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' VB Public Shared Sub HandleCallback( _ ByVal result As IAsyncResult) Dim proxy As OrderEntryServiceClient proxy = result.AsyncState Dim ack As OrderAcknowledgement ack = proxy.EndSubmitOrder(result) Console.WriteLine( _ "Order submitted; tracking number: {0}", _ ack.TrackingNumber) End Sub // C# static void HandleCallback(IAsyncResult result) { OrderEntryServiceClient proxy = result.AsyncState as OrderEntryServiceClient; OrderAcknowledgement ack = proxy.EndSumbitOrder(result); Console.WriteLine( "Order submitted; tracking number: {0}", ack.TrackingNumber); }

The commandId value is the one that you (using the command s Id attribute) or the UICC.EXE compiler gave to the command, and it s declared in the header file that was generated as part

It is a good idea to analyze storage requirements carefully before configuring the disk subsystem of a server. Administrators typically elect to install the operating system on a logical volume separate from applications and data. By isolating the operating system, it is easier to secure the operating system volume and to manage disk utilization so that the volume does not run out of space. It is also usual to configure some kind of fault tolerance for the operating system. Applications are generally stored in a separate volume and user data and files in a third. Again, isolation of data types allows you to manage security, performance, and fault tolerance separately for each data type. If an application uses a transaction log to prepare entries into a database, as do Active Directory directory service and Microsoft Exchange Server, it is typical to store those logs in volumes that reside on physical disks separate from the database itself, allowing the application to rebuild the database from the logs if the database fails. Once you have thoroughly analyzed your storage requirements as they relate to the data type, security, performance, and fault tolerance, you can begin to determine how many disks you require and how those disks should be configured.

figures from a store on a daily basis, defects from a manufacturing line every hour, or inventory levels from a series of sequentially numbered bins When trying to solve missing gaps problems, many developers turn to some kind of cursor operation Either a database cursor is used, or the data is pulled back into an application that sorts the data and then moves across every record, keeping track of the previous and subsequent values to find all the missing values One particular problem that I see is finding gaps in a sequence I had to solve this type of problem several years ago for a customer who was trying to place advertisements automatically within a time sequence where you had to repeatedly find gaps that matched a set of criteria The existing solution used a lot of trial and error with nested cursors.

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Using subqueries and unequal joins solved the problem with a 90 percent reduction in time and resources A colleague of mine sent me an e-mail with a solution to the problem of figuring out a correlation between billboard postings and wildfires that used multiple WHILE loops and table variables While the code worked and produced the required result, it could have been collapsed into a set of noncorrelated subqueries with unequal joins that would perform much better against a large result set (Clint, I hope you found my solution useful) Even as I was writing this chapter, another customer needed to validate data sets in a data warehouse to find data that was missing from the loads While the one-row-at-a-time solutions get the job done, finding gaps within a large set of data can require a significant amount of time.