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Before introducing fourth and fth normal forms, let me brie y mention domain-key normal form (DKNF). In DKNF, all constraints come from domains (types) and keys (candidate keys and foreign keys). A table in DKNF is free of violating entity, referential, and domain integrity rules, as described previously. It is in fth normal form as well and thus fully normalized. However, DKNF is a more theoretical than practical normal form. To achieve it, you would have to create many, many different types. This is a nearly impossible mission, especially if your types need to be widely accepted and your type constraints need to be agreed on. In addition, users of your types (the database and other developers) would have to learn a lot just to start using your types. C. J. Date also proposed sixth normal form a normal form that solves possible temporal data anomalies. However, to solve temporal data problems, I would also have to introduce the Interval data type, implement the Pack and Unpack operators, and solve some other problems as well. Refer to Inside Microsoft SQL Server 2008: T-SQL Programming to nd a deeper discussion of temporal data and suggested solutions for temporal problems.



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query; it provides none of the advanced updating, backward scrolling, or sensitivity features of the JDBC ResultSet interface.

using System.Data; Using System.Data.Common; public partial class UseDataList : System.Web.UI.Page { protected DataTable GetInventory() { string strConnection = @"Data Source= .\SQLEXPRESS; AttachDbFilename=|DataDirectory|\ASPNETStepByStep4.mdf; Integrated Security=True; User Instance=True"; DbProviderFactory f = DbProviderFactories.GetFactory("System.Data.SqlClient"); DataTable dt = new DataTable(); using (DbConnection connection = f.CreateConnection()) { connection.ConnectionString = strConnection; connection.Open(); DbCommand command = f.CreateCommand();





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You should always try to reach at least 3NF when designing a database. However, sometimes you have to turn the process around and, after fully normalizing a database, start denormalizing it. The two main reasons for denormalization are performance and history, as explained in Designing Database Solutions by Using Microsoft SQL Server 2005 by Dejan Sarka, Andy Leonard, Javier Loria, and Adolfo Wiernik (Microsoft Press, 2007). A classic business question is, how much of a product is currently in stock You can calculate quantities on stock by summarizing shipments and subtracting deliveries. States and levels can always be calculated from events. However, this question could be very frequent. Therefore, it makes sense to aggregate events to levels and states and maintain these aggregates with every new event. In addition, you could speed joins by replicating a foreign key from the rst child table to the second one. This way queries might involve fewer tables to join. In both cases, you denormalized to improve performance. Imagine another example. An invoicing application uses a fully normalized database design. A customer s address is stored in the Customers table only. If a customer moves, you update that customer s address with the new one. Let s say that after the update, the customer asks you to reprint an old invoice. Now you have a problem because you didn t store the old address. You can solve this problem by maintaining a copy of the customer address on the invoice date in the Invoices table. (I should mention that this might not be treated as denormalization you probably just missed that InvoiceAddress attribute when analyzing the business problem!) Figure 3-13 shows the fully normalized Invoices database.

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You can denormalize in multiple places. For example, you might transfer the EmployeeId column to the Invoices table to avoid a join to the Customers table when you are analyzing invoices over employees only. You could include the CustomerName and CustomerAddress columns in the Invoices table to maintain history. You could maintain aggregates, such as stock level per warehouse (in a separate table), total stock level per product, year-to-date sales per customers, and more. Figure 3-14 shows a denormalized version of the invoices database.

The columns returned in a Java ResultSet are numbered from 1, while the columns returned in a .NET IDataReader are numbered from 0 (zero).

Products PK ProductId ProductName Price TotalInStock InvoiceDetails PK,FK1 PK FK2 InvoiceId ItemId ProductId Quantity Discount WarehouseId

command.CommandText = "Select * from DotNetReferences"; command.Connection = connection; IDataReader reader = command.ExecuteReader(); dt.Load(reader); reader.Close(); connection.Close();

Remember to denormalize very deliberately. After denormalization, you introduce possible update anomalies back to the database. You have to maintain redundant data in user-de ned transactions. If you insert a new event, for example, take care to update

Member IdataReader[key]

the level or the state derived from events in the same transaction. Triggers are especially useful for maintaining denormalized data. With triggers, which are automatically part of a transaction, you transfer the burden of maintaining the denormalized data on your RDBMS.

} return dt;

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