[jira] [Created] (SPARK-17333) Make pyspark interface friendly with static analysis

["Assaf Mendelson (JIRA)" <ji...@apache.org>]

Assaf Mendelson created SPARK-17333:
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             Summary: Make pyspark interface friendly with static analysis
                 Key: SPARK-17333
                 URL: https://issues.apache.org/jira/browse/SPARK-17333
             Project: Spark
          Issue Type: Improvement
          Components: PySpark
            Reporter: Assaf Mendelson
            Priority: Trivial


Static analysis tools such as those common to IDE for auto completion and error marking, tend to have poor results with pyspark.

This is cause by two separate issues:
The first is that many elements are created programmatically such as the max function in pyspark.sql.functions.
The second is that we tend to use pyspark in a functional manner, meaning that we chain many actions (e.g. df.filter().groupby().agg()....) and since python has no type information this can become difficult to understand.

I would suggest changing the interface to improve it. 

The way I see it we can either change the interface or provide interface enhancements.

Changing the interface means defining (when possible) all functions directly, i.e. instead of having a __functions__ dictionary in pyspark.sql.functions.py and then generating the functions programmatically by using _create_function, create the function directly. 
def max(col):
   """
   docstring
   """
   _create_function(max,"docstring")

Second we can add type indications to all functions as defined in pep 484 or pycharm's legacy type hinting (https://www.jetbrains.com/help/pycharm/2016.1/type-hinting-in-pycharm.html#legacy).
So for example max might look like this:
def max(col):
   """
   does  a max.
  :type col: Column
  :rtype Column
   """
This would provide a wide range of support as these types of hints, while old are pretty common.


A second option is to use PEP 3107 to define interfaces (pyi files)
in this case we might have a functions.pyi file which would contain something like:
def max(col: Column) -> Column:
    """
    Aggregate function: returns the maximum value of the expression in a group.
    """
    ...

This has the advantage of easier to understand types and not touching the code (only supported code) but has the disadvantage of being separately managed (i.e. greater chance of doing a mistake) and the fact that some configuration would be needed in the IDE/static analysis tool instead of working out of the box.




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