Sql-server – SQL Server avoid repeating the collation type multiple times in a query

bulk-insertcollationimportinsertsql server

I have a query which selects rows from a source database (DatabaseA), and inserts them into a target database (Database B). The collation type differs between the databases and they cannot be changed. I need to address the collation difference in my query by explicitly specifying the collation for varchar fields.

Currently my query looks like this:

INSERT INTO DatabaseB.dbo.Users(
    Id,
    UserNumber,
    FirstName,
    Surname,
    Address1,
    Address2,
    AddressTown,
    AddressCity
)
SELECT 
    Id,
    UserNumber,
    FirstName COLLATE SQL_Latin1_General_CI_AS,
    Surname COLLATE SQL_Latin1_General_CI_AS,
    Address1 COLLATE SQL_Latin1_General_CI_AS,
    Address2 COLLATE SQL_Latin1_General_CI_AS,
    AddressTown COLLATE SQL_Latin1_General_CI_AS,
    AddressCity COLLATE SQL_Latin1_General_CI_AS
FROM DatabaseA.dbo.Users

My question is, can I avoid typing the collation type for every string-based field? Is there a way that I can specify the collation type for the whole query at once? If this is not possible, are there any other shortcuts?

Best Answer

You can once create a view using column definitions like this:

FirstName COLLATE SQL_Latin1_General_CI_AS, ...

Then anytime you'd like to use User tables, you can use this view instead.

Related Solutions

Sql-server – SQL Server collation mismatch

You have change all user tables and columns individually, droppng and creating all constraints as needed. It's straightforward, just tedious. (SO link)

You can also use COLLATE DATABASE_DEFAULT to coerce collations in code without actually knowing what one is used.

Sql-server – MERGE a subset of the target table

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 to perform the requested changes. Given the following sample data:

DECLARE @CategoryItem AS TABLE
(
    CategoryId  integer NOT NULL,
    ItemId      integer NOT NULL,

    PRIMARY KEY (CategoryId, ItemId),
    UNIQUE (ItemId, CategoryId)
);

DECLARE @DataSource AS TABLE
(
    CategoryId  integer NOT NULL,
    ItemId      integer NOT NULL

    PRIMARY KEY (CategoryId, ItemId)
);

INSERT @CategoryItem
    (CategoryId, ItemId)
VALUES
    (1, 1),
    (1, 2),
    (1, 3),
    (2, 1),
    (2, 3),
    (3, 5),
    (3, 6),
    (4, 5);

INSERT @DataSource
    (CategoryId, ItemId)
VALUES
    (2, 2);

Target

╔════════════╦════════╗
║ CategoryId ║ ItemId ║
╠════════════╬════════╣
║          1 ║      1 ║
║          2 ║      1 ║
║          1 ║      2 ║
║          1 ║      3 ║
║          2 ║      3 ║
║          3 ║      5 ║
║          4 ║      5 ║
║          3 ║      6 ║
╚════════════╩════════╝

Source

╔════════════╦════════╗
║ CategoryId ║ ItemId ║
╠════════════╬════════╣
║          2 ║      2 ║
╚════════════╩════════╝

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:

MERGE INTO @CategoryItem AS TARGET
USING @DataSource AS SOURCE ON 
    SOURCE.ItemId = TARGET.ItemId 
    AND SOURCE.CategoryId = TARGET.CategoryId
WHEN NOT MATCHED BY SOURCE 
    AND TARGET.CategoryId = 2 
    THEN DELETE
WHEN NOT MATCHED BY TARGET 
    AND SOURCE.CategoryId = 2 
    THEN INSERT (CategoryId, ItemId)
        VALUES (CategoryId, ItemId)
OUTPUT 
    $ACTION, 
    ISNULL(INSERTED.CategoryId, DELETED.CategoryId) AS CategoryId,
    ISNULL(INSERTED.ItemId, DELETED.ItemId) AS ItemId
;

