Add user documentation for UDWF

docs/source/user-guide/common-operations/udf-and-udfa.rst

@@ -18,8 +18,21 @@
 User Defined Functions
 ======================
 
-DataFusion provides powerful expressions and functions, reducing the need for custom Python functions.
-However you can still incorporate your own functions, i.e. User-Defined Functions (UDFs), with the :py:func:`~datafusion.udf.ScalarUDF.udf` function.
+DataFusion provides powerful expressions and functions, reducing the need for custom Python
+functions. However you can still incorporate your own functions, i.e. User-Defined Functions (UDFs).
+
+Scalar Functions
+----------------
+
+When writing a user defined function that can operate on a row by row basis, these are called Scalar
+Functions. You can define your own scalar function by calling
+:py:func:`~datafusion.udf.ScalarUDF.udf` .
+
+The basic definition of a scalar UDF is a python function that takes one or more
+`pyarrow <https://arrow.apache.org/docs/python/index.html>`_ arrays and returns a single array as
+output. DataFusion scalar UDFs operate on an entire batch of record at a time, though the evaluation
+of those records should be on a row by row basis. In the following example, we compute if the input
+array contains null values.
 
 .. ipython:: python
 
@@ -35,14 +48,70 @@ However you can still incorporate your own functions, i.e. User-Defined Function
     ctx = datafusion.SessionContext()
 
     batch = pyarrow.RecordBatch.from_arrays(
-        [pyarrow.array([1, 2, 3]), pyarrow.array([4, 5, 6])],
+        [pyarrow.array([1, None, 3]), pyarrow.array([4, 5, 6])],
         names=["a", "b"],
     )
     df = ctx.create_dataframe([[batch]], name="batch_array")
 
-    df.select(is_null_arr(col("a"))).to_pandas()
+    df.select(col("a"), is_null_arr(col("a")).alias("is_null")).show()
+
+In the previous example, we used the fact that pyarrow provides a variety of built in array
+functions such as ``is_null()``. There are additional pyarrow
+`compute functions <https://arrow.apache.org/docs/python/compute.html>`_ available. When possible,
+it is highly recommended to use these functions because they can perform computations without doing
+any copy operations from the original arrays. This leads to greatly improved performance.
+
+If you need to perform an operation in python that is not available with the pyarrow compute
+functions, you will need to convert the record batch into python values, perform your operation,
+and construct an array. This operation of converting the built in data type of the array into a
+python object can be one of the slowest operations in DataFusion, so it should be done sparingly.
+
+The following example performs the same operation as before with ``is_null`` but demonstrates
+converting to Python objects to do the evaluation.
+
+.. ipython:: python
+
+    import pyarrow
+    import datafusion
+    from datafusion import udf, col
+
+    def is_null(array: pyarrow.Array) -> pyarrow.Array:
+        results = []
+        for value in array:
+            results.append(value.as_py() == None)
+        return pyarrow.array(results)
+
+    is_null_arr = udf(is_null, [pyarrow.int64()], pyarrow.bool_(), 'stable')
+
+    ctx = datafusion.SessionContext()
+
+    batch = pyarrow.RecordBatch.from_arrays(
+        [pyarrow.array([1, None, 3]), pyarrow.array([4, 5, 6])],
+        names=["a", "b"],
+    )
+    df = ctx.create_dataframe([[batch]], name="batch_array")
 
-Additionally the :py:func:`~datafusion.udf.AggregateUDF.udaf` function allows you to define User-Defined Aggregate Functions (UDAFs)
+    df.select(col("a"), is_null_arr(col("a")).alias("is_null")).show()
+
+Aggregate Functions
+-------------------
+
+The :py:func:`~datafusion.udf.AggregateUDF.udaf` function allows you to define User-Defined
+Aggregate Functions (UDAFs). To use this you must implement an
+:py:class:`~datafusion.udf.Accumulator` that determines how the aggregation is performed.
+
+When defining a UDAF there are four methods you need to implement. The ``update`` function takes the
+array(s) of input and updates the internal state of the accumulator. You should define this function
+to have as many input arguments as you will pass when calling the UDAF. Since aggregation may be
+split into multiple batches, we must have a method to combine multiple batches. For this, we have
+two functions, ``state`` and ``merge``. ``state`` will return an array of scalar values that contain
+the current state of a single batch accumulation. Then we must ``merge`` the results of these
+different states. Finally ``evaluate`` is the call that will return the final result after the
+``merge`` is complete.
+
+In the following example we want to define a custom aggregate function that will return the
+difference between the sum of two columns. The state can be represented by a single value and we can
+also see how the inputs to ``update`` and ``merge`` differ.
 
