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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.

Figure 4-4. A bar chart example The code for this is still very simple: var chart17 = new dojox.charting.Chart2D("test17"); chart17.addPlot("default", {type: "StackedBars"}); chart17.addSeries("Series A", [1, 2, 3, 4, 5], {stroke: {color: "red"}, fill: "lightpink"}); chart17.addSeries("Series B", [2, 1, 2, 1, 2], {stroke: {color: "blue"}, fill: "lightblue"}); chart17.render(); In fact, it looks not much different from what you ve seen before. That s part of the beauty of dojox.charting: a change to the type attribute passed into addPlot() is most times all you need to transform your data into a different type of chart. Virtually everything else can stay the same all the time. That s a tremendous amount of flexibility right at your fingertips.

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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.

You also need to support entries that are marked as drafts, which aren t meant to be shown publicly. This means you ll need some way of recording an entry s status. One way would be to use another BooleanField, with a name like is_draft or, perhaps, is_public. Then you could just query for entries with the appropriate value, and authors could check or uncheck the box to control whether an entry shows up publicly. But it would be better to have something that can be extended later on. If there s ever a need for even one more possible value, the BooleanField won t work. The ideal solution would be some way to specify a list of choices and allow the user to select from them; then if there s ever a need for more choices, they can simply be added to the list. Django provides an easy way to do this via an option called choices. Here s how you ll implement it: STATUS_CHOICES = ( (1, 'Live'), (2, 'Draft'), ) status = models.IntegerField(choices=STATUS_CHOICES, default=1)

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..

Unless this book is your first foray into programming you almost certainly know what a collection is. But, just in case you just stepped off the Computer Science bus, a collection is a group of related data items typically stored in some sort of standard data structure. Examples of such structures include List, Dictionary, Stack, Queue, Map, Tree, Bag, Array, Set, and Vector. In JavaScript there are really only two kinds of built-in collections: arrays and objects (which can be made to function like maps). Generally, if you want the more advanced structures like those listed in the preceding paragraph, you re off coding them yourself. Or, if you re smart, you re looking at what Dojo has to offer! And thankfully, DojoX is home to a number of useful data structures. The dojox.collections namespace is the place to look.

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