## What changes were proposed in this pull request?
d6632d185e added an useful util
```python
contextmanager
def sql_conf(self, pairs):
...
```
to allow configuration set/unset within a block:
```python
with self.sql_conf({"spark.blah.blah.blah", "blah"})
# test codes
```
This PR proposes to use this util where possible in PySpark tests.
Note that there look already few places affecting tests without restoring the original value back in unittest classes.
## How was this patch tested?
Manually tested via:
```
./run-tests --modules=pyspark-sql --python-executables=python2
./run-tests --modules=pyspark-sql --python-executables=python3
```
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#20830 from HyukjinKwon/cleanup-sql-conf.
## What changes were proposed in this pull request?
Added a class method to construct CountVectorizerModel from a list of vocabulary strings, equivalent to the Scala version. Introduced a common param base class `_CountVectorizerParams` to allow the Python model to also own the parameters. This now matches the Scala class hierarchy.
## How was this patch tested?
Added to CountVectorizer doctests to do a transform on a model constructed from vocab, and unit test to verify params and vocab are constructed correctly.
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#16770 from BryanCutler/pyspark-CountVectorizerModel-vocab_ctor-SPARK-15009.
## What changes were proposed in this pull request?
Currently, some tests have an assumption that `spark.sql.sources.default=parquet`. In fact, that is a correct assumption, but that assumption makes it difficult to test new data source format.
This PR aims to
- Improve test suites more robust and makes it easy to test new data sources in the future.
- Test new native ORC data source with the full existing Apache Spark test coverage.
As an example, the PR uses `spark.sql.sources.default=orc` during reviews. The value should be `parquet` when this PR is accepted.
## How was this patch tested?
Pass the Jenkins with updated tests.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#20705 from dongjoon-hyun/SPARK-23553.
## What changes were proposed in this pull request?
This PR proposes to fix the error message for Kinesis in PySpark when its jar is missing but explicitly enabled.
```bash
ENABLE_KINESIS_TESTS=1 SPARK_TESTING=1 bin/pyspark pyspark.streaming.tests
```
Before:
```
Skipped test_flume_stream (enable by setting environment variable ENABLE_FLUME_TESTS=1Skipped test_kafka_stream (enable by setting environment variable ENABLE_KAFKA_0_8_TESTS=1Traceback (most recent call last):
File "/usr/local/Cellar/python/2.7.14_3/Frameworks/Python.framework/Versions/2.7/lib/python2.7/runpy.py", line 174, in _run_module_as_main
"__main__", fname, loader, pkg_name)
File "/usr/local/Cellar/python/2.7.14_3/Frameworks/Python.framework/Versions/2.7/lib/python2.7/runpy.py", line 72, in _run_code
exec code in run_globals
File "/.../spark/python/pyspark/streaming/tests.py", line 1572, in <module>
% kinesis_asl_assembly_dir) +
NameError: name 'kinesis_asl_assembly_dir' is not defined
```
After:
```
Skipped test_flume_stream (enable by setting environment variable ENABLE_FLUME_TESTS=1Skipped test_kafka_stream (enable by setting environment variable ENABLE_KAFKA_0_8_TESTS=1Traceback (most recent call last):
File "/usr/local/Cellar/python/2.7.14_3/Frameworks/Python.framework/Versions/2.7/lib/python2.7/runpy.py", line 174, in _run_module_as_main
"__main__", fname, loader, pkg_name)
File "/usr/local/Cellar/python/2.7.14_3/Frameworks/Python.framework/Versions/2.7/lib/python2.7/runpy.py", line 72, in _run_code
exec code in run_globals
File "/.../spark/python/pyspark/streaming/tests.py", line 1576, in <module>
"You need to build Spark with 'build/sbt -Pkinesis-asl "
Exception: Failed to find Spark Streaming Kinesis assembly jar in /.../spark/external/kinesis-asl-assembly. You need to build Spark with 'build/sbt -Pkinesis-asl assembly/package streaming-kinesis-asl-assembly/assembly'or 'build/mvn -Pkinesis-asl package' before running this test.
```
## How was this patch tested?
Manually tested.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#20834 from HyukjinKwon/minor-variable.
The exit() builtin is only for interactive use. applications should use sys.exit().
## What changes were proposed in this pull request?
All usage of the builtin `exit()` function is replaced by `sys.exit()`.
## How was this patch tested?
I ran `python/run-tests`.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Benjamin Peterson <benjamin@python.org>
Closes#20682 from benjaminp/sys-exit.
## What changes were proposed in this pull request?
This PR proposes to support an alternative function from with group aggregate pandas UDF.
The current form:
```
def foo(pdf):
return ...
```
Takes a single arg that is a pandas DataFrame.
With this PR, an alternative form is supported:
```
def foo(key, pdf):
return ...
