apacheGH-40066: [Python] Support requested_schema in `__arrow_c_str…

…eam__()` (apache#40070)

### Rationale for this change

The `requested_schema` portion of the `__arrow_c_stream__()` protocol methods errored in all cases if passed an unequal schema. There was a note about figuring out how to check the cast before doing it and a comment in apache#40066 about how it should be done lazily. This PR (hopefully) solves both!

### What changes are included in this PR?

- Added `arrow::py::CastingRecordBatchReader`, which wraps a `arrow::RecordBatchReader`, casting each batch as it is pulled.

### Are these changes tested?

Yes.

### Are there any user-facing changes?

Yes: the current approach adds `RecordBatchReader.cast()` as the way to access the casting reader.

* Closes: apache#40066
* GitHub Issue: apache#40066

Lead-authored-by: Dewey Dunnington <dewey@fishandwhistle.net>
Co-authored-by: Dewey Dunnington <dewey@voltrondata.com>
Co-authored-by: Antoine Pitrou <pitrou@free.fr>
Signed-off-by: Joris Van den Bossche <jorisvandenbossche@gmail.com>

python/pyarrow/includes/libarrow_python.pxd

@@ -283,6 +283,14 @@ cdef extern from "arrow/python/ipc.h" namespace "arrow::py":
                                                      object)
 
 
+cdef extern from "arrow/python/ipc.h" namespace "arrow::py" nogil:
+    cdef cppclass CCastingRecordBatchReader" arrow::py::CastingRecordBatchReader" \
+            (CRecordBatchReader):
+        @staticmethod
+        CResult[shared_ptr[CRecordBatchReader]] Make(shared_ptr[CRecordBatchReader],
+                                                     shared_ptr[CSchema])
+
+
 cdef extern from "arrow/python/extension_type.h" namespace "arrow::py":
     cdef cppclass CPyExtensionType \
             " arrow::py::PyExtensionType"(CExtensionType):

python/pyarrow/ipc.pxi

@@ -772,6 +772,38 @@ cdef class RecordBatchReader(_Weakrefable):
     def __exit__(self, exc_type, exc_val, exc_tb):
         self.close()
 
+    def cast(self, target_schema):
+        """
+        Wrap this reader with one that casts each batch lazily as it is pulled.
+        Currently only a safe cast to target_schema is implemented.
+
+        Parameters
+        ----------
+        target_schema : Schema
+            Schema to cast to, the names and order of fields must match.
+
+        Returns
+        -------
+        RecordBatchReader
+        """
+        cdef:
+            shared_ptr[CSchema] c_schema
+            shared_ptr[CRecordBatchReader] c_reader
+            RecordBatchReader out
+
+        if self.schema.names != target_schema.names:
+            raise ValueError("Target schema's field names are not matching "
+                             f"the table's field names: {self.schema.names}, "
+                             f"{target_schema.names}")
+
+        c_schema = pyarrow_unwrap_schema(target_schema)
+        c_reader = GetResultValue(CCastingRecordBatchReader.Make(
+            self.reader, c_schema))
+
+        out = RecordBatchReader.__new__(RecordBatchReader)
+        out.reader = c_reader
+        return out
+
     def _export_to_c(self, out_ptr):
         """
         Export to a C ArrowArrayStream struct, given its pointer.
@@ -827,8 +859,6 @@ cdef class RecordBatchReader(_Weakrefable):
             The schema to which the stream should be casted, passed as a
             PyCapsule containing a C ArrowSchema representation of the
             requested schema.
-            Currently, this is not supported and will raise a
-            NotImplementedError if the schema doesn't match the current schema.
 
         Returns
         -------
@@ -840,11 +870,8 @@ cdef class RecordBatchReader(_Weakrefable):
 
         if requested_schema is not None:
             out_schema = Schema._import_from_c_capsule(requested_schema)
-            # TODO: figure out a way to check if one schema is castable to
-            # another. Once we have that, we can perform validation here and
-            # if successful creating a wrapping reader that casts each batch.
             if self.schema != out_schema:
-                raise NotImplementedError("Casting to requested_schema")
+                return self.cast(out_schema).__arrow_c_stream__()
 
         stream_capsule = alloc_c_stream(&c_stream)
 

python/pyarrow/src/arrow/python/ipc.cc

@@ -19,6 +19,7 @@
 
 #include <memory>
 
+#include "arrow/compute/cast.h"
 #include "arrow/python/pyarrow.h"
 
 namespace arrow {
@@ -63,5 +64,70 @@ Result<std::shared_ptr<RecordBatchReader>> PyRecordBatchReader::Make(
   return reader;
 }
 
