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The real problem in our database connection hog antipattern is idle time. In expectation of the user s return (and thus the next request), you greedily hold onto resources, memory, and database connections. In reality, the user may never return or, conversely, a user may make another request 10 seconds later. If you wait around for the length of the session timeout or for 10 seconds, you ll have monopolized resources for too long. And you can t afford to waste scarce resources in this manner. Let s look at that second possible scenario a 10-second wait. The user browses several records and clicks through to the next page fairly quickly. You ve made use of your database connection only twice over that 10-second span. However, your application could handle hundreds of requests per second. If you could instead return this user connection to the pool at the end of each request, you could use this connection to service hundreds of requests in the time it takes for your user to click through one page. Making resource usage dependent on user behavior will not scale. By keeping as little state as possible and using resources with surgical precision that is, by holding onto resources for the smallest amount of time possible your application can handle thousands of unique users concurrently, rather than a double-digit number smaller than or equal to the number of connections in your connection pool. Bean implementations should retrieve a connection, use it quickly, and return it to the pool. Holding onto database connections between requests is hardly ever a good idea. First, look into optimizing the query time at the database level. When all else fails, look into caching in the application server, preferably using an offthe-shelf solution.

SharePoint provides a couple of ways to install a web part, but the simplest is to use the STSADM.exe utility. To use it, however, the web part must be compiled into a cabinet setup file first. To do so, add a setup project to your solution by selecting File New Project and then select a Cab project, as shown in Figure 4-12.

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Most Page-by-Page Iterator pattern descriptions primarily address a remote interface s location between the client and application tier, which makes transferring the entire result set impractical. However, the descriptions fail to consider the most common mistake we see in the pattern s actual implementation. Developers tend to forget that another remote interface sits between the application and data tiers. Though the throughput may be much higher than that of the client interface, it s a remote interface nonetheless, and you must limit the amount of data that passes across it as much as possible. How you design your Page-by-Page Iterator implementation depends heavily on your application and your environment s unique characteristics. Does the

If you ve got a very nice but long-winded contact that seems to always call at the wrong time, one way to deal with it is to send all of their calls straight to voicemail. When viewing their contact page, click the Menu button, select options, and then check the appropriate box.

database sit on the same machine or across a network How big is a typical result set To whom are the results applicable Developers often incorrectly assume that executing the query consumes most application effort If queries have an optimal configuration and a properly worded SQL command (combined with indexing, partitioning, caching, and other optimizations in the data tier), they typically run surprisingly efficiently Developers often fail to realize the possible efficiency of query execution, nor do they keep in mind that they can t keep the database connection open across requests Consequently, developers often implement a Page-by-Page Iterator that executes the query and pulls back all results up front The implementation stores the entire DTO collection in the user s session, either globally or in a stateful session In any case, the results consume nontrivial amounts of memory, even past the duration of a user s request.

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