How Can I Speed Up Bulk Inserts into MS SQL Server Using pyodbc?

How to Enhance Bulk Insert Performance in MS SQL Server Using pyodbc

You mentioned encountering slow insert speeds when attempting to insert over 1,300,000 rows into an MS SQL Server database using pyodbc. While iterating over individual rows for insertion may contribute to the performance issue, considering bulk insert techniques is a viable solution.

T-SQL BULK INSERT

T-SQL's BULK INSERT command allows for efficient bulk data loading, provided the data file is located on the same machine as the SQL Server instance or in an accessible SMB/CIFS network location. If this condition is met, the following steps can be taken:

1. Create a bulk insert statement and specify the destination table and data columns.
2. Use pyodbc.load_bulk() to load the data from a file or cursor into the database.

fast_executemany in pyodbc

For scenarios where the data file resides on a remote client, pyodbc's Cursor#fast_executemany feature introduced in version 4.0.19 can significantly improve insert performance:

1. Enable fast_executemany by setting it to True on the cursor object.
2. Use Cursor#executemany() to insert multiple rows at once. This technique can drastically reduce execution time compared to the manual iteration you are currently using.

Considerations

• fast_executemany is not enabled by default.
• The data for the bulk insert should be structured according to the target table schema.
• Ensure that the SQL Server user has the necessary permissions to perform bulk inserts.

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