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From "Hyukjin Kwon (JIRA)" <>
Subject [jira] [Commented] (SPARK-23645) pandas_udf can not be called with keyword arguments
Date Sun, 11 Mar 2018 04:17:00 GMT


Hyukjin Kwon commented on SPARK-23645:

Hm .. I think named arguments in Scala side do not work too though. Also, sounds the same
thing applies to normal udf too. From a very quick look, I think the real difficulty is to
properly support when we actually use mixed positional and keyword arguments.

Sounds a good to do if the change is minimal but if the change is big, I doubt if this is
something we should support. Documenting this might be good enough for now.

> pandas_udf can not be called with keyword arguments
> ---------------------------------------------------
>                 Key: SPARK-23645
>                 URL:
>             Project: Spark
>          Issue Type: Improvement
>          Components: PySpark
>    Affects Versions: 2.3.0
>         Environment: python 3.6 | pyspark 2.3.0 | Using Scala version 2.11.8, OpenJDK
64-Bit Server VM, 1.8.0_141
>            Reporter: Stu (Michael Stewart)
>            Priority: Minor
> pandas_udf (all python udfs(?)) do not accept keyword arguments because `pyspark/sql/`
class `UserDefinedFunction` has __call__, and also wrapper utility methods, that only accept args
and not kwargs:
> @ line 168:
> {code:java}
> ...
> def __call__(self, *cols):
>     judf = self._judf
>     sc = SparkContext._active_spark_context
>     return Column(judf.apply(_to_seq(sc, cols, _to_java_column)))
> # This function is for improving the online help system in the interactive interpreter.
> # For example, the built-in help / It wraps the UDF with the docstring and
> # argument annotation. (See: SPARK-19161)
> def _wrapped(self):
>     """
>     Wrap this udf with a function and attach docstring from func
>     """
>     # It is possible for a callable instance without __name__ attribute or/and
>     # __module__ attribute to be wrapped here. For example, functools.partial. In this
>     # we should avoid wrapping the attributes from the wrapped function to the wrapper
>     # function. So, we take out these attribute names from the default names to set and
>     # then manually assign it after being wrapped.
>     assignments = tuple(
>         a for a in functools.WRAPPER_ASSIGNMENTS if a != '__name__' and a != '__module__')
>     @functools.wraps(self.func, assigned=assignments)
>     def wrapper(*args):
>         return self(*args)
> ...{code}
> as seen in:
> {code:java}
> from pyspark.sql import SparkSession
> from pyspark.sql.functions import pandas_udf, PandasUDFType, col, lit
> spark = SparkSession.builder.getOrCreate()
> df = spark.range(12).withColumn('b', col('id') * 2)
> def ok(a,b): return a*b
> df.withColumn('ok', pandas_udf(f=ok, returnType='bigint')('id','b')).show()  # no problems
> df.withColumn('ok', pandas_udf(f=ok, returnType='bigint')(a='id',b='b')).show()  # fail
with ~no stacktrace thanks to wrapper helper
> ---------------------------------------------------------------------------
> TypeError Traceback (most recent call last)
> <ipython-input-2-8ba6c4344dc7> in <module>()
> ----> 1 df.withColumn('ok', pandas_udf(f=ok, returnType='bigint')(a='id',b='b')).show()
> TypeError: wrapper() got an unexpected keyword argument 'a'{code}
> *discourse*: it isn't difficult to swap back in the kwargs, allowing the UDF to be
called as such, but the cols tuple that gets passed in the call method:
> {code:java}
> _to_seq(sc, cols, _to_java_column{code}
>  has to be in the right order based on the functions defined argument inputs, or the
function will return incorrect results. so, the challenge here is to:
> (a) make sure to reconstruct the proper order of the full args/kwargs
> --> args first, and then kwargs (not in the order passed but in the order requested
by the fn)
> (b) handle python2 and python3 `inspect` module inconsistencies 

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