[翻译]High Performance JavaScript(021)
Splitting Up Tasks 分解任务
What we typically think of as one task can often be broken down into a series of subtasks. If a single function is taking too long to execute, check to see whether it can be broken down into a series of smaller functions that complete in smaller amounts of time. This is often as simple as considering a single line of code as an atomic task, even though multiple lines of code typically can be grouped together into a single task. Some functions are already easily broken down based on the other functions they call. For example:
我们通常将一个任务分解成一系列子任务。如果一个函数运行时间太长，那么查看它是否可以分解成一系列能够短时间完成的较小的函数。可将一行代码简单地看作一个原子任务，多行代码组合在一起构成一个独立任务。某些函数可基于函数调用进行拆分。例如：
function saveDocument(id){
//save the document
openDocument(id)
writeText(id);
closeDocument(id);
//update the UI to indicate success
updateUI(id);
}
If this function is taking too long, it can easily be split up into a series of smaller steps by breaking out the individual methods into separate timers. You can accomplish this by adding each function into an array and then using a pattern similar to the array-processing pattern from the previous section:
如果函数运行时间太长，它可以拆分成一系列更小的步骤，把独立方法放在定时器中调用。你可以将每个函数都放入一个数组，然后使用前一节中提到的数组处理模式：
function saveDocument(id){
var tasks = [openDocument, writeText, closeDocument, updateUI];
setTimeout(function(){
//execute the next task
var task = tasks.shift();
task(id);
//determine if there's more
if (tasks.length > 0){
setTimeout(arguments.callee, 25);
}
}, 25);
}
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本文主要是出于有朋友使用我原来写的autocomplete的JS控件。当数据量大的时候，会出现效率极其慢的情况，我在这段时间做出的一些测试也及一些经验，与大家分享，如果有错的地方，还请指出。
经过测试，我们会发现如下的情况或者说的结论，如果您的测试结果与我的不符，请说明原因，以便相互学习。
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