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/* Update the database row with modified project list */ update technicians set projects = proj_coll where empl_num = 1234;
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The example shows several aspects of collection-handling in Informix SPL. First, the collection is retrieved from the database into an SPL variable as a collection data type. It would also be possible to retrieve it into a variable explicitly declared as having a SET type (or in other situations, a LIST or MULTSET type). The collection stored in the variable is then explicitly treated as a table for manipulating items within the collection. To add a new project, an INSERT is performed into the collection table. To find and modify a specific project, a cursor is used to search through the collection table, and a cursor-based UPDATE statement is used to change the value of one member of the collection. Note that the FOREACH loop retrieves each item of the collection into a variable so that the SPL routine can process it. Finally, the collection variable s contents are used to update the collection column within the table. Oracle takes a similar approach to processing varying arrays. The individual elements of an array within an abstract data type are available through subscripted references within a structured data type. The typical Oracle PL/SQL process for accessing variable array elements is: 1. Retrieve the row from the table containing the varying array into a local variable whose data type is defined to match the row structure of the table, or of the particular columns being retrieved. 2. Execute a FOR loop with an index variable, n, that counts from 1 to the number of elements in the varying array. The number of elements is available through the value of a special attribute of the array column named COUNT. 3. Within the FOR loop, a subscript is used on the varying array name to access the n-th element of the varying array. A similar technique can be used to process nested tables; however, it s usually not necessary. Instead, the THE operator is generally used to flatten the table in a SQL query, and the results are processed with a single cursor-driven FOR loop. The processing may still be complex. In particular, the stored procedure may need to detect whether a particular row coming from the query results is from the same main table row as the previous row and, upon detecting a change in main table rows, perform special processing such as computing subtotals. In this aspect, the processing of both varying arrays and nested tables begins to resemble the nested-loop processing typical of the COBOL report-writing programs of 30 years ago that handled master and detail records. As the discussion in this section has illustrated, collection types and the processing of individual collection items tend to call for programmatic access through stored
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procedures rather than for ad hoc SQL use. One of the criticisms of object-oriented databases is that they are a regression from the simplicity of the relational model and reintroduce that need for explicit database navigation that was part of the prerelational databases. Examples like these provide evidence that there is at least a certain amount of truth in the criticism.
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Object-relational data management systems generally provide a mechanism through which a user can extend the built-in data types provided by the DBMS with additional, user-defined data types. For example, a mapping application might need to operate on a LOCATION data type that consists of a pair of latitude and longitude measurements, each consisting of hours, minutes, and seconds. To effectively process location data, the application may need to define special functions, such as a DISTANCE(X,Y) function that computes the distance between two locations. The meanings of some built-in operations, such as a test for equality (=), will need to be redefined for location type data. One way that Informix Universal Server supports user-defined data types is through its OPAQUE data type. An OPAQUE data type is (not surprisingly) opaque to the DBMS. The DBMS can store and retrieve data with this type, but it has no knowledge of the internal workings of the type. In object-oriented terms, the data is completely encapsulated. The user must explicitly provide (in external routines, written in C or some similar programming language) the data structure for the type, code to implement the functions or operations that can be performed on the type (such as comparing two data items of the type for equality), and code to convert the opaque type between internal and external representations. Thus, OPAQUE data types represent a low-level capability to extend the core functionality of the DBMS with data types that appear as if they were built-in. A more basic user-defined data type capability is provided by the implementation of DISTINCT data types within Informix. A DISTINCT type is useful to distinguish among different types of data, all of which use one of the DBMS built-in data types. For example, the city and company name data items in a database might both be defined with the data type VARCHAR(20). Even though they share the same underlying DBMS data type, these data items really represent quite different types of data. You would never normally compare a city value to a company name, and yet the DBMS will let you do this because the two VARCHAR(20) columns are directly comparable. To maintain a higher level of database integrity, you could define each of these three data items as having a DISTINCT data type:
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CREATE DISTINCT TYPE CITY_TYPE AS VARCHAR(20); CREATE DISTINCT TYPE CO_NAME_TYPE AS VARCHAR(20);
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