The MERGE statement has a complex syntax and an even more complex implementation. But essentially, the idea is to join two tables, filter down to rows that need to be changed (inserted, updated, or deleted), and then perform the requested changes. Given the following sample data:
Target: Source: The desired outcome is to replace data in the target with data from the source, but only for CategoryId = 2. Following the description of MERGE given above, we should write a query that joins the source and target on the keys only, and filter rows only in the WHEN clauses:
This gives the following results:
The execution plan is: Notice both tables are scanned fully. We might think this inefficient because only rows where CategoryId = 2 will be affected in the target table. This is where the warnings in Books Online come in. One misguided attempt to optimize to touch only necessary rows in the target is: The logic in the ON clause is applied as part of the join. In this case, the join is a full outer join (see this Books Online entry for why). Applying the check for category 2 on the target rows as part of an outer join ultimately results in rows with a different value being deleted (because they do not match the source):
The root cause is the same reason predicates behave differently in an outer join ON clause than they do if specified in the WHERE clause. The MERGE syntax (and the join implementation depending on the clauses specified) just make it harder to see that this is so. The guidance in Books Online (expanded in the Optimizing Performance entry) offers guidance that will ensure the correct semantic is expressed using MERGE syntax, without the user necessarily having to understand all the implementation details, or account for the ways in which the optimizer might legitimately rearrange things for execution efficiency reasons.
The documentation offers three potential ways to implement early filtering:
Specifying a filtering condition in the WHEN clause guarantees correct results but may mean that more rows are read and processed from the source and target tables than is strictly necessary (as seen in the first example). Updating through a view that contains the filtering condition also guarantees correct results (since changed rows must be accessible for update through the view) but this does require a dedicated view and one that follows the odd conditions for updating views.Using a common table expression carries similar risks to adding predicates to the ON clause, but for slightly different reasons. In many cases, it will be safe, but it requires expert analysis of the execution plan to confirm this (and extensive practical testing). For example:
This produces correct results (not repeated) with a more optimal plan:
The plan only reads rows for category 2 from the target table. This might be an important performance consideration if the target table is large, but it is all too easy to get this wrong using MERGE syntax.Sometimes, it is easier to write the MERGE as separate DML operations. This approach can even perform better than a single MERGE, a fact which often surprises people.
What is SQL Server Merge?
The MERGE statement combines INSERT, UPDATE, and DELETE operations into a single statement, eliminating the need to write separate logic for each. It changes the data in a target table based on the data in a source table. We can also specify the condition on which we want to perform the MERGE statement for INSERT, UPDATE, or DELETE operations.
