refactor: Decorate api methods that require total ordering

bigframes/core/__init__.py

@@ -344,10 +344,12 @@ def project_window_op(
         never_skip_nulls: will disable null skipping for operators that would otherwise do so
         skip_reproject_unsafe: skips the reprojection step, can be used when performing many non-dependent window operations, user responsible for not nesting window expressions, or using outputs as join, filter or aggregation keys before a reprojection
         """
-        if not self.session._strictly_ordered:
-            # TODO: Support unbounded windows with aggregate ops and some row-order-independent analytic ops
-            # TODO: Support non-deterministic windowing
-            raise ValueError("Windowed ops not supported in unordered mode")
+        # TODO: Support non-deterministic windowing
+        if window_spec.row_bounded or not op.order_independent:
+            if not self.session._strictly_ordered:
+                raise ValueError(
+                    "Order-dependent windowed ops not supported in unordered mode"
+                )
         return ArrayValue(
             nodes.WindowOpNode(
                 child=self.node,

bigframes/core/groupby/__init__.py

@@ -27,6 +27,7 @@
 import bigframes.core.blocks as blocks
 import bigframes.core.ordering as order
 import bigframes.core.utils as utils
+import bigframes.core.validations as validations
 import bigframes.core.window as windows
 import bigframes.core.window_spec as window_specs
 import bigframes.dataframe as df
@@ -72,6 +73,10 @@ def __init__(
                 if col_id not in self._by_col_ids
             ]
 
+    @property
+    def _session(self) -> core.Session:
+        return self._block.session
+
     def __getitem__(
         self,
         key: typing.Union[
@@ -229,20 +234,25 @@ def count(self) -> df.DataFrame:
     def nunique(self) -> df.DataFrame:
         return self._aggregate_all(agg_ops.nunique_op)
 
+    @validations.requires_strict_ordering()
     def cumsum(self, *args, numeric_only: bool = False, **kwargs) -> df.DataFrame:
         if not numeric_only:
             self._raise_on_non_numeric("cumsum")
         return self._apply_window_op(agg_ops.sum_op, numeric_only=True)
 
+    @validations.requires_strict_ordering()
     def cummin(self, *args, numeric_only: bool = False, **kwargs) -> df.DataFrame:
         return self._apply_window_op(agg_ops.min_op, numeric_only=numeric_only)
 
+    @validations.requires_strict_ordering()
     def cummax(self, *args, numeric_only: bool = False, **kwargs) -> df.DataFrame:
         return self._apply_window_op(agg_ops.max_op, numeric_only=numeric_only)
 
+    @validations.requires_strict_ordering()
     def cumprod(self, *args, **kwargs) -> df.DataFrame:
         return self._apply_window_op(agg_ops.product_op, numeric_only=True)
 
+    @validations.requires_strict_ordering()
     def shift(self, periods=1) -> series.Series:
         window = window_specs.rows(
             grouping_keys=tuple(self._by_col_ids),
@@ -251,6 +261,7 @@ def shift(self, periods=1) -> series.Series:
         )
         return self._apply_window_op(agg_ops.ShiftOp(periods), window=window)
 
+    @validations.requires_strict_ordering()
     def diff(self, periods=1) -> series.Series:
         window = window_specs.rows(
             grouping_keys=tuple(self._by_col_ids),
@@ -259,6 +270,7 @@ def diff(self, periods=1) -> series.Series:
         )
         return self._apply_window_op(agg_ops.DiffOp(periods), window=window)
 
+    @validations.requires_strict_ordering()
     def rolling(self, window: int, min_periods=None) -> windows.Window:
         # To get n size window, need current row and n-1 preceding rows.
         window_spec = window_specs.rows(
@@ -274,6 +286,7 @@ def rolling(self, window: int, min_periods=None) -> windows.Window:
             block, window_spec, self._selected_cols, drop_null_groups=self._dropna
         )
 
+    @validations.requires_strict_ordering()
     def expanding(self, min_periods: int = 1) -> windows.Window:
         window_spec = window_specs.cumulative_rows(
             grouping_keys=tuple(self._by_col_ids),
@@ -514,6 +527,10 @@ def __init__(
         self._value_name = value_name
         self._dropna = dropna  # Applies to aggregations but not windowing
 
+    @property
+    def _session(self) -> core.Session:
+        return self._block.session
+
     def head(self, n: int = 5) -> series.Series:
         block = self._block
         if self._dropna:
@@ -631,26 +648,31 @@ def agg(self, func=None) -> typing.Union[df.DataFrame, series.Series]:
 
     aggregate = agg
 
+    @validations.requires_strict_ordering()
     def cumsum(self, *args, **kwargs) -> series.Series:
         return self._apply_window_op(
             agg_ops.sum_op,
         )
 
+    @validations.requires_strict_ordering()
     def cumprod(self, *args, **kwargs) -> series.Series:
         return self._apply_window_op(
             agg_ops.product_op,
         )
 
+    @validations.requires_strict_ordering()
     def cummax(self, *args, **kwargs) -> series.Series:
         return self._apply_window_op(
             agg_ops.max_op,
         )
 
+    @validations.requires_strict_ordering()
     def cummin(self, *args, **kwargs) -> series.Series:
         return self._apply_window_op(
             agg_ops.min_op,
         )
 
