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A merge join is a very efficient join algorithm, which relies on two inputs that are sorted on the join columns. With a one-to-many join, a merge join operator scans each input only oncehence, the superiority over the other operators. To have two sorted inputs, the optimizer can use clustered, or better yet, nonclustered covering indexes with the first column or columns being the join column(s). SQL Server will start scanning both sides, moving forward first through the "many" side (say, "one" side is x and "many" is x, x, x, x, y). SQL Server will step through the "one" side when the join column value on the "many" side changes (the "one" side steps through y, and then the "many" side steps through y, y, y, z). Ultimately, each side is scanned only once, in an ordered fashion. Things become more complicated when you have a many-to-many join, where the optimizer might still use a merge join operator with rewind logic. When you see a merge join in the plan, it's usually a good sign. As an example, the following query joins Orders and [Order Details] on equal OrderID values: SELECT O.OrderID, O.OrderDate, OD.ProductID, OD.Quantity FROM dbo.Orders AS O JOIN dbo.[Order Details] AS OD ON O.OrderID = OD.OrderID;

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Finally, I have to describe basic predicates . Basic predicates include numeric and Boolean predicates . Numeric predicates simply select nodes by position . You include them in brackets . For example, /x/y[1] means the first y child element of each x element . You can also use parentheses to apply a numeric predicate to the entire result of a path . For example, (/x/y)[1] means the first element out of all nodes selected by x/y . Boolean predicates select all nodes for which the predicate evaluates to true . XQuery supports logical and and or operators . However, you might be surprised by how comparison operators work . They work on both atomic values and sequences . For sequences, if one atomic value in a sequence leads to a true exit of the expression, the whole expression is evaluated to true . Look at this example:

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Both tables have a clustered index on OrderID, so the optimizer chooses a merge join. The merge join operator appears in the plan for this query, shown in Figure 5-9.

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DECLARE @x AS XML = N''; SELECT @x.query('(1, 2, 3) = (2, 4)'); SELECT @x.query('(5, 6) < (2, 4)'); SELECT @x.query('(1, 2, 3) = 1'); SELECT @x.query('(1, 2, 3) != 1'); ----true false true true

Nonclustered 6 Index Seek + Ordered Partial Scan + Lookups Clustered Index Seek + Ordered Partial Scan Covering Nonclustered Index Seek + Ordered Partial Scan 4

The first expression evaluates to true because the number 2 is in both sequences . The second evaluates to false because none of the atomic values from the first sequence is less than any of the values from the second sequence . The third expression is true because there is an atomic value in the sequence on the left that is equal to the atomic value on the right . The fourth expression is true because there is an atomic value in the sequence on the left that is not equal to the atomic value on the right . Interesting result, right Sequence (1, 2, 3) is both equal and not equal to atomic value 1 . If this confuses you, use the value comparison operators . (The familiar symbolic operators in the preceding example are called general comparison operators in XQuery .) Value comparison operators do not work on sequences . Table 13-2 lists the general comparison operators and their value comparison operator counterparts .

Throughout the chapter, I will use the Performance database and its tables in my examples. Run the code in Listing 3-1 to create the database and its tables, and populate them with sample data. Note that it will take a few minutes for the code to finish.

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TABLe 13-2

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