FEAT-modin-project#6398: Improved performance of list-like objects in…

…sertion into DataFrames

Wrap list-like object into a single-column frame before the insertion.
In case of the HDK backend: if the partition contains either pandas DataFrame or
pyarrow Table, insert the object directly into the frame/table, otherwise create
a single-column frame and join the frames by rowid.

Signed-off-by: Andrey Pavlenko <andrey.a.pavlenko@gmail.com>

modin/core/storage_formats/pandas/query_compiler.py

@@ -2751,6 +2751,7 @@ def getitem_row_array(self, key):
         )
 
     def setitem(self, axis, key, value):
+        value = self._wrap_column_data(value)
         return self._setitem(axis=axis, key=key, value=value, how=None)
 
     def _setitem(self, axis, key, value, how="inner"):
@@ -2986,6 +2987,7 @@ def _compute_duplicated(df):  # pragma: no cover
     # return a new one from here and let the front end handle the inplace
     # update.
     def insert(self, loc, column, value):
+        value = self._wrap_column_data(value)
         if isinstance(value, type(self)):
             value.columns = [column]
             return self.insert_item(axis=1, loc=loc, value=value, how=None)
@@ -3018,6 +3020,25 @@ def insert(df, internal_indices=[]):  # pragma: no cover
         )
         return self.__constructor__(new_modin_frame)
 
+    def _wrap_column_data(self, data):
+        """
+        If the data is list-like, create a single column query compiler.
+
+        Parameters
+        ----------
+        data : any
+
+        Returns
+        -------
+        data or PandasQueryCompiler
+        """
+        if is_list_like(data):
+            return self.from_pandas(
+                pandas.DataFrame(pandas.Series(data, index=self.index)),
+                data_cls=type(self._modin_frame),
+            )
+        return data
+
     # END Insert
 
     def explode(self, column):

modin/experimental/core/execution/native/implementations/hdk_on_native/dataframe/dataframe.py

@@ -51,6 +51,7 @@
     check_cols_to_join,
     get_data_for_join_by_index,
     build_categorical_from_at,
+    ensure_supported_dtype,
 )
 from ..db_worker import DbTable
 from ..partitioning.partition_manager import HdkOnNativeDataframePartitionManager
@@ -201,6 +202,9 @@ def __init__(
         self.id = str(type(self)._next_id[0])
         type(self)._next_id[0] += 1
 
+        if op is None and partitions is not None:
+            op = FrameNode(self)
+
         self._op = op
         self._index_cols = index_cols
         self._partitions = partitions
@@ -484,9 +488,7 @@ def _has_arrow_table(self):
         -------
         bool
         """
-        return self._partitions is not None and isinstance(
-            self._partitions[0][0].get(), pyarrow.Table
-        )
+        return self._partitions is not None and self._partitions[0][0].raw
 
     def _dtypes_for_exprs(self, exprs):
         """
@@ -1424,12 +1426,7 @@ def _join_arrow_columns(self, other_modin_frames):
             and isinstance(f._execute(), (DbTable, pyarrow.Table))
             for f in frames
         ):
-            tables = [
-                t
-                if isinstance(t := f._partitions[0][0].get(), pyarrow.Table)
-                else t.to_arrow()
-                for f in frames
-            ]
+            tables = [f._partitions[0][0].get(True) for f in frames]
             column_names = [c for t in tables for c in t.column_names]
             if len(column_names) != len(set(column_names)):
                 raise NotImplementedError("Duplicate column names")
@@ -1637,6 +1634,13 @@ def insert(self, loc, column, value):
         assert column not in self._table_cols
         assert 0 <= loc <= len(self.columns)
 
+        if is_list_like(value):
+            if isinstance(value, pd.Series) and not self.index.equals(value.index):
+                # Align by index
+                value = pd.Series(value, index=self.index)
+                value.reset_index(drop=True, inplace=True)
+            return self._insert_list(loc, column, value)
+
         exprs = self._index_exprs()
         for i in range(0, loc):
             col = self.columns[i]
@@ -1657,6 +1661,159 @@ def insert(self, loc, column, value):
             force_execution_mode=self._force_execution_mode,
         )
 