```
The alternative form takes two argument - a tuple that presents the grouping key, and a pandas DataFrame represents the data.
## How was this patch tested?
GroupbyApplyTests
Author: Li Jin <ice.xelloss@gmail.com>
Closes#20295 from icexelloss/SPARK-23011-groupby-apply-key.
## What changes were proposed in this pull request?
This PR adds a configuration to control the fallback of Arrow optimization for `toPandas` and `createDataFrame` with Pandas DataFrame.
## How was this patch tested?
Manually tested and unit tests added.
You can test this by:
**`createDataFrame`**
```python
spark.conf.set("spark.sql.execution.arrow.enabled", False)
pdf = spark.createDataFrame([[{'a': 1}]]).toPandas()
spark.conf.set("spark.sql.execution.arrow.enabled", True)
spark.conf.set("spark.sql.execution.arrow.fallback.enabled", True)
spark.createDataFrame(pdf, "a: map<string, int>")
```
```python
spark.conf.set("spark.sql.execution.arrow.enabled", False)
pdf = spark.createDataFrame([[{'a': 1}]]).toPandas()
spark.conf.set("spark.sql.execution.arrow.enabled", True)
spark.conf.set("spark.sql.execution.arrow.fallback.enabled", False)
spark.createDataFrame(pdf, "a: map<string, int>")
```
**`toPandas`**
```python
spark.conf.set("spark.sql.execution.arrow.enabled", True)
spark.conf.set("spark.sql.execution.arrow.fallback.enabled", True)
spark.createDataFrame([[{'a': 1}]]).toPandas()
```
```python
spark.conf.set("spark.sql.execution.arrow.enabled", True)
spark.conf.set("spark.sql.execution.arrow.fallback.enabled", False)
spark.createDataFrame([[{'a': 1}]]).toPandas()
```
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#20678 from HyukjinKwon/SPARK-23380-conf.
The `__del__` method that explicitly detaches the object was moved from `JavaParams` to `JavaWrapper` class, this way model summaries could also be garbage collected in Java. A test case was added to make sure that relevant error messages are thrown after the objects are deleted.
I ran pyspark tests agains `pyspark-ml` module
`./python/run-tests --python-executables=$(which python) --modules=pyspark-ml`
Author: Yogesh Garg <yogesh(dot)garg()databricks(dot)com>
Closes#20724 from yogeshg/java_wrapper_memory.
## What changes were proposed in this pull request?
Provide more details in trigonometric function documentations. Referenced `java.lang.Math` for further details in the descriptions.
## How was this patch tested?
Ran full build, checked generated documentation manually
Author: Mihaly Toth <misutoth@gmail.com>
Closes#20618 from misutoth/trigonometric-doc.
## What changes were proposed in this pull request?
Check python version to determine whether to use `inspect.getargspec` or `inspect.getfullargspec` before applying `pandas_udf` core logic to a function. The former is python2.7 (deprecated in python3) and the latter is python3.x. The latter correctly accounts for type annotations, which are syntax errors in python2.x.
## How was this patch tested?
Locally, on python 2.7 and 3.6.
Author: Michael (Stu) Stewart <mstewart141@gmail.com>
Closes#20728 from mstewart141/pandas_udf_fix.
## What changes were proposed in this pull request?
This PR proposes for `pyspark.util._exception_message` to produce the trace from Java side by `Py4JJavaError`.
Currently, in Python 2, it uses `message` attribute which `Py4JJavaError` didn't happen to have:
```python
>>> from pyspark.util import _exception_message
>>> try:
... sc._jvm.java.lang.String(None)
... except Exception as e:
... pass
...
>>> e.message
''
```
Seems we should use `str` instead for now:
aa6c53b590/py4j-python/src/py4j/protocol.py (L412)
but this doesn't address the problem with non-ascii string from Java side -
`https://github.com/bartdag/py4j/issues/306`
So, we could directly call `__str__()`:
```python
>>> e.__str__()
u'An error occurred while calling None.java.lang.String.\n: java.lang.NullPointerException\n\tat java.lang.String.<init>(String.java:588)\n\tat sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)\n\tat sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)\n\tat sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)\n\tat java.lang.reflect.Constructor.newInstance(Constructor.java:422)\n\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:247)\n\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)\n\tat py4j.Gateway.invoke(Gateway.java:238)\n\tat py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:80)\n\tat py4j.commands.ConstructorCommand.execute(ConstructorCommand.java:69)\n\tat py4j.GatewayConnection.run(GatewayConnection.java:214)\n\tat java.lang.Thread.run(Thread.java:745)\n'
```
which doesn't type coerce unicodes to `str` in Python 2.
