gordthompson · October 5, 2024 23:34 · gordthompson · Nov 1, 2022 · npnigro · Nov 1, 2022
diff --git a/mssql_df_upsert.py b/mssql_df_upsert.py
 # Copyright 2024 Gordon D. Thompson, [email protected]
 #
 # 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.

 # version 1.7 - 2024-06-21

 import uuid

 import pandas as pd
 import sqlalchemy as sa


 def df_upsert(data_frame, table_name, engine, schema=None, match_columns=None,
              chunksize=None, dtype=None, skip_inserts=False, skip_updates=False):
    """
    Perform an "upsert" on a SQL Server table from a DataFrame.

    Constructs a T-SQL MERGE statement, uploads the DataFrame to a
    temporary table, and then executes the MERGE.

    Parameters
    ----------
    data_frame : pandas.DataFrame
        The DataFrame to be upserted.
    table_name : str
        The name of the target table.
    engine : sqlalchemy.engine.Engine
        The SQLAlchemy Engine to use.
    schema : str, optional
        The name of the schema containing the target table.
    match_columns : list of str, optional
        A list of the column name(s) on which to match. If omitted, the
        primary key columns of the target table will be used.
    chunksize: int, optional
        Specify chunk size for .to_sql(). See the pandas docs for details.
    dtype : dict, optional
        Specify column types for .to_sql(). See the pandas docs for details.
    skip_inserts : bool, optional
        Skip inserting unmatched rows. (Default: False)
    skip_updates : bool, optional
        Skip updating matched rows. (Default: False)
    """
    if skip_inserts and skip_updates:
        raise ValueError("skip_inserts and skip_updates cannot both be True")

    temp_table_name = "##" + str(uuid.uuid4()).replace("-", "_")

    table_spec = ""
    if schema:
        table_spec += "[" + schema.replace("]", "]]") + "]."
    table_spec += "[" + table_name.replace("]", "]]") + "]"

    df_columns = list(data_frame.columns)
    if not match_columns:
        insp = sa.inspect(engine)
        match_columns = insp.get_pk_constraint(table_name, schema=schema)[
            "constrained_columns"
        ]
    columns_to_update = [col for col in df_columns if col not in match_columns]
    stmt = f"MERGE {table_spec} WITH (HOLDLOCK) AS main\n"
    stmt += f"USING (SELECT {', '.join([f'[{col}]' for col in df_columns])} FROM {temp_table_name}) AS temp\n"
    join_condition = " AND ".join(
        [f"main.[{col}] = temp.[{col}]" for col in match_columns]
    )
    stmt += f"ON ({join_condition})"
    if not skip_updates:
        stmt += "\nWHEN MATCHED THEN\n"
        update_list = ", ".join(
            [f"[{col}] = temp.[{col}]" for col in columns_to_update]
        )
        stmt += f"  UPDATE SET {update_list}"
    if not skip_inserts:
        stmt += "\nWHEN NOT MATCHED THEN\n"
        insert_cols_str = ", ".join([f"[{col}]" for col in df_columns])
        insert_vals_str = ", ".join([f"temp.[{col}]" for col in df_columns])
        stmt += f"  INSERT ({insert_cols_str}) VALUES ({insert_vals_str})"
    stmt += ";"

    with engine.begin() as conn:
        data_frame.to_sql(temp_table_name, conn, index=False, chunksize=chunksize, dtype=dtype)
        conn.exec_driver_sql(stmt)
        conn.exec_driver_sql(f"DROP TABLE IF EXISTS {temp_table_name}")


 if __name__ == "__main__":
    # Usage example adapted from
    # https://stackoverflow.com/a/62388768/2144390

    engine = sa.create_engine(
        "mssql+pyodbc://scott:tiger^[email protected]/test"
        "?driver=ODBC+Driver+17+for+SQL+Server",
        fast_executemany=True,
    )

    # create example environment
    with engine.begin() as conn:
        conn.exec_driver_sql("DROP TABLE IF EXISTS main_table")
        conn.exec_driver_sql(
            "CREATE TABLE main_table (id int primary key, txt nvarchar(50), status nvarchar(50))"
        )
        conn.exec_driver_sql(
            "INSERT INTO main_table (id, txt, status) VALUES (1, N'row 1 old text', N'original')"
        )
        # [(1, 'row 1 old text', 'original')]

    # DataFrame to upsert
    df = pd.DataFrame(
        [(2, "new row 2 text", "upserted"), (1, "row 1 new text", "upserted")],
        columns=["id", "txt", "status"],
    )

    df_upsert(df, "main_table", engine)
    """The MERGE statement generated for this example:

    MERGE [main_table] WITH (HOLDLOCK) AS main
    USING (SELECT [id], [txt], [status] FROM ##955db388_01c5_4e79_a5d1_3e8cfadf400b) AS temp
    ON (main.[id] = temp.[id])
    WHEN MATCHED THEN
      UPDATE SET [txt] = temp.[txt], [status] = temp.[status]
    WHEN NOT MATCHED THEN
      INSERT ([id], [txt], [status]) VALUES (temp.[id], temp.[txt], temp.[status]);

    """

    # check results
    with engine.begin() as conn:
        print(
            conn.exec_driver_sql("SELECT * FROM main_table").all()
        )
        # [(1, 'row 1 new text', 'upserted'), (2, 'new row 2 text', 'upserted')]
	# Copyright 2024 Gordon D. Thompson, [email protected]
	#
	# 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.