+    def _insert_list(self, loc, name, value):
+        """
+        Insert a list-like value.
+
+        Parameters
+        ----------
+        loc : int
+        name : str
+        value : list
+
+        Returns
+        -------
+        HdkOnNativeDataframe
+        """
+        ncols = len(self.columns)
+
+        if loc == -1:
+            loc = ncols
+
+        if ncols == 0:
+            assert loc == 0
+            return self._list_to_df(name, value, True)
+
+        if self._partitions and self._partitions[0][0].raw:
+            return self._insert_list_col(loc, name, value)
+
+        if loc == 0 or loc == ncols:
+            in_idx = 0 if loc == 0 else 1
+            if (
+                isinstance(self._op, JoinNode)
+                and self._op.by_rowid
+                and self._op.input[in_idx]._partitions
+                and self._op.input[in_idx]._partitions[0][0].raw
+            ):
+                lhs = self._op.input[0]
+                rhs = self._op.input[1]
+                if loc == 0:
+                    lhs = lhs._insert_list(0, name, value)
+                    dtype = lhs.dtypes[0]
+                else:
+                    rhs = rhs._insert_list(-1, name, value)
+                    dtype = rhs.dtypes[-1]
+            elif loc == 0:
+                lhs = self._list_to_df(name, value, False)
+                rhs = self
+                dtype = lhs.dtypes[0]
+            else:
+                lhs = self
+                rhs = self._list_to_df(name, value, False)
+                dtype = rhs.dtypes[0]
+        elif isinstance(self._op, JoinNode) and self._op.by_rowid:
+            left_len = len(self._op.input[0].columns)
+            if loc < left_len:
+                lhs = self._op.input[0]._insert_list(loc, name, value)
+                rhs = self._op.input[1]
+                dtype = lhs.dtypes[loc]
+            else:
+                lhs = self._op.input[0]
+                rhs = self._op.input[1]._insert_list(loc - left_len, name, value)
+                dtype = rhs.dtypes[loc]
+        else:
+            lexprs = self._index_exprs()
+            rexprs = OrderedDict()
+            for i, col in enumerate(self.columns):
+                (lexprs if i < loc else rexprs)[col] = self.ref(col)
+            lhs = self.__constructor__(
+                columns=self.columns[0:loc],
+                dtypes=self._dtypes_for_exprs(lexprs),
+                op=TransformNode(self, lexprs),
+                index=self._index_cache,
+                index_cols=self._index_cols,
+                force_execution_mode=self._force_execution_mode,
+            )._insert_list(loc, name, value)
+            rhs = self.__constructor__(
+                columns=self.columns[loc:],
+                dtypes=self._dtypes_for_exprs(rexprs),
+                op=TransformNode(self, rexprs),
+                force_execution_mode=self._force_execution_mode,
+            )
+            dtype = lhs.dtypes[loc]
+
+        op = self._join_by_rowid_op(lhs, rhs)
+        return self._insert_list_col(loc, name, value, dtype, op)
+
+    def _insert_list_col(self, idx, name, value, dtype=None, op=None):
+        """
+        Insert a list-like column.
+
+        Parameters
+        ----------
+        idx : int
+        name : str
+        value : list
+        dtype : dtype, default: None
+        op : DFAlgNode, default: None
+
+        Returns
+        -------
+        HdkOnNativeDataframe
+        """
+        cols = self.columns.tolist()
+        cols.insert(idx, name)
+        if self._index_cols:
+            idx += len(self._index_cols)
+        if dtype is None:
+            part, dtype = self._partitions[0][0].insert(idx, name, value)
+            part = np.array([[part]])
+        else:
+            part = None
+        dtypes = self._dtypes.tolist()
+        dtypes.insert(idx, dtype)
+        return self.copy(partitions=part, columns=cols, dtypes=dtypes, op=op)
+
+    def _list_to_df(self, name, value, add_index):
+        """
+        Create a single-column frame from the list-like value.
+
+        Parameters
+        ----------
+        name : str
+        value : list
+        add_index : bool
+
+        Returns
+        -------
+        HdkOnNativeDataframe
+        """
+        df = pd.DataFrame({name: value}, index=self.index if add_index else None)
+        ensure_supported_dtype(df.dtypes[0])
+        return self.from_pandas(df)
+
+    @staticmethod
+    def _join_by_rowid_op(lhs, rhs):
+        """
+        Create a JoinNode for join by rowid.
+
+        Parameters
+        ----------
+        lhs : HdkOnNativeDataframe
+        rhs : HdkOnNativeDataframe
+
+        Returns
+        -------
+        JoinNode
+        """
+        exprs = lhs._index_exprs() if lhs._index_cols else rhs._index_exprs()
+        exprs.update((c, lhs.ref(c)) for c in lhs.columns)
+        exprs.update((c, rhs.ref(c)) for c in rhs.columns)
+        condition = lhs._build_equi_join_condition(
+            rhs, [ROWID_COL_NAME], [ROWID_COL_NAME]
+        )
+        return JoinNode(lhs, rhs, exprs=exprs, condition=condition)
+
     def cat_codes(self):
         """
         Extract codes for a category column.
@@ -2124,6 +2281,11 @@ def to_arrow(result, op=frame._op, tables=[], frames=iter(input)):
 
