How Can I Efficiently Update 120 Million Records in a Database Table?
Optimizing Mass Record Update Operations
Problem:
Updating a massive table of 120 million records using a straightforward UPDATE statement has failed, even after running for several hours.
Solution:
Instead of updating the existing table, utilize a SELECT statement to populate a new table with the desired values. This approach significantly improves performance and avoids the bottlenecks associated with direct updates.
Steps for Non-Simple Cases:
-
Create a New Table with the Desired Structure:
- Recreate the original table under a different schema with the same clustered index, identity columns, computed columns, and the new column to be updated.
- Exclude triggers, foreign key constraints, and other non-essential elements.
-
Test the Insert Operation:
- Insert a small subset of rows into the new table to ensure correctness.
-
Prepare for Bulk Insert:
- Set the database to bulk-logged or simple recovery model.
- Truncate the new table.
-
Perform the Bulk Insert:
- Insert all records from the original table into the new table using a single INSERT statement with the TABLOCK hint.
-
Verify Data Integrity:
- Check the new table to confirm the successful update.
-
Recreate Constraints and Triggers:
- Recreate all non-clustered constraints, indexes, and triggers in the new table while maintaining the schema in a separate batch.
-
Switch Tables:
- Move the original table to a backup schema and the new table to the desired schema.
- Perform any necessary true-up operations to reconcile any changes that occurred during the update process.
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