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in the section Running Aggregations later in this chapter . The SEARCH clause for recursive common table expressions (CTEs) has not been implemented in SQL Server 2008 . Matching problems are another kind of problem in which cursor solutions are faster than set-based solutions I ll also demonstrate this use of cursors . With matching problems, I haven t found set-based solutions that perform nearly as well as cursor solutions . Of course, from time to time you should revisit problems for which cursor solutions perform better then set-based ones . You might come up with a new set-based approach, and you might be able to benefit from enhancements in SQL Server .
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In this chapter s introduction, I talked about the benefits that set-based solutions have over cursor-based ones . I mentioned both logical and performance benefits . For the most part, efficiently written set-based solutions will outperform cursor-based solutions for two reasons . First, you empower the optimizer to do what it s so good at generating multiple valid execution plans and choosing the most efficient one . When you apply a cursor-based solution, you re basically forcing the optimizer to go with a rigid plan that doesn t leave much room for optimization at least not as much room as with set-based solutions . Second, the processing of each cursor row involves overhead that is not applicable when processing set-based code . You can run some simple tests to witness and measure this overhead for example, by comparing the costs of scanning a table by using a simple query and by using a cursor . If you express the cost of scanning n rows with a query as n, you can express the cost of scanning the same number of rows with a cursor as n + n o, where o represents the overhead, or extra cost, associated with the processing of a single cursor row . You can eliminate the actual disk I/O cost by running the code twice . (The first run will load the data to cache .) To eliminate the time it takes to generate the output, you should run your code with the Discard results after execution option in SQL Server Management Studio (SSMS) is turned on . The difference in performance between the set-based code and the cursor code will then be the cursor s overhead . I will now demonstrate how to compare scanning the same amount of data with set-based code versus with a cursor . Run the following code to generate a table called T1, with a million rows, each containing slightly more than 200 bytes:
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SET NOCOUNT ON; USE tempdb; IF OBJECT_ID('dbo.T1', 'U') IS NOT NULL DROP TABLE dbo.T1; GO SELECT n AS keycol, CAST('a' AS CHAR(200)) AS filler INTO dbo.T1 FROM dbo.Nums WHERE n <= 1000000; CREATE UNIQUE CLUSTERED INDEX idx_keycol ON dbo.T1(keycol);
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You can find the code to create and populate the Nums table in 2, User-Defined Functions . Turn on the Discard results after execution option in SSMS (under Query | Query Options | Results | Grid Or Text) . Now run the following code to issue a checkpoint to ensure that all dirty pages are flushed from cache to disk, and then clear the cache:
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CHECKPOINT; DBCC DROPCLEANBUFFERS;
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Run the following set-based code twice the first run measures performance against a cold cache, and the second measures it against a warm cache:
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SELECT keycol, filler FROM dbo.T1;
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On my system, this query ran for 10 seconds against a cold cache and 1 second against a warm cache . Clear the cache again, and then run the cursor code twice:
DECLARE @keycol AS INT, @filler AS CHAR(200); DECLARE C CURSOR FAST_FORWARD FOR SELECT keycol, filler FROM dbo.T1; OPEN C; FETCH NEXT FROM C INTO @keycol, @filler; WHILE @@fetch_status = 0 BEGIN -- Process data here FETCH NEXT FROM C INTO @keycol, @filler; END CLOSE C; DEALLOCATE C;
This code ran for 25 seconds against a cold cache and 19 seconds against a warm cache . Considering the warm cache example, in which no physical I/O is involved, the cursor code ran almost 20 times longer than the set-based code, and notice that I used a FAST_FORWARD cursor, which is a FORWARD_ONLY, READ_ONLY cursor with performance optimization enabled . Both solutions scanned the same amount of data . In addition to the performance overhead, you also have the development and maintenance overhead of your code . This is a very basic example involving little code; in production environments with more complex code, the problem is, of course, much worse . Tip SQL Server allows you to declare a cursor variable, assign it with a cursor definition, and
manipulate the cursor through the variable . Because variables are local to the batch, SQL Server automatically closes and deallocates the cursor after the batch expires, even if you don t specify the CLOSE and DEALLOCATE commands explicitly . Here s the code you would use to substitute the cursor from the previous example with a cursor variable:
DECLARE @C AS CURSOR, @keycol AS INT, @filler AS CHAR(200); SET @C = CURSOR FAST_FORWARD FOR SELECT keycol, filler FROM dbo.T1; OPEN @C; FETCH NEXT FROM @C INTO @keycol, @filler;
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