out pyspark doctest is calling saveAsTable without ever dropping them. Since we have separate python tests for bucketed table, and there is no checking of results, there is really no need to run the doctest, other than leaving it as an example in the generated doc
## How was this patch tested?
Jenkins
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#17932 from felixcheung/pytablecleanup.
## What changes were proposed in this pull request?
- Replace `getParam` calls with `getOrDefault` calls.
- Fix exception message to avoid unintended `TypeError`.
- Add unit tests
## How was this patch tested?
New unit tests.
Author: zero323 <zero323@users.noreply.github.com>
Closes#17891 from zero323/SPARK-20631.
## What changes were proposed in this pull request?
Remove ML methods we deprecated in 2.1.
## How was this patch tested?
Existing tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#17867 from yanboliang/spark-20606.
## What changes were proposed in this pull request?
Adds Python wrappers for `DataFrameWriter.bucketBy` and `DataFrameWriter.sortBy` ([SPARK-16931](https://issues.apache.org/jira/browse/SPARK-16931))
## How was this patch tested?
Unit tests covering new feature.
__Note__: Based on work of GregBowyer (f49b9a23468f7af32cb53d2b654272757c151725)
CC HyukjinKwon
Author: zero323 <zero323@users.noreply.github.com>
Author: Greg Bowyer <gbowyer@fastmail.co.uk>
Closes#17077 from zero323/SPARK-16931.
## What changes were proposed in this pull request?
- Move udf wrapping code from `functions.udf` to `functions.UserDefinedFunction`.
- Return wrapped udf from `catalog.registerFunction` and dependent methods.
- Update docstrings in `catalog.registerFunction` and `SQLContext.registerFunction`.
- Unit tests.
## How was this patch tested?
- Existing unit tests and docstests.
- Additional tests covering new feature.
Author: zero323 <zero323@users.noreply.github.com>
Closes#17831 from zero323/SPARK-18777.
## What changes were proposed in this pull request?
Adds `hint` method to PySpark `DataFrame`.
## How was this patch tested?
Unit tests, doctests.
Author: zero323 <zero323@users.noreply.github.com>
Closes#17850 from zero323/SPARK-20584.
## What changes were proposed in this pull request?
Use midpoints for split values now, and maybe later to make it weighted.
## How was this patch tested?
+ [x] add unit test.
+ [x] revise Split's unit test.
Author: Yan Facai (颜发才) <facai.yan@gmail.com>
Author: 颜发才（Yan Facai） <facai.yan@gmail.com>
Closes#17556 from facaiy/ENH/decision_tree_overflow_and_precision_in_aggregation.
Add PCA and SVD to PySpark's wrappers for `RowMatrix` and `IndexedRowMatrix` (SVD only).
Based on #7963, updated.
## How was this patch tested?
New doc tests and unit tests. Ran all examples locally.
Author: MechCoder <manojkumarsivaraj334@gmail.com>
Author: Nick Pentreath <nickp@za.ibm.com>
Closes#17621 from MLnick/SPARK-6227-pyspark-svd-pca.
Add Python API for `ALSModel` methods `recommendForAllUsers`, `recommendForAllItems`
## How was this patch tested?
New doc tests.
Author: Nick Pentreath <nickp@za.ibm.com>
Closes#17622 from MLnick/SPARK-20300-pyspark-recall.
## What changes were proposed in this pull request?
Adds Python bindings for `Column.eqNullSafe`
## How was this patch tested?
Manual tests, existing unit tests, doc build.
Author: zero323 <zero323@users.noreply.github.com>
Closes#17605 from zero323/SPARK-20290.
## What changes were proposed in this pull request?
Currently pyspark Dataframe.fillna API supports boolean type when we pass dict, but it is missing in documentation.
## How was this patch tested?
>>> spark.createDataFrame([Row(a=True),Row(a=None)]).fillna({"a" : True}).show()
+----+
| a|
+----+
|true|
|true|
+----+
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Srinivasa Reddy Vundela <vsr@cloudera.com>
Closes#17688 from vundela/fillna_doc_fix.
## What changes were proposed in this pull request?
This PR proposes to fill up the documentation with examples for `bitwiseOR`, `bitwiseAND`, `bitwiseXOR`. `contains`, `asc` and `desc` in `Column` API.
Also, this PR fixes minor typos in the documentation and matches some of the contents between Scala doc and Python doc.
Lastly, this PR suggests to use `spark` rather than `sc` in doc tests in `Column` for Python documentation.
## How was this patch tested?
Doc tests were added and manually tested with the commands below:
`./python/run-tests.py --module pyspark-sql`
`./python/run-tests.py --module pyspark-sql --python-executable python3`
`./dev/lint-python`
Output was checked via `make html` under `./python/docs`. The snapshots will be left on the codes with comments.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#17737 from HyukjinKwon/SPARK-20442.
## What changes were proposed in this pull request?
Some PySpark & SparkR tests run with tiny dataset and tiny ```maxIter```, which means they are not converged. I don’t think checking intermediate result during iteration make sense, and these intermediate result may vulnerable and not stable, so we should switch to check the converged result. We hit this issue at #17746 when we upgrade breeze to 0.13.1.
## How was this patch tested?
Existing tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#17757 from yanboliang/flaky-test.
## What changes were proposed in this pull request?
Upgrade breeze version to 0.13.1, which fixed some critical bugs of L-BFGS-B.
## How was this patch tested?
Existing unit tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#17746 from yanboliang/spark-20449.
## What changes were proposed in this pull request?
Add docstrings to column.py for the Column functions `rlike`, `like`, `startswith`, and `endswith`. Pass these docstrings through `_bin_op`
There may be a better place to put the docstrings. I put them immediately above the Column class.
## How was this patch tested?
I ran `make html` on my local computer to remake the documentation, and verified that the html pages were displaying the docstrings correctly. I tried running `dev-tests`, and the formatting tests passed. However, my mvn build didn't work I think due to issues on my computer.
These docstrings are my original work and free license.
davies has done the most recent work reorganizing `_bin_op`
Author: Michael Patterson <map222@gmail.com>
Closes#17469 from map222/patterson-documentation.
## What changes were proposed in this pull request?
Improve combineByKey documentation:
* Add note on memory allocation
* Change example code to use different mergeValue and mergeCombiners
## How was this patch tested?
Doctest.
## Legal
This is my original work and I license the work to the project under the project’s open source license.
Author: David Gingrich <david@textio.com>
Closes#17545 from dgingrich/topic-spark-20232-combinebykey-docs.
## What changes were proposed in this pull request?
SPARK-15236 do this for scala shell, this ticket is for pyspark shell. This is not only for pyspark itself, but can also benefit downstream project like livy which use shell.py for its interactive session. For now, livy has no control of whether enable hive or not.
## How was this patch tested?
I didn't find a way to add test for it. Just manually test it.
Run `bin/pyspark --master local --conf spark.sql.catalogImplementation=in-memory` and verify hive is not enabled.
Author: Jeff Zhang <zjffdu@apache.org>
Closes#16906 from zjffdu/SPARK-19570.
## What changes were proposed in this pull request?
This PR proposes corrections related to JSON APIs as below:
- Rendering links in Python documentation
- Replacing `RDD` to `Dataset` in programing guide
- Adding missing description about JSON Lines consistently in `DataFrameReader.json` in Python API
- De-duplicating little bit of `DataFrameReader.json` in Scala/Java API
## How was this patch tested?
Manually build the documentation via `jekyll build`. Corresponding snapstops will be left on the codes.
Note that currently there are Javadoc8 breaks in several places. These are proposed to be handled in https://github.com/apache/spark/pull/17477. So, this PR does not fix those.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#17602 from HyukjinKwon/minor-json-documentation.
## What changes were proposed in this pull request?
Added `util._message_exception` helper to use `str(e)` when `e.message` is unavailable (Python3). Grepped for all occurrences of `.message` in `pyspark/` and these were the only occurrences.
## How was this patch tested?
- Doctests for helper function
## Legal
This is my original work and I license the work to the project under the project’s open source license.
Author: David Gingrich <david@textio.com>
Closes#16845 from dgingrich/topic-spark-19505-py3-exceptions.
## What changes were proposed in this pull request?
Saw the following failure locally:
```
Traceback (most recent call last):
File "/home/jenkins/workspace/python/pyspark/streaming/tests.py", line 351, in test_cogroup
self._test_func(input, func, expected, sort=True, input2=input2)
File "/home/jenkins/workspace/python/pyspark/streaming/tests.py", line 162, in _test_func
self.assertEqual(expected, result)
AssertionError: Lists differ: [[(1, ([1], [2])), (2, ([1], [... != []
First list contains 3 additional elements.
First extra element 0:
[(1, ([1], [2])), (2, ([1], [])), (3, ([1], []))]
+ []
- [[(1, ([1], [2])), (2, ([1], [])), (3, ([1], []))],
- [(1, ([1, 1, 1], [])), (2, ([1], [])), (4, ([], [1]))],
- [('', ([1, 1], [1, 2])), ('a', ([1, 1], [1, 1])), ('b', ([1], [1]))]]
```
It also happened on Jenkins: http://spark-tests.appspot.com/builds/spark-branch-2.1-test-sbt-hadoop-2.7/120
It's because when the machine is overloaded, the timeout is not enough. This PR just increases the timeout to 30 seconds.
## How was this patch tested?
Jenkins
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#17597 from zsxwing/SPARK-20285.
## What changes were proposed in this pull request?
The Dataframes-based support for the correlation statistics is added in #17108. This patch adds the Python interface for it.
## How was this patch tested?
Python unit test.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#17494 from viirya/correlation-python-api.
## What changes were proposed in this pull request?
Update doc to remove external for createTable, add refreshByPath in python
## How was this patch tested?
manual
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#17512 from felixcheung/catalogdoc.
## What changes were proposed in this pull request?
PySpark version in version.py was lagging behind
Versioning is in line with PEP 440: https://www.python.org/dev/peps/pep-0440/
## How was this patch tested?
Simply rebuild the project with existing tests
Author: setjet <rubenljanssen@gmail.com>
Author: Ruben Janssen <rubenljanssen@gmail.com>
Closes#17523 from setjet/SPARK-20064.
## What changes were proposed in this pull request?
`_convert_to_vector` converts a scipy sparse matrix to csc matrix for initializing `SparseVector`. However, it doesn't guarantee the converted csc matrix has sorted indices and so a failure happens when you do something like that:
from scipy.sparse import lil_matrix
lil = lil_matrix((4, 1))
lil[1, 0] = 1
lil[3, 0] = 2
_convert_to_vector(lil.todok())
File "/home/jenkins/workspace/python/pyspark/mllib/linalg/__init__.py", line 78, in _convert_to_vector
return SparseVector(l.shape[0], csc.indices, csc.data)
File "/home/jenkins/workspace/python/pyspark/mllib/linalg/__init__.py", line 556, in __init__
% (self.indices[i], self.indices[i + 1]))
TypeError: Indices 3 and 1 are not strictly increasing
A simple test can confirm that `dok_matrix.tocsc()` won't guarantee sorted indices:
>>> from scipy.sparse import lil_matrix
>>> lil = lil_matrix((4, 1))
>>> lil[1, 0] = 1
>>> lil[3, 0] = 2
>>> dok = lil.todok()
>>> csc = dok.tocsc()
>>> csc.has_sorted_indices
0
>>> csc.indices
array([3, 1], dtype=int32)
I checked the source codes of scipy. The only way to guarantee it is `csc_matrix.tocsr()` and `csr_matrix.tocsc()`.
## How was this patch tested?
Existing tests.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#17532 from viirya/make-sure-sorted-indices.
## What changes were proposed in this pull request?
- Allows skipping `value` argument if `to_replace` is a `dict`:
```python
df = sc.parallelize([("Alice", 1, 3.0)]).toDF()
df.replace({"Alice": "Bob"}).show()
````
- Adds validation step to ensure homogeneous values / replacements.
- Simplifies internal control flow.
- Improves unit tests coverage.
## How was this patch tested?
Existing unit tests, additional unit tests, manual testing.
Author: zero323 <zero323@users.noreply.github.com>
Closes#16793 from zero323/SPARK-19454.
## What changes were proposed in this pull request?
This PR proposes to use `XXX` format instead of `ZZ`. `ZZ` seems a `FastDateFormat` specific.
`ZZ` supports "ISO 8601 extended format time zones" but it seems `FastDateFormat` specific option.
I misunderstood this is compatible format with `SimpleDateFormat` when this change is introduced.
Please see [SimpleDateFormat documentation]( https://docs.oracle.com/javase/7/docs/api/java/text/SimpleDateFormat.html#iso8601timezone) and [FastDateFormat documentation](https://commons.apache.org/proper/commons-lang/apidocs/org/apache/commons/lang3/time/FastDateFormat.html).
It seems we better replace `ZZ` to `XXX` because they look using the same strategy - [FastDateParser.java#L930](8767cd4f1a/src/main/java/org/apache/commons/lang3/time/FastDateParser.java (L930)), [FastDateParser.java#L932-L951 ](8767cd4f1a/src/main/java/org/apache/commons/lang3/time/FastDateParser.java (L932-L951)) and [FastDateParser.java#L596-L601](8767cd4f1a/src/main/java/org/apache/commons/lang3/time/FastDateParser.java (L596-L601)).
I also checked the codes and manually debugged it for sure. It seems both cases use the same pattern `( Z|(?:[+-]\\d{2}(?::)\\d{2}))`.
_Note that this should be rather a fix about documentation and not the behaviour change because `ZZ` seems invalid date format in `SimpleDateFormat` as documented in `DataFrameReader` and etc, and both `ZZ` and `XXX` look identically working with `FastDateFormat`_
Current documentation is as below:
```
* <li>`timestampFormat` (default `yyyy-MM-dd'T'HH:mm:ss.SSSZZ`): sets the string that
* indicates a timestamp format. Custom date formats follow the formats at
* `java.text.SimpleDateFormat`. This applies to timestamp type.</li>
```
## How was this patch tested?
Existing tests should cover this. Also, manually tested as below (BTW, I don't think these are worth being added as tests within Spark):
**Parse**
```scala
scala> new java.text.SimpleDateFormat("yyyy-MM-dd'T'HH:mm:ss.SSSXXX").parse("2017-03-21T00:00:00.000-11:00")
res4: java.util.Date = Tue Mar 21 20:00:00 KST 2017
scala> new java.text.SimpleDateFormat("yyyy-MM-dd'T'HH:mm:ss.SSSXXX").parse("2017-03-21T00:00:00.000Z")
res10: java.util.Date = Tue Mar 21 09:00:00 KST 2017
scala> new java.text.SimpleDateFormat("yyyy-MM-dd'T'HH:mm:ss.SSSZZ").parse("2017-03-21T00:00:00.000-11:00")
java.text.ParseException: Unparseable date: "2017-03-21T00:00:00.000-11:00"
at java.text.DateFormat.parse(DateFormat.java:366)
... 48 elided
scala> new java.text.SimpleDateFormat("yyyy-MM-dd'T'HH:mm:ss.SSSZZ").parse("2017-03-21T00:00:00.000Z")
java.text.ParseException: Unparseable date: "2017-03-21T00:00:00.000Z"
at java.text.DateFormat.parse(DateFormat.java:366)
... 48 elided
```
```scala
scala> org.apache.commons.lang3.time.FastDateFormat.getInstance("yyyy-MM-dd'T'HH:mm:ss.SSSXXX").parse("2017-03-21T00:00:00.000-11:00")
res7: java.util.Date = Tue Mar 21 20:00:00 KST 2017
scala> org.apache.commons.lang3.time.FastDateFormat.getInstance("yyyy-MM-dd'T'HH:mm:ss.SSSXXX").parse("2017-03-21T00:00:00.000Z")
res1: java.util.Date = Tue Mar 21 09:00:00 KST 2017
scala> org.apache.commons.lang3.time.FastDateFormat.getInstance("yyyy-MM-dd'T'HH:mm:ss.SSSZZ").parse("2017-03-21T00:00:00.000-11:00")
res8: java.util.Date = Tue Mar 21 20:00:00 KST 2017
scala> org.apache.commons.lang3.time.FastDateFormat.getInstance("yyyy-MM-dd'T'HH:mm:ss.SSSZZ").parse("2017-03-21T00:00:00.000Z")
res2: java.util.Date = Tue Mar 21 09:00:00 KST 2017
```
**Format**
```scala
scala> new java.text.SimpleDateFormat("yyyy-MM-dd'T'HH:mm:ss.SSSXXX").format(new java.text.SimpleDateFormat("yyyy-MM-dd'T'HH:mm:ss.SSSXXX").parse("2017-03-21T00:00:00.000-11:00"))
res6: String = 2017-03-21T20:00:00.000+09:00
```
```scala
scala> val fd = org.apache.commons.lang3.time.FastDateFormat.getInstance("yyyy-MM-dd'T'HH:mm:ss.SSSZZ")
fd: org.apache.commons.lang3.time.FastDateFormat = FastDateFormat[yyyy-MM-dd'T'HH:mm:ss.SSSZZ,ko_KR,Asia/Seoul]
scala> fd.format(fd.parse("2017-03-21T00:00:00.000-11:00"))
res1: String = 2017-03-21T20:00:00.000+09:00
scala> val fd = org.apache.commons.lang3.time.FastDateFormat.getInstance("yyyy-MM-dd'T'HH:mm:ss.SSSXXX")
fd: org.apache.commons.lang3.time.FastDateFormat = FastDateFormat[yyyy-MM-dd'T'HH:mm:ss.SSSXXX,ko_KR,Asia/Seoul]
scala> fd.format(fd.parse("2017-03-21T00:00:00.000-11:00"))
res2: String = 2017-03-21T20:00:00.000+09:00
```
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#17489 from HyukjinKwon/SPARK-20166.
