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Inside Microsoft SQL Server 2008: T-SQL Programming
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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;
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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:
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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);
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8 Cursors
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CHECKPOINT; DBCC FREEPROCCACHE WITH NO_INFOMSGS; DBCC DROPCLEANBUFFERS WITH NO_INFOMSGS; DECLARE @dt1 AS DATETIME, @dt2 AS DATETIME, @dt3 AS DATETIME, @dt4 AS DATETIME; SET @dt1 = GETDATE(); -- Set-Based Solution SELECT app, MAX(concurrent) AS mx FROM (SELECT app, (SELECT COUNT(*) FROM dbo.Sessions AS S WHERE T.app = S.app AND T.ts >= S.starttime AND T.ts < S.endtime) AS concurrent FROM (SELECT app, starttime AS ts FROM dbo.Sessions) AS T) AS C GROUP BY app; SET @dt2 = GETDATE(); DBCC FREEPROCCACHE WITH NO_INFOMSGS; DBCC DROPCLEANBUFFERS WITH NO_INFOMSGS; SET @dt3 = GETDATE(); -- Cursor-Based Solution DECLARE @app AS VARCHAR(10), @prevapp AS VARCHAR (10), @ts AS datetime, @event_type AS INT, @concurrent AS INT, @mx AS INT; DECLARE @Result TABLE(app VARCHAR(10), mx INT); DECLARE C CURSOR FAST_FORWARD FOR SELECT app, starttime AS ts, 1 AS event_type FROM dbo.Sessions UNION ALL SELECT app, endtime, -1 FROM dbo.Sessions ORDER BY app, ts, event_type; OPEN C; FETCH NEXT FROM C INTO @app, @ts, @event_type; SELECT @prevapp = @app, @concurrent = 0, @mx = 0; WHILE @@FETCH_STATUS = 0 BEGIN IF @app <> @prevapp BEGIN INSERT INTO @Result VALUES(@prevapp, @mx); SELECT @prevapp = @app, @concurrent = 0, @mx = 0; END SET @concurrent = @concurrent + @event_type; IF @concurrent > @mx SET @mx = @concurrent; FETCH NEXT FROM C INTO @app, @ts, @event_type; END
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Inside Microsoft SQL Server 2008: T-SQL Programming
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IF @prevapp IS NOT NULL INSERT INTO @Result VALUES(@prevapp, @mx); CLOSE C; DEALLOCATE C; SELECT * FROM @Result; SET @dt4 = GETDATE(); PRINT CAST(@numrows AS VARCHAR(10)) + ' rows, set-based: ' + CAST(DATEDIFF(ms, @dt1, @dt2) / 1000. AS VARCHAR(30)) + ', cursor: ' + CAST(DATEDIFF(ms, @dt3, @dt4) / 1000. AS VARCHAR(30)) + ' (sec)';
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Change the value of the @numrows variable to determine the number of rows in the table . I ran this code with numbers varying from 10,000 through 100,000 in steps of 10,000 . Figure 8-4 shows a graphical depiction of the benchmark test I ran .
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Max Concurrent Sessions Benchmark 60 50 Run Time (seconds) 40 30 20 10 0
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40 50 60 Rows (thousands) Set-Based Cursor
FiguRe 8-4 Benchmark for Maximum Concurrent Sessions solutions
Again, you can see a nicely shaped parabola in the set-based solution s graph, and now you know how to explain it: remember if traffic increases by a factor of f, the number of leaf-level rows inspected by the set-based query grows by a factor of f 2 . In other words, the algorithmic complexity of the set-based solution is O(n2).
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