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One good habit you should start embracing when writing code is to comment your code. Right now, the code for the problems we are solving isn t too complicated. But keep in mind that adding comments serves the following purpose: your code becomes much more maintainable because you can return six months later and, if the comments are good, be able to understand what you developed. It also makes your code more readable and facilitates getting help from somebody. Write your comments in plain language without too much jargon. The comments are never compiled in the application you execute, so they will never slow down the performance of your application. As you can see in the code in step 7 of the following section (code for the FileOK event), you can comment your code by inserting two forward slashes (//) for a one-line comment or by using /* and */ on the same line or on a separate line for a multiline comment. Your comment should appear in green; if not, then the compiler does not consider your line to be a comment. Another good way to comment your code is to use two buttons from one of the toolbars. Let s say you decide that the code in the FileOk event is not the code you want to execute because you want to test something else. You do not want to delete all the text, but you can comment out the code by selecting it and then clicking the Comment Out the Selected Lines button. And if you want to uncomment a block of code, you just have to select the code you want to uncomment and then click the Uncomment the Selected Lines button.

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Barcode using font CODE 128 — OpenText - Forums
I am using CODE 128 font to generate Barcode in report. Its working fine with BIRT Viewer and .xls output, but it appears as number when ...

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Eclipse BIRT Code 128 Barcode Maker Add-in | Generate Code 128 ...
Eclipse BIRT Code 128 Barcode Maker add-ins is a Java Code 128 barcode generator designed for BIRT reports. The Code 128 BIRT reporting maker can be  ...

Updating partitioned tables is somewhat more complicated than nonpartitioned equivalents. Instead of a single physical table (heap) or B+ tree, the query processor has to handle one heap or B+ tree per partition. It needs to gure out where each row belongs, and rows can move between partitions in some update plans. In addition, each index can be partitioned using a separate partition function. Even indexed views can be partitioned, and they too can be partitioned differently than the other access paths associated with a table. Luckily, partitioned update plans SQL Server 2008 are an extension of the update plan shapes already discussed in this chapter. So this section discusses only how those plans are different when using partitioning. In the description of SELECT plans over partitioned tables earlier in this chapter, you may recall that the partitioning ID was represented within the query processor as a virtual leading column on every access method (heap or index). Partitioned table updates also use this representation, which makes the plans look a lot like the plans to update indexes. This leading column also appears in some of the other operators used in update plans, such as the Split/Sort/Collapse operators. The following examples demonstrate how partitioning ts into these plans. In the rst example, we create a partitioned table and then insert a single row into it. The plan can be seen in Figure 8-64.

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8

PARTITION FUNCTION pfinsert(INT) AS RANGE RIGHT FOR VALUES (100, 200, 300); PARTITION SCHEME psinsert AS PARTITION pfinsert ALL TO ([PRIMARY]); TABLE testinsert(ptncol INT, col2 INT) ON psinsert(ptncol); INTO testinsert(ptncol, col2) VALUES (1, 2);

Looking at the query plan, this matches the behavior one would expect from a nonpartitioned table. It has a single operator that just inserts into the table. Underneath the covers, however, the Query Execution operator has to determine which partition needs to be updated, load that partition, and set the appropriate value. Looking at the properties for the INSERT operator, we can see (in Figure 8-65) the partitioning-speci c logic that makes this happen. First, Expr1005 is computed to determine the target partition to use, and you can see the partition boundaries passed to an internal function called RangePartitionNew. In the Predicate section, the extra Expr1005 computed value is used to set the PtnId1001 column, which is the virtual partition ID column that is exposed in the system to support partitioning. The rest of the Predicate list supports setting values for the regular columns ptncol and col2.

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If you want to look at all the possible events that can be fired for a particular control, you can click the yellow lightning icon at the top of the Properties window. To return to the properties, you just have to click the little sheet symbol to the left of the yellow lightning icon.

a particular query The Distributed Query feature covers a number of scenarios and can help make solving those scenarios much easier Distributed Query is implemented within the Query Optimizer s plan-searching framework With the exception of pass-through queries, which are not modi ed during optimization, distributed queries initially are represented using the same operators as regular queries Each base table represented in the Query Optimizer tree contains metadata collected from the remote source using OLE DB metadata interfaces such as OLE DB schema rowsets The information collected is very similar to the information that the query processor collects for local tables, including column information, index information, and statistics One additional piece of collected information includes information about what SQL grammar constructs the remote source supports, which are used later in optimization Once metadata is collected, the Query Optimizer derives special property information for each operator that manipulates remote data.

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