This can be actually a problem:
```python
from pyspark.sql.functions import udf
spark.conf.set("spark.sql.execution.arrow.enabled", True)
spark.range(1).select(udf(lambda x: [[]])()).toPandas()
```
**Before**
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/.../spark/python/pyspark/sql/dataframe.py", line 2009, in toPandas
raise RuntimeError("%s\n%s" % (_exception_message(e), msg))
RuntimeError:
Note: toPandas attempted Arrow optimization because 'spark.sql.execution.arrow.enabled' is set to true. Please set it to false to disable this.
```
**After**
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/.../spark/python/pyspark/sql/dataframe.py", line 2009, in toPandas
raise RuntimeError("%s\n%s" % (_exception_message(e), msg))
RuntimeError: An error occurred while calling o47.collectAsArrowToPython.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 7 in stage 0.0 failed 1 times, most recent failure: Lost task 7.0 in stage 0.0 (TID 7, localhost, executor driver): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "/.../spark/python/pyspark/worker.py", line 245, in main
process()
File "/.../spark/python/pyspark/worker.py", line 240, in process
...
Note: toPandas attempted Arrow optimization because 'spark.sql.execution.arrow.enabled' is set to true. Please set it to false to disable this.
```
## How was this patch tested?
Manually tested and unit tests were added.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#20680 from HyukjinKwon/SPARK-23517.
## What changes were proposed in this pull request?
Clarify JSON and CSV reader behavior in document.
JSON doesn't support partial results for corrupted records.
CSV only supports partial results for the records with more or less tokens.
## How was this patch tested?
Pass existing tests.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#20666 from viirya/SPARK-23448-2.
## What changes were proposed in this pull request?
Fix the build instructions supplied by exception messages in python streaming tests.
I also added -DskipTests to the maven instructions to avoid the 170 minutes of scala tests that occurs each time one wants to add a jar to the assembly directory.
## How was this patch tested?
- clone branch
- run build/sbt package
- run python/run-tests --modules "pyspark-streaming" , expect error message
- follow instructions in error message. i.e., run build/sbt assembly/package streaming-kafka-0-8-assembly/assembly
- rerun python tests, expect error message
- follow instructions in error message. i.e run build/sbt -Pflume assembly/package streaming-flume-assembly/assembly
- rerun python tests, see success.
- repeated all of the above for mvn version of the process.
Author: Bruce Robbins <bersprockets@gmail.com>
Closes#20638 from bersprockets/SPARK-23417_propa.
## What changes were proposed in this pull request?
The PR adds the `distanceMeasure` param to ClusteringEvaluator in the Python API. This allows the user to specify `cosine` as distance measure in addition to the default `squaredEuclidean`.
## How was this patch tested?
added UT
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#20627 from mgaido91/SPARK-23217_python.
## What changes were proposed in this pull request?
Murmur3 hash generates a different value from the original and other implementations (like Scala standard library and Guava or so) when the length of a bytes array is not multiple of 4.
## How was this patch tested?
Added a unit test.
**Note: When we merge this PR, please give all the credits to Shintaro Murakami.**
Author: Shintaro Murakami <mrkm4ntrgmail.com>
Author: gatorsmile <gatorsmile@gmail.com>
Author: Shintaro Murakami <mrkm4ntr@gmail.com>
Closes#20630 from gatorsmile/pr-20568.
## What changes were proposed in this pull request?
This PR explicitly specifies and checks the types we supported in `toPandas`. This was a hole. For example, we haven't finished the binary type support in Python side yet but now it allows as below:
```python
spark.conf.set("spark.sql.execution.arrow.enabled", "false")
df = spark.createDataFrame([[bytearray("a")]])
df.toPandas()
spark.conf.set("spark.sql.execution.arrow.enabled", "true")
df.toPandas()
```
```
_1
0 [97]
_1
0 a
```
This should be disallowed. I think the same things also apply to nested timestamps too.
I also added some nicer message about `spark.sql.execution.arrow.enabled` in the error message.
## How was this patch tested?
Manually tested and tests added in `python/pyspark/sql/tests.py`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#20625 from HyukjinKwon/pandas_convertion_supported_type.
## What changes were proposed in this pull request?
Deprecating the field `name` in PySpark is not expected. This PR is to revert the change.
## How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#20595 from gatorsmile/removeDeprecate.
## What changes were proposed in this pull request?
This PR targets to explicitly specify supported types in Pandas UDFs.
The main change here is to add a deduplicated and explicit type checking in `returnType` ahead with documenting this; however, it happened to fix multiple things.
1. Currently, we don't support `BinaryType` in Pandas UDFs, for example, see:
```python
from pyspark.sql.functions import pandas_udf
pudf = pandas_udf(lambda x: x, "binary")
df = spark.createDataFrame([[bytearray(1)]])
df.select(pudf("_1")).show()
```
```
...
TypeError: Unsupported type in conversion to Arrow: BinaryType
```
We can document this behaviour for its guide.
