After the execution of the update from a select query the output of the Persons table will be as shown below;. After the SET keyword, we specified the column names to be updated, and also, we matched them with the referenced table columns. After the FROM clause, we retyped the table name, which will be updated. In addition to this, we can specify a WHERE clause and filter any columns of the referenced or updated table.
We can also rewrite the query by using aliases for tables. Indexes are very helpful database objects to improve query performance in SQL Server.
Particularly, if we are working on the performance of the update query, we should take into account of this probability. The following execution plan illustrates an execution plan of the previous query. The only difference is that this query updated the 3.
This query was completed within 68 seconds. We added a non-clustered index on Persons table before to update and the added index involves the PersonCityName and PersonPostCode columns as the index key. The following execution plan is demonstrating an execution plan of the same query, but this query was completed within seconds because of the added index, unlike the first one. We have seen this obvious performance difference between the same query because of index usage on the updated columns.
As a result, if the updated columns are being used by the indexes, like this, for example, the query performance might be affected negatively. In particular, we should consider this problem if we will update a large number of rows. To overcome this issue, we can disable or remove the index before executing the update query. On the other hand, a warning sign is seen on the Sort operator, and it indicates something does not go well for this operator.
When we hover the mouse over this operator, we can see the warning details. During the execution of the query, the query optimizer calculates a required memory consumption for the query based on the estimated row numbers and row size.
However, this consumption estimation can be wrong for a variety of reasons, and if the query requires more memory than the estimation, it uses the tempdb data. This mechanism is called a tempdb spill and causes performance loss. The reason for this: the memory always faster than the tempdb database because the tempdb database uses the disk resources. Now, if we go back to our position, the MERGE statement can be used as an alternative method for updating data in a table with those in another table.
PersonAddress" table. Since each tuple in each row is unique, we could use the standard form "update — set" method, but that would take too long since it will have to be done one row at a time. Using this method, we will update the values in the "City" and "PostalCode" columns of the customers table with the data held in the "City" and "PostalCode" columns of the "Test.
With the SET keyword, we specified which columns in the target table we want updated and set them to equal the values found in the source table, matching the columns by aliases.
We could take it a step further by adding a WHERE clause to filter out any columns from the referenced or updated tables.
This will allow for updating only certain rows while leaving the other rows untouched. For example, if you only wanted to update the tuples in the Test. So, I am returning rows 21 through 26 for the sake of clarity in the result set. Feel free to alter the code to suit your needs. The MERGE statement can be very useful for synchronizing the target table with data from any source table that has the correct corresponding data and datatypes. We listed the Test.
In a production environment, the later would be the preferred method. For this test sample, we went with the simpler method of simply selecting all. Finally, when those match up, we update the target table with the corresponding values pulled from the source table by using the aliases assigned earlier in the script.
A subquery is a query that is nested inside another query. It also allows for fine tuning exactly what you want the select statements to do and how they should behave. This one is a relatively easy script to follow. One thing you will notice that is different in this sample as opposed to the previous two, is this one is actually two different scripts that are independent of each other.
Each one updating the values of a single column. A look at the over-all execution plan for the JOIN option. Notice that this returns in two parts. A quick look at the performance cost just on the "Clustered Index Scan" for each of these methods after updating the City and PostalCode columns. Now for the cleanup process if you choose to do so. You are certainly welcome to keep these new tables and the corresponding schema for future testing. Should you decide to remove them, the following block of code will remove the tables and schema from your AdventureWorks database.
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