diff options
Diffstat (limited to 'lib/sqlalchemy/dialects/mssql/pyodbc.py')
| -rw-r--r-- | lib/sqlalchemy/dialects/mssql/pyodbc.py | 20 |
1 files changed, 7 insertions, 13 deletions
diff --git a/lib/sqlalchemy/dialects/mssql/pyodbc.py b/lib/sqlalchemy/dialects/mssql/pyodbc.py index f9f2479ad..08c6bc48f 100644 --- a/lib/sqlalchemy/dialects/mssql/pyodbc.py +++ b/lib/sqlalchemy/dialects/mssql/pyodbc.py @@ -290,25 +290,19 @@ Pyodbc have been resolved as of SQLAlchemy 2.0.5. See the notes at Fast Executemany Mode --------------------- -.. note:: SQLAlchemy 2.0 now includes an equivalent "fast executemany" - handler for INSERT statements that is more robust than the PyODBC feature - (but is not quite as performant particularly for very large datasets); - the feature is called :ref:`insertmanyvalues <engine_insertmanyvalues>` - and is enabled for all INSERT statements by default. - SQLAlchemy's feature integrates with the PyODBC ``setinputsizes()`` method - which allows for more accurate specification of datatypes, and additionally - uses a dynamically sized, batched approach that scales to any number of - columns and/or rows. - - The SQL Server ``fast_executemany`` parameter may be used at the same time - as ``insertmanyvalues`` is enabled; however, the parameter will not be used - for INSERT statements that include RETURNING. + .. note:: SQLAlchemy 2.0 introduced the :ref:`engine_insertmanyvalues` + feature for SQL Server, which is used by default to optimize many-row + INSERT statements; however as of SQLAlchemy 2.0.9 this feature had + to be turned off for SQL Server as the database does not support + deterministic RETURNING of INSERT rows for a multi-row INSERT statement. .. versionchanged:: 2.0.9 - ``fast_executemany`` executions will be used for INSERT statements that don't include RETURNING, when ``fast_executemany`` is set. Previously, ``use_insertmanyvalues`` would cause ``fast_executemany`` to not be used in most cases. + ``use_insertmanyvalues`` is disabled for SQL Server overall as of 2.0.9. + The PyODBC driver includes support for a "fast executemany" mode of execution which greatly reduces round trips for a DBAPI ``executemany()`` call when using Microsoft ODBC drivers, for **limited size batches that fit in memory**. The |