This gives the following results:

╔═════════╦════════════╦════════╗
║ $ACTION ║ CategoryId ║ ItemId ║
╠═════════╬════════════╬════════╣
║ DELETE  ║          2 ║      1 ║
║ INSERT  ║          2 ║      2 ║
║ DELETE  ║          2 ║      3 ║
╚═════════╩════════════╩════════╝
╔════════════╦════════╗
║ CategoryId ║ ItemId ║
╠════════════╬════════╣
║          1 ║      1 ║
║          1 ║      2 ║
║          1 ║      3 ║
║          2 ║      2 ║
║          3 ║      5 ║
║          3 ║      6 ║
║          4 ║      5 ║
╚════════════╩════════╝

The execution plan is: Merge plan

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:

MERGE INTO @CategoryItem AS TARGET
USING 
(
    SELECT CategoryId, ItemId
    FROM @DataSource AS ds 
    WHERE CategoryId = 2
) AS SOURCE ON
    SOURCE.ItemId = TARGET.ItemId
    AND TARGET.CategoryId = 2
WHEN NOT MATCHED BY TARGET THEN
    INSERT (CategoryId, ItemId)
    VALUES (CategoryId, ItemId)
WHEN NOT MATCHED BY SOURCE THEN
    DELETE
OUTPUT 
    $ACTION, 
    ISNULL(INSERTED.CategoryId, DELETED.CategoryId) AS CategoryId,
    ISNULL(INSERTED.ItemId, DELETED.ItemId) AS ItemId
;

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):

╔═════════╦════════════╦════════╗
║ $ACTION ║ CategoryId ║ ItemId ║
╠═════════╬════════════╬════════╣
║ DELETE  ║          1 ║      1 ║
║ DELETE  ║          1 ║      2 ║
║ DELETE  ║          1 ║      3 ║
║ DELETE  ║          2 ║      1 ║
║ INSERT  ║          2 ║      2 ║
║ DELETE  ║          2 ║      3 ║
║ DELETE  ║          3 ║      5 ║
║ DELETE  ║          3 ║      6 ║
║ DELETE  ║          4 ║      5 ║
╚═════════╩════════════╩════════╝

╔════════════╦════════╗
║ CategoryId ║ ItemId ║
╠════════════╬════════╣
║          2 ║      2 ║
╚════════════╩════════╝

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:

WITH TARGET AS 
(
    SELECT * 
    FROM @CategoryItem
    WHERE CategoryId = 2
)
MERGE INTO TARGET
USING 
(
    SELECT CategoryId, ItemId
    FROM @DataSource
    WHERE CategoryId = 2
) AS SOURCE ON
    SOURCE.ItemId = TARGET.ItemId
    AND SOURCE.CategoryId = TARGET.CategoryId
WHEN NOT MATCHED BY TARGET THEN
    INSERT (CategoryId, ItemId)
    VALUES (CategoryId, ItemId)
WHEN NOT MATCHED BY SOURCE THEN
    DELETE
OUTPUT 
    $ACTION, 
    ISNULL(INSERTED.CategoryId, DELETED.CategoryId) AS CategoryId,
    ISNULL(INSERTED.ItemId, DELETED.ItemId) AS ItemId
;

This produces correct results (not repeated) with a more optimal plan:

Merge plan 2

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.

DELETE ci
FROM @CategoryItem AS ci
WHERE ci.CategoryId = 2
AND NOT EXISTS 
(
    SELECT 1 
    FROM @DataSource AS ds 
    WHERE 
        ds.ItemId = ci.ItemId
        AND ds.CategoryId = ci.CategoryId
);

INSERT @CategoryItem
SELECT 
    ds.CategoryId, 
    ds.ItemId
FROM @DataSource AS ds
WHERE
    ds.CategoryId = 2;

Best Answer

Related Solutions

Sql-server – SQL Server collation mismatch

Sql-server – MERGE a subset of the target table

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