+CastingRecordBatchReader::CastingRecordBatchReader() = default;
+
+Status CastingRecordBatchReader::Init(std::shared_ptr<RecordBatchReader> parent,
+                                      std::shared_ptr<Schema> schema) {
+  std::shared_ptr<Schema> src = parent->schema();
+
+  // The check for names has already been done in Python where it's easier to
+  // generate a nice error message.
+  int num_fields = schema->num_fields();
+  if (src->num_fields() != num_fields) {
+    return Status::Invalid("Number of fields not equal");
+  }
+
+  // Ensure all columns can be cast before succeeding
+  for (int i = 0; i < num_fields; i++) {
+    if (!compute::CanCast(*src->field(i)->type(), *schema->field(i)->type())) {
+      return Status::TypeError("Field ", i, " cannot be cast from ",
+                               src->field(i)->type()->ToString(), " to ",
+                               schema->field(i)->type()->ToString());
+    }
+  }
+
+  parent_ = std::move(parent);
+  schema_ = std::move(schema);
+
+  return Status::OK();
+}
+
+std::shared_ptr<Schema> CastingRecordBatchReader::schema() const { return schema_; }
+
+Status CastingRecordBatchReader::ReadNext(std::shared_ptr<RecordBatch>* batch) {
+  std::shared_ptr<RecordBatch> out;
+  ARROW_RETURN_NOT_OK(parent_->ReadNext(&out));
+  if (!out) {
+    batch->reset();
+    return Status::OK();
+  }
+
+  auto num_columns = out->num_columns();
+  auto options = compute::CastOptions::Safe();
+  ArrayVector columns(num_columns);
+  for (int i = 0; i < num_columns; i++) {
+    const Array& src = *out->column(i);
+    if (!schema_->field(i)->nullable() && src.null_count() > 0) {
+      return Status::Invalid(
+          "Can't cast array that contains nulls to non-nullable field at index ", i);
+    }
+
+    ARROW_ASSIGN_OR_RAISE(columns[i],
+                          compute::Cast(src, schema_->field(i)->type(), options));
+  }
+
+  *batch = RecordBatch::Make(schema_, out->num_rows(), std::move(columns));
+  return Status::OK();
+}
+
+Result<std::shared_ptr<RecordBatchReader>> CastingRecordBatchReader::Make(
+    std::shared_ptr<RecordBatchReader> parent, std::shared_ptr<Schema> schema) {
+  auto reader = std::shared_ptr<CastingRecordBatchReader>(new CastingRecordBatchReader());
+  ARROW_RETURN_NOT_OK(reader->Init(parent, schema));
+  return reader;
+}
+
+Status CastingRecordBatchReader::Close() { return parent_->Close(); }
+
 }  // namespace py
 }  // namespace arrow

python/pyarrow/src/arrow/python/ipc.h

@@ -48,5 +48,25 @@ class ARROW_PYTHON_EXPORT PyRecordBatchReader : public RecordBatchReader {
   OwnedRefNoGIL iterator_;
 };
 
+class ARROW_PYTHON_EXPORT CastingRecordBatchReader : public RecordBatchReader {
+ public:
+  std::shared_ptr<Schema> schema() const override;
+
+  Status ReadNext(std::shared_ptr<RecordBatch>* batch) override;
+
+  static Result<std::shared_ptr<RecordBatchReader>> Make(
+      std::shared_ptr<RecordBatchReader> parent, std::shared_ptr<Schema> schema);
+
+  Status Close() override;
+
+ protected:
+  CastingRecordBatchReader();
+
+  Status Init(std::shared_ptr<RecordBatchReader> parent, std::shared_ptr<Schema> schema);
+
+  std::shared_ptr<RecordBatchReader> parent_;
+  std::shared_ptr<Schema> schema_;
+};
+
 }  // namespace py
 }  // namespace arrow

python/pyarrow/table.pxi

@@ -2742,6 +2742,29 @@ cdef class RecordBatch(_Tabular):
 
         return pyarrow_wrap_batch(c_batch)
 
