revert branch UNPICK

Author: kosiew

Cargo.lock

@@ -26,7 +26,7 @@ readme = "README.md"
 license = "Apache-2.0"
 edition = "2021"
 rust-version = "1.78"
-include = ["/src", "/datafusion", "/LICENSE.txt", "pyproject.toml", "Cargo.toml", "Cargo.lock"]
+include = ["/src", "/datafusion", "/LICENSE.txt", "build.rs", "pyproject.toml", "Cargo.toml", "Cargo.lock"]
 
 [features]
 default = ["mimalloc"]
@@ -48,7 +48,6 @@ uuid = { version = "1.18", features = ["v4"] }
 mimalloc = { version = "0.1", optional = true, default-features = false, features = ["local_dynamic_tls"] }
 async-trait = "0.1.89"
 futures = "0.3"
-rayon = "1.10"
 object_store = { version = "0.12.3", features = ["aws", "gcp", "azure", "http"] }
 url = "2"
 log = "0.4.27"

Cargo.toml

@@ -47,26 +47,5 @@ a :py:class:`~datafusion.context.SessionConfig` and :py:class:`~datafusion.conte
     print(ctx)
 
 
-.. _target_partitions:
-
-Target partitions and threads
------------------------------
-
-The :py:meth:`~datafusion.context.SessionConfig.with_target_partitions` method
-controls how many partitions DataFusion uses when executing a query. Each
-partition is processed on its own thread, so this setting effectively limits
-the number of threads that will be scheduled.
-
-For most workloads a good starting value is the number of logical CPU cores on
-your machine. You can use :func:`os.cpu_count` to automatically configure this::
-
-    import os
-    config = SessionConfig().with_target_partitions(os.cpu_count())
-
-Choosing a value significantly higher than the available cores can lead to
-excessive context switching without performance gains, while a much lower value
-may underutilize the machine.
-
-
 You can read more about available :py:class:`~datafusion.context.SessionConfig` options in the `rust DataFusion Configuration guide <https://arrow.apache.org/datafusion/user-guide/configs.html>`_,
 and about :code:`RuntimeEnvBuilder` options in the rust `online API documentation <https://docs.rs/datafusion/latest/datafusion/execution/runtime_env/struct.RuntimeEnvBuilder.html>`_.

benchmarks/collect_gil_bench.py

@@ -25,10 +25,8 @@ The ``DataFrame`` class is the core abstraction in DataFusion that represents ta
 on that data. DataFrames provide a flexible API for transforming data through various operations such as
 filtering, projection, aggregation, joining, and more.
 
-A DataFrame represents a logical plan that is lazily evaluated. The actual execution occurs only when
-terminal operations like ``collect()``, ``show()``, or ``to_pandas()`` are called. ``collect()`` loads
-all record batches into Python memory; for large results you may want to stream data instead using
-``execute_stream()`` or ``__arrow_c_stream__()``.
+A DataFrame represents a logical plan that is lazily evaluated. The actual execution occurs only when 
+terminal operations like ``collect()``, ``show()``, or ``to_pandas()`` are called.
 
 Creating DataFrames
 -------------------
@@ -130,47 +128,27 @@ DataFusion's DataFrame API offers a wide range of operations:
 
 Terminal Operations
 -------------------
-``collect()`` materializes every record batch in Python. While convenient, this
-eagerly loads the full result set into memory and can overwhelm the Python
-process for large queries. Alternatives that stream data from Rust avoid this
-memory growth:
+
+To materialize the results of your DataFrame operations:
 
