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In this chapter, we focused on the structural aspect of the object/relational paradigm mismatch and discussed the first four generic ORM problems. We explored the programming model for persistent classes and the NHibernate ORM metadata for finegrained classes, object identity, inheritance, and associations. You now understand that persistent classes in a domain model should be free of cross-cutting concerns such as transactions and security. Even persistence-related concerns shouldn t leak into the domain model. We no longer entertain the use of restrictive programming models such as DataSets for our domain model. Instead, we use transparent persistence, together with the unrestrictive POCO programming model which is really a set of best practices for the creation of properly encapsulated .NET types. You also learned about the important differences between entities and value-typed objects in NHibernate. Entities have their own identity and lifecycle, whereas valuetyped objects are dependent on an entity and are persisted with by-value semantics. NHibernate allows fine-grained object models with fewer tables than persistent classes. Finally, we introduced the three well-known inheritance-mapping strategies in NHibernate. We also covered associations and collections mapping; and you implemented and mapped your first parent/child association between persistent classes, using database foreign key fields and the cascading of operations. With this understanding, you can experiment with NHibernate and handle most common mapping scenarios, and perhaps some of the thornier ones too. As you become familiar with creating domain models and persisting them with NHibernate, you may face other architectural challenges. We next investigate the dynamic aspects of the object/relational mismatch, including a much deeper study of the cascaded operations we introduced and the lifecycle of persistent objects.

Modifying the program in this way is obviously not a very tricky change to make: you can do it with a simple search-and-replace action, and only two places need to be changed. However, doing a similar search and replace in a larger program, consisting of, say, tens of thousands of lines of code would be a lot trickier. We would have to be careful about simply replacing 5 with 10: no doubt, there would be other instances of the number five that aren t related to this and so shouldn t be changed to ten. Solving this problem is what variables are for. Rather than sticking the upper loop value (five or ten) directly in the code, we can solve this problem by putting the number in a variable, thus adding a layer of indirection. When you add the variable, instead of telling the program to go through the loop five times, you re telling it to go look in this variable named count, which will say how many times to run the loop. Now, the program is called Count-3 and looks like this:

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The Weather app bundled with the iPhone 4 is certainly functional, but there are other alternatives. Most of the weather apps are free in the App Store, and some offer premium versions for a modest fee. NOTE: Most of the free weather apps are supported by ads in the app. For the most part, these are not intrusive. The easiest way to find alternative weather apps is to go to the App Store and touch the Categories icon at the bottom. There is a separate category of the store simply called Weather. In the Weather category, touch Top Free at the top and then search for apps. See more about downloading apps in 26: The Amazing App Store.

IList results = session.CreateFilter( item.Bids, "select this.Bidder" ) .List();

The preceding tables have made a number of changes to the OnPaint method. The following code pulls together all of the pieces presented in the preceding tables. We will not discuss these changes in additional detail.

Aggregations don t have to exactly match the queried dimension attributes If userdefined hierarchies exist that relate higher levels to lower levels in each dimension, queries at higher levels can leverage aggregations at the lower level Let s assume the three user-defined hierarchies represented in table 1 and the same aggregation that was previously mentioned (summing values of sales by state, month, and product category) What happens if a follow-up query is issued to ask, What is the percentage of total sales for nonfiction books in the state of Texas, compared to the total sales across all states in the US This answer is also easy for Analysis Services to answer Because the values for total sales by state per product category are precalculated, only 50 statelevel aggregations are affected by this query.

PS (3) > get-item c:\ | format-table -autosize Directory: Mode ---d--hs LastWriteTime Length Name ------------- ------ ---4/9/2006 10:04 PM C:\

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This compares to summing values down to the lowest level of granularity, which could include anywhere from thousands to millions, or even billions, of values! If you come from a relational database background, you can loosely compare aggregations to indexes In this comparison, you know that indexes provide an expeditious way of retrieving query results Unlike indexes, though, aggregations provide the best benefit to queries that select data at higher levels, which would require accessing a very large number of cells Just as with indexes, the savings at query time come at a cost, and the goal is to design the right indexes and aggregations to match your query load With aggregations, this results in an OLAP solution that responds well to a wide variety of queries with the minimum cube-processing overhead.

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