2. Also, the grouped aggregate Pandas UDF fails fast on `ArrayType` but seems we can support this case.
```python
from pyspark.sql.functions import pandas_udf, PandasUDFType
foo = pandas_udf(lambda v: v.mean(), 'array<double>', PandasUDFType.GROUPED_AGG)
df = spark.range(100).selectExpr("id", "array(id) as value")
df.groupBy("id").agg(foo("value")).show()
```
```
...
NotImplementedError: ArrayType, StructType and MapType are not supported with PandasUDFType.GROUPED_AGG
```
3. Since we can check the return type ahead, we can fail fast before actual execution.
```python
# we can fail fast at this stage because we know the schema ahead
pandas_udf(lambda x: x, BinaryType())
```
## How was this patch tested?
Manually tested and unit tests for `BinaryType` and `ArrayType(...)` were added.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#20531 from HyukjinKwon/pudf-cleanup.
## What changes were proposed in this pull request?
Expose range partitioning shuffle introduced by spark-22614
## How was this patch tested?
Unit test in dataframe.py
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: xubo245 <601450868@qq.com>
Closes#20456 from xubo245/SPARK22624_PysparkRangePartition.
## What changes were proposed in this pull request?
Added unboundedPreceding(), unboundedFollowing() and currentRow() to PySpark, also updated the rangeBetween API
## How was this patch tested?
did unit test on my local. Please let me know if I need to add unit test in tests.py
Author: Huaxin Gao <huaxing@us.ibm.com>
Closes#20400 from huaxingao/spark_23084.
## What changes were proposed in this pull request?
When tz_localize a tz-naive timetamp, pandas will throw exception if the timestamp is during daylight saving time period, e.g., `2015-11-01 01:30:00`. This PR fixes this issue by setting `ambiguous=False` when calling tz_localize, which is the same default behavior of pytz.
## How was this patch tested?
Add `test_timestamp_dst`
Author: Li Jin <ice.xelloss@gmail.com>
Closes#20537 from icexelloss/SPARK-23314.
## What changes were proposed in this pull request?
SPARK-22119 introduced a new parameter for KMeans, ie. `distanceMeasure`. The PR adds it also to the Python interface.
## How was this patch tested?
added UTs
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#20520 from mgaido91/SPARK-23344.
## What changes were proposed in this pull request?
Currently we use `tzlocal()` to get Python local timezone, but it sometimes causes unexpected behavior.
I changed the way to get Python local timezone to use pytz if the timezone is specified in environment variable, or timezone file via dateutil .
## How was this patch tested?
Added a test and existing tests.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#20559 from ueshin/issues/SPARK-23360/master.
## What changes were proposed in this pull request?
This PR proposes to disallow default value None when 'to_replace' is not a dictionary.
It seems weird we set the default value of `value` to `None` and we ended up allowing the case as below:
```python
>>> df.show()
```
```
+----+------+-----+
| age|height| name|
+----+------+-----+
| 10| 80|Alice|
...
```
```python
>>> df.na.replace('Alice').show()
```
```
+----+------+----+
| age|height|name|
+----+------+----+
| 10| 80|null|
...
```
**After**
This PR targets to disallow the case above:
```python
>>> df.na.replace('Alice').show()
```
```
...
TypeError: value is required when to_replace is not a dictionary.
```
while we still allow when `to_replace` is a dictionary:
```python
>>> df.na.replace({'Alice': None}).show()
```
```
+----+------+----+
| age|height|name|
+----+------+----+
| 10| 80|null|
...
```
## How was this patch tested?
Manually tested, tests were added in `python/pyspark/sql/tests.py` and doctests were fixed.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#20499 from HyukjinKwon/SPARK-19454-followup.
## What changes were proposed in this pull request?
This is a followup pr of #20487.
When importing module but it doesn't exists, the error message is slightly different between Python 2 and 3.
E.g., in Python 2:
```
No module named pandas
```
in Python 3:
```
No module named 'pandas'
```
So, one test to check an import error fails in Python 3 without pandas.
This pr fixes it.
## How was this patch tested?
Tested manually in my local environment.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#20538 from ueshin/issues/SPARK-23319/fup1.
## What changes were proposed in this pull request?
This PR proposes to explicitly specify Pandas and PyArrow versions in PySpark tests to skip or test.
We declared the extra dependencies:
b8bfce51ab/python/setup.py (L204)
In case of PyArrow:
Currently we only check if pyarrow is installed or not without checking the version. It already fails to run tests. For example, if PyArrow 0.7.0 is installed:
```
======================================================================
ERROR: test_vectorized_udf_wrong_return_type (pyspark.sql.tests.ScalarPandasUDF)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/.../spark/python/pyspark/sql/tests.py", line 4019, in test_vectorized_udf_wrong_return_type
f = pandas_udf(lambda x: x * 1.0, MapType(LongType(), LongType()))
File "/.../spark/python/pyspark/sql/functions.py", line 2309, in pandas_udf
return _create_udf(f=f, returnType=return_type, evalType=eval_type)
File "/.../spark/python/pyspark/sql/udf.py", line 47, in _create_udf
require_minimum_pyarrow_version()
File "/.../spark/python/pyspark/sql/utils.py", line 132, in require_minimum_pyarrow_version
"however, your version was %s." % pyarrow.__version__)
ImportError: pyarrow >= 0.8.0 must be installed on calling Python process; however, your version was 0.7.0.
