feat: Implementation of udf and udaf decorator

python/datafusion/udf.py

@@ -19,6 +19,7 @@
 
 from __future__ import annotations
 
+import functools
 from abc import ABCMeta, abstractmethod
 from enum import Enum
 from typing import TYPE_CHECKING, Callable, List, Optional, TypeVar
@@ -110,43 +111,102 @@ def __call__(self, *args: Expr) -> Expr:
         args_raw = [arg.expr for arg in args]
         return Expr(self._udf.__call__(*args_raw))
 
-    @staticmethod
-    def udf(
-        func: Callable[..., _R],
-        input_types: list[pyarrow.DataType],
-        return_type: _R,
-        volatility: Volatility | str,
-        name: Optional[str] = None,
-    ) -> ScalarUDF:
-        """Create a new User-Defined Function.
+    class udf:
+        """Create a new User-Defined Function (UDF).
+
+        This class can be used both as a **function** and as a **decorator**.
+
+        Usage:
+            - **As a function**: Call `udf(func, input_types, return_type, volatility,
+              name)`.
+            - **As a decorator**: Use `@udf(input_types, return_type, volatility,
+              name)`. In this case, do **not** pass `func` explicitly.
 
         Args:
-            func: A callable python function.
-            input_types: The data types of the arguments to ``func``. This list
-                must be of the same length as the number of arguments.
-            return_type: The data type of the return value from the python
-                function.
-            volatility: See ``Volatility`` for allowed values.
-            name: A descriptive name for the function.
+            func (Callable, optional): **Only needed when calling as a function.**
+                Skip this argument when using `udf` as a decorator.
+            input_types (list[pyarrow.DataType]): The data types of the arguments
+                to `func`. This list must be of the same length as the number of
+                arguments.
+            return_type (_R): The data type of the return value from the function.
+            volatility (Volatility | str): See `Volatility` for allowed values.
+            name (Optional[str]): A descriptive name for the function.
 
         Returns:
-            A user-defined aggregate function, which can be used in either data
-                aggregation or window function calls.
+            A user-defined function that can be used in SQL expressions,
+            data aggregation, or window function calls.
+
+        Example:
+            **Using `udf` as a function:**
+            ```
+            def double_func(x):
+                return x * 2
+            double_udf = udf(double_func, [pyarrow.int32()], pyarrow.int32(),
+            "volatile", "double_it")
+            ```
+
+            **Using `udf` as a decorator:**
+            ```
+            @udf([pyarrow.int32()], pyarrow.int32(), "volatile", "double_it")
+            def double_udf(x):
+                return x * 2
+            ```
         """
-        if not callable(func):
-            raise TypeError("`func` argument must be callable")
-        if name is None:
-            if hasattr(func, "__qualname__"):
-                name = func.__qualname__.lower()
+
+        def __new__(cls, *args, **kwargs):
+            """Create a new UDF.
+
+            Trigger UDF function or decorator depending on if the first args is callable
+            """
+            if args and callable(args[0]):
+                # Case 1: Used as a function, require the first parameter to be callable
+                return cls._function(*args, **kwargs)
             else:
-                name = func.__class__.__name__.lower()
-        return ScalarUDF(
-            name=name,
-            func=func,
-            input_types=input_types,
-            return_type=return_type,
-            volatility=volatility,
-        )
+                # Case 2: Used as a decorator with parameters
+                return cls._decorator(*args, **kwargs)
+
+        @staticmethod
+        def _function(
+            func: Callable[..., _R],
+            input_types: list[pyarrow.DataType],
+            return_type: _R,
+            volatility: Volatility | str,
+            name: Optional[str] = None,
+        ) -> ScalarUDF:
+            if not callable(func):
+                raise TypeError("`func` argument must be callable")
+            if name is None:
+                if hasattr(func, "__qualname__"):
+                    name = func.__qualname__.lower()
+                else:
+                    name = func.__class__.__name__.lower()
+            return ScalarUDF(
+                name=name,
+                func=func,
+                input_types=input_types,
+                return_type=return_type,
+                volatility=volatility,
+            )
+
+        @staticmethod
+        def _decorator(
+            input_types: list[pyarrow.DataType],
+            return_type: _R,
+            volatility: Volatility | str,
+            name: Optional[str] = None,
+        ):
+            def decorator(func):
+                udf_caller = ScalarUDF.udf(
+                    func, input_types, return_type, volatility, name
+                )
+
+                @functools.wraps(func)
+                def wrapper(*args, **kwargs):
+                    return udf_caller(*args, **kwargs)
+
+                return wrapper
+
+            return decorator
 
 
 class Accumulator(metaclass=ABCMeta):
@@ -212,25 +272,27 @@ def __call__(self, *args: Expr) -> Expr:
         args_raw = [arg.expr for arg in args]
         return Expr(self._udaf.__call__(*args_raw))
 
