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If ever there were a bane of a modern object-oriented developer s existence, it would be the legacy database. Legacy databases are generally the prehistoric remains of what was once an enterprise database. They have all of the complexities,

intricacies, and dependencies of an enterprise database. In addition, they have battle scars from years of modifications, quick fixes, cover-ups, workarounds, bandage solutions, and technical limitations. Furthermore, legacy databases are often implemented on older platforms that are not only outdated but are sometimes totally unsupported. There may not be adequate drivers or tools available for modern developers to work with. iBATIS can still help with legacy databases. As long as there s an appropriate database driver available for the system you re working with, iBATIS will work the same way it does for any database. In fact, iBATIS is probably one of the best persistence frameworks around for dealing with legacy data, because it makes no assumptions about the database design and can therefore deal with even the most nightmarish of legacy designs.

Deques (and Other Collections)

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On modern software projects databases are often considered legacy components. They have a history of being difficult to work with for both technical and nontechnical reasons. Most developers probably wish that they could simply start over and rebuild the database entirely. If the database is to remain, some developers might just wish that the DBAs responsible for it would take a long walk off a short pier. Both of these cases are impractical and unlikely to ever happen. Believe it or not, databases are usually the way they are for a reason even if the reason isn t a good one. It may be that the change would be too costly or there may be other dependencies barring us from changing it. Regardless of why the database is challenged, we have to learn to work effectively with all databases, even challenged ones. iBATIS was developed mostly in response to databases that had very complex designs or even poor designs. The following sections describe some common database challenges and how iBATIS can help with them.

Figure 4-9. Initial CarModel database Explorer pane Note that the session name in the Quadrant status bar (lower-right corner of the window) has changed to the name of the current database session: CarModel. Click on the Database arrowhead icon to expand the schemas. Car.Model should be the first item displayed, as shown in Figure 4-10. Click on the CarComponents table icon and drag this to the right onto the Quadrant canvas. Double-clicking the square icon will have the same effect.

Double-ended queues, or deques, can be useful when you need to remove elements in the order in which they were added. In Python 2.4, the collections module was added, which contains the deque type.

<select id="getProductById" resultClass="Product" parameterClass="Product" cacheModel="productCache"> SELECT * FROM Product WHERE productId=#productId# </select>

This final case study is a somewhat unusual situation, but it s a fun test case nonetheless. Situations arise where you need to deal with smaller static portions of data that become less relevant as time goes on. Often this type of caching is associated with time-based analytics. Examples of time-based analytics include performance

The find method finds a substring within a larger string. It returns the leftmost index where the substring is found. If it is not found, 1 is returned: >>> 'With a moo-moo here, and a moo-moo there'.find('moo') 7 >>> title = "Monty Python's Flying Circus" >>> title.find('Monty') 0

At this point, you should see an Explorer window showing the CarComponents table (shown in Figure 4-11). The table is empty, of course, because you haven t yet created any data.

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