----------------------------------------------------------------------
Ran 33 tests in 8.098s
FAILED (errors=33)
```
In case of Pandas:
There are few tests for old Pandas which were tested only when Pandas version was lower, and I rewrote them to be tested when both Pandas version is lower and missing.
## How was this patch tested?
Manually tested by modifying the condition:
```
test_createDataFrame_column_name_encoding (pyspark.sql.tests.ArrowTests) ... skipped 'Pandas >= 1.19.2 must be installed; however, your version was 0.19.2.'
test_createDataFrame_does_not_modify_input (pyspark.sql.tests.ArrowTests) ... skipped 'Pandas >= 1.19.2 must be installed; however, your version was 0.19.2.'
test_createDataFrame_respect_session_timezone (pyspark.sql.tests.ArrowTests) ... skipped 'Pandas >= 1.19.2 must be installed; however, your version was 0.19.2.'
```
```
test_createDataFrame_column_name_encoding (pyspark.sql.tests.ArrowTests) ... skipped 'Pandas >= 0.19.2 must be installed; however, it was not found.'
test_createDataFrame_does_not_modify_input (pyspark.sql.tests.ArrowTests) ... skipped 'Pandas >= 0.19.2 must be installed; however, it was not found.'
test_createDataFrame_respect_session_timezone (pyspark.sql.tests.ArrowTests) ... skipped 'Pandas >= 0.19.2 must be installed; however, it was not found.'
```
```
test_createDataFrame_column_name_encoding (pyspark.sql.tests.ArrowTests) ... skipped 'PyArrow >= 1.8.0 must be installed; however, your version was 0.8.0.'
test_createDataFrame_does_not_modify_input (pyspark.sql.tests.ArrowTests) ... skipped 'PyArrow >= 1.8.0 must be installed; however, your version was 0.8.0.'
test_createDataFrame_respect_session_timezone (pyspark.sql.tests.ArrowTests) ... skipped 'PyArrow >= 1.8.0 must be installed; however, your version was 0.8.0.'
```
```
test_createDataFrame_column_name_encoding (pyspark.sql.tests.ArrowTests) ... skipped 'PyArrow >= 0.8.0 must be installed; however, it was not found.'
test_createDataFrame_does_not_modify_input (pyspark.sql.tests.ArrowTests) ... skipped 'PyArrow >= 0.8.0 must be installed; however, it was not found.'
test_createDataFrame_respect_session_timezone (pyspark.sql.tests.ArrowTests) ... skipped 'PyArrow >= 0.8.0 must be installed; however, it was not found.'
```
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#20487 from HyukjinKwon/pyarrow-pandas-skip.
## What changes were proposed in this pull request?
Replace `registerTempTable` by `createOrReplaceTempView`.
## How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#20523 from gatorsmile/updateExamples.
## What changes were proposed in this pull request?
Update the description and tests of three external API or functions `createFunction `, `length` and `repartitionByRange `
## How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#20495 from gatorsmile/updateFunc.
## What changes were proposed in this pull request?
In b2ce17b4c9, I mistakenly renamed `VectorizedUDFTests` to `ScalarPandasUDF`. This PR fixes the mistake.
## How was this patch tested?
Existing tests.
Author: Li Jin <ice.xelloss@gmail.com>
Closes#20489 from icexelloss/fix-scalar-udf-tests.
## What changes were proposed in this pull request?
In Python 2, when `pandas_udf` tries to return string type value created in the udf with `".."`, the execution fails. E.g.,
```python
from pyspark.sql.functions import pandas_udf, col
import pandas as pd
df = spark.range(10)
str_f = pandas_udf(lambda x: pd.Series(["%s" % i for i in x]), "string")
df.select(str_f(col('id'))).show()
```
raises the following exception:
```
...
java.lang.AssertionError: assertion failed: Invalid schema from pandas_udf: expected StringType, got BinaryType
at scala.Predef$.assert(Predef.scala:170)
at org.apache.spark.sql.execution.python.ArrowEvalPythonExec$$anon$2.<init>(ArrowEvalPythonExec.scala:93)
...
```
Seems like pyarrow ignores `type` parameter for `pa.Array.from_pandas()` and consider it as binary type when the type is string type and the string values are `str` instead of `unicode` in Python 2.
This pr adds a workaround for the case.