-    @staticmethod
-    def udaf(
-        accum: Callable[[], Accumulator],
-        input_types: pyarrow.DataType | list[pyarrow.DataType],
-        return_type: pyarrow.DataType,
-        state_type: list[pyarrow.DataType],
-        volatility: Volatility | str,
-        name: Optional[str] = None,
-    ) -> AggregateUDF:
-        """Create a new User-Defined Aggregate Function.
+    class udaf:
+        """Create a new User-Defined Aggregate Function (UDAF).
 
-        If your :py:class:`Accumulator` can be instantiated with no arguments, you
-        can simply pass it's type as ``accum``. If you need to pass additional arguments
-        to it's constructor, you can define a lambda or a factory method. During runtime
-        the :py:class:`Accumulator` will be constructed for every instance in
-        which this UDAF is used. The following examples are all valid.
+        This class allows you to define an **aggregate function** that can be used in
+        data aggregation or window function calls.
 
-        .. code-block:: python
+        Usage:
+            - **As a function**: Call `udaf(accum, input_types, return_type, state_type,
+                volatility, name)`.
+            - **As a decorator**: Use `@udaf(input_types, return_type, state_type,
+                volatility, name)`.
+            When using `udaf` as a decorator, **do not pass `accum` explicitly**.
 
+        **Function example:**
+
+            If your `:py:class:Accumulator` can be instantiated with no arguments, you
+            can simply pass it's type as `accum`. If you need to pass additional
+            arguments to it's constructor, you can define a lambda or a factory method.
+            During runtime the `:py:class:Accumulator` will be constructed for every
+            instance in which this UDAF is used. The following examples are all valid.
+            ```
             import pyarrow as pa
             import pyarrow.compute as pc
 
@@ -253,12 +315,24 @@ def evaluate(self) -> pa.Scalar:
             def sum_bias_10() -> Summarize:
                 return Summarize(10.0)
 
-            udaf1 = udaf(Summarize, pa.float64(), pa.float64(), [pa.float64()], "immutable")
-            udaf2 = udaf(sum_bias_10, pa.float64(), pa.float64(), [pa.float64()], "immutable")
-            udaf3 = udaf(lambda: Summarize(20.0), pa.float64(), pa.float64(), [pa.float64()], "immutable")
+            udaf1 = udaf(Summarize, pa.float64(), pa.float64(), [pa.float64()],
+                "immutable")
+            udaf2 = udaf(sum_bias_10, pa.float64(), pa.float64(), [pa.float64()],
+                "immutable")
+            udaf3 = udaf(lambda: Summarize(20.0), pa.float64(), pa.float64(),
+                [pa.float64()], "immutable")
+            ```
+
+        **Decorator example:**
+            ```
+            @udaf(pa.float64(), pa.float64(), [pa.float64()], "immutable")
+            def udf4() -> Summarize:
+                return Summarize(10.0)
+            ```
 
