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forks it found when supplied with a particular command line argument. The ability to make this sort of modification is, of course, exactly the sort of motivation we have for rolling our own scripts in the first place.

In fact, by mixing these three levels of intelligence in the right proportion, you can develop a rock-solid application.

Machine intelligence is inclined toward the programmatic perspective of an application. The best way to implement this intelligence is by religiously following the best programming practices and applying well-proven architectural standards. The most common practices followed in high-performance applications are applying parallelism, which is achieved using multithreading; devising a highly optimized algorithm; and exploiting platform- or hardware-specific capabilities.

. . . @In(value="gadget", create=true) @Out(value="gadget", required=false) private Gadget mActiveGadget; . . .

There are myriad ways to approach the problem of analyzing your script s performance. Ruby ships with a couple of libraries that make this process easier and more productive. We ll discuss both simple benchmarking and more in-depth profiling in this section, but before we get to either of these, it is worth emphasizing that for ultraquick benchmarking, the time command that is available on most popular UNIX-like systems is still a firm favorite.

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Domain intelligence goes hand in hand with machine intelligence To apply such intelligence, the team must be cognizant of both domain- and technology-specific details When domain intelligence is applied at the right place, it gives a multifold increase in the performance of the application This intelligence is often ignored during the performance-engineering phase of a system where a team s energy is primarily mobilized toward implementing all sorts of machine-level intelligence Let s take a market data example where introducing domain intelligence along with machine intelligence provides a tremendous performance boost: Market data applications broadcast the latest price of a stock Several consumers then process this information These real-time prices are often displayed on most financial Web sites The price of stocks tends to lose tempo whenever there is a swing in the market behavior.

This swing period lasts for a short span of time, and during this period almost every stock price is affected This period is called a data-quake because the system will notice a huge surge of data, and this sometimes may even lead to a system crash Even if you assume that the first line of defense is strong and the market data service is able to survive this dataquake, remember that this information needs to further trickle down to the consumers; thus, the consumers of this information could also face the same disaster So, how do you escape from such a deluge of data Although no easy solution exists, you can deal with this by applying domain-level intelligence Before applying this intelligence, you need to understand the business implication behind this data that will help control the flow of data.

Our pageflow is set up in faces-config.xml to take the user to the editGadget.jsp page when the pickGadget() action listener returns editGadget :

You may not have had occasion to use time before, but it couldn t be simpler: $ time ruby -e '10000.times { 1 + 2 }' On my system this yields the following: real user sys 0m0.283s 0m0.105s 0m0.046s

Let s say your investigation disclosed that most stock prices tend to change at least 100 times a second So, instead of pushing this change immediately to downstream systems, you can throttle it for a second During this throttling period, data will not be pushed; rather, it will undergo a price-replacement process that will blindly override the old price of a stock with the new price As data gets published only after the expiry of 1 second, it effectively controls the flow of data from flooding the market data ecosystems Even though the first line of market data service will receive roughly about 100 messages per second for any given stock, this will not affect downstream systems because they will receive throttled messages at the rate of one message per second..

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