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You ve probably heard discussions about normalization that range from treating it like the Holy Grail to viewing it as a feature that adversely affects performance. What is it about normalization that gets people going so You can put all your data somewhere in a table, and as long as you can write SQL code to retrieve the necessary data, and you have a good RDBMS running on a machine with plenty of fast processors, you shouldn t have a slow-performing database, right The truth is that poorly designed relations and tables in a database can have serious effects, not only on the efficacy of your database, but also on the validity of the data itself. Let s look at an example of an ordering system in a warehouse. Imagine a simple table with each customer s information contained in a single tuple or row. What happens if customer A has 1,000 transactions and customer B has only one or two transactions Either customer A s transactions will not all fit in the row, or customer B s row will be mostly empty. Either you will not be able

to cater to the customer, or you will waste a tremendous amount of space in the database. Simple queries turn into terrible resource wasters under this design. You can try another variation on the previous design by creating a much more compact table by allowing repeatable values of the attributes. That is, for each transaction, each customer s complete information would be repeated. Now you have just traded one set of problems for another. If customer A s information changes, each of that customer s rows in the table would need to be updated. For such repeated groups, when you perform updates, you have to make sure to update all occurrences of the particular customer s data, or you will end up with an inconsistent set of data.

Figure 10-57. An example of a system using a router The system consists of two major components, SubSystem1 and SubSystem2, with a router sitting between them. The Run command on SubSystem1 causes a flurry of activity, as shown in the diagram. Note that some of the objects, like C1, must be reentrant. When C1 fires the DoSomething event, the calls blocks until the event notification is processed. To process it, the Router forwards the notification to C2, which calls back through the Router into C1. To remove the reentrancy requirement, some of the events would have to be fired asynchronously. Listing 10-53 and Listing 10-54 show a C# and VB .NET implementation of the system. Listing 10-53. A C# Implementation of a System Using a Router public delegate void UniversalHandler(); public class Router { public void DoSomething() { FireDoSomething(); } public void NowDoThis() { FireNowDoThis(); } public event UniversalHandler OnDoSomething; void FireDoSomething() { if (OnDoSomething != null) OnDoSomething(); }

You can see on an intuitive level that designing without a solid design strategy, based on sound mathematical principles, will lead to several problems. Although it is easy to see the inefficiency involved in the unnecessary consumption of storage space and longer query-execution times, other, more serious problems occur with off-the-cuff design of tables in a database these are the socalled data anomaly problems. Three types of data anomalies can result from improperly designed databases: The update anomaly: In this well-known anomaly, you could fail to update all the occurrences of a certain attribute because of the repeating values problem. The insertion anomaly: In this anomaly, you are prevented from inserting certain data because you are missing other pieces of information. For example, you cannot insert a customer s data because that customer has not bought a product from your warehouse yet. The deletion anomaly: In this anomaly, you could end up losing data inadvertently because you are trying to remove some duplicate attributes from a customer s data.

Note The debate between database developers and designers continues over denormalization. Many believe it s okay to break almost all design rules and denormalize for performance gains. However, others believe that this isn t correct and that the act of denormalization reduces the integrity of the database by removing the controls that lie at the heart of RDBMS design.

SQL statements pass through several stages during their processing: parsing, binding, and executing. Oracle uses cursors, private SQL areas, to store parsed statements and other information relating to the statements it s currently processing. Oracle automatically opens a cursor for all SQL statements.

public event UniversalHandler OnNowDoThis; void FireNowDoThis() { if (OnNowDoThis != null) OnNowDoThis(); } public event UniversalHandler OnMethod1; void FireMethod1() { if (OnMethod1 != null) OnMethod1(); } public event UniversalHandler OnMethod2; void FireMethod2() { if (OnMethod2 != null) OnMethod2(); } } public class Coordinator1 { public void Run() { FireDoSomething(); } public void NowDoThis() { FireMethod1(); FireMethod2(); } public event UniversalHandler OnDoSomething; void FireDoSomething() { if (OnDoSomething != null) OnDoSomething(); } public event UniversalHandler OnMethod1; void FireMethod1() { if (OnMethod1 != null) OnMethod1(); }