## How was this patch tested?
Added a test and existing tests.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#20507 from ueshin/issues/SPARK-23334.
## What changes were proposed in this pull request?
This PR proposes to log if PyArrow and Pandas are installed or not so we can check if related tests are going to be skipped or not.
## How was this patch tested?
Manually tested:
I don't have PyArrow installed in PyPy.
```bash
$ ./run-tests --python-executables=python3
```
```
...
Will test against the following Python executables: ['python3']
Will test the following Python modules: ['pyspark-core', 'pyspark-ml', 'pyspark-mllib', 'pyspark-sql', 'pyspark-streaming']
Will test PyArrow related features against Python executable 'python3' in 'pyspark-sql' module.
Will test Pandas related features against Python executable 'python3' in 'pyspark-sql' module.
Starting test(python3): pyspark.mllib.tests
Starting test(python3): pyspark.sql.tests
Starting test(python3): pyspark.streaming.tests
Starting test(python3): pyspark.tests
```
```bash
$ ./run-tests --modules=pyspark-streaming
```
```
...
Will test against the following Python executables: ['python2.7', 'pypy']
Will test the following Python modules: ['pyspark-streaming']
Starting test(pypy): pyspark.streaming.tests
Starting test(pypy): pyspark.streaming.util
Starting test(python2.7): pyspark.streaming.tests
Starting test(python2.7): pyspark.streaming.util
```
```bash
$ ./run-tests
```
```
...
Will test against the following Python executables: ['python2.7', 'pypy']
Will test the following Python modules: ['pyspark-core', 'pyspark-ml', 'pyspark-mllib', 'pyspark-sql', 'pyspark-streaming']
Will test PyArrow related features against Python executable 'python2.7' in 'pyspark-sql' module.
Will test Pandas related features against Python executable 'python2.7' in 'pyspark-sql' module.
Will skip PyArrow related features against Python executable 'pypy' in 'pyspark-sql' module. PyArrow >= 0.8.0 is required; however, PyArrow was not found.
Will test Pandas related features against Python executable 'pypy' in 'pyspark-sql' module.
Starting test(pypy): pyspark.streaming.tests
Starting test(pypy): pyspark.sql.tests
Starting test(pypy): pyspark.tests
Starting test(python2.7): pyspark.mllib.tests
```
```bash
$ ./run-tests --modules=pyspark-sql --python-executables=pypy
```
```
...
Will test against the following Python executables: ['pypy']
Will test the following Python modules: ['pyspark-sql']
Will skip PyArrow related features against Python executable 'pypy' in 'pyspark-sql' module. PyArrow >= 0.8.0 is required; however, PyArrow was not found.
Will test Pandas related features against Python executable 'pypy' in 'pyspark-sql' module.
Starting test(pypy): pyspark.sql.tests
Starting test(pypy): pyspark.sql.catalog
Starting test(pypy): pyspark.sql.column
Starting test(pypy): pyspark.sql.conf
```
After some modification to produce other cases:
```bash
$ ./run-tests
```
```
...
Will test against the following Python executables: ['python2.7', 'pypy']
Will test the following Python modules: ['pyspark-core', 'pyspark-ml', 'pyspark-mllib', 'pyspark-sql', 'pyspark-streaming']
Will skip PyArrow related features against Python executable 'python2.7' in 'pyspark-sql' module. PyArrow >= 20.0.0 is required; however, PyArrow 0.8.0 was found.
Will skip Pandas related features against Python executable 'python2.7' in 'pyspark-sql' module. Pandas >= 20.0.0 is required; however, Pandas 0.20.2 was found.
Will skip PyArrow related features against Python executable 'pypy' in 'pyspark-sql' module. PyArrow >= 20.0.0 is required; however, PyArrow was not found.
Will skip Pandas related features against Python executable 'pypy' in 'pyspark-sql' module. Pandas >= 20.0.0 is required; however, Pandas 0.22.0 was found.
Starting test(pypy): pyspark.sql.tests
Starting test(pypy): pyspark.streaming.tests
Starting test(pypy): pyspark.tests
Starting test(python2.7): pyspark.mllib.tests
```
```bash
./run-tests-with-coverage
```
```
...
Will test against the following Python executables: ['python2.7', 'pypy']
Will test the following Python modules: ['pyspark-core', 'pyspark-ml', 'pyspark-mllib', 'pyspark-sql', 'pyspark-streaming']
Will test PyArrow related features against Python executable 'python2.7' in 'pyspark-sql' module.
Will test Pandas related features against Python executable 'python2.7' in 'pyspark-sql' module.
Coverage is not installed in Python executable 'pypy' but 'COVERAGE_PROCESS_START' environment variable is set, exiting.
```
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#20473 from HyukjinKwon/SPARK-23300.
## What changes were proposed in this pull request?
This PR proposes to add `columnSchema` in Python side too.