         Args:
-            accum: The accumulator python function.
+            accum: The accumulator python function. **Only needed when calling as a
+                function. Skip this argument when using `udaf` as a decorator.**
             input_types: The data types of the arguments to ``accum``.
             return_type: The data type of the return value.
             state_type: The data types of the intermediate accumulation.
@@ -268,26 +342,69 @@ def sum_bias_10() -> Summarize:
         Returns:
             A user-defined aggregate function, which can be used in either data
             aggregation or window function calls.
-        """  # noqa W505
-        if not callable(accum):
-            raise TypeError("`func` must be callable.")
-        if not isinstance(accum.__call__(), Accumulator):
-            raise TypeError(
-                "Accumulator must implement the abstract base class Accumulator"
+        """
+
+        def __new__(cls, *args, **kwargs):
+            """Create a new UDAF.
+
+            Trigger UDAF function or decorator depending on if the first args is
+            callable
+            """
+            if args and callable(args[0]):
+                # Case 1: Used as a function, require the first parameter to be callable
+                return cls._function(*args, **kwargs)
+            else:
+                # Case 2: Used as a decorator with parameters
+                return cls._decorator(*args, **kwargs)
+
+        @staticmethod
+        def _function(
+            accum: Callable[[], Accumulator],
+            input_types: pyarrow.DataType | list[pyarrow.DataType],
+            return_type: pyarrow.DataType,
+            state_type: list[pyarrow.DataType],
+            volatility: Volatility | str,
+            name: Optional[str] = None,
+        ) -> AggregateUDF:
+            if not callable(accum):
+                raise TypeError("`func` must be callable.")
+            if not isinstance(accum.__call__(), Accumulator):
+                raise TypeError(
+                    "Accumulator must implement the abstract base class Accumulator"
+                )
+            if name is None:
+                name = accum.__call__().__class__.__qualname__.lower()
+            if isinstance(input_types, pyarrow.DataType):
+                input_types = [input_types]
+            return AggregateUDF(
+                name=name,
+                accumulator=accum,
+                input_types=input_types,
+                return_type=return_type,
+                state_type=state_type,
+                volatility=volatility,
             )
-        if name is None:
-            name = accum.__call__().__class__.__qualname__.lower()
-        assert name is not None
-        if isinstance(input_types, pyarrow.DataType):
-            input_types = [input_types]
-        return AggregateUDF(
-            name=name,
-            accumulator=accum,
-            input_types=input_types,
-            return_type=return_type,
-            state_type=state_type,
-            volatility=volatility,
-        )
+
+        @staticmethod
+        def _decorator(
+            input_types: pyarrow.DataType | list[pyarrow.DataType],
+            return_type: pyarrow.DataType,
+            state_type: list[pyarrow.DataType],
+            volatility: Volatility | str,
+            name: Optional[str] = None,
+        ):
+            def decorator(accum: Callable[[], Accumulator]):
+                udaf_caller = AggregateUDF.udaf(
+                    accum, input_types, return_type, state_type, volatility, name
+                )
+
+                @functools.wraps(accum)
+                def wrapper(*args, **kwargs):
+                    return udaf_caller(*args, **kwargs)
+
+                return wrapper
+
+            return decorator
 
 
 class WindowEvaluator(metaclass=ABCMeta):

python/tests/test_udaf.py

@@ -117,6 +117,26 @@ def test_udaf_aggregate(df):
     assert result.column(0) == pa.array([1.0 + 2.0 + 3.0])
 
 
+def test_udaf_decorator_aggregate(df):
+    @udaf(pa.float64(), pa.float64(), [pa.float64()], "immutable")
+    def summarize():
+        return Summarize()
+
+    df1 = df.aggregate([], [summarize(column("a"))])
+
+    # execute and collect the first (and only) batch
+    result = df1.collect()[0]
+
+    assert result.column(0) == pa.array([1.0 + 2.0 + 3.0])
+
+    df2 = df.aggregate([], [summarize(column("a"))])
+
+    # Run a second time to ensure the state is properly reset
+    result = df2.collect()[0]
+
+    assert result.column(0) == pa.array([1.0 + 2.0 + 3.0])
+
+
 def test_udaf_aggregate_with_arguments(df):
     bias = 10.0
 
@@ -143,6 +163,28 @@ def test_udaf_aggregate_with_arguments(df):
     assert result.column(0) == pa.array([bias + 1.0 + 2.0 + 3.0])
 
 
+def test_udaf_decorator_aggregate_with_arguments(df):
+    bias = 10.0
+
+    @udaf(pa.float64(), pa.float64(), [pa.float64()], "immutable")
+    def summarize():
+        return Summarize(bias)
+
+    df1 = df.aggregate([], [summarize(column("a"))])
+
+    # execute and collect the first (and only) batch
+    result = df1.collect()[0]
+
+    assert result.column(0) == pa.array([bias + 1.0 + 2.0 + 3.0])
+
+    df2 = df.aggregate([], [summarize(column("a"))])
+
+    # Run a second time to ensure the state is properly reset
+    result = df2.collect()[0]
+
+    assert result.column(0) == pa.array([bias + 1.0 + 2.0 + 3.0])
+
+
 def test_group_by(df):
     summarize = udaf(
         Summarize,