Backport PR pandas-dev#57311: Fixing multi method for to_sql for non-…

…oracle databases

doc/source/whatsnew/v2.2.1.rst

@@ -31,6 +31,7 @@ Fixed regressions
 - Fixed regression in :meth:`DataFrame.sort_index` not producing a stable sort for a index with duplicates (:issue:`57151`)
 - Fixed regression in :meth:`DataFrame.to_dict` with ``orient='list'`` and datetime or timedelta types returning integers (:issue:`54824`)
 - Fixed regression in :meth:`DataFrame.to_json` converting nullable integers to floats (:issue:`57224`)
+- Fixed regression in :meth:`DataFrame.to_sql` when ``method="multi"`` is passed and the dialect type is not Oracle (:issue:`57310`)
 - Fixed regression in :meth:`DataFrameGroupBy.idxmin`, :meth:`DataFrameGroupBy.idxmax`, :meth:`SeriesGroupBy.idxmin`, :meth:`SeriesGroupBy.idxmax` ignoring the ``skipna`` argument (:issue:`57040`)
 - Fixed regression in :meth:`DataFrameGroupBy.idxmin`, :meth:`DataFrameGroupBy.idxmax`, :meth:`SeriesGroupBy.idxmin`, :meth:`SeriesGroupBy.idxmax` where values containing the minimum or maximum value for the dtype could produce incorrect results (:issue:`57040`)
 - Fixed regression in :meth:`ExtensionArray.to_numpy` raising for non-numeric masked dtypes (:issue:`56991`)

pandas/core/generic.py

@@ -2969,6 +2969,9 @@ def to_sql(
         database. Otherwise, the datetimes will be stored as timezone unaware
         timestamps local to the original timezone.
 
+        Not all datastores support ``method="multi"``. Oracle, for example,
+        does not support multi-value insert.
+
         References
         ----------
         .. [1] https://docs.sqlalchemy.org

pandas/io/sql.py

@@ -1012,22 +1012,19 @@ def _execute_insert(self, conn, keys: list[str], data_iter) -> int:
 
     def _execute_insert_multi(self, conn, keys: list[str], data_iter) -> int:
         """
-        Alternative to _execute_insert for DBs support multivalue INSERT.
+        Alternative to _execute_insert for DBs support multi-value INSERT.
 
         Note: multi-value insert is usually faster for analytics DBs
         and tables containing a few columns
         but performance degrades quickly with increase of columns.
+
         """
 
         from sqlalchemy import insert
 
         data = [dict(zip(keys, row)) for row in data_iter]
-        stmt = insert(self.table)
-        # conn.execute is used here to ensure compatibility with Oracle.
-        # Using stmt.values(data) would produce a multi row insert that
-        # isn't supported by Oracle.
-        # see: https://docs.sqlalchemy.org/en/20/core/dml.html#sqlalchemy.sql.expression.Insert.values
-        result = conn.execute(stmt, data)
+        stmt = insert(self.table).values(data)
+        result = conn.execute(stmt)
         return result.rowcount
 
     def insert_data(self) -> tuple[list[str], list[np.ndarray]]: