How Can Cursor#fast_executemany in pyodbc Speed Up Bulk Inserts to MS SQL Server?

Speeding Up Bulk Inserts to MS SQL Server Using pyodbc: Insights and Solutions

To enhance the efficiency of bulk inserts into an MS SQL Server table using pyodbc, consider leveraging the advantages of its Cursor#fast_executemany feature. Introduced in version 4.0.19, this feature optimizes the insertion process, significantly reducing execution time.

When the CSV file containing the data for insertion resides on a remote client rather than the local machine hosting the SQL Server instance (or an accessible SMB/CIFS network location), the T-SQL BULK INSERT command may not be feasible. In such cases, Cursor#fast_executemany offers a compelling alternative.

Demonstration:

Consider the scenario where you're inserting 1000 rows of data into a "fast_executemany_test" table. Using the default settings, the operation takes approximately 22 seconds:

<code class="python">crsr.executemany(sql, params)</code>

By simply enabling Cursor#fast_executemany via:

<code class="python">crsr.fast_executemany = True</code>

You can accelerate the insertion to a mere 1 second, slashing the execution time by a significant factor. Leverage this feature to streamline your bulk inserts and improve the overall performance of your data import tasks.

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