+    def cast(self, Schema target_schema, safe=None, options=None):
+        """
+        Cast batch values to another schema.
+
+        Parameters
+        ----------
+        target_schema : Schema
+            Schema to cast to, the names and order of fields must match.
+        safe : bool, default True
+            Check for overflows or other unsafe conversions.
+        options : CastOptions, default None
+            Additional checks pass by CastOptions
+
+        Returns
+        -------
+        RecordBatch
+        """
+        # Wrap the more general Table cast implementation
+        tbl = Table.from_batches([self])
+        casted_tbl = tbl.cast(target_schema, safe=safe, options=options)
+        casted_batch, = casted_tbl.to_batches()
+        return casted_batch
+
     def _to_pandas(self, options, **kwargs):
         return Table.from_batches([self])._to_pandas(options, **kwargs)
 

python/pyarrow/tests/test_cffi.py

@@ -633,9 +633,8 @@ def test_roundtrip_reader_capsule(constructor):
 
     obj = constructor(schema, batches)
 
-    # TODO: turn this to ValueError once we implement validation.
     bad_schema = pa.schema({'ints': pa.int32()})
-    with pytest.raises(NotImplementedError):
+    with pytest.raises(pa.lib.ArrowTypeError, match="Field 0 cannot be cast"):
         obj.__arrow_c_stream__(bad_schema.__arrow_c_schema__())
 
     # Can work with matching schema
@@ -647,6 +646,21 @@ def test_roundtrip_reader_capsule(constructor):
         assert batch.equals(expected)
 
 
+def test_roundtrip_batch_reader_capsule_requested_schema():
+    batch = make_batch()
+    requested_schema = pa.schema([('ints', pa.list_(pa.int64()))])
+    requested_capsule = requested_schema.__arrow_c_schema__()
+    batch_as_requested = batch.cast(requested_schema)
+
+    capsule = batch.__arrow_c_stream__(requested_capsule)
+    assert PyCapsule_IsValid(capsule, b"arrow_array_stream") == 1
+    imported_reader = pa.RecordBatchReader._import_from_c_capsule(capsule)
+    assert imported_reader.schema == requested_schema
+    assert imported_reader.read_next_batch().equals(batch_as_requested)
+    with pytest.raises(StopIteration):
+        imported_reader.read_next_batch()
+
+
 def test_roundtrip_batch_reader_capsule():
     batch = make_batch()
 

python/pyarrow/tests/test_ipc.py

@@ -1226,10 +1226,15 @@ def __arrow_c_stream__(self, requested_schema=None):
     reader = pa.RecordBatchReader.from_stream(wrapper, schema=data[0].schema)
     assert reader.read_all() == expected
 
-    # If schema doesn't match, raises NotImplementedError
-    with pytest.raises(NotImplementedError):
+    # Passing a different but castable schema works
+    good_schema = pa.schema([pa.field("a", pa.int32())])
+    reader = pa.RecordBatchReader.from_stream(wrapper, schema=good_schema)
+    assert reader.read_all() == expected.cast(good_schema)
+
+    # If schema doesn't match, raises TypeError
+    with pytest.raises(pa.lib.ArrowTypeError, match='Field 0 cannot be cast'):
         pa.RecordBatchReader.from_stream(
-            wrapper, schema=pa.schema([pa.field('a', pa.int32())])
+            wrapper, schema=pa.schema([pa.field('a', pa.list_(pa.int32()))])
         )
 
