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High-frequency access and low-frequency access are part of the game when it comes to caching. It doesn t matter which you experience as long as there is a true benefit from the fact that it is or may be accessed more than once. Fine-tuning the size is the only thing we need to be concerned with. Depending on the number of people who are accessing the current popular purchases or past popular purchases, we can set our cache size to accommodate. Now that we have looked at the list of requirements and how the FIFO cache fits with them, let s move on to configuring the cache model. Our cache model is quite simple. As shown in listing 9.12, we want to display correct product information for purchased products. This requires that we first set up the appropriate <flushOnExcecute> tags to flush hotProductsCache when product data changes. We only need to specify Product.update or Product.delete to flush the cache. We do not include insert here because we don t care about products that don t exist. If a product is added and then becomes a popular purchase, we will have no problem adding it to hotProductCache. It is only when the product is updated or deleted that we need to worry at all about a flush. We would not want to display a cached product that is no longer for sale (i.e., deleted). We would also not want to display an old pricing on a product that has had a pricing update.

Note The string method find does not return a Boolean value. If find returns 0, as it did here, it means

This will result in a detail pane for adding a new CarComponent entity, as shown in Figure 4-13. The red squiggles indicate the parts of the record that can t be null, so these must have values entered before the item can be saved. The values without squiggles are nullable (except for Id), and entering these values is optional. The first record entered for the model should be the top-level item, which is My Car.

<cacheModel id="hotProductCache" type="FIFO"> <flushOnExecute statement="Product.update"/> <flushOnExecute statement="Product.delete"/> <property name="size" value="12"/> </cacheModel> <select id="getPopularProductsByPurchaseDate" parameterClass="Product" resultClass="Product" cacheModel="hotProductsCache"> SELECT count(productId) countNum, productId FROM productPurchase WHERE purchaseDate BETWEEN #startDate# AND #endDate# GROUP BY productId ORDER BY countNum DESC LIMIT 5 </select>

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With the cache flush configuration behind us, let s move on to setting up the FIFO cache size property. Setting the size property to 12 allows us to hold a maximum of 12 results in the cache (see listing 9.12). Once 12 results occupy the cache, the oldest item is discarded and the newest item is added to the beginning of the cache. Since the SQL we use will only provide our cache with a new result every hour or more, our cache should hold up just fine to aging the data appropriately. The newer results will always occupy the front of the cache while the older results are discarded.

You may also supply a starting point for your search and, optionally, an ending point: >>> >>> 0 >>> 20 >>> 16 >>> -1 subject = '$$$ Get rich now!!! $$$' subject.find('$$$') subject.find('$$$', 1) # Only supplying the start subject.find('!!!') subject.find('!!!', 0, 16) # Supplying start and end

9.7 Summary

Figure 4-13. Detail window (right) for creating the first item Enter the following data for the top-level record: Name: My Car Level: 1 (This is the highest system level, so it will have the lowest possible number; zero is equivalent to a null and would not be accepted.) Description: 1954 Buick Wildcat II Quantity: 1 PartOfComponent: Leave this as null, since this is the top-level system.

appropriate choice requires that you exercise thought and care. It s important to remember that iBATIS caching is value based (versus OID) and intended to handle caching specifically on the persistence layer. If the default iBATIS cache options do not meet your needs, you can look into extending iBATIS (chapter 12) to provide a cache that does. The examples in this chapter can help you get started; experience will be the best guide. Spend time getting to know the various types of caches and their options. Become familiar with each type s particular strengths and weaknesses. This knowledge will help you determine which option will meet your particular needs.

Note that the range specified by the start and stop values (second and third parameters) includes the first index but not the second. This is common practice in Python. In Appendix B: rfind, index, rindex, count, startswith, endswith.

Applications often need to access data from multiple data sources: relational databases, file systems, directory services, web services, and other providers. Each of these data stores has a different API to access the underlying storage mechanism, along with a whole set of idiosyncrasies. The Data Access Object (DAO) pattern is used to hide the unique implementation quirks of these APIs. It provides a simple and common API for application developers so that the consumers of the data can be free of the complexities of the data access APIs. The iBATIS Data Access Objects subproject is a simple implementation of the core J2EE pattern of the same name.

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