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So remember, don t assume any particular order for a table s rows. Conversely, don t specify an ORDER BY clause unless you really need the rows sorted or need to describe the ordering for a TOP option. Sorting has a cost SQL Server needs to perform an ordered index scan or apply a sort operator. The ORDER BY clause considers NULLs as equal. That is, NULLs are sorted together. ANSI leaves the question of whether NULLs are sorted lower or higher than known values up to implementations, which must be consistent. T-SQL sorts NULLs as lower than known values ( rst). Apply this step to the sample query:

FETCH NEXT FROM C INTO @app, @ts, @event_type; END IF @prevapp IS NOT NULL INSERT INTO @Result VALUES(@prevapp, @mx); CLOSE C; DEALLOCATE C; SELECT * FROM @Result;

You get the cursor VC6 shown in Table 1-9.

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The query returns all 91 customers. Because there's no distinction between an ON clause and a WHERE clause, I specified both expressions in the WHERE clause separated by the logical operator AND. You have no control over which part of the filter will take place before adding the outer rows and which part will take place afterwards. That's at the sole discretion of SQL Server. By looking at the result, you can guess what SQL Server did. Logically, it applied the whole expression before adding outer rows. Obviously, there's no row in the Cartesian product for which C.CustomerID = O.CustomerID and O.CustomerID IS NULL. So the second phase in query logical processing yields an empty set. The third phase adds outer rows for rows from the preserved table (Customers) with no match. Because none of the rows matched the join condition, all customers are added back as outer rows. That's why this query returned all 91 customers. Bearing in mind that you got this surprising result because both expressions were applied before adding the outer rows, and being familiar with query logical processing, there is a way to "fix" the problem. Remember that there's another filter available to you in a querythe HAVING filter. First write a join query without the filter to isolate outer rows. Even though the rows in the result of the join are unique, group them by all columns to allow the query to include a HAVING clause. Then put the filter that isolates the outer rows into the HAVING clause: SELECT C.CustomerID, CompanyName, OrderID FROM dbo.Customers AS C, dbo.Orders AS O WHERE C.CustomerID *= O.CustomerID GROUP BY C.CustomerID, CompanyName, OrderID HAVING OrderID IS NULL;

The cursor solution scans the leaf of the index only twice . You can represent its cost as 2 n + 2 n o, where o is the cursor overhead involved with each single row manipulation . Also, if the traffic grows by a factor of f, the performance degrades linearly to 2 n f + 2 n f o . You realize that unless you re dealing with a very small input set, the cursor solution has the potential to perform much faster, and as proof, you can use the following code to conduct a benchmark test:

TABLE 1-9

This section covers further aspects of logical query processing, including table operators (JOIN, APPLY, PIVOT, and UNPIVOT), the OVER clause, and set operators (UNION, EXCEPT, and INTERSECT). Note that I could say much more about these language elements besides their logical query processing aspects, but that s the focus of this chapter. Also, if a language element described in this section is completely new to you (for example, PIVOT, UNPIVOT, or APPLY), it might be a bit hard to fully comprehend its meaning at this point. Later in the book I ll conduct more detailed discussions including uses, performance aspects, and so on. You can then return to this chapter and read about the logical query processing aspects of that language element again to better comprehend its meaning.

SET NOCOUNT ON; USE tempdb; IF OBJECT_ID('dbo.Sessions', 'U') IS NOT NULL DROP TABLE dbo.Sessions GO DECLARE @numrows AS INT; SET @numrows = 10000; -- Test with 10K - 100K SELECT IDENTITY(int, 1, 1) AS keycol, D.*, DATEADD( second, 1 + ABS(CHECKSUM(NEWID())) % (20*60), starttime) AS endtime INTO dbo.Sessions FROM ( SELECT 'app' + CAST(1 + ABS(CHECKSUM(NEWID())) % 10 AS VARCHAR(10)) AS app, 'user1' AS usr, 'host1' AS host, DATEADD( second, 1 + ABS(CHECKSUM(NEWID())) % (30*24*60*60), '20090101') AS starttime FROM dbo.Nums WHERE n <= @numrows ) AS D; ALTER TABLE dbo.Sessions ADD PRIMARY KEY(keycol); CREATE INDEX idx_app_st_et ON dbo.Sessions(app, starttime, endtime);

In this manner, you control which filter will be applied before adding outer rows and which will be applied after.

SQL Server 2008 supports four types of table operators in the FROM clause of a query: JOIN, APPLY, PIVOT, and UNPIVOT.

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