## What changes were proposed in this pull request?
A pyspark wrapper for spark.ml.stat.ChiSquareTest
## How was this patch tested?
unit tests
doctests
Author: Bago Amirbekian <bago@databricks.com>
Closes#17421 from MrBago/chiSquareTestWrapper.
## What changes were proposed in this pull request?
This PR proposes to match minor documentations changes in https://github.com/apache/spark/pull/17399 and https://github.com/apache/spark/pull/17380 to R/Python.
## How was this patch tested?
Manual tests in Python , Python tests via `./python/run-tests.py --module=pyspark-sql` and lint-checks for Python/R.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#17429 from HyukjinKwon/minor-match-doc.
## What changes were proposed in this pull request?
- Add `HasSupport` and `HasConfidence` `Params`.
- Add new module `pyspark.ml.fpm`.
- Add `FPGrowth` / `FPGrowthModel` wrappers.
- Provide tests for new features.
## How was this patch tested?
Unit tests.
Author: zero323 <zero323@users.noreply.github.com>
Closes#17218 from zero323/SPARK-19281.
Add Python wrapper for `Imputer` feature transformer.
## How was this patch tested?
New doc tests and tweak to PySpark ML `tests.py`
Author: Nick Pentreath <nickp@za.ibm.com>
Closes#17316 from MLnick/SPARK-15040-pyspark-imputer.
## What changes were proposed in this pull request?
An additional trigger and trigger executor that will execute a single trigger only. One can use this OneTime trigger to have more control over the scheduling of triggers.
In addition, this patch requires an optimization to StreamExecution that logs a commit record at the end of successfully processing a batch. This new commit log will be used to determine the next batch (offsets) to process after a restart, instead of using the offset log itself to determine what batch to process next after restart; using the offset log to determine this would process the previously logged batch, always, thus not permitting a OneTime trigger feature.
## How was this patch tested?
A number of existing tests have been revised. These tests all assumed that when restarting a stream, the last batch in the offset log is to be re-processed. Given that we now have a commit log that will tell us if that last batch was processed successfully, the results/assumptions of those tests needed to be revised accordingly.
In addition, a OneTime trigger test was added to StreamingQuerySuite, which tests:
- The semantics of OneTime trigger (i.e., on start, execute a single batch, then stop).
- The case when the commit log was not able to successfully log the completion of a batch before restart, which would mean that we should fall back to what's in the offset log.
- A OneTime trigger execution that results in an exception being thrown.
marmbrus tdas zsxwing
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Tyson Condie <tcondie@gmail.com>
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#17219 from tcondie/stream-commit.
## What changes were proposed in this pull request?
This PR proposes to support _not_ trimming the white spaces when writing out. These are `false` by default in CSV reading path but these are `true` by default in CSV writing in univocity parser.
Both `ignoreLeadingWhiteSpace` and `ignoreTrailingWhiteSpace` options are not being used for writing and therefore, we are always trimming the white spaces.
It seems we should provide a way to keep this white spaces easily.
WIth the data below:
```scala
val df = spark.read.csv(Seq("a , b , c").toDS)
df.show()
```
```
+---+----+---+
|_c0| _c1|_c2|
+---+----+---+
| a | b | c|
+---+----+---+
```
**Before**
```scala
df.write.csv("/tmp/text.csv")
spark.read.text("/tmp/text.csv").show()
```
```
+-----+
|value|
+-----+
|a,b,c|
+-----+
```
It seems this can't be worked around via `quoteAll` too.
```scala
df.write.option("quoteAll", true).csv("/tmp/text.csv")
spark.read.text("/tmp/text.csv").show()
```
```
+-----------+
| value|
+-----------+
|"a","b","c"|
+-----------+
```
**After**
```scala
df.write.option("ignoreLeadingWhiteSpace", false).option("ignoreTrailingWhiteSpace", false).csv("/tmp/text.csv")
spark.read.text("/tmp/text.csv").show()
```
```
+----------+
| value|
+----------+
|a , b , c|
+----------+
```
Note that this case is possible in R
```r
> system("cat text.csv")
f1,f2,f3
a , b , c
> df <- read.csv(file="text.csv")
> df
f1 f2 f3
1 a b c
> write.csv(df, file="text1.csv", quote=F, row.names=F)
> system("cat text1.csv")
f1,f2,f3
a , b , c
```
## How was this patch tested?
Unit tests in `CSVSuite` and manual tests for Python.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#17310 from HyukjinKwon/SPARK-18579.
## What changes were proposed in this pull request?
This PR proposes to make `mode` options in both CSV and JSON to use `cass object` and fix some related comments related previous fix.
Also, this PR modifies some tests related parse modes.
## How was this patch tested?
Modified unit tests in both `CSVSuite.scala` and `JsonSuite.scala`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#17377 from HyukjinKwon/SPARK-19949.
## What changes were proposed in this pull request?
Update docs for NaN handling in approxQuantile.
## How was this patch tested?
existing tests.
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#17369 from zhengruifeng/doc_quantiles_nan.
## What changes were proposed in this pull request?
API documentation and collaborative filtering documentation page changes to clarify inconsistent description of ALS rank parameter.
- [DOCS] was previously: "rank is the number of latent factors in the model."
- [API] was previously: "rank - number of features to use"
This change describes rank in both places consistently as:
- "Number of features to use (also referred to as the number of latent factors)"
Author: Chris Snow <chris.snowuk.ibm.com>
Author: christopher snow <chsnow123@gmail.com>
Closes#17345 from snowch/SPARK-20011.
## What changes were proposed in this pull request?
This PR proposes to support an array of struct type in `to_json` as below:
```scala
import org.apache.spark.sql.functions._
val df = Seq(Tuple1(Tuple1(1) :: Nil)).toDF("a")
df.select(to_json($"a").as("json")).show()
```
```
+----------+
| json|
+----------+
|[{"_1":1}]|
+----------+
```
Currently, it throws an exception as below (a newline manually inserted for readability):
```
org.apache.spark.sql.AnalysisException: cannot resolve 'structtojson(`array`)' due to data type
mismatch: structtojson requires that the expression is a struct expression.;;
```
This allows the roundtrip with `from_json` as below:
```scala
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types._
val schema = ArrayType(StructType(StructField("a", IntegerType) :: Nil))
val df = Seq("""[{"a":1}, {"a":2}]""").toDF("json").select(from_json($"json", schema).as("array"))
df.show()
// Read back.
df.select(to_json($"array").as("json")).show()
```
```
+----------+
| array|
+----------+
|[[1], [2]]|
+----------+
+-----------------+
| json|
+-----------------+
|[{"a":1},{"a":2}]|
+-----------------+
```
Also, this PR proposes to rename from `StructToJson` to `StructsToJson ` and `JsonToStruct` to `JsonToStructs`.
## How was this patch tested?
Unit tests in `JsonFunctionsSuite` and `JsonExpressionsSuite` for Scala, doctest for Python and test in `test_sparkSQL.R` for R.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#17192 from HyukjinKwon/SPARK-19849.
## What changes were proposed in this pull request?
Sometimes, CheckpointTests will hang on a busy machine because the streaming jobs are too slow and cannot catch up. I observed the scheduled delay was keeping increasing for dozens of seconds locally.
This PR increases the batch interval from 0.5 seconds to 2 seconds to generate less Spark jobs. It should make `pyspark.streaming.tests.CheckpointTests` more stable. I also replaced `sleep` with `awaitTerminationOrTimeout` so that if the streaming job fails, it will also fail the test.
## How was this patch tested?
Jenkins
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#17323 from zsxwing/SPARK-19986.
## What changes were proposed in this pull request?
This PR proposes to use the correct deserializer, `BatchedSerializer` for RDD construction for coalesce/repartition when the shuffle is enabled. Currently, it is passing `UTF8Deserializer` as is not `BatchedSerializer` from the copied one.
with the file, `text.txt` below:
```
a
b
d
e
f
g
h
i
j
k
l
```
- Before
```python
>>> sc.textFile('text.txt').repartition(1).collect()
```
```
UTF8Deserializer(True)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".../spark/python/pyspark/rdd.py", line 811, in collect
return list(_load_from_socket(port, self._jrdd_deserializer))
File ".../spark/python/pyspark/serializers.py", line 549, in load_stream
yield self.loads(stream)
File ".../spark/python/pyspark/serializers.py", line 544, in loads
return s.decode("utf-8") if self.use_unicode else s
File "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/encodings/utf_8.py", line 16, in decode
return codecs.utf_8_decode(input, errors, True)
UnicodeDecodeError: 'utf8' codec can't decode byte 0x80 in position 0: invalid start byte
```
- After
```python
>>> sc.textFile('text.txt').repartition(1).collect()
```
```
[u'a', u'b', u'', u'd', u'e', u'f', u'g', u'h', u'i', u'j', u'k', u'l', u'']
```
## How was this patch tested?
Unit test in `python/pyspark/tests.py`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#17282 from HyukjinKwon/SPARK-19872.
## What changes were proposed in this pull request?
As timezone setting can also affect partition values, it works for all formats, we should make it clear.
## How was this patch tested?
N/A
Author: Liwei Lin <lwlin7@gmail.com>
Closes#17299 from lw-lin/timezone.
## What changes were proposed in this pull request?
As timezone setting can also affect partition values, it works for all formats, we should make it clear.
## How was this patch tested?
Existing tests.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#17281 from ueshin/issues/SPARK-19817.
Beside the issue in spark api, also fix 2 minor issues in pyspark
- support read from multiple input paths for orc
- support read from multiple input paths for text
Author: Jeff Zhang <zjffdu@apache.org>
Closes#10307 from zjffdu/SPARK-12334.
## What changes were proposed in this pull request?
Add handling of input of type `Int` for dataType `TimestampType` to `EvaluatePython.scala`. Py4J serializes ints smaller than MIN_INT or larger than MAX_INT to Long, which are handled correctly already, but values between MIN_INT and MAX_INT are serialized to Int.
These range limits correspond to roughly half an hour on either side of the epoch. As a result, PySpark doesn't allow TimestampType values to be created in this range.
Alternatives attempted: patching the `TimestampType.toInternal` function to cast return values to `long`, so Py4J would always serialize them to Scala Long. Python3 does not have a `long` type, so this approach failed on Python3.
## How was this patch tested?
Added a new PySpark-side test that fails without the change.
The contribution is my original work and I license the work to the project under the project’s open source license.
Resubmission of https://github.com/apache/spark/pull/16896. The original PR didn't go through Jenkins and broke the build. davies dongjoon-hyun
cloud-fan Could you kick off a Jenkins run for me? It passed everything for me locally, but it's possible something has changed in the last few weeks.
Author: Jason White <jason.white@shopify.com>
Closes#17200 from JasonMWhite/SPARK-19561.
## What changes were proposed in this pull request?
PySpark ```GeneralizedLinearRegression``` supports tweedie distribution.
## How was this patch tested?
Add unit tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#17146 from yanboliang/spark-19806.
## What changes were proposed in this pull request?
Cast the output of `TimestampType.toInternal` to long to allow for proper Timestamp creation in DataFrames near the epoch.
## How was this patch tested?
Added a new test that fails without the change.
dongjoon-hyun davies Mind taking a look?
The contribution is my original work and I license the work to the project under the project’s open source license.
Author: Jason White <jason.white@shopify.com>
Closes#16896 from JasonMWhite/SPARK-19561.
## What changes were proposed in this pull request?
This PR proposes to remove incorrect implementation that has been not executed so far (at least from Spark 1.5.2) for `in` operator and throw a correct exception rather than saying it is a bool. I tested the codes above in 1.5.2, 1.6.3, 2.1.0 and in the master branch as below:
**1.5.2**
```python
>>> df = sqlContext.createDataFrame([[1]])
>>> 1 in df._1
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".../spark-1.5.2-bin-hadoop2.6/python/pyspark/sql/column.py", line 418, in __nonzero__
raise ValueError("Cannot convert column into bool: please use '&' for 'and', '|' for 'or', "
ValueError: Cannot convert column into bool: please use '&' for 'and', '|' for 'or', '~' for 'not' when building DataFrame boolean expressions.
```
**1.6.3**
```python
>>> 1 in sqlContext.range(1).id
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".../spark-1.6.3-bin-hadoop2.6/python/pyspark/sql/column.py", line 447, in __nonzero__
raise ValueError("Cannot convert column into bool: please use '&' for 'and', '|' for 'or', "
ValueError: Cannot convert column into bool: please use '&' for 'and', '|' for 'or', '~' for 'not' when building DataFrame boolean expressions.
```
**2.1.0**
```python
>>> 1 in spark.range(1).id
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".../spark-2.1.0-bin-hadoop2.7/python/pyspark/sql/column.py", line 426, in __nonzero__
raise ValueError("Cannot convert column into bool: please use '&' for 'and', '|' for 'or', "
ValueError: Cannot convert column into bool: please use '&' for 'and', '|' for 'or', '~' for 'not' when building DataFrame boolean expressions.
```
**Current Master**
```python
>>> 1 in spark.range(1).id
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".../spark/python/pyspark/sql/column.py", line 452, in __nonzero__
raise ValueError("Cannot convert column into bool: please use '&' for 'and', '|' for 'or', "
ValueError: Cannot convert column into bool: please use '&' for 'and', '|' for 'or', '~' for 'not' when building DataFrame boolean expressions.
```
**After**
```python
>>> 1 in spark.range(1).id
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".../spark/python/pyspark/sql/column.py", line 184, in __contains__
raise ValueError("Cannot apply 'in' operator against a column: please use 'contains' "
ValueError: Cannot apply 'in' operator against a column: please use 'contains' in a string column or 'array_contains' function for an array column.
```
In more details,
It seems the implementation intended to support this
```python
1 in df.column
```
However, currently, it throws an exception as below:
```python
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".../spark/python/pyspark/sql/column.py", line 426, in __nonzero__
raise ValueError("Cannot convert column into bool: please use '&' for 'and', '|' for 'or', "
ValueError: Cannot convert column into bool: please use '&' for 'and', '|' for 'or', '~' for 'not' when building DataFrame boolean expressions.
```
What happens here is as below:
```python
class Column(object):
def __contains__(self, item):
print "I am contains"
return Column()
def __nonzero__(self):
raise Exception("I am nonzero.")
>>> 1 in Column()
I am contains
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<stdin>", line 6, in __nonzero__
Exception: I am nonzero.
```
It seems it calls `__contains__` first and then `__nonzero__` or `__bool__` is being called against `Column()` to make this a bool (or int to be specific).
