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The NOT NULL constraint can only be used as a column constraint. It is not supported for table constraints, assertions, or domain constraints. Implementing a NOT NULL constraint is a very straightforward process. Simply use the following syntax when creating a column definition: <column name> { <data type> | <domain> } NOT NULL For example, suppose you want to create a table named COMPACT_DISC_ARTISTS that requires three columns: ARTIST_ID, ARTIST_NAME, and PLACE_OF_BIRTH. You want to make sure that any new rows that are added to the table include a value for the ARTIST_ID column and a value for the ARTIST_NAME column. To do this, you add a NOT NULL constraint to both column definitions, as shown in the following SQL statement:

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CREATE TABLE COMPACT_DISC_ARTISTS ( ARTIST_ID INT NOT NULL, ARTIST_NAME VARCHAR(60) NOT NULL, PLACE_OF_BIRTH VARCHAR(60) );

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.

Leisenring: Some people believe that you can t put it in the denominator of EPS (earnings per share) and the expense numerator of EPS. But that s wrong. If you bought a truck with stock options, wouldn t you depreciate the truck Or if you bought office supplies, would you not expense them Would you pretend that you didn t use them If you do use them and expense those office supplies, that s going to be in the numerator, and the two shares that you gave up for those office supplies are going to be in the denominator.

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Notice that the PLACE_OF_BIRTH column does not include a NOT NULL constraint. As a result, if a value isn t supplied for this column (when a row is inserted), a null value will be inserted. (The null flag will be set.) Figure 4-2 shows how the table might look if rows were inserted that contained no value for the PLACE_OF_BIRTH column. As you can see, the ARTIST_ID and ARTIST_NAME columns do not , and cannot, contain null values. The PLACE_OF_BIRTH column, on the other hand, contains two null values.

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To access the data in the database, a program could: find a particular part by number (such as the left door), move "down" to the first child (the door handle), move "up" to its parent (the body), or 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. 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. 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." 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. IMS is still a very widely used DBMS on IBM mainframes. Its raw performance makes it the database of choice in high-volume transaction processing applications such as processing bank ATM transactions, verifying credit card numbers, and tracking the delivery of overnight packages. Although relational database performance has improved dramatically over the last decade, the performance requirements of applications such as these have also increased, insuring a continued role for IMS.

n 2006, the International Organization for Standardization (ISO) and the American National Standards Institute (ANSI) published revisions to their SQL standard, which I will call SQL:2006. As you will see later, the standard is divided in parts, and each part is approved and published on its own timeline, so different parts have different publication years; it is common to use the latest year as the collective name for the set of all parts published up through that year. The SQL:2006 standard, like its predecessors SQL:2003, SQL:1999 (also known as SQL3), and SQL-92, is based on the relational data model, which defines how data can be stored and manipulated within a relational database. Relational database management systems (RDBMSs) such as Oracle, Sybase, DB2, MySQL, and Microsoft SQL Server (or just SQL Server) use the SQL standard as a foundation for their technology, providing database environments that support both SQL and the relational data model. There is more information on the SQL standard later in this chapter.

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