 .. code-block:: python
 
@@ -57,30 +126,122 @@ Additionally the :py:func:`~datafusion.udf.AggregateUDF.udaf` function allows yo
         Interface of a user-defined accumulation.
         """
         def __init__(self):
-            self._sum = pyarrow.scalar(0.0)
+            self._sum = 0.0
 
-        def update(self, values: pyarrow.Array) -> None:
-            # not nice since pyarrow scalars can't be summed yet. This breaks on `None`
-            self._sum = pyarrow.scalar(self._sum.as_py() + pyarrow.compute.sum(values).as_py())
+        def update(self, values_a: pyarrow.Array, values_b: pyarrow.Array) -> None:
+            self._sum = self._sum + pyarrow.compute.sum(values_a).as_py() - pyarrow.compute.sum(values_b).as_py()
 
         def merge(self, states: List[pyarrow.Array]) -> None:
-            # not nice since pyarrow scalars can't be summed yet. This breaks on `None`
-            self._sum = pyarrow.scalar(self._sum.as_py() + pyarrow.compute.sum(states[0]).as_py())
+            self._sum = self._sum + pyarrow.compute.sum(states[0]).as_py()
 
         def state(self) -> pyarrow.Array:
-            return pyarrow.array([self._sum.as_py()])
+            return pyarrow.array([self._sum])
 
         def evaluate(self) -> pyarrow.Scalar:
-            return self._sum
+            return pyarrow.scalar(self._sum)
 
     ctx = datafusion.SessionContext()
     df = ctx.from_pydict(
         {
-            "a": [1, 2, 3],
-            "b": [4, 5, 6],
+            "a": [4, 5, 6],
+            "b": [1, 2, 3],
         }
     )
 
-    my_udaf = udaf(MyAccumulator, pyarrow.float64(), pyarrow.float64(), [pyarrow.float64()], 'stable')
+    my_udaf = udaf(MyAccumulator, [pyarrow.float64(), pyarrow.float64()], pyarrow.float64(), [pyarrow.float64()], 'stable')
+
+    df.aggregate([], [my_udaf(col("a"), col("b")).alias("col_diff")])
+
+Window Functions
+----------------
+
+To implement a User-Defined Window Function (UDWF) you must call the
+:py:func:`~datafusion.udf.WindowUDF.udwf` function using a class that implements the abstract
+class :py:class:`~datafusion.udf.WindowEvaluator`.
+
+There are three methods of evaluation of UDWFs.
+
+- ``evaluate`` is the simplest case, where you are given an array and are expected to calculate the
+  value for a single row of that array. This is the simplest case, but also the least performant.
+- ``evaluate_all`` computes the values for all rows for an input array at a single time.
+- ``evaluate_all_with_rank`` computes the values for all rows, but you only have the rank
+  information for the rows.
+
+Which methods you implement are based upon which of these options are set.
+
+.. list-table:: Title
+   :header-rows: 1
+
+   * - ``uses_window_frame``
+     - ``supports_bounded_execution``
+     - ``include_rank``
+     - function_to_implement
+   * - False (default)
+     - False (default)
+     - False (default)
+     - ``evaluate_all``
+   * - False
+     - True
+     - False
+     - ``evaluate``
+   * - False
+     - True
+     - False
+     - ``evaluate_all_with_rank``
+   * - True
+     - True/False
+     - True/False
+     - ``evaluate``
+
+UDWF options
+^^^^^^^^^^^^
+
+When you define your UDWF you can override the functions that return these values. They will
+determine which evaluate functions are called.
+
+- ``uses_window_frame`` is set for functions that compute based on the specified window frame. If
+  your function depends upon the specified frame, set this to ``True``.
+- ``supports_bounded_execution`` specifies if your function can be incrementally computed.
+- ``include_rank`` is set to ``True`` for window functions that can be computed only using the rank
+  information.
+
+
+.. code-block:: python
+
+    import pyarrow as pa
+    from datafusion import udwf, col, SessionContext
+    from datafusion.udf import WindowEvaluator
+
+    class ExponentialSmooth(WindowEvaluator):
+        def __init__(self, alpha: float) -> None:
+            self.alpha = alpha
+
+        def evaluate_all(self, values: list[pa.Array], num_rows: int) -> pa.Array:
+            results = []
+            curr_value = 0.0
+            values = values[0]
+            for idx in range(num_rows):
+                if idx == 0:
+                    curr_value = values[idx].as_py()
+                else:
+                    curr_value = values[idx].as_py() * self.alpha + curr_value * (
+                        1.0 - self.alpha
+                    )
+                results.append(curr_value)
+
+            return pa.array(results)
+
+    exp_smooth = udwf(
+        ExponentialSmooth(0.9),
+        pa.float64(),
+        pa.float64(),
+        volatility="immutable",
+    )
+
+    ctx = SessionContext()
+
+    df = ctx.from_pydict({
+        "a": [1.0, 2.1, 2.9, 4.0, 5.1, 6.0, 6.9, 8.0]
+    })
 
-    df.aggregate([],[my_udaf(col("a"))])
+    df.select("a", exp_smooth(col("a")).alias("smooth_a")).show()