 .. code-block:: python
 
-    # Collect all data as PyArrow RecordBatches (loads entire result set)
+    # Collect all data as PyArrow RecordBatches
     result_batches = df.collect()
-
-    # Stream batches using the native API
-    stream = df.execute_stream()
-    for batch in stream:
-        ...  # process each RecordBatch
-
-    # Stream via the Arrow C Data Interface
-    import pyarrow as pa
-    reader = pa.ipc.RecordBatchStreamReader._import_from_c(df.__arrow_c_stream__())
-    for batch in reader:
-        ...
-
-    # Convert to various formats (also load all data into memory)
+    
+    # Convert to various formats
     pandas_df = df.to_pandas()        # Pandas DataFrame
     polars_df = df.to_polars()        # Polars DataFrame
     arrow_table = df.to_arrow_table() # PyArrow Table
     py_dict = df.to_pydict()          # Python dictionary
     py_list = df.to_pylist()          # Python list of dictionaries
-
+    
     # Display results
     df.show()                         # Print tabular format to console
-
+    
     # Count rows
     count = df.count()
 
-For large outputs, prefer engine-level writers such as ``df.write_parquet()``
-or other DataFusion writers. These stream data directly to the destination and
-avoid buffering the entire dataset in Python.
-
-For more on parallel record batch conversion and the Python GIL, see
-:doc:`collect-gil`.
-
 HTML Rendering
 --------------
 
@@ -229,4 +207,3 @@ For a complete list of available functions, see the :py:mod:`datafusion.function
    :maxdepth: 1
 
    rendering
-   collect-gil

docs/source/user-guide/configuration.rst

@@ -57,34 +57,17 @@ and returns a ``StructArray``. Common pyarrow sources you can use are:
 Exporting from DataFusion
 -------------------------
 
-DataFusion DataFrames implement ``__arrow_c_stream__`` so any Python library
-that accepts this interface can import a DataFusion ``DataFrame`` directly.
+DataFusion DataFrames implement ``__arrow_c_stream__`` PyCapsule interface, so any
+Python library that accepts these can import a DataFusion DataFrame directly.
 
-``collect()`` or ``pa.table(df)`` will materialize every record batch in
-Python. For large results this can quickly exhaust memory. Instead, stream the
-output incrementally:
+.. warning::
+    It is important to note that this will cause the DataFrame execution to happen, which may be
+    a time consuming task. That is, you will cause a
+    :py:func:`datafusion.dataframe.DataFrame.collect` operation call to occur.
 
-.. ipython:: python
-
-    # Stream batches with DataFusion's native API
-    stream = df.execute_stream()
-    for batch in stream:
-        ...  # process each RecordBatch as it arrives
-
-.. ipython:: python
-
-    # Expose a C stream that PyArrow can consume lazily
-    import pyarrow as pa
-    reader = pa.ipc.RecordBatchStreamReader._import_from_c(df.__arrow_c_stream__())
-    for batch in reader:
-        ...  # process each batch without buffering the entire table
-
-If the goal is simply to persist results, prefer engine-level writers such as
-``df.write_parquet()``. These writers stream data from Rust directly to the
-destination and avoid Python-side memory growth.
 
 .. ipython:: python
 
     df = df.select((col("a") * lit(1.5)).alias("c"), lit("df").alias("d"))
-    pa.table(df)  # loads all batches into memory
+    pa.table(df)
 

docs/source/user-guide/dataframe/collect-gil.md

@@ -161,13 +161,7 @@ def with_batch_size(self, batch_size: int) -> SessionConfig:
     def with_target_partitions(self, target_partitions: int) -> SessionConfig:
         """Customize the number of target partitions for query execution.
 
-        Each partition is processed on its own thread, so this value controls
-        the degree of parallelism. A good starting point is the number of
-        logical CPU cores on your machine, for example
-        ``SessionConfig().with_target_partitions(os.cpu_count())``.
-
-        See the :ref:`configuration guide <target_partitions>` for more
-        discussion on choosing a value.
+        Increasing partitions can increase concurrency.
 
         Args:
             target_partitions: Number of target partitions.

