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===========================================
Python User-defined Table Functions (UDTFs)
===========================================

Spark 3.5 introduces the Python user-defined table function (UDTF), a new type of user-defined function. 
Unlike scalar functions that return a single result value from each call, each UDTF is invoked in 
the ``FROM`` clause of a query and returns an entire table as output.
Each UDTF call can accept zero or more arguments.
These arguments can either be scalar expressions or table arguments that represent entire input tables.

Implementing a Python UDTF
--------------------------

.. currentmodule:: pyspark.sql.functions

To implement a Python UDTF, you first need to define a class implementing the methods:

.. code-block:: python

    class PythonUDTF:

        def __init__(self) -> None:
            """
            Initializes the user-defined table function (UDTF). This is optional.

            This method serves as the default constructor and is called once when the
            UDTF is instantiated on the executor side.
            
            Any class fields assigned in this method will be available for subsequent
            calls to the `eval` and `terminate` methods. This class instance will remain
            alive until all rows in the current partition have been consumed by the `eval`
            method.

            Notes
            -----
            - This method does not accept any extra arguments. Only the default
              constructor is supported.
            - You cannot create or reference the Spark session within the UDTF. Any
              attempt to do so will result in a serialization error.
            """
            ...

        def eval(self, *args: Any) -> Iterator[Any]:
            """
            Evaluates the function using the given input arguments.

            This method is required and must be implemented.

            Argument Mapping:
            - Each provided scalar expression maps to exactly one value in the
              `*args` list.
            - Each provided table argument maps to a pyspark.sql.Row object containing
              the columns in the order they appear in the provided input table,
              and with the names computed by the query analyzer.

            This method is called on every input row, and can produce zero or more
            output rows. Each element in the output tuple corresponds to one column
            specified in the return type of the UDTF.

            Parameters
            ----------
            *args : Any
                Arbitrary positional arguments representing the input to the UDTF.

            Yields
            ------
            tuple
                A tuple representing a single row in the UDTF result table.
                Yield as many times as needed to produce multiple rows.

            Notes
            -----
            - The result of the function must be a tuple representing a single row
              in the UDTF result table.
            - UDTFs currently do not accept keyword arguments during the function call.

            Examples
            --------
            eval that returns one row and one column for each input.

            >>> def eval(self, x: int):
            ...     yield (x, )

            eval that returns two rows and two columns for each input.

            >>> def eval(self, x: int, y: int):
            ...     yield (x + y, x - y)
            ...     yield (y + x, y - x)
            """
            ...

        def terminate(self) -> Iterator[Any]:
            """
            Called when the UDTF has processed all input rows.

            This method is optional to implement and is useful for performing any
            cleanup or finalization operations after the UDTF has finished processing
            all rows. It can also be used to yield additional rows if needed.
            Table functions that consume all rows in the entire input partition
            and then compute and return the entire output table can do so from
            this method as well (please be mindful of memory usage when doing
            this).

            Yields
            ------
            tuple
                A tuple representing a single row in the UDTF result table.
                Yield this if you want to return additional rows during termination.

            Examples
            --------
            >>> def terminate(self) -> Iterator[Any]:
            >>>     yield "done", None
            """
            ...


The return type of the UDTF defines the schema of the table it outputs. 
It must be either a ``StructType``, for example ``StructType().add("c1", StringType())``
or a DDL string representing a struct type, for example ``c1: string``.

**Example of UDTF Class Implementation**

Here is a simple example of a UDTF class implementation:

.. literalinclude:: ../../../../../examples/src/main/python/sql/udtf.py
    :language: python
    :lines: 36-40
    :dedent: 4


**Instantiating a UDTF with the ``udtf`` Decorator**

To make use of the UDTF, you'll first need to instantiate it using the ``@udtf`` decorator:

.. literalinclude:: ../../../../../examples/src/main/python/sql/udtf.py
    :language: python
    :lines: 42-55
    :dedent: 4


**Instantiating a UDTF with the ``udtf`` Function**

An alternative way to create a UDTF is to use the :func:`udtf` function:

.. literalinclude:: ../../../../../examples/src/main/python/sql/udtf.py
    :language: python
    :lines: 60-77
    :dedent: 4

For more detailed usage, please see :func:`udtf`.


Registering and Using Python UDTFs in SQL
-----------------------------------------

Python UDTFs can also be registered and used in SQL queries.

.. literalinclude:: ../../../../../examples/src/main/python/sql/udtf.py
    :language: python
    :lines: 82-116
    :dedent: 4


Arrow Optimization
------------------
Apache Arrow is an in-memory columnar data format used in Spark to efficiently transfer
data between Java and Python processes. Apache Arrow is disabled by default for Python UDTFs.

Arrow can improve performance when each input row generates a large result table from the UDTF.

To enable Arrow optimization, set the ``spark.sql.execution.pythonUDTF.arrow.enabled``
configuration to ``true``. You can also enable it by specifying the ``useArrow`` parameter
when declaring the UDTF.

.. literalinclude:: ../../../../../examples/src/main/python/sql/udtf.py
    :language: python
    :lines: 121-126
    :dedent: 4


For more details, please see `Apache Arrow in PySpark <../arrow_pandas.rst>`_.


TABLE input argument
~~~~~~~~~~~~~~~~~~~~
Python UDTFs can also take a TABLE as input argument, and it can be used in conjunction 
with scalar input arguments.
By default, you are allowed to have only one TABLE argument as input, primarily for 
performance reasons. If you need to have more than one TABLE input argument, 
you can enable this by setting the ``spark.sql.tvf.allowMultipleTableArguments.enabled``
configuration to ``true``.

.. literalinclude:: ../../../../../examples/src/main/python/sql/udtf.py
    :language: python
    :lines: 191-210
    :dedent: 4


More Examples
-------------

A Python UDTF that expands date ranges into individual dates:

.. literalinclude:: ../../../../../examples/src/main/python/sql/udtf.py
    :language: python
    :lines: 131-152
    :dedent: 4


A Python UDTF with ``__init__`` and ``terminate``:

.. literalinclude:: ../../../../../examples/src/main/python/sql/udtf.py
    :language: python
    :lines: 157-186
    :dedent: 4