Amazon Redshift will no longer support the use of Python UDFs after June 30, 2026. We will start enforcing it in phases. For more information on the details of Python end of life and migration options, see the [ blog post ](https://aws.amazon.com/blogs/big-data/amazon-redshift-python-user-defined-functions-will-reach-end-of-support-after-june-30-2026/) that was published on June 30, 2025. 

# Performing a merge operation by replacing existing rows
<a name="merge-replacing-existing-rows"></a>

When you run the merge operation detailed in the procedure, put all of the steps except for creating and dropping the temporary staging table in a single transaction. The transaction rolls back if any step fails. Using a single transaction also reduces the number of commits, which saves time and resources.

**To perform a merge operation by replacing existing rows**

1. Create a staging table, and then populate it with data to be merged, as shown in the following pseudocode.

   ```
   CREATE temp table stage (like target); 
   
   INSERT INTO stage 
   SELECT * FROM source 
   WHERE source.filter = 'filter_expression';
   ```

1.  Use MERGE to perform an inner join with the staging table to update the rows from the target table that match the staging table, then insert all the remaining rows into the target table that don't match the staging table.

    We recommend you run the update and insert operations in a single MERGE command.

   ```
   MERGE INTO target 
   USING stage [optional alias] on (target.primary_key = stage.primary_key)
   WHEN MATCHED THEN 
   UPDATE SET col_name1 = stage.col_name1 , col_name2= stage.col_name2, col_name3 = {expr}
   WHEN NOT MATCHED THEN
   INSERT (col_name1 , col_name2, col_name3) VALUES (stage.col_name1, stage.col_name2, {expr});
   ```

1. Drop the staging table. 

   ```
   DROP TABLE stage;
   ```