Revert "UNPICK changes to review"

This reverts commit df2fe36.

Author: kosiew

docs/source/conf.py

@@ -72,6 +72,12 @@
 suppress_warnings = ["autoapi.python_import_resolution"]
 autoapi_python_class_content = "both"
 autoapi_keep_files = False  # set to True for debugging generated files
+autoapi_options = [
+    "members",
+    "undoc-members",
+    "special-members",
+    "show-inheritance",
+]
 
 
 def autoapi_skip_member_fn(app, what, name, obj, skip, options) -> bool:  # noqa: ARG001

docs/source/user-guide/dataframe/index.rst

@@ -145,10 +145,66 @@ To materialize the results of your DataFrame operations:
     
     # Display results
     df.show()                         # Print tabular format to console
-    
+
     # Count rows
     count = df.count()
 
+PyArrow Streaming
+-----------------
+
+DataFusion DataFrames implement the ``__arrow_c_stream__`` protocol, enabling
+zero-copy streaming into libraries like `PyArrow <https://arrow.apache.org/>`_.
+Earlier versions eagerly converted the entire DataFrame when exporting to
+PyArrow, which could exhaust memory on large datasets. With streaming, batches
+are produced lazily so you can process arbitrarily large results without
+out-of-memory errors.
+
+.. code-block:: python
+
+    import pyarrow as pa
+
+    # Create a PyArrow RecordBatchReader without materializing all batches
+    reader = pa.RecordBatchReader.from_stream(df)
+    for batch in reader:
+        ...  # process each batch as it is produced
+
+DataFrames are also iterable, yielding :class:`datafusion.RecordBatch`
+objects lazily so you can loop over results directly without importing
+PyArrow:
+
+.. code-block:: python
+
+    for batch in df:
+        ...  # each batch is a ``datafusion.RecordBatch``
+
+Each batch exposes ``to_pyarrow()``, allowing conversion to a PyArrow
+table without collecting everything eagerly:
+
+.. code-block:: python
+
+    import pyarrow as pa
+    table = pa.Table.from_batches(b.to_pyarrow() for b in df)
+
+Asynchronous iteration is supported as well, allowing integration with
+``asyncio`` event loops:
+
+.. code-block:: python
+
+    async for batch in df:
+        ...  # process each batch as it is produced
+
+To work with the stream directly, use
+``to_record_batch_stream()``, which returns a
+:class:`~datafusion.RecordBatchStream`:
+
+.. code-block:: python
+
+    stream = df.to_record_batch_stream()
+    for batch in stream:
+        ...
+
+See :doc:`../io/arrow` for additional details on the Arrow interface.
+
 HTML Rendering
 --------------
 

python/datafusion/dataframe.py

@@ -25,7 +25,9 @@
 from typing import (
     TYPE_CHECKING,
     Any,
+    AsyncIterator,
     Iterable,
+    Iterator,
     Literal,
     Optional,
     Union,
@@ -42,7 +44,7 @@
 from datafusion._internal import ParquetWriterOptions as ParquetWriterOptionsInternal
 from datafusion.expr import Expr, SortExpr, sort_or_default
 from datafusion.plan import ExecutionPlan, LogicalPlan
-from datafusion.record_batch import RecordBatchStream
+from datafusion.record_batch import RecordBatch, RecordBatchStream
 
 if TYPE_CHECKING:
     import pathlib
@@ -289,6 +291,9 @@ def __init__(
 class DataFrame:
     """Two dimensional table representation of data.
 
+    DataFrame objects are iterable; iterating over a DataFrame yields
+    :class:`pyarrow.RecordBatch` instances lazily.
+
     See :ref:`user_guide_concepts` in the online documentation for more information.
     """
 
@@ -305,7 +310,7 @@ def into_view(self) -> pa.Table:
         return self.df.into_view()
 
     def __getitem__(self, key: str | list[str]) -> DataFrame:
-        """Return a new :py:class`DataFrame` with the specified column or columns.
+        """Return a new :py:class:`DataFrame` with the specified column or columns.
 
         Args:
             key: Column name or list of column names to select.
@@ -1026,6 +1031,10 @@ def execute_stream(self) -> RecordBatchStream:
         """
         return RecordBatchStream(self.df.execute_stream())
 
+    def to_record_batch_stream(self) -> RecordBatchStream:
+        """Return a :class:`RecordBatchStream` executing this DataFrame."""
+        return self.execute_stream()
+
     def execute_stream_partitioned(self) -> list[RecordBatchStream]:
         """Executes this DataFrame and returns a stream for each partition.
 
@@ -1035,6 +1044,15 @@ def execute_stream_partitioned(self) -> list[RecordBatchStream]:
         streams = self.df.execute_stream_partitioned()
         return [RecordBatchStream(rbs) for rbs in streams]
 
+    def to_record_batch_stream(self) -> RecordBatchStream:
+        """Return a :py:class:`RecordBatchStream` over this DataFrame's results.
+
+        Returns:
+            A ``RecordBatchStream`` representing the lazily generated record
+            batches for this DataFrame.
+        """
+        return self.execute_stream()
+
     def to_pandas(self) -> pd.DataFrame:
         """Execute the :py:class:`DataFrame` and convert it into a Pandas DataFrame.
 
