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In the example in listing 9.1, you can see the two major components: the cache model and the mapped statement (select). The cache model defines how the cache will store fresh results and clear stale data from the cache. Mapped statements that want to use the cache just need to reference it using the cacheModel attributes of the <select> and <procedure> tags. In listing 9.1 the cache model specifies a cache type of MEMORY. This is a builtin iBATIS caching that stores results into memory. This is usually the most-used means of caching in iBATIS. Within the body of the cache model are a couple of

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>>> sorted('Python') ['P', 'h', 'n', 'o', 't', 'y'] If you want to sort the elements in reverse order, you can use sort (or sorted), followed by a call to the reverse method, or you could use the reverse argument, described in the following section.

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tags. <flushOnExecute> specifies that stored results will be flushed when a particular cache is accessed. It s important to note that all of the cache contents are cleared. This means that if you have several mapped statements that use the same cache model, then all of the results of those mapped statements will be flushed. The final tag to notice is named <property>. Each type of cache model has properties that can be specified for custom configuration of the cache model. The name attribute is the name of the property that will be set. The value, of course, provides the assigned value to the defined property. As you can see, the iBATIS cache is quite simple. The most work you will experience is discovering the properties that are available for each of the cache model types. We will take time in this chapter to explain what cache models are available and provide some direction on when and how to use them after we take a quick look at the iBATIS caching philosophy.

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Figure 3-47. The Condiments table Finally, Figure 3-48 shows the values in the Stuffs table, which includes a single many-to-one relationship: Order 4 has two Stuff values (Grilled Tofu and Portabella). Again, this is confirmed by the expression of the fourth order in the list: Grilled Tofu & Portabella in Pita with Lettuce. So the database instances appear to be entirely consistent with the model.

Most caching that developers incorporate into their apps is for long-term, seemingly unchanging data. This is the type of data you usually see in drop-down or selection lists. Data like states, cities, and countries are prime candidates for this type of caching. But caching can reach beyond long-term read-only data and can be used to cache objects that are read/write as well. The difficulties at this point often have to do with the manual process of checking to see whether the data exists in the cache and then storing it into the cache yourself. Another difficulty with a cache is figuring out how to know whether the data contained within is either stale or fresh. It s easy enough to write simple rules like timed cache flushes, but it is not so easy when the execution of several processes should invalidate a cache s contents. Once dependencies between executing code become important to maintaining the integrity of your cached objects, things begin to get difficult. Often when caching reaches this point of complexity, the performance-to-effort ratio can become less than convincing or at minimum be seen as a very annoying task. This is why iBATIS has focused itself on providing caching implementations and strategies for the data access layer only. This focus on the data access layer enables the framework to manage the cache in accordance with an easy-to-manage configuration. Consider the philosophy of the iBATIS caching framework and how it differs from other persistence solutions. This can often be a sore point for some who are used to how traditional O/RM solutions perform caching. IBATIS is built on the idea of mapping SQL to objects, not mapping database tables to objects. This is an

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