	# version 1.7 - 2024-06-21

	import uuid

	import pandas as pd
	import sqlalchemy as sa


	def df_upsert(data_frame, table_name, engine, schema=None, match_columns=None,
	chunksize=None, dtype=None, skip_inserts=False, skip_updates=False):
	"""
	Perform an "upsert" on a SQL Server table from a DataFrame.

	Constructs a T-SQL MERGE statement, uploads the DataFrame to a
	temporary table, and then executes the MERGE.

	Parameters
	----------
	data_frame : pandas.DataFrame
	The DataFrame to be upserted.
	table_name : str
	The name of the target table.
	engine : sqlalchemy.engine.Engine
	The SQLAlchemy Engine to use.
	schema : str, optional
	The name of the schema containing the target table.
	match_columns : list of str, optional
	A list of the column name(s) on which to match. If omitted, the
	primary key columns of the target table will be used.
	chunksize: int, optional
	Specify chunk size for .to_sql(). See the pandas docs for details.
	dtype : dict, optional
	Specify column types for .to_sql(). See the pandas docs for details.
	skip_inserts : bool, optional
	Skip inserting unmatched rows. (Default: False)
	skip_updates : bool, optional
	Skip updating matched rows. (Default: False)
	"""
	if skip_inserts and skip_updates:
	raise ValueError("skip_inserts and skip_updates cannot both be True")

	temp_table_name = "##" + str(uuid.uuid4()).replace("-", "_")

	table_spec = ""
	if schema:
	table_spec += "[" + schema.replace("]", "]]") + "]."
	table_spec += "[" + table_name.replace("]", "]]") + "]"

	df_columns = list(data_frame.columns)
	if not match_columns:
	insp = sa.inspect(engine)
	match_columns = insp.get_pk_constraint(table_name, schema=schema)[
	"constrained_columns"
	]
	columns_to_update = [col for col in df_columns if col not in match_columns]
	stmt = f"MERGE {table_spec} WITH (HOLDLOCK) AS main\n"
	stmt += f"USING (SELECT {', '.join([f'[{col}]' for col in df_columns])} FROM {temp_table_name}) AS temp\n"
	join_condition = " AND ".join(
	[f"main.[{col}] = temp.[{col}]" for col in match_columns]
	)
	stmt += f"ON ({join_condition})"
	if not skip_updates:
	stmt += "\nWHEN MATCHED THEN\n"
	update_list = ", ".join(
	[f"[{col}] = temp.[{col}]" for col in columns_to_update]
	)
	stmt += f" UPDATE SET {update_list}"
	if not skip_inserts:
	stmt += "\nWHEN NOT MATCHED THEN\n"
	insert_cols_str = ", ".join([f"[{col}]" for col in df_columns])
	insert_vals_str = ", ".join([f"temp.[{col}]" for col in df_columns])
	stmt += f" INSERT ({insert_cols_str}) VALUES ({insert_vals_str})"
	stmt += ";"

	with engine.begin() as conn:
	data_frame.to_sql(temp_table_name, conn, index=False, chunksize=chunksize, dtype=dtype)
	conn.exec_driver_sql(stmt)
	conn.exec_driver_sql(f"DROP TABLE IF EXISTS {temp_table_name}")


	if __name__ == "__main__":
	# Usage example adapted from
	# https://stackoverflow.com/a/62388768/2144390

	engine = sa.create_engine(
	"mssql+pyodbc://scott:tiger^[email protected]/test"
	"?driver=ODBC+Driver+17+for+SQL+Server",
	fast_executemany=True,
	)

	# create example environment
	with engine.begin() as conn:
	conn.exec_driver_sql("DROP TABLE IF EXISTS main_table")
	conn.exec_driver_sql(
	"CREATE TABLE main_table (id int primary key, txt nvarchar(50), status nvarchar(50))"
	)
	conn.exec_driver_sql(
	"INSERT INTO main_table (id, txt, status) VALUES (1, N'row 1 old text', N'original')"
	)
	# [(1, 'row 1 old text', 'original')]

	# DataFrame to upsert
	df = pd.DataFrame(
	[(2, "new row 2 text", "upserted"), (1, "row 1 new text", "upserted")],
	columns=["id", "txt", "status"],
	)

	df_upsert(df, "main_table", engine)
	"""The MERGE statement generated for this example:

	MERGE [main_table] WITH (HOLDLOCK) AS main
	USING (SELECT [id], [txt], [status] FROM ##955db388_01c5_4e79_a5d1_3e8cfadf400b) AS temp
	ON (main.[id] = temp.[id])
	WHEN MATCHED THEN
	UPDATE SET [txt] = temp.[txt], [status] = temp.[status]
	WHEN NOT MATCHED THEN
	INSERT ([id], [txt], [status]) VALUES (temp.[id], temp.[txt], temp.[status]);

	"""

	# check results
	with engine.begin() as conn:
	print(
	conn.exec_driver_sql("SELECT * FROM main_table").all()
	)
	# [(1, 'row 1 new text', 'upserted'), (2, 'new row 2 text', 'upserted')]