perf: Optimize dataframe-series alignment on axis=1

bigframes/core/blocks.py

@@ -155,7 +155,13 @@ def __init__(
         self._transpose_cache: Optional[Block] = transpose_cache
 
     @classmethod
-    def from_local(cls, data: pd.DataFrame, session: bigframes.Session) -> Block:
+    def from_local(
+        cls,
+        data: pd.DataFrame,
+        session: bigframes.Session,
+        *,
+        cache_transpose: bool = True,
+    ) -> Block:
         # Assumes caller has already converted datatypes to bigframes ones.
         pd_data = data
         column_labels = pd_data.columns
@@ -169,12 +175,21 @@ def from_local(cls, data: pd.DataFrame, session: bigframes.Session) -> Block:
         pd_data = pd_data.reset_index(names=index_ids)
         as_pyarrow = pa.Table.from_pandas(pd_data, preserve_index=False)
         array_value = core.ArrayValue.from_pyarrow(as_pyarrow, session=session)
-        return cls(
+        block = cls(
             array_value,
             column_labels=column_labels,
             index_columns=index_ids,
             index_labels=index_labels,
         )
+        if cache_transpose:
+            try:
+                # this cache will help when aligning on axis=1
+                block = block.with_transpose_cache(
+                    cls.from_local(data.T, session, cache_transpose=False)
+                )
+            except Exception:
+                pass
+        return block
 
     @property
     def index(self) -> BlockIndexProperties:
@@ -724,12 +739,18 @@ def with_column_labels(
                 f"The column labels size `{len(label_list)} ` should equal to the value"
                 + f"columns size: {len(self.value_columns)}."
             )
-        return Block(
+        block = Block(
             self._expr,
             index_columns=self.index_columns,
             column_labels=label_list,
             index_labels=self.index.names,
         )
+        singleton_label = len(list(value)) == 1 and list(value)[0]
+        if singleton_label is not None and self._transpose_cache is not None:
+            new_cache, label_id = self._transpose_cache.create_constant(singleton_label)
+            new_cache = new_cache.set_index([label_id])
+            block = block.with_transpose_cache(new_cache)
+        return block
 
     def with_transpose_cache(self, transposed: Block):
         return Block(
@@ -1935,6 +1956,153 @@ def merge(
         expr = joined_expr.promote_offsets(offset_index_id)
         return Block(expr, index_columns=[offset_index_id], column_labels=labels)
 
+    def _align_both_axes(
+        self, other: Block, how: str
+    ) -> Tuple[Block, pd.Index, Sequence[Tuple[ex.Expression, ex.Expression]]]:
+        # Join rows
+        aligned_block, (get_column_left, get_column_right) = self.join(other, how=how)
+        # join columns schema
+        # indexers will be none for exact match
+        if self.column_labels.equals(other.column_labels):
+            columns, lcol_indexer, rcol_indexer = self.column_labels, None, None
+        else:
+            columns, lcol_indexer, rcol_indexer = self.column_labels.join(
+                other.column_labels, how="outer", return_indexers=True
+            )
+        lcol_indexer = (
+            lcol_indexer if (lcol_indexer is not None) else range(len(columns))
+        )
+        rcol_indexer = (
+            rcol_indexer if (rcol_indexer is not None) else range(len(columns))
+        )
+
+        left_input_lookup = (
+            lambda index: ex.free_var(get_column_left[self.value_columns[index]])
+            if index != -1
+            else ex.const(None)
+        )
+        righ_input_lookup = (
+            lambda index: ex.free_var(get_column_right[other.value_columns[index]])
+            if index != -1
+            else ex.const(None)
+        )
+
+        left_inputs = [left_input_lookup(i) for i in lcol_indexer]
+        right_inputs = [righ_input_lookup(i) for i in rcol_indexer]
+        return aligned_block, columns, tuple(zip(left_inputs, right_inputs))
+
+    def _align_axis_0(
+        self, other: Block, how: str
+    ) -> Tuple[Block, pd.Index, Sequence[Tuple[ex.Expression, ex.Expression]]]:
+        assert len(other.value_columns) == 1
+        aligned_block, (get_column_left, get_column_right) = self.join(other, how=how)
+
+        series_column_id = other.value_columns[0]
+        inputs = tuple(
+            (
+                ex.free_var(get_column_left[col]),
+                ex.free_var(get_column_right[series_column_id]),
+            )
+            for col in self.value_columns
+        )
+        return aligned_block, self.column_labels, inputs
+
+    def _align_series_block_axis_1(
+        self, other: Block, how: str
+    ) -> Tuple[Block, pd.Index, Sequence[Tuple[ex.Expression, ex.Expression]]]:
+        assert len(other.value_columns) == 1
+        if other._transpose_cache is None:
+            raise ValueError(
+                "Wrong align method, this approach requires transpose cache"
+            )
+
+        # Join rows
+        aligned_block, (get_column_left, get_column_right) = join_with_single_row(
+            self, other.transpose()
+        )
+        # join columns schema
+        # indexers will be none for exact match
+        if self.column_labels.equals(other.transpose().column_labels):
+            columns, lcol_indexer, rcol_indexer = self.column_labels, None, None
+        else:
+            columns, lcol_indexer, rcol_indexer = self.column_labels.join(
+                other.transpose().column_labels, how=how, return_indexers=True
+            )
+        lcol_indexer = (
+            lcol_indexer if (lcol_indexer is not None) else range(len(columns))
+        )
+        rcol_indexer = (
+            rcol_indexer if (rcol_indexer is not None) else range(len(columns))
+        )
+
+        left_input_lookup = (
+            lambda index: ex.free_var(get_column_left[self.value_columns[index]])
+            if index != -1
+            else ex.const(None)
+        )
+        righ_input_lookup = (
+            lambda index: ex.free_var(
+                get_column_right[other.transpose().value_columns[index]]
+            )
+            if index != -1
+            else ex.const(None)
+        )
+
+        left_inputs = [left_input_lookup(i) for i in lcol_indexer]
+        right_inputs = [righ_input_lookup(i) for i in rcol_indexer]
+        return aligned_block, columns, tuple(zip(left_inputs, right_inputs))
+
+    def _align_pd_series_axis_1(
+        self, other: pd.Series, how: str
+    ) -> Tuple[Block, pd.Index, Sequence[Tuple[ex.Expression, ex.Expression]]]:
+        if self.column_labels.equals(other.index):
+            columns, lcol_indexer, rcol_indexer = self.column_labels, None, None
+        else:
+            if not (self.column_labels.is_unique and other.index.is_unique):
+                raise ValueError("Cannot align non-unique indices")
+            columns, lcol_indexer, rcol_indexer = self.column_labels.join(
+                other.index, how=how, return_indexers=True
+            )
+        lcol_indexer = (
+            lcol_indexer if (lcol_indexer is not None) else range(len(columns))
+        )
+        rcol_indexer = (
+            rcol_indexer if (rcol_indexer is not None) else range(len(columns))
+        )
+
+        left_input_lookup = (
+            lambda index: ex.free_var(self.value_columns[index])
+            if index != -1
+            else ex.const(None)
+        )
+        righ_input_lookup = (
+            lambda index: ex.const(other.iloc[index]) if index != -1 else ex.const(None)
+        )
+
+        left_inputs = [left_input_lookup(i) for i in lcol_indexer]
+        right_inputs = [righ_input_lookup(i) for i in rcol_indexer]
+        return self, columns, tuple(zip(left_inputs, right_inputs))
+
+    def _apply_binop(
+        self,
+        op: ops.BinaryOp,
+        inputs: Sequence[Tuple[ex.Expression, ex.Expression]],
+        labels: pd.Index,
+        reverse: bool = False,
+    ) -> Block:
+        block = self
+        binop_result_ids = []
+        for left_input, right_input in inputs:
+            expr = (
+                op.as_expr(right_input, left_input)
+                if reverse
+                else op.as_expr(left_input, right_input)
+            )
+            block, result_col_id = block.project_expr(expr)
+            binop_result_ids.append(result_col_id)
+
+        return block.select_columns(binop_result_ids).with_column_labels(labels)
+
     def join(
         self,
         other: Block,
@@ -2256,15 +2424,6 @@ def column_ids(self) -> Sequence[str]:
         """Column(s) to use as row labels."""
         return self._block._index_columns
 