```python
>>> from pyspark.ml.image import ImageSchema
>>> ImageSchema.columnSchema.simpleString()
'struct<origin:string,height:int,width:int,nChannels:int,mode:int,data:binary>'
```
## How was this patch tested?
Manually tested and unittest was added in `python/pyspark/ml/tests.py`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#20475 from HyukjinKwon/SPARK-23256.
## What changes were proposed in this pull request?
This is a follow-up pr of #19872 which uses `assertRaisesRegex` but it doesn't exist in Python 2, so some tests fail when running tests in Python 2 environment.
Unfortunately, we missed it because currently Python 2 environment of the pr builder doesn't have proper versions of pandas or pyarrow, so the tests were skipped.
This pr modifies to use `assertRaisesRegexp` instead of `assertRaisesRegex`.
## How was this patch tested?
Tested manually in my local environment.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#20467 from ueshin/issues/SPARK-22274/fup1.
## What changes were proposed in this pull request?
In the current PySpark code, Python created `jsparkSession` doesn't add to JVM's defaultSession, this `SparkSession` object cannot be fetched from Java side, so the below scala code will be failed when loaded in PySpark application.
```scala
class TestSparkSession extends SparkListener with Logging {
override def onOtherEvent(event: SparkListenerEvent): Unit = {
event match {
case CreateTableEvent(db, table) =>
val session = SparkSession.getActiveSession.orElse(SparkSession.getDefaultSession)
assert(session.isDefined)
val tableInfo = session.get.sharedState.externalCatalog.getTable(db, table)
logInfo(s"Table info ${tableInfo}")
case e =>
logInfo(s"event $e")
}
}
}
```
So here propose to add fresh create `jsparkSession` to `defaultSession`.
## How was this patch tested?
Manual verification.
Author: jerryshao <sshao@hortonworks.com>
Author: hyukjinkwon <gurwls223@gmail.com>
Author: Saisai Shao <sai.sai.shao@gmail.com>
Closes#20404 from jerryshao/SPARK-23228.
## What changes were proposed in this pull request?
Rename the public APIs and names of pandas udfs.
- `PANDAS SCALAR UDF` -> `SCALAR PANDAS UDF`
- `PANDAS GROUP MAP UDF` -> `GROUPED MAP PANDAS UDF`
- `PANDAS GROUP AGG UDF` -> `GROUPED AGG PANDAS UDF`
## How was this patch tested?
The existing tests
Author: gatorsmile <gatorsmile@gmail.com>
Closes#20428 from gatorsmile/renamePandasUDFs.
## What changes were proposed in this pull request?
It's not obvious from the comments that any added column must be a
function of the dataset that we are adding it to. Add a comment to
that effect to Scala, Python and R Data* methods.
Author: Henry Robinson <henry@cloudera.com>
Closes#20429 from henryr/SPARK-23157.
## What changes were proposed in this pull request?
Reproducer:
```python
from pyspark.sql.functions import udf
f = udf(lambda x: x)
spark.range(1).select(f("id")) # cache JVM UDF instance.
f = f.asNondeterministic()
spark.range(1).select(f("id"))._jdf.logicalPlan().projectList().head().deterministic()
```
It should return `False` but the current master returns `True`. Seems it's because we cache the JVM UDF instance and then we reuse it even after setting `deterministic` disabled once it's called.
## How was this patch tested?
Manually tested. I am not sure if I should add the test with a lot of JVM accesses with the intetnal stuff .. Let me know if anyone feels so. I will add.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#20409 from HyukjinKwon/SPARK-23233.
## What changes were proposed in this pull request?
Bucketizer support multi-column in the python side
## How was this patch tested?
existing tests and added tests
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#19892 from zhengruifeng/20542_py.
## What changes were proposed in this pull request?
Add colRegex API to PySpark
## How was this patch tested?
add a test in sql/tests.py
Author: Huaxin Gao <huaxing@us.ibm.com>
Closes#20390 from huaxingao/spark-23081.
## What changes were proposed in this pull request?
This syncs the ML Python API with Scala for differences found after the 2.3 QA audit.
## How was this patch tested?
NA
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#20354 from BryanCutler/pyspark-ml-doc-sync-23163.
## What changes were proposed in this pull request?
We extract Python UDFs in logical aggregate which depends on aggregate expression or grouping key in ExtractPythonUDFFromAggregate rule. But Python UDFs which don't depend on above expressions should also be extracted to avoid the issue reported in the JIRA.