It seems `__nonzero__` (for Python 2), `__bool__` (for Python 3) and `__contains__` forcing the the return into a bool unlike other operators. There are few references about this as below:
https://bugs.python.org/issue16011http://stackoverflow.com/questions/12244074/python-source-code-for-built-in-in-operator/12244378#12244378http://stackoverflow.com/questions/38542543/functionality-of-python-in-vs-contains/38542777
It seems we can't overwrite `__nonzero__` or `__bool__` as a workaround to make this working because these force the return type as a bool as below:
```python
class Column(object):
def __contains__(self, item):
print "I am contains"
return Column()
def __nonzero__(self):
return "a"
>>> 1 in Column()
I am contains
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: __nonzero__ should return bool or int, returned str
```
## How was this patch tested?
Added unit tests in `tests.py`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#17160 from HyukjinKwon/SPARK-19701.
## What changes were proposed in this pull request?
This PR proposes to both,
**Do not allow json arrays with multiple elements and return null in `from_json` with `StructType` as the schema.**
Currently, it only reads the single row when the input is a json array. So, the codes below:
```scala
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types._
val schema = StructType(StructField("a", IntegerType) :: Nil)
Seq(("""[{"a": 1}, {"a": 2}]""")).toDF("struct").select(from_json(col("struct"), schema)).show()
```
prints
```
+--------------------+
|jsontostruct(struct)|
+--------------------+
| [1]|
+--------------------+
```
This PR simply suggests to print this as `null` if the schema is `StructType` and input is json array.with multiple elements
```
+--------------------+
|jsontostruct(struct)|
+--------------------+
| null|
+--------------------+
```
**Support json arrays in `from_json` with `ArrayType` as the schema.**
```scala
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types._
val schema = ArrayType(StructType(StructField("a", IntegerType) :: Nil))
Seq(("""[{"a": 1}, {"a": 2}]""")).toDF("array").select(from_json(col("array"), schema)).show()
```
prints
```
+-------------------+
|jsontostruct(array)|
+-------------------+
| [[1], [2]]|
+-------------------+
```
## How was this patch tested?
Unit test in `JsonExpressionsSuite`, `JsonFunctionsSuite`, Python doctests and manual test.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#16929 from HyukjinKwon/disallow-array.
## What changes were proposed in this pull request?
The `keyword_only` decorator in PySpark is not thread-safe. It writes kwargs to a static class variable in the decorator, which is then retrieved later in the class method as `_input_kwargs`. If multiple threads are constructing the same class with different kwargs, it becomes a race condition to read from the static class variable before it's overwritten. See [SPARK-19348](https://issues.apache.org/jira/browse/SPARK-19348) for reproduction code.
This change will write the kwargs to a member variable so that multiple threads can operate on separate instances without the race condition. It does not protect against multiple threads operating on a single instance, but that is better left to the user to synchronize.
## How was this patch tested?
Added new unit tests for using the keyword_only decorator and a regression test that verifies `_input_kwargs` can be overwritten from different class instances.
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#16782 from BryanCutler/pyspark-keyword_only-threadsafe-SPARK-19348.
## What changes were proposed in this pull request?
Update doc for R, programming guide. Clarify default behavior for all languages.
## How was this patch tested?
manually
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#17128 from felixcheung/jsonwholefiledoc.
## What changes were proposed in this pull request?
Updates the doc string to match up with the code
i.e. say dropLast instead of includeFirst
## How was this patch tested?
Not much, since it's a doc-like change. Will run unit tests via Jenkins job.
Author: Mark Grover <mark@apache.org>
Closes#17127 from markgrover/spark_19734.
## What changes were proposed in this pull request?
Remove `org.apache.spark.examples.` in
Add slash in one of the python doc.
## How was this patch tested?
Run examples using the commands in the comments.
Author: Yun Ni <yunn@uber.com>
Closes#17104 from Yunni/yunn_minor.
## What changes were proposed in this pull request?
This PR proposes the support for multiple lines for CSV by resembling the multiline supports in JSON datasource (in case of JSON, per file).
So, this PR introduces `wholeFile` option which makes the format not splittable and reads each whole file. Since Univocity parser can produces each row from a stream, it should be capable of parsing very large documents when the internal rows are fix in the memory.
## How was this patch tested?
Unit tests in `CSVSuite` and `tests.py`
Manual tests with a single 9GB CSV file in local file system, for example,
```scala
spark.read.option("wholeFile", true).option("inferSchema", true).csv("tmp.csv").count()
```
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#16976 from HyukjinKwon/SPARK-19610.
This PR adds a param to `ALS`/`ALSModel` to set the strategy used when encountering unknown users or items at prediction time in `transform`. This can occur in 2 scenarios: (a) production scoring, and (b) cross-validation & evaluation.
The current behavior returns `NaN` if a user/item is unknown. In scenario (b), this can easily occur when using `CrossValidator` or `TrainValidationSplit` since some users/items may only occur in the test set and not in the training set. In this case, the evaluator returns `NaN` for all metrics, making model selection impossible.
The new param, `coldStartStrategy`, defaults to `nan` (the current behavior). The other option supported initially is `drop`, which drops all rows with `NaN` predictions. This flag allows users to use `ALS` in cross-validation settings. It is made an `expertParam`. The param is made a string so that the set of strategies can be extended in future (some options are discussed in [SPARK-14489](https://issues.apache.org/jira/browse/SPARK-14489)).
## How was this patch tested?
New unit tests, and manual "before and after" tests for Scala & Python using MovieLens `ml-latest-small` as example data. Here, using `CrossValidator` or `TrainValidationSplit` with the default param setting results in metrics that are all `NaN`, while setting `coldStartStrategy` to `drop` results in valid metrics.
Author: Nick Pentreath <nickp@za.ibm.com>
Closes#12896 from MLnick/SPARK-14489-als-nan.
## What changes were proposed in this pull request?
self.environment will be propagated to executor. Should set PYTHONHASHSEED as long as the python version is greater than 3.3
## How was this patch tested?
Manually tested it.
Author: Jeff Zhang <zjffdu@apache.org>
Closes#11211 from zjffdu/SPARK-13330.
## What changes were proposed in this pull request?
Replaces `UserDefinedFunction` object returned from `udf` with a function wrapper providing docstring and arguments information as proposed in [SPARK-19161](https://issues.apache.org/jira/browse/SPARK-19161).
### Backward incompatible changes:
- `pyspark.sql.functions.udf` will return a `function` instead of `UserDefinedFunction`. To ensure backward compatible public API we use function attributes to mimic `UserDefinedFunction` API (`func` and `returnType` attributes). This should have a minimal impact on the user code.
An alternative implementation could use dynamical sub-classing. This would ensure full backward compatibility but is more fragile in practice.
### Limitations:
Full functionality (retained docstring and argument list) is achieved only in the recent Python version. Legacy Python version will preserve only docstrings, but not argument list. This should be an acceptable trade-off between achieved improvements and overall complexity.
### Possible impact on other tickets:
This can affect [SPARK-18777](https://issues.apache.org/jira/browse/SPARK-18777).
## How was this patch tested?
Existing unit tests to ensure backward compatibility, additional tests targeting proposed changes.
Author: zero323 <zero323@users.noreply.github.com>
Closes#16534 from zero323/SPARK-19161.
## What changes were proposed in this pull request?
Fixed the PySpark Params.copy method to behave like the Scala implementation. The main issue was that it did not account for the _defaultParamMap and merged it into the explicitly created param map.
## How was this patch tested?
Added new unit test to verify the copy method behaves correctly for copying uid, explicitly created params, and default params.
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#16772 from BryanCutler/pyspark-ml-param_copy-Scala_sync-SPARK-14772.
## What changes were proposed in this pull request?
to be consistent with the scala API, we should also add `contains` to `Column` in pyspark.
## How was this patch tested?
updated unit test
Author: Wenchen Fan <wenchen@databricks.com>
Closes#17036 from cloud-fan/pyspark.
## What changes were proposed in this pull request?
This pr added a logic to put malformed tokens into a new field when parsing CSV data in case of permissive modes. In the current master, if the CSV parser hits these malformed ones, it throws an exception below (and then a job fails);
```
Caused by: java.lang.IllegalArgumentException
at java.sql.Date.valueOf(Date.java:143)
at org.apache.spark.sql.catalyst.util.DateTimeUtils$.stringToTime(DateTimeUtils.scala:137)
at org.apache.spark.sql.execution.datasources.csv.CSVTypeCast$$anonfun$castTo$6.apply$mcJ$sp(CSVInferSchema.scala:272)
at org.apache.spark.sql.execution.datasources.csv.CSVTypeCast$$anonfun$castTo$6.apply(CSVInferSchema.scala:272)
at org.apache.spark.sql.execution.datasources.csv.CSVTypeCast$$anonfun$castTo$6.apply(CSVInferSchema.scala:272)
at scala.util.Try.getOrElse(Try.scala:79)
at org.apache.spark.sql.execution.datasources.csv.CSVTypeCast$.castTo(CSVInferSchema.scala:269)
at
```
In case that users load large CSV-formatted data, the job failure makes users get some confused. So, this fix set NULL for original columns and put malformed tokens in a new field.
## How was this patch tested?
Added tests in `CSVSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#16928 from maropu/SPARK-18699-2.
## What changes were proposed in this pull request?
This PR adds a special streaming deduplication operator to support `dropDuplicates` with `aggregation` and watermark. It reuses the `dropDuplicates` API but creates new logical plan `Deduplication` and new physical plan `DeduplicationExec`.
The following cases are supported:
- one or multiple `dropDuplicates()` without aggregation (with or without watermark)
- `dropDuplicates` before aggregation
Not supported cases:
- `dropDuplicates` after aggregation
Breaking changes:
- `dropDuplicates` without aggregation doesn't work with `complete` or `update` mode.
## How was this patch tested?
The new unit tests.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#16970 from zsxwing/dedup.
- Add dependency on aws-java-sdk-sts
- Replace SerializableAWSCredentials with new SerializableCredentialsProvider interface
- Make KinesisReceiver take SerializableCredentialsProvider as argument and
pass credential provider to KCL
- Add new implementations of KinesisUtils.createStream() that take STS
arguments
- Make JavaKinesisStreamSuite test the entire KinesisUtils Java API
- Update KCL/AWS SDK dependencies to 1.7.x/1.11.x
## What changes were proposed in this pull request?
[JIRA link with detailed description.](https://issues.apache.org/jira/browse/SPARK-19405)
* Replace SerializableAWSCredentials with new SerializableKCLAuthProvider class that takes 5 optional config params for configuring AWS auth and returns the appropriate credential provider object
* Add new public createStream() APIs for specifying these parameters in KinesisUtils
## How was this patch tested?
* Manually tested using explicit keypair and instance profile to read data from Kinesis stream in separate account (difficult to write a test orchestrating creation and assumption of IAM roles across separate accounts)
* Expanded JavaKinesisStreamSuite to test the entire Java API in KinesisUtils
## License acknowledgement
This contribution is my original work and that I license the work to the project under the project’s open source license.
Author: Budde <budde@amazon.com>
Closes#16744 from budde/master.
## What changes were proposed in this pull request?
Fix typo in docstring.
Author: Rolando Espinoza <rndmax84@gmail.com>
Closes#16967 from rolando/pyspark-doc-typo.
## What changes were proposed in this pull request?
If a new option `wholeFile` is set to `true` the JSON reader will parse each file (instead of a single line) as a value. This is done with Jackson streaming and it should be capable of parsing very large documents, assuming the row will fit in memory.
Because the file is not buffered in memory the corrupt record handling is also slightly different when `wholeFile` is enabled: the corrupt column will contain the filename instead of the literal JSON if there is a parsing failure. It would be easy to extend this to add the parser location (line, column and byte offsets) to the output if desired.
These changes have allowed types other than `String` to be parsed. Support for `UTF8String` and `Text` have been added (alongside `String` and `InputFormat`) and no longer require a conversion to `String` just for parsing.
I've also included a few other changes that generate slightly better bytecode and (imo) make it more obvious when and where boxing is occurring in the parser. These are included as separate commits, let me know if they should be flattened into this PR or moved to a new one.
## How was this patch tested?
New and existing unit tests. No performance or load tests have been run.
Author: Nathan Howell <nhowell@godaddy.com>
Closes#16386 from NathanHowell/SPARK-18352.
## What changes were proposed in this pull request?
This pull request includes python API and examples for LSH. The API changes was based on yanboliang 's PR #15768 and resolved conflicts and API changes on the Scala API. The examples are consistent with Scala examples of MinHashLSH and BucketedRandomProjectionLSH.
## How was this patch tested?
API and examples are tested using spark-submit:
`bin/spark-submit examples/src/main/python/ml/min_hash_lsh.py`
`bin/spark-submit examples/src/main/python/ml/bucketed_random_projection_lsh.py`
User guide changes are generated and manually inspected:
`SKIP_API=1 jekyll build`
Author: Yun Ni <yunn@uber.com>
Author: Yanbo Liang <ybliang8@gmail.com>
Author: Yunni <Euler57721@gmail.com>
Closes#16715 from Yunni/spark-18080.
## What changes were proposed in this pull request?
This is a follow-up pr of #16308.
This pr enables timezone support in CSV/JSON parsing.
We should introduce `timeZone` option for CSV/JSON datasources (the default value of the option is session local timezone).
The datasources should use the `timeZone` option to format/parse to write/read timestamp values.
Notice that while reading, if the timestampFormat has the timezone info, the timezone will not be used because we should respect the timezone in the values.
For example, if you have timestamp `"2016-01-01 00:00:00"` in `GMT`, the values written with the default timezone option, which is `"GMT"` because session local timezone is `"GMT"` here, are:
```scala
scala> spark.conf.set("spark.sql.session.timeZone", "GMT")
scala> val df = Seq(new java.sql.Timestamp(1451606400000L)).toDF("ts")
df: org.apache.spark.sql.DataFrame = [ts: timestamp]
scala> df.show()
+-------------------+
|ts |
+-------------------+
|2016-01-01 00:00:00|
+-------------------+
scala> df.write.json("/path/to/gmtjson")
```
```sh
$ cat /path/to/gmtjson/part-*
{"ts":"2016-01-01T00:00:00.000Z"}
```
whereas setting the option to `"PST"`, they are:
```scala
scala> df.write.option("timeZone", "PST").json("/path/to/pstjson")
```
```sh
$ cat /path/to/pstjson/part-*
{"ts":"2015-12-31T16:00:00.000-08:00"}
```
We can properly read these files even if the timezone option is wrong because the timestamp values have timezone info:
```scala
scala> val schema = new StructType().add("ts", TimestampType)
schema: org.apache.spark.sql.types.StructType = StructType(StructField(ts,TimestampType,true))
scala> spark.read.schema(schema).json("/path/to/gmtjson").show()
+-------------------+
|ts |
+-------------------+
|2016-01-01 00:00:00|
+-------------------+
scala> spark.read.schema(schema).option("timeZone", "PST").json("/path/to/gmtjson").show()
+-------------------+
|ts |
+-------------------+
|2016-01-01 00:00:00|
+-------------------+
```
And even if `timezoneFormat` doesn't contain timezone info, we can properly read the values with setting correct timezone option:
```scala
scala> df.write.option("timestampFormat", "yyyy-MM-dd'T'HH:mm:ss").option("timeZone", "JST").json("/path/to/jstjson")
```
```sh
$ cat /path/to/jstjson/part-*
{"ts":"2016-01-01T09:00:00"}
```
```scala
// wrong result
scala> spark.read.schema(schema).option("timestampFormat", "yyyy-MM-dd'T'HH:mm:ss").json("/path/to/jstjson").show()
+-------------------+
|ts |
+-------------------+
|2016-01-01 09:00:00|
+-------------------+
// correct result
scala> spark.read.schema(schema).option("timestampFormat", "yyyy-MM-dd'T'HH:mm:ss").option("timeZone", "JST").json("/path/to/jstjson").show()
+-------------------+
|ts |
+-------------------+
|2016-01-01 00:00:00|
+-------------------+
```
This pr also makes `JsonToStruct` and `StructToJson` `TimeZoneAwareExpression` to be able to evaluate values with timezone option.
## How was this patch tested?
Existing tests and added some tests.
