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DECLARE @empid @prvempid @dt @qty @sumqty AS AS AS AS AS INT, INT, DATETIME, INT, BIGINT;
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DECLARE C CURSOR FAST_FORWARD FOR SELECT empid, dt, qty FROM dbo.Sales ORDER BY empid, dt; OPEN C; FETCH NEXT FROM C INTO @empid, @dt, @qty; SELECT @prvempid = @empid, @sumqty = 0; WHILE @@fetch_status = 0 BEGIN IF @empid <> @prvempid SELECT @prvempid = @empid, @sumqty = 0; SET @sumqty = @sumqty + @qty; INSERT INTO @Result VALUES(@empid, @dt, @qty, @sumqty); FETCH NEXT FROM C INTO @empid, @dt, @qty; END CLOSE C; DEALLOCATE C; SELECT * FROM @Result;
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Provided that a covering index is in place defined with the cursor ordering columns as the index keys and the measure you re aggregating as an included column, the cursor solution scans the data only once without the need for sorting . If you have p partitions with an average of r rows per partition, and a relatively uniform distribution of rows per partition, you can express the cost of this solution as p r + p r o, where o, as before, represents the cursor overhead . Therefore, the complexity of this solution is linearly proportional to the number of partitions and linearly proportional to the partition size . Assuming you have the aforementioned index in place, the cost of the set-based solution can be expressed as p r + p (r + r 2)/2. With respect to the number of partitions, the complexity of the set-based solution is linear, but with respect to the partition size, it s quadratic, or O(n2) . If the partition size grows by a factor of f, the cost increases by almost f 2 . Without the aforementioned index in place, of course, things are even worse . When the partition size is small, the set-based solution will typically be faster than the cursor solution . That s when the cursor s overhead exceeds the set-based solution s extra work of
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Inside Microsoft SQL Server 2008: T-SQL Programming
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scanning more data . However, as the partition size increases, the cursor solution s cost grows linearly while the set-based solution s cost grows much more rapidly quadratically, and at some point the cursor solution becomes faster . Tests that I ve done show that up to a partition size of about 500 rows the set-based solution tends to perform better, whereas beyond 500 rows the cursor solution performs better . I ll provide actual benchmark results later in this section . So now you can see that in certain cases a T-SQL cursor-based solution can be faster than a set-based solution . If you are willing to consider an iterative approach based on cursors, you should consider a CLR-based solution . Using a CLR procedure, you can use a data reader to iterate through the records one at a time, then pipe the rows in the result back to the caller . Think of a .NET data reader as a faster cursor than the T-SQL one . You still pay some overhead to manipulate each row, but the overhead is much lower than that of the T-SQL cursor . Here s the definition of a CLR stored procedure called SalesRunningSum that implements our task in C#:
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using using using using using System; System.Data; System.Data.SqlClient; System.Data.SqlTypes; Microsoft.SqlServer.Server;
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public partial class StoredProcedures { [Microsoft.SqlServer.Server.SqlProcedure] public static void SalesRunningSum() { using (SqlConnection conn = new SqlConnection("context connection=true;")) { SqlCommand comm = new SqlCommand(); comm.Connection = conn; comm.CommandText = "" + "SELECT empid, dt, qty " + "FROM dbo.Sales " + "ORDER BY empid, dt;"; SqlMetaData[] columns = new SqlMetaData[4]; columns[0] = new SqlMetaData("empid", SqlDbType.Int); columns[1] = new SqlMetaData("dt", SqlDbType.DateTime); columns[2] = new SqlMetaData("qty", SqlDbType.Int); columns[3] = new SqlMetaData("sumqty", SqlDbType.BigInt); SqlDataRecord record = new SqlDataRecord(columns); SqlContext.Pipe.SendResultsStart(record); conn.Open(); SqlDataReader reader = comm.ExecuteReader(); SqlInt32 prvempid = 0; SqlInt64 sumqty = 0;
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