feat: support UUIDs to pyarrow on more backends

ibis/backends/flink/__init__.py

@@ -24,6 +24,7 @@
 from ibis.backends.sql import SQLBackend
 from ibis.backends.tests.errors import Py4JJavaError
 from ibis.expr.operations.udf import InputType
+from ibis.formats.pyarrow import PyArrowData
 from ibis.util import gen_name
 
 if TYPE_CHECKING:
@@ -940,7 +941,6 @@
         limit: int | str | None = None,
         **kwargs: Any,
     ):
-        import pyarrow as pa
         import pyarrow_hotfix  # noqa: F401
 
         ibis_table = expr.as_table()
@@ -963,9 +963,7 @@
         # TODO (mehmet): `limit` is discarded in `execute()`. Is this intentional?
         df = df.head(limit)
 
-        ibis_schema = ibis_table.schema()
-        arrow_schema = ibis_schema.to_pyarrow()
-        arrow_table = pa.Table.from_pandas(df, schema=arrow_schema)
+        arrow_table = PyArrowData.convert_table(df, ibis_table.schema())
         return arrow_table.to_reader()
 
     def _from_ibis_table_to_pyflink_table(self, table: ir.Table) -> Table | None:

ibis/backends/impala/__init__.py

@@ -1144,20 +1144,16 @@ def to_pyarrow(
         limit: int | str | None = None,
         **kwargs: Any,
     ) -> pa.Table:
-        import pyarrow as pa
         import pyarrow_hotfix  # noqa: F401
 
         from ibis.formats.pyarrow import PyArrowData
 
         self._run_pre_execute_hooks(expr)
 
         table_expr = expr.as_table()
-        output = pa.Table.from_pandas(
-            self.execute(table_expr, params=params, limit=limit, **kwargs),
-            preserve_index=False,
-        )
-        table = PyArrowData.convert_table(output, table_expr.schema())
-        return expr.__pyarrow_result__(table)
+        df = self.execute(table_expr, params=params, limit=limit, **kwargs)
+        pa_table = PyArrowData.convert_table(df, table_expr.schema())
+        return expr.__pyarrow_result__(pa_table)
 
     def to_pyarrow_batches(
         self,

ibis/backends/mysql/__init__.py

@@ -26,6 +26,7 @@
 from ibis.backends.mysql.compiler import MySQLCompiler
 from ibis.backends.sql import SQLBackend
 from ibis.backends.sql.compiler import TRUE, C
+from ibis.formats.pyarrow import PyArrowData
 
 if TYPE_CHECKING:
     from collections.abc import Mapping
@@ -509,19 +510,14 @@ def to_pyarrow_batches(
         chunk_size: int = 1_000_000,
         **_: Any,
     ) -> pa.ipc.RecordBatchReader:
-        import pyarrow as pa
-
         self._run_pre_execute_hooks(expr)
-
         schema = expr.as_table().schema()
         with self._safe_raw_sql(
             self.compile(expr, limit=limit, params=params)
         ) as cursor:
             df = self._fetch_from_cursor(cursor, schema)
-        table = pa.Table.from_pandas(
-            df, schema=schema.to_pyarrow(), preserve_index=False
-        )
-        return table.to_reader(max_chunksize=chunk_size)
+        pa_table = PyArrowData.convert_table(df, schema)
+        return pa_table.to_reader(max_chunksize=chunk_size)
 
     def _fetch_from_cursor(self, cursor, schema: sch.Schema) -> pd.DataFrame:
         import pandas as pd

ibis/backends/sqlite/__init__.py

@@ -21,6 +21,7 @@
 from ibis.backends.sqlite.compiler import SQLiteCompiler
 from ibis.backends.sqlite.converter import SQLitePandasData
 from ibis.backends.sqlite.udf import ignore_nulls, register_all
+from ibis.formats.pyarrow import PyArrowData
 
 if TYPE_CHECKING:
     from collections.abc import Iterator, Mapping
@@ -269,19 +270,15 @@ def to_pyarrow_batches(
         chunk_size: int = 1_000_000,
         **_: Any,
     ) -> pa.ipc.RecordBatchReader:
-        import pyarrow as pa
-
         self._run_pre_execute_hooks(expr)
 
