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In this example, the WHERE clause identifies a single row of the CUSTOMERS table, and the SET clause assigns new values to two of the columns in that row. The WHERE clause is exactly the same one you would use in a DELETE or SELECT statement to identify the row. In fact, the search conditions that can appear in the WHERE clause of an UPDATE statement are exactly the same as those available in the SELECT and DELETE statements. Like the DELETE statement, the UPDATE statement can update several rows at once with the proper search condition, as in this example: Transfer all salespeople from the Chicago of ce (number 12) to the New York of ce (number 11), and lower their quotas by 10 percent.

One of the most important applications for the earliest database management systems was production planning for manufacturing companies. If an automobile manufacturer decided to produce 10,000 units of one car model and 5000 units of another model, it needed to know how many parts to order from its suppliers. To answer the question, the product (a car) had to be decomposed into assemblies (engine, body, chassis), which were decomposed into subassemblies (valves, cylinders, spark plugs), and then into sub-subassemblies, and so on. Handling this list of parts, known as a bill of materials, was a job tailor-made for computers. The bill of materials for a product has a natural hierarchical structure. To store this data, the hierarchical data model, illustrated in Figure 4-2, was developed. In this model, each record in the database represented a specific part. The records had parent/child relationships, linking each part to its subpart, and so on.

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A complete discussion of triggers is beyond the scope of this book, but even these simple examples shows the power of the trigger mechanism. The major advantage of triggers is that business rules can be stored in the database and enforced consistently with each update to the database. This can dramatically reduce the complexity of application programs that access the database. Triggers also have some disadvantages, including these: Database complexity. When the rules are moved into the database, setting up the database becomes a more complex task. Users who could reasonably be expected to create small, ad hoc applications with SQL will find that the programming logic of triggers makes the task much more difficult. Hidden rules. With the rules hidden away inside the database, programs that appear to perform straightforward database updates may, in fact, generate an enormous amount of database activity. The programmer no longer has total control over what happens to the database. Instead, a program-initiated database action may cause other, hidden actions. Hidden performance implications. With triggers stored inside the database, the consequences of executing a SQL statement are no longer completely visible to the programmer. In particular, an apparently simple SQL statement could, in concept, trigger a process that involves a sequential scan of a very large database table, which would take a long time to complete. These performance implications of any given SQL statement are invisible to the programmer.

Figure 4-2.

FIGURE 10-6

Part III:

To access the data in the database, a program could perform the following tasks: I Find a particular part by number (such as the left door) I Move down to the first child (the door handle) I Move up to its parent (the body) I Move sideways to the next child (the right door) Retrieving the data in a hierarchical database thus required navigating through the records, moving up, down, and sideways one record at a time. One of the most popular hierarchical database management systems was IBM s Information Management System (IMS), first introduced in 1968. The advantages of IMS and its hierarchical model follow. I Simple structure. The organization of an IMS database was easy to understand. The database hierarchy paralleled that of a company organization chart or a family tree. I Parent/child organization. An IMS database was excellent for representing parent/child relationships, such as A is a part of B or A is owned by B. I Performance. IMS stored parent/child relationships as physical pointers from one data record to another, so that movement through the database was rapid. Because the structure was simple, IMS could place parent and child records close to one another on the disk, minimizing disk input/output.

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