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The tasks we have created were done so with database schema migration and synchronization in mind, although as stated, the manual task will handle any arbitrary SQL We do need to consider how to handle data synchronization In the scenario earlier, the task synchronizes the schema for an integration database that may be used for unit-testing purposes, and a database used for system testing, which may contain a different set of data for example, a representation of the production system data Therefore, the data migration requirements could well be different for each database: Integration database The integration database may need to be fully refreshed with data for unit testing, or just some standing data such as ISO country codes Good unit tests should create their own data and then destroy it, but we all know that unit tests come in all varieties System database.

private PasswordHome getPasswordHome() {

However, it s very likely that they will run on multiple machines, such as a web server or a smart client and an application server Given these varied physical environments, you re faced with the following questions: Where do the objects reside Are the objects designed to maintain state, or should they be stateless How is object-relational mapping handled when retrieving or storing data in the database How are database transactions managed Before discussing some answers to these questions, it s important that you fully understand the difference between a physical architecture and a logical architecture After defining these terms, I ll define objects and mobile objects and show you how they fit into the architectural discussion.

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If the system database does contain a representation of the live data, then migration scripts are needed rather than refresh scripts This is the ideal time to test those scripts for eventual use in the production environment So how to do this The options are certainly the same the processing of scripts that are manually generated or the use of SQL Data Compare (for instance) to automatically synchronize the data but the decision may be different Whereas synchronization is what we were trying to achieve with the database schemas, it is not our goal with data migration Data migration also tends to be a more semantic affair than schema migration, and thus developer decision-making skills could be called for My feeling is that it is probably best to manually create scripts for data migration and manipulation, using the manual task to run these scripts as part of the delivery process.

InitialContext try { instance Context ic = new InitialContext(); PasswordHome passwordHome = ( PasswordHome ) ic.lookup("ejbAccess.passwordHome");

When most people talk about n-tier applications, they re talking about physical models in which the application is spread across multiple machines with different functions: a client, a web server, an application server, a database server, and so on And this isn t a misconception these are indeed n-tier systems The problem is that many people tend to assume there s a one-to-one relationship between the layers (tiers) in a logical model and the tiers in a physical model, when in fact that s not always true A physical n-tier architecture is quite different from a logical n-layer architecture An n-layer architecture has nothing to do with the number of machines or network hops involved in running the application Rather, a logical architecture is all about separating different types of functionality The most common logical separation is into an Interface layer, a Business layer, and a Data layer.

These layers may exist on a single machine or on three separate machines the logical architecture doesn t define those details..

return passwordHome; } catch (NamingException ne) { return null; } }

Tools such as SQL Data Compare could then be used to verify these scripts if desired Martin Fowler s article on agile databases and Scott Ambler s book Agile Database Techniques (see Further Reading ) describe toolkits of scripts for data migration to speed up and standardize this work This works by taking an approach to database manipulation similar to one a developer might take to code refactoring: creating specific mechanics for performing specific tasks under specific contexts So, for example, in code refactoring we may choose to Extract Method and follow a mechanism to perform this refactoring; in database manipulation we may choose to Introduce Trigger following a mechanism to perform this work The database examples are not quite so rounded as the original work on code refactoring, but there are a lot of ideas and examples in both of these sources.

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