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A Queue data structure is sometimes referred to as a rst-in- rst-out structure, as this is descriptive of the way that items are added to and removed from it The most obvious analogy for a Queue structure is the real queue that you join to receive service at the bank or post of ce Every (well mannered) person who joins the line of customers attaches to the end of the queue, and customers leave the queue to get service only after all of the customers before them have left so that they are at the front In a bank or post of ce, if it is a quiet period there may be no queue, since if a customer arrives and there is a free service window they can be served immediately During busy periods, customers arrive at intervals more quickly than they can be served and in these circumstances a customer who arrives will join the queue, wait until all of the queue members in front of them have been served, and then leave the queue to get service We can see from this that a queue is a good way of providing fair access to a limited resource (eg a bank teller) Microsoft Windows uses Queue data structures in several ways most notably when dealing with user interactions Consider the action of a Windows PC that is running several application programs simultaneously The rst thing to be aware of is that a normal PC cannot do more than one thing at once (you can spend a lot on a multi-processor PC so that it can do more than one job at a time, but most people s needs would not justify this expense) When you see the computer apparently doing two jobs simultaneously, you are in fact watching it switching so rapidly between the two tasks that you cannot see the join Now think of what happens if you select some time-consuming task for your computer to do, such as saving a long text le, or redrawing a complex graphic While it is busy performing this lengthy task, it will probably appear to ignore your commands For example, if you click on the Start button to start nother program up, the Start menu might not appear immediately You will most likely notice that after a delay (once it had completed the time-consuming task) the Start menu will pop up In fact, we are so used to this behaviour in Windows that we tend not to notice unless the delay between initiating some action and the action occurring becomes very long usually an indication that the computer is stressed When you clicked on the Start button, your command was not ignored, but simply placed into a queue until the PC could get round to it Any other commands that were in front of it in the queue would be processed before your mouse click, but you can be quite sure that, failing crashes, Windows will get around to displaying the Start menu eventually Typically you will see this behaviour whenever you try to access a printer, open a network drive or deal with some other slow media devices The Windows message queue is central to making a Windows computer usable It ensures that commands are processed fairly in the order that they were issued, and.

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Consider the performance consequences of your API design decisions aking a public type mutable may require a lot of needless defensive copying (Item 24) Similarly, using inheritance in a public class where composition would have been appropriate ties the class forever to its superclass, which can place artificial limits on the performance of the subclass (Item 14) As a final example, using an implementation type rather than an interface in an API ties you to a specific implementation, even though faster implementations may be written in the future (Item 34) The effects of API design on performance are very real Consider the getSize method in the javaawtComponent class The decision that this performance-critical method was to return a Dimension instance, coupled with the decision that Dimension instances are mutable, forces any implementation of this method to allocate a new Dimension instance on every invocation Even though, as of release 13, allocating small objects is relatively inexpensive, allocating millions of objects needlessly can do real harm to performance In this case, several alternatives existed Ideally, Dimension should have been immutable (Item 13); alternatively, the getSize method could have been replaced by two methods returning the individual primitive components of a Dimension object In fact, two such methods were added to the Component API in the 12 release for performance reasons Preexisting client code, however, still uses the getSize method and still suffers the performance consequences of the original API design decisions Luckily, it is generally the case that good API design is consistent with good performance It is a very bad idea to warp an API to achieve good performance The performance issue that caused you to warp the API may go away in a future release of the platform or other underlying software, but the warped API and the support headaches that it causes will be with you for life Once you've carefully designed your program and produced a clear, concise, and wellstructured implementation, then it may be time to consider optimization, assuming you're not already satisfied with the performance of the program Recall that Jackson's two rules of optimization were Don't do it, and (for experts only) Don't do it yet He could have added one more: Measure performance before and after each attempted optimization You may be surprised by what you find Often attempted optimizations have no measurable effect on performance; sometimes they make it worse The main reason is that it's difficult to guess where your program is spending its time The part of the program that you think is slow may not be at fault, in which case you'd be wasting your time trying to optimize it Common wisdom reveals that programs spend 80 percent of their time in 20 percent of their code Profiling tools can help you decide where to focus your optimization efforts Such tools give you run-time information such as roughly how much time each method is consuming and how many times it is invoked In addition to focusing your tuning efforts, this can alert you to the need for algorithmic changes If a quadratic (or worse) algorithm lurks inside your program, no amount of tuning will fix the problem You must replace the algorithm with one that's more efficient The more code in the system, the more important it is to use a profiler It's like looking for a needle in a haystack: The bigger the haystack, the more useful it is to have a metal detector The Java 2 SDK comes with a simple profiler, and several more sophisticated profiling tools are available commercially.

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