Author: Takuya UESHIN <ueshin@happy-camper.st>
Closes#16750 from ueshin/issues/SPARK-18937.
## What changes were proposed in this pull request?
Add coalesce on DataFrame for down partitioning without shuffle and coalesce on Column
## How was this patch tested?
manual, unit tests
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#16739 from felixcheung/rcoalesce.
## What changes were proposed in this pull request?
This PR adds `udf` decorator syntax as proposed in [SPARK-19160](https://issues.apache.org/jira/browse/SPARK-19160).
This allows users to define UDF using simplified syntax:
```python
from pyspark.sql.decorators import udf
udf(IntegerType())
def add_one(x):
"""Adds one"""
if x is not None:
return x + 1
```
without need to define a separate function and udf.
## How was this patch tested?
Existing unit tests to ensure backward compatibility and additional unit tests covering new functionality.
Author: zero323 <zero323@users.noreply.github.com>
Closes#16533 from zero323/SPARK-19160.
## What changes were proposed in this pull request?
This PR is to document the changes on QuantileDiscretizer in pyspark for PR:
https://github.com/apache/spark/pull/15428
## How was this patch tested?
No test needed
Signed-off-by: VinceShieh <vincent.xieintel.com>
Author: VinceShieh <vincent.xie@intel.com>
Closes#16922 from VinceShieh/spark-19590.
## What changes were proposed in this pull request?
Add a `metadata` keyword parameter to `pyspark.sql.Column.alias()` to allow users to mix-in metadata while manipulating `DataFrame`s in `pyspark`. Without this, I believe it was necessary to pass back through `SparkSession.createDataFrame` each time a user wanted to manipulate `StructField.metadata` in `pyspark`.
This pull request also improves consistency between the Scala and Python APIs (i.e. I did not add any functionality that was not already in the Scala API).
Discussed ahead of time on JIRA with marmbrus
## How was this patch tested?
Added unit tests (and doc tests). Ran the pertinent tests manually.
Author: Sheamus K. Parkes <shea.parkes@milliman.com>
Closes#16094 from shea-parkes/pyspark-column-alias-metadata.
## What changes were proposed in this pull request?
UDF constructor checks if `func` argument is callable and if it is not, fails fast instead of waiting for an action.
## How was this patch tested?
Unit tests.
Author: zero323 <zero323@users.noreply.github.com>
Closes#16535 from zero323/SPARK-19162.
## What changes were proposed in this pull request?
- Provides correct description of the semantics of a `dict` argument passed as `to_replace`.
- Describes type requirements for collection arguments.
- Describes behavior with `to_replace: List[T]` and `value: T`
## How was this patch tested?
Manual testing, documentation build.
Author: zero323 <zero323@users.noreply.github.com>
Closes#16792 from zero323/SPARK-19453.
## What changes were proposed in this pull request?
- Add support for `slice` arguments in `Column.__getitem__`.
- Remove obsolete `__getslice__` bindings.
## How was this patch tested?
Existing unit tests, additional tests covering `[]` with `slice`.
Author: zero323 <zero323@users.noreply.github.com>
Closes#16771 from zero323/SPARK-19429.
## What changes were proposed in this pull request?
Add support for data type string as a return type argument of `UserDefinedFunction`:
```python
f = udf(lambda x: x, "integer")
f.returnType
## IntegerType
```
## How was this patch tested?
Existing unit tests, additional unit tests covering new feature.
Author: zero323 <zero323@users.noreply.github.com>
Closes#16769 from zero323/SPARK-19427.
## What changes were proposed in this pull request?
Add missing `warnings` import.
## How was this patch tested?
Manual tests.
Author: zero323 <zero323@users.noreply.github.com>
Closes#16846 from zero323/SPARK-19506.
## What changes were proposed in this pull request?
This pull request adds two new user facing functions:
- `to_date` which accepts an expression and a format and returns a date.
- `to_timestamp` which accepts an expression and a format and returns a timestamp.
For example, Given a date in format: `2016-21-05`. (YYYY-dd-MM)
### Date Function
*Previously*
```
to_date(unix_timestamp(lit("2016-21-05"), "yyyy-dd-MM").cast("timestamp"))
```
*Current*
```
to_date(lit("2016-21-05"), "yyyy-dd-MM")
```
### Timestamp Function
*Previously*
```
unix_timestamp(lit("2016-21-05"), "yyyy-dd-MM").cast("timestamp")
```
*Current*
```
to_timestamp(lit("2016-21-05"), "yyyy-dd-MM")
```
### Tasks
- [X] Add `to_date` to Scala Functions
- [x] Add `to_date` to Python Functions
- [x] Add `to_date` to SQL Functions
- [X] Add `to_timestamp` to Scala Functions
- [x] Add `to_timestamp` to Python Functions
- [x] Add `to_timestamp` to SQL Functions
- [x] Add function to R
## How was this patch tested?
- [x] Add Functions to `DateFunctionsSuite`
- Test new `ParseToTimestamp` Expression (*not necessary*)
- Test new `ParseToDate` Expression (*not necessary*)
- [x] Add test for R
- [x] Add test for Python in test.py
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: anabranch <wac.chambers@gmail.com>
Author: Bill Chambers <bill@databricks.com>
Author: anabranch <bill@databricks.com>
Closes#16138 from anabranch/SPARK-16609.
## What changes were proposed in this pull request?
Remove cyclic imports between `pyspark.ml.pipeline` and `pyspark.ml`.
## How was this patch tested?
Existing unit tests.
Author: zero323 <zero323@users.noreply.github.com>
Closes#16814 from zero323/SPARK-19467.
## What changes were proposed in this pull request?
Methods `numClasses` and `numFeatures` in LinearSVCModel are already usable by inheriting `JavaClassificationModel`
we should not explicitly add them.
## How was this patch tested?
existing tests
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#16727 from zhengruifeng/nits_in_linearSVC.
## What changes were proposed in this pull request?
* Removed Since tags in Python Params since they are inherited by other classes
* Fixed doc links for LinearSVC
## How was this patch tested?
* doc tests
* generating docs locally and checking manually
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#16723 from jkbradley/pyparam-fix-doc.
## What changes were proposed in this pull request?
1, add the multi-cols support based on current private api
2, add the multi-cols support to pyspark
## How was this patch tested?
unit tests
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Author: Ruifeng Zheng <ruifengz@foxmail.com>
Closes#12135 from zhengruifeng/quantile4multicols.
## What changes were proposed in this pull request?
Defer `UserDefinedFunction._judf` initialization to the first call. This prevents unintended `SparkSession` initialization. This allows users to define and import UDF without creating a context / session as a side effect.
[SPARK-19163](https://issues.apache.org/jira/browse/SPARK-19163)
## How was this patch tested?
Unit tests.
Author: zero323 <zero323@users.noreply.github.com>
Closes#16536 from zero323/SPARK-19163.
## What changes were proposed in this pull request?
Adding convenience function to Python `JavaWrapper` so that it is easy to create a Py4J JavaArray that is compatible with current class constructors that have a Scala `Array` as input so that it is not necessary to have a Java/Python friendly constructor. The function takes a Java class as input that is used by Py4J to create the Java array of the given class. As an example, `OneVsRest` has been updated to use this and the alternate constructor is removed.
## How was this patch tested?
Added unit tests for the new convenience function and updated `OneVsRest` doctests which use this to persist the model.
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#14725 from BryanCutler/pyspark-new_java_array-CountVectorizer-SPARK-17161.
## What changes were proposed in this pull request?
This removes from the `__all__` list class names that are not defined (visible) in the `pyspark.sql.column`.
## How was this patch tested?
Existing unit tests.
Author: zero323 <zero323@users.noreply.github.com>
Closes#16742 from zero323/SPARK-19403.
## What changes were proposed in this pull request?
Add Python API for the newly added LinearSVC algorithm.
## How was this patch tested?
Add new doc string test.
Author: wm624@hotmail.com <wm624@hotmail.com>
Closes#16694 from wangmiao1981/ser.
## What changes were proposed in this pull request?
This pr is to fix an issue occurred when resharding Kinesis streams; the resharding makes the KCL throw an exception because Spark does not checkpoint `SHARD_END` when finishing reading closed shards in `KinesisRecordProcessor#shutdown`. This bug finally leads to stopping subscribing new split (or merged) shards.
## How was this patch tested?
Added a test in `KinesisStreamSuite` to check if it works well when splitting/merging shards.
Author: Takeshi YAMAMURO <linguin.m.s@gmail.com>
Closes#16213 from maropu/SPARK-18020.
The code was failing to propagate the user conf in the case where the
JVM was already initialized, which happens when a user submits a
python script via spark-submit.
Tested with new unit test and by running a python script in a real cluster.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#16682 from vanzin/SPARK-19307.
### What changes were proposed in this pull request?
It is weird to create Hive source tables when using InMemoryCatalog. We are unable to operate it. This PR is to block users to create Hive source tables.
### How was this patch tested?
Fixed the test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes#16587 from gatorsmile/blockHiveTable.
## What changes were proposed in this pull request?
PythonUDF is unevaluable, which can not be used inside a join condition, currently the optimizer will push a PythonUDF which accessing both side of join into the join condition, then the query will fail to plan.
This PR fix this issue by checking the expression is evaluable or not before pushing it into Join.
## How was this patch tested?
Add a regression test.
Author: Davies Liu <davies@databricks.com>
Closes#16581 from davies/pyudf_join.
## What changes were proposed in this pull request?
add loglikelihood in GMM.summary
## How was this patch tested?
added tests
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Author: Ruifeng Zheng <ruifengz@foxmail.com>
Closes#12064 from zhengruifeng/gmm_metric.
## What changes were proposed in this pull request?
For some datasources which are based on HadoopRDD or NewHadoopRDD, such as spark-xml, InputFileBlockHolder doesn't work with Python UDF.
The method to reproduce it is, running the following codes with `bin/pyspark --packages com.databricks:spark-xml_2.11:0.4.1`:
from pyspark.sql.functions import udf,input_file_name
from pyspark.sql.types import StringType
from pyspark.sql import SparkSession
def filename(path):
return path
session = SparkSession.builder.appName('APP').getOrCreate()
session.udf.register('sameText', filename)
sameText = udf(filename, StringType())
df = session.read.format('xml').load('a.xml', rowTag='root').select('*', input_file_name().alias('file'))
df.select('file').show() # works
df.select(sameText(df['file'])).show() # returns empty content
The issue is because in `HadoopRDD` and `NewHadoopRDD` we set the file block's info in `InputFileBlockHolder` before the returned iterator begins consuming. `InputFileBlockHolder` will record this info into thread local variable. When running Python UDF in batch, we set up another thread to consume the iterator from child plan's output rdd, so we can't read the info back in another thread.
To fix this, we have to set the info in `InputFileBlockHolder` after the iterator begins consuming. So the info can be read in correct thread.
## How was this patch tested?
Manual test with above example codes for spark-xml package on pyspark: `bin/pyspark --packages com.databricks:spark-xml_2.11:0.4.1`.
Added pyspark test.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#16585 from viirya/fix-inputfileblock-hadooprdd.
## What changes were proposed in this pull request?
The `jdbc` API do not check the `lowerBound` and `upperBound` when we
specified the ``column``, and just throw the following exception:
>```int() argument must be a string or a number, not 'NoneType'```
If we check the parameter, we can give a more friendly suggestion.
## How was this patch tested?
Test using the pyspark shell, without the lowerBound and upperBound parameters.
Author: DjvuLee <lihu@bytedance.com>
Closes#16599 from djvulee/pysparkFix.
## What changes were proposed in this pull request?
Currently, PySpark does not work with Python 3.6.0.
Running `./bin/pyspark` simply throws the error as below and PySpark does not work at all:
```
Traceback (most recent call last):
File ".../spark/python/pyspark/shell.py", line 30, in <module>
import pyspark
File ".../spark/python/pyspark/__init__.py", line 46, in <module>
from pyspark.context import SparkContext
File ".../spark/python/pyspark/context.py", line 36, in <module>
from pyspark.java_gateway import launch_gateway
File ".../spark/python/pyspark/java_gateway.py", line 31, in <module>
from py4j.java_gateway import java_import, JavaGateway, GatewayClient
File "<frozen importlib._bootstrap>", line 961, in _find_and_load
File "<frozen importlib._bootstrap>", line 950, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 646, in _load_unlocked
File "<frozen importlib._bootstrap>", line 616, in _load_backward_compatible
File ".../spark/python/lib/py4j-0.10.4-src.zip/py4j/java_gateway.py", line 18, in <module>
File "/usr/local/Cellar/python3/3.6.0/Frameworks/Python.framework/Versions/3.6/lib/python3.6/pydoc.py", line 62, in <module>
import pkgutil
File "/usr/local/Cellar/python3/3.6.0/Frameworks/Python.framework/Versions/3.6/lib/python3.6/pkgutil.py", line 22, in <module>
ModuleInfo = namedtuple('ModuleInfo', 'module_finder name ispkg')
File ".../spark/python/pyspark/serializers.py", line 394, in namedtuple
cls = _old_namedtuple(*args, **kwargs)
TypeError: namedtuple() missing 3 required keyword-only arguments: 'verbose', 'rename', and 'module'
```
The root cause seems because some arguments of `namedtuple` are now completely keyword-only arguments from Python 3.6.0 (See https://bugs.python.org/issue25628).
We currently copy this function via `types.FunctionType` which does not set the default values of keyword-only arguments (meaning `namedtuple.__kwdefaults__`) and this seems causing internally missing values in the function (non-bound arguments).
This PR proposes to work around this by manually setting it via `kwargs` as `types.FunctionType` seems not supporting to set this.
Also, this PR ports the changes in cloudpickle for compatibility for Python 3.6.0.
## How was this patch tested?
Manually tested with Python 2.7.6 and Python 3.6.0.
```
./bin/pyspsark
```
, manual creation of `namedtuple` both in local and rdd with Python 3.6.0,
and Jenkins tests for other Python versions.
Also,
```
./run-tests --python-executables=python3.6
```
```
Will test against the following Python executables: ['python3.6']
Will test the following Python modules: ['pyspark-core', 'pyspark-ml', 'pyspark-mllib', 'pyspark-sql', 'pyspark-streaming']
Finished test(python3.6): pyspark.sql.tests (192s)
Finished test(python3.6): pyspark.accumulators (3s)
Finished test(python3.6): pyspark.mllib.tests (198s)
Finished test(python3.6): pyspark.broadcast (3s)
Finished test(python3.6): pyspark.conf (2s)
Finished test(python3.6): pyspark.context (14s)
Finished test(python3.6): pyspark.ml.classification (21s)
Finished test(python3.6): pyspark.ml.evaluation (11s)
Finished test(python3.6): pyspark.ml.clustering (20s)
Finished test(python3.6): pyspark.ml.linalg.__init__ (0s)
Finished test(python3.6): pyspark.streaming.tests (240s)
Finished test(python3.6): pyspark.tests (240s)
Finished test(python3.6): pyspark.ml.recommendation (19s)
Finished test(python3.6): pyspark.ml.feature (36s)
Finished test(python3.6): pyspark.ml.regression (37s)
Finished test(python3.6): pyspark.ml.tuning (28s)
Finished test(python3.6): pyspark.mllib.classification (26s)
Finished test(python3.6): pyspark.mllib.evaluation (18s)
Finished test(python3.6): pyspark.mllib.clustering (44s)
Finished test(python3.6): pyspark.mllib.linalg.__init__ (0s)
Finished test(python3.6): pyspark.mllib.feature (26s)
Finished test(python3.6): pyspark.mllib.fpm (23s)
Finished test(python3.6): pyspark.mllib.random (8s)
Finished test(python3.6): pyspark.ml.tests (92s)
Finished test(python3.6): pyspark.mllib.stat.KernelDensity (0s)
Finished test(python3.6): pyspark.mllib.linalg.distributed (25s)
Finished test(python3.6): pyspark.mllib.stat._statistics (15s)
Finished test(python3.6): pyspark.mllib.recommendation (24s)
Finished test(python3.6): pyspark.mllib.regression (26s)
Finished test(python3.6): pyspark.profiler (9s)
Finished test(python3.6): pyspark.mllib.tree (16s)
Finished test(python3.6): pyspark.shuffle (1s)
Finished test(python3.6): pyspark.mllib.util (18s)
Finished test(python3.6): pyspark.serializers (11s)
Finished test(python3.6): pyspark.rdd (20s)
Finished test(python3.6): pyspark.sql.conf (8s)
Finished test(python3.6): pyspark.sql.catalog (17s)
Finished test(python3.6): pyspark.sql.column (18s)
Finished test(python3.6): pyspark.sql.context (18s)
Finished test(python3.6): pyspark.sql.group (27s)
Finished test(python3.6): pyspark.sql.dataframe (33s)
Finished test(python3.6): pyspark.sql.functions (35s)
Finished test(python3.6): pyspark.sql.types (6s)
Finished test(python3.6): pyspark.sql.streaming (13s)
Finished test(python3.6): pyspark.streaming.util (0s)
Finished test(python3.6): pyspark.sql.session (16s)
Finished test(python3.6): pyspark.sql.window (4s)
Finished test(python3.6): pyspark.sql.readwriter (35s)
Tests passed in 433 seconds
```
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#16429 from HyukjinKwon/SPARK-19019.