@@ -1098,21 +1116,33 @@ def unnest_columns(self, *columns: str, preserve_nulls: bool = True) -> DataFram
         return DataFrame(self.df.unnest_columns(columns, preserve_nulls=preserve_nulls))
 
     def __arrow_c_stream__(self, requested_schema: object | None = None) -> object:
-        """Export an Arrow PyCapsule Stream.
+        """Export the DataFrame as an Arrow C Stream.
 
-        This will execute and collect the DataFrame. We will attempt to respect the
-        requested schema, but only trivial transformations will be applied such as only
-        returning the fields listed in the requested schema if their data types match
-        those in the DataFrame.
+        The DataFrame is executed using DataFusion's streaming APIs and exposed via
+        Arrow's C Stream interface. Record batches are produced incrementally, so the
+        full result set is never materialized in memory. When ``requested_schema`` is
+        provided, only straightforward projections such as column selection or
+        reordering are applied.
 
         Args:
             requested_schema: Attempt to provide the DataFrame using this schema.
 
         Returns:
-            Arrow PyCapsule object.
+            Arrow PyCapsule object representing an ``ArrowArrayStream``.
         """
+        # ``DataFrame.__arrow_c_stream__`` in the Rust extension leverages
+        # ``execute_stream_partitioned`` under the hood to stream batches while
+        # preserving the original partition order.
         return self.df.__arrow_c_stream__(requested_schema)
 
+    def __iter__(self) -> Iterator[RecordBatch]:
+        """Return an iterator over this DataFrame's record batches."""
+        return iter(self.to_record_batch_stream())
+
+    def __aiter__(self) -> AsyncIterator[RecordBatch]:
+        """Return an async iterator over this DataFrame's record batches."""
+        return self.to_record_batch_stream().__aiter__()
+
     def transform(self, func: Callable[..., DataFrame], *args: Any) -> DataFrame:
         """Apply a function to the current DataFrame which returns another DataFrame.
 

python/datafusion/record_batch.py

@@ -46,6 +46,26 @@ def to_pyarrow(self) -> pa.RecordBatch:
         """Convert to :py:class:`pa.RecordBatch`."""
         return self.record_batch.to_pyarrow()
 
+    def __arrow_c_array__(
+        self, requested_schema: object | None = None
+    ) -> tuple[object, object]:
+        """Export the record batch via the Arrow C Data Interface.
+
+        This allows zero-copy interchange with libraries that support the
+        `Arrow PyCapsule interface <https://arrow.apache.org/docs/format/
+        CDataInterface/PyCapsuleInterface.html>`_.
+
+        Args:
+            requested_schema: Attempt to provide the record batch using this
+                schema. Only straightforward projections such as column
+                selection or reordering are applied.
+
+        Returns:
+            Two Arrow PyCapsule objects representing the ``ArrowArray`` and
+            ``ArrowSchema``.
+        """
+        return self.record_batch.__arrow_c_array__(requested_schema)
+
 
 class RecordBatchStream:
     """This class represents a stream of record batches.
@@ -63,19 +83,19 @@ def next(self) -> RecordBatch:
         return next(self)
 
     async def __anext__(self) -> RecordBatch:
-        """Async iterator function."""
+        """Return the next :py:class:`RecordBatch` in the stream asynchronously."""
         next_batch = await self.rbs.__anext__()
         return RecordBatch(next_batch)
 
     def __next__(self) -> RecordBatch:
-        """Iterator function."""
+        """Return the next :py:class:`RecordBatch` in the stream."""
         next_batch = next(self.rbs)
         return RecordBatch(next_batch)
 
     def __aiter__(self) -> typing_extensions.Self:
-        """Async iterator function."""
+        """Return an asynchronous iterator over record batches."""
         return self
 
     def __iter__(self) -> typing_extensions.Self:
-        """Iterator function."""
+        """Return an iterator over record batches."""
         return self

python/tests/conftest.py

@@ -17,7 +17,7 @@
 
 import pyarrow as pa
 import pytest
-from datafusion import SessionContext
+from datafusion import DataFrame, SessionContext
 from pyarrow.csv import write_csv
 
 
@@ -49,3 +49,12 @@ def database(ctx, tmp_path):
         delimiter=",",
         schema_infer_max_records=10,
     )
+
+
+@pytest.fixture
+def fail_collect(monkeypatch):
+    def _fail_collect(self, *args, **kwargs):  # pragma: no cover - failure path
+        msg = "collect should not be called"
+        raise AssertionError(msg)
+
+    monkeypatch.setattr(DataFrame, "collect", _fail_collect)