     def _build_index_cache(self):
         """Materialize index and store it in the cache."""
+        if self._partitions and not self._index_cols:
+            nrows = self._partitions[0][0]._length_cache
+            self.set_index_cache(Index.__new__(RangeIndex, data=range(nrows)))
+            return
+
         obj = self._execute()
 
         if self._index_cols is None:
@@ -2593,8 +2755,8 @@ def to_pandas(self):
             assert len(df.columns) == len(self.columns)
         else:
             assert self._index_cols is None
-            assert df.index.name is None or isinstance(
-                self._partitions[0][0].get(), pd.DataFrame
+            assert (
+                df.index.name is None or self._has_unsupported_data
             ), f"index name '{df.index.name}' is not None"
             if self.has_materialized_index:
                 df.index = self._index_cache.get().copy()

modin/experimental/core/execution/native/implementations/hdk_on_native/dataframe/utils.py

@@ -536,6 +536,52 @@ def get_common_arrow_type(t1: pa.lib.DataType, t2: pa.lib.DataType) -> pa.lib.Da
     return pa.from_numpy_dtype(np.promote_types(t1, t2))
 
 
+def is_supported_arrow_type(dtype: pa.lib.DataType) -> bool:
+    """
+    Return True if the specified arrow type is supported by HDK.
+
+    Parameters
+    ----------
+    dtype : pa.lib.DataType
+
+    Returns
+    -------
+    bool
+    """
+    if (
+        pa.types.is_string(dtype)
+        or pa.types.is_time(dtype)
+        or pa.types.is_dictionary(dtype)
+        or pa.types.is_null(dtype)
+    ):
+        return True
+    if isinstance(dtype, pa.ExtensionType) or pa.types.is_duration(dtype):
+        return False
+    try:
+        pandas_dtype = dtype.to_pandas_dtype()
+        return pandas_dtype != np.dtype("O")
+    except NotImplementedError:
+        return False
+
+
+def ensure_supported_dtype(dtype: pa.lib.DataType):
+    """
+    Check if the specified `dtype` is supported by HDK.
+
+    If `dtype` is not supported, `NotImplementedError` is raised.
+
+    Parameters
+    ----------
+    dtype : dtype
+    """
+    try:
+        if is_supported_arrow_type(pa.from_numpy_dtype(dtype)):
+            return
+    except pa.ArrowNotImplementedError:
+        ...
+    raise NotImplementedError(f"Type {dtype}")
+
+
 def arrow_to_pandas(at: pa.Table) -> pandas.DataFrame:
     """
     Convert the specified arrow table to pandas.

modin/experimental/core/execution/native/implementations/hdk_on_native/df_algebra.py

@@ -434,7 +434,9 @@ def execute_arrow(self, ignore=None) -> Union[DbTable, pa.Table, pandas.DataFram
         """
         frame = self.modin_frame
         if frame._partitions is not None:
-            return frame._partitions[0][0].get()
+            part = frame._partitions[0][0]
+            to_arrow = part.raw and not frame._has_unsupported_data
+            return part.get(to_arrow)
         if frame._has_unsupported_data:
             return pandas.DataFrame(
                 index=frame._index_cache, columns=frame._columns_cache
@@ -836,6 +838,48 @@ def __init__(
         self.exprs = exprs
         self.condition = condition
 
+    @property
+    def by_rowid(self):
+        """
+        Return True if this is a join by the rowid column.
+
+        Returns
+        -------
+        bool
+        """
+        return (
+            isinstance(self.condition, OpExpr)
+            and self.condition.op == "="
+            and all(
+                isinstance(o, InputRefExpr) and o.column == ColNameCodec.ROWID_COL_NAME
+                for o in self.condition.operands
+            )
+        )
+
+    @_inherit_docstrings(DFAlgNode.require_executed_base)
+    def require_executed_base(self) -> bool:
+        return self.by_rowid and any(
+            not isinstance(i._op, FrameNode) for i in self.input
+        )
+
+    @_inherit_docstrings(DFAlgNode.can_execute_arrow)
+    def can_execute_arrow(self) -> bool:
+        return self.by_rowid and all(
+            isinstance(e, InputRefExpr) for e in self.exprs.values()
+        )
+
+    @_inherit_docstrings(DFAlgNode.execute_arrow)
+    def execute_arrow(self, tables: List[pa.Table]) -> pa.Table:
+        t1 = tables[0]
+        t2 = tables[1]
+        cols1 = t1.column_names
+        cols = [
+            (t1 if (col := ColNameCodec.encode(e.column)) in cols1 else t2).column(col)
+            for e in self.exprs.values()
+        ]
+        names = [ColNameCodec.encode(c) for c in self.exprs]
+        return pa.table(cols, names)
+
     def copy(self):
         """
         Make a shallow copy of the node.