Conclusion in Java

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Conclusion
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In this chapter we have provided a brief introduction to SQLJ and JDBC, the standard mechanisms for database access in Java. The two frameworks are closely related a JDBC driver typically is used to run translated SQLJ code but at the same time they have
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Table 3.2
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Comparison of SQLJ and JDBC SQLJ Yes, e.g., via host expressions JDBC No, e.g., get/set calls for data
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FEATURE Compact embedded syntax
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Early checking of static SQL Typed iterators and connection contexts Profile customization
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Yes, by SQLJ translator No, runtime checking only Yes, for query shapes No, has generic result sets and schema structures and connections Yes, for binary portability No, but interoperates with JDBC Managed by SQLJ runtime
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Yes, fully dynamic API
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Dynamic SQL
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Statement handles
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Object type support Portable source code
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Yes, ANSI standard
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SQLJ and JDBC: Database Access in Java
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different programming models. The JDBC library is an API, whereas SQLJ supports embedded SQL it hides many underlying details to provide a simpler model for static SQL. We have given an overview of the basic concepts in SQLJ and JDBC: single-row queries via the convenient SELECT..INTO construct in SQLJ, multirow queries through SQLJ iterators that encode query shapes or through JDBC result sets, and JDBC statements and prepared statements. Both frameworks support efficient SQL execution through powerful datatypes such as LOBs and object types, and performance features such as batching of updates and connection pooling. It is worth repeating here that SQLJ and JDBC are complementary approaches. SQLJ does not preclude the use of JDBC or vice-versa. In fact, we can easily mix and match SQLJ and JDBC code in the same source file, depending on whether the SQL operation is static or dynamic. SQLJ is designed to interoperate nicely with the JDBC API for example, they can share a single database connection so we can easily switch between the two styles of programming as per the requirements of our application.
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CHAPTER
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Penguin: Objects for Programs, Relations for Persistence
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Penguin is designed to support object-orientation for application programs while using relational databases as the persistent backing store. Objects are attractive to customers and programmers of applications because they structure information into a relevant and effective view. The use of relational databases that store large amounts of base data for long periods of time enables Penguin to take advantage of mature solutions for sharing information, concurrency, transactions, and recovery. We expect that application programs will be designed best with their own object schemata, so each object schema is supported as a series of views of the underlying relational databases. Penguin provides for multiple mappings to diverse object configurations, enhancing interapplication interoperation. This approach supports coexistence and sharing data among programs using relational technology with diverse application programs using object technology, as well as facilitating a migration to object technology. Penguin is an object data management system that relies on relational databases for persistent storage. Object views of the relational databases allow each application to have its own object schema rather than requiring all applications to share the same object schema [Wiederhold 1986]. In this chapter, we discuss the principles, architecture, and implementation of the Penguin approach to sharing persistent objects. The primary motivation for using a database management system (DBMS) is to allow sharing of data among multiple customers and multiple applications. To support sharing among independent transactions, DBMSs have evolved services including transaction independence, persistence, and concurrency control. When a database is shared among multiple applications, these applications typically have differing requirements for data access and representation. Such differences are supported by having views, which present diverse subsets of the base data [Chamberlin 1975]. 75
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76 4
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The primary motivation for defining objects is to include shareable semantics and structure in the information. Must all applications sharing objects use the same object schema, or is it better to give each application its own object schema and somehow to integrate them If multiple applications differ in view, the needed compromise reduces the relevance and effectiveness of the object representation [Abiteboul 1991]. For instance, customers will have an orthogonal view of an inventory versus the suppliers. When combining independently developed applications, we do not have the luxury of choosing a common object schema. Many legacy databases and legacy data are still being used. We must retain the investment in existing application software and databases while building new software using the object approach. When creating a federation of heterogeneous (preexisting) databases, we must support a degree of interoperation among these databases and their schemas. Consider also that current projects will become legacy in a few years hence, but their semantics will remain. Whatever solutions we create in shared settings must support evolution and maintenance. Object-oriented database management systems (OODBMSs) define an object schema that is used to support all information for its applications. Typically, that object schema is designed for one initial application, and other applications that want to share the information will have their needs grafted on [Tsichritzis 1989]. To us, it appears preferable for each application to use the object schema most convenient for it. Typical OODBMSs do not support mappings between object schemas. Encapsulation provides data independence only from outsiders, but does not support integrating new applications. Database development was greatly influenced by the three-level data modeling architecture from the ANSI/SPARC committee [Steel 1980]. Their model partitions the concerns of information management into a view level, conceptual level, and physical level. The view level is composed of multiple user data models, one for each application, which cover only part of the database and provide for logical data independence. The conceptual level describes a common data model for the entire database and provides for physical data independence. Today, in a distributed environment, there may not be a single conceptual model, but rather one for each configuration of databases used in a set of applications. The physical level describes the implementation of the database at an abstract level, including distribution, data structures, and access paths. Most RDBMSs support the view level, and many views may be defined corresponding to various user roles. In contrast, OODBMSs typically do not support the view level; there is only one view corresponding to the conceptual level [Shilling 1989]. The Penguin approach builds on mature relational database management system (RDBMS) technology by creating object views. Our techniques could be used to create object views of alternate object databases, but we have not yet done this work, so it is beyond the scope of this chapter.