## What changes were proposed in this pull request?
In https://github.com/apache/spark/pull/16296 , we reached a consensus that we should hide the external/managed table concept to users and only expose custom table path.
This PR renames `Catalog.createExternalTable` to `createTable`(still keep the old versions for backward compatibility), and only set the table type to EXTERNAL if `path` is specified in options.
## How was this patch tested?
new tests in `CatalogSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#16528 from cloud-fan/create-table.
Change is for SQLContext to reuse the active SparkSession during construction if the sparkContext supplied is the same as the currently active SparkContext. Without this change, a new SparkSession is instantiated that results in a Derby error when attempting to create a dataframe using a new SQLContext object even though the SparkContext supplied to the new SQLContext is same as the currently active one. Refer https://issues.apache.org/jira/browse/SPARK-18687 for details on the error and a repro.
Existing unit tests and a new unit test added to pyspark-sql:
/python/run-tests --python-executables=python --modules=pyspark-sql
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Vinayak <vijoshi5@in.ibm.com>
Author: Vinayak Joshi <vijoshi@users.noreply.github.com>
Closes#16119 from vijoshi/SPARK-18687_master.
## What changes were proposed in this pull request?
In SparkSession initialization, we store created the instance of SparkSession into a class variable _instantiatedContext. Next time we can use SparkSession.builder.getOrCreate() to retrieve the existing SparkSession instance.
However, when the active SparkContext is stopped and we create another new SparkContext to use, the existing SparkSession is still associated with the stopped SparkContext. So the operations with this existing SparkSession will be failed.
We need to detect such case in SparkSession and renew the class variable _instantiatedContext if needed.
## How was this patch tested?
New test added in PySpark.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#16454 from viirya/fix-pyspark-sparksession.
## What changes were proposed in this pull request?
Removes `UserDefinedFunction._broadcast` and `UserDefinedFunction.__del__` method.
## How was this patch tested?
Existing unit tests.
Author: zero323 <zero323@users.noreply.github.com>
Closes#16538 from zero323/SPARK-19164.
## What changes were proposed in this pull request?
This PR allow update mode for non-aggregation streaming queries. It will be same as the append mode if a query has no aggregations.
## How was this patch tested?
Jenkins
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#16520 from zsxwing/update-without-agg.
## What changes were proposed in this pull request?
Add FDR test case in ml/feature/ChiSqSelectorSuite.
Improve some comments in the code.
This is a follow-up pr for #15212.
## How was this patch tested?
ut
Author: Peng, Meng <peng.meng@intel.com>
Closes#16434 from mpjlu/fdr_fwe_update.
## What changes were proposed in this pull request?
Copy `GaussianMixture` implementation from mllib to ml, then we can add new features to it.
I left mllib `GaussianMixture` untouched, unlike some other algorithms to wrap the ml implementation. For the following reasons:
- mllib `GaussianMixture` allows k == 1, but ml does not.
- mllib `GaussianMixture` supports setting initial model, but ml does not support currently. (We will definitely add this feature for ml in the future)
We can get around these issues to make mllib as a wrapper calling into ml, but I'd prefer to leave mllib untouched which can make ml clean.
Meanwhile, There is a big performance improvement for `GaussianMixture` in this PR. Since the covariance matrix of multivariate gaussian distribution is symmetric, we can only store the upper triangular part of the matrix and it will greatly reduce the shuffled data size. In my test, this change will reduce shuffled data size by about 50% and accelerate the job execution.
Before this PR:
After this PR:
## How was this patch tested?
Existing tests and added new tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#15413 from yanboliang/spark-17847.
## What changes were proposed in this pull request?
- [X] Make sure all join types are clearly mentioned
- [X] Make join labeling/style consistent
- [X] Make join label ordering docs the same
- [X] Improve join documentation according to above for Scala
- [X] Improve join documentation according to above for Python
- [X] Improve join documentation according to above for R
## How was this patch tested?
No tests b/c docs.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: anabranch <wac.chambers@gmail.com>
Closes#16504 from anabranch/SPARK-19126.
## What changes were proposed in this pull request?
- [X] Fix inconsistencies in function reference for dense rank and dense
- [X] Make all languages equivalent in their reference to `dense_rank` and `rank`.
## How was this patch tested?
N/A for docs.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: anabranch <wac.chambers@gmail.com>
Closes#16505 from anabranch/SPARK-19127.
## What changes were proposed in this pull request?
It seems allowed to not set a key and value for a dict to represent the value is `None` or missing as below:
``` python
spark.createDataFrame([{"x": 1}, {"y": 2}]).show()
```
```
+----+----+
| x| y|
+----+----+
| 1|null|
|null| 2|
+----+----+
```
However, it seems it is not for `Row` as below:
``` python
spark.createDataFrame([Row(x=1), Row(y=2)]).show()
```
``` scala
16/06/19 16:25:56 ERROR Executor: Exception in task 6.0 in stage 66.0 (TID 316)
java.lang.IllegalStateException: Input row doesn't have expected number of values required by the schema. 2 fields are required while 1 values are provided.
at org.apache.spark.sql.execution.python.EvaluatePython$.fromJava(EvaluatePython.scala:147)
at org.apache.spark.sql.SparkSession$$anonfun$7.apply(SparkSession.scala:656)
at org.apache.spark.sql.SparkSession$$anonfun$7.apply(SparkSession.scala:656)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$4.apply(SparkPlan.scala:247)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$4.apply(SparkPlan.scala:240)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:780)
```
The behaviour seems right but it seems it might confuse users just like this JIRA was reported.
This PR adds the explanation for `Row` class.
## How was this patch tested?
N/A
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#13771 from HyukjinKwon/SPARK-13748.
## What changes were proposed in this pull request?
There are many locations in the Spark repo where the same word occurs consecutively. Sometimes they are appropriately placed, but many times they are not. This PR removes the inappropriately duplicated words.
## How was this patch tested?
N/A since only docs or comments were updated.
Author: Niranjan Padmanabhan <niranjan.padmanabhan@gmail.com>
Closes#16455 from neurons/np.structure_streaming_doc.
## What changes were proposed in this pull request?
Univariate feature selection works by selecting the best features based on univariate statistical tests.
FDR and FWE are a popular univariate statistical test for feature selection.
In 2005, the Benjamini and Hochberg paper on FDR was identified as one of the 25 most-cited statistical papers. The FDR uses the Benjamini-Hochberg procedure in this PR. https://en.wikipedia.org/wiki/False_discovery_rate.
In statistics, FWE is the probability of making one or more false discoveries, or type I errors, among all the hypotheses when performing multiple hypotheses tests.
https://en.wikipedia.org/wiki/Family-wise_error_rate
We add FDR and FWE methods for ChiSqSelector in this PR, like it is implemented in scikit-learn.
http://scikit-learn.org/stable/modules/feature_selection.html#univariate-feature-selection
## How was this patch tested?
ut will be added soon
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Author: Peng <peng.meng@intel.com>
Author: Peng, Meng <peng.meng@intel.com>
Closes#15212 from mpjlu/fdr_fwe.
### What changes were proposed in this pull request?
Currently, we only have a SQL interface for recovering all the partitions in the directory of a table and update the catalog. `MSCK REPAIR TABLE` or `ALTER TABLE table RECOVER PARTITIONS`. (Actually, very hard for me to remember `MSCK` and have no clue what it means)
After the new "Scalable Partition Handling", the table repair becomes much more important for making visible the data in the created data source partitioned table.
Thus, this PR is to add it into the Catalog interface. After this PR, users can repair the table by
```Scala
spark.catalog.recoverPartitions("testTable")
```
### How was this patch tested?
Modified the existing test cases.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#16356 from gatorsmile/repairTable.
## What changes were proposed in this pull request?
Adds basic TaskContext information to PySpark.
## How was this patch tested?
New unit tests to `tests.py` & existing unit tests.
Author: Holden Karau <holden@us.ibm.com>
Closes#16211 from holdenk/SPARK-18576-pyspark-taskcontext.
## What changes were proposed in this pull request?
There is a timeout failure when using `rdd.toLocalIterator()` or `df.toLocalIterator()` for a PySpark RDD and DataFrame:
df = spark.createDataFrame([[1],[2],[3]])
it = df.toLocalIterator()
row = next(it)
df2 = df.repartition(1000) # create many empty partitions which increase materialization time so causing timeout
it2 = df2.toLocalIterator()
row = next(it2)
The cause of this issue is, we open a socket to serve the data from JVM side. We set timeout for connection and reading through the socket in Python side. In Python we use a generator to read the data, so we only begin to connect the socket once we start to ask data from it. If we don't consume it immediately, there is connection timeout.
In the other side, the materialization time for RDD partitions is unpredictable. So we can't set a timeout for reading data through the socket. Otherwise, it is very possibly to fail.
## How was this patch tested?
Added tests into PySpark.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#16263 from viirya/fix-pyspark-localiterator.
## What changes were proposed in this pull request?
`_to_seq` wasn't imported.
## How was this patch tested?
Added partitionBy to existing write path unit test
Author: Burak Yavuz <brkyvz@gmail.com>
Closes#16297 from brkyvz/SPARK-18888.
## What changes were proposed in this pull request?
Right now `StreamingQuery.lastProgress` throws NoSuchElementException and it's hard to be used in Python since Python user will just see Py4jError.
This PR just makes it return null instead.
## How was this patch tested?
`test("lastProgress should be null when recentProgress is empty")`
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#16273 from zsxwing/SPARK-18852.
## What changes were proposed in this pull request?
Updated Scala param and Python param to have quotes around the options making it easier for users to read.
## How was this patch tested?
Manually checked the docstrings
Author: krishnakalyan3 <krishnakalyan3@gmail.com>
Closes#16242 from krishnakalyan3/doc-string.
### What changes were proposed in this pull request?
Currently, when users use Python UDF in Filter, BatchEvalPython is always generated below FilterExec. However, not all the predicates need to be evaluated after Python UDF execution. Thus, this PR is to push down the determinisitc predicates through `BatchEvalPython`.
```Python
>>> df = spark.createDataFrame([(1, "1"), (2, "2"), (1, "2"), (1, "2")], ["key", "value"])
>>> from pyspark.sql.functions import udf, col
>>> from pyspark.sql.types import BooleanType
>>> my_filter = udf(lambda a: a < 2, BooleanType())
>>> sel = df.select(col("key"), col("value")).filter((my_filter(col("key"))) & (df.value < "2"))
>>> sel.explain(True)
```
Before the fix, the plan looks like
```
== Optimized Logical Plan ==
Filter ((isnotnull(value#1) && <lambda>(key#0L)) && (value#1 < 2))
+- LogicalRDD [key#0L, value#1]
== Physical Plan ==
*Project [key#0L, value#1]
+- *Filter ((isnotnull(value#1) && pythonUDF0#9) && (value#1 < 2))
+- BatchEvalPython [<lambda>(key#0L)], [key#0L, value#1, pythonUDF0#9]
+- Scan ExistingRDD[key#0L,value#1]
```
After the fix, the plan looks like
```
== Optimized Logical Plan ==
Filter ((isnotnull(value#1) && <lambda>(key#0L)) && (value#1 < 2))
+- LogicalRDD [key#0L, value#1]
== Physical Plan ==
*Project [key#0L, value#1]
+- *Filter pythonUDF0#9: boolean
+- BatchEvalPython [<lambda>(key#0L)], [key#0L, value#1, pythonUDF0#9]
+- *Filter (isnotnull(value#1) && (value#1 < 2))
+- Scan ExistingRDD[key#0L,value#1]
```
### How was this patch tested?
Added both unit test cases for `BatchEvalPythonExec` and also add an end-to-end test case in Python test suite.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#16193 from gatorsmile/pythonUDFPredicatePushDown.
## What changes were proposed in this pull request?
Fixes a bug in the python implementation of rdd cartesian product related to batching that showed up in repeated cartesian products with seemingly random results. The root cause being multiple iterators pulling from the same stream in the wrong order because of logic that ignored batching.
`CartesianDeserializer` and `PairDeserializer` were changed to implement `_load_stream_without_unbatching` and borrow the one line implementation of `load_stream` from `BatchedSerializer`. The default implementation of `_load_stream_without_unbatching` was changed to give consistent results (always an iterable) so that it could be used without additional checks.
`PairDeserializer` no longer extends `CartesianDeserializer` as it was not really proper. If wanted a new common super class could be added.
Both `CartesianDeserializer` and `PairDeserializer` now only extend `Serializer` (which has no `dump_stream` implementation) since they are only meant for *de*serialization.
## How was this patch tested?
Additional unit tests (sourced from #14248) plus one for testing a cartesian with zip.
Author: Andrew Ray <ray.andrew@gmail.com>
Closes#16121 from aray/fix-cartesian.
## What changes were proposed in this pull request?
`input_file_name` doesn't return filename when working with UDF in PySpark. An example shows the problem:
from pyspark.sql.functions import *
from pyspark.sql.types import *
def filename(path):
return path
sourceFile = udf(filename, StringType())
spark.read.json("tmp.json").select(sourceFile(input_file_name())).show()
+---------------------------+
|filename(input_file_name())|
+---------------------------+
| |
+---------------------------+
The cause of this issue is, we group rows in `BatchEvalPythonExec` for batching processing of PythonUDF. Currently we group rows first and then evaluate expressions on the rows. If the data is less than the required number of rows for a group, the iterator will be consumed to the end before the evaluation. However, once the iterator reaches the end, we will unset input filename. So the input_file_name expression can't return correct filename.
This patch fixes the approach to group the batch of rows. We evaluate the expression first and then group evaluated results to batch.
## How was this patch tested?
Added unit test to PySpark.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#16115 from viirya/fix-py-udf-input-filename.
Based on an informal survey, users find this option easier to understand / remember.
Author: Michael Armbrust <michael@databricks.com>
Closes#16182 from marmbrus/renameRecentProgress.
## What changes were proposed in this pull request?
Here are the major changes in this PR.
- Added the ability to recover `StreamingQuery.id` from checkpoint location, by writing the id to `checkpointLoc/metadata`.
- Added `StreamingQuery.runId` which is unique for every query started and does not persist across restarts. This is to identify each restart of a query separately (same as earlier behavior of `id`).
- Removed auto-generation of `StreamingQuery.name`. The purpose of name was to have the ability to define an identifier across restarts, but since id is precisely that, there is no need for a auto-generated name. This means name becomes purely cosmetic, and is null by default.
- Added `runId` to `StreamingQueryListener` events and `StreamingQueryProgress`.
Implementation details
- Renamed existing `StreamExecutionMetadata` to `OffsetSeqMetadata`, and moved it to the file `OffsetSeq.scala`, because that is what this metadata is tied to. Also did some refactoring to make the code cleaner (got rid of a lot of `.json` and `.getOrElse("{}")`).
- Added the `id` as the new `StreamMetadata`.
- When a StreamingQuery is created it gets or writes the `StreamMetadata` from `checkpointLoc/metadata`.