         schema = expr.as_table().schema()
         with self._safe_raw_sql(
             self.compile(expr, limit=limit, params=params)
         ) as cursor:
             df = self._fetch_from_cursor(cursor, schema)
-        table = pa.Table.from_pandas(
-            df, schema=schema.to_pyarrow(), preserve_index=False
-        )
-        return table.to_reader(max_chunksize=chunk_size)
+        pa_table = PyArrowData.convert_table(df, schema)
+        return pa_table.to_reader(max_chunksize=chunk_size)
 
     def _generate_create_table(self, table: sge.Table, schema: sch.Schema):
         column_defs = [

ibis/backends/tests/test_client.py

@@ -1556,10 +1556,15 @@ def test_close_connection(con):
     reason="JSON type not implemented",
 )
 @pytest.mark.notimpl(
-    ["risingwave", "sqlite"],
+    ["risingwave"],
     raises=pa.ArrowTypeError,
     reason="mismatch between output value and expected input type",
 )
+@pytest.mark.notimpl(
+    ["sqlite"],
+    raises=pa.ArrowInvalid,
+    reason="cannot mix list and non-list, non-null values",
+)
 @pytest.mark.never(
     ["snowflake"],
     raises=TypeError,

ibis/backends/tests/test_uuid.py

@@ -3,6 +3,7 @@
 import contextlib
 import uuid
 
+import pyarrow as pa
 import pytest
 
 import ibis
@@ -63,3 +64,17 @@ def test_uuid_unique_each_row(con):
         con.tables.functional_alltypes.mutate(uuid=ibis.uuid()).limit(2).uuid.nunique()
     )
     assert expr.execute() == 2
+
+
+@pytest.mark.notimpl(
+    ["druid", "exasol", "oracle", "polars", "pyspark", "risingwave"],
+    raises=com.OperationNotDefinedError,
+)
+@pytest.mark.notimpl(
+    ["clickhouse", "postgres", "trino"],
+    reason="Expected bytes, got a 'UUID' object. https://github.com/ibis-project/ibis/issues/8902",
+    raises=pa.lib.ArrowTypeError,
+)
+@pytest.mark.notimpl(["pandas", "dask"], raises=com.OperationNotDefinedError)
+def test_uuid_to_pyarrow(con):
+    con.to_pyarrow(ibis.uuid())

ibis/formats/pyarrow.py

@@ -2,6 +2,7 @@
 
 from typing import TYPE_CHECKING, Any
 
+import pandas as pd
 import pyarrow as pa
 import pyarrow_hotfix  # noqa: F401
 
@@ -243,15 +244,30 @@ def convert_scalar(cls, scalar: pa.Scalar, dtype: dt.DataType) -> pa.Scalar:
             return scalar
 
     @classmethod
-    def convert_column(cls, column: pa.Array, dtype: dt.DataType) -> pa.Array:
+    def convert_column(
+        cls, column: pa.Array | pd.Series, dtype: dt.DataType
+    ) -> pa.Array:
+        if isinstance(column, pd.Series):
+            if dtype.is_uuid():
+                # pyarrow doesn't support UUIDs, so we need to convert them to strings
+                column = column.astype(str)
+            column = pa.Array.from_pandas(column)
         desired_type = PyArrowType.from_ibis(dtype)
         if column.type != desired_type:
             return column.cast(desired_type)
         else:
             return column
 
     @classmethod
-    def convert_table(cls, table: pa.Table, schema: Schema) -> pa.Table:
+    def convert_table(cls, table: pa.Table | pd.DataFrame, schema: Schema) -> pa.Table:
+        if isinstance(table, pd.DataFrame):
+            table = pa.Table.from_arrays(
+                [
+                    cls.convert_column(table[col], dtype)
+                    for col, dtype in schema.items()
+                ],
+                names=schema.names,
+            )
         desired_schema = PyArrowSchema.from_ibis(schema)
         pa_schema = table.schema