     # Proper type errors for wrong input
@@ -1238,3 +1243,60 @@ def __arrow_c_stream__(self, requested_schema=None):
 
     with pytest.raises(TypeError):
         pa.RecordBatchReader.from_stream(expected, schema=data[0])
+
+
+def test_record_batch_reader_cast():
+    schema_src = pa.schema([pa.field('a', pa.int64())])
+    data = [
+        pa.record_batch([pa.array([1, 2, 3], type=pa.int64())], names=['a']),
+        pa.record_batch([pa.array([4, 5, 6], type=pa.int64())], names=['a']),
+    ]
+    table_src = pa.Table.from_batches(data)
+
+    # Cast to same type should always work
+    reader = pa.RecordBatchReader.from_batches(schema_src, data)
+    assert reader.cast(schema_src).read_all() == table_src
+
+    # Check non-trivial cast
+    schema_dst = pa.schema([pa.field('a', pa.int32())])
+    reader = pa.RecordBatchReader.from_batches(schema_src, data)
+    assert reader.cast(schema_dst).read_all() == table_src.cast(schema_dst)
+
+    # Check error for field name/length mismatch
+    reader = pa.RecordBatchReader.from_batches(schema_src, data)
+    with pytest.raises(ValueError, match="Target schema's field names"):
+        reader.cast(pa.schema([]))
+
+    # Check error for impossible cast in call to .cast()
+    reader = pa.RecordBatchReader.from_batches(schema_src, data)
+    with pytest.raises(pa.lib.ArrowTypeError, match='Field 0 cannot be cast'):
+        reader.cast(pa.schema([pa.field('a', pa.list_(pa.int32()))]))
+
+
+def test_record_batch_reader_cast_nulls():
+    schema_src = pa.schema([pa.field('a', pa.int64())])
+    data_with_nulls = [
+        pa.record_batch([pa.array([1, 2, None], type=pa.int64())], names=['a']),
+    ]
+    data_without_nulls = [
+        pa.record_batch([pa.array([1, 2, 3], type=pa.int64())], names=['a']),
+    ]
+    table_with_nulls = pa.Table.from_batches(data_with_nulls)
+    table_without_nulls = pa.Table.from_batches(data_without_nulls)
+
+    # Cast to nullable destination should work
+    reader = pa.RecordBatchReader.from_batches(schema_src, data_with_nulls)
+    schema_dst = pa.schema([pa.field('a', pa.int32())])
+    assert reader.cast(schema_dst).read_all() == table_with_nulls.cast(schema_dst)
+
+    # Cast to non-nullable destination should work if there are no nulls
+    reader = pa.RecordBatchReader.from_batches(schema_src, data_without_nulls)
+    schema_dst = pa.schema([pa.field('a', pa.int32(), nullable=False)])
+    assert reader.cast(schema_dst).read_all() == table_without_nulls.cast(schema_dst)
+
+    # Cast to non-nullable destination should error if there are nulls
+    # when the batch is pulled
+    reader = pa.RecordBatchReader.from_batches(schema_src, data_with_nulls)
+    casted_reader = reader.cast(schema_dst)
+    with pytest.raises(pa.lib.ArrowInvalid, match="Can't cast array"):
+        casted_reader.read_all()

python/pyarrow/tests/test_table.py

@@ -635,9 +635,18 @@ def __arrow_c_stream__(self, requested_schema=None):
     result = pa.table(wrapper, schema=data[0].schema)
     assert result == expected
 
+    # Passing a different schema will cast
+    good_schema = pa.schema([pa.field('a', pa.int32())])
+    result = pa.table(wrapper, schema=good_schema)
+    assert result == expected.cast(good_schema)
+
     # If schema doesn't match, raises NotImplementedError
-    with pytest.raises(NotImplementedError):
-        pa.table(wrapper, schema=pa.schema([pa.field('a', pa.int32())]))
+    with pytest.raises(
+        pa.lib.ArrowTypeError, match="Field 0 cannot be cast"
+    ):
+        pa.table(
+            wrapper, schema=pa.schema([pa.field('a', pa.list_(pa.int32()))])
+        )
 
 
 def test_recordbatch_itercolumns():
@@ -2620,6 +2629,25 @@ def test_record_batch_sort():
     assert sorted_rb_dict["c"] == ["foobar", "bar", "foo", "car"]
 
 
+def test_record_batch_cast():
+    rb = pa.RecordBatch.from_arrays([
+        pa.array([None, 1]),
+        pa.array([False, True])
+    ], names=["a", "b"])
+    new_schema = pa.schema([pa.field("a", "int64", nullable=True),
+                            pa.field("b", "bool", nullable=False)])
+
+    assert rb.cast(new_schema).schema == new_schema
+
+    # Casting a nullable field to non-nullable is invalid
+    rb = pa.RecordBatch.from_arrays([
+        pa.array([None, 1]),
+        pa.array([None, True])
+    ], names=["a", "b"])
+    with pytest.raises(ValueError):
+        rb.cast(new_schema)
+
+
 @pytest.mark.parametrize("constructor", [pa.table, pa.record_batch])
 def test_numpy_asarray(constructor):
     table = constructor([[1, 2, 3], [4.0, 5.0, 6.0]], names=["a", "b"])