A small code snippet to reproduce that issue looks like:
```python
import pyspark.sql.functions as f
df = spark.createDataFrame([(1,2), (3,4)])
f_udf = f.udf(lambda: str("const_str"))
df2 = df.distinct().withColumn("a", f_udf())
df2.show()
```
Error exception is raised as:
```
: org.apache.spark.sql.catalyst.errors.package$TreeNodeException: Binding attribute, tree: pythonUDF0#50
at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:56)
at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1.applyOrElse(BoundAttribute.scala:91)
at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1.applyOrElse(BoundAttribute.scala:90)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:266)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:272)
at org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:256)
at org.apache.spark.sql.catalyst.expressions.BindReferences$.bindReference(BoundAttribute.scala:90)
at org.apache.spark.sql.execution.aggregate.HashAggregateExec$$anonfun$38.apply(HashAggregateExec.scala:514)
at org.apache.spark.sql.execution.aggregate.HashAggregateExec$$anonfun$38.apply(HashAggregateExec.scala:513)
```
This exception raises because `HashAggregateExec` tries to bind the aliased Python UDF expression (e.g., `pythonUDF0#50 AS a#44`) to grouping key.
## How was this patch tested?
Added test.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#20360 from viirya/SPARK-23177.
## What changes were proposed in this pull request?
Add support for using pandas UDFs with groupby().agg().
This PR introduces a new type of pandas UDF - group aggregate pandas UDF. This type of UDF defines a transformation of multiple pandas Series -> a scalar value. Group aggregate pandas UDFs can be used with groupby().agg(). Note group aggregate pandas UDF doesn't support partial aggregation, i.e., a full shuffle is required.
This PR doesn't support group aggregate pandas UDFs that return ArrayType, StructType or MapType. Support for these types is left for future PR.
## How was this patch tested?
GroupbyAggPandasUDFTests
Author: Li Jin <ice.xelloss@gmail.com>
Closes#19872 from icexelloss/SPARK-22274-groupby-agg.
## What changes were proposed in this pull request?
Note that this PR was made based on the top of https://github.com/apache/spark/pull/20151. So, it almost leaves the main codes intact.
This PR proposes to add a script for the preparation of automatic PySpark coverage generation. Now, it's difficult to check the actual coverage in case of PySpark. With this script, it allows to run tests by the way we did via `run-tests` script before. The usage is exactly the same with `run-tests` script as this basically wraps it.
This script and PR alone should also be useful. I was asked about how to run this before, and seems some reviewers (including me) need this. It would be also useful to run it manually.
It usually requires a small diff in normal Python projects but PySpark cases are a bit different because apparently we are unable to track the coverage after it's forked. So, here, I made a custom worker that forces the coverage, based on the top of https://github.com/apache/spark/pull/20151.
I made a simple demo. Please take a look - https://spark-test.github.io/pyspark-coverage-site.
To show up the structure, this PR adds the files as below:
```
python
├── .coveragerc # Runtime configuration when we run the script.
├── run-tests-with-coverage # The script that has coverage support and wraps run-tests script.
└── test_coverage # Directories that have files required when running coverage.
├── conf
│ └── spark-defaults.conf # Having the configuration 'spark.python.daemon.module'.
├── coverage_daemon.py # A daemon having custom fix and wrapping our daemon.py
└── sitecustomize.py # Initiate coverage with COVERAGE_PROCESS_START
```
Note that this PR has a minor nit:
[This scope](04e44b37cc/python/pyspark/daemon.py (L148-L169)) in `daemon.py` is not in the coverage results as basically I am producing the coverage results in `worker.py` separately and then merging it. I believe it's not a big deal.
In a followup, I might have a site that has a single up-to-date PySpark coverage from the master branch as the fallback / default, or have a site that has multiple PySpark coverages and the site link will be left to each pull request.
## How was this patch tested?
Manually tested. Usage is the same with the existing Python test script - `./python/run-tests`. For example,
```
sh run-tests-with-coverage --python-executables=python3 --modules=pyspark-sql
```
Running this will generate HTMLs under `./python/test_coverage/htmlcov`.
Console output example:
```
sh run-tests-with-coverage --python-executables=python3,python --modules=pyspark-core
Running PySpark tests. Output is in /.../spark/python/unit-tests.log
Will test against the following Python executables: ['python3', 'python']
Will test the following Python modules: ['pyspark-core']
Starting test(python): pyspark.tests
Starting test(python3): pyspark.tests
...
Tests passed in 231 seconds
Combining collected coverage data under /.../spark/python/test_coverage/coverage_data
Reporting the coverage data at /...spark/python/test_coverage/coverage_data/coverage
Name Stmts Miss Branch BrPart Cover
--------------------------------------------------------------
pyspark/__init__.py 41 0 8 2 96%
...
pyspark/profiler.py 74 11 22 5 83%
pyspark/rdd.py 871 40 303 32 93%
pyspark/rddsampler.py 68 10 32 2 82%
...
--------------------------------------------------------------
TOTAL 8521 3077 2748 191 59%
Generating HTML files for PySpark coverage under /.../spark/python/test_coverage/htmlcov
```
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#20204 from HyukjinKwon/python-coverage.