- All internal logging in `StreamExecution` uses `(name, id, runId)` instead of just `name`
TODO
- [x] Test handling of name=null in json generation of StreamingQueryProgress
- [x] Test handling of name=null in json generation of StreamingQueryListener events
- [x] Test python API of runId
## How was this patch tested?
Updated unit tests and new unit tests
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#16113 from tdas/SPARK-18657.
## What changes were proposed in this pull request?
As reported in the Jira, there are some weird issues with exploding Python UDFs in SparkSQL.
The following test code can reproduce it. Notice: the following test code is reported to return wrong results in the Jira. However, as I tested on master branch, it causes exception and so can't return any result.
>>> from pyspark.sql.functions import *
>>> from pyspark.sql.types import *
>>>
>>> df = spark.range(10)
>>>
>>> def return_range(value):
... return [(i, str(i)) for i in range(value - 1, value + 1)]
...
>>> range_udf = udf(return_range, ArrayType(StructType([StructField("integer_val", IntegerType()),
... StructField("string_val", StringType())])))
>>>
>>> df.select("id", explode(range_udf(df.id))).show()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/spark/python/pyspark/sql/dataframe.py", line 318, in show
print(self._jdf.showString(n, 20))
File "/spark/python/lib/py4j-0.10.4-src.zip/py4j/java_gateway.py", line 1133, in __call__
File "/spark/python/pyspark/sql/utils.py", line 63, in deco
return f(*a, **kw)
File "/spark/python/lib/py4j-0.10.4-src.zip/py4j/protocol.py", line 319, in get_return_value py4j.protocol.Py4JJavaError: An error occurred while calling o126.showString.: java.lang.AssertionError: assertion failed
at scala.Predef$.assert(Predef.scala:156)
at org.apache.spark.sql.execution.CodegenSupport$class.consume(WholeStageCodegenExec.scala:120)
at org.apache.spark.sql.execution.GenerateExec.consume(GenerateExec.scala:57)
The cause of this issue is, in `ExtractPythonUDFs` we insert `BatchEvalPythonExec` to run PythonUDFs in batch. `BatchEvalPythonExec` will add extra outputs (e.g., `pythonUDF0`) to original plan. In above case, the original `Range` only has one output `id`. After `ExtractPythonUDFs`, the added `BatchEvalPythonExec` has two outputs `id` and `pythonUDF0`.
Because the output of `GenerateExec` is given after analysis phase, in above case, it is the combination of `id`, i.e., the output of `Range`, and `col`. But in planning phase, we change `GenerateExec`'s child plan to `BatchEvalPythonExec` with additional output attributes.
It will cause no problem in non wholestage codegen. Because when evaluating the additional attributes are projected out the final output of `GenerateExec`.
However, as `GenerateExec` now supports wholestage codegen, the framework will input all the outputs of the child plan to `GenerateExec`. Then when consuming `GenerateExec`'s output data (i.e., calling `consume`), the number of output attributes is different to the output variables in wholestage codegen.
To solve this issue, this patch only gives the generator's output to `GenerateExec` after analysis phase. `GenerateExec`'s output is the combination of its child plan's output and the generator's output. So when we change `GenerateExec`'s child, its output is still correct.
## How was this patch tested?
Added test cases to PySpark.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#16120 from viirya/fix-py-udf-with-generator.
## What changes were proposed in this pull request?
- Add StreamingQuery.explain and exception to Python.
- Fix StreamingQueryException to not expose `OffsetSeq`.
## How was this patch tested?
Jenkins
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#16125 from zsxwing/py-streaming-explain.
## What changes were proposed in this pull request?
Makes `Window.unboundedPreceding` and `Window.unboundedFollowing` backward compatible.
## How was this patch tested?
Pyspark SQL unittests.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: zero323 <zero323@users.noreply.github.com>
Closes#16123 from zero323/SPARK-17845-follow-up.
## What changes were proposed in this pull request?
In`JavaWrapper `'s destructor make Java Gateway dereference object in destructor, using `SparkContext._active_spark_context._gateway.detach`
Fixing the copying parameter bug, by moving the `copy` method from `JavaModel` to `JavaParams`
## How was this patch tested?
```scala
import random, string
from pyspark.ml.feature import StringIndexer
l = [(''.join(random.choice(string.ascii_uppercase) for _ in range(10)), ) for _ in range(int(7e5))] # 700000 random strings of 10 characters
df = spark.createDataFrame(l, ['string'])
for i in range(50):
indexer = StringIndexer(inputCol='string', outputCol='index')
indexer.fit(df)
```
* Before: would keep StringIndexer strong reference, causing GC issues and is halted midway
After: garbage collection works as the object is dereferenced, and computation completes
* Mem footprint tested using profiler
* Added a parameter copy related test which was failing before.
Author: Sandeep Singh <sandeep@techaddict.me>
Author: jkbradley <joseph.kurata.bradley@gmail.com>
Closes#15843 from techaddict/SPARK-18274.
## What changes were proposed in this pull request?
added the new handleInvalid param for these transformers to Python to maintain API parity.
## How was this patch tested?
existing tests
testing is done with new doctests
Author: Sandeep Singh <sandeep@techaddict.me>
Closes#15817 from techaddict/SPARK-18366.
## What changes were proposed in this pull request?
- Add StreamingQueryStatus.json
- Make it not case class (to avoid unnecessarily exposing implicit object StreamingQueryStatus, consistent with StreamingQueryProgress)
- Add StreamingQuery.status to Python
- Fix post-termination status
## How was this patch tested?
New unit tests
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#16075 from tdas/SPARK-18516-1.
## What changes were proposed in this pull request?
Add python api for KMeansSummary
## How was this patch tested?
unit test added
Author: Jeff Zhang <zjffdu@apache.org>
Closes#13557 from zjffdu/SPARK-15819.
## What changes were proposed in this pull request?
make a pass through the items marked as Experimental or DeveloperApi and see if any are stable enough to be unmarked. Also check for items marked final or sealed to see if they are stable enough to be opened up as APIs.
Some discussions in the jira: https://issues.apache.org/jira/browse/SPARK-18319
## How was this patch tested?
existing ut
Author: Yuhao <yuhao.yang@intel.com>
Author: Yuhao Yang <hhbyyh@gmail.com>
Closes#15972 from hhbyyh/experimental21.
This PR separates the status of a `StreamingQuery` into two separate APIs:
- `status` - describes the status of a `StreamingQuery` at this moment, including what phase of processing is currently happening and if data is available.
- `recentProgress` - an array of statistics about the most recent microbatches that have executed.
A recent progress contains the following information:
```
{
"id" : "2be8670a-fce1-4859-a530-748f29553bb6",
"name" : "query-29",
"timestamp" : 1479705392724,
"inputRowsPerSecond" : 230.76923076923077,
"processedRowsPerSecond" : 10.869565217391303,
"durationMs" : {
"triggerExecution" : 276,
"queryPlanning" : 3,
"getBatch" : 5,
"getOffset" : 3,
"addBatch" : 234,
"walCommit" : 30
},
"currentWatermark" : 0,
"stateOperators" : [ ],
"sources" : [ {
"description" : "KafkaSource[Subscribe[topic-14]]",
"startOffset" : {
"topic-14" : {
"2" : 0,
"4" : 1,
"1" : 0,
"3" : 0,
"0" : 0
}
},
"endOffset" : {
"topic-14" : {
"2" : 1,
"4" : 2,
"1" : 0,
"3" : 0,
"0" : 1
}
},
"numRecords" : 3,
"inputRowsPerSecond" : 230.76923076923077,
"processedRowsPerSecond" : 10.869565217391303
} ]
}
```
Additionally, in order to make it possible to correlate progress updates across restarts, we change the `id` field from an integer that is unique with in the JVM to a `UUID` that is globally unique.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Author: Michael Armbrust <michael@databricks.com>
Closes#15954 from marmbrus/queryProgress.
## What changes were proposed in this pull request?
This PR fixes SparkContext broken state in which it may fall if spark driver get crashed or killed by OOM.
## How was this patch tested?
1. Start SparkContext;
2. Find Spark driver process and `kill -9` it;
3. Call `sc.stop()`;
4. Create new SparkContext after that;
Without this patch you will crash on step 3 and won't be able to do step 4 without manual reset private attibutes or IPython notebook / shell restart.
Author: Alexander Shorin <kxepal@apache.org>
Closes#15961 from kxepal/18523-make-spark-context-stop-more-reliable.
## What changes were proposed in this pull request?
Remove deprecated methods for ML.
## How was this patch tested?
Existing tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#15913 from yanboliang/spark-18481.
## What changes were proposed in this pull request?
This PR adds two of the newly added methods of `Dataset`s to Python:
`withWatermark` and `checkpoint`
## How was this patch tested?
Doc tests
Author: Burak Yavuz <brkyvz@gmail.com>
Closes#15921 from brkyvz/py-watermark.
## What changes were proposed in this pull request?
Expose RDD's localCheckpoint() and associated functions in PySpark.
## How was this patch tested?
I added a UnitTest in python/pyspark/tests.py which passes.
I certify that this is my original work, and I license it to the project under the project's open source license.
Gabriel HUANG
Developer at Cardabel (http://cardabel.com/)
Author: Gabriel Huang <gabi.xiaohuang@gmail.com>
Closes#15811 from gabrielhuang/pyspark-localcheckpoint.
## What changes were proposed in this pull request?
Add model summary APIs for `GaussianMixtureModel` and `BisectingKMeansModel` in pyspark.
## How was this patch tested?
Unit tests.
Author: sethah <seth.hendrickson16@gmail.com>
Closes#15777 from sethah/pyspark_cluster_summaries.
## What changes were proposed in this pull request?
I found the documentation for the sample method to be confusing, this adds more clarification across all languages.
- [x] Scala
- [x] Python
- [x] R
- [x] RDD Scala
- [ ] RDD Python with SEED
- [X] RDD Java
- [x] RDD Java with SEED
- [x] RDD Python
## How was this patch tested?
NA
Please review https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark before opening a pull request.
Author: anabranch <wac.chambers@gmail.com>
Author: Bill Chambers <bill@databricks.com>
Closes#15815 from anabranch/SPARK-18365.
## What changes were proposed in this pull request?
This PR aims to provide a pip installable PySpark package. This does a bunch of work to copy the jars over and package them with the Python code (to prevent challenges from trying to use different versions of the Python code with different versions of the JAR). It does not currently publish to PyPI but that is the natural follow up (SPARK-18129).
Done:
- pip installable on conda [manual tested]
- setup.py installed on a non-pip managed system (RHEL) with YARN [manual tested]
- Automated testing of this (virtualenv)
- packaging and signing with release-build*
Possible follow up work:
- release-build update to publish to PyPI (SPARK-18128)
- figure out who owns the pyspark package name on prod PyPI (is it someone with in the project or should we ask PyPI or should we choose a different name to publish with like ApachePySpark?)
- Windows support and or testing ( SPARK-18136 )
- investigate details of wheel caching and see if we can avoid cleaning the wheel cache during our test
- consider how we want to number our dev/snapshot versions
Explicitly out of scope:
- Using pip installed PySpark to start a standalone cluster
- Using pip installed PySpark for non-Python Spark programs
*I've done some work to test release-build locally but as a non-committer I've just done local testing.
## How was this patch tested?
Automated testing with virtualenv, manual testing with conda, a system wide install, and YARN integration.
release-build changes tested locally as a non-committer (no testing of upload artifacts to Apache staging websites)
Author: Holden Karau <holden@us.ibm.com>
Author: Juliet Hougland <juliet@cloudera.com>
Author: Juliet Hougland <not@myemail.com>
Closes#15659 from holdenk/SPARK-1267-pip-install-pyspark.
## What changes were proposed in this pull request?
SPARK-18459: triggerId seems like a number that should be increasing with each trigger, whether or not there is data in it. However, actually, triggerId increases only where there is a batch of data in a trigger. So its better to rename it to batchId.
SPARK-18460: triggerDetails was missing from json representation. Fixed it.
## How was this patch tested?
Updated existing unit tests.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#15895 from tdas/SPARK-18459.
## What changes were proposed in this pull request?
Currently the error message is correct but doesn't provide additional hint to new users. It would be better to hint related configuration to users in the message.
## How was this patch tested?
N/A because it only changes error message.
Please review https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark before opening a pull request.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#15822 from viirya/minor-pyspark-worker-errmsg.
## What changes were proposed in this pull request?
Currently we use java serialization for the WAL that stores the offsets contained in each batch. This has two main issues:
It can break across spark releases (though this is not the only thing preventing us from upgrading a running query)
It is unnecessarily opaque to the user.
I'd propose we require offsets to provide a user readable serialization and use that instead. JSON is probably a good option.
## How was this patch tested?
Tests were added for KafkaSourceOffset in [KafkaSourceOffsetSuite](external/kafka-0-10-sql/src/test/scala/org/apache/spark/sql/kafka010/KafkaSourceOffsetSuite.scala) and for LongOffset in [OffsetSuite](sql/core/src/test/scala/org/apache/spark/sql/streaming/OffsetSuite.scala)
Please review https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark before opening a pull request.
zsxwing marmbrus
Author: Tyson Condie <tcondie@gmail.com>
Author: Tyson Condie <tcondie@clash.local>
Closes#15626 from tcondie/spark-8360.
## What changes were proposed in this pull request?
Gradient Boosted Tree in R.
With a few minor improvements to RandomForest in R.
Since this is relatively isolated I'd like to target this for branch-2.1
## How was this patch tested?
manual tests, unit tests
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#15746 from felixcheung/rgbt.
## What changes were proposed in this pull request?
minor doc update that should go to master & branch-2.1
## How was this patch tested?
manual
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#15747 from felixcheung/pySPARK-14393.
## What changes were proposed in this pull request?
Document that Java 7, Python 2.6, Scala 2.10, Hadoop < 2.6 are deprecated in Spark 2.1.0. This does not actually implement any of the change in SPARK-18138, just peppers the documentation with notices about it.
## How was this patch tested?
Doc build
Author: Sean Owen <sowen@cloudera.com>
Closes#15733 from srowen/SPARK-18138.
## What changes were proposed in this pull request?
Add missing 'subsamplingRate' of pyspark GBTClassifier
## How was this patch tested?
existing tests
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#15692 from zhengruifeng/gbt_subsamplingRate.
## What changes were proposed in this pull request?
- Renamed kbest to numTopFeatures
- Renamed alpha to fpr
- Added missing Since annotations
- Doc cleanups
## How was this patch tested?
Added new standardized unit tests for spark.ml.
Improved existing unit test coverage a bit.
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#15647 from jkbradley/chisqselector-follow-ups.
## What changes were proposed in this pull request?
This PR proposes to add `to_json` function in contrast with `from_json` in Scala, Java and Python.
It'd be useful if we can convert a same column from/to json. Also, some datasources do not support nested types. If we are forced to save a dataframe into those data sources, we might be able to work around by this function.
The usage is as below:
``` scala
val df = Seq(Tuple1(Tuple1(1))).toDF("a")
df.select(to_json($"a").as("json")).show()
```
``` bash
+--------+
| json|
+--------+
|{"_1":1}|
+--------+
```
## How was this patch tested?
Unit tests in `JsonFunctionsSuite` and `JsonExpressionsSuite`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#15354 from HyukjinKwon/SPARK-17764.
## What changes were proposed in this pull request?
Add subsmaplingRate to randomForestClassifier
Add varianceCol to randomForestRegressor
In Python
## How was this patch tested?
manual tests
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#15638 from felixcheung/pyrandomforest.
## What changes were proposed in this pull request?
This PR is an enhancement of PR with commit ID:57dc326bd00cf0a49da971e9c573c48ae28acaa2.
NaN is a special type of value which is commonly seen as invalid. But We find that there are certain cases where NaN are also valuable, thus need special handling. We provided user when dealing NaN values with 3 options, to either reserve an extra bucket for NaN values, or remove the NaN values, or report an error, by setting handleNaN "keep", "skip", or "error"(default) respectively.
