FIX-#2311: fixed performance regression at reduction operations

modin/backends/pandas/query_compiler.py

@@ -171,8 +171,9 @@ def _numeric_only_reduce_fn(applier: Type[Function], *funcs) -> Callable:
     """
 
     def caller(self, *args, **kwargs):
-        # If `numeric_only` is None then we don't know what columns/indices will
-        # be dropped at the result of reduction function, and so can't preserve labels
+        # If `numeric_only` is None and the frame contains non-numeric columns,
+        # then we don't know what columns/indices will be dropped at the result
+        # of reduction function, and so can't preserve labels
         preserve_index = kwargs.get("numeric_only", None) is not None
         return applier.register(*funcs, preserve_index=preserve_index)(
             self, *args, **kwargs

modin/engines/base/frame/data.py

@@ -330,12 +330,14 @@ def _validate_axis_equality(self, axis: int, force: bool = False):
     def _validate_internal_indices(self, mode=None, **kwargs):
         """
         Validates and optionally updates internal and external indices
-        of modin_frame in specified mode. There is 3 modes supported:
-            1. "reduced" - force validates on that axes
-                where external indices is ["__reduced__"]
-            2. "all" - validates indices at all axes, optionally force
+        of modin_frame in specified mode. There is 4 modes supported:
+            1. "reduced" - validates on that axis where external
+                indices is ["__reduced__"] for not force
+            2. "reduced+other" - validates on axis where external
+                indices is ["__reduced__"] for not force, and force on another axis
+            3. "all" - validates indices at all axes, optionally force
                 if `force` parameter specified in kwargs
-            3. "custom" - validation follows arguments specified in kwargs.
+            4. "custom" - validation follows arguments specified in kwargs.
 
         Parameters
         ----------
@@ -353,26 +355,38 @@ def _validate_internal_indices(self, mode=None, **kwargs):
             is_force = kwargs.get("force", False)
 
         reduced_sample = pandas.Index(["__reduced__"])
+
+        is_axis_reduced = [self.axes[i].equals(reduced_sample) for i in [0, 1]]
+
         args_dict = {
             "custom": kwargs,
             "reduced": {
-                "validate_index": self.index.equals(reduced_sample),
-                "validate_columns": self.columns.equals(reduced_sample),
-                "force": False,
+                "validate_index": is_axis_reduced[0],
+                "validate_columns": is_axis_reduced[1],
+                "force": [False, False],
+            },
+            "reduced+other": {
+                "validate_index": True,
+                "validate_columns": True,
+                "force": [not is_axis_reduced[0], not is_axis_reduced[1]],
             },
             "all": {
                 "validate_index": True,
                 "validate_columns": True,
-                "force": is_force,
+                "force": [is_force, is_force],
             },
         }
 
         args = args_dict.get(mode, args_dict["custom"])
 
+        def force_getter(axis):
+            force = args.get("force", False)
+            return force[axis] if isinstance(force, list) else force
+
         if args.get("validate_index", True):
-            self._validate_axis_equality(axis=0, force=args.get("force"))
+            self._validate_axis_equality(axis=0, force=force_getter(0))
         if args.get("validate_columns", True):
-            self._validate_axis_equality(axis=1, force=args.get("force"))
+            self._validate_axis_equality(axis=1, force=force_getter(1))
 
     def _apply_index_objs(self, axis=None):
         """Lazily applies the index object (Index or Columns) to the partitions.
@@ -1026,18 +1040,12 @@ def _compute_map_reduce_metadata(self, axis, new_parts, preserve_index=True):
         new_axes, new_axes_lengths = [0, 0], [0, 0]
 
         new_axes[axis] = ["__reduced__"]
-        new_axes[axis ^ 1] = (
-            self.axes[axis ^ 1]
-            if preserve_index
-            else self._compute_axis_labels(axis ^ 1, new_parts)
-        )
+        new_axes[axis ^ 1] = self.axes[axis ^ 1]
 
         new_axes_lengths[axis] = [1]
-        new_axes_lengths[axis ^ 1] = (
-            self._axes_lengths[axis ^ 1] if preserve_index else None
-        )
+        new_axes_lengths[axis ^ 1] = self._axes_lengths[axis ^ 1]
 
-        if (axis == 0 or self._dtypes is None) and preserve_index:
+        if axis == 0 or self._dtypes is None:
             new_dtypes = self._dtypes
         elif preserve_index:
             new_dtypes = pandas.Series(
@@ -1051,7 +1059,7 @@ def _compute_map_reduce_metadata(self, axis, new_parts, preserve_index=True):
             *new_axes,
             *new_axes_lengths,
             new_dtypes,
-            validate_axes="reduced",
+            validate_axes=("reduced" if preserve_index else "reduced+other"),
         )
 
     def _fold_reduce(self, axis, func, preserve_index=True):

modin/pandas/base.py

@@ -1449,8 +1449,18 @@ def _stat_operation(
 
         """
         axis = self._get_axis_number(axis)
-        if numeric_only is not None and not numeric_only:
-            self._validate_dtypes(numeric_only=True)
+        # If `numeric_only` is None, then we can do this precheck to whether or not
+        # frame contains non-numeric columns, if it doesn't, then we can pass to a backend
+        # `numeric_only=False` parameter and make its work easier in that case, rather than
+        # performing under complicate `numeric_only=None` parameter
+        if not numeric_only:
+            try:
+                self._validate_dtypes(numeric_only=True)
+            except TypeError:
+                if numeric_only is not None:
+                    raise
+            else:
+                numeric_only = False
 
         data = self._get_numeric_data(axis) if numeric_only else self
 

modin/pandas/test/dataframe/test_reduction.py

@@ -358,11 +358,14 @@ def test_sum_single_column(data):
 @pytest.mark.parametrize(
     "fn", ["max", "min", "median", "mean", "skew", "kurt", "sem", "std", "var"]
 )
+@pytest.mark.parametrize("axis", [0, 1])
 @pytest.mark.parametrize(
     "numeric_only", bool_arg_values, ids=arg_keys("numeric_only", bool_arg_keys)
 )
-def test_reduction_specific(fn, numeric_only):
+def test_reduction_specific(fn, numeric_only, axis):
+    if fn == "mean" and axis == 1:
+        pytest.skip("See issue #2313 for details")
     eval_general(
         *create_test_dfs(test_data_diff_dtype),
-        lambda df: getattr(df, fn)(numeric_only=numeric_only),
+        lambda df: getattr(df, fn)(numeric_only=numeric_only, axis=axis),
     )