Test again PyArrow 17.0.0

pyiceberg/io/pyarrow.py

@@ -1050,11 +1050,6 @@ def _task_to_record_batches(
 
         fragment_scanner = ds.Scanner.from_fragment(
             fragment=fragment,
-            # With PyArrow 16.0.0 there is an issue with casting record-batches:
-            # https://github.com/apache/arrow/issues/41884
-            # https://github.com/apache/arrow/issues/43183
-            # Would be good to remove this later on
-            schema=_pyarrow_schema_ensure_large_types(physical_schema),
             # This will push down the query to Arrow.
             # But in case there are positional deletes, we have to apply them first
             filter=pyarrow_filter if not positional_deletes else None,
@@ -1070,12 +1065,7 @@ def _task_to_record_batches(
                 batch = batch.take(indices)
                 # Apply the user filter
                 if pyarrow_filter is not None:
-                    # we need to switch back and forth between RecordBatch and Table
-                    # as Expression filter isn't yet supported in RecordBatch
-                    # https://github.com/apache/arrow/issues/39220
-                    arrow_table = pa.Table.from_batches([batch])
-                    arrow_table = arrow_table.filter(pyarrow_filter)
-                    batch = arrow_table.to_batches()[0]
+                    batch = batch.filter(pyarrow_filter)
             yield _to_requested_schema(projected_schema, file_project_schema, batch, downcast_ns_timestamp_to_us=True)
             current_index += len(batch)
 

pyiceberg/table/__init__.py

@@ -2020,7 +2020,7 @@ def to_arrow_batch_reader(self) -> pa.RecordBatchReader:
                 case_sensitive=self.case_sensitive,
                 limit=self.limit,
             ),
-        )
+        ).cast(target_schema=target_schema)
 
     def to_pandas(self, **kwargs: Any) -> pd.DataFrame:
         return self.to_arrow().to_pandas(**kwargs)

pyproject.toml

@@ -58,7 +58,7 @@ fsspec = ">=2023.1.0,<2025.1.0"
 pyparsing = ">=3.1.0,<4.0.0"
 zstandard = ">=0.13.0,<1.0.0"
 tenacity = ">=8.2.3,<9.0.0"
-pyarrow = { version = ">=9.0.0,<18.0.0", optional = true }
+pyarrow = { version = ">=17.0.0,<18.0.0", optional = true }
 pandas = { version = ">=1.0.0,<3.0.0", optional = true }
 duckdb = { version = ">=0.5.0,<2.0.0", optional = true }
 ray = { version = ">=2.0.0,<2.10.0", optional = true }

tests/integration/test_add_files.py

@@ -549,7 +549,7 @@ def test_add_files_with_large_and_regular_schema(spark: SparkSession, session_ca
     tbl.add_files([file_path])
 
     table_schema = tbl.scan().to_arrow().schema
-    assert table_schema == arrow_schema_large
+    assert table_schema == arrow_schema
 
     file_path_large = f"s3://warehouse/default/unpartitioned_with_large_types/v{format_version}/test-1.parquet"
     _write_parquet(

tests/integration/test_writes/test_writes.py

@@ -357,7 +357,8 @@ def test_python_writes_dictionary_encoded_column_with_spark_reads(
     tbl.overwrite(arrow_table)
     spark_df = spark.sql(f"SELECT * FROM {identifier}").toPandas()
     pyiceberg_df = tbl.scan().to_pandas()
-    assert spark_df.equals(pyiceberg_df)
+    assert spark_df['id'].equals(pyiceberg_df['id'])
+    assert all(spark_df['name'].values == pyiceberg_df['name'].values)
 
 
 @pytest.mark.integration
@@ -401,12 +402,12 @@ def test_python_writes_with_small_and_large_types_spark_reads(
     assert arrow_table_on_read.schema == pa.schema([
         pa.field("foo", pa.large_string()),
         pa.field("id", pa.int32()),
-        pa.field("name", pa.large_string()),
+        pa.field("name", pa.string()),
         pa.field(
             "address",
             pa.struct([
-                pa.field("street", pa.large_string()),
-                pa.field("city", pa.large_string()),
+                pa.field("street", pa.string()),
+                pa.field("city", pa.string()),
                 pa.field("zip", pa.int32()),
                 pa.field("bar", pa.large_string()),
             ]),