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[SPARK-9014][SQL] Allow Python spark API to use built-in exponential operator #8658

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[SPARK-9014][SQL] Allow Python spark API to use built-in exponential operator #8658

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[SPARK-9014][SQL] Allow Python spark API to use built-in exponential …
0x0FFF Sep 8, 2015
aecc0c2
[SPARK-9014][SQL] Reverted to the previous option and replaced float(…
0x0FFF Sep 11, 2015
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13 changes: 13 additions & 0 deletions python/pyspark/sql/column.py
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Original file line number Diff line number Diff line change
Expand Up @@ -91,6 +91,17 @@ def _(self):
return _


def _bin_func_op(name, reverse=False, doc="binary function"):
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If we are just looking at adding pow then it might make more sense to add pow to column, but if we are looking at making it easy to access the rest of the functions in functions.scala this sounds good (although maybe add a tiny comment explaining the purpose).

def _(self, other):
sc = SparkContext._active_spark_context
fn = getattr(sc._jvm.functions, name)
jc = other._jc if isinstance(other, Column) else _create_column_from_literal(other)
njc = fn(self._jc, jc) if not reverse else fn(jc, self._jc)
return Column(njc)
_.__doc__ = doc
return _


def _bin_op(name, doc="binary operator"):
""" Create a method for given binary operator
"""
Expand Down Expand Up @@ -151,6 +162,8 @@ def __init__(self, jc):
__rdiv__ = _reverse_op("divide")
__rtruediv__ = _reverse_op("divide")
__rmod__ = _reverse_op("mod")
__pow__ = _bin_func_op("pow")
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So this doesn't quite match the scala API (which I suppose isn't the end of the world), but would it possible make sense to have a similar functions.py file to match the scala API?

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We already have pow in pyspark.sql.functions.

For here, it's easy to do like this:

from pyspark.sql.function import pow
__pow__ = pow
__rpow__ = lambda c, other: pow(other, c)

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@davies, pow function in pyspark.sql.functions is created with function _create_binary_mathfunction which uses Column internally, thus it cannot be simply imported from pyspark.sql.fuction
So as I outlined below, there are two options: do it like I did or add pow and ** implementation to Column in Scala. In Scala you can reuse the same Pow class as it does not depend on Column

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I see, I'd like to go with current approach.

We could change to _bin_func_op to _pow, use it for __pow__ and __rpow__, it would be more clear.

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I can rename, but what if later we would implement something like __lshift__ or __rshift__ to allow the syntax df.a << 2, then we would have to either rename _pow back to _bin_func_op and utilize it, or add one more functions. Now its clear that _bin_func_op allows you to utilize binary function. What do you think?

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That make sense, but _bin_func_op expect that other should be Column or float, it's not in a shape that we could easily reused for others.

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Agree. What if I replace

jc = other._jc if isinstance(other, Column) else float(other)

with

jc = other._jc if isinstance(other, Column) else _create_column_from_literal(other)

Would it still worth renaming to _pow?

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This sounds better, go for it, thanks!

__rpow__ = _bin_func_op("pow", reverse=True)

# logistic operators
__eq__ = _bin_op("equalTo")
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2 changes: 1 addition & 1 deletion python/pyspark/sql/tests.py
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Expand Up @@ -568,7 +568,7 @@ def test_column_operators(self):
cs = self.df.value
c = ci == cs
self.assertTrue(isinstance((- ci - 1 - 2) % 3 * 2.5 / 3.5, Column))
rcc = (1 + ci), (1 - ci), (1 * ci), (1 / ci), (1 % ci)
rcc = (1 + ci), (1 - ci), (1 * ci), (1 / ci), (1 % ci), (1 ** ci), (ci ** 1)
self.assertTrue(all(isinstance(c, Column) for c in rcc))
cb = [ci == 5, ci != 0, ci > 3, ci < 4, ci >= 0, ci <= 7]
self.assertTrue(all(isinstance(c, Column) for c in cb))
Expand Down