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stretch the relational model some would say past the breaking point to incorporate features such as tables within tables, which model the relationships between object classes. One of the major vendors, Informix Software, gained its object-relational capabilities by acquisition, buying Illustra Software. Illustra s object-relational technology was based on the Postgres work at the University of California at Berkeley, a follow-up to the university s pioneering relational database system, Ingres. The Informix version of the Illustra product was renamed Informix Universal Server. Another of the major vendors, Oracle Corporation, evolved its own mainstream database system to include object-relational technologies. Oracle8, introduced in 1998, embodies several years of intensive Oracle development in this area, and Oracle9 further expanded it. The object-oriented database vendors and the relational vendors response to it have also had a major impact on the SQL standards efforts. The most significant change to the SQL2 standard addressed in the work on SQL3 was the addition of object capabilities. When the SQL3 work was finally approved as the SQL:1999 official standard, the new object-oriented capabilities nearly doubled the size of the SQL language specification in terms of page count. The acquisition and development of object-relational databases by the industry leaders, and the formal adoption of object extensions to SQL, signal the growing synergy between SQL and the world of object technology.
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Object-relational databases typically begin with a relational database foundation, and add selected features that provide object-oriented capabilities. This approach simplifies the addition of object capabilities for the major RDBMS vendors, whose enterprise-class RDBMS products have been developed over the course of 15 or more years and would be tremendously costly to reproduce from scratch. It also recognizes the large installed base of relational systems and gives those customers a smoother upgrade path (not to mention an upgrade revenue stream for the vendors). The object extensions that are commonly found in object-relational databases are: I Large data objects. Traditional relational data types are small in size integers, dates, short character strings; large data objects can store documents, audio and video clips, web pages, and other new media data types. I Structured/abstract data types. Relational data types are atomic and indivisible; structured data types allow groups of individual data items to be grouped into higher-level structures that can be treated as entities of their own. I User-defined data types. Relational databases typically provide a limited range of built-in data types; object-oriented systems and databases emphasize the user s ability to define his or her own new data types.
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I Tables within tables. Relational database columns store individual data items; object-relational databases allow columns to contain complex data items, such as structured types or even entire tables. This can be used to represent object hierarchies. I Sequences, sets, and arrays. In a traditional relational database, sets of data are represented by rows in their own table, linked to an owning entity by a foreign key; object-relational databases may allow the direct storage of collections of data items (sequences, sets, arrays) within a single column. I Stored procedures. Traditional relational databases provide set-based interfaces, such as SQL, for storing, selecting, and retrieving data; object-relational databases provide procedural interfaces, such as stored procedures, that encapsulate the data and provide strictly defined interactions. I Handles and object-ids. A pure relational database requires that data within each row of the database itself (the primary key) uniquely identifies the row; object-relational databases provide built-in support for row-ids or other unique identifiers for objects.
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Relational databases have traditionally focused on business data processing. They store and manipulate data items that represent money amounts, names, addresses, unit quantities, dates, times, and the like. These data types are relatively simple and require small amounts of storage space, from a few bytes for an integer that holds order or inventory quantities to a few dozen bytes for a customer name, employee address, or product description. Relational databases have been optimized to manage rows containing up to a few dozen columns of this type of data. The techniques they use to manage disk storage and to index data assume that data rows will occupy a few hundred to a few thousand bytes. The programs that store and retrieve data can easily hold dozens or hundreds of these types of data items in memory, and can easily store and retrieve entire rows of data at a time through reasonably sized memory buffers. The row-at-a-time processing techniques for relational query results work well. Many modern types of data have quite different characteristics from traditional business data. A single high-resolution graphical image to be displayed on a PC screen can require hundreds of thousands of bytes of storage or more. A word processing document, such as a contract or the text of this book, can take even more storage. The HTML text that defines web pages and the PostScript files that define printed images are other examples of larger, document-oriented data items. Even a relatively short high-quality audio track can occupy millions of bytes, and video clips can run to hundreds of megabytes or even gigabytes of data. As multimedia applications have become more important, users have wanted to manage these types of data along with the other data in their databases. The capability to efficiently manage large objects, often called binary large objects (BLOBs), was one of the earliest advantages claimed for object-oriented databases.
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