leftover useage

python/cudf/cudf/core/groupby/groupby.py

@@ -14,6 +14,8 @@
 import numpy as np
 import pandas as pd
 
+import pylibcudf as plc
+
 import cudf
 from cudf import _lib as libcudf
 from cudf._lib import groupby as libgroupby
@@ -25,6 +27,7 @@
 from cudf.api.types import is_list_like, is_numeric_dtype
 from cudf.core._compat import PANDAS_LT_300
 from cudf.core.abc import Serializable
+from cudf.core.buffer import acquire_spill_lock
 from cudf.core.column.column import ColumnBase, StructDtype, as_column
 from cudf.core.column_accessor import ColumnAccessor
 from cudf.core.copy_types import GatherMap
@@ -770,9 +773,22 @@ def agg(self, func=None, *args, engine=None, engine_kwargs=None, **kwargs):
                 join_keys = map(list, zip(*join_keys))
                 # By construction, left and right keys are related by
                 # a permutation, so we can use an inner join.
-                left_order, right_order = libcudf.join.join(
-                    *join_keys, how="inner"
-                )
+                with acquire_spill_lock():
+                    plc_tables = [
+                        plc.Table(
+                            [col.to_pylibcudf(mode="read") for col in cols]
+                        )
+                        for cols in join_keys
+                    ]
+                    left_plc, right_plc = plc.join.inner_join(
+                        plc_tables[0],
+                        plc_tables[1],
+                        plc.types.NullEquality.EQUAL,
+                    )
+                    left_order = libcudf.column.Column.from_pylibcudf(left_plc)
+                    right_order = libcudf.column.Column.from_pylibcudf(
+                        right_plc
+                    )
                 # left order is some permutation of the ordering we
                 # want, and right order is a matching gather map for
                 # the result table. Get the correct order by sorting

python/cudf/cudf/core/index.py

@@ -16,6 +16,8 @@
 import pyarrow as pa
 from typing_extensions import Self
 
+import pylibcudf as plc
+
 import cudf
 from cudf import _lib as libcudf
 from cudf._lib.filling import sequence
@@ -32,6 +34,7 @@
 )
 from cudf.core._base_index import BaseIndex, _return_get_indexer_result
 from cudf.core._compat import PANDAS_LT_300
+from cudf.core.buffer import acquire_spill_lock
 from cudf.core.column import (
     CategoricalColumn,
     ColumnBase,
@@ -1360,7 +1363,14 @@ def get_indexer(self, target, method=None, limit=None, tolerance=None):
         except ValueError:
             return _return_get_indexer_result(result.values)
 
-        scatter_map, indices = libcudf.join.join([lcol], [rcol], how="inner")
+        with acquire_spill_lock():
+            left_plc, right_plc = plc.join.inner_join(
+                plc.Table([lcol.to_pylibcudf(mode="read")]),
+                plc.Table([rcol.to_pylibcudf(mode="read")]),
+                plc.types.NullEquality.EQUAL,
+            )
+            scatter_map = libcudf.column.Column.from_pylibcudf(left_plc)
+            indices = libcudf.column.Column.from_pylibcudf(right_plc)
         result = libcudf.copying.scatter([indices], scatter_map, [result])[0]
         result_series = cudf.Series._from_column(result)
 

python/cudf/cudf/core/multiindex.py

@@ -14,6 +14,8 @@
 import numpy as np
 import pandas as pd
 
+import pylibcudf as plc
+
 import cudf
 import cudf._lib as libcudf
 from cudf._lib.types import size_type_dtype
@@ -22,6 +24,7 @@
 from cudf.core import column
 from cudf.core._base_index import _return_get_indexer_result
 from cudf.core.algorithms import factorize
+from cudf.core.buffer import acquire_spill_lock
 from cudf.core.column_accessor import ColumnAccessor
 from cudf.core.frame import Frame
 from cudf.core.index import (
@@ -1919,10 +1922,18 @@ def get_indexer(self, target, method=None, limit=None, tolerance=None):
             for lcol, rcol in zip(target._columns, self._columns)
         ]
         join_keys = map(list, zip(*join_keys))
-        scatter_map, indices = libcudf.join.join(
-            *join_keys,
-            how="inner",
-        )
+        with acquire_spill_lock():
+            plc_tables = [
+                plc.Table([col.to_pylibcudf(mode="read") for col in cols])
+                for cols in join_keys
+            ]
+            left_plc, right_plc = plc.join.inner_join(
+                plc_tables[0],
+                plc_tables[1],
+                plc.types.NullEquality.EQUAL,
+            )
+            scatter_map = libcudf.column.Column.from_pylibcudf(left_plc)
+            indices = libcudf.column.Column.from_pylibcudf(right_plc)
         result = libcudf.copying.scatter([indices], scatter_map, [result])[0]
         result_series = cudf.Series._from_column(result)