'''Before:
val bucketizer: Bucketizer = new Bucketizer()
.setInputCol("feature")
.setOutputCol("result")
.setSplits(splits)
'''After:
val bucketizer: Bucketizer = new Bucketizer()
.setInputCol("feature")
.setOutputCol("result")
.setSplits(splits)
.setHandleNaN("keep")
## How was this patch tested?
Tests added in QuantileDiscretizerSuite, BucketizerSuite and DataFrameStatSuite
Signed-off-by: VinceShieh <vincent.xieintel.com>
Author: VinceShieh <vincent.xie@intel.com>
Author: Vincent Xie <vincent.xie@intel.com>
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#15428 from VinceShieh/spark-17219_followup.
## What changes were proposed in this pull request?
API and programming guide doc changes for Scala, Python and R.
## How was this patch tested?
manual test
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#15629 from felixcheung/jsondoc.
## What changes were proposed in this pull request?
StreamingQueryStatus exposed through StreamingQueryListener often needs to be recorded (similar to SparkListener events). This PR adds `.json` and `.prettyJson` to `StreamingQueryStatus`, `SourceStatus` and `SinkStatus`.
## How was this patch tested?
New unit tests
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#15476 from tdas/SPARK-17926.
## What changes were proposed in this pull request?
This change is a followup for #15389 which calls `_to_java_object_rdd()` to solve this issue. Due to the concern of the possible expensive cost of the call, we can choose to decrease the batch size to solve this issue too.
Simple benchmark:
import time
num_partitions = 20000
a = sc.parallelize(range(int(1e6)), 2)
start = time.time()
l = a.repartition(num_partitions).glom().map(len).collect()
end = time.time()
print(end - start)
Before: 419.447577953
_to_java_object_rdd(): 421.916361094
decreasing the batch size: 423.712255955
## How was this patch tested?
Jenkins tests.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#15445 from viirya/repartition-batch-size.
## What changes were proposed in this pull request?
Add a crossJoin function to the DataFrame API similar to that in Scala. Joins with no condition (cartesian products) must be specified with the crossJoin API
## How was this patch tested?
Added python tests to ensure that an AnalysisException if a cartesian product is specified without crossJoin(), and that cartesian products can execute if specified via crossJoin()
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Please review https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark before opening a pull request.
Author: Srinath Shankar <srinath@databricks.com>
Closes#15493 from srinathshankar/crosspython.
Currently pyspark can only call the builtin java UDF, but can not call custom java UDF. It would be better to allow that. 2 benefits:
* Leverage the power of rich third party java library
* Improve the performance. Because if we use python UDF, python daemons will be started on worker which will affect the performance.
Author: Jeff Zhang <zjffdu@apache.org>
Closes#9766 from zjffdu/SPARK-11775.
[SPARK-11905](https://issues.apache.org/jira/browse/SPARK-11905) added support for `persist`/`cache` for `Dataset`. However, there is no user-facing API to check if a `Dataset` is cached and if so what the storage level is. This PR adds `getStorageLevel` to `Dataset`, analogous to `RDD.getStorageLevel`.
Updated `DatasetCacheSuite`.
Author: Nick Pentreath <nickp@za.ibm.com>
Closes#13780 from MLnick/ds-storagelevel.
Signed-off-by: Michael Armbrust <michael@databricks.com>
## What changes were proposed in this pull request?
For feature selection method ChiSquareSelector, it is based on the ChiSquareTestResult.statistic (ChiSqure value) to select the features. It select the features with the largest ChiSqure value. But the Degree of Freedom (df) of ChiSqure value is different in Statistics.chiSqTest(RDD), and for different df, you cannot base on ChiSqure value to select features.
So we change statistic to pValue for SelectKBest and SelectPercentile
## How was this patch tested?
change existing test
Author: Peng <peng.meng@intel.com>
Closes#15444 from mpjlu/chisqure-bug.
## What changes were proposed in this pull request?
Since ```ml.evaluation``` has supported save/load at Scala side, supporting it at Python side is very straightforward and easy.
## How was this patch tested?
Add python doctest.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#13194 from yanboliang/spark-15402.
## What changes were proposed in this pull request?
Follow-up work of #13675, add Python API for ```RFormula forceIndexLabel```.
## How was this patch tested?
Unit test.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#15430 from yanboliang/spark-15957-python.
## What changes were proposed in this pull request?
Metrics are needed for monitoring structured streaming apps. Here is the design doc for implementing the necessary metrics.
https://docs.google.com/document/d/1NIdcGuR1B3WIe8t7VxLrt58TJB4DtipWEbj5I_mzJys/edit?usp=sharing
Specifically, this PR adds the following public APIs changes.
### New APIs
- `StreamingQuery.status` returns a `StreamingQueryStatus` object (renamed from `StreamingQueryInfo`, see later)
- `StreamingQueryStatus` has the following important fields
- inputRate - Current rate (rows/sec) at which data is being generated by all the sources
- processingRate - Current rate (rows/sec) at which the query is processing data from
all the sources
- ~~outputRate~~ - *Does not work with wholestage codegen*
- latency - Current average latency between the data being available in source and the sink writing the corresponding output
- sourceStatuses: Array[SourceStatus] - Current statuses of the sources
- sinkStatus: SinkStatus - Current status of the sink
- triggerStatus - Low-level detailed status of the last completed/currently active trigger
- latencies - getOffset, getBatch, full trigger, wal writes
- timestamps - trigger start, finish, after getOffset, after getBatch
- numRows - input, output, state total/updated rows for aggregations
- `SourceStatus` has the following important fields
- inputRate - Current rate (rows/sec) at which data is being generated by the source
- processingRate - Current rate (rows/sec) at which the query is processing data from the source
- triggerStatus - Low-level detailed status of the last completed/currently active trigger
- Python API for `StreamingQuery.status()`
### Breaking changes to existing APIs
**Existing direct public facing APIs**
- Deprecated direct public-facing APIs `StreamingQuery.sourceStatuses` and `StreamingQuery.sinkStatus` in favour of `StreamingQuery.status.sourceStatuses/sinkStatus`.
- Branch 2.0 should have it deprecated, master should have it removed.
**Existing advanced listener APIs**
- `StreamingQueryInfo` renamed to `StreamingQueryStatus` for consistency with `SourceStatus`, `SinkStatus`
- Earlier StreamingQueryInfo was used only in the advanced listener API, but now it is used in direct public-facing API (StreamingQuery.status)
- Field `queryInfo` in listener events `QueryStarted`, `QueryProgress`, `QueryTerminated` changed have name `queryStatus` and return type `StreamingQueryStatus`.
- Field `offsetDesc` in `SourceStatus` was Option[String], converted it to `String`.
- For `SourceStatus` and `SinkStatus` made constructor private instead of private[sql] to make them more java-safe. Instead added `private[sql] object SourceStatus/SinkStatus.apply()` which are harder to accidentally use in Java.
## How was this patch tested?
Old and new unit tests.
- Rate calculation and other internal logic of StreamMetrics tested by StreamMetricsSuite.
- New info in statuses returned through StreamingQueryListener is tested in StreamingQueryListenerSuite.
- New and old info returned through StreamingQuery.status is tested in StreamingQuerySuite.
- Source-specific tests for making sure input rows are counted are is source-specific test suites.
- Additional tests to test minor additions in LocalTableScanExec, StateStore, etc.
Metrics also manually tested using Ganglia sink
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#15307 from tdas/SPARK-17731.
## What changes were proposed in this pull request?
update python api for NaiveBayes: add weight col parameter.
## How was this patch tested?
doctests added.
Author: WeichenXu <WeichenXu123@outlook.com>
Closes#15406 from WeichenXu123/nb_python_update.
## What changes were proposed in this pull request?
This patch improves the window function frame boundary API to make it more obvious to read and to use. The two high level changes are:
1. Create Window.currentRow, Window.unboundedPreceding, Window.unboundedFollowing to indicate the special values in frame boundaries. These methods map to the special integral values so we are not breaking backward compatibility here. This change makes the frame boundaries more self-evident (instead of Long.MinValue, it becomes Window.unboundedPreceding).
2. In Python, for any value less than or equal to JVM's Long.MinValue, treat it as Window.unboundedPreceding. For any value larger than or equal to JVM's Long.MaxValue, treat it as Window.unboundedFollowing. Before this change, if the user specifies any value that is less than Long.MinValue but not -sys.maxsize (e.g. -sys.maxsize + 1), the number we pass over to the JVM would overflow, resulting in a frame that does not make sense.
Code example required to specify a frame before this patch:
```
Window.rowsBetween(-Long.MinValue, 0)
```
While the above code should still work, the new way is more obvious to read:
```
Window.rowsBetween(Window.unboundedPreceding, Window.currentRow)
```
## How was this patch tested?
- Updated DataFrameWindowSuite (for Scala/Java)
- Updated test_window_functions_cumulative_sum (for Python)
- Renamed DataFrameWindowSuite DataFrameWindowFunctionsSuite to better reflect its purpose
Author: Reynold Xin <rxin@databricks.com>
Closes#15438 from rxin/SPARK-17845.
## What changes were proposed in this pull request?
In PySpark, the invalid join type will not throw error for the following join:
```df1.join(df2, how='not-a-valid-join-type')```
The signature of the join is:
```def join(self, other, on=None, how=None):```
The existing code completely ignores the `how` parameter when `on` is `None`. This patch will process the arguments passed to join and pass in to JVM Spark SQL Analyzer, which will validate the join type passed.
## How was this patch tested?
Used manual and existing test suites.
Author: Bijay Pathak <bkpathak@mtu.edu>
Closes#15409 from bkpathak/SPARK-14761.
## What changes were proposed in this pull request?
SQLConf is session-scoped and mutable. However, we do have the requirement for a static SQL conf, which is global and immutable, e.g. the `schemaStringThreshold` in `HiveExternalCatalog`, the flag to enable/disable hive support, the global temp view database in https://github.com/apache/spark/pull/14897.
Actually we've already implemented static SQL conf implicitly via `SparkConf`, this PR just make it explicit and expose it to users, so that they can see the config value via SQL command or `SparkSession.conf`, and forbid users to set/unset static SQL conf.
## How was this patch tested?
new tests in SQLConfSuite
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15295 from cloud-fan/global-conf.
## What changes were proposed in this pull request?
The root cause that we would ignore SparkConf when launching JVM is that SparkConf require JVM to be created first. https://github.com/apache/spark/blob/master/python/pyspark/conf.py#L106
In this PR, I would defer the launching of JVM until SparkContext is created so that we can pass SparkConf to JVM correctly.
## How was this patch tested?
Use the example code in the description of SPARK-17387,
```
$ SPARK_HOME=$PWD PYTHONPATH=python:python/lib/py4j-0.10.3-src.zip python
Python 2.7.12 (default, Jul 1 2016, 15:12:24)
[GCC 5.4.0 20160609] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> from pyspark import SparkContext
>>> from pyspark import SparkConf
>>> conf = SparkConf().set("spark.driver.memory", "4g")
>>> sc = SparkContext(conf=conf)
```
And verify the spark.driver.memory is correctly picked up.
```
...op/ -Xmx4g org.apache.spark.deploy.SparkSubmit --conf spark.driver.memory=4g pyspark-shell
```
Author: Jeff Zhang <zjffdu@apache.org>
Closes#14959 from zjffdu/SPARK-17387.
## What changes were proposed in this pull request?
Quoted from JIRA description:
Calling repartition on a PySpark RDD to increase the number of partitions results in highly skewed partition sizes, with most having 0 rows. The repartition method should evenly spread out the rows across the partitions, and this behavior is correctly seen on the Scala side.
Please reference the following code for a reproducible example of this issue:
num_partitions = 20000
a = sc.parallelize(range(int(1e6)), 2) # start with 2 even partitions
l = a.repartition(num_partitions).glom().map(len).collect() # get length of each partition
min(l), max(l), sum(l)/len(l), len(l) # skewed!
In Scala's `repartition` code, we will distribute elements evenly across output partitions. However, the RDD from Python is serialized as a single binary data, so the distribution fails. We need to convert the RDD in Python to java object before repartitioning.
## How was this patch tested?
Jenkins tests.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#15389 from viirya/pyspark-rdd-repartition.
## What changes were proposed in this pull request?
address post hoc review comments for https://github.com/apache/spark/pull/14897
## How was this patch tested?
N/A
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15424 from cloud-fan/global-temp-view.
## What changes were proposed in this pull request?
Upgraded to a newer version of Pyrolite which supports serialization of a BinaryType StructField for PySpark.SQL
## How was this patch tested?
Added a unit test which fails with a raised ValueError when using the previous version of Pyrolite 4.9 and Python3
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#15386 from BryanCutler/pyrolite-upgrade-SPARK-17808.
## What changes were proposed in this pull request?
When I was creating the example code for SPARK-10496, I realized it was pretty convoluted to define the frame boundaries for window functions when there is no partition column or ordering column. The reason is that we don't provide a way to create a WindowSpec directly with the frame boundaries. We can trivially improve this by adding rowsBetween and rangeBetween to Window object.
As an example, to compute cumulative sum using the natural ordering, before this pr:
```
df.select('key, sum("value").over(Window.partitionBy(lit(1)).rowsBetween(Long.MinValue, 0)))
```
After this pr:
```
df.select('key, sum("value").over(Window.rowsBetween(Long.MinValue, 0)))
```
Note that you could argue there is no point specifying a window frame without partitionBy/orderBy -- but it is strange that only rowsBetween and rangeBetween are not the only two APIs not available.
This also fixes https://issues.apache.org/jira/browse/SPARK-17656 (removing _root_.scala).
## How was this patch tested?
Added test cases to compute cumulative sum in DataFrameWindowSuite for Scala/Java and tests.py for Python.
Author: Reynold Xin <rxin@databricks.com>
Closes#15412 from rxin/SPARK-17844.
## What changes were proposed in this pull request?
Global temporary view is a cross-session temporary view, which means it's shared among all sessions. Its lifetime is the lifetime of the Spark application, i.e. it will be automatically dropped when the application terminates. It's tied to a system preserved database `global_temp`(configurable via SparkConf), and we must use the qualified name to refer a global temp view, e.g. SELECT * FROM global_temp.view1.
changes for `SessionCatalog`:
1. add a new field `gloabalTempViews: GlobalTempViewManager`, to access the shared global temp views, and the global temp db name.
2. `createDatabase` will fail if users wanna create `global_temp`, which is system preserved.
3. `setCurrentDatabase` will fail if users wanna set `global_temp`, which is system preserved.
4. add `createGlobalTempView`, which is used in `CreateViewCommand` to create global temp views.
5. add `dropGlobalTempView`, which is used in `CatalogImpl` to drop global temp view.
6. add `alterTempViewDefinition`, which is used in `AlterViewAsCommand` to update the view definition for local/global temp views.
7. `renameTable`/`dropTable`/`isTemporaryTable`/`lookupRelation`/`getTempViewOrPermanentTableMetadata`/`refreshTable` will handle global temp views.
changes for SQL commands:
1. `CreateViewCommand`/`AlterViewAsCommand` is updated to support global temp views
2. `ShowTablesCommand` outputs a new column `database`, which is used to distinguish global and local temp views.
3. other commands can also handle global temp views if they call `SessionCatalog` APIs which accepts global temp views, e.g. `DropTableCommand`, `AlterTableRenameCommand`, `ShowColumnsCommand`, etc.
changes for other public API
1. add a new method `dropGlobalTempView` in `Catalog`
2. `Catalog.findTable` can find global temp view
3. add a new method `createGlobalTempView` in `Dataset`
## How was this patch tested?
new tests in `SQLViewSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#14897 from cloud-fan/global-temp-view.
## What changes were proposed in this pull request?
If given a list of paths, `pyspark.sql.readwriter.text` will attempt to use an undefined variable `paths`. This change checks if the param `paths` is a basestring and then converts it to a list, so that the same variable `paths` can be used for both cases
## How was this patch tested?
Added unit test for reading list of files
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#15379 from BryanCutler/sql-readtext-paths-SPARK-17805.
## What changes were proposed in this pull request?
1,parity check and add missing test suites for ml's NB
2,remove some unused imports
## How was this patch tested?
manual tests in spark-shell
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#15312 from zhengruifeng/nb_test_parity.
## What changes were proposed in this pull request?
Replaces` ValueError` with `IndexError` when index passed to `ml` / `mllib` `SparseVector.__getitem__` is out of range. This ensures correct iteration behavior.