+    @validations.requires_strict_ordering()
     def cumcount(self, *args, **kwargs) -> series.Series:
         return (
             self._apply_window_op(
@@ -660,6 +682,7 @@ def cumcount(self, *args, **kwargs) -> series.Series:
             - 1
         )
 
+    @validations.requires_strict_ordering()
     def shift(self, periods=1) -> series.Series:
         """Shift index by desired number of periods."""
         window = window_specs.rows(
@@ -669,6 +692,7 @@ def shift(self, periods=1) -> series.Series:
         )
         return self._apply_window_op(agg_ops.ShiftOp(periods), window=window)
 
+    @validations.requires_strict_ordering()
     def diff(self, periods=1) -> series.Series:
         window = window_specs.rows(
             grouping_keys=tuple(self._by_col_ids),
@@ -677,6 +701,7 @@ def diff(self, periods=1) -> series.Series:
         )
         return self._apply_window_op(agg_ops.DiffOp(periods), window=window)
 
+    @validations.requires_strict_ordering()
     def rolling(self, window: int, min_periods=None) -> windows.Window:
         # To get n size window, need current row and n-1 preceding rows.
         window_spec = window_specs.rows(
@@ -696,6 +721,7 @@ def rolling(self, window: int, min_periods=None) -> windows.Window:
             is_series=True,
         )
 
+    @validations.requires_strict_ordering()
     def expanding(self, min_periods: int = 1) -> windows.Window:
         window_spec = window_specs.cumulative_rows(
             grouping_keys=tuple(self._by_col_ids),

bigframes/core/indexes/base.py

@@ -30,6 +30,7 @@
 import bigframes.core.expression as ex
 import bigframes.core.ordering as order
 import bigframes.core.utils as utils
+import bigframes.core.validations as validations
 import bigframes.dtypes
 import bigframes.formatting_helpers as formatter
 import bigframes.operations as ops
@@ -114,6 +115,10 @@ def from_frame(
         index._linked_frame = frame
         return index
 
+    @property
+    def _session(self):
+        return self._block.session
+
     @property
     def name(self) -> blocks.Label:
         names = self.names
@@ -179,6 +184,7 @@ def empty(self) -> bool:
         return self.shape[0] == 0
 
     @property
+    @validations.requires_strict_ordering()
     def is_monotonic_increasing(self) -> bool:
         """
         Return a boolean if the values are equal or increasing.
@@ -192,6 +198,7 @@ def is_monotonic_increasing(self) -> bool:
         )
 
     @property
+    @validations.requires_strict_ordering()
     def is_monotonic_decreasing(self) -> bool:
         """
         Return a boolean if the values are equal or decreasing.
@@ -341,6 +348,7 @@ def max(self) -> typing.Any:
     def min(self) -> typing.Any:
         return self._apply_aggregation(agg_ops.min_op)
 
+    @validations.requires_strict_ordering()
     def argmax(self) -> int:
         block, row_nums = self._block.promote_offsets()
         block = block.order_by(
@@ -353,6 +361,7 @@ def argmax(self) -> int:
 
         return typing.cast(int, series.Series(block.select_column(row_nums)).iloc[0])
 
+    @validations.requires_strict_ordering()
     def argmin(self) -> int:
         block, row_nums = self._block.promote_offsets()
         block = block.order_by(
@@ -424,6 +433,8 @@ def dropna(self, how: typing.Literal["all", "any"] = "any") -> Index:
         return Index(result)
 
     def drop_duplicates(self, *, keep: str = "first") -> Index:
+        if keep is not False:
+            validations.enforce_ordered(self, "drop_duplicates")
         block = block_ops.drop_duplicates(self._block, self._block.index_columns, keep)
         return Index(block)
 

bigframes/core/validations.py

@@ -0,0 +1,51 @@
+# Copyright 2023 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""DataFrame is a two dimensional data structure."""
+
+from __future__ import annotations
+
+import functools
+from typing import Protocol, TYPE_CHECKING
+
+import bigframes.constants
+import bigframes.exceptions
+
+if TYPE_CHECKING:
+    from bigframes import Session
+
+
+class HasSession(Protocol):
+    @property
+    def _session(self) -> Session:
+        ...
+
+
+def requires_strict_ordering():
+    def decorator(meth):
+        @functools.wraps(meth)
+        def guarded_meth(object: HasSession, *args, **kwargs):
+            enforce_ordered(object, meth.__name__)
+            return meth(object, *args, **kwargs)
+
+        return guarded_meth
+
+    return decorator
+
+
+def enforce_ordered(object: HasSession, opname: str) -> None:
+    if not object._session._strictly_ordered:
+        raise bigframes.exceptions.OrderRequiredError(
+            f"Op {opname} not supported when strict ordering is disabled. {bigframes.constants.FEEDBACK_LINK}"
+        )

bigframes/core/window_spec.py

@@ -152,3 +152,13 @@ class WindowSpec:
     ordering: Tuple[orderings.OrderingExpression, ...] = tuple()
     bounds: Union[RowsWindowBounds, RangeWindowBounds, None] = None
     min_periods: int = 0
+
+    @property
+    def row_bounded(self):
+        """
+        Whether the window is bounded by row offsets.
+
+        This is relevant for determining whether the window requires a total order
+        to calculate deterministically.
+        """
+        return isinstance(self.bounds, RowsWindowBounds)