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the table region. Once we created the image, we used the table region properties to set the BackgroundImage Source property to External, Value to Confidential.jpg, and BackgroundRepeat to NoRepeat. Finally, you might want to experiment with borders, fonts, colors, and formatting to make the report more eye-catching. 4.2.2 Parameterized tabular reports OK, we can almost feel the resentment growing for the now ubiquitous Sales by Territory report. You can relax we re going to create a new report from scratch to learn more about working with table regions. Let s say that the AWC management has requested a report that tracks employee performance for a given period of time. To allow users to see the sales data filtered for a given time period and salesperson, the report needs to be designed as parameterized. The employee sales data must be grouped by employee and then by product subcategory and sorted by the employee sales total in descending order. In its final version, the report will look like the one in figure 4.8. Because you are already familiar with the table region, we ll highlight a few things worth mentioning, rather than provide step-by-step implementation instructions.
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Choosing an implementation approach Figure 6.5 shows the logical architecture view of our solution. Our report uses embedded code to call a shared method in a custom assembly (AwRsLibrary) and get the forecasted data. AwRsLibrary loads the existing sales data into an OpenForecast dataset and obtains a forecasting model from OpenForecast. Then, it calls down to OpenForecast to get the forecasted values for the requested number of months. AwRsLibrary returns the forecasted data to the report, which in turn displays it. We have at least two implementation options for passing the crosstab sales data to AwRsLibrary: Fetch the sales data again from the database. To accomplish this, the report could pass the selected product category and month values on a row-by-row basis. Then, AwRsLibrary could make a database call to retrieve the matching sales data. Load the existing sales data in a structure of some kind using embedded code inside the report and pass the structure to AwRsLibrary. The advantages of the latter approach are as follows: The custom code logic is self-contained We don t have to query the database again. It uses the default custom code security policy We don t have to elevate the default code access security policy for the AwRsLibrary assembly. If we choose the first option, we won t be able to get away with the default code access security setup, because RS only grants our custom assemblies Execution rights, which are not sufficient to make a database call. Actually, in the case of OpenForecast, we had to grant both assemblies FullTrust rights because any J# code requires FullTrust to execute successfully. However, we wouldn t have had to do this if we had chosen C# as a programming language. No data synchronization is required We don t have to worry about synchronizing the data containers, the matrix region, and the AwRsLibrary dataset.
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The following take on this if statement is a bit safer:
precise performance requirements of a database application. Today s off-the-shelf/ commodity database servers from the major system vendors are both powerful and flexible enough for almost all database implementations. Given that, regardless of the available power, one of the fundamental truths of any computing system is that there will always be a bottleneck somewhere (and in most cases, particularly for SQL Server systems, the bottleneck is usually in the disk subsystem), making disk configuration an important DBA skill. Multicore CPUs and higher-capacity (and cheaper) memory chips have made CPU and memory configuration reasonably straightforward. Disk configuration, on the other hand, is more involved, and for a disk-intensive server application such as SQL Server, correctly configuring disk storage components is critical in ensuring ongoing performance and stability. As well as being the most complicated hardware bottleneck to fix once in production, incorrectly configured disks and poor data placement are arguably the most common cause of SQL Server performance problems. 9 will tackle disk management from a SQL Server data placement perspective. For now, let s focus on disk configuration from a hardware and operating system perspective. In this section, we ll take a look at disk drive anatomy, partition offsets, allocation unit size, using multipathing software, and configuring storage cache.
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