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Sets, Arrays, and Collections
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In a relational database, tables are the only database structure used to represent a set of objects. For example, the set of engineers in our PERSONNEL database is represented by the rows in the ENGINEERS table. Suppose each engineer has a set of academic degrees (a BS in science from MIT, a PhD in electrical engineering from Michigan, etc.) that are to be stored in the database. The number of degrees for each engineer will vary from none for some engineers to perhaps half a dozen for others. A pure relational database has only one correct way to add this information to the data model. A new table, DEGREES, must be created, as shown in Figure 24-5. Each row in the DEGREES table represents one individual academic degree held by one of the engineers. A column in the DEGREES table holds the employee number of the engineer holding the degree described by that particular row and serves as a foreign key to the ENGINEERS table, linking the two tables in a parent/child relationship. The other columns in the DEGREES table describe the particulars of the degree. You have seen the type of parent/child relational table structure shown in Figure 24-5 many times in the earlier chapters of this book, and it has been a basic construct of relational databases since the beginning. However, there are some disadvantages to having this be the only way in which sets of data attributes can be modeled. First, the database tends to have a great many tables and foreign key relationships, and becomes hard to understand. Second, many common queries need to join three, four, or more tables to get the required answers. Third, with the implementations of relational joins provided by most DBMS systems, the performance of queries will deteriorate as they involve more and more joins. The table structure of Figure 24-5 cannot be implemented directly in an object-oriented model. The degrees are not substantial objects in their own right and do not deserve their own table. Instead, they are must be implemented as attributes of the engineer holding the degrees. True, a variable number of degrees is associated with each engineer, but the objectoriented model would have no problem with representing this situation as an array or a set of data within the engineer object.
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ENGINEERS Table NAME EMPL_NUM 1234 1374 1421 1532 F_NAME Bob Sam Sally M_INIT J. F. P. ADDRESS POSTCODE L_NAME STREET CITY STATE MAIN SFX SALARY YRS_EXPER Smith 956 Elm Rd. Forest NY 38294 4567 $45,000 6 Wilson 564 Birch Rd. Marion KY 82942 3524 $30,000 12 Watson 87 Dry Lane Mt Erie DL 73853 2394 $34,500 9 DEGREES Table EMPL_NUM DEGREE SCHOOL Michigan 1245 BS 1245 MS Purdue 1374 BS Lehigh 1439 BS MIT BS MIT 1436 MBA Stanford 1439
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FIGURE 24-5
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A relational modeling of engineers and their degrees
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SQL and Objects
The object-relational databases support this object-oriented view of data by supporting sets, arrays, or other collection data types. A column within a table can be defined to have one of these data types. It will then contain not a single data item value, but a set of data item values. Special SQL extensions allow a user, or more often a stored procedure, to manipulate the set of data items as a whole or to access individual members of the set.
Defining Collections
Informix Universal Server supports collections of attributes through its collection data types. Three different collection data types are supported: Lists A list is an ordered collection of data items, all of which have the same type. Within a list is the concept of a first item, a last item, and the nth item. The items in the list are not required to be unique. For example, a list of the first names of the employees hired in the last year, in order of hire, might be { Jim , Mary , Sam , Jim , John }. Multisets A multiset is an unordered collection of data items, all of which have the same type. There is no concept of sequencing the items in a multiset; its items have no implied ordering. The items are not required to be unique. The list of employee first names could be considered a multiset if you didn t care about the order of hire: { Jim , Sam , John , Jim , Mary }. Sets A set is an unordered collection of unique data items, all of which have the same type. As in a multiset, there is no concept of first or last; the set has no implied order. The items must have unique values. The first names in the previous examples wouldn t qualify, but the last names might: { Johnson , Samuels , Wright , Jones , Smith }. To illustrate the concept of collection data, we will expand the tables in our example object-relational database as follows: The REPS table will include sales targets for each of the first, second, third, and fourth quarters. The quarterly targets can naturally be represented as a list column added to the REPS table. The quarters have a natural ordering (first through fourth); the quota for each quarter has the same data type (DECIMAL); and the values are not necessarily unique (that is, the quotas for the first and second quarters might be the same). The ENGINEERS table will include information about the academic degrees that each engineer holds. Two items of data will actually be stored about each degree the actual degree (BS, PhD, MBA, etc.) and the school. This data will be stored as a multiset column added to the ENGINEERS table, because it s possible to have two identical entries for example, an engineer may have a BS degree in engineering and a BS degree in business from the same school. The TECHNICIANS table will include information about the projects to which each technician is assigned. Each technician may be assigned to two or more projects, but each project has a unique name. This data will be stored as a set column added to the TECHNICIANS table. The data values must be unique, but no particular order is associated with them.
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