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Consider the performance consequences of your API design decisions Making 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existed Ideally, Dimension should have been immutable (Item 13); alternatively, the getSize method could have been replaced by two methods returning the individual primitive components of a Dimension object In fact, two such methods were added to the Component API in the 12 release for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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The need to measure the effects of optimization is even greater on the Java platform than on more traditional platforms, as the Java programming language does not have a strong performance model The relative costs of the various primitive operations are not well defined The semantic gap between what the programmer writes and what the CPU executes is far greater than in traditional compiled languages which makes it very difficult to reliably predict the performance consequences of any optimization There are plenty of performance myths floating around that turn out to be half-truths or outright lies Not only is the performance model ill-defined, but it varies from JVM implementation to JVM implementation and from release to release If you will be running your program on multiple JVM implementations, it is important that you measure the effects of your optimization on each Occasionally you may be forced to make trade-offs between performance on different JVM implementations To summarize, do not strive to write fast programs strive to write good ones; speed will follow Do think about performance issues while you're designing systems and especially while you're designing APIs, wire-level protocols, and persistent data formats When you've finished building the system, measure its performance If it's fast enough, you're done If not, locate the source of the problems with the aid of a profiler, and go to work optimizing the relevant parts of the system The first step is to examine your choice of algorithms: No amount of low-level optimization can make up for a poor choice of algorithm Repeat this process as necessary, measuring the performance after every change, until you're satisfied
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