Replaces `ValueError` with `IndexError` for `DenseMatrix` and `SparkMatrix` in `ml` / `mllib`.
## How was this patch tested?
PySpark `ml` / `mllib` unit tests. Additional unit tests to prove that the problem has been resolved.
Author: zero323 <zero323@users.noreply.github.com>
Closes#15144 from zero323/SPARK-17587.
## What changes were proposed in this pull request?
Partial revert of #15277 to instead sort and store input to model rather than require sorted input
## How was this patch tested?
Existing tests.
Author: Sean Owen <sowen@cloudera.com>
Closes#15299 from srowen/SPARK-17704.2.
Spark SQL has great support for reading text files that contain JSON data. However, in many cases the JSON data is just one column amongst others. This is particularly true when reading from sources such as Kafka. This PR adds a new functions `from_json` that converts a string column into a nested `StructType` with a user specified schema.
Example usage:
```scala
val df = Seq("""{"a": 1}""").toDS()
val schema = new StructType().add("a", IntegerType)
df.select(from_json($"value", schema) as 'json) // => [json: <a: int>]
```
This PR adds support for java, scala and python. I leveraged our existing JSON parsing support by moving it into catalyst (so that we could define expressions using it). I left SQL out for now, because I'm not sure how users would specify a schema.
Author: Michael Armbrust <michael@databricks.com>
Closes#15274 from marmbrus/jsonParser.
## What changes were proposed in this pull request?
Add Python API for multinomial logistic regression.
- add `family` param in python api.
- expose `coefficientMatrix` and `interceptVector` for `LogisticRegressionModel`
- add python-side testcase for multinomial logistic regression
- update python doc.
## How was this patch tested?
existing and added doc tests.
Author: WeichenXu <WeichenXu123@outlook.com>
Closes#14852 from WeichenXu123/add_MLOR_python.
## What changes were proposed in this pull request?
#14597 modified ```ChiSqSelector``` to support ```fpr``` type selector, however, it left some issue need to be addressed:
* We should allow users to set selector type explicitly rather than switching them by using different setting function, since the setting order will involves some unexpected issue. For example, if users both set ```numTopFeatures``` and ```percentile```, it will train ```kbest``` or ```percentile``` model based on the order of setting (the latter setting one will be trained). This make users confused, and we should allow users to set selector type explicitly. We handle similar issues at other place of ML code base such as ```GeneralizedLinearRegression``` and ```LogisticRegression```.
* Meanwhile, if there are more than one parameter except ```alpha``` can be set for ```fpr``` model, we can not handle it elegantly in the existing framework. And similar issues for ```kbest``` and ```percentile``` model. Setting selector type explicitly can solve this issue also.
* If setting selector type explicitly by users is allowed, we should handle param interaction such as if users set ```selectorType = percentile``` and ```alpha = 0.1```, we should notify users the parameter ```alpha``` will take no effect. We should handle complex parameter interaction checks at ```transformSchema```. (FYI #11620)
* We should use lower case of the selector type names to follow MLlib convention.
* Add ML Python API.
## How was this patch tested?
Unit test.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#15214 from yanboliang/spark-17017.
## What changes were proposed in this pull request?
Match ProbabilisticClassifer.thresholds requirements to R randomForest cutoff, requiring all > 0
## How was this patch tested?
Jenkins tests plus new test cases
Author: Sean Owen <sowen@cloudera.com>
Closes#15149 from srowen/SPARK-17057.
## What changes were proposed in this pull request?
Move the internals of the PySpark accumulator API from the old deprecated API on top of the new accumulator API.
## How was this patch tested?
The existing PySpark accumulator tests (both unit tests and doc tests at the start of accumulator.py).
Author: Holden Karau <holden@us.ibm.com>
Closes#14467 from holdenk/SPARK-16861-refactor-pyspark-accumulator-api.
## What changes were proposed in this pull request?
Add treeAggregateDepth parameter for AFTSurvivalRegression to keep consistent with LiR/LoR.
## How was this patch tested?
Existing tests.
Author: WeichenXu <WeichenXu123@outlook.com>
Closes#14851 from WeichenXu123/add_treeAggregate_param_for_survival_regression.
## What changes were proposed in this pull request?
This PR includes the changes below:
1. Upgrade Univocity library from 2.1.1 to 2.2.1
This includes some performance improvement and also enabling auto-extending buffer in `maxCharsPerColumn` option in CSV. Please refer the [release notes](https://github.com/uniVocity/univocity-parsers/releases).
2. Remove useless `rowSeparator` variable existing in `CSVOptions`
We have this unused variable in [CSVOptions.scala#L127](29952ed096/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVOptions.scala (L127)) but it seems possibly causing confusion that it actually does not care of `\r\n`. For example, we have an issue open about this, [SPARK-17227](https://issues.apache.org/jira/browse/SPARK-17227), describing this variable.
This variable is virtually not being used because we rely on `LineRecordReader` in Hadoop which deals with only both `\n` and `\r\n`.
3. Set the default value of `maxCharsPerColumn` to auto-expending.
We are setting 1000000 for the length of each column. It'd be more sensible we allow auto-expending rather than fixed length by default.
To make sure, using `-1` is being described in the release note, [2.2.0](https://github.com/uniVocity/univocity-parsers/releases/tag/v2.2.0).
## How was this patch tested?
N/A
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#15138 from HyukjinKwon/SPARK-17583.
## What changes were proposed in this pull request?
This PR fixes an issue when Bucketizer is called to handle a dataset containing NaN value.
Sometimes, null value might also be useful to users, so in these cases, Bucketizer should
reserve one extra bucket for NaN values, instead of throwing an illegal exception.
Before:
```
Bucketizer.transform on NaN value threw an illegal exception.
```
After:
```
NaN values will be grouped in an extra bucket.
```
## How was this patch tested?
New test cases added in `BucketizerSuite`.
Signed-off-by: VinceShieh <vincent.xieintel.com>
Author: VinceShieh <vincent.xie@intel.com>
Closes#14858 from VinceShieh/spark-17219.
## What changes were proposed in this pull request?
Univariate feature selection works by selecting the best features based on univariate statistical tests. False Positive Rate (FPR) is a popular univariate statistical test for feature selection. We add a chiSquare Selector based on False Positive Rate (FPR) test in this PR, like it is implemented in scikit-learn.
http://scikit-learn.org/stable/modules/feature_selection.html#univariate-feature-selection
## How was this patch tested?
Add Scala ut
Author: Peng, Meng <peng.meng@intel.com>
Closes#14597 from mpjlu/fprChiSquare.
## What changes were proposed in this pull request?
Users would like to add a directory as dependency in some cases, they can use ```SparkContext.addFile``` with argument ```recursive=true``` to recursively add all files under the directory by using Scala. But Python users can only add file not directory, we should also make it supported.
## How was this patch tested?
Unit test.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#15140 from yanboliang/spark-17585.
## What changes were proposed in this pull request?
The Scala version of `SparkContext` has a handy field called `uiWebUrl` that tells you which URL the SparkUI spawned by that instance lives at. This is often very useful because the value for `spark.ui.port` in the config is only a suggestion; if that port number is taken by another Spark instance on the same machine, Spark will just keep incrementing the port until it finds a free one. So, on a machine with a lot of running PySpark instances, you often have to start trying all of them one-by-one until you find your application name.
Scala users have a way around this with `uiWebUrl` but Java and Python users do not. This pull request fixes this in the most straightforward way possible, simply propagating this field through the `JavaSparkContext` and into pyspark through the Java gateway.
Please let me know if any additional documentation/testing is needed.
## How was this patch tested?
Existing tests were run to make sure there were no regressions, and a binary distribution was created and tested manually for the correct value of `sc.uiWebPort` in a variety of circumstances.
Author: Adrian Petrescu <apetresc@gmail.com>
Closes#15000 from apetresc/pyspark-uiweburl.
## What changes were proposed in this pull request?
In optimizer, we try to evaluate the condition to see whether it's nullable or not, but some expressions are not evaluable, we should check that before evaluate it.
## How was this patch tested?
Added regression tests.
Author: Davies Liu <davies@databricks.com>
Closes#15103 from davies/udf_join.
## Problem
CSV in Spark 2.0.0:
- does not read null values back correctly for certain data types such as `Boolean`, `TimestampType`, `DateType` -- this is a regression comparing to 1.6;
- does not read empty values (specified by `options.nullValue`) as `null`s for `StringType` -- this is compatible with 1.6 but leads to problems like SPARK-16903.
## What changes were proposed in this pull request?
This patch makes changes to read all empty values back as `null`s.
## How was this patch tested?
New test cases.
Author: Liwei Lin <lwlin7@gmail.com>
Closes#14118 from lw-lin/csv-cast-null.
## What changes were proposed in this pull request?
This pull request changes the behavior of `Word2VecModel.findSynonyms` so that it will not spuriously reject the best match when invoked with a vector that does not correspond to a word in the model's vocabulary. Instead of blindly discarding the best match, the changed implementation discards a match that corresponds to the query word (in cases where `findSynonyms` is invoked with a word) or that has an identical angle to the query vector.
## How was this patch tested?
I added a test to `Word2VecSuite` to ensure that the word with the most similar vector from a supplied vector would not be spuriously rejected.
Author: William Benton <willb@redhat.com>
Closes#15105 from willb/fix/findSynonyms.
## What changes were proposed in this pull request?
For large objects, pickle does not raise useful error messages. However, we can wrap them to be slightly more user friendly:
Example 1:
```
def run():
import numpy.random as nr
b = nr.bytes(8 * 1000000000)
sc.parallelize(range(1000), 1000).map(lambda x: len(b)).count()
run()
```
Before:
```
error: 'i' format requires -2147483648 <= number <= 2147483647
```
After:
```
pickle.PicklingError: Object too large to serialize: 'i' format requires -2147483648 <= number <= 2147483647
```
Example 2:
```
def run():
import numpy.random as nr
b = sc.broadcast(nr.bytes(8 * 1000000000))
sc.parallelize(range(1000), 1000).map(lambda x: len(b.value)).count()
run()
```
Before:
```
SystemError: error return without exception set
```
After:
```
cPickle.PicklingError: Could not serialize broadcast: SystemError: error return without exception set
```
## How was this patch tested?
Manually tried out these cases
cc davies
Author: Eric Liang <ekl@databricks.com>
Closes#15026 from ericl/spark-17472.
## What changes were proposed in this pull request?
In PySpark, `df.take(1)` runs a single-stage job which computes only one partition of the DataFrame, while `df.limit(1).collect()` computes all partitions and runs a two-stage job. This difference in performance is confusing.
The reason why `limit(1).collect()` is so much slower is that `collect()` internally maps to `df.rdd.<some-pyspark-conversions>.toLocalIterator`, which causes Spark SQL to build a query where a global limit appears in the middle of the plan; this, in turn, ends up being executed inefficiently because limits in the middle of plans are now implemented by repartitioning to a single task rather than by running a `take()` job on the driver (this was done in #7334, a patch which was a prerequisite to allowing partition-local limits to be pushed beneath unions, etc.).
In order to fix this performance problem I think that we should generalize the fix from SPARK-10731 / #8876 so that `DataFrame.collect()` also delegates to the Scala implementation and shares the same performance properties. This patch modifies `DataFrame.collect()` to first collect all results to the driver and then pass them to Python, allowing this query to be planned using Spark's `CollectLimit` optimizations.
## How was this patch tested?
Added a regression test in `sql/tests.py` which asserts that the expected number of jobs, stages, and tasks are run for both queries.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#15068 from JoshRosen/pyspark-collect-limit.
## What changes were proposed in this pull request?
This pull request removes the SparkContext.clearFiles() method from the PySpark API as the method was removed from the Scala API in 8ce645d4ee. Using that method in PySpark leads to an exception as PySpark tries to call the non-existent method on the JVM side.
## How was this patch tested?
Existing tests (though none of them tested this particular method).
Author: Sami Jaktholm <sjakthol@outlook.com>
Closes#15081 from sjakthol/pyspark-sc-clearfiles.
## What changes were proposed in this pull request?
When there is any Python UDF in the Project between Sort and Limit, it will be collected into TakeOrderedAndProjectExec, ExtractPythonUDFs failed to pull the Python UDFs out because QueryPlan.expressions does not include the expression inside Option[Seq[Expression]].
Ideally, we should fix the `QueryPlan.expressions`, but tried with no luck (it always run into infinite loop). In PR, I changed the TakeOrderedAndProjectExec to no use Option[Seq[Expression]] to workaround it. cc JoshRosen
## How was this patch tested?
Added regression test.
Author: Davies Liu <davies@databricks.com>
Closes#15030 from davies/all_expr.
## What changes were proposed in this pull request?
#14956 reduced default k-means|| init steps to 2 from 5 only for spark.mllib package, we should also do same change for spark.ml and PySpark.
## How was this patch tested?
Existing tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#15050 from yanboliang/spark-17389.
## What changes were proposed in this pull request?
```weights``` of ```MultilayerPerceptronClassificationModel``` should be the output weights of layers rather than initial weights, this PR correct it.
## How was this patch tested?
Doc change.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#14967 from yanboliang/mlp-weights.
## What changes were proposed in this pull request?
Related to https://github.com/apache/spark/pull/14524 -- just the 'fix' rather than a behavior change.
- PythonMLlibAPI methods that take a seed now always take a `java.lang.Long` consistently, allowing the Python API to specify "no seed"
- .mllib's Word2VecModel seemed to be an odd man out in .mllib in that it picked its own random seed. Instead it defaults to None, meaning, letting the Scala implementation pick a seed
- BisectingKMeansModel arguably should not hard-code a seed for consistency with .mllib, I think. However I left it.
## How was this patch tested?
Existing tests
Author: Sean Owen <sowen@cloudera.com>
Closes#14826 from srowen/SPARK-16832.2.
## What changes were proposed in this pull request?
Require the use of CROSS join syntax in SQL (and a new crossJoin
DataFrame API) to specify explicit cartesian products between relations.
By cartesian product we mean a join between relations R and S where
there is no join condition involving columns from both R and S.
If a cartesian product is detected in the absence of an explicit CROSS
join, an error must be thrown. Turning on the
"spark.sql.crossJoin.enabled" configuration flag will disable this check
and allow cartesian products without an explicit CROSS join.
The new crossJoin DataFrame API must be used to specify explicit cross
joins. The existing join(DataFrame) method will produce a INNER join
that will require a subsequent join condition.
That is df1.join(df2) is equivalent to select * from df1, df2.
## How was this patch tested?
Added cross-join.sql to the SQLQueryTestSuite to test the check for cartesian products. Added a couple of tests to the DataFrameJoinSuite to test the crossJoin API. Modified various other test suites to explicitly specify a cross join where an INNER join or a comma-separated list was previously used.
Author: Srinath Shankar <srinath@databricks.com>
Closes#14866 from srinathshankar/crossjoin.
## What changes were proposed in this pull request?
Set SparkSession._instantiatedContext as None so that we can recreate SparkSession again.
## How was this patch tested?
Tested manually using the following command in pyspark shell
```
spark.stop()
spark = SparkSession.builder.enableHiveSupport().getOrCreate()
spark.sql("show databases").show()
```
Author: Jeff Zhang <zjffdu@apache.org>
Closes#14857 from zjffdu/SPARK-17261.
## What changes were proposed in this pull request?
Clarify that only parquet files are supported by DataStreamWriter now
## How was this patch tested?
(Doc build -- no functional changes to test)
Author: Sean Owen <sowen@cloudera.com>
Closes#14860 from srowen/SPARK-17264.
## What changes were proposed in this pull request?
Allow centering / mean scaling of sparse vectors in StandardScaler, if requested. This is for compatibility with `VectorAssembler` in common usages.
## How was this patch tested?
Jenkins tests, including new caes to reflect the new behavior.
Author: Sean Owen <sowen@cloudera.com>
Closes#14663 from srowen/SPARK-17001.
## What changes were proposed in this pull request?
Method `SQLContext.parseDataType(dataTypeString: String)` could be removed, we should use `SparkSession.parseDataType(dataTypeString: String)` instead.
This require updating PySpark.
## How was this patch tested?
Existing test cases.
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes#14790 from jiangxb1987/parseDataType.
