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The Toolkit animation framework consists of a group of client classes, each of which describes a particular type of animation. These classes derive, directly or indirectly, from a base class called Animation. Table 10.4 lists all the available animations along with their descriptions. As you can see, the list of animations is exhaustive it includes fading effects, and you can move, resize, and scale elements; animate colors; and manage multiple animations.

The easiest way to generate a WSDL document is using a tool like Visual Studio .NET. There is very much a chicken-and-the-egg relationship between a WSDL document and the Web service implementation that it describes. That is, you can write the code first, and generate the WSDL document later. Or, you can manually write the WSDL document first, and then use it to auto-generate the code definition. Because it is very difficult to generate a WSDL document by hand, you are better off writing the code implementation first, and then using Visual Studio .NET to generate the WSDL document for you.

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Web services are an area of heated development. Axis will evolve, Sun is coming out with its own web service package, and, inevitably, Ant will acquire wrapper tasks to simplify the stages of the build using Apache, Sun, and other toolkits. Ultimately, web services are distributed applications scaled up. If you are writing one, you are writing an application to work across the Internet, interoperating with systems written in other languages, communicating over a protocol (HTTP) that is chosen because it can get through firewalls, not because it is the best protocol for such things (it isn t). This is a major undertaking. Ant alone is not adequate. What Ant gives you is the means to build, deploy, and test your code, including automated generation of client-side stub classes and test cases. It is not a silver bullet. It is, however, along with JUnit, an essential tool for this kind of project.

Joins are a common cause of poorly performing SQL A large part of the optimizer s work lies in optimizing joins, in making them perform at the least possible cost Sometimes the optimizer needs a little help Following are some possible problems to look at: Inefficient join order caused by the optimizer selecting the wrong starting row source: It is crucial for joins to use a join order that starts with the tables/row sources (in the case of nonmergeable views) that produce the least number of rows, possibly after applying filter predicates, which ideally produces a single-row row source.

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If the single-table access path estimates are incorrect (see the section Generating 10053 Cost-Based Optimizer Traces in 9 to determine how to obtain the single-table access estimates for all tables involved), then the optimizer might start the join with the wrong table/row source, causing the joins to process more rows than expected When an underestimate of cardinality is encountered, check the object statistics and predicates applied In particular, when using functions in predicates, the default of 1 percent or 5 percent selectivity depending on the comparison method (equality or inequality comparisons such as ranges, respectively) is used by the optimizer, which might be significantly different from the actual selectivity of the function General inefficient join order: Estimating the correct join selectivity/cardinality is one of the biggest challenges to the optimizer If the join selectivity/cardinality estimate is wrong, the effects of the miscalculation will echo through the remaining plan.

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This potentially leads to an inefficient overall join order used by the optimizer, which then results in more rows being processed than necessary The objective here is to eliminate rows as early as possible Multicolumn joins: When joining tables on multiple columns, the optimizer might significantly underestimate the resulting join cardinality if the join columns are correlated If the number of distinct values of the combined join columns is less than the multiplied number of distinct values for each individual column (for example, 20 distinct values in COL1 and 10 distinct values in COL2, but only 30 distinct values for COL1, COL2 instead of 20 10 = 200), then you have identified.

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