-    def __repr__(self) -> str:
-        """Converts an Index to a string."""
-        # TODO(swast): Add a timeout here? If the query is taking a long time,
-        # maybe we just print the job metadata that we have so far?
-        # TODO(swast): Avoid downloading the whole index by using job
-        # metadata, like we do with DataFrame.
-        preview = self.to_pandas()
-        return repr(preview)
-
     def to_pandas(self) -> pd.Index:
         """Executes deferred operations and downloads the results."""
         if len(self.column_ids) == 0:
@@ -2359,6 +2518,61 @@ def join_indexless(
     )
 
 
+def join_with_single_row(
+    left: Block,
+    single_row_block: Block,
+) -> Tuple[Block, Tuple[Mapping[str, str], Mapping[str, str]],]:
+    """
+    Special join case where other is a single row block.
+    This property is not validated, caller responsible for not passing multi-row block.
+    Preserves index of the left block, ignoring label of other.
+    """
+    left_expr = left.expr
+    # ignore index columns by dropping them
+    right_expr = single_row_block.expr.select_columns(single_row_block.value_columns)
+    left_mappings = [
+        join_defs.JoinColumnMapping(
+            source_table=join_defs.JoinSide.LEFT,
+            source_id=id,
+            destination_id=guid.generate_guid(),
+        )
+        for id in left_expr.column_ids
+    ]
+    right_mappings = [
+        join_defs.JoinColumnMapping(
+            source_table=join_defs.JoinSide.RIGHT,
+            source_id=id,
+            destination_id=guid.generate_guid(),
+        )
+        for id in right_expr.column_ids  # skip index column
+    ]
+
+    join_def = join_defs.JoinDefinition(
+        conditions=(),
+        mappings=(*left_mappings, *right_mappings),
+        type="cross",
+    )
+    combined_expr = left_expr.join(
+        right_expr,
+        join_def=join_def,
+    )
+    get_column_left = join_def.get_left_mapping()
+    get_column_right = join_def.get_right_mapping()
+    # Drop original indices from each side. and used the coalesced combination generated by the join.
+    index_cols_post_join = [get_column_left[id] for id in left.index_columns]
+
+    block = Block(
+        combined_expr,
+        index_columns=index_cols_post_join,
+        column_labels=left.column_labels.append(single_row_block.column_labels),
+        index_labels=[left.index.name],
+    )
+    return (
+        block,
+        (get_column_left, get_column_right),
+    )
+
+
 def join_mono_indexed(
     left: Block,
     right: Block,
@@ -2546,7 +2760,7 @@ def coalesce_columns(
 ) -> Tuple[core.ArrayValue, Sequence[str]]:
     result_ids = []
     for left_id, right_id in zip(left_ids, right_ids):
-        if how == "left" or how == "inner":
+        if how == "left" or how == "inner" or how == "cross":
             result_ids.append(left_id)
             expr = expr.drop_columns([right_id])
         elif how == "right":

bigframes/core/groupby/__init__.py

@@ -560,7 +560,7 @@ def size(self) -> series.Series:
             by_column_ids=self._by_col_ids,
             dropna=self._dropna,
         )
-        return series.Series(agg_block, name=self._value_name)
+        return series.Series(agg_block.with_column_labels([self._value_name]))
 
     def skew(self, *args, **kwargs) -> series.Series:
         block = block_ops.skew(self._block, [self._value_column], self._by_col_ids)