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The previous problem, which discussed custom aggregates, used a cursor-based solution that scanned the data only once, but so did the pivoting solution, the UDA solution, and some of the specialized set-based solutions . If you consider that cursors incur more overhead than set-based solutions that scan the same amount of data, you can see that the cursor-based solutions are

bound to be slower . On the other hand, set-based solutions for running aggregation problems (also known as running totals) in SQL Server 2008 involve rescanning portions of the data multiple times, whereas the cursor-based solutions scan the data only once . I covered set-based solutions to running aggregations in T-SQL Querying . Here, I ll demonstrate cursor-based solutions . Run the following code, which creates and populates the Sales table in the CLRUtilities database:

Session Bean (BugAccessor)

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SET NOCOUNT ON; IF DB_ID('CLRUtilities') IS NULL CREATE DATABASE CLRUtilities; GO USE CLRUtilities; IF OBJECT_ID('dbo.Sales', 'U') IS NOT NULL DROP TABLE dbo.Sales; CREATE TABLE dbo.Sales ( empid INT NOT NULL, -dt DATETIME NOT NULL, -qty INT NOT NULL DEFAULT (1), -val MONEY NOT NULL DEFAULT (1.00), -CONSTRAINT PK_Sales PRIMARY KEY(empid, dt) ); GO DECLARE @num_partitions AS INT, @rows_per_partition AS INT, @start_dt AS DATETIME; SET @num_partitions = 10000; SET @rows_per_partition = 10; SET @start_dt = '20090101'; TRUNCATE TABLE dbo.Sales; INSERT INTO dbo.Sales WITH (TABLOCK) (empid, dt) SELECT NP.n AS empid, DATEADD(day, RPP.n - 1, @start_dt) AS dt FROM dbo.Nums AS NP CROSS JOIN dbo.Nums AS RPP WHERE NP.n <= @num_partitions AND RPP.n <= @rows_per_partition;

Each row in the table holds the sales quantity and value for a given employee and date . The table is initially populated with 100,000 rows, representing 10,000 partitions (employees), with 10 rows in each partition . To run performance tests with a different number of partitions and rows per partition, simply rerun this code after assigning the desired values to the variables @num_partitions and @rows_per_partition, respectively . As an example of a running aggregate problem, suppose that you need to calculate the running total of qty for each employee and date . That is, for each employee and date you need to return the current quantity and also the total quantity from the beginning of the employee s activity until the current date .

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The common set-based solutions for this task that are supported by SQL Server 2008 use either subqueries or joins . Here s an example using a subquery:

Now that we have completed our requirement-gathering process and developed the analysis architecture, we move into design. During this phase, we detail each component and add any necessary implementation classes to our application. Also, we apply the principles and design patterns discussed throughout this book to create a more robust architecture.

SELECT empid, dt, qty, (SELECT SUM(S2.qty) FROM dbo.Sales AS S2 WHERE S2.empid = S1.empid AND S2.dt <= S1.dt) AS sumqty FROM dbo.Sales AS S1;

For each row from the outer instance of the Sales table (S1), a subquery calculates the sum of quantities of all rows from a second instance of Sales (S2), that have the same employee ID as in the outer row, and a date that is smaller than or equal to the one in the outer row . This query generates the following output, shown here in abbreviated form:

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