How efficient is SQL MERGE?
The basic set-up data is as follows. We’ve purposely set up our source table so that the INSERTs it will do when merged with the target are interleaved with existing records for the first 500,000 rows. These indicate that MERGE took about 28% more CPU and 29% more elapsed time than the equivalent INSERT/UPDATE.
How do you optimize a MERGE statement in SQL?
Our first task was to change the MERGE statement to meet all of the required conditions for optimization:
- Target table’s join column has a unique or primary key constraint.
- UPDATE and INSERT clauses include every column in the target table.
- UPDATE and INSERT clause column attributes are identical.
What is difference between UPDATE and MERGE?
Both the MERGE and UPDATE statements are designed to modify data in one table based on data from another, but MERGE can do much more. Whereas UPDATE can only modify column values you can use the MERGE statement to synchronize all data changes such as removal and addition of row.
What is the purpose of merge in SQL?
MERGE is designed to apply both UPDATE and INSERTs into a target table from a source table. The statement can do both at once, or simply do INSERTs or only UPDATEs.
How to improve the performance of the MERGE statement?
To improve the performance of the MERGE statement and ensure correct results are obtained, we recommend the following join guidelines: Specify only search conditions in the ON clause that determine the criteria for matching data in the source and target tables.
How is the join operation in a MERGE statement optimized?
The join operation in the MERGE statement is optimized in the same way as a join in a SELECT statement. That is, when SQL Server processes joins, the query optimizer chooses the most efficient method (out of several possibilities) of processing the join.
What are the query plans for MERGE statement?
The actual query plans for the four statements ( combined MERGE and the separate UPDATE, INSERT & DELETE ) are attached. SQL Code to create the source and target tables and the actual queries themselves are below. I’ve also included the statistics created by my test run. Nothing else was running on the server when I ran the test.