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

@@ -20,7 +20,6 @@
 import re
 import threading
 import time
-import tracemalloc
 from typing import Any
 
 import pyarrow as pa
@@ -253,6 +252,13 @@ def test_filter(df):
     assert result.column(2) == pa.array([5])
 
 
+def test_show_empty(df, capsys):
+    df_empty = df.filter(column("a") > literal(3))
+    df_empty.show()
+    captured = capsys.readouterr()
+    assert "DataFrame has no rows" in captured.out
+
+
 def test_sort(df):
     df = df.sort(column("b").sort(ascending=False))
 
@@ -1384,27 +1390,6 @@ def test_collect_partitioned():
     assert [[batch]] == ctx.create_dataframe([[batch]]).collect_partitioned()
 
 
-def test_collect_multiple_batches_to_pyarrow():
-    ctx = SessionContext()
-
-    batch1 = pa.RecordBatch.from_arrays(
-        [pa.array([1, 2])],
-        names=["a"],
-    )
-    batch2 = pa.RecordBatch.from_arrays(
-        [pa.array([3, 4])],
-        names=["a"],
-    )
-
-    df = ctx.create_dataframe([[batch1], [batch2]])
-
-    batches = df.collect()
-
-    assert len(batches) == 2
-    table = pa.Table.from_batches(batches)
-    assert table.column("a").to_pylist() == [1, 2, 3, 4]
-
-
 def test_union(ctx):
     batch = pa.RecordBatch.from_arrays(
         [pa.array([1, 2, 3]), pa.array([4, 5, 6])],
@@ -1485,24 +1470,6 @@ def test_empty_to_pandas(df):
     assert set(pandas_df.columns) == {"a", "b", "c"}
 
 
-def test_show_no_batches(capsys):
-    """Ensure showing a query with no batches still prints headers."""
-    ctx = SessionContext()
-    df = ctx.sql("SELECT 1 AS a WHERE 1=0")
-    df.show()
-    captured = capsys.readouterr()
-    assert "| a |" in captured.out
-    assert "Empty DataFrame" not in captured.out
-
-
-def test_show_empty_dataframe(df, capsys):
-    """Ensure showing an empty DataFrame prints a helpful message."""
-    empty_df = df.limit(0)
-    empty_df.show()
-    captured = capsys.readouterr()
-    assert "Empty DataFrame" in captured.out
-
-
 def test_to_polars(df):
     # Skip test if polars is not installed
     pl = pytest.importorskip("polars")
@@ -1607,23 +1574,6 @@ async def test_execute_stream_partitioned_async(df):
         assert not remaining_batches
 
 
-def test_arrow_c_stream_streaming(large_df):
-    df = large_df.repartition(4)
-    capsule = df.__arrow_c_stream__()
-    ctypes.pythonapi.PyCapsule_GetPointer.restype = ctypes.c_void_p
-    ctypes.pythonapi.PyCapsule_GetPointer.argtypes = [ctypes.py_object, ctypes.c_char_p]
-    ptr = ctypes.pythonapi.PyCapsule_GetPointer(capsule, b"arrow_array_stream")
-    reader = pa.RecordBatchReader._import_from_c(ptr)
-
-    tracemalloc.start()
-    batch_count = sum(1 for _ in reader)
-    _current, peak = tracemalloc.get_traced_memory()
-    tracemalloc.stop()
-
-    assert batch_count > 1
-    assert peak < 50 * MB
-
-
 def test_empty_to_arrow_table(df):
     # Convert empty datafusion dataframe to pyarrow Table
     pyarrow_table = df.limit(0).to_arrow_table()
@@ -2714,3 +2664,19 @@ def trigger_interrupt():
 
     # Make sure the interrupt thread has finished
     interrupt_thread.join(timeout=1.0)
+
+
+def test_show_select_where_no_rows(capsys) -> None:
+    ctx = SessionContext()
+    df = ctx.sql("SELECT 1 WHERE 1=0")
+    df.show()
+    out = capsys.readouterr().out
+    assert "DataFrame has no rows" in out
+
+
+def test_show_from_empty_batch(capsys) -> None:
+    ctx = SessionContext()
+    batch = pa.record_batch([pa.array([], type=pa.int32())], names=["a"])
+    ctx.create_dataframe([[batch]]).show()
+    out = capsys.readouterr().out
+    assert "| a |" in out