Merge branch 'main' into dependabot/pip/development-dependencies-8400…

…e7d1a7

awswrangler/distributed/ray/datasources/arrow_parquet_datasource.py

@@ -243,7 +243,6 @@ def __init__(  # noqa: PLR0912,PLR0915
                 paths,
                 **dataset_kwargs,
                 filesystem=filesystem,
-                use_legacy_dataset=False,
             )
         except OSError as e:
             _handle_read_os_error(e, paths)

awswrangler/distributed/ray/modin/_data_types.py

@@ -15,7 +15,7 @@ def pyarrow_types_from_pandas_distributed(
 ) -> dict[str, pa.DataType]:
     """Extract the related Pyarrow data types from a pandas DataFrame."""
     func = ray_remote()(pyarrow_types_from_pandas)
-    first_block_object_ref = _ray_dataset_from_df(df).get_internal_block_refs()[0]
+    first_block_object_ref = next(_ray_dataset_from_df(df).iter_internal_ref_bundles()).block_refs[0]
     return ray_get(  # type: ignore[no-any-return]
         func(
             df=first_block_object_ref,

awswrangler/distributed/ray/modin/_utils.py

@@ -51,15 +51,21 @@ def _to_modin(
 
     return from_partitions(
         partitions=[
-            _block_to_df(block=block, to_pandas_kwargs=_to_pandas_kwargs) for block in dataset.get_internal_block_refs()
+            _block_to_df(block=block_ref, to_pandas_kwargs=_to_pandas_kwargs)
+            for ref_bundle in dataset.iter_internal_ref_bundles()
+            for block_ref in ref_bundle.block_refs
         ],
         axis=0,
         index=index,
     )
 
 
 def _split_modin_frame(df: modin_pd.DataFrame, splits: int) -> list[ObjectRef[Any]]:
-    object_refs: list[ObjectRef[Any]] = _ray_dataset_from_df(df).get_internal_block_refs()
+    object_refs: list[ObjectRef[Any]] = [
+        block_ref
+        for ref_bundle in _ray_dataset_from_df(df).iter_internal_ref_bundles()
+        for block_ref in ref_bundle.block_refs
+    ]
     return object_refs
 
 

awswrangler/distributed/ray/modin/s3/_write_dataset.py

@@ -145,7 +145,11 @@ def write_partitions(df: pd.DataFrame, block_index: int) -> tuple[list[str], dic
             )
             return paths, partitions_values
 
-        block_object_refs = _ray_dataset_from_df(df).get_internal_block_refs()
+        block_object_refs = (
+            block_ref
+            for ref_bundle in _ray_dataset_from_df(df).iter_internal_ref_bundles()
+            for block_ref in ref_bundle.block_refs
+        )
         result = ray_get(
             [write_partitions(object_ref, block_index) for block_index, object_ref in enumerate(block_object_refs)]
         )

awswrangler/s3/_select.py

@@ -17,6 +17,7 @@
 from awswrangler import _data_types, _utils, exceptions
 from awswrangler._distributed import engine
 from awswrangler._executor import _BaseExecutor, _get_executor
+from awswrangler.annotations import Deprecated
 from awswrangler.distributed.ray import ray_get
 from awswrangler.s3._describe import size_objects
 from awswrangler.s3._list import _path2list
@@ -148,6 +149,7 @@ def _select_query(
     )
 
 
+@Deprecated
 @_utils.validate_distributed_kwargs(
     unsupported_kwargs=["boto3_session"],
 )

tests/unit/test_athena.py

@@ -135,7 +135,6 @@ def test_athena_ctas(path, path2, path3, glue_table, glue_table2, glue_database,
     assert len(wr.s3.list_objects(path=path3)) == 0
 
 
-@pytest.mark.modin_index
 def test_athena_read_sql_ctas_bucketing(path, path2, glue_table, glue_table2, glue_database, glue_ctas_database):
     df = pd.DataFrame({"c0": [0, 1], "c1": ["foo", "bar"]})
     wr.s3.to_parquet(
@@ -1013,7 +1012,6 @@ def test_bucketing_catalog_parquet_table(path, glue_database, glue_table):
     assert table["StorageDescriptor"]["BucketColumns"] == bucket_cols
 
 
-@pytest.mark.modin_index
 @pytest.mark.parametrize("bucketing_data", [[0, 1, 2], [False, True, False], ["b", "c", "d"]])
 @pytest.mark.parametrize(
     "dtype",
@@ -1102,7 +1100,6 @@ def test_bucketing_catalog_csv_table(path, glue_database, glue_table):
     assert table["StorageDescriptor"]["BucketColumns"] == bucket_cols
 
 
-@pytest.mark.modin_index
 @pytest.mark.parametrize("bucketing_data", [[0, 1, 2], [False, True, False], ["b", "c", "d"]])
 @pytest.mark.parametrize(
     "dtype",
@@ -1168,7 +1165,6 @@ def test_bucketing_csv_dataset(path, glue_database, glue_table, bucketing_data,
         assert all(x in bucketing_data for x in loaded_df["c0"].to_list())
 
