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There are two other aspects to versioning apart from the assemblies themselves. The first is the labeling of the published assets. We will handle this in the Publish step. The second is the labeling of the VSS database. At this point, we do not perform any other action with VSS, but in future scripts we may want to manipulate assets in VSS later in the process. Therefore, we do not want to update the VSS label until later. We can split the version target into two sections: version1 and version2. version1 contains all of the work up until this point, such as obtaining a number and rewriting CommonAssemblyInfo.cs. version2 will contain directions for updating the VSS database. <target name="version2"> <ifnot test="${debug}"> <vsslabel user="builder" password="builder" dbpath="D:\dotNetDelivery\VSS\srcsafe.ini" path="$/Solutions/Transformer/" comment="Automated Label" label="NAnt - ${sys.version}" /> </ifnot> </target> The label we want to apply is NAnt - 1.0.x.0, which corresponds to the version marked on the assemblies and aids the versioning process. I have also included a test for the debug property so that the version 0.0.0.0 is not labeled throughout the VSS database when we are debugging the script, which would also be confusing. All of this scripting means that the assembly will be versioned as required when the code is compiled in the next step. It has taken longer to explain the necessary steps than it takes to actually implement the solution. The full script for the version1 target follows: <target name="version1" description="Apply versioning to the source code files."> <property name="sys.version" value="0.0.0.0"/> <ifnot test="${debug}"> <version buildtype="increment" revisiontype="increment" path="Transformer.Build.Number" /> </ifnot> <attrib file="D:\dotNetDelivery\BuildArea\Source\CommonAssemblyInfo.cs" readonly="false" /> <asminfo output="D:\dotNetDelivery\BuildArea\Source\CommonAssemblyInfo.cs"



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Some of the statistics gathered about rows (or tuples) in tables (or relations) for use in cost-based optimizers include the following: The number of tuples in the table The number of blocks containing rows (the block count) The size of the row in bytes The number of distinct values for each attribute (or column) The selection cardinality of each attribute (sometimes represented as evenly distributed) The fan-out of internal nodes of an index (the number of children resulting in subtrees) The height of the B-tree for an index The number of blocks at the leaf level of the index The cost of writing the final result of an operation back to disk is ignored Regardless of the query-evaluation plan used, this cost does not change; therefore, not including it in the calculations does not affect the choice of the plan.





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language="CSharp"> <imports> <import name="System" /> <import name="System.Reflection"/> </imports> <attributes> <attribute type="AssemblyVersionAttribute" value="${sys.version}" /> <attribute type="AssemblyProductAttribute" value="Transformer" /> <attribute type="AssemblyCopyrightAttribute" value="Copyright (c) 2005, Etomic Ltd."/> </attributes> </asminfo> <attrib file="D:\dotNetDelivery\BuildArea\Source\CommonAssemblyInfo.cs" readonly="true" /> </target> Because we have added a new target, we must change the go target to account for the new dependency. It should now look like this: <target name="go" description="The main target for full build process execution." depends="clean, get, version1, version2, build, test, document, publish, notify" />

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Most database systems today use a form of dynamic programming to generate all possible query plans While dynamic programming offers good performance for cost optimization, it is a complex algorithm that can require more resources for the more complex queries While most database systems do not encounter these types of queries, researchers in the areas of distributed database systems and high-performance computing have explored alternatives and variants to dynamic programming techniques The recent research by Kossmann and Stocker shows that we are beginning to see the limits of traditional approaches to query optimization6 What are needed are more efficient optimization techniques that generate execution plans that follow good practices rather than exhaustive exploration In other words, we need optimizers that perform well in a variety of general environments as well as optimizers that perform well in unique database environments..

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The goal of heuristic optimization is to apply rules that ensure good practices for query execution. Systems that use heuristic optimizers include INGRES and various academic variants. Most systems typically use heuristic optimization as a means of avoiding the really bad plans rather than as a primary means of optimization. Heuristic optimizers use rules on how to shape the query into the most optimal form prior to choosing alternative implementations. The application of heuristics, or rules, can eliminate queries that are likely to be inefficient. Using heuristics as a basis to form the query plan ensures

As I mentioned earlier, it is the scaffolding of the process that takes the time (the versioning effort is a testament to this!) while the actual compilation activities are quite simple. This target makes use of the <solution> task to compile the solution file and output the assemblies into the output folder. Since the solution is entirely .NET-based and not complex, there is no need for any pre- or post-compilation activities here. The target looks like this: <target name="build" description="Compile the application."> <solution solutionfile="D:\dotNetDelivery\BuildArea\Source\Transformer.sln" configuration="Debug" outputdir="D:\dotNetDelivery\BuildArea\Output\" /> </target> Here I have hard-coded the configuration attribute and I will not address this attribute any further, but of course you may wish to have a conditional (or otherwise) process based on a variety of configuration options.

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