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This would be ideal in terms of performance, but it d be feasible only if the data you needed was limited to these specific rows in this particular sequence. However, if you were building a more elaborate system, it might be better to pull all the data once from the database (as you do here) and then filter and sort it in different ways. ADO.NET s rich suite of methods for manipulating datasets and their components gives you a broad range of techniques for meeting specific needs in an optimal way.

You can use CONTAINS to find words in data that are not even next to each other by using AND, OR, and NEAR. You can use the operator AND NOT to find results with one term and not another. This syntax is similar to searches with Google or other search engines. Listing 2-20 demonstrates this technique. Listing 2-20. Multiple Terms in CONTAINS USE AdventureWorks2008; GO --1 SELECT FileName, DocumentSummary FROM Production.Document WHERE Contains(DocumentSummary,'bicycle AND reflectors'); --2 SELECT FileName, DocumentSummary FROM Production.Document WHERE CONTAINS(DocumentSummary,'bicycle AND NOT reflectors'); --3 SELECT FileName, DocumentSummary FROM Production.Document WHERE CONTAINS(DocumentSummary,'maintain NEAR bicycle AND NOT reflectors');

SELECT ProductCategoryID, Name FROM MyCopy ProductCategoryID ----------------1 2 3 4 Name ----------Bikes Components Clothing Accessories

s In general, try to exploit SQL, rather than code VB procedures, to get the data you need from the dataTip

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One area in which to look for differences between the two tables is the definition of their column values. The following pair of SELECT statements draws on the sys.columns and sys.types system views to characterize the columns in the MyCopy and ProductCategory tables. ( 7 examines the topic of system views, such as sys.columns, in greater depth.) In the current context, all you need to know is that you can obtain information about the columns and user-defined data types from the sys.columns and sys.types views. The OBJECT_ID function returns the database identification number of an object, which SQL Server Express uses to track database objects, such as tables. The next pair of SELECT statements and their result sets show the column properties for the ProductCategory and MyCopy tables. Notice that the column names and the property values (the max_length column to the identity column) are identical in the two tables. However, the data type names are not identical. The ProductCategory table defines its Name column as a Name data type, which is a user-defined data type in the AdventureWorks database. The MyCopy table defines its Name column as an nvarchar data type with a maximum length of 100 bytes (or 50 characters because the column uses Unicode characters). In fact, the properties for the Name columns are the same in both tables although they have different type names. SELECT c.name 'Column name', t.name 'Data type name', c.max_length, c.precision, c.scale, c.is_nullable 'nullable', c.is_identity 'identity' FROM sys.columns c JOIN sys.types t ON c.user_type_id = t.user_type_id WHERE object_id = OBJECT_ID('Production.ProductCategory') Column name -----------------ProductCategoryID Name rowguid ModifiedDate Data type name ----------------int Name uniqueidentifier datetime max_length ---------4 100 16 8 precision --------10 0 0 23 scale ----0 0 0 3 nullable -------0 0 0 0 identity -------1 0 0 0

Figure 2-17 shows the results. In this case, a regular string data column, DocumentSummary, is searched so that you can verify the results.

base. Database servers are optimized to perform selections and sorts, as well as other things. Queries can be far more sophisticated and powerful than the ones you ve been playing with in this book. By carefully (and creatively) coding queries to return exactly what you need, you not only minimize resource demands (on memory, network bandwidth, and so on) but also reduce the code you must write to manipulate and format result set data.

SELECT c.name 'Column name', t.name 'Data type name', c.max_length, c.precision, c.scale, c.is_nullable 'nullable', c.is_identity 'identity' FROM sys.columns c JOIN sys.types t ON c.user_type_id = t.user_type_id WHERE object_id = OBJECT_ID('MyCopy') Column name -----------------ProductCategoryID Name rowguid ModifiedDate Data type name ----------------int nvarchar uniqueidentifier datetime max_length ---------4 100 16 8 precision --------10 0 0 23 scale ----0 0 0 3 nullable -------0 0 0 0 identity -------1 0 0 0

The loop through the second data table is interesting mainly for its first line:

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