 
-@pytest.mark.modin_index
 @pytest.mark.parametrize("bucketing_data", [[0, 1, 2, 3], [False, True, False, True], ["b", "c", "d", "e"]])
 def test_combined_bucketing_partitioning_parquet_dataset(path, glue_database, glue_table, bucketing_data):
     nb_of_buckets = 2
@@ -1296,7 +1292,6 @@ def test_combined_bucketing_partitioning_csv_dataset(path, glue_database, glue_t
         assert all(x in bucketing_data for x in loaded_df["c0"].to_list())
 
 
-@pytest.mark.modin_index
 def test_multiple_bucketing_columns_parquet_dataset(path, glue_database, glue_table):
     nb_of_buckets = 2
     df = pd.DataFrame({"c0": [0, 1, 2, 3], "c1": [4, 6, 5, 7], "c2": ["foo", "bar", "baz", "boo"]})

tests/unit/test_athena_csv.py

@@ -372,7 +372,6 @@ def test_athena_csv_types(path, glue_database, glue_table):
     ensure_data_types_csv(df2)
 
 
-@pytest.mark.modin_index
 @pytest.mark.parametrize("use_threads", [True, False])
 @pytest.mark.parametrize("ctas_approach", [True, False])
 @pytest.mark.parametrize("line_count", [1, 2])

tests/unit/test_athena_parquet.py

@@ -613,7 +613,6 @@ def test_schema_evolution_disabled(path, glue_table, glue_database):
     assert df2.c0.sum() == 3
 
 
-@pytest.mark.modin_index
 def test_date_cast(path, glue_table, glue_database):
     df = pd.DataFrame(
         {

tests/unit/test_s3_parquet.py

@@ -410,7 +410,6 @@ def test_index_recovery_simple_str(path, use_threads):
     assert_pandas_equals(df, df2)
 
 
-@pytest.mark.modin_index
 @pytest.mark.xfail(
     raises=AssertionError,
     reason="https://github.com/ray-project/ray/issues/37771",
@@ -447,7 +446,6 @@ def test_range_index_recovery_simple(path, use_threads):
     assert_pandas_equals(df.reset_index(level=0), df2.reset_index(level=0))
 
 
-@pytest.mark.modin_index
 @pytest.mark.xfail(
     raises=AssertionError,
     reason="https://github.com/ray-project/ray/issues/37771",
@@ -498,7 +496,6 @@ def test_multi_index_recovery_nameless(path, use_threads):
     assert_pandas_equals(df.reset_index(), df2.reset_index())
 
 
-@pytest.mark.modin_index
 @pytest.mark.xfail(
     raises=(wr.exceptions.InvalidArgumentCombination, AssertionError),
     reason="Named index not working when partitioning to a single file",
@@ -535,7 +532,6 @@ def test_index_schema_validation(path, glue_database, glue_table, index):
     assert_pandas_equals(pd.concat([df, df]), df2)
 
 
-@pytest.mark.modin_index
 @pytest.mark.xfail(
     raises=AssertionError,
     reason="https://github.com/ray-project/ray/issues/37771",
@@ -625,7 +621,6 @@ def test_to_parquet_dataset_sanitize(path):
     assert df2.par.to_list() == ["a", "b"]
 
 
-@pytest.mark.modin_index
 @pytest.mark.parametrize("use_threads", [False, True, 2])
 def test_timezone_file(path, use_threads):
     file_path = f"{path}0.parquet"
@@ -636,7 +631,6 @@ def test_timezone_file(path, use_threads):
     assert_pandas_equals(df, df2)
 
 
-@pytest.mark.modin_index
 @pytest.mark.parametrize("use_threads", [True, False, 2])
 def test_timezone_file_columns(path, use_threads):
     file_path = f"{path}0.parquet"
@@ -690,7 +684,6 @@ def test_validate_columns(path, partition_cols) -> None:
         wr.s3.read_parquet(path, columns=["a", "b", "c"], dataset=True, validate_schema=True)
 
 
-@pytest.mark.modin_index
 @pytest.mark.xfail(
     raises=AssertionError,
     reason="https://github.com/ray-project/ray/issues/37771",
@@ -715,7 +708,6 @@ def test_mixed_types_column(path) -> None:
         wr.s3.to_parquet(df, path, dataset=True, partition_cols=["par"])
 
 
-@pytest.mark.modin_index
 @pytest.mark.parametrize("compression", [None, "snappy", "gzip", "zstd"])
 def test_parquet_compression(path, compression) -> None:
     df = pd.DataFrame({"id": [1, 2, 3]}, dtype="Int64")

tests/unit/test_s3_text.py

@@ -188,7 +188,6 @@ def test_csv_dataset_header_modes(path, mode, glue_database, glue_table):
         assert df_res.equals(dfs[-1])
 
 
-@pytest.mark.modin_index
 @pytest.mark.xfail(
     raises=AssertionError,
     reason="https://github.com/ray-project/ray/issues/37771",
@@ -205,7 +204,6 @@ def test_json(path):
     assert df1.equals(wr.s3.read_json(path=[path0, path1], use_threads=True))
 
 
-@pytest.mark.modin_index
 @pytest.mark.xfail(
     raises=AssertionError,
     reason="https://github.com/ray-project/ray/issues/37771",
@@ -366,7 +364,6 @@ def test_csv_line_terminator(path, line_terminator):
     assert df.equals(df2)
 
 
-@pytest.mark.modin_index
 def test_read_json_versioned(path) -> None:
     path_file = f"{path}0.json"
     dfs = [