fix: do not warn with DefaultIndexWarning in partial ordering mode

bigframes/session/_io/bigquery/read_gbq_table.py

@@ -402,6 +402,7 @@ def get_index_cols(
     | bigframes.enums.DefaultIndexKind,
     *,
     rename_to_schema: Optional[Dict[str, str]] = None,
+    default_index_type: bigframes.enums.DefaultIndexKind = bigframes.enums.DefaultIndexKind.SEQUENTIAL_INT64,
 ) -> List[str]:
     """
     If we can get a total ordering from the table, such as via primary key
@@ -471,7 +472,11 @@ def get_index_cols(
         # find index_cols to use. This is to avoid unexpected performance and
         # resource utilization because of the default sequential index. See
         # internal issue 335727141.
-        if _is_table_clustered_or_partitioned(table) and not primary_keys:
+        if (
+            _is_table_clustered_or_partitioned(table)
+            and not primary_keys
+            and default_index_type == bigframes.enums.DefaultIndexKind.SEQUENTIAL_INT64
+        ):
             msg = bfe.format_message(
                 f"Table '{str(table.reference)}' is clustered and/or "
                 "partitioned, but BigQuery DataFrames was not able to find a "

bigframes/session/loader.py

@@ -696,6 +696,7 @@ def read_gbq_table(
             table=table,
             index_col=index_col,
             rename_to_schema=rename_to_schema,
+            default_index_type=self._default_index_type,
         )
         _check_index_col_param(
             index_cols,

tests/unit/session/test_read_gbq_table.py

@@ -15,10 +15,13 @@
 """Unit tests for read_gbq_table helper functions."""
 
 import unittest.mock as mock
+import warnings
 
 import google.cloud.bigquery
 import pytest
 
+import bigframes.enums
+import bigframes.exceptions
 import bigframes.session._io.bigquery.read_gbq_table as bf_read_gbq_table
 from bigframes.testing import mocks
 
@@ -143,3 +146,43 @@ def test_check_if_index_columns_are_unique(index_cols, values_distinct, expected
     )
 
     assert result == expected
+
+
+def test_get_index_cols_warns_if_clustered_but_sequential_index():
+    table = google.cloud.bigquery.Table.from_api_repr(
+        {
+            "tableReference": {
+                "projectId": "my-project",
+                "datasetId": "my_dataset",
+                "tableId": "my_table",
+            },
+            "clustering": {
+                "fields": ["col1", "col2"],
+            },
+        },
+    )
+    table.schema = (
+        google.cloud.bigquery.SchemaField("col1", "INT64"),
+        google.cloud.bigquery.SchemaField("col2", "INT64"),
+        google.cloud.bigquery.SchemaField("col3", "INT64"),
+        google.cloud.bigquery.SchemaField("col4", "INT64"),
+    )
+
+    with pytest.warns(bigframes.exceptions.DefaultIndexWarning, match="is clustered"):
+        bf_read_gbq_table.get_index_cols(
+            table,
+            index_col=(),
+            default_index_type=bigframes.enums.DefaultIndexKind.SEQUENTIAL_INT64,
+        )
+
+    # Ensure that we don't raise if using a NULL index by default, such as in
+    # partial ordering mode. See: internal issue b/356872356.
+    with warnings.catch_warnings():
+        warnings.simplefilter(
+            "error", category=bigframes.exceptions.DefaultIndexWarning
+        )
+        bf_read_gbq_table.get_index_cols(
+            table,
+            index_col=(),
+            default_index_type=bigframes.enums.DefaultIndexKind.NULL,
+        )